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A simulation study of chickpea crop response to limited irrigation in a semiarid environment
Agricultural Water Management 49 (2001) 225±237
A simulation study of chickpea crop response to limited irrigation in a semiarid environment A. Soltania,*, F.R. Khooieb, K. Ghassemi-Golezanib, M. Moghaddamb a
Department of Agronomy, Gorgan University of Agricultural Sciences, Gorgan, Iran b Department of Agronomy, Tabriz University, Tabriz, Iran Accepted 16 October 2000
Abstract In West Asia and North Africa (WANA) including northwest (NW) Iran irrigation is becoming increasingly available and investigation of the effect of limited irrigation (LI) is a research need. Only a few seasons of successful experimentation exist with LI effects. Thus, the objective of this simulation study was to examine potential long-term bene®ts of limited irrigation in NW Iran in terms of grain yield. To do this, a simple, mechanistic chickpea (Cicer arietinum L.) model and 16 years of weather data of Maragheh (NW Iran) were used. Three LI systems with one, two and three irrigations and each with three plant population densities (25, 38 and 50 plants m 2) were simulated. Results showed chickpea crop experiences terminal drought stress that is started at a time between ¯owering and beginning seed growth (BSG). This terminal drought stress severely reduces grain yield by 67%, from 2766 kg ha 1 under full-irrigated conditions to 909 kg ha 1 under rainfed conditions. Grain yield was signi®cantly increased with LI compared to rainfed conditions. Grain yields were reached to 60, 75 and 90% of grain yield simulated under full-irrigated (generally requires ®ve irrigations) conditions. In LI with one irrigation its application at BSG, and in LI with two and three irrigations, application of ®rst irrigation at ¯owering and application of one or two other irrigations when fraction of transpirable soil water dropped to 0.5 in the root zone resulted in higher grain yield. Water use ef®ciency was, also, increased with LI by 28, 39 and 52% for one, two and three irrigations, respectively. In LI systems with two and three irrigations it was required to a higher plant density (38 or 50 plants m 2) to capture and to use applied water more ef®ciently. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Chickpea; Limited irrigation; Terminal drought stress; Model; Simulation
* Corresponding author. Fax: 98-171-2220981/438. E-mail address: [email protected] (A. Soltani).
0378-3774/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 0 ) 0 0 1 4 3 - 8
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1. Introduction Crop simulation models that predict plant growth, water use and yield are being used to understand the response of crops to the dynamics of climate±plant±water systems. These models are also used in crop production to help make decisions that optimize use of available resources (Boote et al., 1996). For example, a number of modeling groups have attempted to optimize water management over long-term historical weather years (Hook, 1994; Aggarwal and Kalra, 1994; O'Leary and Connor, 1998). The crop simulation model assesses alternate management options, allowing the most suitable option to be adopted (Loomis and Connor, 1992, Chapter 13). Chickpea (Cicer arietinum L.) is one of the major pulse crops in West Asia and North Africa (WANA) region. It is cultivated on a large scale in arid and semiarid environments, and has considerable importance as food, feed and fodder. Due to the increasing need for legumes, chickpea is no longer considered a subsistence crop. The upward trend in its trade suggests that the crop is grown increasingly for the market (Saxena et al., 1996). In Iran, chickpea is the most important legume crop, and is sown on more than 50% of total legume area. The crop is predominately rainfed and only 10% is grown with irrigation. Average yields of rainfed chickpea range from 400 to 600 kg ha 1. Irrigated yields are ranged 1000±1500 kg ha 1 (Sadri and Banai, 1996). Most chickpea growing areas of Iran which have cool and cold semiarid climates with terminal drought stress (similar to that of Maragheh, the site chosen for this study) are located in northwest of the country. Nearly 50% of total chickpea production in Iran is concentrated in NW. Terminal drought is a widely occurred pattern of soil water availability in which drought is terminal following the major growth period during which water availability is adequate (Ludlow and Muchow, 1990; Loomis and Connor, 1992). In NW Iran, chickpea is sown from the mid of March to the end of April and grows mainly on stored moisture which is progressively depleted with crop growth. The crop experiences drought stress from late vegetative stage until maturity. The intensity of drought stress varies from year to year, depending on the amount and distribution of rainfall and on spring and early summer temperatures. Thus, large responses in grain yield are expected when one to three irrigation are applied (limited irrigation, LI). In this area irrigation is becoming increasingly available and investigation on the chickpea crop response to LI is a research need and opportunity. However, there are a few seasons of experimentation with LI management so questions remain about its performance and its benefits over seasons with differing weather conditions. Recently, a study was initiated to analyze the biophysical limitations in chickpea production using crop simulation approach in Iran (Soltani, 1999). To do this, a mechanistically based model for chickpea was developed and tested under rainfed and irrigated conditions (Soltani et al., 1999). Hence, the objective of this research was to examine potential long-term benefits of the LI in NW Iran in terms of grain yield. Therefore, this modeling analysis takes the `heuristic' approach recommended by Sinclair and Seligman (1996) whereby a model is used as a tool to explore possible long-term yield consequences of LI.
