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Constraints and applicability of irrigation scheduling under limited water resources, variable rainfall and saline conditions

R. Ragab, Institute of Hydrology, Wallingford, UK


This report focuses on the issue of irrigation water scheduling under limited water resources, variable rainfall and salinity conditions. These constraints make irrigation scheduling more challenging for irrigation managers. Deficit irrigation is widely used with different degrees of success. It requires a reliable estimate of soil water content and crop water stress and an accurate crop water production function. Irrigation scheduling under variable annual rainfall requires a good weather forecast to avoid either waterlogging or water stress. There are a number of models available to account for rainfall forecast for short periods and to adjust the irrigation time and amount accordingly. Irrigation under saline conditions requires careful water management to avoid crop yield losses. Salt tolerant crops and proper water management to allow leaching of the accumulated salt in the root zone are essential. The presence of shallow water tables, drainage facilities, type of irrigation system, and mixing waters of different qualities are important issues to consider when scheduling irrigation under saline conditions. The report describes examples of work carried out in different parts of the world. The bottlenecks and future work needed are also highlighted.

The need for increased food and fibre production in many parts of the world has resulted in an increase in irrigated areas regardless of water resource availability. For many irrigation projects, water becomes a limiting factor for development. Proper water management would maximize the water use efficiency of the irrigated crops. In some cases, drainage water or other low quality water might be used to irrigate salt tolerant crops. Scheduling irrigation under limited water resources and saline conditions requires a different approach from those known for unconstrained conditions. In addition, scheduling irrigation under variable rainfall requires weather forecasting and a flexible management system to cope with rainfall uncertainty. This report will highlight the issues of irrigation scheduling under limited water resources, salinity, and rainfall variability and will identify the bottlenecks and future work needed in order to solve the remaining problems.


Crop response to water can be described by the so-called crop-water-yield function (CWYF). It is important in defining the marginal crop production for maximum profit. The CWYF has been used to evaluate the economic viability of irrigation management schemes.

De Wit (1958) suggested the following CWYF, based on transpiration [Y(T)]:

, (1)

where Y is the dry matter yield, T is seasonal transpiration, E0 is evaporation from free water surface, and M is the slope of the straight line representing the function. The term M accounts for crop variety, soil type, water availability and weather conditions not accounted for by E0.

Based on the de Wit function, Hanks (1974) suggested the following function:

, (2)

where TMAX is maximum seasonal transpiration and YMAX is the yield at TMAX.

Stewart et al. (1977) suggested a function based on evapotranspiration [Y(ET)]:

, (3)

where ET is the actual seasonal evapotranspiration, ETMAX is the maximum seasonal evapotranspiration and B is an empirical coefficient.

These three relations are linear functions. The crop yield as a function of water applied (IRR) can be described generally as:

Y = F (IRR), (4)

The relation in this case is curvilinear. It coincides with the Y (ET) up to a point and deviates from linearity with increased water application. This departure occurs because the irrigation efficiency decreases. Y (ET) and Y (IRR) are identical as long as the irrigation efficiency is 100%. The departure from linearity is a non-ET contribution and results from deep percolation, runoff, drainage, change of soil moisture content or other components of the soil water balance equation.

This relation is important for irrigators since the water applied is the water paid for. It is a variable under their control as compared to T or ET. Transferability of Y (T) or Y (ET) to Y (IRR) is an interesting issue which represents a bottleneck.

The growth stage effect was taken into account by Jensen (1968) who developed a function which divided the growing season into stages with ET in each stage having a unique effect on yield:

, (5)

where bi is the relative sensitivity of the crop to water stress in the ith growth stage and n is the number of growth stages. Similarly Hanks' model can be written as:

, (6)

where bn is the sensitivity to water stress in the n growth stage.

Stewart's model can also be used to include the growth stage effect as follows:

, (7)

where ETMAX is the ET for the whole season.

Salinity affects the CWYF. Therefore, under saline conditions present in soil or irrigation water, the CWYF takes the following form (Maas and Hoffman, 1977):

for ECe £ As, (8)

for ECe > = As, (9)

where As is the salinity threshold in ds m-1, bs is sensitivity of the crop to salinity above the threshold value in ds-1 m-2 and ECe is the electrical conductivity of the soil solution of the saturation extract in ds m-1.

Van Genuchten and Hoffman (1984) suggested the following model:

, (10)

where OP is the current soil osmotic potential in J kg-1 and OP50 is the soil osmotic potential in J kg-1 when yield is reduced by 50% and b is an empirical constant. This was found to be around 3 for some crops.

