The procedures described in the preceding chapters relate to the assessment of carbon stock aboveground and belowground. Simulation models were used to predict the turnover of SOC in SOM at different time periods. Having computed values for carbon stock in each of the carbon pools,attention can turn to the completion of the carbon accounting process.
Total carbon stock for present land use
For carbon accounting purposes, the total carbon stock for a given area, which may be a soil or LUT polygon, or a PCC, present in the current land-use pattern, can be calculated from:
Cstock total = Cag + Cbg
Cbg = Cbg-biom + Csoil
Cstock total = Cag + (Cbg-biom + Csoil)
where Cstock total is the total stock of C in the ecosystem, including aboveground (Cag) and belowground (Cbg) pools. The constituents of the belowground pool are the carbon content in roots and all belowground biomass (Cbg-biom) and the C in the soil (Csoil) as organic C in SOM.
The values of Cstock total after the estimation of aboveground biomass, its conversion to C, the estimation of C in belowground biomass (roots, etc.), and the modelling of SOM turnover to establish SOC are calculated for particular sites where the biomass measurements have taken place, in this case the 10 × 10 m quadrats.
The calculated carbon stock values implicitly assume permanence of the present land-use pattern in the area of study. This is important given that the SOM starting parameters for the simulation models required rates of addition of organic materials to the soil. However, the land-use pattern in a watershed is subject to year-to-year changes, even though they may be minor.
Upscaling and mapping total carbon stocks
Methods for upscaling from values measured, estimated or modelled for sites, polygons or PCCs are discussed in Chapter 3.
Assessment of carbon stock and sequestration in PLUTs
The methodological details considered so far in this chapter involve the determination of the stocks of C under present land use. However, for many practical and strategic reasons it may be pertinent to know what the implications would be, in terms of carbon stock and sequestration, biodiversity, land degradation and food security, of alternative scenarios of LUC.
The assessment of PLUTs is fundamental to the creation of LUC scenarios, which may represent advantageous options for farmers and land users in terms not only of the production of food, fodder and fibre, but also of the potential accrual of carbon stock and sequestration credits. Thus, in order to be considered realistically for implementation in a LUC scenario, any PLUT should:
represent advantages in terms of increases of carbon stock and sequestration potential;
represent gains in food production and food security;
not affect biodiversity adversely;
not promote or increase the degradation of the land;
be biophysically and economically suitable for the characteristics of the area where it would be implemented.
Later sections of this report deal with the conditions on biodiversity, food production and security, and land degradation. This section concentrates on the other two criteria. Thus, the selection of a PLUT to become part of a land-use pattern in a potential LUC scenario, as far as C is concerned, is based on two criteria:
the enhanced carbon sequestration criterion: the PLUT should represent potential increases in total carbon stock and sequestration potential over the time involved in the planning horizon.
the biophysical land suitability criterion: the land where the PLUT is to be implemented should be biophysically suitable. That is, the biophysical requirements of the PLUT should be met entirely or to a large extent by the biophysical conditions of the land to which it is intended. The PLUT must also be economically viable.
A land evaluation scheme can include both criteria. The criteria of land suitability for a given crop can be supplemented by additional criteria that reflect enhancement of carbon sequestration. Thus, the land evaluation exercise will accomplish both: selecting LUTs that are biophysically suitable and enhancing carbon sequestration. The selection of PLUTs for a given area of interest rests on a series of procedures. The following section describes these in more detail. Figure 20 summarizes the overall methodological framework for the assessment of carbon sequestration in PLUTs.
Land evaluation for PLUTs -with carbon sequestration criteria
This procedure is one of the central components of the overall methodological framework shown in Figure 20. The land evaluation process follows the methodological framework proposed by FAO (1986) and adopted almost globally as the standard method for land evaluation for rainfed and irrigated agriculture. Land evaluation is crucial to ensure that PLUTs are suitable for the area by meeting the biophysical characteristics and qualities of the local environment, in addition to having high CSP.
FIGURE 20 - Assessment of carbon sequestration in PLUTs
FIGURE 21 - Land suitability assessment for PLUTs,including carbon sequestration potential
The procedural stages for evaluating the suitability of PLUTs are standard practice in land evaluation. Figure 21 illustrates the stages in land suitability assessment incorporating criteria for carbon sequestration. The key aspects of the procedures in land suitability assessment can be consulted in FAO (1986). Such procedures, some of which are charted in Figure 21, are described in some detail in later sections of this report.