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2. Material and methods 2.1. The crop model The chickpea crop model of Soltani et al. (1999) was used to simulate chickpea growth and yield. The model is based on a daily time step and simulates crop growth and development as a function of temperature, solar radiation and water availability. Crop phenology is divided into two growth stages (before and after beginning seed growth), the duration of them are predicted based on daily temperature and water deficit. Leaf area development is calculated as functions of expansion and senescence of leaves. These functions are sensitive to temperature and water deficit. Daily biomass production is predicted from leaf area index, light extinction coefficient and radiation use efficiency. Transpiration is calculated as a function of daily biomass production, transpiration efficiency coefficient and daily vapor pressure deficit. Daily biomass production and leaf growth decrease below potential values once the fraction of transpirable soil water (FTSW) declines below threshold values (0.34 for biomass production and 0.48 for leaf expansion, Soltani et al., 2000a). After the beginning of seed growth the accumulated biomass is partitioned into the grains, the rate of which depending on climatic conditions at and after beginning of seed growth. Soil evaporation, soil water drainage, and runoff are also calculated in the soil water balance sub-model. The model uses readily available weather and soil information. The model was tested using independent data from a range of Iran's environmental conditions. In most cases, simulated grain yields were similar to that of observed yields. At this stage the model does not account for the effects of pests, diseases and soil fertility. Modifications were made to the model. In the original model, potential evapotranspiration was calculated with a modified Penman equation as presented by Sinclair (1986) and Amir and Sinclair (1991), that requires minimum and maximum temperatures and solar radiation data. And, soil evaporation was estimated using a two-stage model used by Amir and Sinclair (1991), and originally proposed by Doraiswamy and Thompson (1982) and O'Leary et al. (1985). Now, potential evapotranspiration is calculated according to Priestly and Taylor model (Priestley and Taylor, 1972; Ritchie, 1972). Inputs required for this model are also minimum and maximum temperatures and solar radiation. Using these three inputs and soil albedo, this model estimates potential evapotranspiration. Soil evaporation from the top 20 cm layer is slightly modified from Ritchie two-stage soil evaporation model (Ritchie, 1972, 1985, 1998) as used by Chapman et al. (1993) in their sunflower model. During Stage 1, water freely evaporates according to the Priestly and Taylor equation depending on crop cover. After a soil type specific amount of water has evaporated, Stage 2 begins. During Stage 2, soil evaporation is dependent on the square root of the number of days since the beginning of the stage. Details for these current methods can be find in Ritchie (1972, 1985, 1998). The procedures used in modified model are as simple as original methods. Another modification was related to root growth. In the original model, the whole soil volume, and consequently the total amount of stored soil water, was made available to the crop at sowing and onwards. To allow for root growth, the model was modified by defining the initial depth of soil water extraction as 200 mm at the full-emergence. Each
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day, up to beginning of seed growth (BSG), when root depth growth stopped, Silim and Saxena (1993), the depth of extractable soil water profile was increased by GRTD
EED 200 DTT TTS TTE
where GRTD is the daily root depth increase in mm per day, EED the effective extraction depth in mm, 200 the initial extraction depth, TTS the thermal time from sowing to BSG (8C day), TTE the thermal time from sowing to full-emergence (8C day) and DTT the thermal time occurred each day (8C per day). The TTE was set at 2308C day and EED (1200 mm) and TTS were as described by Soltani et al. (1999). The amount of irrigation water required to rise the available water to ®eld capacity, calculated by APLDWi