The main difficulty in applying such a function is that the soil salinity profile is quite dynamic depending on the salinity of the applied irrigation water, the crop water extraction pattern and the soil chemical reaction with irrigation water. Crop tolerance to salinity varies with growth stage. Tolerance at emergence is based on survival, while after emergence it is based on relative growth or yield. In the field, distribution of salts is neither spatially uniform nor constant with depth. Moreover, plant response to salinity that varies with time and depth is not well understood, but some results suggest that crops respond to the mean salinity of the root zone during the growing season. Integrating salinity over time is difficult because for some crops sensitivity varies with growth stage. Times of high and low salt concentrations, monsoon, crop dormancy and evaporation demand, represent problems in evaluating the response of a crop to a seasonal mean salinity.

Transient models use the basic flow equation of water and solute to compute the soil water and solute contents as a function of time and depth. These models use a root extraction term added to the flow equation which relate the soil water salinity level and the crop yield. The sink term accounts for the osmotic potential. When the osmotic and matric potential exceed a critical level, transpiration ceases. These models do not account for crop salt tolerance.

Under saline conditions, the Y (ET) functions are not affected by salinity level. It is unknown whether reduction in water uptake with increasing salinity is the cause or the result of a reduction in crop growth in general.

Crop growth models can be used to produce daily CWYF. Its accuracy depends on the accuracy of the input to the model (Ragab et al., 1990). The assumptions and limitations of the model will be reflected in the output similar to those of the empirical models.

The empirical relations based only on T or ET are usually valid for a single crop at a specific location. Using a relative transpiration or relative evapotranspiration ratio would make the CWYF a more generalized function and, therefore, transferable to different sites.

Field values of T, unlike ET, are difficult or nearly impossible to determine or estimate accurately and, therefore, ET is more reliable. From an economic point of view the Y (IRR) is the most important function for growers because it reflects better the cost of irrigation water, although it does not represent the actual water used by the crop as well as the ET. The relationship between ET and IRR applied is not well understood but it is known to depend mainly on irrigation system design and management. Soil water stress will usually have an inconsistent effect on yield under field conditions. The relation is also affected by the soil permeability (Ragab and Cooper, 1993), soil moisture uniformity within fields and other factors. The effect of variation in spatial application of irrigation and the spatial uniformity of ET over the field is an unresolved issue. Integrating all the factors affecting the CWYF is difficult. There is no universal relation between Y and IRR.

The inherent uncertainties create an element of risk in applying these functions. This uncertainty is attributed to the impact of production factors such as fertilizers, climate, water quality, soil variability, water distribution uniformity, pests, soil characteristics, etc. Therefore, irrigators do not precisely know the impact of any of these factors on the CWYF. There is a need for more process-oriented dynamic models which integrate the various factors affecting the crop growth (Van Aelst et al., 1988, Ragab et al., 1990) instead of such simple statistical models describing the CWYF.

An example of CWYF and its application was given by Plauborg et al. (1996). The function is used in the MARKVAND model which gives daily information on the timing, the amount and the economic return of irrigation for a large selection of agricultural crops in Denmark. Crop development is based on heat sums, while soil water balance is based on field capacity and the wilting point concept. These and other empirical models using the stress day concept are used to compute yield. A priority module was used to rank the crops with respect to their economic net return of irrigation and thus recommends the order in which crops should be irrigated. Calculations in the priority module are based on the calculated yield increase due to irrigation, cost of irrigation, and crop prices. The irrigation demand is calculated for a five-day forecast period to help farmers in planning their activities.

The CWYF is in the following form:

, (11)

where K is a drought sensitivity parameter which varies with crop species and growth stage. Daily K values have been estimated from Danish experiments using the stress index on a daily basis with the water balance model. The latter was calculated as:

SI = (1 - T/TMAX), (12)

the K parameter is calculated as follows:

, (13)

the calculated K values are fitted to a polynomial function of the temperature sums from emergence as follows:

K = a + a1t + a2t2 + a3t3, (14)

where a, a1 a2, etc. are regression coefficients. From these equations Plauborg et al. (1996) derived this general equation:

, (15)

A functional relationship between yield decrease in kg ha-1 against stress day for several spring crops was shown. The relationship indicated that the order of priority for irrigation in mid-June would be potatoes, peas, spring barley and spring rape if the same level of drought were imposed on all crops.

MARKVAND has been applied on several crops. The calculated soil water balance and yield decrease were generally in good agreement with the observed values.