Pre-selection of land utilization type by climate suitability and photosynthetic pathway
The initial step in the land suitability assessment involves the compilation of a preliminary list of PLUT by climate suitability (temperature, radiation and soil moisture regimes) and photosynthetic pathway. This starts with the identification and compilation of a list of plant species that are actually grown or can be found in the area of study that meet two requirements: (i) there are official records or anecdotal evidence indicating that they have adapted to, and have been grown in the area of study; and (ii) such species possess advantageous photosynthetic characteristics, particularly as they relate to CO2 assimilation efficiency, including variations under different management and growing periods in the study area.
Of particular interest are certain agricultural, agroforestry or forestry species that: (i) can accumulate biomass rapidly; (ii) are well adapted climatically, in addition to producing food, fibre and fodder for the local populations; and (iii) are economically viable. Two types of databases and knowledge bases need to be examined:
crop requirements databases. The adaptability requirements to climate and soil.
physiological and phenological databases for selection of crops, trees and grass species based on photosynthetic pathway (efficiency).
Each species has different photosynthetic pathways, which determine the speed of biomass accumulation. The types of plant species of interest, according to their photosynthetic efficiency and speed of biomass accumulation, are groups C4 (typically, 70-100 mg CO2/dm2/h; Group III and IV crops) and C3 (40-50 mg CO2/dm2/h; Group II and V crops). Photosynthetic efficiency refers to the net speed of CO2 interchange in saturation by light, and to the terminal velocity of growth (accumulated biomass) of each plant species. Tables of data on photosynthetic efficiency by species can be found in topical and specialized sources (e.g. Hall and Rao, 1999; FAO, 1978b).
The activities described above correspond to conducting research for the selection of LUT with maximum CSP. At this stage, it is important to conduct intensive consultation with farmers and local experts regarding the initial list of LUTs and then, on the basis of their feedback, refine the list of promising LUTs. Once the initial list has been compiled, the selected PLUTs must be characterized in terms of their infrastructural setting, socioeconomic conditions, level of inputs, cropping system and land management, particularly as it pertains to SOM. Comprehensive FAO guidelines for LUT description have been published to aid in this process (FAO, 1986).
The identification of LUT requirements is of central importance in the suitability assessment. Knowledge bases containing specific information on plant species requirements are not common. Ecocrop (FAO, 1999) is one of the few resources available. It is a crop environmental requirements database developed in the Land and Water Development Division of FAO, pooling information from its many projects around the world. This database provides a large list of climate and soil requirements for crop, tree and grass species (about 1 700). The database is generic in terms of the nature of the requirements listed. Knowledge of specific crop requirements is not abundant as it is usually derived from long-term field experience and research. However, there is some very useful published work including knowledge bases of this kind, e.g. Sys (1985). Syss database is really a knowledge base in that it includes threshold values and their ranking into suitability classes. Both threshold values and designated suitability classes are expressions of long-term experience and accumulated knowledge.
The final list of promising plant species is compiled following the criteria indicated above. Climate and soil requirements per species are identified and listed with the species identity. The requirements can be considered pointers to the type of data on land characteristics that need to be collected in order to assess the suitability (climate and soil) of land polygons for each of the species in thelist.
Land suitability assessment
The requirements of each plant species in the list should be matched to the status of equivalent land qualities. The matching process takes a variety of approaches to its implementation with data from the soil and climate databases. FAO (1986) has prepared detailed guidelines on the topic for a variety of infrastructural, management and ecological conditions. A description of a methodology of that kind is beyond the scope of this report. It should suffice to indicate the stages of the process for the suitability assessment and to describe in brief one of the approaches to carrying out the assessment.