TTSW
ATSW IE
where APLDWi is the amount of irrigation water (mm), TTSW the total transpirable soil water (mm), ATSW the actual transpirable soil water (mm), and IE the irrigation ef®ciency. An evaluation of the chickpea crop model for grain yield predictions under LI conditions was performed using field data collected at the IDARC (Iran Dryland Agriculture Research Center), Maragheh (378240 N, 478160 E and 1476 m asl). The data were taken in 1996 from a chickpea LI experiment. Chickpea (var. Jam and Kaka) were sown April 15 in a randomized complete block design of six irrigation cultivar treatments with four replications. Irrigation treatments were rainfed, one LI at flowering and one LI at BSG. Grain yield was obtained by hand from 3 m2 of each plot. Evaluation of the model for simulating crop evapotranspiration was performed for rainfed conditions, because required data for irrigated conditions were not available. Data were extracted from various chickpea crop experiment reports from Kermanshah (348190 N, 47870 E and 1322 m asl), Maragheh and Tabriz (38850 N, 468170 E and 1361 m asl) (Soltani, 1999) for soil water at planting, rainfall during growing season and soil water at crop harvest. In these semiarid environments deep drainage is negligible and plot preparation methods used in the experiments prevented surface runoff. Crop evapotranspiration then was calculated as soil water at planting rainfall during growth season
soil water at harvest
In both cases, the relevant input requirements were collated to simulate these crops. Daily solar radiation (estimated from sunshine hours), minimum and maximum temperatures and rainfall were available for each case. Date of planting, plant density, and soil water attributes were input from known values for each experiment. The relevant cultivar data were specified as Soltani et al. (1999). Simulated outcomes compared with those measured in experiments by linear regression analysis and calculating root mean square error (RMSE). 2.2. Limited irrigation systems In this simulation study, three LI systems with one, two and three irrigations were evaluated. In NW Iran, terminal drought stress is initiated between flowering to BSG of
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chickpea crop and before these stages water deficit stress is not considerably occurred (Figs. 4 and 5, see Section 3.2). Therefore, first irrigation in LI systems was applied at flowering or BSG. Because of the better conditions for crop growth with LI, each LI system with three plant population densities (PPD) was simulated. Hence, considering LI with one irrigation, there were six treatments as: one irrigation at flowering with 25, 38, and 50 plants m 2, and one irrigation at BSG with 25, 38, and 50 plants m 2. In LI systems with two or three irrigations, the time of first irrigation was considered as LI with one irrigation, but second (in LI with two and three irrigations) and third (in the case of LI with three irrigations) irrigations were applied when the simulated available soil water dropped to 50% in the root zone. LI systems with two or three irrigations were also simulated with three PPD as mentioned above for LI with one irrigation. For comparison, simulations were run for rainfed conditions with 25 plants m 2 and full-irrigated conditions with 50 plants m 2. Under irrigated conditions, water was applied to fill the soil of the root zone to field capacity when the simulated available soil water dropped to 50% in the root zone. An application efficiency of 100% was used for full-irrigated and LI systems. 