Deficit irrigation is needed where essential resources such as water, capital, energy, and labour are limited. Under deficit irrigation, crops are deliberately allowed to sustain some water deficit and yield reduction. The irrigator aims to increase water use efficiency (WUE) by reducing the amount of water at irrigation or by reducing the number of irrigations. The irrigator has a difficult task. He must decide what deficit level to allow, what level has been reached, when not to allow a deficit to occur and when to apply water at a lower level of adequacy to achieve the highest WUE at minimum cost (English et al., 1990).

The uncertainty associated with this type of irrigation stems from the fact that the cost of the water used and the yield function are not precisely known. If precisely known, it would be a simple matter to choose an optimum level of water use. The yield function tends to be uncertain due to the difficulty in estimating the water losses to inefficient application (like evaporative losses), to deep percolation and to surface and subsurface runoff, particularly when variability of weather, soil and topography are present. It is also difficult to predict what yield would be produced by a given amount of water stored in the root zone. All these uncertainties imply economic risk.

The use of deficit irrigation requires selection of drought resistant crops, low soil salinity and alkalinity levels, suitable cultural practices, and deep soils with high water holding capacity, though high frequency irrigation may compensate for lower capacity.

Drought tolerant crops are adapted to water stress by limiting transpiration losses. Deep rooted crops allow water deficit to develop gradually, which also facilitates short-term adaptation to water stress. Deep soils with high water holding capacity provide a buffering capacity to allow water stress to develop gradually, and not suddenly as in sandy soils.

Deficit irrigation requires modification of some cultural practices which may include: lower plant densities; reduced fertilizer and chemical use; flexible planting dates and selection of shorter season crops; and use of fallow when a fallow interval is desired for rain storage.

Strategies for deficit irrigation may include: allocation of less water to the more drought tolerant crops; irrigating crops only during critical growth stages; planting crop so as to stagger the critical demand periods; and planning for an average or wetter than average weather year.

Scheduling for deficit irrigation is more challenging than for full irrigation. The manager must evaluate the level of soil water storage in the root zone, the level of the crop water stress, and how that level will affect the yield.

The relationship between the crop water stress and yield is very important (Misra, 1973) in scheduling for deficit irrigation. Appropriate water stress indicators should be used to determine when to irrigate and how much to apply. These indicators would measure soil water depletion and crop water stress via the measurement of soil water content/potential, crop water content/potential and canopy temperature. Crop water potential and canopy temperature are the most relevant indicators for crop water stress but soil water content and potential are the most widely used for practical reasons. On the other hand, the spatial variability of soil and applied irrigation water will cause non-uniformity of water storage which results in some parts of the field showing signs of stress before others. Consequently, instruments which give point measurements, such as tensiometers and gypsum blocks, are difficult to use. Therefore they should be placed to represent the range of conditions in the field.

Using the soil water content as an indicator is easy to define but difficult to determine accurately in practice. The percent of soil water depletion in the root zone can be calculated as:

, (16)

where AW is percent available water in the root zone, SWCl is soil water content below which no root water uptake occurs (usually corresponding to the permanent wilting point), SWCu is the upper limit of soil water content above which drainage occurs (usually taken as field capacity), and Z is the depth to the bottom of the root zone. Here also, the soil variability and non-uniformity of water distribution can cause significant errors. Lower and upper limits and precise depth of the active root zone are difficult to measure. Errors in determining these parameters could cause more adverse effects on deficit irrigation than in the case of conventional scheduling. In addition, it is not easy to relate AW to crop yields in order to decide when to irrigate with a limited water supply. We can conclude that the AW is not the best scheduling index for deficit irrigation. It should be taken into account that crops under deficit irrigation tend to extract more water from lower parts of the root zone than fully irrigated crops. Moisture measurements should therefore cover greater depths. The range of tensiometers is less than 1 bar, which make them more suitable for full irrigation than deficit irrigation.

The operating range of gypsum blocks is suitable for deficit irrigation, but they are less accurate. Since gypsum blocks are inexpensive they can be used to include a range of different conditions in the field to obtain a more realistic average of the field. However they need to be calibrated.

Stenitzer (1996) explained the principles and practical instructions for irrigation scheduling with gypsum blocks in eastern Austria. He suggested using at least two gypsum blocks for each measuring station. One block should be placed at the middle of the root zone to indicate the irrigation need and a second one at the bottom of the root zone to indicate percolation losses.

Plant water stress varies with time during the day. It changes very quickly in response to wind, temperature, solar radiation, cloud cover and humidity. Plant stress is also caused by other factors such as salinity, diseases and insect damage. As a result, monitoring plant water stress is far more complex than monitoring the soil water content. The measurements must be conducted under consistent conditions at a certain time of the day and be corrected for cloud cover, humidity, and other uncontrollable factors.

