Cover illustrations:

Margherita Bongiovanni
(Formerly professor of Design and Art History, and Fine Art, Ministry of Education, Rome, Italy)

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© FAO 2012

Foreword

Sustainable management and utilization of natural resources is part of the Global Goals of FAO Member Countries and essential to the mandate of FAO.

The latest FAO assessment of the state of the world’s land and water resources clearly indicated that these resources, already scarce today, will be increasingly scarce as we move into the future, threatening food security. In fact, the outstanding food demand projected for the next decades, due to the world population growth and to the anticipated shift in consumption patterns, will face very limited opportunities for further land expansion and the finite availability of fresh water resources. Such a food demand may be satisfied only if we are able to act effectively and sustainably on both sides of the food equation, i.e., production and consumption, and on the inter-linkages between these two variables, including trade, distribution and access.

Efforts are being made by FAO to address major issues on the production side, on the fairness of trade, on the consumption side (reduction of post-harvest losses and food waste; promoting nutritious and healthy diets) and other emerging challenges. Among these emerging challenges are: food price volatility, revealing the vulnerability of some countries in their dependency on imports, leading to increase production inside their national boundaries; climate change, causing greater uncertainties on rainfall patterns, thus requiring higher levels of adaptation and increased resilience of the local production systems; transboundary rivers and competing demands for land and water resources by other sectors of society and by ecosystems.

Under such circumstances, and looking into the future food demand, it is imperative that agriculture improve the efficiencies of use of the limited resources and ensure substantial productivity gains. In the case of water, scarcity is a major threat to the sustainability of food production in many areas of the world. The effective management of water in rainfed and irrigated agriculture is thus a major knowledge-based pathway to increase productivity and farmers’ income. To combine increased productivity with sustainable management of natural resources, without repeating the mistakes made in the past, will be a challenge.

With the contribution of numerous experts, professionals and scientific institutions around the world, including a few Institutes of the Consultative Group on International Agricultural Research (CGIAR), “Crop yield response to water” is published at a time of high demand for assistance by member countries in order to implement effective water management strategies and practices that are environmentally safe and climate-resilient, and enhance sustainable water productivity and yield of their farming systems, therefore alleviating the risks of food insecurity.

Preface

The FAO Land and Water Division is engaged extensively in the enhancement of global agricultural performance. A part of this effort is the production of landmark publications and guidelines that address food production and water use problems using analytical methods that often serve as standards worldwide.

In the face of growing water scarcity, declining water quality, and the uncertainties of climate change, improving the efficiency and productivity of crop water use, while simultaneously reducing negative environmental impact, is of utmost importance in responding to the increasing food demand of the growing world population. To this end, irrigated and rainfed agriculture must adopt more knowledge-intensive management solutions.

Moreover, competing demands for water from other economic sectors and for ecosystem services will continue to grow. As agriculture is by far the largest consumer of water, efficiency and productivity gains in this sector would free significant amounts of water for other uses.

Abstracting from the scientific understanding and technological advances achieved over the last few decades, and relying on a network of several scientific institutions, FAO has packaged a set of tools in this Irrigation and Drainage Paper to better assess and enhance crop yield response to water. These tools provide the means to sharpen assessment and management capacities required to: sustainably intensify crop production; close the yield-gap in many regions of the world; quantify the impact of climate variability and change on cropping systems; more efficiently use natural resources; and minimize the negative impact on the environment caused by agriculture. These tools are invaluable to various agricultural practitioners including, but not limited to: water managers and planners; extension services; consulting engineers; governmental agencies; non-governmental organizations and farmers' associations; agricultural economists and research scientists.

Representing FAO’s state-of-the-art work in water and crop productivity, it is our hope that this publication provides easy access to, and better understanding of, the complex relationships between water and food production and, in this way, help improve the management of our precious water resources.

Contents

ACKNOWLEDGMENTS

ACRONYMS OF INSTITUTIONS

LIST OF PRINCIPAL SYMBOLS AND ACRONYMS

1.

INTRODUCTION

2.

YIELD RESPONSE TO WATER: THE ORIGINAL FAO WATER PRODUCTION FUNCTION

3.

YIELD RESPONSE TO WATER OF HERBACEOUS CROPS: THE AQUACROP SIMULATION MODEL

3.1

AquaCrop: concepts, rationale and operation

3.2

AquaCrop applications

3.3

AquaCrop parameterization, calibration, and validation guide

3.4

Herbaceous Crops

Wheat

Rice

Maize

Soybean

Barley

Sorghum

Cotton

Sunflower

Sugarcane

Potato

Tomato

Sugar Beet

Alfalfa

Bambara Groundnut

Quinoa

Tef

4.

YIELD RESPONSE TO WATER OF FRUIT TREES AND VINES: GUIDELINES

4.1

Fruit Trees and Vines

Olive

Citrus

Apple

Plum

Almond

Pear

Peach

Walnut

Pistachio

Apricot

Avocado

Sweet cherry

Grapevine

Kiwifruit

5.

EPILOGUE

Acknowledgments

This publication has relied on inputs from a large number of people and institutions in various forms, including expert consultations, contributions at project workshops, experimental data and authorship.

Universities and national and international research institutions from many regions of the world have provided data and insights, which forms part of the vast amount of information and knowledge condensed in this state-of-the-art publication Crop yield response to water.

Particularly significant has been the involvement of key CGIAR Centres, specifically IRRI, ICARDA, ICRISAT, CIMMYT and CIP, and the FAO/IAEA Joint Division. Working together with the colleagues from these Centres has strengthened the institutional partnership and enhanced the synergy towards filling the gaps between scientific research and field implementation, theoretical knowledge and field practice, investigation and actual operation.

Most of these scientists, experts and colleagues are listed in this publication either as editors, authors or as scientists who have contributed with data and tests for the model Aquacrop. We are grateful to all of them for their highly valuable inputs.

We would like to thank the following experts who were involved in the initial stage of this endeavour: John Passioura (CSIRO, Canberra, Australia), Luigi Cavazza (University of Bologna, Italy), Hugh Turral (formerly IWMI, Colombo, Sri Lanka), John Annandale (University of Pretoria, South Africa), Dogo Seck (CERAAS, Dakar, Senegal), Lamourdia Thiombiano (FAO, Sub-Regional Office for Central Africa, Libreville, Gabon), Netij Ben-Mechlia (INAT, Tunis, Tunisia), Claudio Stockle (Washington State University, Pullman, United States), Florent Maraux (CIRAD, Montpellier, France), François Tardieu (INRA, Montpellier, France), Tsugihiro Watanabe (RIHN, Tokyo, Japan), Hiroshi Nakagawa (Ishikawa Prefectural University, Ishikawa, Japan), Long Nguyen (Joint FAO/IAEA Division, Vienna, Austria), Tim Wheeler (University of Reading, Reading, United Kingdom), Jan Doorenbos and Amir Kassam (formerly FAO, Land and Water Division, Rome, Italy) and Gerardo van Halsema (Wageningen University and Research Centre, Wageningen, The Netherlands). Their inputs have been determinant in correctly setting the initial condition of this work.

The support of colleagues at FAO at different stages has been indispensable for this publication. Special thanks go to Martin Smith, who started the process and later overviewed the various chapters, providing inputs and feedback to the editors to improve harmonization among the chapters and between the present and past FAO publications. Martin was ably assisted by Giovanni Munoz and Robina Wahaj. Shortly after the initial phase, an intense period of work started and we are particularly grateful to Gabriella Izzi and Lee K. Heng who managed the extensive experimental data set collected for calibration, maintained the liaison between all the collaborators, and contributed considerably to the various calibration and tests of Aquacrop. After one year of diligent effort at FAO, Lee moved to the FAO/IAEA Joint Division in Vienna, but continued to cooperate enthusiastically on model calibration and testing. Gabriella moved to the World Bank after two years of intense work on the model at FAO. More recently, support has been provided by Michela Marinelli, Patricia Mejías Moreno and Jippe Hoogeveen. We thank them for their continuous help and critical exchange in the completion of this product.

The design and accurate execution of the layout and editing of the manuscript are critical for this publication, requiring meticulous attention to the numerous details in the text, graphs, pictures, drawings, equations, symbols and citations. For their professional and immensely patient devotion to these tasks, we would like to thank the FAO collaborators Gabriele Zanolli, Paolo Mander, Rosemary Allison and Jim Morgan. A very special recognition goes to Margherita Bongiovanni for providing artistic, beautiful and accurate botanical illustrations of the crops.

Finally, we thank the former Assistant Director-General of Agriculture and Consumer Protection Department, Ms Louise Fresco, for her early endorsment and support of the iniciative, and both the Land and Water Division, Mr Parviz Koohafkan, and the Assistant Director-General of Natural Resources Management and Environment Department, Mr Alexander Müller, for their strong and continuous support of the ‘Water’ programme making possible this publication and the development of the model Aquacrop.

Without this collective and interdisciplinary effort, this outcome could not have been achieved.

Acronyms of institutions

CER:

Canale Emiliano Romagnolo, Italy

CNR:

Consiglio Nazionale delle Ricerche, Italy

CMMYT:

Centro Internacional de Mejoramiento de Maíz y Trigo, Mexico

CIP:

Centro Internacional de la Papa, Peru

CONICET:

Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina

CRA:

Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Italy

CRI:

Cotton Research Institute, Uzbekistan

CSIRO:

Commonwealth Scientific and Industrial Research Organisation, Australia

DSIR:

Department of Scientific & Industrial Research, India

EEAD-CSIC:

Estación Experimental Aula Dei - Consejo Superior de Investigaciones Científicas, Spain

EMBRAPA:

Empresa Brasileira de Pesquisa Agropecuária, Brazil

FAO:

Food and Agriculture Organization of the United Nations, Italy

GRI:

Golan Research Institute, Israel

GRC:

Geisenheim Research Centre for Viticulture and Grapevine Breeding, Germany

IAM-B:

Mediterranean Agronomic Institute of Bari, Italy

IAEA:

International Atomic Energy Agency, Austria

IAS-CSIC:

Instituto de Agricultura Sostenible - Consejo Superior de Investigaciones Científicas, Spain

ICARDA:

International Center for Agricultural Research in the Dry Areas, Syria

ICREA:

Institució Catalana de Recerca i Estudis Avançats, Spain

ICRISAT:

International Crops Research Institute for the Semi-Arid Tropics, India

INIA:

Instituto Nacional de Investigaciones Agropecuarias, Chile

INRA:

Institute National de la Recherche Agronomique, France

INTA:

Instituto Nacional de Tecnologia Agropecuaria, Argentina

IRD CEFE-CNRS:

Institut de Recherche pour le Développement Centre d’Ecologie Fonctionnelle & Evolutive - Centre National de la Recherche Scientifique, France

IRRI:

International Rice Research Institute, Philippines

IRTA:

Institut de Recerca i Tecnologia Agroalimentaries, Spain

ISPA:

Istituto di Scienze delle Produzioni Alimentari, Italy

IVIA:

Instituto Valenciano de Investigaciones Agrarias, Spain

KESREF:

Kenya Sugar Research Foundation, Kenya

LARI:

Lebanese Agricultural Research Institute, Lebanon

LLNL:

Laurence Livermore National Laboratory, USA

SARDI:

South Australian Research and Development Institute, Australia

SASRI:

South African Sugarcane Research Institute, South Africa

SIA:

Servicio de Investigación Agraria, Spain

USDA-ARS:

United States Department of Agriculture – Agricultural Research Service, USA

WB:

World Bank, USA

VLIR-UOS:

Flemish Inter-University Council, Belgium

ZIS:

Zhanghe Irrigation System, China

List of principal symbols and acronyms

B

Dry biomass, of shoot for non-root crops, and of shoot plus storage root or tuber for root crops [tonne/ha or kg/m^2]

Ca

Mean atmospheric CO_2 concentration for the actual year [ppm]

Ca,o

Mean atmospheric CO_2 concentration for the year 2000 [ppm]

cco

Canopy size of the average seedling at 90% emergence [cm^2]

CC

Green canopy cover [percent or fraction]

CC*

Green canopy cover adjusted for micro advection [percent or fraction]

CCmeas

Canopy cover measured [percent or fraction]

CCsim

Canopy cover simulated [percent or fraction]

CCo

Initial canopy cover, canopy cover at 90% emergence [percent or fraction]

CCpot

Potential canopy cover [percent or fraction]

CCx

Maximum green canopy cover [percent or fraction]

CDC

Canopy decline coefficient [percent or fraction of canopy decline per unit time]

CGC

Canopy growth coefficient [percent or fraction of canopy growth per unit time]

CN

Curve number or surface runoff coefficient

[CO_2]

Carbon dioxide concentration of the atmosphere [ppm]

CWSI

Crop water stress index

d

Day

dp

Plant density [plants per unit surface]

dref

Reference plant density [plants per unit surface]

Dr

Soil water depletion of the root zone [mm]

DAE

Days after emergence [day]

DAP

Days after planting [day]

DI

Deficit irrigation

DP

Deep percolation [mm per unit time]

DU

Distribution uniformity

E

Soil evaporation [mm per unit time]

Edz

Surface evaporation from the rest of the soil surface outside the emitter wetting pattern [mm per unit time]

EStage I

Soil evaporation at Stage I (wet soil surface) [mm per unit time]

EStage II

Soil evaporation at Stage II (drying soil surface) [mm per unit time]

Ewz

Surface evaporation from the soil wetted by the emitters [mm per unit time]

ECe

Electrical conductivity of the saturated soil-paste extract [dS/m]

ECen

Electrical conductivity of the saturated soil-paste extract: lower threshold (at which soil salinity stress starts occurring) [dS/m]

ECex

Electrical conductivity of the saturated soil-paste extract: upper threshold (at which soil salinity stress has reached its maximum effect) [dS/m]

ET

Evapotranspiration [mm per unit time]

ETa

Actual evapotranspiration [mm per unit time]

ETc

Crop evapotranspiration under standard conditions [mm per unit time]

ETx

Maximum evapotranspiration [mm per unit time]

ETo

Reference evapotranspiration [mm per unit time]

fage

Reduction coefficient describing the effect of ageing on Kc,Trx [1/d]

fcc

Fraction of the orchard ground surface occupied by the cover crop

fCO2

CO_2 factor for atmospheric CO_2 normalization

fHI

Adjustment factor for HIo

FC

Field capacity

FI

Full irrigation

FDR

Frequency domain reflectometry

FTSW

fraction of transpiring soil water

gs

Stomatal conductance [m/s]

G

Ground cover fraction of the tree canopy

GDD

Growing degree days [°C d]

GIR

Gross irrigation requirement [mm or m3/ha per unit time]

GIS

Geographical information systems

HI

Harvest index [percent or fraction]

HIo

Reference harvest index [percent or fraction]

I

Infiltration [mm per unit time]

Kc

Crop coefficient

Kcb

Basal crop coefficient representing Kc for a dry soil surface having little evaporation but full transpiration

Kcc

Cover crop coefficient

Kext

Radiation extinction coefficient

Kp

Pan coefficient (for the pan evaporation method to determine ETo)

Kr,t

Empirical coefficient relating the ETc of an orchard of incomplete cover to that of a mature orchard

Ksat

Saturated hydraulic conductivity [mm per unit time]

Ks,e

Empirical soil evaporation coefficient

Ky

Yield response factor

Kc,Tr

Crop transpiration coefficient

Kc,Tr x

Crop transpiration coefficient for when the canopy fully covers the ground (CC = 1) and stresses are absent

Ke

Soil evaporation coefficient for fully wet soil surface

Kex

Soil evaporation coefficient for fully wet and non-shaded soil surface

Kr

Evaporation reduction coefficient

Ks

Stress coefficient

Ksb,c

Cold stress coefficient for biomass production

Kspol,c

Cold stress coefficient for pollination

Kspol,h

Heat stress coefficient for pollination

Ksaer

Water stress coefficient for water logging (aeration stress)

Ksexp,w

Water stress coefficient for canopy expansion

Kssen

Water stress coefficient for canopy senescence

Kssto

Water stress coefficient for stomatal closure

LAI

Leaf area index [m^2 leaf area/m^2 soil surface]

LAIref

LAI of the same crop planted at a reference density

LWP

Leaf water potential [MPa]

NIR

Net irrigation requirement [mm per unit time or m^3/ha per unit time]

p

Fractional depletion of TAW

pupper

Upper threshold of p (no water stress: Ks = 1)

plower

Lower threshold of p (maximum water stress: Ks = 0)

P

Precipitation or rainfall [mm]

PAR

Photosythetically active radiation [μmol per m^2 of surface per s]

PRD

Partial root drying

PWP

Permanent wilting point

RDI

Regulated deficit irrigation

RAW

Readily available soil water in the root zone [mm]

REW

Readily evaporable water at the top of the soil profile [mm]

RUE

Radiation use efficiency [Kg of biomass per MJ of intercepted solar radiation]

RO

Surface runoff [mm per unit time]

SDI

Sustained (or continuous) deficit irrigation

t

Time [GDD or d]

T

Air temperature [°C]

Tbase

Base temperature (below which crop development does not progress) [°C]

Tc

Canopy temperature [°C]

Tmax = Tx

Daily maximum air temperature [°C]

Tmin = Tn

Daily minimum air temperature [°C]

Tn,cold

Minimum air temperature at upper threshold for cold stress affecting pollination [°C]

Tx,heat

Maximum air temperature at lower threshold for heat stress affecting pollination [°C]

Topt

Crop optimal daily temperature [°C]

Tupper

Upper temperature (above which crop development no longer increases with an increase in air temperature) [°C]

Tr

Crop transpiration [mm per unit time]

Trcc

Cover Crop transpiration [mm per unit time]

Trx

Maximum crop transpiration (for a well watered crop) [mm per unit time]

TAW

Total Available soil Water (between FC and PWP), equivalent to the soil water holding capacity in the root zone [mm/m]

TDR

Time domain reflectometry

TE

Transpiration efficiency [Kg of biomass per unit of water transpired]

VPD

Air vapor pressure deficit [kPa]

wz

Fraction of the soil surface wetted by the emitters

Wr

Soil water content of the root zone expressed as an equivalent depth [mm]

WP

Crop biomass water productivity [tonne of biomass per ha and per mm of water transpired or kg of biomass per m^3 of water transpired]

WP*

WP normalized for ETo and air CO_2 concentration [tonne /ha or kg/m^2]

WPB/ET

WP as the ratio of biomass to ET [kg/m^3]

WPB/Tr

WP as the ratio of biomass to Tr [kg/m^3]

WPfresh Y/ET

WP as the ratio of yield measured as fresh biomass to ET [kg/m^3]

WPlint/ET

WP as the ratio of lint (of cotton) to ET [kg/m^3]

WPsucrose/ET

WP as the ratio of sucrose (for sugar cane) to ET [kg/m^3]

WPY/ET

WP as the ratio of yield (as dry matter) to ET [kg/m^3]

WPY/Tr

WP as the ratio of yield (as dry matter) to Tr [kg/m^3]

X

Irrigation depth [mm]

XR

Required irrigation depth [mm]

Y

Yield [tonne/ha or kg/ha]

Ya

Actual yield [tonne/ha or kg/ha]

Yx

Maximum yield [tonne/ha or kg/ha]

Ze

Effective rooting depth [m]

Zx

Maximum effective rooting depth [m]

Zn

Minimum effective rooting depth [m]

θ

Volumetric soil water content [m^3 of water / m^3 of soil]

Ψstem = SWP

Stem water potential [MPa]

Ψleaf = LWP

Leaf water potential [MPa]

1. Introduction

Food production and water use are inextricably linked. Water has always been the main factor limiting crop production in much of the world where rainfall is insufficient to meet crop demand. With the ever-increasing competition for finite water resources worldwide and the steadily rising demand for agricultural commodities, the call to improve the efficiency and productivity of water use for crop production, to ensure future food security and address the uncertainties associated with climate change, has never been more urgent.

To examine the pathways for increasing the efficiency and productivity of water use, the yield response of crops to water must be known. This relationship is complex in nature and various attempts have been made to provide simplified, though sound, approaches to capture the basic features of the response.

FAO’s first publication that presented a relationship between crop yield and water consumed was Irrigation and Drainage Paper No. 33 Yield Response to Water (Doorenbos and Kassam, 1979). This approach, discussed in Chapter 2, is based on one single equation relating the relative yield loss of any crop (either herbaceous or woody species) to the relative reduction of water consumption, i.e. evapotranspiration, by way of a coefficient (ky), which is specific for any given crop and condition. This approach has provided a widely-used standard for synthetic water production functions, still in use today. This simplification, however, made this approach more suitable for general planning, project design and rapid appraisal purposes, often providing a first-order approximation.

Over the last three and half decades, new knowledge has enlighten processes underlying the relationship between crop yield and water use and technology has improved. Further, novel needs have emerged related to the planning and management of water in agriculture, including those arising from climate change. FAO has, therefore, revisited the approach to quantify crop yields in response to water use and water deficit. The end product of this effort is a crop simulation model named AquaCrop, which balances accuracy, simplicity and robustness and is described in Chapter 3. The conceptualization and development of this modelling approach is the result of a number of years of consultation and collaboration with scientists, crop specialists and practitioners worldwide, consolidating the vast amount of knowledge and information available since 1979.

AquaCrop uses the original equation of Doorenbos and Kassam (1979) as a point of departure and evolves from it by calculating the crop biomass, based on the amount of water transpired, and the crop yield as the proportion of biomass that goes into the harvestable parts. An important evolution is the separation of the non-productive consumption of water (soil evaporation) from the productive consumption of water (transpiration). Furthermore, the timescale of the original equation is seasonal, or growth-stages that are weeks long in duration, while the timescale used in AquaCrop is daily, in order to better represent the dynamics of crop response to water. Finally, the model allows for the assessment of responses under different climate change scenarios in terms of altered water and temperature regimes and elevated carbon dioxide concentration in the atmosphere. AquaCrop simulates growth, productivity and water use of a crop day-by-day, as affected by changing water availability and environmental conditions. The results of calibration and testing of the model so far provide grounds for confidence in its performance.

The development of standard crop parameters has made the model accessible to several types of users in different disciplines and for a wide-range of applications. AquaCrop is mainly aimed at practitioner-type end users such as those working for extension services, consulting engineers, irrigation districts, governmental agencies, nongovernmental organizations, and various kinds of farmer associations for use in the development of irrigation schedules and management decisions. Economists and policy specialists can also use this model for planning and scenario analysis. In addition, research scientists should find the model valuable as a tool for analysis and conceptualization. Overall, AquaCrop allows proper investigation of strategic planning and management to improve the efficiency and productivity of water use in herbaceous crop production. It is not designed for use with trees and vines.

Chapter 3 not only describes AquaCrop but also provides samples of applications for specific purposes and guidelines for calibration.

Chapter 3 also provides the agronomic features of the sixteen crops for which the model has been calibrated and validated. The crops covered are: wheat, rice, maize, soybean, barley, sorghum, cotton, sunflower, sugarcane, potato, tomato, sugar beet, alfalfa, bambara groundnut, quinoa and tef. Additional crops will soon be calibrated and their agronomic features described. The goal is to provide an overview of each crop’s physiology and agronomy for users interested in applying the model to a particular crop at a given location. Furthermore, the overview can serve as a reference when calibrating the model for different crop classes. The description of each crop includes crop growth and development, water use and productivity, responses to water deficits and expected yields.

Fruit production has risen in importance over the past decades for increasing the productivity and competitiveness of small-scale farmers around the world. Fruit not only provides better income opportunities for growers, but is also pivotal in providing more healthy diets to consumers. The yield response to water of fruit trees and vines forms the second major part of this publication, presented in Chapter 4. The complexity of tree crops resulting from carryover effects from one year to the next and the large divergence among cultivars, however, precluded using a relatively simple modelling approach, as that used for herbaceous crops. Therefore, a Guideline is presented instead, which includes a general section on the irrigation of fruit trees and vines, and a special section covering physiological and agronomic features of each individual crop species. While the general section provides the technical background and guidelines for efficient irrigation management, the sections on individual crops give specific responses to water, with a common format, covering the following key items: growth and development, crop water requirements, yield response to water supply, and recommended strategies for deficit irrigation. The focus of Chapter 4, in fact, is to synthesize available data and to generate production functions to glean opportunities in many cases for reducing water supply without yield or net income penalties. Particular attention in this chapter is paid to safeguarding farmers’ net income and, in some cases, to enhancing fruit quality. Crops covered in Chapter 4 include olive, citrus, apple, plum, almond, pear, peach, walnut, pistachio, apricot, avocado, sweet cherry, grapevine and kiwi. As more information becomes available, other fruit and plantation crops will be described and made available to users via the Internet.

Finally, Chapter 4 provides some closing remarks and the way forward from this FAO I&D Paper No. 66. A compact disc accompanies this publication, where the user will find most of the information products and guidelines relevant to her/his work.

This new publication will provide the practitioner with strengthened skills to: assess the effect of water shortages on crop production; investigate the impact of climate change on crop yield; compare the results of several water allocations plans; optimize irrigation scheduling (either full, deficit or supplementary); and enhance management strategies for increased water productivity and water savings.

Yield response to water:
the original FAO water
production function

2. Yield response to water: the original FAO water production function

LEAD AUTHORS

Martin Smith
(formerly FAO, Land and Water Division, Rome, Italy; currently retired)

Pasquale Steduto
(FAO, Land and Water Division, Rome, Italy)

GENERAL DESCRIPTION

FAO addressed the relationship between crop yield and water use in the late seventies proposing a simple equation where relative yield reduction is related to the corresponding relative reduction in evapotranspiration (ET). Specifically, the yield response to ET is expressed as:

where Yx and Ya are the maximum and actual yields, ETx and ETa are the maximum and actual evapotranspiration, and Ky is a yield response factor representing the effect of a reduction in evapotranspiration on yield losses. Equation 1 is a water production function and can be applied to all agricultural crops, i.e. herbaceous, trees and vines.

The yield response factor (Ky) captures the essence of the complex linkages between production and water use by a crop, where many biological, physical and chemical processes are involved. The relationship has shown a remarkable validity and allowed a workable procedure to quantify the effects of water deficits on yield.

This approach and the calculation procedures for estimating yield response to water were published in the FAO Irrigation and Drainage Paper No. 33 (Doorenbos and Kassam, 1979), which was considered one of FAO's milestone publications, and were used widely worldwide for a broad range of applications.

In this Chapter, the procedures used to quantify the yield response to water deficits using Equation 1 are briefly described. To get fully acquainted with the original procedures, the Ky use and related applications, the reader is referred to the original publication.

THE YIELD RESPONSE FACTOR (Ky)

The Ky values are crop specific and vary over the growing season according to growth stages with:

Ky >1: crop response is very sensitive to water deficit with proportional larger yield reductions when water use is reduced because of stress.

Ky <1: crop is more tolerant to water deficit, and recovers partially from stress, exhibiting less than proportional reductions in yield with reduced water use.

Ky =1: yield reduction is directly proportional to reduced water use.

Based on the analysis of an extensive amount of the available literature on crop-yield and water relationships and deficit irrigation, Ky values were derived for several crops (Table 1).

The analysis of deficit irrigation studies also allowed, for a majority of crops, the development of crop response functions when water deficits occur at different crop stages. As illustrated for maize in Figure 1, yield response will differ largely depending on the stage the water stress occurs. Typically flowering and yield formation stages are sensitive to stress, while stress occurring during the ripening phases has a limited impact, as in the vegetative phase, provided the crop is able to recover from stress in subsequent stages.

CALCULATION PROCEDURES

The calculation procedure for Equation 1 to determine actual yield Ya has four steps:

i.

Estimate maximum yield (Yx) of an adapted crop variety, as determined by its genetic makeup and climate, assuming agronomic factors (e.g. water, fertilizers, pest and diseases) are not limiting.

ii.

Calculate maximum evapotranspiration (ETx) according to established methodologies and considering that crop-water requirements are fully met.

iii.

Determine actual crop evapotranspiration (ETa) under the specific situation, as determined by the available water supply to the crop.

iv.

Evaluate actual yield (Ya) through the proper selection of the response factor (Ky) for the full growing season or over the different growing stages.

MAXIMUM YIELD (Yx)

The FAO I&D No. 33 recommended procedures for estimating maximum yield either from available local data for maximum crop yields or based on the calculation of maximum biomass and a corresponding harvest index, following two different procedures:

I.

Wageningen procedure (De Wit, 1968; Slabbers, 1978)

II.

Ecological zone approach (Kassam, 1977)

These procedures for yield estimation were developed in the late sixties and seventies. The considerable advances in agronomy and crop physiology, though, allow for the use of more precise methods to estimate maximum yields.

MAXIMUM CROP EVAPOTRANSPIRATION (ETx)

Procedures for determining ETx were based on FAO guidelines for crop-water requirements (ETc), and the ETx component of Equation 1, which is equal to ETc, was determined through the product of the reference-crop evapotranspiration (ETo) times the crop coefficient (Kc), i.e.

Original procedures for determining ETo are described in FAO I&D No. 24 (Doorenbos and Pruitt, 1977), offering different equations for its calculation according to the available climate data. Kc values were provided for a large number of crops and procedures to determine ETc over the growing season. Subsequently, revised procedures for calculating ETo were introduced in FAO I&D No. 56 (Allen et al., 1998), according to the FAO Penman-Monteith equation, which has now become the standard for estimating reference crop evapotranspiration.

ACTUAL CROP EVAPOTRANSPIRATION (ETa)

It is very difficult to estimate the actual crop evapotranspiration with precision. FAO I&D No. 33 provided tables from which ETa could be estimated from data on evapotranspiration rate, available soil water and wetting intervals. The tables however proved cumbersome and later were replaced by more accurate ETa calculations based on daily water balance calculations and digital computation methods.

Water balance calculations allow the level of available soil water in the root zone to be determined on a daily basis. As long as soil water is readily available for the crop, then ETa = ETx. When a critical soil moisture level is reached, defined as a fraction of the total available soil water content (p), transpiration is reduced because the stomata close and thus ETa < ETx, until the level of soil water in the root zone reaches the permanent wilting point, when ETa is assumed to be zero. This critical soil-water content is estimated from soil, crop and rooting characteristics and from the ETo rate. Depletion of soil-water content between p and the permanent wilting point will result in a proportional reduction of ETa.

FAO I&D No. 56 provides detailed procedures to assess the impact of stress on reduced evapotranspiration based on the water balance calculations with parameters on critical soil-water content values and rooting depth.

ACTUAL CROP YIELD (Ya) AND YIELD REDUCTION

Based on the estimated Yx and the calculated ETx and ETa , actual yield (Ya) may be determined using Equation (1).

However, in many planning and management studies requiring the estimation of yield in relation to the water availability, the yield reduction is expressed in relative terms, e.g. as a fraction or percentage rather than absolute (Ya).

As a matter of fact, the errors in estimating actual yields with water production functions are quite important, given the empirical nature of the relationships and the uncertainty of estimating the parameters discussed above.

COMPUTERIZED CALCULATION PROCEDURES (CROPWAT)

The use of the water production functions, Equation (1), is facilitated using the CROPWAT model (Smith, 1992) that provides computation procedures to determine yield reductions based on the FAO I&D No. 33 approach using daily water balance calculations. CROPWAT has been widely used as a practical management tool for irrigation scheduling and to estimate yield reductions under water deficit condition. Standard values for crop parameters (Kc, p, rooting depth, etc.) and Ky values are included in the model and can be modified to adjust to local conditions.

CROPWAT includes various modules to calculate reference evapotranspiration from daily, decade or monthly climatic data, crop-water requirements and irrigation water requirements from climatic and crop data, as well as scheme water supply for varying cropping patterns. CROPWAT was designed as a practical tool to carry out standard calculations for design and management of irrigation schemes, and for improving irrigation practices. It may also be used for irrigation scheduling under full or deficit irrigation conditions and for this, it uses the yield response factors derived from the crop-water production functions synthesized in FAO I&D No. 33. In order to allow the calculation from a wide-range of countries a climatic database CLIMWAT (Smith, 1993) has been included in the CROPWAT software, based on agro-meteorological data compiled by the FAO agro-meteorological service with over 3 200 stations from 144 countries and spanning the years from 1961 to 1990.

LIMITATIONS AND APPLICATIONS OF FAO I&D NO. 33

Procedures for estimating yield response to water developed in FAO I&D No. 33 have been very popular among economists and engineers, and have been used in several practical applications at field, scheme, regional and national level. For many years, this water production function approach has been the standard for planning and was an input to many economic models dealing with water allocation. It is still useful when a quick, first approximation of yield reduction related to water limitations is needed, especially when both herbaceous crops, trees and vines have to be considered simultaneously. Recent examples of applications can be found at basin scale (e.g. Xiaojuan et al., 2011), at field scale (e.g. Yacoubi et al., 2010) and in decision support systems (e.g. Gastélum et al., 2008).

While the FAO I&D No. 33 approach is solidly based on crop-water use principles, the simplification introduced by using one empirical yield response factor (Ky) to integrate the complex linkages between production and water use for crop production, limits its applicability for making accurate estimates of yield responses to water. Moreover, factors other than water such as nutrients, different cultivars, etc. also affect the response to water. In fact, adjustments for site-specific conditions would be needed if greater accuracy is sought. Determination of Ky values after adaptive research has been carried out in numerous studies for various crops and under different environments. Results showed a wide range of variations of Ky values and suggest that the within-crop variation in Ky may be as large as that between crops (Stanhill et al., 1985).

As an example of the differences in Ky values from different studies, it is instructive to compare the results under a cooperative research programme carried out by the International Atomic Energy Agency (IAEA) against the original Ky values of the FAO I&D No. 33. Table 2 summarizes the comparison of Ky values as published in the FAO Water Report No. 22, Deficit Irrigation, 2002.