2.3. Simulations Maragheh was selected as a site to compare the LI systems. Maragheh is one of the most important chickpea-producing regions in NW Iran, and IDARC is located in this city. For running the model, maximum temperature, minimum temperature, precipitation and solar radiation data are required. Precipitation and maximum and minimum temperatures data were available for 16 years (1980±1995). Solar radiation data were calculated from sunshine hours and extraterrestrial radiation as outlined by Doorenbos and Pruitt (1977) or were generated by a synthesis program (WGEN, Richarsdon and Wright, 1984; Pickering et al., 1994; Soltani et al., 2000b). With estimated solar radiation data, a complete 16-years data set of daily precipitation, maximum and minimum temperatures and solar radiation data was available. The model was run for each treatment in each of the 16 seasons by inputting the daily weather data. For all treatments sowing date was considered 1 April (usual sowing date of the region). The soil and cultivar were same for all treatments. The soil that was chosen was sandy loam (typical of the location) with a total plant available water of 158 mm, a depth of 120 cm and a curve number of 75 (for calculation of run-off, only under rainfed treatment). Available soil water at sowing date was calculated for each year by running the soil water balance sub-model of the model from October 1 of the pervious year until sowing date. The chickpea variety Jam (typical) was chosen for simulations. The inputs of this variety was same as Soltani et al. (1999). Daily estimates of the soil water and various crop state variables were calculated. The results presented here focus on the final status of the crop at maturity. Grain yield is presented with 14% moisture content. Applied-water use efficiency was calculated for each year as crop yield obtained with LI (or full irrigation) minus rainfed yield divided by irrigation applied water. Means of variables for treatments of each LI system and rainfed and full-irrigated treatments were compared using Duncan's multiple range test P 0:05. Analysis of variance was performed on log transformed
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data using a model as Xij M Yi Tj Eij where Xij is observation, M the mean, Yi the effect of year, Tj the effect of treatment and Eij the residual effects. 3. Results 3.1. Crop model evaluation A plot of simulated versus observed crop yield is presented in Fig. 1. Simulated grain yield varied from 0.79 to 1.40 t ha 1 and measured yields from 0.77 to 1.35 t ha 1, and the means (S.D.) were 1.11 (0.22) t ha 1 and 1.07 (0.20) t ha 1, respectively. The model generally provided good estimates of crop yield with RMSE of 0.05 t ha 1 which is 5% of the observed mean grain yield. Fig. 2 shows simulated and observed crop evapotranspiration for rainfed conditions. While observed crop evapotranspiration ranged from 120 to 330 mm, simulated crop evapotranspiration varied from 165 to 300 mm. Mean (S.D.) of observed and simulated crop evapotranspiration were 236 (57) and 238 (59), respectively. In spite of some overprediction and under-prediction of crop evapotranspiration in the range 120±250 and 250±330 mm, respectively, the model acceptably simulated crop evapotranspiration. Model accounted for 64% of variations in crop evapotranspiration with an RMSE of 37 mm which is 16% of the observed mean crop evapotranspiration. 3.2. Climate and the time when terminal drought stress is initiated Mean monthly maximum temperature, minimum temperature, solar radiation and the monthly precipitation of Maragheh are presented in Fig. 3. In NW Iran, rainfall occurs in autumn, winter and early spring when temperatures are low. From March onwards the
Fig. 1. Simulated versus observed grain yields for six limited irrigation cultivar treatments, squares: cultivar Jam, triangles: cultivar Kaka. The solid line is 1:1 line and the broken line is the regression.
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Fig. 2. Simulated versus observed crop evapotranspiration. The solid line is 1:1 line and the broken line is the regression.
Fig. 3. Long term (16-year) means of monthly maximum and minimum temperatures (a) and solar radiation (pluses) and monthly precipitation (squares) (b) of Maragheh, NW Iran.
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Fig. 4. Simulated FTSW during chickpea growing season for a year with highest simulated rainfed yield (1995, dotted line) and for a year with lowest simulated rainfed yield (1989, solid line) in Maragheh, NW Iran. Arrows show the time of flowering.