Plant water potential measurements are both difficult and subjective. They are affected by the time of day and weather conditions. At least ten samples are required to obtain a reliable estimate.

Canopy temperature of a freely transpiring plant is a few degrees cooler than the air temperature. A plant under water stress which is transpiring at a lower rate will be warmer than the air. The difference between the air temperature and plant temperature can be used as crop water stress index (CWSI):

, (17)

where Tc is crop temperature, Ta is air temperature, the subscript l refers to the temperature difference when the crop is well watered while the subscript u refers to temperature difference during severe stress when transpiration approaches zero. The temperature difference also depends on vapour pressure deficit, air temperature and solar radiation.

The CWSI is related to the relative evapotranspiration (Jackson 1982) by the following equation:

, (18)

Since the ET is related to the yield, this can also be considered a function of CWSI. The ratio of ET/ETMAX on its own is a good indicator for deficit irrigation scheduling.

Stress day index (SDI) has been used as an indicator for plant water stress. It is a product of a plant stress factor and a crop susceptibility factor (Hiler and Clark, 1971):

, (19)

where SDi is plant water stress and CSi is the crop susceptibility factor (a measure of a crop's susceptibility to water deficit at i growth stage) and i and n are growth stage numbers.

Remote sensing can be used to monitor leaf area index (LAI), dry matter and drought stress of field crops. LAI could be used in evapotranspiration models and, in conjunction with other data, it can be used in irrigation scheduling programmes, e.g., LAI can be related to the crop coefficient that is utilized in the widely known evapotranspiration model of Doorenboos and Pruitt (1977) to estimate the crop water requirements. Remote sensing can provide an estimate of a crop coefficient that indicates the degree of coverage and variation of growth within a region. This could be used in a regional evapotranspiration model to provide a better estimation of evapotranspiration.

In recent years microwave radar techniques have been used as a remote sensing tool for direct detection of soil moisture (Ragab, 1992; Blyth et al,. 1993; Ragab, 1995). The back scattering coefficient for the microwave radar can be related to soil moisture and crop water status.

The remotely sensed canopy temperature, when taken into account with other environmental factors, could provide a good indicator of crop water status and demand. The advantage is that a large area can be quickly surveyed rather than a few plants or points within the field. The canopy temperature can be incorporated into the crop water stress index and used to schedule irrigations.

Scheduling methods that are based on soil or plant measurements are reactive management tools. As such they require: 1. observations to be made; 2. the observations need to be compared against the threshold values that indicate if irrigation is required; and 3. a decision to be made of whether to irrigate or not.

Management of deficit irrigation can be carried out in different ways. If there is a possibility of future rainfall, or if early season irrigations are likely to be inefficient, the manager can reduce the irrigation amount leaving some reserve storage capacity in the root zone. Alternatively, the manager might assume a high application efficiency as a way of systematic reduction of gross water application, or might choose a lower irrigation adequacy to meet field average crop water requirements rather than the crop requirements of the lowest quarter. In general, these strategies involve the same principle: increasing the application efficiency by allowing some portions of the field to be underirrigated. When more severe deficits are planned, the whole field may be underirrigated. In the latter case the application efficiency will not be of primary concern and the manager should focus on the time and frequency of a limited number of irrigations and management of salinity in the soil profile.

In furrow irrigation systems, deficit irrigation can be conducted by irrigating alternate furrows (i.e., irrigating every second or third furrow) or by using widely spaced furrows.

More options for deficit irrigation scheduling are needed. In some areas the annual or multi-seasonal allocation of water already limits production. Decisions must be made how to distribute the water over a seasonal or a multi-seasonal period. Crop response to water stress and its relation with ET rate should be considered in scheduling.

Non-uniformity of irrigation systems and fields requires more intensive monitoring of soil water content over greater depth, of plant canopy temperature and of soil water balance components. A method to determine the amount of water to apply to a specific area in the field, rather than the best average depth of water to apply, is critically needed.

Faci and Bercero reported the effect of water deficit at various growth stages on the grain yield of sorghum. A field experiment was conducted to study the effect of lack of irrigation at three growth stages on the grain yield of sorghum. Eight irrigation treatments were set by applying irrigation or not in the vegetative, reproductive, and maturity phases. A simple linear regression equation was obtained relating the yield and water applied as:

Y = -0.107 + 0.131 X, R2 = 0.65, n = 16, (20)

where Y is the grain yield at 12% moisture, Mg ha-1 and X is the seasonal amount of rainfall and irrigation applied, in cm. The multiple regression between grain yield and the amount of irrigation and precipitation received in each phase is as follows:

Y = 0.381NS + 0.166**X1 + 0.163**X2 + 0.057NSX3, R2 = 0.68, n = 16, (21)

where X1 is the amount of irrigation and precipitation in the first phase, while X2 and X3 are for the second and third phases respectively. The ** means significant at 0.01 level of significance, and NS means not significant. These results indicate that the grain yield was affected significantly by lack of irrigation at any of the three growth stages. The F value was highest for a vegetative stage followed by a reproductive stage followed by a maturity stage. The authors suggested that the equation can be used to predict the grain yield for a given amount of water applied.