Once the biophysical characterization of the area and the definition and mapping of ecological or PCCs have been completed, the land suitability assessment can proceed. This consists of the matching of requirements to qualities. Two contrasting approaches can be adopted:
manual matching of LUT requirements to land qualities through their characteristics and allocation to suitability classes through the use of tabular knowledge bases (e.g. Sys, 1985; Ponce-Hernandez and Beernaert, 1991). The actual value of a given climate or soil characteristic is allocated to a suitability class on the basis of the ranges of each class in the table. The characteristics are evaluated one by one.
development of an automated suitability assessment model. This is an automated procedure, which requires the development of a multicriteria assessment model. Software shells for automation, based on the construction of decision trees, such as the Automated Land Evaluation System (ALES), can be very useful for achieving these aims. Detailed explanation of the process of construction of decision trees in ALES is beyond the scope of this report. The reader is referred to Rossiter (1995).
The manual approach is labour intensive and only recommended where the data sets to be matched are not large (i.e. the number of land units and LUTs), or the assessment is straightforward, involving only a few land qualities. Conversely, the need for automation becomes clear where the process may become a routine operation in an organization or the volumes of data are sufficiently large to make manual matching prohibitive. In this report, a combination of manual and automated methods was applied to the case studies presented, depending on considerations of: volume of data to be processed, viability of decision-tree model development, effort involved and time.
The matching process results in the generation of potential land-use information, which consists of a suitability matrix with LUT and land unit polygons or PCCs as columns and rows, respectively.
Mapping potential land use
Transfer of the land suitability assessment ratings (suitability classes) to the soil/land/ecological zone polygon map (vector), or to the pedo-climatic raster map in the GIS, allows for the generation of a series of map coverages or thematic layers in the GIS. Each layer represents the spatial variability of suitability ratings for a given PLUT. Therefore, there are as many thematic map layers as there are PLUTs.
Selection criteria for mapping PLUTs
Not all the land units evaluated are biophysically suitable for all the PLUTs considered. Therefore, to create the first viable scenario of potential land use, selection criteria need to be identified in order to build the first suitability assessment scenario. The selection rule used was:
select only the LUTs with the two highest suitability classes for each land unit polygon/cell, and attribute such ratings to the corresponding polygons or grid cells.
Thus, only PLUTs with highly suitable (S1-0) and suitable (S1-1) classes were selected. Hence, the final LUC scenario consisted of those PLUTs that attained such ratings, making it possible to generate one map showing the spatial variability of highly suitable PLUTs in the area, and another map with the suitable PLUTs in the area. These two maps are the scenarios for LUC.
FIGURE 22 - Estimation of carbon sequestration by PLUT
Although very useful, these scenarios only establish whether the PLUTs are ecologically suited and feasible in the area of concern. The fluxes, balances and the stock and sequestration of C in all its pools, which are implicit in each PLUT, are yet to be determined for carbon accounting purposes, should the LUT be implemented on the ground.
The main difficulty with the estimation of carbon stock and sequestration in PLUTs is the fact that these LUTs are conceptual and yet to materialize. Thus, physical measurements for the estimation of aboveground and belowground biomass, which were the main instrument for estimation in present land use, are not yet possible. Estimation processes based on theoretical constructs, state-of-the-art knowledge and simulation modelling, in this case, will determine whether or not there are advantages in implementing the potential LUC scenarios.
Estimation of carbon stock and sequestration in PLUTs - aboveground and belowground pools
Figure 22 provides an outline of the procedural stages for the generation of these estimates.
The estimation of biomass of potential crops not yet grown poses a methodological problem: the lack of physical presence of those plant species on the ground, which would allow measuring or physically estimating their biomass. Three solutions can be envisaged to this problem. In order of accuracy and intricacy, from the most simple and least accurate to the most complex and most accurate, these are:
· inference from the suitability classes and expected yields. Biomass can be estimated through knowledge of the genetic potential of the species involved in the LUT, and knowledge of the range of the potential yields from the range of yield values equivalent to the suitability class of that LUT in the land polygon of concern.
· calculation from standard phenological equations of net biomass as a function of climate parameters and LAI. Net biomass for a given crop can be calculated from standard crop growth (phenological) equations, where it is set as a function of the maximum velocity of biomass production and respiration. This is in effect a simplified model of plant growth based on certain assumptions about the shape of the curve of biomass accumulation as a function of effective LGP (time), the maximum slope of such curve (first derivative), and its relation to LAI and main climate parameters influencing the speed of biomass accumulation and plant respiration, such as temperature and radiation (FAO, 1978b, 1981; De Wit et al., 1978).