Despite the robustness of the production function approach, the differences in Ky values between the two publications are important, and no specific trend can be extracted from the deviations in the Ky values under different conditions. It can be concluded that application of the water production function approach has proved useful for general planning, design and operation of irrigation projects and for the rapid assessment of yield reductions under limited water supply. It has found applications from water supply allocation among crops during periods of water shortage to various studies at national or regional scales, where generalized crop conditions prevail.

For improved strategies and practices related to on-farm water management aiming to increasing efficiency and productivity of water use, Equation 1 is of limited use and more accurate predictions are required for yield response under actual field conditions. AquaCrop (Chapter 3), provides a valid alternative for herbaceous crops, as the incorporation of advanced knowledge of crop-water relationships allows a more accurate modelling of actual crop growth and yield formation processes under various soil water availability, climate and soil fertility conditions.

REFERENCES

Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. In: FAO Irrigation and Drainage Paper No. 56. Rome, FAO.

De Wit, C.T. 1968. Plant production. In: Miscellaneous Papers Landbouw Hogeschool, Wageningen No. 3: 25-50.

Doorenbos, J. & Kassam, A.H. 1979. Yield response to water. FAO Irrigation and Drainage Paper No. 33. Rome, FAO.

Doorenbos, J. & Pruitt, W.O. 1977. Crop water requirements. FAO Irrigation and Drainage Paper No. 24. Rome, FAO.

FAO. 2002. Deficit Irrigation Practices. Water Reports No. 22. Rome.

Gastélum, J. R., Valdés, J.B. & Stewart, S. 2008. A decision support system to improve water resources management in the Conchos basin. Water Resources Management 23:1519-1548.

Kassam, A.H. 1977. Net biomass production and yield of crops. Present and potential land use by agro-ecological zones project. Rome, FAO.

Slabbers, P.J., Sorbello, V. & Stapper, M. 1978. Evaluation of simplified water-crop yield models. Agricultural Water Management.

Smith, M. 1992. CROPWAT. A computer programme for irrigation planning and management. In: FAO Irrigation and Drainage Paper No. 46. Rome, FAO.

Smith M. 1993. CLIMWAT for CROPWAT. FAO Irrigation and Drainage Paper No. 49. Rome, FAO.

Stanhill, G., Lake, J.V. & Rudd-Jones, D. 1985. The Water Resource for Agriculture. Philosophical Transactions. Royal Society Biological Series. London 310:161-173.

Xiaojuan, L., Ling, T., Shaozhong, K. & Fusheng, L. 2011. Comparison of spatial interpolation methods for yield response factor of winter wheat and its spatial distribution in Haihe basin of north China. Irrigation Science Volume 29, Issue 6, pp 455-468.

Yacoubi, S., Zayani, K., Zapata, N., Zairi, A., Slatni, A., Salvador, R. & Playa´n, E. 2010. Day and night time sprinkler irrigated tomato: Irrigation performance and crop yield. Biosystems Engineering, Volume 107, pp 25-35.

Yield response to water of herbaceous crops: the AquaCrop simulation model

Editors:

Pasquale Steduto
(FAO, Land and Water Division, Rome, Italy)

Dirk Raes
(KU Leuven University, Leuven, Belgium)

3. Yield response to water of herbaceous crops: the AquaCrop simulation model

This Chapter presents the main features of AquaCrop, the dynamic crop-growth model developed to predict yield response to water of herbaceous crops. The scientific basis of AquaCrop has been previously described (Steduto et al., 2009; Raes et al., 2009; Hsiao et al., 2009) and only the basic concepts and fundamental calculation procedures are briefly explained here, along with additional descriptions related to the input requirements, the user interface and the model outputs. Sample applications are provided to illustrate the usefulness of AquaCrop for benchmarking, irrigation scheduling, and for studying the effect of various soils, crop management practices, and the impact of climate change, on crop yield and water productivity. Finally, guidelines for parameterizing, calibrating and validating AquaCrop are presented. For further insights on the operation of the model and on the full algorithms details, the reader is referred to the AquaCrop Reference Manual (Raes et al., 2011).

3.1 AquaCrop: concepts, rationale and operation

LEAD AUTHORS

Pasquale Steduto
(FAO, Land and Water Division, Rome, Italy)

Dirk Raes
(KU Leuven University, Leuven, Belgium)

Theodore C. Hsiao
(University of California, Davis, USA)

Elias Fereres
(University of Cordoba and IAS-CSIC, Cordoba, Spain)

EVOLVING CONCEPTS IN YIELD RESPONSE TO WATER

Intercepted solar radiation is the driving force for both crop transpiration and photosynthesis. A direct relation exists therefore between biomass production and water consumed through transpiration. Water stress and reduced transpiration result in a reduced biomass production that normally also reduces yields. The yield response to water approach adopted in the FAO Irrigation and Drainage Paper No. 33 (Doorenbos and Kassam, 1979) linked a reduction in evapotranspiration to a proportional reduction in yield. As discussed in Chapter 2, the approach suffers drawbacks as a result of the aggregation of variables, i.e. final yield rather than its components and evapotranspiration rather than transpiration only. As a result, the yield response factor has proved, in several cases, to be significantly variable.

Maintaining the original concept of a direct link between crop water use and crop yield, the AquaCrop model evolved from the FAO I&D Paper No. 33 approach (Equation 1, Chapter 2) by separating non-productive soil evaporation (E) from productive crop transpiration (Tr) and estimating biomass production directly from actual crop transpiration through a water productivity parameter. The changes lead to the following equation, which is at the core of the AquaCrop growth engine:

Where, B is the biomass produced cumulatively (kg per m^2), Tr is the crop transpiration (either mm or m^3 per unit surface), with the summation over the time period in which the biomass is produced, and WP is the water productivity parameter (either kg of biomass per m^2 and per mm, or kg of biomass per m^3 of water transpired).

For most crops, only part of the biomass produced is partitioned to the harvested organs to give yield (Y), and the ratio of yield to biomass is known as harvest index (HI), hence:

The underlying processes culminating in B and in HI are largely distinct from each other. Therefore, separation of Y into B and HI makes it possible to consider effects of environmental conditions and stresses on B and HI separately.

Understanding of crop-water-yield relationships has improved markedly since 1979 and made the step-up from Equation (1) of Chapter 2 to Equation (1) and (2) of this Chapter possible. WP, when normalized for evaporative demand, behaves conservatively (Steduto et al, 2007). That is, normalized WP (designated as WP*) remains virtually constant over a range of environments. This has fundamental implications for the robustness of the model, which is further enhanced by quantification of the harvest index day-by-day over the yield formation period. Improved knowledge of plant responses to water stress on short time scales (from second to hours), enhanced computation capacity, and more accurate procedures to determine daily soil water status made it possible to simulate in daily time steps. This allowed the important change from a static approach to a dynamic growth model. A schematic representation of the evolution of AquaCrop from Equation (1) of Chapter 2 to Equation (1) and (2) of this Chapter is shown in Figure 1.

STRUCTURE AND COMPONENTS OF AQUACROP

AquaCrop is a dynamic model that simulates the attainable yield of herbaceous crops as a function of water consumption. In addition to its core functions, represented by equations (1) and (2), an extensive set of additional model components have been incorporated that includes:

the climate, with its thermal regime, rainfall, evaporative demand and carbon dioxide concentration;

the crop, with its development, growth and yield processes;

the soil, with its water (and salt) balance;

the management, with practices including irrigation, fertilization and mulching.

AquaCrop allows simulations of yield response to water under various management and environmental conditions, including climate change scenarios but, like most crop models, it does not account for the effects of pests and diseases.

These fundamental model components of AquaCrop, and their functions, are briefly described in this Section. For more detailed information, the user is referred to the AquaCrop Reference Manual (Raes et al., 2011), which is regularly updated as the model develops.

The climate

The atmospheric environment is identified by four daily weather variables: maximum and minimum air temperatures (Tx and Tn, respectively), rainfall and the evaporative demand of the atmosphere expressed as reference evapotranspiration (ETo) to be calculated according to the FAO Penman-Monteith equation (Allen et al., 1998). In addition, the annual mean carbon dioxide concentration (CO_2) of the atmosphere is required. Temperature influences crop development (phenology). Additional effects of more extreme temperatures are reduction of WP (hence biomass accumulation) when it is too cold, and reduction in pollination (hence HI) when it is either too cold or too hot. Rainfall, irrigation and ETo are determinants of water balance of the soil root zone and water stress. Atmospheric CO_2 concentration affects WP, canopy expansion and stomatal conductance. Tx, Tn, ETo and rainfall are derived from typical records of agrometeorological stations. Aside from its continuous rise over years, atmospheric CO_2 varies with an annual cycle and also with location. These variation are small and of minimal significance in terms of impact on crops. For simplicity, AquaCrop provides as default values the annual mean atmospheric CO_2 concentration from 1902 to the last year measured at Mauna Loa Observatory in Hawaii. Users may enter their own data set or the forecasted CO_2 following pre-determined climate change scenarios.

The crop

The crop component of the model includes the following subcomponents: phenology, canopy cover, rooting depth, crop transpiration, soil evaporation, biomass production, and harvestable yield.

After emergence, the crop grows and develops over its growth cycle by expanding its canopy and deepening its root system, transpiring water and cumulating biomass, while progressing through its phenological stages. The harvest index (HI) alters the portion of biomass that will be harvestable. It is important to note that in AquaCrop, beyond the partitioning of biomass into yield, there is no other partitioning among the various plant organs. This choice avoids dealing with the complexity and uncertainties associated with the partitioning processes, which remain among the most difficult to model. The relationships between root and shoot (biomass) or canopy in AquaCrop are not direct. Instead, root deepening rate is slowed by an empirical function once the stress becomes severe enough to initiate partial stomatal closure.

Phenology

The stages of crop development and their duration are characteristics frequently differentiating cultivars of the same crop from each other, and needs to be specified by the user for the cultivar in question. AquaCrop uses the growing degree days (GDD) as the internal default clock to account for effects of temperature regimes on phenology. The simulation runs and displays, however, in daily (calendar) time step. GDD is calculated following procedures described by McMaster and Wilhelm (1997), but with the exception that the minimum temperature (Tn) is not changed to be equal to the base temperature when it drops below the base temperature in the calculation. This is believed to represent better the damaging or inhibitory effects of cold on plant processes.

AquaCrop is applicable to all major herbaceous crop types: fruit or grain crops; root and tuber or storage-stem crops; leafy or floral vegetable crops, and forage crops typically subjected to several cuttings per season. For all but forage crops, the key developmental stages are: emergence, start of flowering (anthesis) or root/tuber/storage-stem initiation, time when maximum rooting depth is reached, start of canopy senescence, and physiological maturity. For forage cops, the list may be shortened to only emergence or start of regrowth in spring, time of cuttings, and start of senescence.

Genetic differences among species require calibration of the model for each species. Although some crop cultivars may require some adjustment of parameters in the calibrated model, in addition to phenology, calibration and validation using data from different studies in different parts of the world have given confidence that most of the fundamental parameters considered to be conservative (virtually constant) will be applicable even to different cultivars. The calibrated parameters available should at least serve as solid starting values, and can be adjusted if good data sets, used to test the values, indicate clearly a need. In this regard, it must be pointed out that calibrations should be done with data obtained from crops grown without any mineral nutrient limitation, as deficiencies of major nutrients (N, P, and K) do alter, to some extent, a number of the conservative parameters in AquaCrop.

Canopy development

Canopy cover (CC), more precisely green canopy cover, is a crucial feature of AquaCrop. Its expansion, ageing, and senescence, along with its conductance as controlled by stomata, determine the amount of water transpired, which in turn determines the amount of biomass produced. Expressing amount of foliage in terms of canopy cover (in fraction or percentage) and not as leaf area index (LAI) is one of the distinctive features of AquaCrop. This results in a significant simplification of the simulation, allowing the user to enter actual values of CC, even if only estimated visually. Moreover, CC is easily obtained from remote-sensing sources, either to check the simulated CC or as input for AquaCrop.

For the first half of the CC increase or development curve, an exponential equation, analogous to the equation for relative growth rate, is used for the simulation. Specifically,

where CC is the fractional coverage of the soil by the canopy at time t, CCo is initial CC (at t = 0) also in fraction, and CGC is canopy growth coefficient in fraction or percentage of existing CC at time t. CCo is a composite of canopies of individual plants and is calculated by multiplying plant density by the mean canopy size per plant (cco). This feature is used by the model to account for effects of plant density on canopy size. For simulations starting at emergence, cco is defined as the canopy size for the average seedling at the time of 90 percent emergence. For a number of crop species the value of cco has been assessed and found to be conservative; only small adjustments may be required for specific cultivars. CGC is also conservative, as long as time is expressed as GDD. This was demonstrated for a number of crop species when the same CGC gave good prediction of canopy development over time for a number of cultivars at different locations around the world (e.g. Hsiao et al., 2009; Heng et al., 2009, for maize).

CC calculated with Equation (3) over the canopy development period is compared with measured values in Figure 2. Also shown is the difference in canopy development due to plant density. As noted earlier, the fact that CCo is the product of cco and plant density provides a simply but fundamentally based procedure to account for variations in density.

The concept underlying Equation (3) (Bradford and Hsiao, 1982) is based on the reasoning that when green canopy cover is sparse, the growth of canopy, being dependent on the existing canopy size to capture radiation and carry out photosynthesis, should be proportional to the canopy size existing on that day. This led to the use of an exponential growth equation with a constant coefficient to simulate canopy development up to half of the maximum CC. When canopy grows further and covers more than half of the soil, radiation capture and photosynthesis begin to increase less than in proportion to the increase in CC because of mutual shading among the plants.

Therefore, Equation (3) no longer applies and for the second half of canopy development, CC follows an exponential decay equation,

where CCx is the maximum canopy cover for optimal conditions. AquaCrop simulates with Equation (3) up to the point when CC = 0.5 CCx, then switches to simulate with Equation (4) until CCx is reached. Default values for CCx are provided for the calibrated crops, based on various studies. Since CCx is determined also by plant density, a farm management option, the user should adjust the default CCx to the actual field situation.

As the crop approaches maturity, CC enters a declining phase resulting from leaf senescence. The decline of green canopy cover in AquaCrop is characterized by an empirical canopy decline coefficient (CDC), with units of fractional reduction in CC per unit of time, and can be adjusted to either lengthen or shorten the time span required to go from the start of senescence to the time when no green canopy remains (CC = 0).

The starting time for canopy senescence is critical because it determines the duration of the canopy when it is most effective in photosynthesis. As senescence starts both transpiration and photosynthesis decline, and biomass accumulations slow. Canopy senescence should be considered to start at the time when leaf senescence (indicated by yellowing) becomes significant, but only when canopy cover of the soil is incomplete and LAI is no more than 3 to 4.

Calibration of senescence requires accurate field observation or measurement of LAI during the late phase near maturity, as there is no effective way to assess green canopy cover during this phase because of the interference by the yellow or dead leaves. LAI can be converted to CC using equations in the literature arrived at by regressing CC against LAI (see Section 3.3). The progression of CC over a full crop cycle under non-stress conditions, as simulated with Equation (3) and (4) and CDC, and as measured on a crop, is depicted in Figure 3.

Root deepening

Root water uptake in AquaCrop is simulated by defining effective rooting depth (Ze) and the water extraction pattern. Ze at planting to near emergence is the soil depth from which the germinating seed or the young seedling can extract water. For water balance calculation by AquaCrop, a minimum effective rooting depth of 0.2 to 0.3 m (Zn) at the beginning is generally considered appropriate. Studies show that under favourable conditions, roots deepen at a relatively constant rate up to the time when fruit/grain begin to accumulate the major portion of photosynthetic assimilates. At this time root deepening is likely to slow. AquaCrop simulates this with an exponential function that makes the deepening of the root zone faster after planting in an early stage than later in the life-cycle of the crop (Figure 4).

Under optimal conditions, with no soil restrictions, the maximum effective rooting depth (Zx) is expected to be reached near the end of the crop’s life cycle, around the beginning of canopy senescence. If, at a certain depth, a soil layer is restricting root growth, roots will deepen at the normal rate until the restrictive layer is reached and then stops completely (Figure 4). Also a shallow groundwater table will limit rooting to the depth of the water table.

Water extraction by roots follows the common pattern used in simulations. Namely, 40 percent, 30 percent, 20 percent, and 10 percent of the required water is taken from the upper to the lower quarter of Ze, when water content is adequate. The pattern can be changed by the user, in cases warranted by specific physical or chemical characteristics of the soil.

Crop transpiration

Transpiration per unit land area is dependent on the fraction of land area covered by the canopy (CC) when there is insufficient stress to limit stomatal opening. The dependence is not strictly linear, because inter-row micro-advection supplies energy to the canopy in addition to that supplied by radiation, causing Tr to be somewhat more than being proportional to CC when CC is substantially incomplete. AquaCrop adjusts for this by assuming a slightly larger effective canopy cover with an empirical equation, developed from literature data. Tr is calculated from ETo with crop transpiration coefficient, denoted by Kc,Trx, defined as the crop coefficient (Kc) for transpiration when the canopy fully covers the ground (CC is close to and approaching 1.0) and stresses are absent. The effective CC is then multiplied by Kc,Trx and ETo to arrive at Tr. Restriction of Tr by water stress is elaborated on later in this section.

After maximum canopy cover (CCx) is reached and before the onset of senescence, the canopy ages slowly and undergoes a progressive though small reduction in transpiration and photosynthetic capacity. This is simulated by applying an ageing coefficient (fage) that decreases Kc,Trx by a constant and slight fraction (e.g. 0.3 percent) per day. After senescence is triggered, transpiration and photosynthetic capacity of the canopy drop more markedly with time.

Soil evaporation

Evaporation is mostly from the wetted soil surface unshaded by the canopy. AquaCrop calculates soil evaporation (E) separately from Tr, and for simplicity assumes that E takes place only from unshaded soil and is slightly less than being proportional to (1-CC) as the results of the adjustment for inter-row advection. The other key factor determining E is the wetness of the soil surface layer. When the soil surface is fully wet, E proceeds at the potential rate determined by the energy supply, and is about 10 percent more than the rate of ETo. This phase is known as Stage I evaporation and lasts from less than to a little more than 1 day, and can be adjusted in the model. As the soil surface begins to dry and water vapour pressure at the surface drops, E declines exponentially with the decline of the soil water in the top soil (a very thin surface layer). This phase is known as Stage II evaporation. AquaCrop simulate this by multiplying the potential E rate with an exponentially declining coefficient.

As the canopy senesces, it still shades the soil, but not as effectively, because canopy structure begins to disintegrate and dead leaves may be lost. The model continues to base soil E on CCx, but applies a simple factor to reduce the sheltering effect of the dying canopy.

Biomass production

The biomass water productivity (WP) is central to the operation of AquaCrop (Equation 1) and has shown a remarkable conservative behaviour (remaining nearly constant) when normalized for different evaporative demands. This has been demonstrated already in early studies of, among others, de Wit (1958) and was further advanced in studies by Tanner and Sinclair (1983), Hsiao and Bradford (1983) and Steduto et al. (2007).

The WP parameter introduced in AquaCrop is normalized for atmospheric evaporative demand, defined by ETo, and for the CO_2 concentration of the atmosphere. The normalized biomass water productivity (WP*) proved to be nearly constant for a given crop when mineral nutrients are not limiting, regardless of water stress except for extremely severe cases. Calibration of WP and normalization for evaporative demands has been based on the equation:

The summation is taken over the time intervals spanning the period when B is produced. [CO_2] outside the bracket indicates that the normalized value is for a particular air CO_2 concentration. For most crop species, WP* increases as air CO_2 concentration increases, allowing the simulation of impact on yield under various CO_2 and climate change scenarios. The equation is directly applicable when Tr and ETo data are for daily time intervals. When Tr and ETo are available for time interval larger than daily, the normalization requires caution. Background information and more details on normalization, including that for CO_2 concentration, are given in Steduto et al. (2007).

In the literature WP is commonly normalized for evaporative demand using air vapour pressure deficit (VPD) instead of ETo. The choice of using ETo was made because it has been demonstrated to be superior and accounts for advective energy transfer, which is ignored using VPD (Steduto et al., 2007). WP* is conservative for a given level of mineral nutrition, but may be reduced by nutrient deficiencies, particularly nitrogen. The calibrated WP* in the model for various crops are for situations where nutrients are ample. For nutrient limited situations, the model provides categories of soil fertility stress ranging from mild to severe nutrient deficiencies, with corresponding lower default WP* values.

The conservative nature of WP* is demonstrated in Figure 5, where cumulative B vs. cumulative Tr are plotted in (a), and cumulative B vs. cumulative normalized Tr (Tr/ETo) in (b), over the season for sweet sorghum (a C_4 crop), sunflower, wheat and chickpea (all three are C_3). It is seen in Figure 5a that the regression lines for different crops are linear but with different slopes. This means WP is constant for each crop but differs among the crops. In Figure 5b it is seen that normalization by ETo has coalesced the lines for the three C_3 crops into one, meaning their WP* are very similar. In this study sunflower was grown in May-August, wheat in February-May, and chickpea in April-June. So growth of these crops occurred in periods differing in atmospheric evaporative demand. Normalizing by ETo accounted for the difference in evaporative demand and showed that the three crops have very similar intrinsic water productivity (very similar WP*).

The single value of WP*, as show in Figure 5b, is used for the entire crop cycle for most of the crops. However, for crops with yields high in fat and protein content, more photosynthetic assimilates or energy is required per unit of dry matter produced after flowering and during the grain/fruit filling stage. For such crops, AquaCrop uses a single value for the WP* up to flowering, then declining gradually towards a lower WP* value to account for yield composition.

Harvestable yield

The partition of biomass into yield part (Y) is simulated by means of a harvest index (HI). For fruit or grain crops, published data on different species indicate there is a linear increase with time in the ratio of fruit or grain biomass to total above-ground biomass, from the time not too long after pollination and fruit set until maturity or near maturity. In common usage, HI is this ratio at maturity or harvest time. In AquaCrop, this ratio at earlier stages is also referred to as HI, for simplicity. For fruit/grain crops, HI is set to increase from zero at flowering, first over a short lag phase, when the increase starts slowly but accelerates with time, followed by a steady phase with the highest, but at constant rate of, increase (Figure 6). For root/tuber crops, HI is the ratio of the storage organ biomass to the total biomass (root plus shoot). The limited published data on root/tuber crops indicate that instead of increasing linearly after a lag phase, HI increases quickly shortly after storage organ initiation, then gradually slows until maturity. So HI is described by a logistic curve for these crops.

A reference point is needed for the upper range of HI. This point, termed reference HI (HIo), is the HI representative of well-developed cultivars adapted to their environments and grown under optimal conditions without limiting inputs. Calibrated HIo can be changed based on good data for a particular cultivar. The progression of HI for fruit/grain crops is exemplified in Figure 6.

The soil

In AquaCrop the soil is described by a soil profile and the characteristics of the groundwater table (if any). In AquaCrop the soil can be subdivided vertically up to five layers of variable depth, each layer (or horizon) accommodating different soil physical characteristics: the soil-water content at saturation; the upper limit of water content under gravity (commonly referred as field capacity (FC) for easy of reference); the lower limit of water content where a crop can reach the permanent wilting point (PWP); and the hydraulic conductivity at saturation (Ksat). From these characteristics AquaCrop derives other parameters governing soil evaporation, internal drainage and deep percolation, surface runoff, and capillary rise. The considered characteristics of the groundwater table are its depth below the soil surface and its salinity. The characteristics can remain constant during the season or vary throughout the simulation period.

By keeping track of the incoming (rainfall, irrigation and capillary rise) and outgoing (runoff, evapotranspiration and deep percolation) water and salt fluxes at the boundaries of the root zone, the amount of water and salt retained in the root zone can be calculated at any moment of the season (Figure 7).

When calculating the soil-water balance, the amount of water stored in the root zone can be expressed as an equivalent water depth (Wr) or as root zone depletion (Dr). The total available soil water (TAW) is the amount of water held in the root zone between field capacity and permanent wilting point. At field capacity root zone depletion (Dr) is zero, and at permanent wilting point Dr is equal to TAW.

To accurately describe surface runoff, the retention and movement of water and salt in the soil profile, soil evaporation and crop transpiration throughout the simulation period, AquaCrop divides both the soil profile and time into small fractions. AquaCrop divides the soil profile into 12 soil compartments with thickness Δz and runs with a time step Δt of 1 day. As such the one-dimensional vertical water and salt flow and root water uptake can be solved by means of a finite difference technique. Each of the 12 soil compartment has the hydraulic characteristics of the soil layer to which it belongs (Figure 8). The default size of the compartments (0.10 m) is automatically adjusted to cover the entire root zone. For deep root zones, ΔZ is not constant but increases exponentially with depth, so that infiltration, evaporation and transpiration from the top soil layers can be described with sufficient detail.

To simulate water movement in and out of the soil profile, AquaCrop considers surface runoff, infiltration, capillary rise, soil evaporation and crop transpiration. To simulate the redistribution of water into a soil layer, the drainage out of a soil profile, and the infiltration of rainfall and/or irrigation, AquaCrop makes use of an exponential drainage function that describes the declining water movement between saturation and field capacity. Upward water movement from a groundwater table to the soil profile is described by an exponential relationship between the capacity for capillary rise and the height above the groundwater table. The amount of water that moves upward depends not only on the depth of the groundwater table but also on the wetness of the top soil and the hydraulic characteristics of the soil layers. By considering the water fluxes in response to the processes listed above, the soil-water content is updated at the end of the daily time step in each of the 12 compartments (for full details see Raes et al., 2011).

While performing the water balance, AquaCrop also deploys the salt balance. Salts enter the soil profile by capillary rise from a saline groundwater table or together with the irrigation water. Salts are leached out of the soil profile by excessive rainfall or irrigation. Vertical salt movement in a soil profile is described by assuming that salts are transferred downwards by soil-water flow in macro pores as simulated by the drainage function. Since the solute transport in the macro pores bypass the soil water in the matrix, a diffusion process is considered to describe the transfer of solutes from macro pores to the soil matrix. Therefore the soil compartments are divided into a number of cells where salts can be figuratively stored. A cell is a representation of a bundle of pores with a specific diameter. The driving force for the horizontal diffusion is the salt concentration gradient that exists between the water solution in the cells at a particular soil depth. To avoid the building up of high salt concentrations at a particular depth, vertical salt diffusion is also taken into account. The driving force for this vertical redistribution process is the salt concentration gradient that builds up at various soil depths in the soil matrix.

The management

AquaCrop encompasses two categories of management practices: the irrigation management, which is quite complete in its various features, and the field management, which is limited to selected aspects and is relatively simple in approaches.

Irrigation management

Here options are provided to assess and analyse crop production and water management and use, under either rainfed or irrigated conditions. Management options include the selection of water application methods (sprinkler, surface, or drip either surface or underground), defining the schedule by specifying the time, depth and quality of the irrigation water of each application, or let the model automatically generate the schedule based on fixed time interval, fixed depth per application, or fixed percentage of allowable water depletion. An additional feature is the estimation of full water requirement of a crop in a given climate.

Field management

Three aspects are considered here: (i) fertility of the soil for growing the crop, whether native or by fertilization; (ii) mulching of the soil to reduce soil evaporation; and (iii) use of soil bunds (small dykes) to pond water or control surface runoff and enhance infiltration.

Effects of fertility on crop growth and productivity are not directly simulated. Instead, AquaCrop provides default adjustments of the pivotal crop parameters for several limiting fertility categories, ranging from near optimal to poor. The adjustments are multipliers, used to reduce: (1) CGC; (2) CCx; (3) CC, from the time when CCx is reached to maturity, but only gradually; and (4) WP*. These adjustments are based on the pattern of canopy evolution, photosynthesis, and WP at different fertility levels reported in several studies (e.g. Wolfe et al., 1988). To make the adjustments more reliable, biomass production data and observed canopy development, obtained at different fertility levels, should be used to do a local calibration, as provided for in AquaCrop.

Mulching is considered only for its effect on reducing soil E, and is to be specified by the user in terms of the percentage of soil surface covered and effectiveness of the mulching material.

The last management aspect concerns soil bunds and runoff. A bund and its height can be specified to prevent runoff and force all water from rain or irrigation to infiltrate the soil. Equally important, bunds allow the simulation of crops under ponding water such as paddy rice. For soils that are especially permeable, it is also possible to choose ‘no runoff’ without building bunds.

THE DYNAMICS OF CROP RESPONSES TO STRESSES IN AquaCrop

Environmental abiotic stresses such as water and temperature can have major negative impacts on canopy development, biomass production and yield, depending on timing of occurrence, severity and duration. In addition, stress from soil salinity or low soil fertility may have similar negative impacts, but be less dynamic in terms of speed of response and recovery. AquaCrop is designed to simulate crop responses first to water, but with sufficient attention also to temperature. AquaCrop takes an indirect approach to the deficiencies of mineral nutrients or the presence of salts in the root zone, avoiding attempts to simulate nutrient balances and their complex cycles that would make the model too complex. This indirect approach is outlined in the Fertility and Salinity stress section below.

The structural components of AquaCrop, including stress responses, and the functional linkages among them, are shown schematically in the diagram of Figure 9, to serve as a framework for the following discussion.

Stress response functions

Any type of stress is described in AquaCrop by means of a stress coefficient (Ks) which is an indicator of the relative intensity of the effect on a specific growth process and growth stage. In essence, Ks is a modifier of its target model parameter, and varies in value from one (no stress) to zero (full stress).

Above the upper threshold of a stress indicator, the stress is non-existent and Ks is 1. Below the lower threshold, the effect is maximum and Ks is 0 (Figure 10). For water stresses, the thresholds are soil water depletions (Dr) from the root zone. The upper threshold refers to the soil water that can be depleted before the stress starts to affect the process, while the lower threshold is the root zone depletion at which the stress inhibits the process completely. Indicators for air temperature stress are growing degrees, minimum air temperatures (cold stress) or maximum air temperatures (heat stress), while the electrical conductivity of the soil water in the root zone (ECe) determines salinity stress. When running a simulation, the degree of soil fertility selected as the Field management practice is the indicator for soil fertility stress. It varies from 0 percent, when soil fertility is non-limiting (Ks = 1), to a theoretical 100 percent when soil fertility stress is so severe that crop production is no longer possible (Ks = 0).

The relative stress level and the shape of the Ks curve determines the magnitude of the effect of the stress on the process between the thresholds. The relative stress is 0.0 at the upper threshold and 1.0 at the lower threshold (Figure 10). The shape of most of the Ks curves are typically convex, and the degree of curvature is set during model calibration.

Water stress

AquaCrop distinguishes stresses related to deficit and to excess water. In this publication, water stress routinely refers to the stress caused by a lack of water, and stress caused by excessive water is referred to as aeration stress. Water stress effects on productivity and water use processes are simulated by impacting: (1) canopy growth; (2) stomata conductance; (3) canopy senescence; (4) root deepening, and (5) harvest index. The normalized water productivity is assumed to be not impacted, based on extensive evaluation of the literature. The discourse that follows discusses the first three impacted processes together, and includes root depending at the end. Harvest index, a complex subject, is covered on its own in the last section on water stress.

Water stress response functions

For water stresses, the stress indicator is the root zone depletion (Dr), and the thresholds are soil water depletions from the root zone expressed as fractions (p) of the total available soil water (TAW). At the point when there is no depletion Ks = 1.0. As depletion progresses Ks does not drop below 1.0 until the upper threshold for stress effect is reached. This threshold is referred to as pupper. Further increase in root zone depletion, brings about lower values of Ks, until the lower threshold (designated as plower) is reached, where Ks becomes zero and the stress effect is maximum (Figure 11). Further depletion below plower has no additional effect and Ks remains zero. For water stresses the shape of the curve can vary between very convex to mildly convex to linear. Conceptually, the more convex the curve, the higher is the crop’s capacity to adjust and acclimate to the stress. A linear relationship indicates minimal or no acclimation. The stress thresholds, as well as the curve shape, are set by calibration and should be based on knowledge of the crop’s drought resistance or tolerance.

Being the middle link in the soil-plant-atmosphere continuum, the plant water status depends not only on soil-water status, but also on the rate of transpiration determined by atmospheric evaporative demand. The crop is more sensitive to soil-water depletion on days of high ETo, and less on days of low ETo. For simplicity, instead of modelling the soil-plant-atmosphere continuum, AquaCrop adjusts the thresholds of the Ks curve according to ETo, a measure of evaporative demand. As the threshold is set for environments with ETo = 5 mm/day, the model automatically adjusts the thresholds each day according to daily ETo when running a simulation. The extent of the adjustment is depicted in Figure 11.

Of the first three processes affected by water stress, extensive studies have shown that expansion of the leaf (hence the canopy) is the most sensitive, and stomatal conductance is substantially less sensitive. Depending on the species, leaf (hence canopy) senescence may be equally or slightly less sensitive than stomatal conductance (Bradford and Hsiao, 1982). Setting of the three upper thresholds for water stress for a crop should be consistent with these observations. Differences in the Ks curves for the three processes can be seen in the example for maize in Figure 12.

Quantifying stress dynamics with Ks

Generally, Ks is used as a multiplier to modulate the processes in question. For canopy expansion, its CGC (Equation 3 and 4) is actually multiplied by its specific Ks. This has no effect on the value of CGC as long as p is small (little depletion) and Ks remains 1.0. As soil water depletion pass the upper threshold (point a in Figure 12), Ks drops to less than 1.0, causing a reduction in the calculated effective CGC, and the canopy development slows as a result. As water depletes further, canopy grows even slower because of further decreases in Ks, and stops completely when the depletion reaches the lower threshold (point b in Figure 12) where Ks = 0.