Varlev et al. (1996) discussed irrigation scheduling using the application of yield water relationships. Field trials to evaluate maize response to water deficit during a particular growth period were carried out. The ratio of relative yield - relative evapotranspiration was obtained for different growth stages. The results showed that one should satisfy 75-80% of crop water requirements starting from the most sensitive to the less sensitive phases. It is advisable to keep relative ET over 0.7 so that crop development would not be stressed during the following period. Usually the yield obtained under rainfed conditions in regions with a semi-humid climate, such as Bulgaria, amounts to 40-50% of the potential level. If two-thirds of the required water is available one could obtain 90-95% of the maximum yield. If the water available is only 50% of that required, the yield could be 80-85% under optimal management.

Tarjuelo et al. (1995) developed a model for optimum irrigation water management using a simple relation between yield and amount of irrigation water which takes into account the uniformity of water application. The objective was to provide a procedure by which farmers can evaluate and compare alternative water regimes for the following year in order to optimize crop rotations, crop production and farm incomes, and to attain the optimum use of irrigation equipment, farmland and other resources. The method requires data that is readily available to the farmer. The normal strategy of adopting a single value to the application efficiency 'water depth needed/gross water depth applied' has been proved not to be acceptable. This is because agronomic and economic factors should also be taken into account, along with the volume of available water and the price per cubic metre of water applied to the field.

Zhi (1996) reported on the impact of water-saving irrigation for rice. The basic feature of this water-saving irrigation is that there is no water layer above the soil surface on rice fields for 75%-85% of the entire growing season of rice.


Irrigation management in humid or in semi-arid areas should take rainfall into account. In humid regions, rainfall data must be used to compute the irrigation water requirements and minimize the adverse effects of: 1. soil waterlogging; 2. reduced soil aeration; and 3. soil erosion produced by surface runoff.

For these reasons, irrigation scheduling needs information on future rainfall events. This can be in the form of forecasts, long-term records, etc. (Martin et al., 1990).

Irrigation scheduling under variable rainfall is usually developed to react to the actual rainfall received rather than to predict rain. This is so that the date of the next irrigation can be adjusted according to the rain occurred between irrigations. For example, if the earliest irrigation date was planned to be on 30 July and the amount of rainfall received was 12 mm and the average actual evapotranspiration was 6 mm/day, the irrigation will be postponed by two days. This is particularly useful in semi-arid regions where, during the irrigation season, rainfall occurs from individual storms and is highly variable in frequency and amount. Another approach would consist of using a rainfall allowance to store rain in the root zone, so that net irrigation will be less than that required to refill the root zone to field capacity. During the peak ET period, the rainfall allowances should be smaller to avoid any risk due to water stress effects. The rainfall allowance suits deep rooted crops and soils with medium to high water holding capacities.

In humid areas, where there is a high probability of rainfall, irrigations can be scheduled according to the expected rainfall either by using historical daily records or short-term weather forecasts.

In areas with shallow water tables, the irrigation manager might consider rainfall and the upward capillary flow from the water table as adequate to meet the crop water requirements, Ragab et al. (1988). The manager should carefully assess the risk of water stress while waiting for rain, taking into account not only the cost of irrigation and the potential crop loss, but also the leaching of crop nutrients and the groundwater quality. Rainfall should be measured at the field being scheduled. The spatial variability of rainfall should be accounted for by installing several rain gauges.

Untimely or non-uniform rain occurring soon after or during irrigation could cause leaching in areas already irrigated but not in areas yet to be irrigated. If rain occurs during irrigation, the manager would not complete the current irrigation leaving the remaining portion of the field drier than the part already irrigated. If the irrigation system can apply water at variable depth, it would be best to apply enough water on the remaining portion of the field to even out the soil water content. If the system cannot operate to apply variable depth, other adjustments should be made. A similar problem occurs if rainfall is not uniformly distributed. Stress might appear in one part while leaching occurs in another. In such cases soil and plant monitoring is necessary.