· computation from crop and forest growth simulation models. Plant growth and biomass accumulation models allow for the simulation of biomass accumulation in crops and tree species. Aboveground biomass estimation is also a compartment of some organic matter turnover models (e.g. CENTURY). There is a relatively long list of simulation models that could provide estimates of biomass in crops. It would seem logical that universal crop simulation models, e.g. the erosion/productivity impact calculator, known as EPIC (Sharpley and Williams, 1990) and WOFOST (van Diepen et al., 1989), should be more complex than single-crop simulation models, e.g. CERES (Jones and Kiniry, 1986) and SOYGRO (Jones et al., 1989). However, this does not seem to be the case. Several researchers have attempted a detailed review of these types of models. Such discussion is beyond the scope of this report. However, these types of models can be very useful in predicting total biomass accumulation of potential crops, shrubs and trees in a PLUT mix. The Decision Support System for Agrotechnology Transfer (DSSAT) from the International Benchmark Soils Network for Agrotechnology Transfer (IBSNAT, 1989) is considered of particular interest to users in the developing world. The DSSAT includes CERES, CROPGRO and other models with different degrees of sophistication and detail.
Suitability classes and crop performance as a percentage of maximum potential yields
Biomass estimation by the AEZ approach and through suitability class and expected potential yields
Biomass estimation has attracted considerable research effort. There is a relative wealth of published work on the subject. This section focuses only on the readily applicable approaches to biomass estimation.
In its AEZs Project, FAO (1978b) defined a method for estimating the biomass of cropping systems. The method is based on calculations supported by knowledge of the relationships of climate parameters and phenological stages of crop growth. The AEZ method has been expanded substantially through the incorporation of many capabilities for land resources assessment. This was demonstrated particularly clearly in the very complete case study of Kenya (FAO, 1993a). The AEZ methodology has evolved to become part of a computerized decision-support system (known as AEZWIN) for multicriteria analysis applied to land resources appraisal (FAO, 1999). The AEZ approach and methodology have been advanced by FAO through land resources assessments in the developing world. The method for biomass estimation used by the FAOs AEZ team is the basis of the calculations suggested below.
On the other hand, in situations where the method, the software or the data for the AEZ method are not available, a simple method consists of converting the suitability class pertaining to each LUT to a range of crop yields. In turn, these could be transformed to biomass through using a ratio of grain-fruit to aboveground biomass (grain-fruit/biomass), specific for each species. As an example, Table 12 provides an indication of how the ranges of suitability classes can be related to crop yields (Sys, 1985). Such relationships could be useful for estimating crop yields from a simple suitability rating as a measure of crop performance.
An investigation into historical records of potential, constraint-free yields and potential constrained yields (usual constraints are climate, soil and pests) attained in the study area would produce valuable data to aid in biomass estimation without the use of models. Information may be obtained from local research stations, agriculture statistics agencies or, better, from the local farmers in each of the ecozones in the study area. A combination of data on potential crop yields, both constrained and constraint-free (high levels of inputs), will provide the reference yields from which to calculate a fraction according to the suitability class assigned to the LUT and crop or crops for a given land unit. In this case, as the PLUTs have already been selected, only the two highest suitability classes are considered. The figures derived in this way will provide a range of yields. A conservative approach would be to adopt the lower limit of the percentage range of the potential yields, converted into kilograms per hectare. For example, an LUT with a crop (e.g. maize) given a suitability class S1-1 would consist of 85 percent of potential yields attained in the area.
The harvest index (Hi) relates crop yields (By) as a fraction of net biomass (Bn):
By = Hi × Bn
Thus, the net biomass can be approximated by:
Bn = By / Hi
FAO (1981) has reported values of Hi for a relatively wide range of crops for Latin America and the Caribbean region. The report also contains tables of net biomass estimates for selected constraint-free crops, grouped by photosynthetic pathway and climate suitability. Then, with imposed constraints (climate, soils, topography and pests), the report also gives expected yields at two levels of inputs. These data provide a starting point to the initial values of net biomass and crop yields. Such values can be fine-tuned with local information from farmers and agencies about maximum attained yields and potential yields in the area of concern. This approach provides an alternative to using crop growth models or similar tools.