region experiences increasing radiation and a rapid rise in temperature. The growing season of chickpea is started from the April (month 4) when minimum temperature rises to above 2.58C. Thus, the crop may experiences drought stress from the flowering onwards. Fig. 4 shows simulated dynamics of FTSW (Sinclair and Ludlow, 1986) during chickpea growing season in a year with the highest simulated yield (1995) and in a year with the lowest simulated yield (1989) under rainfed conditions. The FTSW lower than 0.34 and 0.48 can adversely affect photosynthesis and leaf growth of chickpea, respectively (Soltani et al., 1999, 2000a). As presented in Fig. 4, during the 1/2±2/3 of growing season, the crop did not experienced drought stress seriously, but thereafter, the crop experiences an increasing water deficit stress. In 1989, terminal drought stress was begun at flowering, while in 1995 terminal drought stress started about 12 days after flowering (i.e. at BSG phase). Therefore, in this environment, terminal drought stress is started at a time between flowering till BSG of the chickpea crop. Fig. 5 confirms this
Fig. 5. Cumulative probability of days to beginning terminal drought stress (from flowering as an origin) under rainfed conditions of Maragheh, NW Iran.
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fact, by showing the cumulative probability of days to beginning terminal drought stress (from flowering as an origin). In this figure, numbers mean after flowering. For example, 2 shows that terminal drought stress is started 2 days after flowering. Median of days to beginning drought stress was 4 days after flowering (with a mean of 5.1 days), and the probability of the occurrence of terminal drought stress 1 week or more after flowering is 0.3 (30%). In 100% of years terminal drought stress is occurred at a time between flowering and BSG. 3.3. Limited irrigation effects Grain yields are 909 and 2766 kg ha 1 with coefficient of variations (CV: standard deviation/mean) of 29 and 4% under rainfed and full-irrigated conditions, respectively. Rainfed yield is 33% of irrigated yield with a CV of seven times greater, showing that terminal drought stress in this semiarid environment can greatly reduce chickpea yield, but greatly increases grain yield variability (Table 1). Results of the effect of one LI on simulated grain yield (YLD), crop evapotranspiration (ET), water use efficiency (WUE), applied irrigation water (APLDW), and applied-water use efficiency (EAW) are shown in Table 1. The highest grain yield is obtained with LI application at BSG and the differences between PPDs were not significant. Because of the lower seed requirement and lower yield variability, PPD of 25 plants m 2 combined with LI at BSG may be considered as the best irrigation management option (the median of flowering-BSG interval was 12 days (data not shown)). This treatment increases simulated grain yield 81%, in respect to rainfed conditions. If grain yield to be considered as a product of ET and WUE, the increase of grain yield is a result of the rising in the both components. LI at BSG compared to LI at flowering requires more irrigation water, due to lower FTSW at BSG. For the best option, 124 mm irrigation water is required, assuming an irrigation efficiency of 100%. EAW is dependent on APLDW and is increased from F1 to IRR through P1. Table 1 Effect of LI with one irrigation at flowering (F1) or beginning of seed growth (pod filling, P1) on grain yield (YLD, kg ha 1 ), CV of grain yield, crop evapotranspiration (ET, mm), water use efficiency (WUE, kg ha 1 mm 1), applied irrigation water (APLDW, mm) and applied-water use efficiency (EAW, kg ha 1 mm 1). Rainfed (RFD) and full-irrigated (IRR) variables are included for comparison. Numbers in parenthesis show plant density. The numbers with different letters indicate significant differences at 5% level of probability Treatment
YLD
CV
ET
RFD F1 (25) F1 (38) F1 (50) P1 (25) P1 (38) P1 (50) IRR
909 d 1442 c 1503 c 1524 c 1648 b 1701 b 1721 b 2766 a
29 16 19 19 18 19 20 4
228 296 305 310 323 334 338 441
g f ef de cd bc b a
WUE
APLDW
EAW
3.99 4.90 4.93 4.93 5.12 5.10 5.10 6.31
0 68 d 74 c 78 c 124 b 127 b 128 b 325 a
0 7.84 8.01 7.94 5.99 6.25 6.34 5.71
c b b b b b b a
a a a bc bc b c