If there is no replenishment of water in the root zone, the final size of CC would be less than the specified CCx. If the crop is indeterminant with the potential of growing leaves over much of its life-cycle, late replenishment of water would raise Ks above the lower threshold and restart canopy expansion. If the crop is determinant, however, late replenishment of water would not renew canopy expansion because the crop has no potential for leaf growth past the peak of the flowering period, and the model is programmed to end CC expansion.

As mentioned, stomata are considerably less sensitive to soil-water depletion than canopy growth, so its Ks is set not to decrease until the soil water is substantially more depleted. Tr is also calculated by multiplying with its Ks, and is not affected by water stress as long as root zone depletion is less than the upper threshold for its Ks. As more water depletes and the upper threshold (point c in Figure 12) is passed, Ks drops below 1.0 and calculated Tr becomes less than potential. Further depletion causes more reduction in Tr, and if it passes the upper threshold for senescence (point d in Figure 12), canopy starts to senesce and CC, made up of green foliage, decreases. If root zone water is replenished to above the upper thresholds at this point, stomata would open fully and Tr will increase, and canopy senescence will cease. Tr, however, will be lower than if there had not been water stress, because CC is now smaller. CC would increase gradually if the crop is at a stage when the potential for leaf growth is still there; otherwise CC would remain smaller, but would endure to the normal time of maturation if there is no additional depletion passing the upper threshold for senescence.

Senescence of the canopy can be triggered and accelerated by water stress any time during the crop life-cycle, provided the stress is severe enough. This is simulated by adjusting CDC, in units of fractional reduction of CC per unit of time, with an empirical equation based on Ks for senescence arranged in such a way that the value of CDC is zero when Ks is 1.0, but rises exponentially above zero when Ks falls below 1.0.

Root deepening is another process affected by water stress. It is well established that root growth is substantially less sensitive to water stress than leaves, and that the ratio of root to shoot is enhanced by mild to moderate water stress (Hsiao and Xu, 2000). In AquaCrop there is no link between roots and shoot (canopy and biomass) except indirectly via the effect of root zone water depletion on components of the production process. Specifically, deepening enlarges the root zone and reduces Dr (fractional water depletion) if the deeper soil layers are high in water content. This raises the value of particular Ks, leading to favourable changes in shoot processes. On the other hand, deepening into quite dry soil layers may actually increase Dr, because volume of the root zone becomes larger but there is little increase in its water the volume. Fractional depletion could then become larger with lower Ks and negative consequences on shoot processes.

Because root growth is less sensitive to water stress than leaves, root deepening is simulated in AquaCrop to proceed normally as root zone water depletes until pupper for stomatal closure is reached. At this point, a reduction as a function of Tr (hence Ks for stomata) is applied to the deepening rate. In this simple way, the model mimics the increase in root-shoot ratio under mild to moderate water stress, because canopy expansion starts to be inhibited at a much higher fractional water content of the root zone than Tr. So roots grow better than the canopy, down at least to the upper threshold for stomata.

Water stress effects on harvest index

So far attention has been on processes leading to biomass production, on which yield depends (Equation 2). Yield also depends on HI, and the impact of water stresses on HI can be pronounced, depending on the timing and extent of stress during the crop cycle. Effects of water stress on HI can be negative or positive.

Two of the negative effects are more straightforward. One is the inhibition of water stress on pollination and fruit set (successful formation of the embryo). If the stress is severe and long enough, the number of set fruit (or grain) would be reduced sufficiently to reduce HI and limit yield, in some cases drastically. Under good conditions most, if not all crop species, have been selected with a tendency to set more fruit than can be filled with the available photosynthetic assimilates, leading to the abortion of a portion of the set fruit early in their development. So reduction of fruit set by water stress may or may not reduce HI, depending on the extent of the reduction and the extent of the excessive fruit setting. AquaCrop simulates this also with the Ks approach, to reduce pollination (hence fruit set) each day according to the extent of water depletion. The effect on HI is adjusted for tendency to set excess fruit by providing categories differing in excessiveness.

Another negative impact on HI is the underfilling and abortion of younger fruits resulting from a lack of photosynthetic assimilates. Photosynthesis is tightly correlated with stomatal conductance. Water stress, by reducing stomatal opening, diminishes the amount of assimilates available to fill all developing fruit. The youngest fruit are then the most likely to be aborted and only the older fruit mature, but likely underfilled. This occurs during the grain filling and maturing period, when most of the vegetative growth has already taken place and most of the assimilates go to the grain. AquaCrop simulates this in two ways, one is simply by reducing HI with a coefficient that is a function of Ks for stomata. Stomatal closure may often be only the minor cause, however, because water stress at this growth stage commonly accelerates canopy senescence, resulting in an early decline in photosynthetic surface area and shortens the duration of the canopy. As programmed in AquaCrop, HI increases continuously up to the time of normal maturity (Figure 6), but only if a portion of the green canopy remains. As CC declines to some low limit value, HI is considered to have reached its final value. With CC reaching this low limit earlier because of stress induced early senescence, HI is automatically reduced. This effect can be dramatic if canopy duration is shortened substantially.

The last of the negative impacts on HI has to do with not having sufficient water stress. This centres on the competition between vegetative and reproductive growth, which also accounts for the positive impact of water stress on HI. As demonstrated for cotton and some other crops, HI can be reduced by overly luxurious vegetative (leaf) growth during the reproductive phase when water is fully available, while restricting vegetative growth by mild water (and nitrogen) stress is known to enhance HI. The cause is apparently the competition for assimilates. Negative effect on HI comes about when high water availability stimulates fast leaf growth, with too many assimilates diverted to the vegetative organs, depriving the younger potential flowers or nascent fruits so they drop off the crop. The end result is that too few fruits mature, reducing HI. On the other hand, mild water stress would reduce leaf growth substantially because it is most sensitive to water stress, while stomata, being substantially less sensitive, would remain open to maintain photosynthesis. Consequently, without the excessive diversion to vegetative organs, an ample amount of assimilates are available to enhance fruit retention and growth, leading to higher HI. AquaCrop simulates this behaviour relying on the Ks functions for leaf growth (Ksexp,w) and for stomata closure (Kssto), with HI being enhanced as Ksexp,w declines, and being reduced as Kssto declines. In the adjustment, HI is first enhanced as stress develops and vegetative growth is inhibited, then is more enhanced as stress intensifies, until stomata begin to close restricting photosynthesis, at which point the HI does not change. At some level of stress severity HI is reduced to the normal value because the positive effect of leaf growth inhibition is counterbalanced by the negative effect of stomata closure. As stress intensifies beyond this level, the overall effects would switch to negative with proper programme setting parameters (Figure 13).

In addition to water stress effects on competition for assimilates during fruit set and grain filling, studies have shown that mild to moderate water stress just before the reproductive phase (pre-anthesis) can enhance HI in some cases. The increase is correlated to the reduction in the accumulation biomass. AquaCrop includes an algorithm that operates in some crops to enhance HI based on the stress effect on reduction (relative to the potential) in biomass accumulated up to the start of flowering. The effect is dependent on the extent of reduction and limited to a range with optimal effect before the midpoint of the range.

Overall, in AquaCrop the reference HI is adjusted daily for water stress effects based on the inhibition of leaf growth, closure of stomata, reduction in biomass at pre-anthesis, reduction of green canopy duration resulting from accelerated senescence and failure of pollination.

Schematic representation

A schematic representation of the dynamics of the crop response to water stress, as simulated by AquaCrop, is given in Figure 14.

Temperature stress

By using GDD as the thermal clock, much of the temperature effects on crops, such as on phenology and canopy expansion rate, are presumably accounted for. The effect of temperature on transpiration is accounted for separately by ETo. Damaging effects of extreme or close to extreme temperatures, however, fall into the stress category and require different considerations.

In general AquaCrop simulates temperature stress effects with temperature stress coefficients, which vary from zero to 1.0 and are functions of air temperature or GDD. Value of GDD for a given day may be considered as an integrated measure of the daily temperature. Lower and upper thresholds delineate the temperature window wherein the process is affected. Lacking more definitive data currently, the shape of the Ks vs. temperature curve (Figure 15) is taken to be logistic, and may be changed in the future when better data become available.

One important temperature stress effect is on pollination, which is inhibited by temperatures either too high or too low. The left graph in Figure 15 illustrates the Kspol,c curve for cold stress on pollination, with daily minimum temperature (Tn) as the independent variable and the upper threshold set at a specified threshold temperature (Tn,cold) and lower threshold at 5 oC below Tn,cold. The curve for heat stress on pollination is the mirror image of the cold stress (right graph in Figure 15), except the independent variable is maximum temperature (Tx) and the range would be higher and the thresholds also higher. Analogous to the case of water stress, for cold stress pollination begins to be inhibited once the Tn drops below the upper threshold and Kspol,c drops below 1.0. Pollination decreases further as Tn and Kspol,c drop further, and is halted (Kspol,c = 0) at the lower Tn threshold or below. For heat stress it is the other way round: below the lower threshold Kspol,h is 1.0 and pollination is unaffected, and above the upper threshold Ks is zero and pollination is halted (Figure 15). The ultimate effect of temperature stresses on pollination is on HI, in exactly the same way as the effect of water stress.

In addition to effects on pollination, cold temperature may hamper biomass production beyond the restriction accounted for by GDD and irrespective of Tr and ETo. AquaCrop adjusts for this with again the stress coefficient approach. The biomass produced each day is multiplied by the Ks for cold stress (Ksb,c) to account for the restriction on production. Since biomass is derived from Tr using WP*, a constant, adjusting biomass this way, in essence, is an adjustment of WP*.

Aeration stress

The lack of soil aeration is another abiotic stress considered by AquaCrop. The treatment is simple, using the stress coefficient approach to modulate Tr, hence biomass production and ET. The independent variable for the Ks function (Ksaer) is the percentage of soil pore volume occupied by air in the root zone. The function is assumed to be linear with a settable upper threshold and the lower threshold fixed at zero (fully saturated soil). When the percentage air volume drops below the upper threshold, Ksaer starts to decrease below 1.0, causing proportional reduction in Tr.

The sensitivity of the crop to waterlogging is specified by setting the upper threshold, and by indicating the number of days waterlogging must remain before the stress becomes fully effective and Tr is affected. It should be pointed out that so far aeration stress parameters given for the crops already calibrated are all default values, because definitive data for crop under aeration stress are rare.

Low soil fertility (or mineral nutrient stress)

As already mentioned under Field management, AquaCrop does not simulate nutrient cycles and balances, but provides the means to adjust for fertility effects with a set of soil fertility stress coefficients, to simulate the impact on the growing capacity of the crop in terms of four pivotal components of productivity: canopy growth coefficient (CGC), maximum canopy cover (CCx), canopy decline, which includes a slow but substantial decline upon reaching CCx in addition to the senescence near maturity, and WP*. Accounting for the first three of these components, as affected by fertility, results in simulated pattern of CC vs. time very similar to plots based on measured data (Figure 16). The last component, WP*, is also adjusted downward for low fertility. The basis for making these adjustments are the following observations, well established in the literature: plants grown on soil deficient in nutrients (N, P, and/or K) produce leaves more slowly, with lower leaves senescing quite or very early but the upper and youngest leaves remain green until maturity or very near maturity. Photosynthetic capacity of the deficient leaves is less and their ratio of photosynthesis to transpiration is lower, consistent with the observed changes in WP in field studies.

AquaCrop provides default adjustments of the pivotal components for several categories differing in fertility limitation, ranging from near optimal to poor. To make the adjustments more reliable, biomass production data obtained at different fertility levels at the same location and time should be used to make a local calibration, as provided for in AquaCrop.

Soil salinity stress

The average electrical conductivity of saturation soil paste extract (ECe) from the root zone is the indicator of soil salinity stress. At the lower threshold of soil salinity (ECen), Ks becomes smaller than 1 and the stress starts to affect biomass production. Ks becomes zero at the upper threshold for soil salinity (ECex) and the stress becomes so severe that biomass production ceases. Values for ECen and ECex for many agriculture crops are given by Ayers and Westcot (1985) in the FAO Irrigation & Drainage Paper No. 29.

The soil water in the root zone becomes less available for root extraction when salts build up in the soil profile. This affects crop development, crop transpiration and hence biomass production and harvestable yield. AquaCrop does not simulate each of these crop responses but simulates only its global effect on biomass production. Given a user calibrated relationship between soil salinity stress and relative biomass production, AquaCrop translates the expected reduction in production into a stress resulting in stomata closure (Kssto) and affecting the canopy development (CGC, CCx and canopy decline upon reaching CCx). The simulation is similar as the approach used to simulate the crop response to low soil fertility.

INPUTS

AquaCrop uses a relative small number of parameters and fairly intuitive input variables, either widely used or largely requiring simple methods for their determination. Input consist of weather data, crop and soil characteristics, and management practices that define the environment in which the crop will develop, and are summarized schematically in Figure 17. The inputs are stored in climate, crop, soil and management files and can be easily retrieved from AquaCrop’s database and adjusted with the user interface.

Climate data

For each day of the simulation period, AquaCrop requires minimum (Tn) and maximum (Tx) air temperature, rainfall, and reference evapotranspiration (ETo) as a measure of the evaporative demand of the atmosphere. Further, the yearly mean atmospheric CO_2 concentration has to be known.

For consistency and as the standard, ETo is to be calculated using the Penman-Monteith equation (Allen et al., 1998), from full daily weather data sets. The full data set consists of radiation, Tx and Tn, wind run or speed, and humidity, all daily. An ETo calculator, a free public domain software, is available from the FAO website for the calculation (FAO, 2009). The calculator accepts weather data given in a wide variety of units. In the absence of full daily data set, the calculator can also estimate ETo from 10-day or monthly mean data, and make approximations when one or several kinds of the required weather data are missing. This makes it possible for a user to run rough simulations even when the weather data are minimal. Care must be taken, however, to avoid misuse of the calculator’s versatility. For validation and parameterization of the model for a particular crop, such approximation should not be relied on. The more the weather elements are missing the rougher the approximation of ETo, the less reliable would be the simulated results and derived AquaCrop parameters.

The daily, 10-day or monthly air temperature, ETo and rainfall data for each specific environment are stored in their own climate folder in the AquaCrop database from where the programme retrieves data at run time. In the absence of daily weather data, because the programme runs in daily steps, it invokes built-in procedures to approximate the required daily data from the 10-day or monthly means. Again, the more approximate, the less reliable is the outcome. This is particularly an acute problem for rainfall data. With its extremely heterogeneous distribution over time, the use of 10-day or monthly rainfall data completely grosses over the dynamic nature of crop response to water stress.

Additionally, AquaCrop provides the mean yearly CO_2 concentration required for the simulation, applicable for most locations. These yearly values are measured at the Mauna Loa Observatory in Hawaii and encompass the period from 1902 to the most recent available data. Several projected values can be retrieved from the AquaCrop database or entered by the user, following the climate change scenario to be investigated.

Crop parameters

Although grounded on basic and complex biophysical processes, AquaCrop uses a relative small number of crop parameters to characterize the crop. FAO has calibrated crop parameters for several crops (Section 3.4), and provides them as default values in the crop files stored in AquaCrop database. The parameters fall into two categories, distinguished as conservative or cultivar and conditions dependent (see also Section 3.3).

The conservative crop parameters do not change with time, management practices, climate, or geographical location. Regarding cultivar differences, so far tests show the same value of a conservative parameter is applicable to many cultivars, although some deviation may be expected for cultivars of extreme characteristics. The decision to assign a particular parameter to the conservative category is based on conceptual and theoretical analysis, and on extensive empirical data demonstrating near constancy. Depending on extensiveness of the data sets used for the calibration, the calibrated value for a conservative parameter may require some small adjustment. This should be done, however, only if the adjustment is based on high quality experimental data. Generally and in principle, the conservative parameters require no adjustment to the local conditions or for the common cultivars, and can be used as such in simulations. The conservative crop parameters are listed in Table 1.

The cultivar and condition dependent crop parameters are generally known to vary with cultivars and situations. Outstanding examples are life-cycle length and phenology of cultivars. In Table 2, an overview is given of crop parameters that are likely to require an adjustment to account for the local cultivar and or local environmental and management conditions. Reference HI (HIo) is usually conservative for well developed high yielding cultivars, and therefore is not included in Table 2 as a cultivar specific parameter. It is known, however, that some special cultivar may have HI consistently either slightly higher or lower than the common cultivars. Adjustment in HIo would be justified in such cases.

TABLE 1

Conservative crop parameters.

Crop growth and development

- Base temperature and upper temperature for growing degree days

- Canopy size of the average seedling at 90 percent emergence (cco)

- Canopy growth coefficient (CGC); Canopy decline coefficient (CDC)

- Crop determinacy linked/unlinked with flowering; Excess of potential fruit (%)

Crop transpiration

- Decline of crop coefficient as a result of ageing

Biomass production and yield formation

- Water productivity normalized for ETo and CO_2 (WP*)

- Reduction coefficient describing the effect of the products synthesized during yield formation on the normalized water productivity

- Reference harvest index (HIo)

Stresses

Water stresses

- Upper and lower thresholds of soil-water depletion for canopy expansion and shape of the stress curve

- Upper threshold of soil-water depletion for stomatal closure and shape of the stress curve

- Upper threshold of soil-water depletion for early senescence and shape of the stress curve

- Upper threshold of soil-water depletion for failure of pollination and shape of the stress curve

- Possible increase of HI resulting from water stress before flowering

- Coefficient describing positive impact of restricted vegetative growth during yield formation on HI

- Coefficient describing negative impact of stomatal closure during yield formation on HI

- Allowable maximum increase of specified HI

- Anaerobiotic point (for effect of waterlogging on Tr)

Temperature stress

- Minimum and maximum air temperature below which pollination starts to fail

- Minimum growing degrees required for full biomass production

TABLE 2

List of crop parameters likely to require adjustments to account for the characteristics of the cultivar and local environment and management.

Phenology (cultivar specific)

- Time to flowering or the start of yield formation

- Length of the flowering stage

- Time to start of canopy senescence

- Time to maturity (i.e. the length of crop cycle)

Management dependent

- Plant density

- Time to 90 percent emergence

- Maximum canopy cover (depends on plant density and cultivar, see Section 3.3)

Soil dependent

- Maximum rooting depth

- Time to reach maximum rooting depth

Soil and management dependent

- Response to soil fertility

- Soil salinity stress

It should be emphasized that for temperature dependent processes, such as canopy expansion with its conservative parameter CGC, the constancy of their parameters is entirely based on operating the model in the GDD mode. It is obvious, that for simulation of production and water use under different yearly climate or different times of the season, AquaCrop must be run in the GDD mode, otherwise temperature effects on key crop processes would be completely ignored by the model.

Another important consideration is the thoroughness of the calibration and the extensiveness of the data set on which the calibration is based. Diverse data sets are necessary to cover a wide-range of climate and soil conditions, and more cultivars. Particularly crucial are data sets for water-deficient conditions, on which the calibration of the water-stress parameters depend, and are often not readily available.

Of the number of crops calibrated by FAO, the thoroughness ranges from very good to fair and limited. Users need to consult the rating, available on the AquaCrop website, to determine the firmness of the conservative parameters. With time, calibration of the various crops will be improved based on additional data sets, and more crop species will be calibrated.

The reader is referred to Section 3.3 of this Chapter and the AquaCrop Reference Manual (Raes et al., 2011) for procedures on how to calibrate a crop for local conditions and how to modify the crop parameters in the data files.

Soil data

Needed parameters are: volumetric water content at field capacity (FC), permanent wilting point (PWP), and saturation, and the saturated hydraulic conductivity (Ksat), for each differentiated soil layers encompassing the root zone. From these characteristics AquaCrop derives other parameters governing soil evaporation, internal drainage and deep percolation, surface runoff and capillary rise (Raes et al., 2011). The default values for these parameters can be adjusted if the user has access to more precise information. In case some of the first four parameters values are missing, the user can make use of the indicative values provided by AquaCrop for various soil texture classes, or import locally-determined or derived data from soil texture with the help of pedo-transfer functions (see for example The Hydraulic Properties Calculator on the web: http://hydrolab.arsusda.gov/soilwater/Index.htm). These functions are based on primary particle size distribution of the different soil textures. Since these functions depend on texture class only, they do not account for differences in soil aggregation and should be taken as rough approximations. Users should adjust their estimates based on their own data and experience.

If a layer exists in the soil to stop root deepening, its depth has to be specified as well. In addition, the water content of the soil profile layers at the start of the simulation period need to be specified if it is not at field capacity.

Management data

Management practices are divided into irrigation management and field management. Under field management practices are choices of soil fertility levels, level of weed infestation, and practices that affect the soil-water balance such as mulching to reduce soil evaporation, soil bunds to store water on the field, and the elimination of runoff by conservation practices. The fertility levels range from non-limiting to poor, with effects on WP, on the rate of canopy growth, on the maximum canopy cover and on senescence.

Under irrigation management the user chooses whether the crop is rainfed or irrigated. If irrigated, the user specifies the application method (sprinkler, drip or surface), the fraction of surface wetted, and for each irrigation event, the irrigation water quality, the timing and the applied irrigation amount.

There are also options to asses the net irrigation requirement and to generate irrigation schedules based on specified time and depth criteria. Since the criteria can be changed during the season, the programme provides the means to test deficit irrigation strategies by applying chosen amounts of water at various stages of crop development.

THE USER INTERFACE AND OUTPUT

AquaCrop has a menu-driven software programme with a well-developed user interface. Multiple graphs and schematic displays in the menus help the user to discern the consequences of input changes and to analyse the simulation results.

The main menu

The Main Menu of AquaCrop provides three panels (Figure 18): Environment and Crop, Simulation and Project.

On the Environment and Crop panel of the Main menu, users have access to a whole set of menus of the four structural components of AquaCrop (climate, crop, management, soil), where files are selected, input data are displayed or updated and the planting date is specified. Data can be retrieved from input files stored in the database. In the absence of input files, default settings are provided.

On the Simulation panel, a simulation period different from life-cycle of the crop, and conditions of the soil water and salt content in the soil profile at the start of the simulation can be specified. Also, off-season (outside the growing period) practices (mulching or irrigation) can be specified. These features make it possible to simulate effects of fallow and pre-season irrigation.

On the Project panel, users can define projects to simulate multi-year cropping, either of the same crop or crop rotations. Note that the climate file needs to span the total simulation years. Under Project, users can also specify all the input files for any simulation trial for a single year or season, to avoid having to choose again each file individually when resuming the trial after exiting AquaCrop.

Display of simulation results

When running a Simulation, the user can track changes in soil-water and salt content, components of soil water balance, canopy development, transpiration, biomass accumulation, and yield and water productivity. The key simulation results are displayed in a number of graphs, updated at the end of each daily time step. From these graphs and associated displays the user can follow the dynamic effects of water, temperature, fertility and salinity stress on crop development and production and water use. By switching among different output displays on tab sheets for different aspect of crop, soil water and salt balance, the user can observe and analyse a particular event on a specific parameter.

Climate-Crop-Soil water is the most useful of the tab sheets (Figure 19). It displays three graphs plotted as a function of time: (i) depletion of root zone soil water (Dr), with the three water stress thresholds represents by lines of different colours; (ii) the corresponding progression of green canopy cover (CC), with the potential CC (no stresses) shaded gray; and (iii) the transpiration (Tr) of the canopy (for the simulated CC size), with potential Tr shaded gray. On top of the menu the biomass and yield are displayed along with the status of the water, temperature, soil fertility and salinity stresses. The graphs vividly show how canopy expansion and transpiration are affected when the absence of rain and irrigation led to drops in root zone water content below the threshold (green line, bottom graph) affecting canopy expansion, below the threshold for stomata (red line) affecting Tr, and below the threshold (yellow line) triggering canopy senescence. The reversal effects of water supply or irrigation are also obvious in the graphs.

One feature of the Simulation run menu is particularly helpful to users seeking to develop a regulated deficit irrigation schedule to optimize water use. By selecting short simulation time steps (1 to 3 days), a chosen amount of irrigation can be specified on the upper left panel at any time step (and date) during a simulation run, allowing quick and close scrutiny of the resultant benefits in the context of irrigation time, frequency, and amount. For more details, see Section 3.3 and the AquaCrop Reference Manual (Raes et al., 2011).

Output

On exiting the Simulation run menu, the user is asked whether to save the output, and can choose one or more of the categories of output: daily (Table 3) and/or seasonal. The files are automatically assigned the file extension OUT, with the name of the category of file contents forming the last part of the default file name as shown in Table 3.

TABLE 3

Default file name and content of the seven output files with daily simulation results.

Default file name

Nature and number (in parenthesis) of output variables in the file

ProjectCrop.OUT

crop processes, production, & related data (18)

ProjectWabal.OUT

soil water balance and related data (16)

ProjectProf.OUT

water content of root zone profile (10)

ProjectSalt.OUT

soil salinity of root zone profile (8)

ProjectCompWC.OUT

soil water content of model compartments (12)

ProjectCompEC.OUT

soil salinity of model compartments (12)

ProjectInet.OUT

net irrigation requirement (if simulated) (5)

Users should change the first part (Project) of file name to identify the particular simulation, otherwise the next simulation would be automatically assigned the same default file name and overwrite the files resulting from the preceding simulation. Daily simulation results are also summarized as seasonal totals. The files are stored by default in the OUTP directory of AquaCrop. The data in the files can be retrieved in spreadsheet programmes for further processing and analysis.

REFERENCES

Adams, J.E., Arkin, G.F. & Ritchie, J. T. 1976. Influence of row spacing and straw mulch on first stage drying. Soil Science Society American Journal 40: 436-442.

Allen R.G., Pereira L.S., Raes D. & Smith M. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56.

Ayers, R.S. & Westcot, D.W. 1985. Water quality for agriculture. FAO Irrigation and Drainage Paper No. 29. FAO, Rome.

Bradford, K.J. & Hsiao, T.C. 1982. Physiological responses to moderate water stress. pages 264-324. In: Lange, O. R., P. S. Nobel, C.B. Osmond & H. Ziegler, eds. Encyclopedia Plant Physiol. New Series, Vol. 12B. Physiological Plant Ecology II. Berlin, Springer-Verlag.

de Wit, C.T. 1958. Transpiration and crop yields. Versl. Landbouwk. Onderz. 64.6 Institute of Biological Chemistry Research On Field Crops and Herbage, Wageningen, The Netherlands.

Doorenbos, J. & Kasssam, A.H. 1979. Yield response to Water. FAO Irrigation and Drainage Paper No. 33. Rome, FAO.

FAO, 2009. ETo Calculator, Land and Water Digital Media Series No. 36, Rome.

Heng, L.K., Hsiao, T.C., Evett, S., Howell, T. & Steduto, P. 2009. Validating the FAO Aquacrop model for irrigated and water deficient field maize. Agronomy Journal 101:488-498.

Hsiao, T.C. & Bradford K.J. 1983. Physiological consequences of cellular water deficits. In: Taylor H.M., Jordan, W.A., Sinclair, T.R., eds. Limitations to efficient water use in crop production. Madison, Wisconsin, USA, ASA, pp. 227–265.

Hsiao, T.C., Lee H., Steduto, B., Basilio, R.L., Raes, D. & Fereres, E. 2009. Aquacrop - The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal 101:448–459.

Hsiao, T.C. & Xu, L.K. 2000. Sensitivity of growth of roots vs. leaves to water stress: biophysical analysis and relation to water transport. Journal of Experimental Botany 51: 1595-1616.

McMaster, G.S. & Wilhelm, W.W. 1997. Growing degree-days: one equation, two interpretations. Agricultural and Forestry Meteorology, 87: 291-300.

Raes, D., Steduto, P., Hsiao, T.C., & Fereres, E. 2009. Aquacrop - The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Agronomy Journal 101:438–447.

Raes, D., Steduto, P., Hsiao, T.C. & Fereres, E. 2011. Aquacrop – Reference Manual. Available at: http://www.fao.org/nr/water/aquacrop.html

Steduto P. & Albrizio R., 2005. Resource use efficiency of field-grown sunflower, sorghum, wheat and chickpea - II. Water use efficiency and comparison with radiation use efficiency. Journal of Agricultural And Forest Meteorology, 130 (3-4), 269-281.

Steduto P., Hsiao, T.C. & Fereres, E. 2007. On the conservative behavior of biomass water productivity. Irrigation Science, 25:189–207.

Steduto, P., Hsiao, T.C., Raes, D. & Fereres, E. 2009. Aquacrop - The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles. Agronomy Journal, 101:426–437.

Tanner C.B. & Sinclair T.R. 1983. Efficient water use in crop production: research or re-search? In: Taylor H.M., Jordan W.A., Sinclair T.R. (eds) Limitations to efficient water use in crop production. Madison, Wisconsin, USA, ASA, pp 1–27.

Villalobos, F. J. & Fereres, E. 1990. Evaporation measurements beneath corn, cotton, and sunflower canopies. Agronomy Journal 82:1153-1159.

3.2 AquaCrop applications

LEAD AUTHOR

Elias Fereres
(University of Cordoba and IAS-CSIC, Cordoba, Spain)

CONTRIBUTING AUTHORS

Sue Walker
(University of the Free State, Bloemfontein, South Africa),

Lee K. Heng
(Soil and Water Management & Crop Nutrition Section, Joint FAO/IAEA Division, Vienna, Austria),

Theodore C. Hsiao
(University of California, Davis, USA),

Pasquale Steduto
(FAO, Land and Water Division, Rome, Italy),

Dirk Raes
(KU Leuven University, Leuven, Belgium),

Gabriella Izzi
(formerly FAO, Land and Water Division, Rome, Italy; currently World Bank, Washington, USA),

Senthold Asseng
(formerly CSIRO, Wembley, Australia; currently University of Florida, Gainesville, USA),

Steve R. Evett
(USDA-ARS, Conservation & Production Research Laboratory, Bushland, Texas, USA)

INTRODUCTION

Agricultural production takes place in an environment characterized by risk and uncertainty. This is particularly so in arid and semi-arid zones where water supply to crops from rainfall is variable and erratic. Even in areas under irrigation, water scarcity is not uncommon and yields are often affected, therefore procedures and tools are needed to predict the crop response to a given supply of water, so as to reduce uncertainty and to manage risk. For a long time, FAO has worked on providing methods to assist a diverse range of users in determining the yield response to water. Recently, the development of AquaCrop by FAO provides an improved and powerful approach for the assessment of the attainable yield of the major herbaceous crops as a function of water supply.

The main outputs of AquaCrop are the yield and water use (E and Tr) of a crop grown at a specific location, with that climate, soil, and with a certain water supply (Steduto et al., 2009). When the input information is precise, its performance is accurate, as shown in the validation tests conducted in many locations (e.g. Mainuddin et al., 2010; Todorovic et al., 2009; Heng et al., 2009; Farahani et al., 2009). The information provided by crop simulation models such as AquaCrop may be used in a myriad of ways and by many different types of users. Yield predictions may be useful for farmers, extension specialists, field consultants, engineers, water planners, economists, policy analysts, and scientists. AquaCrop simulation results may also be inputs to other types of tools and models.

The type of application depends on the type of user, on the objective the user wants to achieve, and on the temporal scale of the analysis. At the farmers and agricultural technician level, the simulation of yield provides the information needed to explore the outcomes of decisions that can be made at three temporal levels:

Days to weeks: Decisions made at the operational level refer to those taken within a growing season, on a scale of days to few weeks, such as determining the date and amount of next irrigation or of a fertilizer topdressing application.

Weeks to months: Tactical farming decisions have a time frame of weeks to months and are typically made at the start or different times during the growing season. An example would be the determination of the seasonal irrigation scheduling programme, or a decision concerning the best planting density.

Years: Strategic decisions are long-term, when a series of years are considered in the analysis. Strategic decisions may be made with the aid of AquaCrop, for example in evaluating when the optimal planting date would be to exploit the stored soil water, based on the anticipated long-term rainfall, by running the model with different planting dates over a series of years.

There are many different farm management decisions at the three levels described, and the use of AquaCrop simulations can help in making better informed decisions.

Engineers involved in irrigation management over large areas, at scales above that of an individual farm, need to assess the impact of a number of decisions dealing with irrigation water allocation that scales up from a single farm to groups of farms, single or various irrigation districts, up to the river basin or catchment level. Water is typically allocated according to historical customs, or legal, institutional, political, or social criteria. In situations of water scarcity, economic considerations take a higher priority, and the focus must be placed on achieving efficient and equitable use of the limited resources; this is often accomplished by managing water more as an economic factor.

The economics and management of agricultural water demand and use require information on crop productivity as affected by water supply. This information has been typically obtained by engineers, water planners, and economists from empirical crop-water production functions that use a simple equation to relate yield to the amount of water consumed. AquaCrop, however, by dynamically simulating the yield response to different amounts of applied water under a specific set of agronomic conditions, provides a more powerful and flexible alternative and a more realistic range of results as compared to the traditional water production functions.

There could be many applications of AquaCrop at different scales, from the plot to the watershed. It can assist in benchmarking irrigation performance or the yield gap, and in making informed decisions from operational up to strategic water-related management decisions. It can be used to test the role of different soils-climate systems on water-limited crop production, and, can also be very useful for the analysis of different scenarios, including variations in climate (present and future), water supply, crop type, field management, etc.

It would be nearly impossible to describe all possible applications of AquaCrop. Therefore, what follows is a range of examples and case studies that illustrate some of the applications for different purposes. Users may find the model useful to resolve some of the questions that they face related to different aspects of the prediction of water-limited crop production. The applications described include the range of applicable scales: field to farm to irrigation district and regional scales. Other applications illustrate the usefulness for benchmarking, irrigation scheduling, variations in soils, agronomy and crop management practices as well as effects of variation in climate.