The biggest concern in humid areas is that irrigation is considered to be supplemental and rainfall can mask some of the non-uniformity of irrigation application. This non-uniformity becomes noticeable when irrigation is used for extended periods of time.

Better methods for forecasting precipitation and climate conditions are needed. Computer models for real time irrigation scheduling can be used in combination with rainfall forecast to compute the time and amount of irrigation. Examples are the Danish model MARKVAND (Plauborg et al., 1996) and the French model EPICPHASE, (Cabelguenne et al., 1996). The reliability of the output will depend on the accuracy of the inputs. In general most of these models provide reasonably accurate results over short time periods.

An example of irrigation management with the real-time EPICPHASE model and weather forecast was given by Cabelguenne et al. (1996). This model was tested on a corn experiment to evaluate its potential for real time irrigation scheduling based on model predictions made every five days. Results from this model were compared with the farmer's simple decision-making rules. The model was capable of simulating different scheduling options and hence different irrigation amounts with weather forecast, and its application resulted in an increase in yield and a reduction in irrigation water use as compared to the farmer's management. The model also showed that discrepancies between actual and forecast weather led to different irrigation tactics.

Thoradeniya (1995) reported on optimization of irrigation water release for rice growing in the wet season using probable rainfall. They used the CROPWAT (Smith, 1992) model. The simulation was based on 24 years of statistically stable rainfall data. The expected rainfall computed using the traditional methods of 80% probability of exceedance was compared with the use of rainfall at other probability levels. Framed rules were applied to four rainfall records in Thailand and Surinam. The results showed that a significant water saving of up to 60%, when compared with the usual practice, can be achieved by using a constant lower level of exceedance throughout the crop period or by selecting different levels of exceedance during different crop phases.


Salinity is of great concern in the irrigated lands of arid and semi-arid zones because of the small contribution of rainfall to leaching and the often poor quality of irrigation water. It is well established that soil salinity does not reduce crop yield significantly until a threshold level is exceeded. Beyond this threshold, yield decreases almost linearly as salinity increases as shown in the first section.

To avoid yield loss when salt concentration exceeds the crop tolerance limit, excess salts must be leached below the root zone. In areas where rainfall rate and regime are not adequate to provoke that process, irrigation water must be applied in excess. Therefore, when calculating the irrigation depth, an additional amount of water according to the salinity level should be added for leaching, Oster (1994) and Bresler and Hoffman (1986). The leaching requirement (LR) is usually defined, assuming steady state regime, as:

, (22)

where Dd is the depth of water passing below the root zone as drainage water, Di is the depth of applied irrigation, Cd is the salt concentration of the drainage water above which yield reduction occurs, and Ci is the salt concentration of the irrigation water.

It should be kept in mind that excessive leaching of salts might also lead to leaching of nutrients. In managing irrigation under saline conditions one should consider the salt concentration of the irrigation water, the salt tolerance of the crop, the long-term annual rainfall, the depth to the water table and the drainage facilities.

Leaching frequency has been investigated by many scientists. The indications are that more tolerant crops can withstand delayed leaching. A high concentration of salts in lower parts of the root zone can be tolerated with minimum effect on yield reduction provided that the upper part is maintained at low salt content. Plants compensate for reduced water uptake from a highly saline soil layer by increasing the uptake from a less saline zone without yield reduction. The amount of salt that can be stored in the root zone before leaching is required, and how frequently the leaching requirements should be applied are questions that remain to be answered (Hoffman et al., 1990).

Salt tolerance of many crops increases during the growing season. Leaching might not be required during the season. If salinity levels are low enough during the seedling stage and adequate amounts of low salt water are applied, soil salinity can be permitted to increase over time until the next crop. Rainfall or pre-plant irrigation can replenish soil water and leach accumulated salts to permit irrigation of the next crop without further leaching.

Leaching might be required during the growing season. If the irrigation water was saline, rainfall and pre-planting irrigation might be insufficient to prevent yield loss. One should bear in mind that leaching is required only when the salt concentration exceeds the threshold value, and that it can be applied at each irrigation or less frequently, such as seasonally or at even longer intervals, provided soil salinity levels are kept below that value.

It has long been assumed that more frequent irrigation (excluding drip irrigation) reduces the effect of salinity. Higher soil water content would partly reduce the osmotic potential, but there was no improvement in yield to support this assumption. The actual evapotranspiration rate (ET) stays at its potential level until the allowable depletion is reached. If the soil surface is wetted frequently, soil evaporation will stay high most of the time and will result in concentration of salts in the surface layers. Moreover, the root water uptake takes place preferentially from the upper soil layers when they are frequently wetted, while the uptake proceeds in deeper layers if the soil surface is allowed to dry under less frequent irrigation. Both processes of evaporation and water uptake tend to concentrate salts closer to the surface layers under frequent irrigation. Drip irrigation is an exception where the localized water displaces the salts towards the boundaries of the wetted zone. Here the leaching process dominates over evapotranspiration and water uptake. Increasing the amount of applied water for salinity control is the only acceptable measure.