The estimates of net biomass lead to two useful calculations:
the fraction of net biomass that corresponds to roots and belowground biomass;
the fraction of aboveground net biomass that is left in the field as crop residues and later incorporated into the soil as input into the SOM turnover processes.
The latter is a fraction that is site dependent. It needs to be investigated locally with farmers and agricultural agencies and research stations as it depends exclusively on the local crop and land management. In many areas of the developing world, stalks and crop residues are a valuable resource for backyard and extensive livestock feed. However, in most instances, most or all of the crop residues (and their carbon content) are removed from the system. These leakages cannot be accounted for accurately even where they become animal tissue. Their fate is not certain, and they constitute a removal from the soil of the field from where they were extracted.
Net biomass estimation from standard phenological equations as a function of climate parameters and LAI
The net biomass production of a crop with N days of growing period in the field can be calculated (FAO, 1981) from:
Bn = 0.36 bgm / (1/N + 0.25Ct)
where Bn is the net biomass (kilograms per hectare), bgm is the effective velocity of maximum production of total biomass (kilograms per hectare), which is reached with an LAI = 5. Where LAI is not five, then proportions of bgm can be calculated depending on the value of LAI at the moment of bgm. Ct is the coefficient of maintenance respiration of the crop. It is a function of temperature such as:
Ct = C30 (0.44 + 0.00019T + 0.0010T2)
where C30 is the value of C at 30 °C, 0.02283 for a grain and 0.0108 for a legume; and T is the average daily temperature within the growing period.
The following data sets are necessary for calculating Bn by this procedure:
climate data: latitude; altitude; growing period (days); beginning and end of the growing period (date); mean solar radiation within the growing period (calories per square centimetre per day); mean temperature (diurnal) within the growing period (degrees Celsius); and mean temperature (24 h) within the growing period (degrees Celsius).
crop data: crop species; time to maturity (days); LAI at the point of maximum growth in the crop growth curve or at the time of maximum velocity of growth (usually five); Hi (adimensional); climate suitability group of the crop (see FAO, 1981).
Once the net biomass has been estimated, it is possible to estimate crop yields from it through standard harvest indices. Then, it is possible to estimate the fraction corresponding to crop residues returned to the soil. This is site dependent and should be ascertained from local sources (farmers or local agricultural agencies).
Biomass estimation from crop growth simulation models
Plant growth and biomass accumulation models allow for the simulation of biomass accumulation in crops and tree species. Models of this type, categorized by their degree of complexity and specificity, produce a relatively long list. Among the most commonly available models are: EPIC (Jones et al., 1991), WOFOST (van Diepen et al., 1989) and CERES (Jones and Kiniry, 1986).
Of particular interest to modellers in the developing world could be the DSSAT mentioned above. The DSSAT is a shell that allows the user: (i) to organize and manipulate crop, soils and weather data; (ii) to access and run a collection of crop growth models in various ways; and (iii) to analyse their outputs, rather than running single models.
Biomass estimates for belowground biomass (BGB), i.e. roots, can be estimated as a fraction of aboveground biomass (AGB) by applying the same coefficients as in the estimation for present land use:
BGB = 0.25 AGB for coniferous vegetation;
BGB = 0.30 AGB for broadleaf vegetation and crops.
In the case of crops, the coefficient 0.3 should be used. Then, for a given site or polygon:
Biomass(total) = AGB + BGB
The value of total biomass can be estimated from the equation above. Independently of the choice of model, the biomass estimates obtained, by necessity, will be referenced spatially to either a pixel or a polygon representing the land unit or ecozone or pedo-climatic unit from which the climate, soil and site data were extracted to run the model. Therefore, biomass estimate values must be interpolated spatially by any of the procedures described in the preceding sections.
Upscaling and mapping total biomass implicit in potential land use
Upscaling the estimates of biomass of PLUTs is a relatively straightforward procedure as suitability map layers have already been created for the highly suitable and suitable PLUTs. In this report, these were mapped out by assigning these two suitability ratings from the matching process to each one of the map objects, i.e. land unit polygons or PCCs evaluated.