To fully appreciate the applications reported hereafter, the user must be already familiar with AquaCrop and with the overall data required to run the model adequately.

APPLICATIONS TO IRRIGATION MANAGEMENT AT THE FIELD AND FARM SCALES

Two types of applications are described. The first describes applications when the water supply is adequate, while the second type refers to examples of how to use AquaCrop to assist in coping with irrigation management under water scarcity.

CASE 1 - Developing a seasonal irrigation schedule for a specific crop and field

Specific data requirements:

long-term climatic data (Rain and ETo) statistically processed to determine typical climatic conditions of dry, wet, or average years. Note that average ETo is much less variable than average rainfall; thus, the user could combine average ETo information with seasonal daily rainfall from different years, representing dry, wet, and average years, if long-term ETo data is not available;

soil profile characteristics of the field as needed to run AquaCrop; and

crop characteristics as needed to run AquaCrop.

Approach:

The model is run for the season of typical year (dry, wet, average year) using the feature ‘Generation of Irrigation Schedule’ where the timing and depth of irrigation are determined by selected criteria. The selected time criterion depends on the objectives of the manager; for instance, the user can choose to irrigate every time the root zone water content is depleted down to 50 percent of its total available water or can choose to irrigate every time a certain depth of water has been depleted, such as 25 or 40 mm or even at a fixed time interval as used on many irrigation schemes. A ‘fixed application depth’ is typically selected as depth criterion. The selection of the fixed amount of water to apply depends on many factors such as farmers’ practices, the irrigation method, the irrigation interval, the rooting depth and soil type.

Output:

An indicative irrigation schedule for the crop-climate-soil combination is produced based on the criteria selected by the manager. This simulated schedule may be used for benchmarking the actual irrigation performance of a specific farmer against the ideal for that particular year or different schedules according to different irrigation criteria could be presented to the farmers for discussion.

CASE 2 - Determining the date of next irrigation with AquaCrop

Specific data requirements:

real-time weather data are used to run AquaCrop. Current season daily weather data are used to compute actual ETo and the soil-water balance from planting until the last day of available weather data, before the simulation of next irrigation date;

soil profile characteristics as needed to run AquaCrop; and

crop characteristics as needed to run AquaCrop.

Approach:

The model is run for the current season, using actual ETo data from planting until the last day for which actual weather data and thus ETo is available. From then on, the model is run for daily time steps using the average, long-term ETo information or weather forecast information, and the projected soil water depletion is simulated day by day.

Outputs:

By considering the current status of the soil-water balance and the depletion of soil water relative to thresholds for restricting canopy growth, transpiration and enhancing of senescence, the user can select the date of next irrigation based on his management goals or availability of water. Such a projection may be adjusted daily by entering new actual weather data to modify the long-term average ETo used in the projection.

CASE 3 - Determining the seasonal water requirements and its components for various crops on a farm

Specific data requirements

average or historical climatic data;

soil profile characteristics as needed to run AquaCrop; and

crop characteristics as needed to run AquaCrop for the various crops considered in the case study.

Approach:

AquaCrop is run for the selected crops on the corresponding soils, as selected by the user. For each crop-soil combination, the mode ‘Determination of Net Irrigation Water Requirement’ is used to determine irrigation needs. Then together with the output from ‘Generation of an irrigation schedule’, one can plan the timing and depth of irrigation scheduling across all the crops. The manager may vary the selected criteria for the different crops, depending on several factors such as their sensitivity to water deficits or according to total water available. This will enable him/her to learn how the crop will respond to different water regimes and to balance the requirements of different fields or crops according to water supply, thus providing a farm level management plan.

Outputs:

The seasonal water-balance components, and ETc and its components, E and Tr, will be extracted from the AquaCrop simulations, together with the net irrigation requirements for each crop. A comparison of the ETc of different crops and their irrigation needs, as affected by time of the year (winter vs. summer crops) and by season length and other crop characteristics can be performed by the user in different ‘run’s of the model and ‘saved to disk’. For instance, a farm in a Mediterranean, semi-arid climate with 450 mm/year of annual rainfall, had simulated ETc values for wheat, maize and potatoes of 425, 650 and 500 mm, respectively, while the corresponding net irrigation requirements were 105, 540, and 415 mm. This is because of the differences in the contribution of seasonal rainfall between a winter crop, wheat, where rainfall is a major contributor and ETo is low, and a summer crop, maize, grown in the rainless, warm summer. The differences between the two summer crops, were due to potato having a shorter growing season than maize. This information can then help the manager to make appropriate decisions regarding the distribution of the available irrigation water between crops.

CASE 4 - Benchmarking current irrigation practices

Specific data requirements

actual weather data for the irrigation season;

soil profile characteristics representative for actual farm conditions, as needed to run AquaCrop;

crop specific characteristics as required to run AquaCrop; and

irrigation practice details in terms of timing and amount of each application.

Approach

With the actual field data, a simulation run is carried out with the exact planting dates and plant population, and the model outputs (yield, irrigation, drainage and rainfall amounts from both ‘Production’ and ‘’Climate and Soil Water Balance’ tab sheets) are then compared against the actual field data. By evaluating the model output in this way, it would be possible to decide if the current schedule could be improved by reducing drainage or runoff losses and/or avoiding water deficits that may be less detrimental at other times of the season. By alternative trials in reiterative model runs, the user can improve the current irrigation schedule and propose an alternative schedule using the same amount of seasonal irrigation but that maximizes yield, i.e. an optimal schedule.

Outputs

The water balance components of the current schedule, the simulated yield and the yield water productivity are compared to information obtained from the field. Actual vs. simulated yields, corresponding to the current and optimal schedules, should be compared. Large differences between actual and simulated yield would be an indication that either there may be factors other than water (soil fertility, pests, etc...) that are affecting actual yields or that inadequate assumptions or incorrect inputs were made when running the model. If the yield difference is reasonable (i.e. < 15-20 percent), the improvements in the current schedule as predicted by the simulated optimal schedule are probably realistic and should be recommended for field testing.

CASE 5 - How to make best use of stored soil water when irrigation supply is limited

Specific data requirements

average climate, or real-time weather data;

soil profile characteristics, typical of the farm, as needed to run AquaCrop; and

crop specific characteristics as required to run AquaCrop.

Approach

The objective is to end the season with the soil-water content within the crop root zone fully depleted. For that purpose, the mode ‘Generation of Irrigation Schedule’ is run with two settings in the ‘Time’ and ‘Depth’ criteria so as to change them towards the end of the season. In the first setting an irrigation schedule is generated in which timing and application does not result in water stress. By selecting towards the end of the season a second time criterion (such as an interval longer than the remaining time to reach maturity, or an allowable depletion corresponding with wilting point) further irrigations are no longer generated and the end of the season will be reached with the root zone completely depleted.

Output

An irrigation schedule that leaves the profile completely dry at the end of the season is generated, thus maximizing the use of the water stored in the profile from rainfall and irrigation. A comparison between this schedule, that does not allow significant crop water deficits, and the standard schedule that it generates using the standard ‘Time’ and ‘Depth’ criteria should show the potential irrigation water savings by fully utilising the stored soil water. However, the practical details in terms of the amount of irrigation water applied, number of irrigations, and other parameters of the water balance need to be carefully considered.

CASE 6 - Developing deficit and supplemental irrigation programmes at a field scale

a) Deficit irrigation programme under a moderate (25-35 percent) reduction of normal water supply.

Specific data requirements

standard climate, soil, and crop data needed to run AquaCrop; and

the level of irrigation supply for the season relative to an adequate supply (obtained by running option ’Net Irrigation Requirement’ in AquaCrop) or usual irrigation water (IW) supply must be known.

Approach

The approach to be followed depends on the crop specific sensitivity to water deficits (Fereres and Soriano, 2007). An example for cotton using this model has been published (García-Vila et al., 2009). A standard schedule must first be developed with AquaCrop, as shown in CASE 2 using the normal IW supply for cotton under local conditions. Then, the amount of IW will be reduced by 30 percent, and there are many different choices to generate a deficit irrigation (DI) programme – two approaches may be followed:

plan the last application to end the season with the soil profile completely dry. This would be general methodology for most DI programmes (see Case 5); then, apply the same number of irrigations but reduce each of their depths by 30 percent in order to apply continuous or sustained DI; or

using knowledge of the differential sensitivity of cotton to water stress (see Cotton Section under 3.4) plan the crop water deficits that have the least impact on yield, using a so-called regulated DI (RDI). For instance, delay the timing of the first irrigation, then concentrate the water applications around flowering and early fruit set and finally impose more severe deficits as the season progresses after boll set. Two or three options of RDI should be simulated with the same amount of IW. Then the simulated yield values can be compared and the RDI programme that produces the highest yield for the same level of IW will be selected (García-Vila et al., 2009).

b) Deficit irrigation programme with a severe (50-60 percent) reduction in normal supply.

The approach should be the same as above. However, in this case, the number of irrigations must also be reduced during the beginning of the season and concentrated from early flowering to early fruit set, leading to an early senescence and a shorter growing season. This should have some yield penalty relative to full irrigation supply. Several simulations should be conducted (and saved to disk) to reach the best solution in terms of maximum harvest index which would lead to the maximum yield for the given IW.

c) Supplemental irrigation programme to determine the best timing for a single irrigation application

Specific data requirements

In addition to the standard data requirements of it, it is useful to have rainfall probability information to optimize the timing of a single application.

Approach:

In the real world, the availability of water determines the timing of application. In collective networks, the timing is imposed by the delivery schedule. If farmers have on-farm storage or access to groundwater, then there is flexibility in the timing of applications. The AquaCrop simulations will differ in each of these cases. It is also possible to use AquaCrop to simulate DI programmes in near real-time, i.e. for the current year, by running the model up-to-date, and then use rainfall probabilities for the coming weeks (available from weather services), and simulate the subsequent week (with long-term mean ETo and expected rainfall in the climate file). It is then possible to assess the impact on yield of applying the single irrigation in the following week, relative to postponing it. It is also possible to quantify the E vs. Tr effects of the single irrigation; if canopy cover is still developing, the E component will be more important than if the irrigation is applied when maximum cover is reached. On the other hand, early irrigation would enhance canopy cover leading to more intercepted radiation (and relatively lower E) and consequently more biomass production. But the crop-water requirement of a well-developed crop early in the season might largely exceeds the limited amount of water available in the root zone, triggering an early senescence of the canopy. The user is encouraged to evaluate these trade-offs in each specific case and compare the final yields.

Output:

In an example run of AquaCrop for wheat in a semi-arid climate, on a soil of medium water storage capacity (110 mm of TAW) with an increasing drought probability as the season progresses, the best timing for a single irrigation is around early grain filling. AquaCrop simulated yields with a single 60 mm irrigation just after end of flowering were 4.1 tonne/ha, relative to a yield of 2.4 tonne/ha under rainfed, and 3.5 tonne/ha if the irrigation is delayed 10 days. In another example, when only two irrigations 10 days apart were applied on a very deep soil, maize yielded either 6 tonne/ha or 9 tonne/ha when irrigation started on day 30 and on day 80 after planting, respectively. In this example, early applications were more detrimental to yield as the crop ran out of water too early in the season before its normal senescence date.

One example of the effects on E and Tr of a single irrigation on cotton, when applied during canopy development (at 30-40 percent of maximum), had 7 percent more E than when the single 60 mm irrigation was applied after attaining full canopy. The lower E (and higher Tr) in the second case, together with the beneficial effects of the stress pattern (better water status during reproductive development), led to higher water productivity, with more than 10 percent increase in yield with the same amount of irrigation water (2.7 vs. 2.4 tonne/ha).

A specific case study of simulation of deficit irrigation of cotton is presented in Box 1.

Box 1 Simulating deficit irrigation in cotton production

Background

Cotton is grown in many water limited regions where deficit irrigation may be practised either as a necessity driven by lack of water or for economic reasons (costs of water and/or energy for pumping). The United States southern high plains region is exemplary of both limited water and high pumping costs. AquaCrop simulations were carried out for a Texas location at 35°11′ N, 102°6′ W, 1170 m elevation above sea level. The slowly permeable soil is a Pullman silty clay loam with a strong argillic horizon containing approximately 50 percent clay above a wavy boundary of a calcic horizon at 0.1 to 0.14 m depth. The soil water-holding capacity is about 200 mm to 1.5-m depth (Tolk and Howell 2001). Mean annual precipitation is 490 mm, 65 percent of which falls during the growing season (May-August). ETo greatly exceeds precipitation in all months.

AquaCrop simulation

Simulations were performed for cotton sown in rows on raised beds and with the furrows diked to store irrigation and precipitation. Irrigation was either Full (FI), indicating that soil water was replenished to replace that lost to ET, or one half of that (Deficit, DI). Irrigation scheduling was performed assuming a lateral-move sprinkler irrigation system that applies ~25 mm per irrigation. The sowing rate was at 21 seeds/m^2. Late in the season, FI was reduced relative to the crop-water requirement (ETc demand), so as to enhance crop maturation.

Reference evapotranspiration for input into AquaCrop was calculated using the FAO EToCalc computer programme (FAO, 2009) and weather data measured at a weather station close to the cotton field. AquaCrop field management parameters were set so that no runoff occurred (due to the furrow dikes), and soil fertility was non-limiting. Five soil depths were considered, with initial water contents of 23, 33, 34, 30, and 27 vol percent at depths of 0.10, 0.29, 0.45, 0.66, and 1.00 m, respectively, as measured in the field. The crop calendar was set as 10 days from sowing to emergence, 94 days from sowing to maximum root depth, 121 days from sowing to start of senescence, 140 days from sowing to maturity, 60 days from sowing to flowering, and 71 days the duration of flowering.

Results

Simulated yields were in the range of 3.3 to 3.6 tonne/ha, (equivalent to 1.3 to 1.4 tonne/ha of lint) and were comparable to values reported in the region. Deficit irrigation (DI) seed-lint yields were ~95 percent of full irrigation (FI) yields. The water productivity of DI cotton was ~10 percent greater than that of FI (both in the range of 0.49 to 0.54 kg/m^3(seed plus lint), or 0.19 to 0.21 kg lint/m^3). Crop ET was about 15 percent greater for FI than for DI, both in the range of 625 to 720 mm, which matches well observed values in several regions. However, DI received 240 mm of irrigation, only 43 percent of the FI amount.

Conclusions and recommendations

Farmers in the region pump from a water table about 90 m below ground and, given rising fuel costs, the energy savings of DI were more than US$250/ha. At cotton prices ranging from US$0.4 to 0.8/kg, the loss in production associated with DI represents only US$100 to 200/ha, giving the economic edge to deficit irrigation.

APPLICATIONS RELATED TO THE INFLUENCE OF FIELD MANAGEMENT AND SOIL PROPERTIES ON YIELD AND WATER USE

CASE 7 - Influence of field management on rainfed agriculture

Specific data requirements

typical climate and soil characteristics as needed to run AquaCrop;

crop specific characteristics as required to run AquaCrop; and

current field management practiced by farmer (e.g. mulch or soil bunds under ‘Management’).

Approach

The simulation run with input data will generate a seasonal soil-water balance and yield. The field management practices that can be modified in AquaCrop should be tested, such as applying mulches and/or soil bunds. Also, using data for years of different rainfall amounts/patterns, AquaCrop simulations can help assess the role of different field management on soil E and water supply to the crop and consequently, on yield under different rainfall in different years. The importance of runoff may be assessed by switching off the runoff calculations (under Field Management) or by changing the curve number (CN) or the amount of readily evaporable water (REW) for soil evaporation (under Soil Characteristics).

Output

The role of variations in field surface management on water-limited production may be assessed in order to derive recommendations from the simulations. In one example, run in a semi-arid area with irrigated maize to obtain an estimate of the role of mulches in the reduction of evaporation from soil, the E component under bare soil was 133 mm, and it was reduced to 90 mm when the soil surface was 100 percent covered with an organic mulch.

CASE 8 - Impact of variations in soil water properties and soil fertility levels

Specific data requirements

average or typical climate and soil characteristics as needed to run AquaCrop;

crop specific characteristics as required to run AquaCrop; and

various soil water properties and soil fertility levels.

Approach

If the user is uncertain about the values of soil-water parameters and/or about the level of soil fertility and actual yield measurements for benchmarking, AquaCrop may be run varying the soil-water properties quite drastically (for instance, by selecting various soil types with different soil-water holding capacities) and then comparing the AquaCrop output yield across these simulations. If the user suspects that the level of fertility is not at its optimum, the option Fertility stress, under Field management, provides mild, moderate, and severe fertility stress levels that the user can utilize to simulate possible effects of limited nutrient supply on biomass production.

APPLICATIONS RELATED TO AGRONOMY AND CROP MANAGEMENT AT FIELD AND FARM SCALES.

CASE 9 - Benchmarking yield gaps in rainfed and irrigated agriculture and assessment of long-term productivity

Specific data requirements

climate (long-term data set) and soil profile characteristics as needed to run AquaCrop;

crop specific characteristics as required to run AquaCrop; and

current practices related to irrigation management, fertilization, level of crop protection and other agronomic practices relevant to actual yields.

Approach

It is important to determine the differences between potential, attainable and actual yields (Loomis and Connor, 1992) at various scales, from a field to a region. If all information is available, the model should be run to determine the attainable yield for each year. Several years of data (standard is 30 years) would be desirable for the comparison of the long-term productivity under various production systems, using the cumulative distribution functions to show the relative risk levels.

Output

Given the actual yield information and the simulated yield, the capacity of rainfed environments and the yield gap (simulated minus actual yield) can be determined. Results from different years will give some clues as to the possible reasons for the yield gap (i.e. low soil fertility, pest, disease, and weed limitations, socio-economic constraints, or low-yielding crop varieties, etc.). A specific application of this approach for assessing wheat yield constraints in a region may be found in Calviño and Sadras (2002). Additional simulations with AquaCrop varying the scenarios, with possible remedial actions, would also help in identifying the possible underlying causes of the yield gap and identify regions and crops where substantial improvements in production and productivity may be possible. If combined with geographical information systems (GIS), yield gap maps for regions could be developed.

CASE 10 - Determining the optimal planting date based on probability analysis

Specific data requirements

at least 20 years of ETo and rainfall data are needed for the area; and

crop and soil characteristics as required to run AquaCrop and representative of the area.

Approach:

Early, middle, and late planting dates are used to simulate 20 or more seasons with AquaCrop. For this application, AquaCrop should be run in the multiple project mode, as a minimum of 60 simulations need to be done. If a much larger number of runs are required, the plug-in version of the model should be used (downloadable at www.fao.org/nr/water/aquacrop).

Output

Once the yields for every year and for the different planting dates (keeping all other parameters the same) have been simulated, the values are organized from lowest to highest, for each planting date. If there are 20 years of simulations, each value represents a 5 percent probability. Then, the yield can be plotted against the cumulative probability graphically, and it is possible to choose the most favourable option with least risk from the graph or compare different options for years with differing conditions, say amounts of rainfall or El Niño phases.

A specific example of an AquaCrop application to determine the optimal sowing date for wheat as a function of the initial soil moisture conditions is reported in Box 2.

Box 2 Determining the optimal sowing date for wheat

Background

The AquaCrop model was used to analyse the optimum sowing date at three different initial soil water conditions under rainfed Mediterranean conditions. The importance of early sowing has been emphasized by many authors (Photiades and Hadjichristodoulou, 1984; Anderson and Smith, 1990 and Connor et al., 1992), who reported a decline in yield when sowing is delayed after the first sowing opportunity (initial rainfall in autumn) within an optimum sowing window. Wheat yields are estimated to be reduced by 4.2 percent (Stapper and Harris, 1989) to 10 percent (Asseng et al., 2008) for each week of any delay in sowing in autumn in Mediterranean environment. On the other hand, soil water conditions at sowing can also be important for wheat production, particularly in low rainfall regions (Rinaldi, 2004; Heng et al., 2007; Asseng et al., 2008).

Initial soil water from summer rainfall or left over from the previous year can influence early establishment of the crop and can contribute to water use and yield later in the season, in particularly in low rainfall seasons. Therefore, simulations were carried out with AquaCrop to determine the optimal sowing date in relation to initial soil water to maximize wheat grain yields.

Location and simulation experiments

The site of the simulation experiments was selected within the northern part of the Western Australia wheat-belt, at Buntine (29.51°S, 116.34°E, 365 m elevation) one of the main wheat-growing regions of Australia, where wheat is grown under rainfed conditions. The location is a relative low-yielding environment with a typical Mediterranean-type climate. Rainfall mainly falls in winter, but varies from season-to-season in terms of seasonal distribution and amount. Rainfall quickly declines in spring during grain filling. Average long-term annual rainfall is 329 mm. Average seasonal (May to October is the main growing season in the Southern Hemisphere) rainfall was 243 mm over the last 30 years period (1979-2008), varying between 125 and 417 mm. In such an environment, a mild winter is followed by increasing temperatures in spring.

A common soil for the study region was used in the simulation experiment, a loamy sandy soil with 101 mm of plant available soil water to the maximum rooting depth of 1.7 m.

Simulations were carried out using measured daily weather records from 1999 to 2008. Crops were sown when rainfall was at least 20 mm during the previous 10 days during a sowing window of May to July and again at 30 days after the first sowing opportunity as a delayed sowing practice (e.g. to manage weeds or due to technical limits of sowing all crops early on a farm).

Each sowing date treatment was simulated with an initial soil water of 0, 30 and 60 mm plant available soil water stored below 20 cm depth. The earliest sowing date possible was 1 May, the date at which the initial soil water conditions were set every year. Nitrogen was assumed to be not limiting for crop growth.

A bread-wheat spring cultivar was used in the experiments, cv. Wyalkatchem, a standard early-medium flowering cultivar for this region. Conservative parameters based on typical growth and development in the considered environment were used as inputs (See wheat Section in Chapter 4).

Results

The simulated differences in grain yields between the first and the second sowing dates as a function of the seasonal rainfall for different initial soil-water contents are shown in Figure 1. Simulated differences in grain yield became negative at zero mm of initial soil water, but were positive at 30 and 60 mm initial soil water. When the soil profile was dry, 70 percent of the crops sown with an early sowing opportunity failed, while this percentage decreased to 40 percent with the second sowing date. But, crops which were sown early in to dry subsoil with the first rainfall in autumn which did not fail yielded on average 30 percent more than the second sowing date. On average, the first sowing yielded 35 percent more than the second sowing with 30 and 60 mm of initial water, but 13 percent less with zero mm of initial soil water.

Conclusions and recommendations

The results of the simulation experiments indicate that in a Mediterranean environment, sowing a wheat crop early with the first rainfall events in autumn can give higher yields, consistent with other simulation and field experimental studies. However, early sowing can increase the risk of crop failure if the subsoil profile is dry at sowing. Therefore, early sowing is only warranted if there is some initial soil water in the soil profile from summer rainfall or left over from the previous year. If the soil profile is dry at the beginning of the season, delaying sowing, despite some loss of yield potential, reduces the risk of crop failure in such an environment.

In similar regions, but with water resources available for irrigation, applying a small amount of water (about 30 mm) before sowing will significantly reduce the risk of crop failure with an early sowing opportunities and would allow to maximize yield potential in such an environment.

CASE 11 - Developing water production functions with AquaCrop and using them in Decision Support Systems

Specific data requirements

average or historical series of preferably 20-30 year, or at least 10 years, of data on ETo and daily rainfall; and

crop and soil characteristics necessary to run AquaCrop.

Approach

Two approaches may be used: (i) with the average climatic records, the user will simulate the yield response to different amounts of applied irrigation (IW) changing the level of application in 30-50 mm step intervals (’Irrigation Events’ tab sheet in ’Irrigation Management’); (ii) if a climate dataset is available as historical series, simulate the yield response to different amounts of IW using each year of the available climate records. This will yield a family of curves from which a mean curve and probabilities of exceeding a certain yield value could be derived (see Case 10).

Output

An example is shown in Figure 2 of the results of simulating potato production with AquaCrop over 25 years of climate with varying irrigation levels (García-Vila and Fereres, 2012). The resulting curves could serve as inputs in economic models to build decision support systems that would aid farmers to determine the optimum irrigation level to maximize economic profits under specific sets of conditions. Another example is shown in Figure 3 for the quinoa crop (Geerts et al., 2009). This crop has a unique response in that the yield-ET relationship is not linear but curvilinear (Figure 3a). The simulated yield data points for different levels of ET vary because of differences in irrigation timing. The envelope curve of the data points giving the highest yield values represents optimal DI regimes for the different ET levels. In Figure 3b, the region with the highest yield water productivity is indicated, and from the graph, the optimal level of ET may be defined (Geerts et al., 2009).

CASE 12 - Assessing the effects of plant density on yield

Specific data requirements

average climate data representing a typical year; and

crop and soil data needed to run AquaCrop for average or most probable field conditions.

Approach

Plant densities have been optimized in commercial plantings of most crops; however, there are situations where it is necessary to assess the role of plant density on water use and yield. To judge the impact of drastic changes in plant population on yield, the user should try a range of quite diverse values of plant density (which can be specified in the Development tab sheet under Crop Characteristics) and change accordingly the maximum canopy cover that can be reached (CCx). The resulting changes to the parameters CCo and CCx would allow the evaluation of the role of these two features in canopy development, and hence on yield.

Output

In one example, contrasting densities (30 000 vs. 75 000 plants per ha) for rainfed maize grown in California on a very deep, fertile soil with the profile nearly fully charged at the time of planting were compared. Yield was 4.7 tonne/ha for the low density, and 4.2 tonne/ha for the high density. The main reason for the difference in yield is due to a slightly higher HI for the low density (0.31 vs. 0.28) because less water is transpired early in the season as a result of a smaller canopy, leaving a little more water to allow the canopy to stay green longer, with the corresponding longer build-up of HI.

BEYOND THE LEVEL OF FIELD AND FARM: APPLICATIONS RELATED TO THE EFFECTS OF WEATHER AND CLIMATE ON CROP PRODUCTION AND WATER USE

CASE 13. Assessing the impact of rainfall variability on water-limited yields

Specific data requirements

a long-term series of daily rainfall and ETo, for at least 20 to 30 years;

typical rainfed crop of the area; and

representative soil and management conditions, as needed to run AquaCrop.

Approach

AquaCrop will be run with a selected crop, preferably one grown in the rainy season, for every year where data is available. The climate dataset will include years with a range of annual rainfall, and also some years having the same annual rainfall but with different distribution through the season.

Output

Yields and other parameters will be obtained for all the simulations performed. If the runs cover a sufficiently large number of years, yield probability curves as a function of annual rainfall could be generated (see explanation in Case 9; or example output in Geerts et al., 2009).

CASE 14 - Mapping water-limited yield potential of a region

Specific data requirements

average or historical climate data (rainfall and ETo) processed in GIS mapping data-set; and

typical rainfed crop with representative soil and management conditions, as needed to run AquaCrop.

Approach

This application would require the use of the AquaCrop model with a GIS that would allow the spatial simulation of yield, based on maps of ETo, rainfall, soil profile characteristics, as well as the crop features required to run the model. An example is the FAO-MOSAICC project that being developed in the framework of the EC/FAO Programme on Linking information and decision-making to improve food security, Theme 3 Climate change and food security (http://www.fao.org/climatechange/mosaicc/en/).

CASE 15 - Climate Change effects on crop production and water use

Specific data requirements

climate data processed to simulate future climate change conditions; and

typical crops with representative soil and management conditions, as needed to run AquaCrop.

Approach

a) Global warming effects on simulated yields and water use

The effects of the increased temperatures on ETo and crop development (predicted with climate change) can be simulated with AquaCrop. If there are regional predictions that permit the generation of future daily weather data, such data would be the input of AquaCrop simulations, thus providing prediction of changes in yield and water requirements. The model can also be used to quantify the climatic risk associated with various management options (e.g. changing varieties, short vs. long season cultivars, irrigation input and low dosage of fertilizer) to help farmers choose low-risk management options to suit household resource constraints. Dimes et al., (2009) have assessed the climate change effects in some areas of Southern Africa using the simulation model APSIM.

b) Integration of global warming and of increase in greenhouse gas concentration

Aquacrop simulations respond to changes in CO_2 concentration, thus it is possible to evaluate the interactive effects of the increase in temperatures, the more scattered rainfall and of the increase in CO_2 in the atmosphere in future climates. Different scenarios may be introduced, including a variable sink capacity (Vanuytrecht et al., 2011) as provided in AquaCrop, following predictions of the regional climate change models.

CASE 16 - Using AquaCrop for water allocation decisions at basin or regional levels

Specific data requirements

climate data processed to represent average, adverse or favourable climate conditions; and

typical crops with representative soil and irrigation management conditions, as needed to run AquaCrop.

Approach

AquaCrop outputs may be the input of water allocation optimization models that have strong economic and institutional components. Such models are needed to assist in the management of water by institutions in charge of water governance. AquaCrop inputs would be particularly valuable in the event of a drought, where different scenarios are considered and yield/income predictions for the area are essential to make informed decisions when allocating limited supplies. One example of an application at the farm scale is given in García-Vila and Fereres (2012).

Conclusion

These case studies are a small sample of the applications that may be possible to tackle with the assistance of AquaCrop and illustrate the use of the simulation model. The examples have also illustrated the possibilities that AquaCrop offers for various types of users – namely irrigation specialists, agricultural engineers and agronomists, agricultural extension personnel. Additional users include water engineers, hydrologists, and economists working at catchment scale and climate scientists wanting to investigate the effect of different climate change scenarios on the water-use of various crops. There are many more applications of AquaCrop that may be used in practical ways and which will be revealed as users around the world incorporate this simulation model in their assessments of crop yield response to water.

REFERENCES

Anderson, W.K. & Smith, W.R. 1990. Yield advantage of two semi-dwarf compared with two tall wheats depends on sowing time. Australian Journal Agricultural Research, 41: 811-826.

Asseng, S., Milroy, S. P. & Poole, M. L. 2008. Systems analysis of wheat production on low water-holding soils in a Mediterranean-type environment I. Yield potential and quality. Field Crops Research, 105: 97-106.

Calviño, P. & Sadras, V. 2002. On-farm assessment of constraints to wheat yield in the south-eastern Pampas. Field Crops Research, 74:1-11.

Connor, D.J., Theiveyanathan, S. & Rimmington, G.M. 1992. Development, growth, water-use and yield of a spring and a winter wheat in response to time of sowing. Australian Journal Agricultural Research, 43: 493-516.

Dimes, J., Cooper, P. & Rao, K.P.C. 2009. Climate change impact on crop productivity in the semi-arid tropics of Zimbabwe in the twenty-first century. Proceedings of the Workshop on Increasing the Productivity and Sustainability of Rainfed Cropping Systems of Poor, Smallholder Farmers, Tamale, Ghana, 22-25 September 2008. p189-198.

FAO. 2009. ETo Calculator, land and water digital media Series No. 36., Rome.

Farahani, H. J., Izzi, G. & Oweis, T.Y. 2009. Parameterization and Evaluation of the AquaCrop Model for Full and Deficit Irrigated Cotton. Agronomy Journal 101: 469-476.

Fereres, E. & Soriano, M.A. 2007. Deficit irrigation for reducing agricultural water use. Journal of Experimental Botany 58(2): 147–159.

García-Vila, M. & Fereres. E. 2012. Combining the simulation crop model AquaCrop with an economic model for the optimization of irrigation management at farm level. European Journal of Agronomy, 36: 21-31.

García-Vila, M., Fereres, E., Mateos, L., Orgaz, F. & Steduto, P. 2009. Deficit irrigation optimization of cotton with AquaCrop. Agronomy Journal 101: 477-487.

Geerts, S., Raes, D., Garcia, M., Taboada, C., Miranda, R., Cusicanqui, J., Mhizha, T. & Vacher, J. 2009. Modelling the potential for closing quinoa yield gaps under varying water availability in the Bolivian Altiplano. Agricultural Water Management 96: 1652–1658.

Heng, L.K., Asseng, S., Mejahed, K. & Rusan, M. 2007. Optimizing wheat productivity in two rainfed environments of the West Asia-North Africa region using a simulation model. European Journal of Agronomy, 26: 121-129.

Heng, L.K., Hsiao,T.C., Evett, S., Howell, T. & Steduto P. 2009.Validating the FAO AquaCrop Model for Irrigated and Water Deficient Field Maize. Agronomy Journal 101: 488-498.

Loomis, R.S. & Connor, D.A. 1992. Crop ecology: productivity and management of agricultural systems. UK, Cambridge University Press. 538 p.

Mainuddin, M., Hoanh, C.T., Jirayoot, K., Halls, A.S., Kirby, M., Lacombe, G. & Srinetr, V. 2010. Adaptation options to reduce the vulnerability of mekong water resources, food security and the environment to impacts of development and climate change. CSIRO: Water for a Healthy Country National Research Flagship. 152 pp.

Photiades, I. & Hadjichristodoulou, A. 1984. Sowing date, sowing depth, seed rate and row spacing of wheat and barley under dryland conditions. Field Crops Research, 9: 151-162.

Rinaldi, M. 2004. Water availability at sowing and nitrogen management of durum wheat: a seasonal analysis with the CERES-Wheat model. Field Crops Research 89: 27-37.

Stapper, M. & Harris, H.C. 1989. Assessing the productivity of wheat genotypes in a Mediterranean climate, using a crop simulation model. Field Crops Research, 20: 129-152.

Steduto, P., Hsiao, T.C., Raes, D. & Fereres, E. 2009. AquaCrop – the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal. 101: 426–437.