Impact of shallow, saline groundwater: Salinity can be caused by the upward movement of saline water of shallow water tables and its subsequent evaporation from the soil surface. Under these conditions, irrigation systems must be managed with special care. Both rate of evaporation and upward flow from the water table are important factors (Ragab and Amer, 1986 and 1989). A drainage system to lower the water table can be used to prevent salinization and to provide adequate aeration to crop roots (Ragab and Amer, 1987).

Varying water quality for irrigation: When water resources are limiting and the cost of non-saline water becomes prohibitive, moderate to high salt tolerance crops can be irrigated with saline water especially at later growth stages. The irrigation water can be a mixture of saline with non-saline water when this is scarce (blending). Saline water can be applied in cycles with non-saline water, using it to irrigate salt tolerant crops or to irrigate a salt sensitive crop during a salt tolerant growth stage (non-saline water at all other times). The use of drainage water for irrigation has an environmental advantage. It reduces the amount of non-saline water required for salt tolerant crops and it decreases the volume of drainage water that needs disposal or treatment.

Blending different types of water is a questionable practice. It is preferable to use non-saline water early in the growth season and saline water later (Shalhevet, 1994).

For proper management in scheduling irrigation one should: 1. improve the accuracy of the soil water balance components to calculate a reliable estimate of the leaching fraction; 2. estimate the leaching requirements and add that to the irrigation requirement; 3. consider the water distribution uniformity to decide which part of the field should receive at least the leaching fraction for salinity control; 4. take into account that leaching salts periodically is more practical than every irrigation; 5. consider that there is no need to increase irrigation frequency to control salt concentration (as explained before) except for drip irrigation; and 6. monitor the root zone salinity, especially prior to the times of periodic leaching. This would result in optimum salt control with minimum losses to deep percolation.

Transient models use basic water flow and salt transport equations to compute soil water and salt content profiles before the following irrigation (Bresler, 1973). The root extraction term, which takes into account the salinity level, provides a linkage between the soil water salinity and crop yield.

Drip irrigation (Ragab et al., 1984) provides a greater opportunity for using saline water. Sprinkler irrigation may cause surface sealing (Ragab, 1983) and leaf burn of sensitive crops. Leaf burning can be reduced by night irrigation, and by irrigating continually rather than intermittently.

Basin irrigation has greater potential' for uniform application than other methods of flooding such as border irrigation or wild-flooding irrigation provided that the basins are levelled and sized properly.

Furrow irrigation tends to accumulate salts in the seed beds because leaching occurs primarily below the furrows. The length of the furrow, the slope, size of the stream and time of application are factors that govern the depth and uniformity of application. Leaching and salinity control require a proper balance among these factors.

Subsurface irrigation produces a continuous upward water flow which results in salt accumulation near the surface. This system offers no leaching and therefore periodic leaching by rainfall or surface irrigation is needed.

Sodium causes a reduction in saturated hydraulic conductivity and porosity. Both parameters are important for water movement. A high sodium adsorption ratio (SAR) reduces the hydraulic conductivity, and consequently the drainage and leaching efficiency.

An example of the effect of salinity on soil hydraulic conductivity, soil water retention, and bulk density and porosity was given by Tedeschi et al, (1996). The authors carried out research on spring-summer crops grown on a clay loam soil in the south of Italy. The crops were irrigated using water of different salinity levels. The results showed that there was a significant reduction in the soil hydraulic conductivity, porosity and soil water retention when soil was irrigated with saline water.

Generally the use of saline water for irrigation requires a selection of salt tolerant crops and improvement in water management, and maintenance of soil physical properties to ensure adequate soil permeability to meet leaching.

Better techniques to determine optimum leaching requirements for dynamic situations are needed. There is a gap in our knowledge on how plants respond to salinity stress that varies with time and space. Most of our knowledge on water and solute flow are for deep groundwater conditions. However, our knowledge is insufficient in the presence of a shallow water table. The specific effect of various salt constituents on crops is also not well understood. Periodic information on salinity is required, i.e., inventories, database systems.