The procedures for upscaling estimates of biomass consist of assigning the calculated value of Biomass(total) calculated for a given LUT to the land unit polygon or PCC where this PLUT is assigned in the two scenarios of potential land use, either the highly suitable scenario or the suitable scenario. This will provide at least two mapping scenarios of biomass estimated by each of the estimation procedures above. The upscaling procedure based on spatial interpolation or drawing average means per polygon was not necessary in this case. This is because the objects on which the biomass was estimated were already polygons and not the sampling quadrats used to estimate actual land use.
Estimation of carbon stock implicit in potential land use
Independently of the biomass estimation procedure, carbon values can be derived by using a similar approach for the estimation of C in biomass of current land use (described in preceding sections of this chapter). Therefore, carbon stock in total biomass of PLUT can be estimated from:
Carbon(in biomass)=0.55Biomass(total = AGB + BGB)
Mapping carbon stock implicit in potential land use
A simple GIS scalar operation can generate the spatial distribution of the potential carbon stock that would materialize if the PLUT were implemented. This operation consists essentially of multiplying all the values of the pixels containing the total biomass of the corresponding PLUT by a scalar or constant (0.55). It is a straightforward operation. In the case of polygons, the attribute tables of the polygons containing the values of total biomass are multiplied by the constant coefficient and assigned to a new coverage or thematic layer. These operations allow for the creation of maps depicting the spatial variability of total carbon stock under highly suitable or suitable land use.
Modelling potential carbon sequestration by potential land use - generation of carbon scenarios
Thus far, the procedures for estimating C have considered accounting for belowground and aboveground biomass. Accounting for SOC present in SOM calls for the use of SOM turnover simulation models, as SOC is part of the overall carbon balance: potential carbon stock(total) = C as potential biomass + SOC, where SOC in this case is the organic C that would accumulate or decrease in the soil following the introduction of a PLUT through a LUC from the current land use. Thus, in order to estimate the value of SOC left after turnover of SOM from additions of crop residues incoming from the PLUT to be implemented, it is necessary to model the dynamics of organic matter turnover. The simulation models of SOM turnover should be run under the hypothetical implementation of a PLUT and its crop and land management parameters. Chapter 2 outlines the procedural stages involved in these operations.
The methodological stages for the simulation of SOC dynamics in the belowground pool for PLUT have been outlined in preceding sections in this chapter. The main steps are:
input of organic matter contributions from PLUT (crop residues, manures, litter and any other organic residues).
selection of the carbon dynamics simulation model. In this case, RothC-26.3 or CENTURY is used.
extraction of soil and climate model parameters from each PCC/soil or land polygon, for input into the carbon simulation model.
model simulation execution.
model output and generation of SOC stock scenarios for PLUT.
carbon sequestration calculations and database development.
The generation of carbon scenarios over time and space is achieved by adhering to the following sequence of steps:
linking the SOC simulation model output to a GIS;
preparation, with the aid of GIS functions, of scenarios of carbon stock by LUT and by soil type/land unit or PCC, over different periods of time meaningful to the assessment;
computation of statistics, maps of total carbon stock by both pools for the scenarios desired, legends and output tables.
The analytical steps to link the output from the carbon simulation models to the GIS are not difficult but involved and laborious. They are also GIS software specific and may depend on idiosyncratic architectures and functions whose degree of laboriousness changes with the software package used. For example, in the GIS software ArcView, a series of manipulations of tables and pivots in spreadsheet format allows for the transfer of the carbon simulation model outputs to ArcView tables and their assignment to polygons of soil or landcover.
On the other hand, a customized interface (e.g. Soil-C) could be used to link the model results to their spatial distribution mapped out in the GIS. These steps provide estimates of carbon stock for PLUTs that are to be compared with the stock in present land use.
Carbon sequestration attributable to land-use changes
The preceding sections have assessed the estimates of carbon stock for the actual LUTs and the carbon sequestration implicit in PLUTs. A comparison of estimates between current and potential LUT per each land polygon or PCC should be made. The simple balance could be established in algebraic terms by:
Carbon sequestered = potential carbon stock(total) - actual carbon stock(total)
where the potential carbon stock corresponds to the C in PLUTs, and the actual carbon stock is the carbon stock in present LUTs for a given plot of land or area of the landscape. This comparison would yield the gains or losses in carbon stock resulting from implementing the PLUT in that land polygon or PCC.