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Table 2 Effect of LI with two irrigation first at flowering (F2) or beginning of seed growth (pod filling, P2) and second irrigation when simulated FTSW dropped to 0.5 in the root zone on grain yield (YLD, kg ha 1), CV of grain yield, crop evapotranspiration (ET, mm), water use efficiency (WUE, kg ha 1 mm 1), applied irrigation water (APLDW, mm) and applied-water use efficiency (EAW, kg ha 1 mm 1). Rainfed (RFD) and full-irrigated (IRR) variables are included for comparison. Numbers in parenthesis show plant density. The numbers with different letters indicate significant differences at 5% level of probability Treatment
YLD
CV
ET
RFD F2 (25) F2 (38) F2 (50) P2 (25) P2 (38) P2 (50) IRR
909 f 1909 cd 2041 bc 2101 b 1731 e 1853 de 1902 d 2766 a
29 12 14 14 16 17 19 4
228 352 370 378 329 347 355 441
f d bc b e d cd a
WUE
APLDW
EAW
3.99 5.45 5.54 5.58 5.28 5.34 5.36 6.31
0 144 150 154 200 203 205 325
0 6.96 7.59 7.74 4.14 4.68 4.87 5.71
e bcd bc b d cd bcd a
cd cd c b b b a
b a a e d d c
For LI with two irrigations, the highest grain yields are obtained with first irrigation at flowering and second irrigation when the simulated available soil water dropped to 50% FTSW 0:5 in the root zone, using PPDs of 38 or 50 plants m 2 (Table 2). In these treatments, grain yields increased 125 and 131% compared to rainfed conditions. Grain yield increase is a result of increase in ET and WUE. For the best options, 150 and 154 mm irrigation water are required (sum of two irrigations) with an irrigation efficiency of 100%. The median of interval between these two irrigations was 10 days (data not shown). PPD affects efficiency of applied water. An increase in the efficiency is observed with greater PPD. The efficiency in F2 (50) is 2 and 11% higher than that of F2 (38) and F2 (25), respectively. For LI with three irrigations, application of first irrigation at flowering and using PPD of 38 or 50 plants m 2 resulted in the highest grain yields (2431 and 2554 kg ha 1) compared to rainfed conditions with about 64% lower yield variation (Table 3). In these Table 3 Effect of LI with three irrigation first at flowering (F3) or beginning of seed growth (pod filling, P3) and second and third irrigations when simulated FTSW dropped to 0.5 in the root zone on grain yield (YLD, kg ha 1), CV of grain yield, crop evapotranspiration (ET, mm), water use efficiency (WUE, kg ha 1 mm 1), applied irrigation water (APLDW, mm) and applied-water use efficiency (EAW, kg ha 1 mm 1). Rainfed (RFD) and full-irrigated (IRR) variables are included for comparison. Numbers in parenthesis show plant density. The numbers with different letters indicate significant differences at 5% level of probability Treatment
YLD
CV
ET
RFD F3 (25) F3 (38) F3 (50) P3 (25) P3 (38) P3 (50) IRR
909 f 2133 c 2431 b 2554 b 1731 e 1854 de 1904 d 2766 a
29 7 10 11 16 17 18 4
228 372 406 422 329 348 355 441
f c b b e d d a
WUE
APLDW
EAW
3.99 5.80 6.01 6.08 5.28 5.34 5.36 6.31
0 222 228 232 203 215 226 325
0 5.57 6.70 7.10 4.06 4.40 4.42 5.71
e c bc ab d d d a
bc bc b d cd bc a
b a a c c c b
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treatments grain yields are increased 167 and 181% compared to rainfed treatment. The increase of grain yield is a result of increasing WUE and ET. WUE in the best treatments (F3 (38) and F3 (50)) increased by about 50% compared to rainfed conditions. Amount of irrigation water in F3 (38) and F3 (50) were 228 and 232 mm without a significant difference. The significantly lower grain yield in F3 (25) compared to F3 (38) and F3 (50) is interesting, showing that in LI with three irrigations a higher PPD is necessary to capture available resources completely. Also, EAW is increased from 5.57 kg ha 1 mm 1 in F3 (25) to 6.70 and 7.10 kg ha 1 mm 1 in F3 (38) and F3 (50), respectively. In LI with first irrigation at BSG (P3 treatments), APLDW is similar to LI with two irrigations (first at BSG), suggested that third irrigation is not necessary in the most of the years. Average irrigation water per irrigation for F3 (38) and F3 (50) are 76 and 77 mm. The median of interval between first and second and second and third irrigation was 9 or 10 days in 80% of years (data not shown). 