Ferrer and Stockle (1996) modified the CropSyst model (Stockle et al., 1994) to assess the crop response to water management under saline conditions. The root water uptake term was modified to account for salinity effects on root permeability. The water flow was described by Richard's equation (Darcian flow) with a sink term to represent water uptake. The solute flow was described as convective flow i.e ignoring the diffusion. The water uptake term was given as:

, (23)

where U is the water uptake rate (Kg m-2 day-1), K is a constant = 86400 s day-1, RC is root conductance (kg s m-4), SWP is soil water potential (J kg-1) and LWP is the crop canopy average leaf water potential (J kg-1). The root conductance RC at a certain node i is obtained from:

RCi = fi RCmax fcint, (24)

where fi is a fraction of total root length present at a node, RCmax is a crop parameter that represents the maximum total root conductance, for a fully developed crop well supplied with water, fcint is the fraction of incident radiation intercepted by the crop canopy. The average leaf water potential LWP is calculated by matching the total crop water uptake with transpiration from the canopy using the following equation:

, (25)

where RCt is the total root conductance, SWP is average soil water potential of the profile weighted by root fraction at each node.

Salinity effects on crop water uptake are accounted for in two ways: 1) by adding the osmotic potential to the soil matric potential; and 2) by the direct salt effect on root conductance. The latter decreases as salinity increases. A similar form of Van Genuchten (1987) was used to calculate the modified root conductance RC' (to account for salinity effects):

, (26)

Comparison with field data showed that the model is able to predict the effect of salinity and the combined effect of water stress and salinity on crop yield. Crop salt tolerance parameters are difficult to obtain, mainly because the experimental results vary with environmental conditions and cultivar choices.

Aragues et al. (1995) reported on management practices aimed to reduce the negative effects of sprinkler applied irrigation water on plants. Both pot and field experiments of barley and maize were conducted. Applications of short pre- and post-fresh water irrigations with each saline irrigation cycle, and application of saline sprinklings at night were carried out. The results indicated that short pre-wetting and post-washing with fresh water permit the use of saline waters that otherwise would not be suitable for irrigation because of its effect on yield. With this strategy, ten units of saline water could be used per unit application of fresh water. However, indiscriminate use of saline water for irrigation should be avoided since it could cause soil salinization. This strategy should be used only in areas where rainfall or high leaching fractions will allow seasonal leaching of salts accumulated in the root zone.


In conclusion, one might ask the following questions:

1. To what extent are the crop production functions reliable? Do we need dynamic functions that account for other factors affecting crop production?

The relationship between the yield and water applied is of great importance for farmers. A more reliable function that accounts for the uncertainty due to irrigation performance and uniformity of water distribution is needed. Moreover, others factors affecting yield and root behaviour need to be considered. Examples include salinity levels and shallow water tables. In addition, more dynamic functions are required in order to schedule irrigation more efficiently during the growing season.

2. Are stress indicators suitable and reliable or do we need to develop other techniques? The possibility of using biological water stress indicators should be explored. For example, crops sensitive to water stress, such as corn and sunflowers, can be planted alongside the major crops to indicate when irrigation is required. This idea is worth further investigation.

In order to encourage farmers to use water stress indicators, these indicators should be made available cheap to farmers and given with simple guidelines to operate. The spatial variability of soil moisture contents and crop water stress determined from point measurements leads to uncertainty in the calculation of the crop water requirements and irrigation scheduling. Remote sensing techniques are useful in detecting soil moisture and crop water stress over larger areas and thus are accurate in calculating the evapotranspiration and crop water requirements at a regional scale. Further research and application should be encouraged in this direction.

3. For deficit irrigation, do we need new ideas to schedule irrigation?

In general, more new ideas are needed for scheduling deficit irrigation. A better timing to irrigate in order to prevent yield loss is needed through an improved scheduling system. More efficient irrigation scheduling under variable rainfall is also required. In addition, a priority list of crops and irrigation systems suitable for deficit irrigation needs to be drawn.

4. Do we need more accurate methods for rainfall forecast and new ideas to schedule irrigation under untimely variable rainfall?

For more efficient irrigation scheduling under untimely variable rainfall, a reliable method for rainfall forecast is needed. The present rainfall forecast methods do not predict the quantity of rain. Future work is needed to improve the existing methods to predict both the frequency and quantity of rainfall.

5. Do we need more ideas on how we schedule irrigation under saline conditions in general and in the presence of shallow water table in particular? Guidelines to manage irrigation under saline conditions especially in the presence of shallow water tables is an issue which needs further investigation. Moreover, an assessment of the suitability of low quality water in general for irrigation and its long-term impact on crops, human beings, etc., is worth investigating in view of possible future uses of low quality water.


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