Todorovic, M., Albrizio,R., Zivotic, L., Abi Saab, M.T., Stockle, C. & Steduto, P. 2009. Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes. Agronomy Journal 101: 509-521.

Tolk, J.A. & Howell, T.A. 2001. Measured and simulated evapo-transpiration of grain sorghum with full and limited irrigation in three High Plains soils.Trans. ASAE, 44: 1553–1558.

Vanuytrecht, E., Raes, D. & Willems, P. 2011. Considering sink strength to model crop production under elevated atmospheric CO_2. Agricultural and Forest Meteorology 151: 1753– 1762.

3.3 AquaCrop parameterization, calibration, and validation guide

LEAD AUTHOR

Theodore C. Hsiao
(University of California, Davis, USA)

CONTRIBUTING AUTHORS

Elias Fereres
(University of Cordoba and IAS-CSIC, Cordoba, Spain),

Pasquale Steduto
(FAO, Land and Water Division, Rome, Italy),

Dirk Raes
(KU Leuven University, Leuven, Belgium)

ACKNOWLEDGMENTS

Lee K. Heng
(Soil and Water Management & Crop Nutrition Section, Joint FAO/IAEA Division, Vienna, Austria)

This guide is mainly for AquaCrop users with an agronomic background and some experience of crop modelling, and for those needing to: simulate the productivity of a crop already parameterized, but not yet validated for their specific conditions; calibrate the model for a crop not yet parameterized; or to improve the parameters already worked out by others and to validate them for the same crop. These users should be acquainted with all Chapter 3 of this publication and the Reference Manual which can be downloaded from the FAO AquaCrop website (www.fao.org/nr/water/aquacrop). Users with less experience and background, who need more detailed instructions to run simulations with AquaCrop already calibrated for a particular crop, should download the instructions for Group 1 Users from the same website.

IMPORTANT DISTINCTIONS OF MODEL PARAMETERS

Conservative vs. user-specific parameters

AquaCrop is designed to be widely applicable under different climate and soil conditions, without the need for local calibration, once it has been properly parameterized for a particular crop species. To this end the model is constructed with parameters falling into two groups. One group is considered conservative, in that the parameters should remain basically constant under different growing conditions and water regimes. The other group encompasses parameters that are dependent on location, crop cultivar, and management practices, and must be specified by the user. A critical stipulation for many of the conservative parameters is that their values are based on data obtained from modern high-yielding cultivars grown with optimal soil fertility without limitation by any mineral nutrient, particularly nitrogen. With some notable exceptions, it is also stipulated that values are based on data obtained when water is not limiting. It follows that, if the conservative parameters already calibrated for a given crop do not provide simulated results, matching measured data for a crop in a particular case, the first thing to check is that mineral nutrients are not limiting the growth of the crop. To keep the model relatively simple, AquaCrop does not simulate nutrient cycles and nutritional effects on the crop directly. Instead, a way is provided in the ‘Biomass’ tab sheet to account for nutritional effects after performing a calibration based on the reduction of biomass produced by a nutrient-deficient treatment.

Cultivar classes

For simplicity, the parameters are grouped into two categories, as described above, in reality some of the parameters assigned to the conservative group may vary within small limits for different cultivars of the crop species. This brings up the need for a new term, ‘cultivar class’, to designate cultivars of a crop with very similar values of conservative parameters, to distinguish them from cultivars of the same species but differing by a limited amount in one or more conservative parameters. Take maize cultivars as an example. The reference HI for a number of maize cultivars has been parameterized at 48 percent (Hsiao et al., 2009), and together they comprise one cultivar class. If two or three other cultivars are found to have a higher reference harvest index (e.g., HI=51 percent), they would constitute another cultivar class. It is anticipated that over the long term, plant breeding and biotechnology will alter a number of the conservative parameters, increasing the number of cultivar classes.

INPUT INFORMATION AND DATA FOR VALIDATION AND PARAMETERIZATION

Table 1 lists the required information for using the model to simulate production and water use. The first column lists the absolutely required minimum. If this is the only available information, the simulated results would at best be first order approximations. The second column of the table lists additional information needed to make the simulation more reliable. In this case, agreement between the simulated and observed biomass production and yield should not be considered as validation of the model, unless the agreement is observed for several water regimes and for more than one climate. Essentially, the more exact and detailed information, the more close to reality would be the simulated results.

To validate the model and to parameterize the model, more detailed and exact data are needed, as listed in Table 2. The first column lists the minimum information needed in addition to that in Table 1 for a reasonable validation of the model or initial calibration of the conservative parameters. The second column lists the additional information needed to validate the model for general use, and calibrate the conservative parameters for a wide-range of climate, soil, and water regimes. In the validation and parameterization process, attention must be paid equally to how well the simulated results (canopy cover, biomass, and consumptive water use) agree with the measured values as time progresses through the season, as well as the total biomass, yield and total ET at crop maturity.

ITERATION, ASSESSMENT OF SIMULATION RESULTS, AND REFINING OF PARAMETERS

The common practice is to run simulations with the model starting with estimated or guessed parameter values and than compare the output with the measured experimental data, then adjust the parameters and run the simulation and compare again. This is done repeatedly until the simulated results closely agree with the experimental data. Trial-and-error iterations being the heart of the process, how can the process be streamlined to minimize the time and effort required? Some general rules may be helpful.

Rule 1: The better one understands the principles underlying the model, its flow diagram, and the flow of calculation steps, the more capable one would be in identifying the likely input or parameter to adjust, and in what direction. To this end, any time taken to read the three initial publications on AquaCrop (Steduto et al.; Raes et al.; Hsiao et al., all 2009) is worthwhile.

Rule 2: Always pay attention to the graphic display of the Climate-Crop-Soil water tab sheet on the simulation run page, as well as the output numbers of the Production and the Climate and water balance tab sheets. By switching the simulation run to advance in time steps, one can see how the crop and soil water change step-by-step. The graphic display is particularly useful for water limited conditions to see whether the crop canopy cover (CC), transpiration relative to potential transpiration, and acceleration of canopy senescence are reasonable or need adjustment.

Rule 3: Differentiate the input information and measured or observed data according to their reliability and exactness, and make rational adjustment to the vague or rough estimates of input first to see if the simulated results better match the measured results, before changing the model parameters. In later Sections, many of the uncertainties of the input information or data, and the measurements taken on crops, are mentioned and discussed to decide which inputs can be altered based on a rational evaluation of its likely range of uncertainty.

Rule 4: When simulated results and measured data do not agree, the problem could also be in the measured data. If simulated results coincide with measured data obtained in several different studies, but not with that of another study, the data in the other study are more suspect and additional data sets should be sought to complete the validation or parameterization.

LOCATION AND USER SPECIFIC PARAMETERS AND INPUT

Climate and soil are location specific, and crop cultivar, timing of crop cycle, water management and agronomic practices are user specific.

Climate data and reference evapotranspiration (ETo)

AquaCrop simulates in daily time steps because plant responses to water status are highly dynamic and cannot be easily represented as weekly or 10-day means. The model runs with 10-day or monthly mean temperature and ETo files, through interpolations. The results are, however, obviously approximations, and should not be used to calibrate or validate the model except as the last resort. ETo is a key input for AquaCrop as the model calculates daily crop transpiration (Tr) and soil evaporation (E) using daily ETo values.

ETo is to be calculated using the FAO Penman-Monteith equation from full daily weather data sets, as described by Allen et al. (1998). A programme to do this calculation, named ETo Calculator (FAO, 2009) is available on the FAO website. The ETo Calculator has the advantage of allowing approximations when one or several kinds of the required weather data are missing, also following the approximation procedure of Allen et al. (1998). This makes it possible for a user to run rough simulations, even when the weather data are minimal, but can be easily misused. For validation and parameterization, such approximation should not be relied upon. The rougher the approximation of ETo, the less reliable would be the simulated results and derived AquaCrop parameters. For example, ETo Calculator can use daily maximum and minimum temperature, relative humidity, wind run, and sunshine hours in place of radiation, to calculate ETo, and also can calculate ETo simply from daily minimum and maximum temperature data and general information on site location such as whether it is arid or humid and windy or calm. Obviously, the ETo calculated from the sunshine hours would be somewhat less reliable than that calculated with daily radiation, and the ETo simply estimated with the daily minimum and maximum air temperature would be essentially worthless for the purpose of model validation and parameterization. Thus, it must be understood that reliable climatic data are critical in AquaCrop.

Growing degree day (GDD)

AquaCrop is designed for use under different climatic conditions and hence should be parameterized in the growing degree day (GDD) mode to account for different temperature regimes. This may be difficult, however, because many users may only have data for their specific locations with their limited temperature range. In this case, it is best to select data obtained when cold temperature is not a limiting factor and run the model first in the calendar time mode for parameterization. After arriving at reasonably acceptable parameter values, by switching the model to the GDD mode, the parameters are automatically converted to units in terms of GDD. The challenge is then to define: (1) the base temperature and upper temperature to calculate the GDD, and (2) the temperature thresholds for biomass accumulation and for pollination and fruit set of the specific crop. These are to be discussed later.

Soil water characteristics

In AquaCrop, the extent of water limitation is expressed as a fraction of the total available water (TAW) in the root zone, with TAW defined as the water held in the soil between its field capacity (FC) and permanent wilting point (PWP). In the case that the soil has layers differing in FC and PWP, the different values for the layers encompassing the maximum rooting depth need to be entered into the model.

Accurate FC and PWP are only important to specify the local conditions if water is a significant limiting factor. If the simulation is for conditions where water is either not limiting or only minimally limiting, approximate FC and PWP would do, but the water-stress functions (threshold and curve shape) derived cannot be relied on for conditions limited more by water deficits. Approximate FC and PWP may be estimated simply from the textural class of the soil. In AquaCrop there are default soil files for a number of textural classes. In each file the relevant water parameters are given in the ‘Characteristics of soil horizons’ tab sheet. More accuracy is required to calibrate the model for the various water-stress functions using data obtained when water is limiting.

Spatial heterogeneity of the soil can be a problem for accurate simulation for a given water limiting location, because FC and PWP, and hence TAW, can vary sufficiently from area-to-area in a field, reducing the accuracy of the simulated results for the field. If data are available for different parts of a field, simulation should be run for each part of the field that differs in soil water characteristics.

Saturated hydraulic conductivity (Ksat) of the top soil determines the internal drainage in the soil profile, losses from deep percolation and the amount of water infiltrated in the root zone and the surface runoff after an irrigation or rainfall. Surface runoff is only important if the amount of water applied per irrigation is excessive or the rainfall is intense and heavy. In such situations, the measured Ksat should be used for simulation. If measured value is unavailable, the default value provided by AquaCrop for the given soil textural class (based on the difference in soil-water content (θ) between saturated soil and FC) should be adjusted according to general knowledge of the local condition.

Initial soil water content

Another local specific factor is the initial water content of the soil for the maximum rooting depth at start of the simulation run. If the values are not measured, estimates may be made based on knowledge of the local climate, particularly rainfall, and the preceding crop or weed history. For example, for a climate with winter rainfall, sufficient to completely charge the soil profile, and dry summer, and the field is kept fallow and weed free, one may assume the deeper layers of the soil to be at FC but reduce soil-water content of the upper layer of the soil by estimating the extent of soil evaporation taking place before the simulation starting time. If weather data before the starting time of simulation are available, AquaCrop can be used to make that estimate by setting the start time as the end of the last significant rain. If weeds are present, however, some estimates would have to be made on canopy cover (CC) of the weed in order for AquaCrop to simulate a reasonable profile of initial soil-water content.

Crop phenology

Many of the differences among crop cultivars are related to the timing of developmental stages. The timing to reach a particular stage, or its duration for the local cultivar, needs to be specified by the user. These stages are: time to 90 percent seedling emergence, to the beginning of flowering, to the beginning of canopy senescence, and to physiological maturity, and the duration of flowering.

Time to 90 percent emergence

The particular choice of time to 90 percent emergence is explained later, under Initial canopy size per seedling. In nearly all the cases, this time is likely to be estimated and not determined by actual counting of the seedlings. It should be adjusted to have a good match between the simulated and measured canopy cover (CC) at the seedling stage and in early season. The adjustment, however, should be taken only after the relevant conservative parameters (initial canopy size per seedling and canopy growth coefficient) are well parameterized and the plant density is ascertained.

Time to start of flowering and duration of flowering

For determinate crops with their short flowering duration (e.g. 15 days), it is important to have an accurate time for start of flowering. For indeterminate crops with their long flowering duration, the timing can be more approximate. In cases where there is no significant water stress, the model is constructed in such a way that timing of flowering does not matter.

Time to maximum canopy cover

This parameter is provided in AquaCrop to allow simulation runs when the conservative parameter, canopy growth coefficient (CGC) of the crop, is not known, and should be used only as a last resort. See the later section Canopy growth coefficient for more explanation.

Time to canopy senescence

In AquaCrop, the timing to the start of canopy senescence is defined as the time when green leaf area falls to or below LAI = 4 as a result of yellowing of leaves, under optimal conditions with no water stress. By this definition, if the plant density is low and the maximum LAI is less than 4.0, canopy senescence starts once there is significant senescence of lower leaves. But if the maximum LAI is considerably higher than 4.0, enough of the lower leaves must senesce to reduce LAI to 4.0 before the canopy is considered to be at the beginning of senescence.

Time to physiological maturity

Different crop species may each have its own specific definition of physiological maturity (e.g. black layer formation in maize grains). To be general, however, AquaCrop uses as default the time when canopy cover is reduced to 5 percent of the achieved maximum canopy cover as the time of maturity. Users can change the maturity time according to their own data on the Canopy development tab sheet. Clearly, maturity is closely linked to the time of canopy senescence, and this may be one practical way to estimate maturity time if no detailed determination of maturity is made. Seed companies usually supply information on the life-cycle duration of their cultivars. This, however, can be very general, in terms of short, medium, or long season. The information can also be given in degree days, but unfortunately defined in ways different from that used in AquaCrop. For accuracy, experimental observations or data are necessary to determine the time to maturity. It would be justified to take the time when only a little green leaf area remains in the canopy as the time of maturity.

Rooting depth and deepening rate

Root development is highly site specific because of differences in soil physical (temperature, mechanical impedance, and aeration) and chemical (pH, salinity, and high levels of aluminum or manganese) characteristics, which strongly affect root growth. When soil conditions are all highly favourable, the root-deepening rate is likely to be in the range of 20 to 25 mm per day for many crops. The probable exceptions are crop species known for their shallow roots.

On deep soils with no layers restricting root growth, as default AquaCrop stops root deepening once the time for canopy senescence is reached (for no stress conditions). There is a notion in the literature that roots do not grow or deepen beyond the pollination stage of a crop. Good data on various crops show, however, that roots deepen after pollination, albeit at a slower rate. For soils of limited depth, but also with no growth-restricting layer in between, roots deepen at the normal rate in AquaCrop but stop abruptly when the bottom of the soil is reached.

In cases where the observed rooting is too shallow; although the soil is deep, some characteristic of the soil or soil layer may be inhibiting root growth. To approximate the situation with AquaCrop there are two possible means. One is simply to reduce the average deepening rate throughout the soil profile, by setting the maximum root depth at the beginning of canopy senescence at a point so that the root depth observed at a particular time matches that displayed by AquaCrop. The other approximation is only applicable to situations where root growth is inhibited more as the soil depth increases. By raising the shape factor of the root depth vs. time curve to the 2.5 to 3.0 range in the Root deepening tab under Development of the Crop file, the deepening rate would start high and slows with time as the roots go deeper. AquaCrop also offers the possibility of specifing the soil depth of a restrictive layer blocking root zone expansion as a soil characteristic in the Restrictive soil layer tab sheet.

CONSERVATIVE PARAMETERS

Temperature effects

Most temperature effects on crops are accounted for by using the GDD in place of calendar time as the driver, for which the setting of base and upper (cutoff) temperature are critical, and also by the use of ETo. In addition, three temperature effects should be accounted for by other means. These are inhibitory effects, of low temperature on the conversion of transpiration to biomass production and on pollination, and of high temperature on pollination.

Base and upper temperature

The base temperature may be thought of as the lower threshold for crop growth and development. The upper temperature is the limit above which further increase in temperature has no effect on the rate of progression. The GDD calculation in AquaCrop is according to ‘Method 2’ as described by McMaster and Wilhelm (1997), but with an important modification, that no adjustment is made of the minimum temperature when it drops below the base temperature. The base and upper temperature are usually selected in modelling work by trial and error by running simulation models for data collected in different temperature regimes. In terms of guiding principles, C_4 species are generally more cold sensitive than C_3 species, winter crops are obviously more cold tolerant than spring and summer crops, and crops with higher base temperature would benefit from warmer temperature (higher upper temperature). Base temperature for crops, such as barley and wheat, are generally taken to be 0 oC in most crop models, whereas for C_4 summer crops such as maize it is 8 or 10 oC. Upper temperature has been set at 30 oC for maize and 32 oC for cotton, but at 26 oC for wheat in AquaCrop.

If the experimental data used to test AquaCrop were obtained in a climate where the temperature does not often fall around the base temperature or above the upper temperature, the exact value of these two thresholds, as long as they are reasonable, would not likely make much difference in the simulated results. On the other hand, the difference may be large if the temperature often hovers around the base temperature or rises substantially above the upper temperature. In this case, it is necessary to refine the threshold values, best by securing data sets of the crop grown in other temperature regimes and by trial-and-error to arrive at the most reasonable temperature thresholds.

Low temperature effect on converting transpiration to biomass production

When simulating periods around the base temperature using AquaCrop, it was found that the model overpredicted production, probably because transpiration, mostly a physical process, is less inhibited by cold than photosynthesis, a complex metabolic process. It was then decided to apply a logistic function to arbitrarily reduce the amount of biomass produced per unit of normalized transpiration according to the magnitude of GDD each day, with an upper threshold GDD where the reduction begins, and a lower GDD fixed at GDD = 0, where conversion is reduced to zero. Generally, the upper threshold should probably be set in the range of 6 to 10 GDD.

Low and high temperature effects on pollination

These effects are also dealt with by arbitrary reductions using logistic functions, with temperature as the independent variable. The reduction starts at the upper threshold temperature for the cold effect, and the lower threshold for the high temperature effect. Pollination is completely inhibited when the temperature drops to 5 oC below the upper threshold for the cold effect, and when the temperature rises 5 oC above the lower threshold for the high temperature effect. Generally these inhibiting temperatures fall outside the temperature regimes favouring the growth and production of a given crop class.

Canopy cover and related parameters

Converting leaf area index (LAI) data to canopy cover (CC)

AquaCrop simulates transpiration in terms of canopy cover (CC) of the crop, but often experimental studies measure LAI but not canopy cover, especially in earlier studies. During the parameterization of AquaCrop for maize (Hsiao et al., 2009), a conversion equation, CC = 1.005 [1 - exp(-0.6 LAI)]1.2 was arrived at and used to analyse the literature of maize and soybean data (Figure 1).

Crops differing substantially in canopy architecture would have CC-LAI relationships different than that of Figure 1. Several recent reports on such relationships may be found in the scientific literature.

It should be noted that, during the canopy senescence phase, there is no simple way to measure CC, which refers to only green cover, because green and yellow leaves intermingle and some leaves are partly green and partly yellow. Hence, converting measured LAI to CC is the only way to obtain CC values during this crop phase.

Initial canopy size per seedling (cco)

Initial canopy cover per unit land area (CCo) is computed from the mean initial canopy size per seedling (cco) and the plant density, that is, CCo = cco x plant density. CCo is taken to be the canopy cover on the day of 90 percent emergence. At this stage, the average seedling is likely to be at the start of autotrophy and its growth begins to obey the equation for the first half of the canopy expansion (Equation 3 of Section 3.1).

Ideally, cco should be measured on seedlings of the chosen species, about 3 to 4 days after emergence, when the leaf or leaves turn fully green. At this stage, instead of measuring CC, the green leaf area of a seedling can be measured and used to approximate cco with a small downward adjustment (e.g. cco being 10 percent or 15 percent less than leaf area per seedling). The alternative is to derive cco indirectly, from data of CC taken at different times and plant density using the CC growth equations of AquaCrop. This approach is described fully later, when the parameterization of CGC is discussed. Regarding cco, one guiding principle is that for the variety of crop species, initial canopy size per seedling (cco) is generally correlated with mass per seed. Take an example of three crops, the relative sizes of cco are: maize > wheat > tomato, the same ranking as the relative mass per seed for these crops. Another guiding principle is that crops of similar nature and similar seed size should have similar cco. Thus, the cco value for wheat should be a good starting point for cco of barley, and the cco value for cabbage should be a good starting point for cco of canola.

Maximum canopy cover (CCx)

When the planting is sufficiently dense, the theoretical upper limit for CCx is 1.0, but in practice CCx seldom reaches 0.99 and often lies in the range of 0.95 to 0.99 even for unusually high plant densities. This range is referred to in this writing as full canopy cover and the time when this is reached is referred to as canopy closure. However, these terms are used rather loosely in the literature, and can be referred to CC substantially less than 0.95, as low as 0.9 or even lower in some writings. As a general guide, CC is 0.95 or higher when LAI exceeds about 4.5 or 5.0 (Figure 1), with some exceptions. One exception is species exhibiting strong sun-tracking behaviour, such as sunflower, which requires an LAI of 3.5 to achieve full canopy cover. Another exception is if the crop is planted in clumps, or very close to each other in rows that are spaced widely apart. In this case LAI significantly higher than 5.0 is necessary to achieve full canopy cover.

As plant density is reduced below a particular level, the density is insufficient for the canopy to close and CC falls substantially below 0.95. This point depends on the kind of crop, each with its particular limit of potential leaf area per plant. Ideally, for each kind of crop a curve of CCx vs. plant density, based on experimental data, should be constructed for use in AquaCrop simulations. Unfortunately, for some species, the required experimental data are lacking. If the user has CCx measurements of his/her crop at the plant density in question under optimal growth conditions, these CCx values are obviously the best to use in the simulation. Otherwise CCx needs to be estimated. One way is simply visual, judging the extent of CC by eye around the time when CC is maximum. A word of caution here, viewing the canopy from the side or even at a downward angle (or photograph taken from similar positions) tends to overestimate the CC because this view may include too many plant layers. Viewing the canopy from directly above, or viewing the proportion of the soil shaded by the canopy when full sun is directly overhead, is the better way to make the estimate. Estimates can also be made based on general knowledge of the crop or similar crops.

If the CCx of a particular crop is known for a particular plant density (reference planting), to estimate CCx of a planting of the same kind of crop but planted at a different density (dp), one can start by estimating the maximum LAI of the planting of known CCx (LAIref) from Figure 1 (or a similar relationship if more accurate), and calculating first the LAI of the planting assuming leaf area per plant is independent of plant density, then make a rough adjustment for the impact of change in plant density. This is summarized as an equation:

where dref is the plant density of the reference, and Fadj is the adjustment factor.

Fadj is limited between the range of d/dref and 1.0, for cases where dp > dref, as well as where d < dref. To illustrate, first take the case of dp > dref. If the dp/dref = 1.3, Fadj would be limited to the range of 1 to 1.3. At one extreme where Fadj = 1, the leaf area per plant would be independent of plant density. At the other extreme where Fadj = 1.3, the leaf area per plant is reduced by the increase in plant density so much that the LAI of the planting remains the same as LAIref. In the case of dp < dref, if dp/dref = 0.7, Fadj would be limited to the range of 1 to 0.7. Obviously, in most cases, the limit values of Fadj should not be used to estimate LAI. The extent the plant adjusts its leaf area in response to crowding is related to how determinate the crop is in growth habit. So the more indeterminate the crop is, the more Fadj should deviate from 1.0, either smaller or larger.

After LAI of the planting is estimated, the corresponding CC can be read off Figure 1 or similar relations, and used as CCx for the simulation. As is obvious in Figure 1, for cases where the canopy is full or nearly full and the plant density is not widely different from that of the reference, the CCx estimate made with the above procedure should be accurate within a few percentage points. The estimates become less and less reliable as the difference in density becomes greater, or if CCx or reference CCx is substantially less than full cover. On the other hand, for cases where there is little interplant competition for PAR because of a sparse canopy (e.g. CC < 0.5), Equation 1 can be used along with Figure 1 to obtain a reasonable estimate of CCx by setting Fadj close to 1.0.

Canopy growth coefficient (CGC)

CGC is a measure of the intrinsic ability of the canopy to expand. A CGC of 0.11, for example, means that each day the CC is 11 percent greater than the CC of the day before during the first half of canopy development. CGC is virtually a constant when temperature effects are accounted for by using GDD as the driver and there is no stress. Because CGC is based on first order kinetics (Bradford and Hsiao, 1982), a good way to derive CGC is to plot the log of CC vs. time and take the slope of the linearly fitted curve to be CGC, provided that only CC data measured from shortly after seedling emergence to approximately 60 percent cover, that do not include periods of heavy fruit load on the crop, are used. If CC data are too limited for the period specified above, but additional data have been collected up to canopy closure or near full cover, CGC can be parameterized using the canopy growth components of AquaCrop. Instead of running the model, which is more time consuming, a simple Excel programme limited only to canopy growth is available on the FAO AquaCrop website for this purpose.

Commonly, both cco and CGC would be unknown, requiring trial-and-error iterations to find the best values for the two parameters. As general guiding principles for parameterizing CGC, the main considerations appear to be whether the crop is C_3 or C_4, and whether the crop is more efficient in the capture of PAR. For maize and sorghum, two important C_4 crops already parameterized for AquaCrop, the CGC is 0.17 per day on a calendar time basis and 0.013 on a GDD basis. For a number of C_3 species, the CGC is around 0.09 to 0.12 per day on a calendar time basis. There are exceptions. One is the C_3 crop sunflower, its CGC is in the order of 0.22 per day (calendar time), presumably because of its solar tracking ability to capture more PAR per unit of canopy. In the trial-and-error runs to parameterize cco and CGC, several scenarios of outcome are possible when the simulated CC over time are compared with the measured data. These are listed in the first column of Table 3. In the second column are given possible causes for the lack of agreement and adjustments to make.

If the comparison of simulated vs. measured data does not follow any of the scenarios in the table, it is possible that either the experimental data are questionable, or the weather data may be deficient. The weather data are particularly suspect if 10-day or monthly minimum and maximum temperature are used instead of daily values

AquaCrop has built in an alternative to estimate CGC, based on the time required for CC to reach CCx. This feature is provided for users who want to simulate roughly the production and water use of a crop with some or many of the crop parameters not known. It should not be relied on to parameterize CGC, because in such cases cco and plant density or initial canopy cover (CCo), which are equally important in determining the time to reach maximum cover, are most certainly not known.

TABLE 3

Comparison of simulated with measured canopy cover and possible adjustments in the model parameters to improve the match.

Agreement between simulated CC (CCsim) and measured CC (CCmeas)

Possible cause(s) of discrepancy and suggested remedial action

CCsim is either lower or higher than CCmeas from time of emergence to CCx. Same CCx reached but at different times. Slopes of the two curves for the period of rapid canopy growth are similar

Either cco is too low or plant density is too low, or, respectively, cco is too high or plant density is too high. Check plant density data and try larger (or smaller) cco. CGC and CCx probably OK

CCsim coincides with CCmeas early in season but gradually becomes either lower or higher. Same CCx reached but at different times

CGC is either too low or too high, respectively. Make the appropriate adjustment in CGC. CCx and cco probably OK

CCsim is either lower or higher than CCmeas early in season but the trend reverses gradually later and the same CCx is reached but at different times

Either cco is too low or plant density is too low, or, respectively, cco is too high or plant density is too high. CGC is either too high or too low respectively. Check plant density data and try larger (or smaller) cco and lower (or higher) CGC. CCx probably OK

CCsim coincide well with CCmeas over the season

Values of cco, CGC, and CCx are good for this set of experimental data

Canopy decline coefficient (CDC)

After the canopy begins to senesce, CC is reduced progressively by applying an empirical canopy decline coefficient (CDC) (Raes et al., 2011). If there are LAI data spanning the senescence phase, they should be converted to CC using Equation 1 and a value for CDC selected to match the simulated CC decline with the measured values. Regrettably, in many studies detailed LAI data are lacking for this phase. In this case, CDC may be set initially according to observations of the canopy's speed of yellowing, and then refined by trial-and-error simulations to find the CDC that gives the best fit of the measured biomass data during the senescence phase. In terms of predicting biomass and yield, AquaCrop is not very sensitive to the extent of CC decline near maturity, because the model assumes a continuous decrease in the efficiency of converting normalized Tr to biomass for that period.

Normalized water productivity (WP*)

The water productivity (WP) of concern here is the ratio of biomass produced to the amount of water transpired (WPB/Tr), and the normalized water productivity (WP*) is the ratio of biomass produced to water transpired, normalized for the evaporative demand and CO_2 concentration of the atmosphere.

WP normalized for evaporative demand

Transpiration, the denominator of WP, is extremely difficult to measure and separate from soil evaporation in the field. Fortunately, there are numerous sets of data on biomass production vs. consumptive water use, which can be used to derive WPB/Tr, and hence WP* if the required weather data are available. Plots of biomass vs. normalized ET, based on sequential sampling over the season, should exhibit a portion of rising slope at the beginning followed by a straight-line portion of near constant slope, and then ending with the slope being reduced for one to several data points sampled near the end of the crop life-cycle. The slope at any given point, of course, is the water productivity at that point in terms of normalized ET, not just normalized transpiration. The early rising slopes represent a period of low water productivity, when CC is small and much of the soil is exposed, and soil evaporation accounts for much of the ET. The middle portion of the plot, encompassing the data points collected from the time when the crop canopy covered more than about 70 percent of the ground to the time when about one-fourth of the maximum LAI has senesced as maturity is approached, are to be fitted with a linear equation. The slope of this linear regression is the WP normalized for evaporative demand, but only after a correction is made for soil evaporation. Once the canopy is nearly full, even when the soil surface is wet, evaporation may constitute only 12 to 18 percent of the total ET (Villalobos and Fereres, 1990). So, depending on how frequently the soil is wetted by rain or irrigation during the period spanning the middle portion of the plot, its slope should be reduced by 5 to 15 percent to obtain normalized WP.

For the plotting of biomass vs. normalized ET, ETo is used to normalize for each time interval encompassing a biomass sample (Steduto et al., 2007) according to the equation:

where i is a running number designating the sequential time interval between two adjacent biomass samples, Tr is the cumulative transpiration within that interval, and is the mean of daily ETo within that interval, and n is the number of the biomass sample in question. The interval may not be fixed in duration and represents the time preceding the biomass sampling to the previous sampling time, e.g. for i = 5, the relevant time interval is the time between sample No. 4 and No. 5. For each biomass sample (n), the summation starts at the beginning (i = 1) and ends when i = n. If there are no ET and weather data for the time preceding the first biomass sample (i = 1), they can be assumed to be zero.

The reason for using Equation 2 to normalize is to account for any variation in ETo among the different time intervals. If daily weather data are lacking and the weather is relatively stable, plots of biomass vs. ET instead of normalized ET can be used to obtain WP in a way analogous to the procedure above. Then the WP can be divided by a mean ETo calculated from less detailed weather data to estimate normalized WP. However, this clearly is a rough approximation.

Normalization for atmospheric CO_2

The concentration of CO_2 in the atmosphere increases each year with time and impacts WP of crops. AquaCrop accounts for this effect by normalizing WP for CO_2 in a general way based on conceptual understanding and empirical data (Steduto et al., 2007). The WP already normalized for evaporative demand is multiplied by a factor, fco_2, defined by Equation 3 below, to obtain WP*.

In Equation 3 Ca is the mean air CO_2 concentration for the year of the experimental data, and Ca,o is the mean CO_2 concentration for the year 2000 (equals 369.77 μLL-1), both measured at the observatory at Mauna Loa, Hawaii. The Ca measured for the years 1980 up to present, are listed in AquaCrop in the climate file under the atmospheric CO_2 tab. The numerical values of the measured data can be found in the Mauna Loa CO_2 file in the SIMUL subdirectory of AquaCrop. The Ca of future years varies with the selected greenhouse gas-emission scenario (e.g. A2, A1B, B2 and B1 storylines). Users can enter their own projections or select one of the CO_2 files available in the DATA subdirectory of AquaCrop.

Reference harvest index (HIo)

The value of reference harvest index is chosen as the middle high end of HI values reported for the majority of the given crop species or class. This value should be carefully chosen and not altered without good reason, because a change in reference HI would require the recalibration of the parameters modulating water stress effects on HI. In terms of guiding principles, reference HI can be 0.50 or even slightly higher for modern high-yielding cultivars of grain crops, but considerably lower for earlier cultivars and land races. Over the last century plant breeders selected for high HI by selecting for higher-yielding ability (Evans, 1993). For example, HI for wheat and rice were in the range of 0.33 at the beginning of the twentieth century and rose to as high as 0.53 in the 1980s (Evans, 1993). Since the 1980s only marginal improvements have been made in the HI of the major crops (Evans and Fischer, 1999). The reason could be that the limits for stems strong enough to support the grain weight and for the amount of leaves needed to support photosynthesis have been reached (Hsiao et al., 2007). It should be noted that HI considerably higher than 0.50 for grain crops have been reported from time to time in the literature. These values should be viewed with caution, to see if there is any indication of substantial loss of biomass such as the old and dead leaves to the wind just before harvest.

HI for oil seed crops and root crops differ from those of grains. Because it takes approximately 2.5 times as much assimilate to make a gram of oil as compared to sugar or starch, HI for oil seed crops are substantially lower than for grain crops, between 0.25 to 0.4. HI for root crops, on the other hand, are usually much higher, with the range of 0.7 to 0.8 being common for high-yielding cultivars of potato, sweet potato, and sugar beet, presumably because strong stems are not required to support the harvestable product.