4. Discussion Model evaluation made here for LI conditions and crop evapotranspiration, and previous model testing (Soltani et al., 1999) show that the model is able to simulate well the crop yield and evapotranspiration. Mean simulated yield and crop evapotranspiration and the associated standard deviations (S.D.) were remarkably close to values for measured ones. None of the simulated means are significantly different from the measured means at 95% confidence level. Likewise, there was a significant P 0:05 relationship between measured and simulated yield and evapotranspiration (R2 values in Figs. 1 and 2). In semiarid environment of NW Iran, chickpea crop experiences terminal drought stress which is started at a time between flowering and BSG in 100% of years, because of the high temperatures and ever-increasing evaporative demand. This terminal drought stress reduces grain yield by 67%, from 2766 kg ha 1 under full-irrigated conditions to 909 kg ha 1 under rainfed conditions. Silim and Saxena (1993) working in north of Syria with 14 chickpea cultivars during three seasons, showed terminal drought stress reduces yield by 61%, which is consistent with result of this study. Grain yield is significantly increased with LI compared to rainfed conditions. These increases were 739, 1162 and 1584 kg ha 1 for LI with one, two and three irrigations, respectively. Grain yields were reached to 60, 75 and 90% of grain yield obtained with full-irrigated (generally with five irrigations) conditions. These results indicate a positive response of chickpea grain yield to irrigation and are consistent with findings of Farah et al. (1988), Saxena (1987), Silim and Saxena (1993). The increase of grain yield was a result of increasing ET and WUE, suggesting that irrigation increases the WUE of chickpea which is in agreement with results of Khana-Chopra and Sinha (1987), Saxena (1984, 1987) and Silim and Saxena (1993). For example, Silim and Saxena (1993) reported that WUE is increased with irrigation. In their study, mean WUE of 14 cultivars of chickpea under rainfed conditions was 1.56 kg ha 1 mm 1 that was increased to 4.74 kg ha 1 mm 1 under full-irrigated conditions.
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Within each LI system, PPD increases efficiency of LI and this increase is more evidence with greater number of LI. In comparison of PPD of 50 plants m 2 with 25 and 38 plants m 2, these increases were 3.5, 6.5 and 16.5% for LI with one, two and three irrigations, respectively. It seems that higher PPD causes a more rapid canopy closure and enhances transpiration at the expense of direct soil evaporation (Ludlow and Muchow, 1990; Loomis and Connor, 1992); hence, applied water is used more efficiently. In conclusion, the results of this study showed that: 1. The chickpea crop model developed by Soltani et al. (1999) and modi®ed here can be used to optimizing irrigation management of chickpea. 2. In NW Iran, chickpea crop experiences terminal drought stress that is started at a time between flowering and BSG and this stress severely reduces grain yield by 67% compared to irrigated conditions. 3. Grain yield shows a large response to LI. In LI with one irrigation, the best results are obtained when first irrigation is applied at BSG and with this irrigation grain yield is increased 81% compared to rainfed conditions. In LI with two irrigations, the application of first irrigation at flowering (and second irrigation when available soil water dropped to 50% in the root zone) and using a PPD of 38 or 50 plants m 2 were the best options. In LI with three irrigations, the application of first irrigation at flowering was also the best option, combined to a PPD of 38 or 50 plants m 2. At end it should be noted that 16 years of simulation is probably a minimum in a simulation study. Further, simulation data are still artificial and only serve to compare scenarios on a longer-term basis. The artificial data do not necessary reflects the real yield variability of 16 years of field experiment. Acknowledgements The authors are thankful for suggestions provided by referees that resulted in significant improvements. References Aggarwal, P.K., Kalra, N., 1994. Analyzing the limitation set by climatic factors, genotype, and water and nitrogen availability on productivity of wheat. II. Climatically potential yield and management strategies. Field Crops Res. 38, 93±103. Amir, J., Sinclair, T.R., 1991. A model of water limitation on spring wheat growth and yield. Field Crops Res. 29, 59±69. Boote, K.J., Jones, J.W., Pickering, N.B., 1996. Potential uses and limitations of crop models. Agron. J. 88, 704± 716. Chapman, S.C., Hammer, G.L., Meinke, H., 1993. A sunflower simulation model. I. Model development. Agron. J. 85, 725±735. Doorenbos, J., Pruitt, W.O., 1977. Guidelines for predicting crop water requirements, 2nd Edition. FAO Irrigation and Drainage Paper 24. FAO, Rome. Doraiswamy, P.C., Thompson, D.R., 1982. A crop moisture stress index for large areas and its application in the prediction of spring wheat phenology. Agric. Meteorol. 27, 1±15.