WATER STRESS RESPONSE FUNCTIONS (KS)

Water stress effects on leaf growth, stomata conductance, and accelerated canopy senescence are mediated through the stress response function (Ks) for these processes, with their characteristic thresholds expressed in terms of the fractional depletion (p) of the potential total available water in the root zone (TAW). As elaborated in Steduto et al. (2009), of the three processes leaf growth is the most sensitive to water stress; hence, its upper threshold (pupper) should not be much below field capacity of the root zone soil (very small depletion) for virtually all the crops. Leaf growth is stopped completely at the lower threshold (plower), a point where water content in the root zone is still considerably above PWP, i.e. depletion is considerably smaller than complete. For stomatal conductance and accelerated senescence, pupper should be considerably larger than that for leaf growth, and plower is fixed as 1 (complete depletion) in AquaCrop. Depending on the tendency to senesce of the kind of crop, pupper for conductance may be the same, slightly or substantially smaller than that for senescence. Senescence is presumably much less sensitive to water stress in ‘stay green’ cultivars. A guiding principle is that crops possessing strong osmotic adjustment capability should have larger pupper for conductance and senescence than those that do not. But pupper for leaf growth may not be that different, although plower could also be larger. In setting the thresholds, it is not necessary to base the values too literally on results reported in short-term physiological studies, because AquaCrop runs in daily time steps and the thresholds represent integrated values over a diurnal cycle.

The shape of each stress response function (Ks vs. p) also needs to be parameterized. In most cases the shape should be convex. The convex shape may be interpreted as a reflection of crop acclimation to water stress, with earlier responses under milder water stresses being modulated by acclimation, and the limits of acclimation as stress becomes more and more severe.

During trial-and-error runs of AquaCrop to calibrate the stress response functions, the choices are to adjust either the thresholds or shape of the curve, or both. Obviously, if the time of the start of the stress effect is either clearly ahead or behind the effect shown by the measured data, the first adjustment should be in pupper, by making it larger and smaller, respectively. After the starting times of the effect are matched between the simulated and measured, the degree of convex curvature can then be adjusted to match the progression of the stress effects between the simulated and measured. The more convex the curve is, the more gradually the stress effect intensifies initially as soil water depletes (p increases), but the stress effect intensifies more readily as p approaches the lower threshold. In the case of the stress function for leaf growth, plower may also need to be adjusted.

Because at the same soil water status plants experience more severe stress on days of high transpiration and less stress on days of low transpiration, AquaCrop automatically adjusts the various stress thresholds according to the evaporative demand of the atmosphere, represented by the daily ETo. In most cases the default setting for this adjustment should suffice. Only in the rarest cases, where good data indicate a clear need, should this setting be changed under the Programme Setting tab sheet.

WATER STRESS EFFECTS ON HARVEST INDEX (HI)

AquaCrop accounts for three different effects of water stress on HI. The first is the effect related to accelerated senescence of canopy, shortening the life-cycle of the crop. In AquaCrop HI increases linearly with time shortly after the start of flowering to the time of maturity, when the reference HI value is reached (provided there is no modulation due to stress along the way). This increase is stopped automatically when CC drops to a threshold value (default value is 5 percent of the maximum CC reached). Early senescence of the canopy reduces HI by shortening the time available for HI to increase, because of the shortened life-span of the crop. If the resultant final HI simulated by the model does not match the measured HI, the match may be improved by altering the parameters that affect the timing and acceleration of canopy senescence, or by changing the threshold of percent CC remaining for stopping HI increase. The latter, however, should not be done unless there are good data supporting the change. Before making either alteration, it is prudent to first examine the impact of the other two stress effects on HI, discussed next, to see if their parameter values and simulated impact on HI are reasonable.

The next stress effect on HI to discuss is apparently the result of the competition for assimilates between vegetative and reproductive growth. A part of this effect is what accounts for higher HI under the right water-stress conditions. This beneficial water-stress effect is well known for cotton, and somewhat less well known for tomato and other vegetable fruit crops such as pepper and eggplant. The increase of HI over time would be accelerated for this situation as long as stress is not severe enough to inhibit photosynthesis. When stress is severe enough to markedly reduce photosynthesis, the increase of HI would be reduced. Three parameters in AquaCrop determine the sensitivity and extent of the changes in HI caused by the vegetative/reproductive competitions. The first parameter (Before flowering tab sheet under Water stresses) determines the increase in HI as the result of a minor reduction in biomass (reduction in leaf growth) caused by water stress for a short period before the start of flowering. This is based on empirical data, but may possibly be the result of flower bud formation and development being stimulated by accumulated assimilates. In many cases, this enhancement should be only a couple of percent. The next two parameters are in the During yield formation tab sheet. On View corresponding HI adjustment the values of the two parameters, ‘a’ and ‘b’, can be changed. Increase ‘a’ to reduce the enhancing effect on HI of leaf growth inhibition, and decrease ’b’ to enhance the reduction of HI caused by stomatal closure.

The third effect of stress on HI is because of failures of pollination and fruit set. The literature often state that pollination is sensitive to water stress. It turns out, however, that in detailed studies pollination and fruit set were found to be resistant to water stress, requiring stress levels much stronger than those inhibiting stomatal opening. Accordingly, in AquaCrop the threshold for pollination failure should be set close to the PWP (e.g. 85 percent depletion of TAW).

Most crops have an excess of potential fruits for the available assimilates to fill, so a portion of the embryos is aborted after pollination. For a stress to diminish HI by inhibiting pollination, it must be sufficiently severe to reduce the number of potential fruits below the number that can be filled by the available assimilates. Hence, the impact of stress on HI depends on the proportion of excessive potential fruits. The default proportion of excessive potential fruits is given in the model for a given crop, but is adjustable by the user on the Water stress/Harvest index/During flowering tab sheet.

REFERENCES

Allen, R., Pereira, L., Raes, D. & Smith, M. 1998. Crop evapotranspiration (Guidelines for computing crop water requirements). FAO Irrigation and Drainage Paper, No. 56. Rome. 297 p.

Bradford, K.J. & Hsiao, T.C. 1982. Physiological responses to moderate water stress. In: Lange, O.R., Nobel, P.S., Osmond, C.B., Ziegler, H., eds. Encyclopedia plant physiology new series, Physiological plant ecology II, Vol. 12B. Heidelberg, Springer. pp. 264–324.

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4. Yield response to water of fruit trees and vines: guidelines

LEAD AUTHORS

Elias Fereres
(University of Cordoba and IAS-CSIC, Cordoba, Spain),

David A. Goldhamer
(formerly University of California, Davis, USA; currently Cooperative Extension Emeritus),

Victor O. Sadras
(SARDI Waite Campus, Australia)

CONTRIBUTING AUTHORS

Martin Smith
(formerly FAO, Land and Water Division, Rome, Italy)

Jordi Marsal
(IRTA, Lleida, Spain),

Joan Girona
(IRTA, Lleida, Spain),

Amos Naor
(GRI, University of Haifa, and Migal - Galilee Technology Center, Israel),

Riccardo Gucci
(University of Pisa, Pisa, Italy),

Angelo Caliandro
(University of Bari, Italy),

Carmen Ruz
(IAS-CSIC, Cordoba, Spain)

ACKNOWLEDGEMENTS

Cristos Xiloyannis
(Università degli studi della Basilicata, Potenza, Italy)

Elias Fereres, Joan Girona and Jordi Marsal are thankful to the Ministry of Education of Spain for the CONSOLIDER-RIDECO grant.

INTRODUCTION AND BACKGROUND

Orchards and vineyards are long-term, costly investments. The development of plantations must avoid two critical issues: a) poor soil conditions (e.g. too shallow, poor drainage, high salinity); and b) uncertainty in irrigation water supply. Limiting soil conditions are a threat to the long-term viability of plantations, and the severe water deficits imposed by lack of irrigation water not only would reduce current year yields but could negatively affect production in subsequent years, enhance alternate bearing and damage or kill trees, either directly or indirectly. In perennial plantations, growers need to keep the risks to the minimum, thus orchards and vineyards traditionally have been developed under good environmental conditions. However, due to water scarcity, orchards and vineyards are subjected to periodic droughts and, more recently, many orchards and vineyards have been planted in situations where the soil and/or the water may be limiting. This is why it is important to understand the responses of orchards and vineyards to variations in water supply so as to manage water judiciously.

Tree crops and vines have more complex behaviour and have been less studied than the major annual crops. Therefore, it is not possible at this time to build a simple and robust dynamic simulation model of the yield response to water, as AquaCrop is for the herbaceous crops in Chapter 3. Alternatively, we provide first an overview of the generalized responses to water supply of tree crops and vines, followed by Sections on each crop that delineate the specific responses of each major tree and vine crop, for which there is sufficient information, grown primarily in temperate and subtropical climates. The material presented focuses on the relevant issues related to orchard and vineyard development in relation to: a) water requirements; b) responses to water deficits; c) irrigation scheduling techniques; d) relations between yield and water use; and e) water management strategies suggested under limited water supply.

World trends in fruit tree and vine production

The rapid rate of world economic development in recent decades has been accompanied by many transformations; an important one is the change in human diet, such as the increase in demand for animal products in many emerging economies. In most countries, health-related concerns have led to a renewed interest and increased consumption of fruit and vegetables. Strong consumer demand has resulted in the production of high-quality horticultural products; a very high priority of both public and private institutions worldwide. This increased demand for fruit is expected to continue and presents an incentive for growers to develop efficient horticultural industries, which in most areas will be dependent on irrigation.

Current water demands by other sectors of society require that the agricultural sector reduces water use for irrigation; the world primary user of diverted (developed) water. Indeed, current agricultural water use is considered by some competing interests simply another ‘source’ of water, since the development of additional water supplies has been limited, especially in developed countries. In many parts of the world, one result of reduced water supply for agriculture has been a shift in cropping patterns to economically more viable crops; from annual field crops to horticultural crops, such as fruit trees and vegetables. Figure 1 shows that the areas devoted to growing fruit and vines in a number of countries have increased significantly over the last 20 years, indicating that this is a general trend in irrigated agriculture in most parts of the world.

Recent improvement of irrigation and management methods have reduced irrigation-related water losses, thus decreasing the amount of applied water per unit of irrigated land. Both the shifts towards high-value crops and towards higher application efficiency suggest that more water should be available for irrigation of trees and vines. However, these improvements have coincided with an expansion of irrigated land, to the extent that most of the water saved has generally been used to irrigate additional land or crops that have higher water requirements. In many cases, the water requirements of tree crops exceed those of the major field crops that were previously grown in these areas. Therefore, the availability of water for tree and vine irrigation most likely will continue to decline. This will be more so in areas where the supplies have been stretched to the limit and where environmental water needs are considered high priority by society and are in direct competition with irrigation needs.

Towards intensification in fruit and vine production

The general trend in fruit-tree production over the last decades, as in other forms of agriculture, has been towards intensification. This trend is manifested as an increase in tree or vine density, often coupled with the use of dwarfing rootstocks to reduce tree vigour. There are many reasons for the success of these intensive plantations, relative to those that are traditional low density. Greater radiation interception results in increased carbon assimilation and productivity and lower vigour reduces pruning needs and often harvesting costs; both of which are significant expenses in orchard production. Since access to the orchard is improved because the drive rows (areas between the tree rows) are not usually wetted by microirrigation, pest control applications are easier and on time. In short, high-intensity orchards are more manageable. Their major limitation is the higher capital requirements per unit land area for orchard establishment, but smaller orchards can be economically sustainable if they are adequately designed for intensive production. Another drawback is the higher water requirements associated with greater radiation interception, especially during the first years of the orchards (Box 1).

Methods of irrigation

Irrigation has both scientific and technological components and recent improvements of tree and vine crops have involved primarily the latter; the adoption of improved irrigation systems. The traditional method used in the past was surface irrigation, primarily with furrows or small basins, as is still practised today in many areas (Figures 2 and 3). The introduction of sprinkler irrigation in the 1950s had limited impact on tree crops, although it was useful to develop new plantations on steep land that was not amenable to surface irrigation (Figure 4). In the mid-1960s, drip and other forms of microirrigation were invented, which portended a drastic change in tree and vine irrigation. For the first time, growers had not only control of how much water was applied but could fully overcome the limitations of harsh topography. Furthermore, it was possible to apply water only to the areas near trees planted on uneven land, thus avoiding conveyance and evaporation losses from the zones not explored by tree roots. This was particularly important in the first years of the orchard when significant water savings could be achieved by using microirrigation. This benefit diminishes with time and as orchards approach maturity, and more ground area is shaded, evaporation losses become only a small component of consumptive water use. Nevertheless, if trees are widely spaced and the wetted soil areas are extensive and sunlit, surface evaporation can still be a significant part of orchard water use, as in the photograph in Figure 5.

Another significant advantage of microirrigation is its adaptability to large and small growers, and the simplicity of its management. The success of the drip method for orchard irrigation has been such that wherever farmers can afford to purchase such systems, they are very much preferred for use in new plantations worldwide. As with other permanent irrigation systems, the most important management issue is to determine the amount of water to apply, while the frequency of application is much less important. While irrigation is applied to meet the sum of transpiration loss from the leaves and evaporation from the soil surface, some losses normally take place in the process of irrigation, even though these should be relatively small with well-designed and managed microirrigation systems.

BOX 1

Investment costs (US$/ha) of conventional and intensive irrigated orchards: an example.

Conventional Tree Spacing: 8 x 6 m equivalent to 208 tree/ha
Intensive Tree Spacing: 5 x 2 m, equivalent to 1 000 tree/ha

208 tree/ha Costs (US$/ha)

1 000 tree/ha Costs (US$/ha)

Trees

505

2 438

Tutoring

169

810

Holes

1 685

8 100

Microirrigation system

3 650

13 740

Total

6 009

25 078

Costs (determined in Spain in 2010) are around four times higher for the intensive orchard in this case; however, benefits will also differ and it is possible that, in certain situations, the greater production in the first years and the higher productivity of intensive orchards may outweigh the costs.

Efficient use of water in orchard irrigation

The key to efficient use of water for irrigation is to quantify the disposition of the water applied to a field. A schematic water balance for an orchard under microirrigation is shown in Figure 6, in which the various losses that occur during and after irrigation are shown. Efficient irrigation is achieved when most of the water applied is consumed as Tr and the losses in E, runoff and percolation are kept to a minimum. Traditionally, irrigation efficiency under surface irrigation was low and less than 50 percent of the applied water was used in ET. Precise land levelling and adequate management can raise efficiency values to 70 percent or more. Efficiency of microirrigation systems that are well designed, operated and maintained may reach 90 percent.

An important contribution of research to improving water management has been the establishment of the consumptive use requirements of crops. In the case of tree crops, because in many areas trees and vines were grown rainfed, the amount of irrigation traditionally used was insufficient to achieve maximum yields. As the accuracy of crop-water use estimates for tree crops improved and, with production intensification, the water requirements of tree crops have been increased. This change has led to greater irrigation water use but to increased production as well.

Worldwide, irrigation management decisions are based primarily on local experience, with very limited technical input. The main reason for the lack of adoption of the many technical procedures that now exist is the growers’ perception that there is no need to improve on current practices. As irrigation methods improve and water becomes scarcer, this perception is changing and growers are beginning to see the need to be much more precise in the management of irrigation for orchards and vineyards. Increased governmental regulation, and even some legal decisions, can force the issue of improved irrigation management. The challenges that fruit tree and vine producers face to maximize their sustained productivity require: i) knowledge of the irrigation requirements to meet the full tree needs; ii) determining the irrigation schedule that will be best in terms of net profits, which may include a moderate reduction in applied water relative to the maximum needs determined in i) ; iii) tailoring that schedule to their own conditions and monitoring the tree response to the water applied, and iv) knowledge of the orchard response to a reduction in irrigation water below that needed for maximum net profits, which may be caused by regional droughts or other restrictions.

Water management under scarcity

Farmers that face irrigation supply restrictions must make decisions at the farm level by allocating the available supply to the various crops. The initial response to water scarcity is normally focused on reducing irrigation system losses thus improving irrigation efficiency (see Figure 6). This is achieved by improving existing or installing newer systems, such as microirrigation, that have high potential application efficiency. Also, technical irrigation scheduling procedures that match water applications close to the water-use rates, are used more often when irrigation water is scarce. However, there are practical limits to reducing water losses associated with irrigation. Once these losses are nearly eliminated, further reductions in water supply will unavoidably result in crop-water deficits. When this occurs, it is important to understand the species-specific physiological responses to water deficits.

Water deficits that reduce plant transpiration also decrease the production of biomass in all crops. If the crop is being grown for its biomass, such as alfalfa or corn silage, there will be a reduction in farmers’ income. For the main annual crops such as wheat, maize, and rice, a reduction in transpiration also decreases grain yield and gross income. However, for many tree crops and vines (and for some annual crops, such as cotton) where the fruit is the economic product, a reduction in biomass production does not always result in a parallel reduction in fruit production. Nevertheless, some quality parameters, such as fruit size or appearance, may be negatively affected.

The other distinctive feature of the response of perennial crops to water deficits is the carryover effects of water deficits that affect production in subsequent years. It is not known if the longevity of plantations may be affected by long-term water deficits; in some species, such as peach or some citrus, shorter longevity may not be too important, as new cultivars with better market opportunities are replacing older ones well ahead of full orchard maturity. In other species, however, it may have important economic consequences that must be considered.

Further, there may be cases of water deficits having beneficial effects on the production of trees and vines. It has long been known that fruit from trees grown under water deficits tasted better than those from fully irrigated trees. Much research in recent years has shown that water deficits affect many fruit quality features, and that they can also positively impact on the quality of products derived from fruit juices, such as wine. Therefore, in addition to saving water, there may be other incentives to applying and managing water stress in perennial crops in terms of improving product quality and growers’ revenues.

EVAPOTRANSPIRATION AND IRRIGATION REQUIREMENTS OF FRUIT TREES AND VINES

Background

Water evaporates from soils (E) and from inside plant leaves, a process that is called transpiration (Tr). The sum of evaporation from soil and plant transpiration (Box 2) from a field is termed evapotranspiration (ET; Figure 6) and is equivalent to the consumptive water use of that field. Evaporation of water requires energy that is provided by solar radiation, the primary energy source and the driving force for the ET process. The ET is the result of the interception of solar radiation by the wet soil surfaces and by the vegetation. Other meteorological factors that influence the rate of ET are temperature, humidity and wind.

Because the ET process is complex, very dynamic and is affected by local environmental conditions, researchers have developed equations to estimate ET using meteorological parameters. The FAO Penman-Monteith equation, currently accepted worldwide as the standard method, calculates the water loss from a theoretical grass surface that fully shades the ground and is never short of water, providing the reference ET (ETo). The procedures to calculate ETo from radiation, wind, humidity and temperature data are presented in the FAO I&D No. 56. The ETo is a measure of the evaporative demand of a given environment. There are many other methods to calculate ETo, some of them use temperature data only and are useful in locations where other climatic data are not available. In any case, they should not be used unless there is insufficient information to calculate ETo with the FAO Penman-Monteith equation.

The ETo provides a reference that is useful for calculating the maximum ET (ETc) from any given crop. Past research has generated information on the ratio between ET and ETo, defined as a crop coefficient, Kc. Thus, if Kc is known, the ETc is calculated as:

This is the standard procedure for estimating the crop consumptive use requirements as shown in FAO I&D No. 56, where a list of Kc values for each crop and developmental stage is provided. This Kc approach may also be used to obtain the ETc for the various tree crops and vines, as shown below.

The FAO I&D No. 56 publication also offers the option of differentiating E from Tr by using a dual crop coefficient approach, according to the equation:

Where Kcb is a transpiration coefficient and Ke is an evaporation coefficient.

BOX 2

Understanding the transpiration process.

Water loss from plant leaves takes place primarily through pores called stomata that are opened during daylight hours and close at night. Stomata are also the path for the entrance of carbon dioxide into the leaves, the necessary input for photosynthesis. Transpiration losses from leaves trigger a sequence of events in the soil tree system; water moves from the shoots into the leaves to compensate for leaf losses, drawing water from the trunk into the shoots; this in turn, draws water from the roots to the trunk, and finally, from the soil to the roots. Thus, liquid water flows from the soil to the sites in the leaves where it evaporates before diffusing through the stomata to the atmosphere. Water moves passively through this path following a gradient in potential energy, from the soil to the leaves. In any location along this path, such energy level may be determined and is called water potential. Furthermore, the water flow encounters resistance all along the pathway from the soil to the atmosphere (see diagram). The tree exerts some control on this water flow, which is needed to match the transpirational loss to the evaporative demand of the environment. When there are imbalances between supply and demand, the tree controls its water loss through the adjustment of the degree of stomatal opening and by other means.

The water flow from soil through tree to the atmosphere.

The orchard ET process

The ETc from an orchard is more complex than from a uniform herbaceous crop because there are different components that contribute to the water loss from an orchard. In addition to tree Tr, there could be Tr losses from a cover crop or from weeds, and there are E losses from the soil. In the case of microirrigation, there are two E components that may differ in their rates: one is the E from the soil areas wetted by the emitters, and the other is the E from the rest of the soil surface which is only wetted by rainfall. Figure 7 shows the four different components of water vapour that contribute to orchard ET and are further explained below.

Orchard transpiration

Tree Tr is determined by the amount of radiation intercepted by the tree canopy and by the behaviour of stomata. The degree of stomatal aperture is influenced by the climate drivers mentioned before: radiation, temperature, humidity and wind. The stomatal behaviour of tree leaves is complex; it reflects a trade-off between maximizing the uptake of carbon dioxide and minimizing Tr loss, and is affected not only by environmental, but by internal tree factors as well. While individual leaf Tr depends on its stomatal conductance and on the environment around it, tree Tr depends on the number and behaviour of all individual leaves and on their disposition in relation to the incoming radiation. The integration of the conductance of individual leaves over the whole tree canopy, yields the canopy conductance, a useful concept to understand the Tr process. In herbaceous crops, the canopy may be considered as a ‘big leaf’; for trees and vines, while the canopies are more complex because of their three dimensional nature and the gaps between individual trees, the concept of canopy conductance is also valid to represent the behaviour of the whole tree. It is important to characterize canopy size because it determines the amount of solar radiation that is intercepted, which is directly related to Tr. Box 3 shows how to estimate some parameters that relate to canopy size.

BOX 3

How to characterize the size of tree canopies.

Tree canopies may be characterized using two parameters: canopy volume (m^3 of tree volume/m^2 of ground surface) and leaf area density (m^2 of leaf area /m^3 of tree volume). The first one may be estimated easily with a measuring rod once the tree shape has been approximated as a sphere, an ellipsoid, or a truncated inverted cone. However, the second parameter is much more difficult to assess and requires specialized instruments. As an alternative to the measurements or calculations of the radiation actually intercepted by the tree, a simple parameter that is easy to determine is the degree of ground cover. The ground cover (normally expressed in percentage) is obtained by measuring the shaded area outlined from the horizontal projection of the tree canopy (See Figure below).

Canopy size and stomatal conductance are the two main parameters determining tree Tr. When orchards and vineyards approach maturity, they intercept much of the incoming radiation, although for most species the horizontal projection of their canopies seldom covers more than 70-75 percent of the ground. This is because their growth is often controlled by pruning to allow mechanization and to achieve a more even distribution of direct solar radiation to fruiting branches. For the same level of intercepted radiation, tree Tr differs among species depending on their growth habit (evergreen vs. deciduous), canopy size and architecture, developmental stage, and on their stomatal behaviour. Therefore, the models needed for calculating Tr in different orchards as a function of the size and conductance of the tree canopy must be specific for each tree species.

Orchard evaporation

The process of evaporation from soil comprises two stages, as presented in detail in Chapter 3 of the Aquacrop Reference Manual. First, after the soil is wetted by rain or irrigation, E from soil takes place at a constant rate and is limited only by the incoming radiation (Figure 8). The first stage continues until the surface dries to a level such that the soil surface layers restrict the E loss. This point marks the beginning of the second stage when E declines more or less exponentially with time. A certain amount of water must be evaporated before E starts to decline (Stage II), and this amount is constant for a given soil, varying from about 5 mm in sandy soils up to 10 mm in clay loam soils. In the declining E rate period, cumulative E can be expressed as a function of the square root of time or as a function of soil-water content in the uppermost soil layers.

The model AquaCrop computes E for the two stages according to the equations:

Where, CC is canopy cover, (1-CC*) is the non-shaded, exposed soil from which evaporation takes place, corrected with an advection coefficient; Kex is a coefficient set at 1.1, and Kr is a reduction coefficient, which is calculated as a function of the soil-water content of the upper soil surface layers.

Figure 9 shows a calculation of bare soil evaporation using the AquaCrop model for a period of 15 days after a thorough irrigation.

The major complication in computing E from an orchard or vineyard is that the soil is partially and dynamically shaded by the crop canopy, and even if the surface is wet, E is limited by the incoming energy.

The spatial variation of incoming energy within the orchard floor depends on tree spacing, canopy size and architecture and on the leaf area density. Some models have been developed to compute E under orchards and they can be used to compute E from soil that is wetted by either rainfall or by full coverage irrigation.

With microirrigation, there are spatial variations in the degree of wetting within the orchard, as some areas are frequently wetted by the emitters while the rest of the soil surface remains dry in the absence of rainfall. Because the drip lines are placed near the trees, the wet areas are generally shaded by the crop canopy, although they are wetted frequently enough to be considered to have radiation-limited E. Measurements and models suggest that the E from the soil areas in orchards, which are wetted frequently (every 1-2 days) by the emitters is equivalent to about 60 percent of the ETo from the wet areas, as a first approximation. More accurate quantification of the E from the wetted spots in a drip-irrigated orchard is available using a semi-empirical model (Bonachela, 2001).

Orchard evapotranspiration

Gross irrigation requirements have two components: orchard ET, which is considered the net irrigation requirement, and inefficiency losses, as discussed in the next section. In well-managed irrigation, ET is the major component of the irrigation requirements and losses are small. Orchard ET may be calculated using crop coefficients and ETo, or by estimating its individual components, namely transpiration and evaporation from soil. Crop coefficients are used when lack of data, particularly tree transpiration, precludes the application of the component approach.

CALCULATING ETc FROM ITS COMPONENTS

Based on Figure 7, orchard or vineyard ET may be calculated as the sum of four components:

Where:

Tr is tree transpiration

Trcc is cover crop (or actively growing weeds) transpiration

Ewz is surface evaporation from the soil wetted by the emitters, and

Edz is surface evaporation from the rest of the soil surface outside the emitter wetting pattern.

This method needs to estimate the four ETc components and is not yet widely used. However, as there are now methods available to measure tree Tr independently of ETc, it will become the standard method in the future when the models used for estimation of the ETc components, such as the one presented here (Testi et al., 2006) will be thoroughly tested. (See Box 4 for sample calculations).

Calculation of Edz using an empirical model

The following equation, derived from research on an olive orchard ET (Testi et al., 2006; Orgaz et al., 2006), for estimating the average monthly value is proposed:

Where, G is the ground cover fraction of the tree canopy, F is the monthly frequency of rainy days, and wz is the fraction of the soil surface wetted by the emitters.

BOX 4

Sample calculation of Edz, Ewz, and Trcc.

Assume an olive orchard with a tree ground cover, G, of 0.33; the average monthly ETo in April is 3.5 mm/day; the emitters wet 7 percent of the ground (wz=0.07), and there are 7 rainy days per month (F=7/30 = 0.23).

a) Calculation of E from soil not wetted by emitters, Edz ,

Using Equation 5 and 6, the average Edz for the month of April is:

Ks,e = 0.31 x (1-0.07) = 0.29

Edz = 0. 29 x 3.5 = 1.01 mm/day or 30.4 mm/month

b) Calculation of E from wet spots Ewz

Equation 8 yields:

Ewz = 0.6 x 3.5 x 0.07 = 0.147 mm/day or 4.4 mm/month

c) Calculation of cover crop Trcc ,

If the olive orchard with 8 x 6 m tree spacing, has a cover crop that covers a strip 4 m wide every tree row (fcc = 4/8), and is fairly dense and kept cut about 5-8 cm height, the water use of the cover crop would be estimated as:

Trcc = 0.45 x 3.5 mm/day x 0.5 = 0.79 mm/day or 23.6 mm for the month of April

Calculation of Ewz

Here, as a first approximation, Ewz is calculated as

Where 0.6 is an empirical factor described above and wz is the fraction of the soil surface that is kept wet by the emitters.

Calculation of transpiration from the cover crop, Trcc

Orchards with cover crops have higher ET rates than orchards that are clean cultivated. The water-use rate of cover crops in orchards is difficult to measure and has not been thoroughly investigated. Either cover crops are planted in strips of variable width between tree rows or, sometimes, weeds are allowed to grow in these areas and are controlled periodically by cutting or with herbicides. Cover crops are shaded at least part of the day, and it is difficult to measure their ET independently of the other ETc components. The Trcc will vary widely, depending on the cutting frequency, plant density, degree of shading by the tree canopies, and whether it has sufficient water available, i.e. whether the cover crop is fully wetted by the irrigation applications. Thus, the estimation of Trcc is site specific. The approach to calculate it uses a coefficient, Kcc and the ETo as follows:

Where, fcc is the fraction of the orchard ground surface occupied by the cover crop, and Kcc is a cover crop coefficient that varies from 0.25-0.35 for sparse vegetation, to 0.4-0.5 for fairly dense, short (less than 10 cm) cover crops, up to 0.6-0.8 for dense cover crops.

Calculation of tree transpiration Tr

The approach here is to use a transpiration coefficient (Kc,Tr) which multiplied by the ETo would yield the Tr.

Tr = Kc,Tr ETo

The different crop sections offer information that could be used to derive the Kc,Tr values; although for many species the information that exists is not sufficiently accurate to generalize the values. There are several factors that affect the seasonal Kc,Tr values of mature orchards or vineyards well supplied with water. In addition to the level of intercepted radiation, whether the species is deciduous or evergreen, the stomatal responses to the environment, the presence or absence of fruit, are factors that influence the Kc,Tr, and even some cultivar differences within a species have been described.

The Kc,Tr values of mature deciduous orchards vary from nearly zero at bud break to a maximum value after leaf growth is sufficient to intercept all incoming radiation. The maximum value (which varies between 0.75 and 1.0) is maintained throughout the rest of the season until leaf senescence starts, provided the tree is supplied with sufficient water. Fruit harvest decreases temporarily the value of the maximum transpiration coefficient (Kc,Trx), down to a level that is species dependent, but the Kc,Tr usually recovers two to three weeks after harvest. Figure 10 shows a typical seasonal pattern of the crop coefficient for apple (obtained in a drip-irrigated lysimeter, thus including the E component of ETc), and similar information for other crops may be found in the various specific sections.

In the case of evergreen fruit trees, the two major evergreen tree crop species are citrus and olive. Citrus trees have Kc,Trx values that depend on air humidity levels; in dry climates mature orchard Kc,Tr values vary within the season between 0.6 and 0.7, while in humid areas it oscillates between 0.7-0.85. Olive Kc,Tr values also vary within the season and are affected by climatic conditions. In temperate, semi-arid climates, it has a minimum of about 0.4 in spring, about 0.5 in the summer and may reach 0.6-0.65 in the autumn. A model to compute Kc,Tr and Tr for olive trees has been developed (Testi et al., 2006 and Orgaz et al., 2006), and is briefly described in Box 5. Similar models are now being developed for other fruit tree species and will be available soon.

BOX 5

Computing olive tree transpiration independently of the other ET components.

After E is calculated following the methods described above in Box 4, tree transpiration, Tr (mm), may be calculated as the product of the intercepted radiation by the tree and two factors (F_1 and F_2) which are related to canopy conductance. These factors were calibrated and tested for olive in a Mediterranean environment (Testi et al., 2006 and Orgaz et al., 2006). The method has also been tested in an arid environment in Argentina and in other Mediterranean-type environments.

Tr = ETo Kc,Tr

Where, Kc,Tr is a transpiration coefficient calculated as:

Kc,Tr = (Qd F_1) F_2

Where, Qd is the intercepted radiation by the tree (fraction), calculated as:

Qd = 1 – e- Kext Vu

Where,

Kext = 0.52 + 0.00079 dp – 0.76 e – 1.25 DAF,

DAF = 2 – 0.53 (Vu – 0.5); (Note: DAF must be < 2),

Vu = Vo (dp/10 000), and,

Vo = 1/6 π D^2 H

Symbols:

Tr :

Tree Transpiration (mm)

ETo:

Reference evapotranspiration (mm)

Kc,Tr :

Transpiration coefficient

F_1 :

Depends on tree density; F_1 = 0.72 for tree densities of < 250 tree/ha and F_1 = 0.66 for tree densities > 250 tree/ha.

F_2 :

Monthly coefficient from Table below

Kext:

Radiation extinction coefficient

dp :

Tree density (tree/ha)

DAF:

Leaf area density

Vu :

Canopy volume per unit ground surface (m^3/m^2)

Vo :

Canopy volume (m^3/tree)

D :

Canopy average diameter (m)

H :

Canopy height (m)

e:

Exponent (2.718)

F_2 VALUES (Northern Hemisphere)

Month

F_2

January

0.70

February

0.75

March

0.80

April

0.90

May

1.05

June

1.25

July

1.25

August

1.20

September

1.10

October

1.20

November

1.10

December

0.70

CALCULATING EVAPOTRANSPIRATION WITH THE Kc METHOD

If it is not possible to calculate Tr separately, what is needed is an overall crop coefficient (Kc) that embodies all the components of ETc. The FAO I&D No. 56 provides the Kc values for tree crops and vines, derived from the original values published in FAO I&D No. 24. The different crop sections in this publication provide sets of Kc values for the different tree crops and vines, which are an update of those published earlier in FAO I&D No. 56.

As shown in Figure 10, the crop coefficient (Kc) for deciduous orchards on bare soil, increases sharply in spring in response to bud break and leafout until it reaches a maximum in early summer. The peak Kc value is maintained until harvest, after which it declines temporarily for some tree crops. In others, the maximum Kc stays more or less the same until the beginning of senescence, when it starts to decline until leaf fall is complete. For evergreen trees, there may be variations in Kc within the season, caused by changes in leaf area, and by responses either to environmental changes or to internal tree signals. It should be emphasized that the standard Kc values include an average E component but do not include the Trcc from cover crops, unless specifically stated.

Determining the ET of partial tree canopies

When information exists to compute ETc as the sum of its components (Equation 4), the same procedure may be applied to non-mature, developing orchards where trees have not yet achieved their mature size. It suffices to measure the canopy parameters that characterize the radiation intercepted by the young trees and to compute the Tr, using a method such as the one in Box 5. The standard Kc procedure described above specifically applies to mature trees that have reached their maximum size. In intensive production systems, this is equivalent to attaining a percent ground cover (horizontal projection) of no more than 70-80 percent; maximum ground cover values are generally dictated by harvesting and other orchard mechanized operations. Mature traditional orchards and vineyards generally have lower ground cover values, although such values can be over 80 percent in a few species, such as walnuts or table grapes.

To schedule irrigation for developing plantations or in sparsely planted orchards, as is the case in traditional plantations under limited water supply, it is necessary to relate the ET of a young orchard to that of a mature orchard, for which the Kc values were developed. If the Kc approach is used, a new coefficient is needed to compute ET as:

Where, Kr,t is an empirical coefficient relating the ET of an orchard of incomplete cover to that of a mature orchard. Here, the Kr,t is related to the horizontal projection of the tree shade (ground cover).

Figure 11 shows the relation between percent ground cover and percent ET of a mature orchard, based on an original one obtained experimentally for almond trees (Fereres et al., 1982) and adjusted with data from experiments on several tree species. Box 6 shows two examples on how to calculate the ET of immature tree canopies of different sizes. The tree canopy parameter used in the equation in Figure 11, is the horizontal projection of the canopy, without correcting for differences in leaf area density that could leave gaps within the tree shade. Note that this relationship has been developed for tree canopies of various shapes but all of them formed as isolated trees, with some sort of spherical or conical shape.

BOX 6

Examples for determining ET of canopies with partial cover.

The relationship shown in Figure 11 may be approximated between 0 and 70 percent ground cover (G%) using the equation:

The two examples below refer to citrus and almond trees where the average horizontal projection of the tree canopy has diameters of 2.8 and 1.2 m, respectively.

[1]ETc = ETo Kc Kr,t

For training systems on a vertical plane, such as many trellis systems used for winegrapes, pears and apples, a different relation from that of Figure 11 between percent ground cover or intercepted radiation at noon and percent of ET would apply. This is because, as these canopies expand, they grow vertically with little increase in percent ground cover or noon intercepted radiation but their Tr increases nevertheless. This is because of the vertical growth (wall) intercepting more radiation in the morning and afternoon and also to more advective energy transfer to the trellis rows. The apple and pear Sections describe specific relations between intercepted radiation and percent mature ET for such crops, which should be used for hedgerows and other types of canopies.

Another aspect specific to young plantations is that their canopies grow and expand as time advances until close to leaf fall. Therefore, the seasonal evolution of Kc is different in young orchards than in mature orchards. Thus, estimates of ground cover should be updated on a monthly or bimonthly basis in young orchards/vineyards to adjust the estimation of ET and the resulting irrigation rates.

Variations in the E and Tr of orchards and vineyards

Site specificity affects the ETc of orchards and vineyards more than that of herbaceous crops. In perennial crops, tree or vine canopy size and leaf area density determine the Tr rate, while rainfall and irrigation frequency determine the E rate. In arboriculture, canopy size may be manipulated by pruning, and hence Tr can vary depending on pruning practices. In intensive production systems, however, pruning is kept to a minimum and Tr is not subjected to wide year-to-year variations, other than those caused by changes in evaporative demand or by internal tree controls, often related to crop load.

The E losses from an orchard or vineyard are somewhat easier to manipulate. Evaporation from soil is minimized when irrigation applications are as infrequent as possible (without causing tree-water deficits). If trees have small canopies and a significant fraction of the soil is exposed to direct solar radiation, E can be an important ET component, in particular if the irrigation method wets a significant portion of the soil surface. In these cases, irrigation frequency should be managed to minimize E loss. Under microirrigation, E losses are comparatively much less, because the wetted areas of soil are smaller and normally located under the canopy shade. Nevertheless, high (daily) irrigation frequency is common for drip systems, and the areas wetted by the emitters always stay wet. If the number of emitters per tree is high and the wetted areas are exposed, significant E losses may occur from these spots. In situations where water is in short supply, microirrigation frequency should be decreased to the longest interval compatible with having an optimal soil water regime, such as a week or even more in extreme cases of very low supply. In these cases, subsurface drip systems that eliminate E from irrigation applications should be considered.

Since Tr and E, do not occur independently, it is important to understand their interactions that are related to the energy balance of the orchard. Adjective energy transfer from the hot, dry soil surfaces in the rows towards the trees will increase Tr. On the other hand, Tr will decrease when E is high following an irrigation or rain. These interactions have not been fully documented, and thus, cannot be included in current procedures for calculating orchard or vineyard ET. However, they need to be considered at least qualitatively.

Determining irrigation requirements

There are a number of losses from irrigated agriculture, often unavoidable, associated with the application of irrigation water that the grower must consider when determining the actual water requirements of an orchard or vineyard. It should be noted that in this context, the term ‘losses’ does not apply to E and Tr, which are consumptive uses of water rather than losses.

Until now, when determining the ETc, a situation has been considered in which the calculation applies to an ideal, uniform orchard with all the trees having the same ETc. However, the uniformity of irrigation water application over a field is not perfect and some areas get more water than others. To adequately irrigate areas that get less, the system must apply more water than required to meet the overall needs of the field. Thus, some areas of the field will receive water in excess of ET and this can result in the deep percolation (loss) of water below the root zone. Additionally, some irrigation water may inadvertently run off the field and this is also considered a loss, at least for that particular field.

Whether deep percolation and runoff are true losses depend on the scale under consideration (field, farm, district, basin), and whether any or all of these losses can be recovered. For example, if runoff from one field is collected and then applied to the same or an adjacent field, it is not a true loss. The same applies to deep percolation that enters a groundwater table and is eventually pumped and reused, although its quality may be degraded. If water enters a saline sink, such as a perched saline water table or the ultimate saline sink, the ocean, it is a true loss.

To maintain a favourable salt balance, some deep percolation of water is needed to transport the salt introduced by the irrigation water out of the root zone. The amount of deep percolation required is referred to as the ‘leaching fraction’ and depends on irrigation water quality as well as the crop sensitivity to salinity. Methods have been established to estimate appropriate leaching fractions and can be obtained in FAO I&D No. 29. Leaching of excess salts is a requisite for the sustainabiliy of irrigated agriculture.

Application efficiency is used to express irrigation efficacy and is described as the percentage of applied water that is available for tree use. Variations depend on the irrigation system and skill of the irrigator and on whether an individual field, series of fields, entire farm or region is considered. The chart in Box 7 indicates the disposition of irrigation water into consumptive and non-consumptive uses, and into beneficial and non-beneficial uses. It is important for the grower to understand that some irrigation losses are unavoidable, but that they should be minimized.

For the grower, it is important to have high application efficiency and as good distribution uniformity of applied water as possible. Distribution uniformity (DU) for surface and sprinkler irrigation methods applied to systems operating with trees and vines can be determined using farm-system evaluation techniques. With microirrigation systems, it is relatively easy to measure DU by checking either emitter flow rates or operating pressures throughout the system. In orchards or vineyards under microirrigation, it is possible to attain DU values of 80 to 90 percent and thus, equally high application efficiencies if the systems are well designed, maintained and managed.

BOX 7

Consumptive and non-consumptive uses of water.

Water may be used beneficially for irrigation if used considering ETc for salt leaching or for frost protection. Irrigation efficiency (IE) is the ratio of beneficial to non-beneficial uses of water. Also shown are the various consumptive and non-consumptive uses of water.

Box 8 illustrates how the distribution of irrigation water varies in different parts of the field; in some, it exceeds the required depth, while in others it is less than required. The shaded area indicates the degree of deficit in the field. This deficit could be reduced or even eliminated by applying a gross irrigation depth in excess of that required, leading to more deep percolation losses. Systems with high DU have water distribution lines in the Figure of Box 8 that are close to horizontal, with little water excess and deficit, while lower DU values cause the line to be steeper (more variation among sites within the field) and hence there will be greater losses and deficits in the field.

In order to calculate the gross irrigation requirement (GIR) of an orchard or vineyard, the computed ETc, which is considered as the net irrigation requirement (NIR), should be divided by the application efficiency (AE), as:

Where AE is expressed as a fraction.

This effectively increases the net irrigation amount by an amount determined by the application efficiency. For example, an application efficiency of 80 percent requires that 20 percent more water than the ETc is applied to the field. Knowing the system application efficiency is just as important as knowing the ETc when calculating irrigation requirements. It would be useless to devote effort to calculate precisely calculating the ETc and then not pay attention to the actual delivery process to the field and the degree of uniformity of the irrigation system.

BOX 8

Spatial relation between the distribution of water over a field and its area.

The Figure shows a simplified diagram of the spatial distribution of irrigation depth, X, as a function of fractional irrigated area. The two straight lines represent the hypothetical relationships between the depth of water applied (X, normalized with respect to the required depth to refill the soil water deficit prior to irrigation) as a function of the fraction of the irrigated area. The two inclined lines represent the distribution of water for full and for deficit irrigation. Note that under full irrigation, 50 percent of the area receives water in excess of the required depth, XR, needed to refill the root zone. The slope of the line represents the degree of uniformity; the steeper the slope, the less uniform the irrigation application would be.

Periodic monitoring of irrigation uniformity is an important component of a good irrigation maintenance programme.

IRRIGATION SCHEDULING

Background

The goal of irrigation scheduling is to determine how to supply the tree or vine with the correct amount of water at the appropriate time. Irrigation is applied to avoid tree water deficits that are not compatible with management objectives. The usual objective of the manager is to maximize net economic benefit, which does not always coincide with maximum yields, as when deficit irrigation can improve fruit quality and thus crop value. Most growers make irrigation decisions based on their practical experience and consider the practical limitations of their systems. Research has developed technical scheduling procedures to optimize orchard water management. Technical procedures are adopted by growers who are seeking more precision in their irrigation management towards their primary objective of greater revenues (net profits). Conserving water reduces grower input that, by itself, increases revenue by a magnitude that depends on the water cost. However, reducing the consumptive use of water also conserves a resource considered quite valuable by the rest of society. Demonstrating that they are good stewards of their water resources should serve growers well in the ever-increasing competition for existing water supplies.

Three technical approaches may be used for making scheduling decisions. They are based on: a) monitoring soil water status; b) monitoring plant water status; and c) computing a water budget of the tree root zone. It is possible, and often convenient, to combine more than one of these approaches to arrive at the desired procedure. Despite the huge amount of scientific literature on the many methods developed using the different approaches, only comparatively few have proven practical and are being used for irrigation scheduling of tree crops and vines. Even the methods that have proven useful are used by a limited number of growers.

There are many reasons for the limited adoption of technical procedures; the one most frequently mentioned by growers is the lack of perceived benefits relative to their current practices, which they considered adequate. However, as water scarcity becomes more common, and when the crop value is high, as is the case for tree crops and vines, more and more growers are attempting to improve their conventional irrigation practices by adopting new technical procedures for irrigation management. The choice of procedure is primarily determined by the degree of precision required by the manager, which would normally increase in deficit irrigation situations, and/or where water supply is limited or expensive. Ease of use and the expenses involved are also important grower considerations. In some countries, irrigation scheduling services are offered to growers either by public agencies or private consultants in the form of software packages and sensor installation and monitoring.

Monitoring soil water status

Instruments to determine either the soil water content or the soil water tension were developed long ago; although in the last decade techniques have become more sophisticated with the improvements in electronics. The usual approach has been to monitor soil water at one or more depths until a threshold that indicates the need for irrigation is reached. Lately, continuous records of soil water status can be obtained and decisions are made based on the water extraction trends rather than on setting an absolute threshold point.

Traditional soil-based sensors include the tensiometer, which measures soil water tension, and the gypsum block, which measures electrical resistance. Both of these devices and others developed more recently, such as the granular matrix sensors, use porous media where water enters and is in energy equilibrium with the surrounding soil. More recently developed sensors are based on measuring the dielectric properties of the soil or the heat dissipation. There are two approaches based on the dielectric constant of the soil media: time domain reflectometry (TDR) and frequency domain reflectometry (FDR). So far the sensors based on heat dissipation are primarily research tools.

There are advantages to knowing the volumetric water content of soils in that it allows the manager to determine quantitatively the amount of water in the soil. In order to accurately determine volumetric soil water content, all the previously-mentioned sensors must be calibrated for a particular soil, with the exception of TDR. Soil water content may be measured gravimetrically or with the neutron probe. A recent study comparing the neutron probe, against many other devices developed more recently, revealed that there is no suitable replacement technology for the neutron probe for measuring volumetric soil water content. The main advantage of the new sensors is that they provide continuous soil water records that can be useful for adjusting the irrigation schedule and they lend themselves to automated irrigation control. One limitation of many of the newer sensors is the very small soil volume that they explore, leading to large variability among replications.

Use of soil water sensors for irrigation scheduling

The two critical issues with this method are where to place the sensors in the field and how many observation locations (sensors) are needed to adequately characterize a field. It is instructive to conduct a soil survey in terms of soil depth and texture to find a location that is representative of a given field. The decision concerning placement will depend on whether the grower wants to irrigate the field according to the areas where the plants exhibit water deficits first (shallower and/or lighter-textured soils), to an average location, or based on any other management criteria. The number of sensors that should be installed depends on their cost and on the degree of precision demanded by the manager. Two sensors placed in the same location at two different depths provide more useful information than if they are installed separately in two different spots because they can detect the direction of soil water movement (Box 9). The sensor at the shallow depth is installed at 20-30 cm deep, while the deeper sensor is placed at 50-60 cm or even deeper, depending on root system depth. More sophisticated instruments can give continuous records of soil moisture at several depths in any one location.

Plant water monitoring

While soil-based instruments give information of soil moisture levels in the plant root zone, the plant itself is the best indicator of its water status. There has been extensive research in measuring plant water status and its impact on plant physiological processes; however, much less work has been conducted on developing specific protocols for using plant-based measurements for irrigation scheduling. The main difficulty is related to the dynamic nature of tree water status, which is affected by both the soil water status and the atmospheric environment. Tree water status changes diurnally and over the season; thus it is not easy to define thresholds for practical use. Tree water status measurements need to be benchmarked against equivalent measurements representing fully irrigated plants in the same environment. This can be accomplished by developing ‘references’ or ‘baselines’, representing the behaviour of plants under non-limiting soil water conditions, which can be used to normalize plant water measurements.

Plant water potential

The parameter used for characterizing the state of water in plants is the water potential. This is commonly measured with a pressure chamber. The measurement requires that a leaf be excised, placed and sealed in a chamber with the cut petiole end sticking out, and then pressurizing the chamber with nitrogen gas until the xylem sap just appears at the cut end of the petiole (Box 10). This pressure balances the leaf water potential under certain assumptions. The established method used to assess water status of trees is the stem-water potential (SWP). To measure SWP, an interior, shaded leaf close to a main branch is covered (a small plastic bag overlaid with aluminum foil or any opaque, sealed bag) for a period of time (around 20 min) prior to excision. The practical elimination of transpiration by enclosing the leaf equilibrates its water status with that of the adjacent stem and presumably, the trunk. The SWP is less affected by evaporative demand (and by leaf-to-leaf variations in stomatal opening) and is more representative of the water status of the whole tree than the water potential of an exposed leaf. In some species, measurements of the water potential of uncovered, shaded leaves inside the canopy have proven to be closely correlated with SWP. Thus, taking the measurement on shaded leaves may be a viable, faster alternative to having to bag the leaves.

BOX 9

Examples of soil water monitoring in different situations

The three soil water status trends were obtained with sensors installed in shallow (dotted lines) and deep (solid lines) soil layers. The top example indicates insufficient water application (deficit irrigation), based on the decline of soil water in the lower depth (solid line). The center graph shows an increase with time in soil water deep in the profile which indicates excessive applications. The soil water fluctuations in the shallow depth (dotted line) show the typical responses to irrigation applications followed by fast extraction at shallow depths. The third graph at the bottom represents a pattern indicative of adequate irrigation applications. Note that even if the water content in the deep layer does not change deep percolation may occur even without apparent water content changes.

Plant water status varies in response to atmospheric demand and soil water levels. Even when the soil water level is high, the SWP varies, declining from predawn hours until about solar noon when a plateau is reached through the midday hours, at least on clear days. The SWP measurements are normally taken during this plateau period; from solar noon to 1400, and represent the minimum for the day. The maximum value taken at predawn is used in some cases to represent a SWP close to equilibrium with the soil water. It is not practical to measure predawn water potential for commercial scheduling given the time of day and the limited time to make the measurements.

The major limitation of using SWP for irrigation management is its labor requirement, a limitation that greatly increases for the predawn measurements, because the measurements require trips to the fields, and they cannot be automated. Alternative techniques to the pressure chamber have been proposed, such as measuring the diurnal trunk diameter changes with dendrometers. This technique provides continuous records automatically and it has been tested in a few species, although it is not widely used outside research activities. Another shortcoming of plant-based scheduling approaches is that they do not provide quantitative information on how much water should be applied, as the soil water monitoring and the water budget methods offer. Research information on SWP of the various tree and vine species is available to diagnose the relative level of water status in an orchard, and this is one of the most useful applications of SWP measurements. Box 11 provides indicative values for some tree species and further details are given in the specific crop chapters. Both evaporative demand and time of the season may affect the reference SWP values.

Canopy temperature

Tree canopy temperature is another indicator of water stress. The underlying mechanism is that the closure of stomata induced by water deficits causes an increase in canopy temperature because of a lower transpirational cooling. On a clear day, plant canopies well supplied with water are cooler than the surrounding air by several degrees. As water supply is restricted and water stress is imposed, the canopies warm up as transpiration is reduced, and their temperatures can be equal or higher than that of the air. In a given environment, canopy temperature increases with the severity of water stress. The development of the infrared thermometer (IRT) made it possible to measure canopy temperature remotely, without physical contact with the plant and this made it possible to use canopy temperatures for irrigation management. As an example, the difference between canopy and air temperature may range from -3 ºC when canopies are well watered to +3 ºC under significant stress, which offers a wide window to detect stress.

BOX 11

Reference values of stem-water potential (SWP) in some species of fruit trees and vines.

Range of SWP values for well-irrigated trees (no stress) of several perennial crop species. Values were observed around midday on clear days. The range indicates the variation observed at the low and high evaporative demands, and early (soon after full canopy development) vs. late in the season (before leaf senescence visual symptoms).

SWP (MPa)

Early season

Mid-to-late season

Olive, Citrus

-0.8 to -1.0

-1.0 to -1.2

Grapes

-0.4 to -0.6

-0.6 to -0.8

Pistachio

-0.7 to -0.8

-1.0 to -1.2

Almonds

-0.6 to -0.8

-0.8 to -1.0

Peach

-0.5 to -0.6

-0.7 to -0.9

Walnuts

-0.4 to -0.5

-0.5 to -0.7

Based on canopy temperatures, a crop water stress index (CWSI) has been developed to quantify the level of water stress of crop canopies. The CWSI uses the temperature difference between the canopy (Tc) and surrounding air (T) which is normalized for differences in climatic conditions using the vapour pressure deficit of the air.

Box 12 shows how the CWSI is calculated using two baselines, one for a fully irrigated canopy (lower baseline) and another for a severely stressed canopy (upper baseline). Both lines are either empirically determined or theoretically derived under equivalent evaporative demand conditions. Protocols for use in irrigation scheduling focus on not exceeding predetermined CWSI thresholds during specific periods of the season. Canopies under no stress have CWSI values near zero while in severely stressed crops, CWSI approaches one.

The CWSI indicator has been successful in detecting the water status of homogeneous canopies of the major field crops. It is more difficult to apply it to the heterogeneous canopies of trees and vines because the bare soil, which has a much higher temperature, interferes with the readings of surface temperature of isolated tree crowns within hot soil surfaces. Also, in the case of tree crops, the use of CWSI has been limited by the smaller differences in the canopy-air temperature gradient because of the rougher tree canopy as compared to the smooth canopies of homogeneous field crops. With the latter, the crop canopy temperatures get hotter than tree canopies for the same relative decline in temperature. However, recently it has been shown that the CWSI approach is capable of detecting water stress in fruit tree canopies, as seen in Box 12 for pistachio trees.

BOX 12

Definition of CWSI and an example showing the relations between CWSI and Leaf WP in pistachio trees.

The point C depicts the measured Tc - T of a canopy; B is the lower limit of Tc - T for the canopy transpiring at its maximum potential, and C is the Tc - T of a non-transpiring canopy, both for the existing VPD.

Diurnal CWSI trends in control pistachio trees (dots) and in trees under water deficits (RDI; black squares) on 3 July 2006, in Madera, California. The values of leaf water potential are also represented in the Figure (adapted from Testi et al., 2008).

A major advantage of the canopy temperature techniques is the possibility of acquiring thermal images that represent complete orchards from aerial vehicles or satellites. This allows an entire field to be characterized rather than monitoring only a few points or trees, as is now the case with soil and plant-based techniques. Additionally, analysing the tree-to-tree variability of remotely sensed canopy temperature data may produce other indicators of orchard water needs. Box 13 shows maps of CWSI for an orchard and the degree of detail that can be obtained from thermal images that have sufficient resolution to map each tree crown with detail.

BOX 13

Thermal orthomosaic obtained from the UAV over the peach orchard at 40-cm resolution.

The zoomed image on the top shows the water stressed trees (warmer, in red and yellow) as compared with the fully irrigated trees (blue). The bottom right image shows a low-altitude image where within-crown thermal variability is observed (adapted from Berni et al., 2009).

The water budget method

With this method, the tree root zone is considered a reservoir of soil water that is depleted as E and Tr take place. The soil reservoir of available water that the tree depletes through ET is allowed to lose water until a soil water threshold (allowable depletion) is reached, below which water stress is detrimental to crop production, quality or both. At this point, irrigation must be applied to refill the soil profile and the amount needed is equivalent to the ETc losses since last irrigation. Box 14 describes the process of how to schedule irrigation with the water budget for tree crops.

BOX 14

Applying the water budget method of irrigation scheduling.

Information needed

Available soil water holding capacity or total available water (TAW)
Defined as the difference between field capacity and permanent wilting point, it varies according to soil texture between 50 mm/m to 200 mm/m.

Rooting depth
Tree roots extend deeply into open soils and can reach several metres, but their depth may be much more limited by mechanical restrictions in the soil profile.

The allowable depletion(AD)
This is the threshold level of the root zone storage capacity below which the level of water deficit in the tree is undesirable. At this point, an irrigation is applied. Usual AD levels vary between 50-70 percent of TAW.

The ETc rate
The ETc losses are accumulated until the allowable depletion level is reached.

Practical considerations

Although tree roots may reach several metres, the effective depth of rooting for irrigation purposes is considered much less for practical reasons. Even in deep, open soils, 1.5 to 2 m is the maximum depth considered for water budget calculations. A 2 m soil profile can hold up to 400 mm of H_2O, and if the AD is 50 percent of the TAW, the crop can extract 200 mm before the next irrigation is applied. At an ETc rate of 5 mm/day, the next irrigation would be applied after 40 days! This is not practical for many reasons; for example, it would be difficult to replenish that deficit in a reasonable time period, as it is very difficult to infiltrate 200 mm of water into most soils within the standard irrigation time. Therefore, what is commonly done is to fix a certain depth of water to be applied, often much less than the AD, (between 50 and 100 mm of water), and vary the irrigation intervals according to the ETc loss. Thus, setting the AD is primarily a management decision, and must consider all practical aspects of the farm irrigation processes.

The water budget technique is very useful when significant labour is needed to irrigate, because it permits water to be applied as infrequently as possible, minimizing the number of irrigations per season and thus, labour costs. This is the case for the surface and portable sprinkler methods. When the irrigation system is permanent and covers the whole orchard or vineyard, such as for microirrigation, the issue of irrigation frequency is much less relevant, and the grower should irrigate as frequently as desired, taking into account the potential E losses. Nevertheless, it is important to keep some account of the water budget for seasonal planning, even under high-frequency irrigation.

Under microirrigation, the goal is not to refill the entire profile but only to replace the water consumed by ETc since the previous application. Thus, the primary role of the water budget using microirrigation is to determine the amount of water that needs to be applied. However, it can also be used to keep track of the soil moisture depletion of the root zone. This can be beneficial if deficit irrigation is practised or, in the event of a water delivery problem, because it can be used to determine how much available water is left in the profile. It is therefore a good planning tool, as shown in Box 15.

RESPONSES TO WATER DEFICITS OF TREE CROPS AND VINES

In every diurnal cycle, water evaporates from the leaves, internal water deficits develop and water flows from the bulk soil towards the leaves to replace the losses. For water to move from the soil to the leaves, the trees must experience some water deficits during the course of the day. However, when the soil cannot supply water at a sufficient rate to replenish the losses, or when Tr is very high because of the evaporative demand, the tree dehydrates partially and experiences water deficits that may be excessive and may affect important physiological processes that result in lower yields, fruit quality deterioration or both.

Effects on phenological development

Water deficits affect the development of fruit trees and vines. Flower bud formation, floral development and fruit set are the main processes relevant to fruit production. For deciduous trees, fruit evolves from bud differentiation that occurs in the previous year. Thus, water deficits in one year may affect the return bloom and production of the following year. For some tree crops, water deficits negatively affect floral viability the following year, but there are also reports of enhanced return bloom following water stress in the previous summer. This response is critical for determining fruit load and therefore yield in relation to water. Since this response is species-dependent it is presented in the specific crop sections. However, a general truism is that periods of floral development and fruit set are very sensitive to water deficits, and thus, damaging water stress should be avoided. Nevertheless, occurrence of water stress during these developmental events is relatively rare for deciduous species since Tr is very low early in the season because of lack of leaf area and the generally low evaporative demand in temperate climates. In subtropical climates and for evergreen species, the likelihood of stress at flowering and fruit set is greater and should be managed accordingly.

Effects on vegetative growth and CO_2 assimilation

Growth is among the first processes that are affected by water deficits. When a water deficit develops, any organ that may be expanding at that time, be it a leaf, a fruit, a branch, or the trunk, slows its rate of growth. This is because high rates of expansion require high internal pressure inside the growing cells, or turgor, which is directly dependent on the water status of the tissue. The high sensitivity of shoot growth to water stress has important implications for tree irrigation; on the one hand, it may be desirable in some cases to reduce the growth of vegetative shoots relative to their potential growth under unlimited water supply. On the other hand, if large fruit size is an important factor in determining growers’ revenues, water stress must be completely avoided during the period of fast fruit expansion. There is evidence for different species that the various growth processes, shoot initiation, shoot extension, leaf and fruit growth, and trunk growth all have differential sensitivity to water stress within a generally high sensitivity level, as discussed in the different crop sections. It should be emphasized that during the first years of the orchard or vineyard, canopy expansion is the most important process leading to high productivity. As the plantation reaches maturity, the importance of water deficits that affect vegetative growth is reduced. Another important response to water deficits is accelerated leaf senescence. In some species such as almonds, this response is quite marked, while in others it requires severe water deficits to be detectable, as for olives.

BOX 15

Evolution of soil water under deficit irrigation.

If irrigation is applied at rates below the ETc , soil water deficits develop. Such deficits may not be detrimental unless they reach levels that negatively impact the orchard or vineyard. When microirrigation is applied with a fixed rate, water budget scheduling keeps track of the soil water reservoir and facilitates the safe extraction of part of this reservoir, making the best use of the stored soil water. However, note that, in areas or years where seasonal rainfall is insufficient to wet the potential root zone, the soil water reservoir with microirrigation may be much smaller than under full-coverage irrigation.

Thus, deficit irrigation will manifest crop water stress sooner with microirrigation under those specific conditions. On the other hand, since irrigation can be applied frequently, it is easier to overcome the onset of detrimental stress with frequent applications under microirrigation, provided the system has enough capacity. The graph below shows the seasonal evolution of soil water under deficit irrigation, with both conventional and microirrigation systems. In both cases, the applied water is less than the ETc and the soil water reservoir is being depleted, but the threshold is not reached.

Carbon assimilation of plants depends on CO_2 uptake through stomata, which responds to water deficits by partially closing. The behaviour of stomata in many fruit trees follows a pattern that maximizes CO_2 uptake per unit water loss. However, stomatal regulation may also involve trade-offs between maximizing CO_2 uptake per unit water loss (which may require stomatal closure at times of high evaporative demand) and tolerance to heat stress (which requires high stomatal conductance to allow for evaporative cooling). Different patterns of stomatal behaviour are shown in Box 16 for several tree species. Stomatal conductance is highest during the morning when the vapour pressure deficit is low and declines to a plateau at midday when VPD is high (Box 16, C). This pattern has been observed in many fruit tree species but is different in others, such as apple and in grapevines (Box16, A) where stomata stay wide open for most of the day if the plant is well supplied with water.

It has been thoroughly documented that stomatal conductance and the rate of CO_2 assimilation per unit leaf area of the major field crops are not as sensitive as is vegetative growth to water deficits. Significant water deficits are required in most annual crops to reduce stomatal opening and photosynthesis relative to maximum levels. The response of many fruit tree species appears to be similar, although there is less solid evidence that for fruit trees and vines these two processes are as insensitive to mild water stress as they are for the major annual crops. In addition to trees' stomatal regulation, in response to water status, crop load and other endogenous factors can also affect gas exchange rates, making it difficult to generalize the observed patterns of stomatal behaviour across species. Also, there are gradients of water status within the tree and these differences in water supply can induce partial stomatal closure for some branches of the tree but not in others. The characteristic response of stomata to water deficits is shown in Box 16, where increasing stress levels impact both the magnitude of the peak and the plateau levels of stomatal conductance.

BOX 16

Examples of diurnal patterns of stomatal conductance

The graph below shows A) wide open stomata with a small midday depression, typical of apple or grapevines under ample water supply; B) continuous decline of stomatal opening following the increase in VPD, typical of citrus; C) Morning peak with substantial midday depression, typical of many deciduous fruit species, and of the olive; D) same pattern as in C but when the trees are under significant water stress. The relative scale may differ for the different patterns.

Mechanisms involved in the responses

The understanding of the physiological mechanisms involved in the response to water deficits is well developed for water relations and hydraulic and chemical signals, but there is a need to have a framework that integrates these processes into a whole-crop model accounting for both resource capture and efficiency in the use of resources. Box 17 shows the mechanisms of crop responses to water deficits grouped into three classes. Pathway (1) involves root-to-shoot and shoot-to-root hydraulic and chemical signals, which have attracted profuse attention. Pathway (2) contains a very strong reinforcing loop, whereby initial reduction in growth of shoot, root or both, forms a loop that may eventually override other processes. Pathway (3) involves changes in radiation- and transpiration-efficiency; these efficiencies are stable except for conditions of severe stress. This model illustrates the interactions and interdependence of the multitude of physiological processes involved in water relations and plant growth and, as such, is an integral part of understanding crop responses to water deficits. This understanding is fundamental and forms the basis for the development of improved irrigation practices.

BOX 17

Physiological mechanisms of crop responses to soil water deficit.

Pathway (1) involves direct root perception of soil water deficit, and root signals inducing reduction in shoot growth; the two-way arrow allows for shoot-to-root feedback signalling. Pathway (2) involves a strong, reinforcing loop of reduced shoot and root growth, which is mediated by impairment of the ability of root systems and canopies to capture resources. Pathway (3) involves reductions in the efficiency in the use of resources, as exemplified by radiation use efficiency (RUE) and transpiration efficiency (TE) (Sadras, 2009).

Effects on yield

Knowledge of yield and fruit quality responses to water deficits is required to predict the orchard response to reductions in water supply in water-short years or in areas of water scarcity, where it is not possible to supply the amounts needed to meet maximum ETc. Normally, the prospect of water deficits increases the risk of yield reductions and these fears are often realized if the water deficits are severe enough and occur at critical stages when some of the components of yield are determined. However, it is sometimes possible to avoid the negative impacts of reduced irrigation supplies by confining the plant water deficits to stress-tolerant periods of the season. Moreover, there are some cases where it is possible to actually exploit the positive responses to water deficits to improve fruit quality and thus, enhance crop value with reduced consumptive use. Thus, growers would profit from both higher gross revenue and reduced water costs.

To assess the response of fruit trees and vines to water deficits, multiyear trials are necessary because perennial plants require time to acclimatize to a new water regime and because there may be carryover affects of water deficits on subsequent season(s)' productivity. In other words, stress history is an important aspect of permanent crop deficit irrigation. Normally, stored soil water, shoot, leaf, root, and fruit development are all affected by the first season of water deficits and all that influences the results of that year. After the first year, a minimum of two additional years are needed to evaluate the response, primarily is the result of both carryover impacts of stress and the alternate bearing tendency of many tree species that would affect the yield, regardless of the water deficits. To characterize the yield response to water for perennial crops it is necessary to conduct experiments for 3-4 years minimum, and preferably more. The tree-to-tree variability increases the number of replicate plots needed to detect statistically significant differences in both physiological processes and yield components among treatments. All these make tree and vine irrigation experiments very time consuming and expensive and thus scarce, particularly those that are long term.

Fruit and product quality

Many fruit quality features may be affected by water deficits and it is ultimately the cumulative impact on yield and quality of a product that will inform deficit irrigation strategies. Thus, only those quality factors that influence product prices should be considered when evaluating the effects of water deficits on quality. The size of fresh fruit is one of the most important quality aspects affecting orchard revenue. Fruit size is generally reduced by water deficits that occur during the periods of fruit growth. However, the impact depends on stress timing; early season growth rate reductions may not be evident at harvest because of accelerated fruit growth if and when full irrigation is restored. Regardless, since large fruit at harvest is generally desirable, water deficits should be avoided during most species' fruit growth. On the contrary, the eating quality of many pome and stone fruit is enhanced by mild water deficits, normally by increasing their sugar content, or by increasing the sugar/acid ratios and other chemical compounds responsible for flavour and aroma. There are other positive responses of fruit quality to water deficits, such as improved colour, but the incidence of a number of disorders have been known to increase with water stress. The responses are certainly species and even cultivar-specific (see specific crop Sections). In the case of grapevines, the quality issue is of paramount importance for wine production. The price that grapes fetch may differ by an order of magnitude in some regions, depending on their fruit size, chemistry, and colour, and water management is the primary factor determining this quality, as discussed in the Section of Grapevine.

WATER PRODUCTION FUNCTIONS IN TREE CROPS AND VINES

From the practical standpoint, it is important to know how yield and quality responds to variations in water used; i.e. the relation between water and yield, which is termed the water production function. Such functions quantify the sensitivity of yield to a reduction in the consumptive use or ET below the maximum potential, and therefore describe the expected yield response to water. Water production functions were defined in the FAO I&D No. 33 Publication for most field crops, but at that time, there were insufficient data to formulate similar functions for the tree crops and vines. An important goal of the following paragraphs is to develop the production functions for different fruit tree species based on research conducted over the last 30 years. While each species has its own response, there is a general pattern of response for most fruit tree species, as shown in Box 18.

The generalized relationship between applied irrigation water (AIW), ETc and yield of tree crops shown in Box 18 has different response regions. Starting at Point 1, maximum yield is normally achieved at maximum ET; at this point, the level of AIW is such that there are no drainage losses and the level of allowable depletion has been reached. To ensure that ET needs are met, some growers apply more irrigation (Region A) but the yield is maintained at maximum, albeit with some drainage losses. For many tree crops, there is an irrigation level beyond which yield starts to decline, either as a result of the direct effects of waterlogging or the indirect effects caused by diseases associated with very high soil water levels, which may even cause tree death in extreme cases (Region B).

When AIW is reduced below the level of Point 1 (deficit irrigation), initially, yield may not be reduced (Region C, Box 18); the extent of this region depends on the species and the irrigation regime, but is generally limited to a range of actual ETc between 75 and 100 percent. As AIW is further reduced, yields finally decline due to the irrigation deficit (Region D), and will be further reduced at a faster rate if AIW is decreased further (Region E). In this last region, the likelihood of large yield losses is high because of the sharp decline in yield in response to the decrease in AIW. Note that because of the steep slope in Region E, the water productivity (yield per unit AIW) of the initial irrigation amounts is highest.

BOX 18

Generalized relationships between yield, ETc and applied irrigation water in fruit trees. The dotted line represents the expected response of fruit and nut trees while the solid blue line indicates the typical response of an annual field crop for comparative purposes.

Yield response functions and fruit quality

Farmers’ objectives are focused on achieving the maximum net income, and in many cases in fruit production, income is not only determined by the amount of production but there are quality factors that may also affect the price of the product. In these cases, the crop value not only depends on the total weight of the harvest, but on its quality as well. As discussed above, water has been shown to affect fruit quality parameters, and water stress can have positive and negative effects on fruit quality.

Markets value fruit quality in complex ways; in some, a high premium is paid for the large fruit of some species (apple, peach) while other markets do not value size as much even for the same species. Markets do not specifically consider quality features of most fruit, such as high sugar or better flavour, because quantification is difficult, and the price is based solely on weight and general appearance. Some varieties are valued more than others but this also depends on the market. The same variety may be appreciated more in some countries and regions than in others. Although fruit quality has not been an important factor in the past for determining the value of fruit, the situation is changing, as a result of both better consumer education and new techniques that permit the quantification of some quality features.

The crop, where price can vary more depending on its quality, is wine grapes. In this case, it is known that prices may vary almost an order of magnitude even for the same variety and region, depending on the quality of the grapes. This is (partly) reflected in the price of the wine, which is known to vary widely depending on its quality, as assessed by the markets. It has been shown that, generally, water stress improves the quality of wine grapes, depending on its severity and on how the stress is managed.

To quantitatively illustrate the influence of quality as affected by irrigation, Box 19 shows the general yield production function of Box 18 with a revenue (relative gross income) production function for two cases, which differ in the response to irrigation of fruit quality. In the first case (Box 19 a), water deficits have beneficial effects on fruit quality up to a point, after which quality is reduced and so is the price of the product (red line).

Strategies for reducing irrigation water use in fruit trees and vines

A major purpose of this publication is to offer practitioners a number of options for dealing with water scarcity; where the supply of irrigation water is insufficient to meet the full crop demand. In some cases, the strategies devised to apply less irrigation than that needed to achieve maximum ETc may also be the best for maximizing revenue. There are many possibilities that can be effective for increasing the efficiency of water use when the supply is scarce, as presented below.

BOX 19

Relative revenue responses to variations in ETc when fruit quality affects product prices.

In the first case (a), the revenue (blue line) increases with water deficits because prices (red line) are directly related to quality, which increases with water deficits until a point, and then decreases as water deficits become more severe. The price curve together with the yield response line yield a revenue production function that has an optimum ETc and AIW below that needed to obtain maximum yields.

(b) In the second case where fruit size determines the crop value, the price (red line) is negatively affected by the water deficits as water stress reduces fruit size, even more severely as the deficits intensify. Here, the revenue function (blue line) is different and steeper than the yield function, thus favouring the application of high levels of AIW at or very close to that needed to ensure maximum ET.

Irrigation system management

Improving the uniformity of water distribution over the field and maximizing application efficiency are two key goals that must be pursued in deficit situations. The goal is to eliminate, as much as possible, any unproductive water loss; to ensure that most of the applied water is available for plant use. High application efficiency requires both good scheduling decisions (when to irrigate and how much water to apply) and irrigation systems designed and maintained to achieve high uniformity. Lack of uniformity, when water is in short supply, might leave areas in the orchard with supply levels so low that severe water stress could be induced. The maximum attainable uniformity depends on the method of irrigation and it is difficult to exceed 90 percent in practice, but it is critical to reach the highest possible level with any method used. An important consideration when irrigation scheduling with limited water is to exploit all the stored soil water available in the root zone of the tree so that the season ends with a dry soil profile ready to be refilled by seasonal rainfall, in the geographical areas where this is feasible.

Modify horticultural practices

Pruning – More severe pruning may be effective in reducing water use since Tr is related to canopy size and leaf area density. However, the relationships between canopy size and Tr are not linear (see Figure 11) and significant pruning may be needed to change Tr. When the reduction in water supply is going to be drastic (a small fraction of tree Tr), a mature plantation can be saved for later years by heavy pruning (sometimes called ‘dehorning’ or ‘stumping’). The objective is to remove most of the canopy, leaving only short primary scaffolds. This effectively eliminates any yield for that season but it permits the tree to survive the drought. Full production may not resume for several years.

Fruit thinning – Heavy fruit thinning under limited water allows the grower to produce fruit of marketable sizes, thus increasing crop value relative to normal thinning. This practice is common and achieves higher grower revenue, even though yields are lower. Also, because the presence of fruit enhances Tr, heavy thinning can reduce Tr rates somewhat in many tree species and decrease the level of water stress in the trees, leading to an acceptable commercial size for the remaining fruit. As a general conclusion, it must be said that there is not much evidence that these measures are very effective, relative to others discussed in this Section, except in extreme, very low water supply situations. Further, because the reduction in transpiration associated with fruit thinning is mediated by reduced stomatal conductance, this practice might increase heat damage where water deficit and high temperatures occur at the same time.

Reduce evaporation from soil – If runoff is avoided and deep percolation is minimized, the only option left to decrease unproductive water use is to reduce or even eliminate E loss. In full coverage systems, irrigating as infrequently as possible would also minimize E. Evaporation losses from drip irrigation are low but they can be reduced further if the systems are not run daily, but every few days. The optimum interval would depend on the depth of water required and on soil type, as deep percolation must also be avoided. Because the depth of applied water is reduced under deficit, the irrigation set (duration of irrigation) should not be changed, but the interval between applications should be expanded (irrigation frequency lowered). Having the drip lines under the canopy will contribute to the E reduction because of both the shade and the mulch layer of dead leaves over the wetted soil. Buried drip networks can theoretically eliminate E. However, some surface wetting has been reported with many buried drip systems, even with line placement 45 cm deep. The magnitude of this problem seems to depend on soil transport properties, the installation depth, the method used for line installation, and the duration of the irrigation. Moreover, these systems are relatively expensive and more difficult to maintain. Thus, the installation of buried drip systems for the purpose of reducing E is generally not justified, as the savings relative to surface drip would be small in absolute terms. In cases where the amount of water applied is very low, the relative importance of E increases, and the small E savings from buried drip may pay off.

Deficit irrigation

Once all the measures described above have been considered and adopted as needed, the only option left to cope with water scarcity is to reduce the application of irrigation water. Deficit irrigation (DI) is defined as a regime where the irrigation water applied is less than the orchard ET requirements. When the irrigation rate is below the ET rate, there will be a net extraction of water from the soil reservoir. Two situations may then develop. In one case, if sufficient water is stored in the soil and transpiration is not limited by soil water, the consumptive use (ET) is unaffected even though the volume of irrigation water was reduced. However, if the soil water supply is insufficient to meet the ET demand, crop water deficits lead to a reduction in growth and transpiration. In the latter situation, DI reduces ET below its maximum potential. It is important to consider the sensitivity of the specific crop to water deficits. As described in the specific sections, some crops are very sensitive to water deficits and are not amenable to DI, such as walnut, avocado and kiwi. Others ranked from moderately sensitive to somewhat tolerant and, therefore, are amenable to DI strategies of variable intensity.

Deficit irrigation strategies

There are many approaches in designing a DI programme but they follow two different strategies. In one, called continuous or sustained DI (SDI), a constant fraction of the crop ET is applied at regular intervals. If the soil profile is full at the start of the season, the trees take up soil water to compensate for the deficits; as the season progresses, the soil is progressively depleted and the water deficits increase with time in the absence of rainfall. The other approach is called regulated DI (RDI), and is defined as a regime that purposely stresses the trees or vines at specific developmental stages of the crop that are considered to be the least sensitive to water deficits. The goal of RDI, when water supplies are relatively high, is to have little, if any, negative impact on the yield of marketable products and on gross profits. It should be emphasized that under RDI, the trees are subjected to irrigation deficits only at certain stages of development but they generally receive full irrigation outside these periods. The water stress is normally imposed in RDI at stages when reproductive growth is relatively low. Water deficits imposed at these stages also generally reduce vegetative growth (and thus pruning costs and agricultural burning potential problems) and may impact on other plant processes, often improving fruit quality. Figure 12 shows graphically one example of the two SDI and RDI strategies in relation to full ETc.

With plentiful water supplies, RDI is designed to reduce consumptive use without negatively impacting, and in the cases where water deficits enhance fruit quality, improving grower income and hopefully net profits. However, the RDI concept can also be used in drought years, where available irrigation supplies are limited. In practice, the manager must decide whether to impose more severe water deficits during the stress-tolerant periods or begin to expand the stress into the less stress-tolerant stages of the season or a combination of both. Thus, the timing, magnitude, and duration of the stress periods will depend on the water supply with the goal being maximum grower profit; both in the current and subsequent season(s).

One form of deficit irrigation is to irrigate alternatively either side of the tree or vine, to generate wet-dry cycles on both sides of the root system. Thus, only half the root system is irrigated at any one time while the other dries out. After some time (every two weeks or so), the system is shifted and the dry side is irrigated; this technique is called Partial Root Drying (PRD). Even though one side of the root system is always in a drying cycle, plant water deficits may not occur under PRD since the other side of the root zone can be irrigated to meet the ET requirements. Experiments and commercial practice has shown that under PRD, less irrigation water can be applied than under full irrigation resulting in higher production per unit water used. The hypothesis is that the drying cycle induces the production of chemical signals in the roots, which are translocated to the leaves and result in partial stomatal closure and the regulation of growth. This, in turn, reduces vegetative growth and enhances fruit cell turgor, resulting in positive impacts on yield and/or quality. In practice, most of the PRD experiments to date have also imposed DI, making it impossible to distinguish impacts of the water deficit versus the alternate drying cycles. Indeed, the comparisons between PRD and other forms of DI, which apply the same irrigation levels under field conditions, have not shown any specific advantage of PRD over RDI in terms of production per unit irrigation water in a significant number of experiments (Figure 13) (Sadras, 2009).

Responses to deficit irrigation

All DI strategies aim to control crop water deficits by manipulating water supply as best as possible to maintain growers’ revenues in water–limiting situations. Contrary to the responses of most annual crops, where yield declines linearly with ET under many watering regimes, the modulation of water deficits have a different impact on tree crops and vines. As an example, Figure 14 shows that three different DI strategies for almond trees had very different impacts on the yield response to applied water.

Most current RDI management approaches are based on irrigating a certain percentage of ET during stress-tolerant periods of the season. This concept works relative well in the mid and later parts of the season, after the soil moisture reservoir has been depleted of winter rainfall. However, early in the season stored water in the soil profile often buffers the impact of deficit irrigation on plant stress. Thus, even though one reduces irrigation, there will likely be a lag in terms of producing the desired plant stress. The duration of the lag depends on the depth of the root zone, soil water holding capacity, effective winter rainfall, and atmospheric evaporative demand. Therefore, there is greater need for precise plant-based water stress indicators, such as pressure chamber measurements, early in the season when using RDI. For example, one winegrape RDI strategy recommends delaying irrigation until a target leaf water potential is reached and then irrigating at a certain percentage of ET until after fruit colour change (veraison stage).

The DI practices discussed here refer to mature trees and vines. It has been shown that developing orchards should not be stressed until their canopies reach mature size to ensure that maximum production is achieved as early in the life of the orchard/vineyard as possible. Vegetative growth is very sensitive to water deficits and should be avoided if possible. Therefore, meeting the full needs of young orchards and vineyards is particularly important from the economic viewpoint, and the allocation of total farm supplies should take into consideration the objective of meeting full needs of the young orchard blocks whenever possible.

Soil fertility and pest management under DI has not been sufficiently researched to make generalizations. Since fertilizers are commonly applied through the irrigation system, reduced applied water will have a concomitant reduction in the amount of applied fertilizer. This should be avoided by adjusting the fertilizer application scheme to ensure that the recommended amounts are added. There is little evidence that RDI results in lower levels of nutrients in the plant, at least based on leaf tissue analysis. This may be because RDI reduces leaf growth and thus, tissue concentrations are the same even though there is less nutrient uptake from the soil. There are positive and negative interactions between DI and the incidence of pest and diseases. In the specific crop sections, stress-related pest management and plant disease considerations are presented where appropriate.

Salinity management under deficit irrigation

All irrigation water contains salts but only pure water evaporates from plants and from the soil and the salts are left behind. Therefore, the process of irrigation concentrates salts in the soil profile to the point that they can reach harmful levels unless they are leached out of the root zone. The rate at which salts are concentrated as a result of irrigation depends on the quality of the irrigation water, the amount of annual rainfall, the irrigation amounts and the ETc. If the seasonal water balance is such that there is a certain amount of drainage, salts will be displaced from the root zone and will move with the drainage waters below the root zone. In many cases, artificial drainage networks are needed to evacuate the excess water and the salts outside irrigated areas to ensure the sustainability of irrigated agriculture. The FAO I&D No. 25 publication provides water quality guidelines and procedures to assess the leaching requirements, and on how to quantify the impact of salinity on crop yields.

When DI is practised, the amount of applied irrigation water is less than the ETc and the water balance of the root zone is such that little or no leaching would occur during the irrigation season. Thus the risks of salinity buildup are higher under DI than under full irrigation. When reductions in water supply last only a year or two, it is possible to use mild to moderate DI with limited impact on yield and net income and with little salt accumulation. This is because normally, there is sufficient stored soil water to contribute to ET from the previous, normal year, and the salinity risks are limited if full irrigation resumes one or two years after the imposition of DI.

If DI is practised over the long term, a strategy for salinity management under DI must be devised to make DI sustainable. In areas where annual rainfall is significant (average, above 300 mm) and drainage is feasible, salinity buildup is controlled by the annual rainfall for waters of good to medium quality. Poor quality water may require some additional leaching in dry years, depending on the rainfall patterns. When annual rainfall is insufficient to leach the salts and microirrigation is practised, salts will accumulate and remain at the boundaries of the wetted zones by the emitters, and they may be harmful to the crop. Light rainfall may move these salts into the active root zones, and this is why in some dry areas drip irrigation systems are turned on when light rainfall is predicted. Sometimes, full coverage irrigation systems are used to leach the salts after leaf fall in deciduous tree plantations. Monitoring salinity is essential to anticipate possible problems, more so in dry areas and poor quality irrigation waters.

An important consideration in the case of salinity is that it is a gradual problem that in most cases takes time to build up. The limited experience with DI is that salts may be periodically controlled, even in dry areas, in the high-rainfall years or when irrigation supply conditions permit the return to full irrigation once every several years. Nevertheless, to sustain the plantations throughout their normal life cycle under DI, salinity monitoring and a sound management strategy for salinity control will be critical.

REFERENCES

Berni, J.A.J., Zarco-Tejada, P.J., Suárez, L., & Fereres, E. 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing 47: 722 – 738.

Bonachela, S., Orgaz, F., Villalobos, F.J. & Fereres, E. 2001. Soil evaporation from drip-irrigated olive orchards. Irrigation Science 20:65-71.

FAO. 2011. FAOSTAT online database, available at link http://faostat.fao.org/. Accessed on December 2011.

Fereres, E., Martinich, D.A., Aldrich, T.M., Castel, J.R., Holzapfel, E. & Schulbach, H.1982. Drip irrigation saves money in young almond orchards. California Agriculture 36:12–13.

Goldhamer, D.A., Viveros, M. & Salinas, M. 2006. Regulated deficit irrigation in almonds: effects of variations in applied water and stress timing on yield and yield components. Irrigation Science 24(2):101-114.

Orgaz, F., Testi, L., Villalobos, F.J. & Fereres, E. 2006. Water requirements of olive orchards II: determination of crop coefficients for irrigation scheduling. Irrigation Science 24:77-84.

Sadras, V.O. 2009. Does partial root-zone drying improve irrigation water productivity in the field? A meta-analysis. Irrigation Science 27: 183-190.

Testi, L., Villalobos, F.J., Orgaz, F. & Fereres, E. 2006. Water requirements of olive orchards: I simulation of daily evapotranspiration for scenario analysis. Irrigation Science 24:69-76.

Testi, L., Goldhamer, D.A., Iniesta, F. & Salinas, M. 2008. Crop Water Stress Index Is a Sensitive Water Stress Indicator in Pistachio Trees. Irrigation Science, 26: 395-405.

ADDITIONAL READING

Following are a number of general references on irrigation of fruit trees and vines that provide additional background and information to this Section.

Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. 1998. Crop Water Requirements. FAO Irrigation and Drainage. Paper No. 56, Rome, FAO.

Ayers, R.S. & Westcot, D.W. 1985. Water quality for agriculture. FAO Irrigation and Drainage. Paper No. 29, Rome, FAO.

Behboudian, M.H., Girona, J., Marsal, J. & Lopez, G. 2010. Quality and yield responses of deciduous fruit to reduced irrigation. Horticultural Reviews. 21: 149-188.

Charlesworth, P. 2005. Soil water monitoring. Irrigation Insights No.1. Second Edition 96p. Available at: http://www.irrigationfutures.org.au/imagesDB/news/soilwatermonitoring2ed.pdf

Doorenbos, J. & Kassam, A.H. 1979. Yield Response to Water. FAO Irrigation Drainage. Paper No. 33, Rome, FAO.

Fereres, E., Goldhamer, D. & Parsons, L.R. 2003. Irrigation water management of horticultural crops. Historical Review Compiled for the American Society of Horticultural Science's Hundredth Anniversary. Horticultural Science 38(5):1036-1042.

Fereres, E. & Soriano, M.A. 2007. Deficit irrigation for reducing agricultural water use. Journal of Experimental Botany 58:147-159.

Fereres, E. & Goldhamer, D.A. 1990. Deciduous fruit and nut trees. In: Stewart, B.A. & Nielsen, D.R., eds. Irrigation of agricultural crops, American Society of Agronomy Monograph. No. 30, Madison, Wisconsin. pp. 987-1017. (Monograph)

Fereres, E. & Evans, R.G. (eds). 2006. Irrigation of Fruit Trees and Vines. Special Issue of Irrigation Science, 24:55

Girona, J., del Campo, J., Mata, M., Lopez, G. & Marsal, J. 2011. A comparative study of apple and pear tree water consumption measured with two weighing lysimeters. Irrigation Science. 29:55-63.

Jones, H. G. 2004. Irrigation Scheduling: advantages and pitfalls of plant-based methods. Journal of Experimental Botany, 55:2427-2436.

Lamm, F.R., Ayars, J.E. & Nakayama, F.S. 2007. Microirrigation for Crop Production. Volume 13: Design, Operation and Management. Elsevier Publications. 642p.

Marsal, J., Lopez, G. & Girona, J. 2008. Recent advances in regulated deficit irrigation (RDI) in woody perennials and future perspectives. Acta Horticulturae 792:429-439

Naor, A. 2006. Irrigation scheduling and evaluation of tree water status in deciduous orchards. Horticultural Reviews 32:111-166.

Shaffer, B. & Anderson, P.C. 1994. Handbook of Environmental Physiology of Fruit Crops: Temperate Crops, Volume I. Boca Raton, FL, USA, CRC Press.

White, S. & Raine, S.R. 2008. A growers guide for plant based sensing for irrigation scheduling. National Centre for Engineering for Agriculture Publication 1001574/6, USQ, Toowoomba, Australia. 52 pp.

5. Epilogue

This publication, Irrigation and Drainage Paper No. 66, follows the tradition of FAO in producing papers addressing pressing land and water issues and providing guidelines to improve agricultural resources management.

With this new I&D paper, FAO is providing an enhanced set of tools, methods and knowledge, for the analysis and assessment of crop production in relation to climate, to water supply and shortage, to advance management strategies for improving crop productivity and water saving. The targeted potential users are agricultural practitioners, including farmer associations, irrigation districts, extension services, consulting engineers, governmental and non-governmental agencies, research scientists, agricultural and natural resource economists.

In the face of the increased threats of water scarcity worldwide, this publication comes at a time of great demand for maximizing the efficiency and productive water use. There is a need to sustainably intensify agricultural production in the face of incessant acceleration of competition for finite water resources, along with the uncertainties arising from climate change.

The tools and methods presented are effective as long as the user is fully aware of the strengths and limitations of their applications. The user is urged to carefully read the various Chapters, so not to fall into simplistic and inappropriate assessments.

Chapter 2 discusses the original FAO water production function method (I&D No. 33) of assessing yield response to water. This empirical approach is mostly suitable when a quick and first approximation of yield reduction related to water limitation is needed. This method has found many applications, particularly in various interdisciplinary studies at regional or even national scales, where generalized crop conditions prevaile and rapid assessments of yield reductions under limited water supply are required. Caution is necessary as the simplification introduced limits its applicability for accurate estimates of yield responses to water.

The use of AquaCrop (Chapter 3) is confined to herbaceous crops only, but with a wide spectrum of applications from the plot to field level and can be scaled up to watershed or regional level through aggregation. It can be used for yield gap assessment, to benchmark irrigation performance, and can assist in making informed water-related operational management decisions ranging from tactical to strategic. It can be used to optimize the cropping system in terms of crop and cultivar choice, planting time, and irrigation schedule for a given soil-climate combination where water is limiting. AquaCrop is also particularly suitable for the analysis of the impact of climate change scenarios on crop productivity, water requirements and consumption. Caution is required in the use of AquaCrop as its performance mostly depends on the accuracy of the input information and how well the crop has been calibrated. A well-calibrated crop and accurate local parameters, particularly the weather data, soil water characteristics, and phenology of the cultivar, are prerequisite for high-quality simulation results. Therefore the users are recommended to not rely solely on the crop file for the parameters of a particular crop, but check the AquaCrop web-page (www.fao.org/nr/water/aquacrop.html) for update of the crop parameters, and pay special attention to rough estimations or approximations involved in determining the local parameters. Questions and request for assistance regarding the model should be directed to the AquaCrop help-desk at its dedicated e-mail address (AquaCrop@fao.org).

The yield response to water of tree crops and vines is tackled through Guidelines for Trees and Vines (Chapter 4), as the current level of knowledge and the complexity of perennial crops prevented the development of a simulation model such as AquaCrop. Irrigation management of fruit and vine production must be based on accurate estimates of crop water requirements and on the crop-specific responses to water deficits, with emphasis on fruit quality. General concepts and applications are discussed first, followed by sections on specific crops where yield responses to water supply are generalized in the form of production functions. For many of these perennial crop plants, there are trade-offs between fruit quality and yield, as the maximum net economic return is achieved at irrigation levels below those needed for maximum yield. One outstanding example of this response is described in the section on grapes for wine production. Given such trade-offs and the increased water scarcity of many world areas, guidelines on the use of regulated deficit irrigation are included in every chapter, under certain assumptions on the restriction levels of water supply. The focus is on providing guidelines to optimize the use of a limited water supply, taking into account the sensitivity of certain growth stages to water deficits.

The perennial crops tackled in Chapter 4 are mostly grown in the temperate zones because insufficient information prevented inclusion of some important tropical tree crops. Given the growing demand for fruit, this subject matter is now receiving increased attention in research programmes. Guidelines for additional perennial crops will be introduced in future versions of this publication. The synthesis responses of fruit trees and vines to water presented here offers guidelines not only for improving the efficiency of water use in plantations but, also, for dealing with situations of water scarcity, which are a major threat to the viability and sustainability of fruit production in many areas of the world.

Included in this new publication is a CD containing a copy of each of the following: (i) this whole publication (I&D Paper No. 66), (ii) the original FAO water production function (I&D Paper No. 33), (iii) the FAO guideline on crop evapotranspiration (I&D Paper No. 56), and (iv) a document listing potentially useful free-ware software and the internet link.

As next steps, the maintenance and development of the model AquaCrop has three aspects. (1) One is the continuous improvement of the model and refinement of the parameters for the crops already calibrated. It is anticipated that a new version of the model, prompted by feedbacks from users and testing with more extensive experimental data sets, may be released on the AquaCrop web-page as and when warranted. Regarding the crop parameters, as indicated on the AquaCrop website, the thoroughness of the parameterization varies from crop-to-crop. As more experimental data becomes available for calibration and testing, refinement in the parameter values are expected, and they will be posted on the website.(2) Additional herbaceous crops are and will continue to be calibrated. These may include common bean, millet, cassava, sweet potato, chickpea, forage crops and leafy vegetables. Some of these are among the so-called locally-important crops, to which particular attention will be paid. New crop sections, as well as the calibrated crop files with the calibrated parameters, will be provided on the AquaCrop web-page as they are finalized. (3) As part of the ongoing development of the software, special versions will be made available for use with various operating systems (e.g., Linux, Unix, etc.) and platforms for spatial analysis (e.g. GIS, DSS, etc.). For update, the user is recommended to visit the AquaCrop web-page periodically.

Special attention will also be given to the community of AquaCrop users and data and information providers. An AquaCrop Network is established with the purpose of sharing experience and scaling-up significant results. Particularly relevant is the scaling-up and propagation of ‘training’ in the use and application of AquaCrop and of the Guidelines for trees and vines, as well as of other related tools. Face-to-face workshops, training-of-trainers approaches, distance-and e-learning methods and other capacity development activities are being developed to respond to this need.

The overall goal is to reach out and engage a large community of users and potential users, and researchers with good experimental data, to enhance and expand the utilization of these tools, methods, and knowledge, and to improve them based on community input and feedback. This will ultimately lead to an augmented capacity of the users to sustainably manage water and crops while enhancing their productivity.

FAO IRRIGATION AND DRAINAGE PAPERS

1

Irrigation practice and water management, 1972 (Ar* E* F* S*)

1 Rev.1

Irrigation practice and water management, 1984 (E)

2

Irrigation canal lining, 1971 (New edition, 1977, available in E, F and S in the FAO Land and Water Development Series, No. 1)

3

Design criteria for basin irrigation systems, 1971 (E*)

4

Village irrigation programmes - a new approach in water economy, 1971 (F E*)

5

Automated irrigation, 1971 (E* F* S*)

6

Drainage of heavy soils, 1971 (E* F* S*)

7

Salinity seminar, Baghdad, 1971 (F E*)

8

Water and the environment, 1971 (E* F* S*)

9

Drainage materials, 1972 (E* F* S*)

10

Integrated farm water management, 1971 (E* F* S*)

11

Planning methodology seminar, Bucharest, 1972 (E* F*)

12

Farm water management seminar, Manila, 1972 (E*)

13

Water use seminar, Damascus, 1972 (F* E*)

14

Trickle irrigation, 1973 (E* F* S*)

15

Drainage machinery, 1973 (F* E*)

16

Drainage of salty soils, 1973 (C* E* F* S*)

17

Man’s influence on the hydrological cycle, 1973 (E* F* S*)

18

Groundwater seminar, Granada, 1973 (E* F S*)

19

Mathematical models in hydrology, 1973 (E)

20/1

Water laws in Moslem countries - Vol. 1, 1973 (F* E*)

20/2

Water laws in Moslem countries - Vol. 2, 1978 (F E)

21

Groundwater models, 1973 (E)

22

Water for agriculture - index, 1973 (E/F/S*)

23

Simulation methods in water development, 1974 (E* F* S*)

24

Crop water requirements, (rev.) 1977 (C* E F S)

25

Effective rainfall, 1974 (C* E* F* S*)

26/1

Small hydraulic structures – Vol. 1, 1975 (E* F* S*)

26/2

Small hydraulic structures – Vol. 2, 1975 (E* F* S*)

27

Agro-meteorological field stations, 1976 (E* F* S*)

28

Drainage testing, 1976 (E* F* S*)

29

Water quality for agriculture, 1976 (E* F* S*)

29

Rev.1 Water quality for agriculture, 1985 (C** E* F* S*)

30

Self help wells, 1977 (E)*

31

Groundwater pollution, 1979 (C* E* S)

32

Deterministic models in hydrology, 1979 (E)*

33

Yield response to water, 1979 (C* E F S)

34

Corrosion and encrustation in water wells, 1980 (E)

35

Mechanized sprinkler irrigation, 1982 (C E* F S*)

36

Localized irrigation, 1980 (Ar C E* F S*)

37

Arid zone hydrology, 1981 (C E)

38

Drainage design factors, 1980 (Ar C E F S)

39

Lysimeters, 1982 (C E* F* S*)

40

Organization, operation and maintenance of irrigation schemes, 1982 (C E* F S*)

41

Environmental management for vector control in rice fields, 1984 (E* F* S*)

42

Consultation on irrigation in Africa, 1987 (F E)

43

Water lifting devices, 1986 (F E)

44

Design and optimization of irrigation distribution networks, 1988 (F E)

45

Guidelines for designing and evaluating surface irrigation systems, 1989 (E)

46

CROPWAT – a computer program for irrigation planning and management, 1992 (E F* S*)

47

Wastewater treatment and use in agriculture, 1992 (E)

48

The use of saline waters for crop production, 1993 (E)

49

CLIMWAT for CROPWAT, 1993 (E)

50

Le pompage éolien, 1994 (F)

51

Prospects for the drainage of clay soils, 1995 (E)

52

Reforming water resources policy, 1995 (E)

53

Environmental impact assessment of irrigation and drainage projects, 1995 (E)

54

Crues et apports, 1996 (F)

55

Control of water pollution from agriculture, 1996 (E* S)

56

Crop evapotranspiration, 2006 (E S)

57

Soil salinity assessment, 1999 (E)

58

Transfer of irrigation management services: guidelines, 1999 (E F R S)

59

Performance analysis of on-demand pressurized irrigation systems, 2000 (E)

60 Rev.1

Materials for subsurface land drainage systems, 2005 (E)

61

Agricultural drainage water management in arid and semi-arid areas, 2002 (E)

62

Guidelines and computer programs for the planning and design of land drainage systems, 2007 (E)

63

Modernizing Irrigation Management – the MASSCOTE approach. Mapping System and Services for Canal Operation Techniques, 2007 (E)

64

Manual on small earth dams. A guide to siting, design and construction, 2010 (E)

65

Guidelines on spate irrigation, 2010 (E)

66

Crop yield response to water, 2012 (E)

Ar

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Arabic

C

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Chinese

E

–

English

F

–

French

P

–

Portuguese

R

–

Russian

S

–

Spanish

Multil

–

Multilingual

*

Out of print

**

In preparation