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Farah, S.M., Atar, A., Miller, D.E., 1988. Water requirements and the irrigation management of pea, lentil, faba bean and chickpea crops. In: Summerfield, R.J. (Ed.), World Crops: Cool Season Food Legumes. Kluwer Academic Publishers, Dordrecht, the Netherlands, pp. 271±278. Hook, J.E., 1994. Using crop models to plan water withdrawals for irrigation in drought years. Agric. Syst. 45, 271±289. Khana-Chopra, R., Sinha, S.K., 1987. Chickpea: physiological aspects of growth and yield. In: Saxena, M.C., Singh, K.B. (Eds.), The Chickpea. CAB International, Wallingford, UK, pp. 163±189. Loomis, R.S., Connor, D.J., 1992. Crop Ecology: Productivity and Management in Agricultural Systems. Cambridge University Press, Cambridge, 538 pp. Ludlow, M.M., Muchow, R.C., 1990. A critical evaluation of traits for improving crop yields in water limited environments. Adv. Agron. 43, 107±153. O'Leary, G.J., Connor, D.J., 1998. A simulation study of wheat crop response to water supply, nitrogen nutrition, stubble retention and tillage. Aust. J. Agric. Res. 49, 11±19. O'Leary, G.J., Connor, D.J., White, D.H., 1985. A simulation model of the development, growth and yield of the wheat crop. Agric. Syst. 17, 1±26. Pickering, N.B., Hansen, J.W., Jones, J.W., Wells, C.M., Chan, V.K., Godwin, D.C., 1994. WeatherMan: a utility for managing and generating daily weather data. Agron. J. 86, 332±337. Priestley, C.H.B., Taylor, R.J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100, 81±92. Richarsdon, C.W., Wright, D.A., 1984. WGEN: a model for generating daily weather variables. US Department of Agriculture, Agriculture Research Service, ARS-8, p. 83. Ritchie, J.T., 1972. Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 8, 1024±1213. Ritchie, J.T., 1985. A user-oriented model of soil water balance in wheat. In: Day, W., Arkin, P.K. (Eds.), Wheat Growth and Modelling. Plenum Press, New York, pp. 293±305. Ritchie, J.T., 1998. Soil water balance and plant water stress. In: Tsuji, G.Y., Hoogenboom, G., Thornton, P.K. (Eds.), Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, pp. 41± 54. Sadri, B., Banai, T., 1996. Chickpea in Iran. In: Saxena, N.P., Saxena, M.C., Johansen, C., Virmani, S.M., Harris, H. (Eds.), Adaptation of Chickpea in the West Asia and North Africa Region. ICARDA, Aleppo, Syria, pp. 23±34. Saxena, N.P., 1984. Chickpea. In: Goldsworthy, P.R., Fisher, N.M. (Eds.), The Physiology of Tropical Field Crops. Wiley, New York, pp. 207±232. Saxena, M.C., 1987. Agronomy of chickpea. In: Saxena, M.C., Singh, K.B. (Eds.), The Chickpea. CAB International, Wallingford, UK, pp. 207±232. Saxena, N.P., Saxena, M.C., Johansen, C., Virmani, S.M., Harris, H., 1996. Future research priorities for chickpea in WANA and SAT. In: Saxena, N.P., Saxena, M.C., Johansen, C., Virmani, S.M., Harris, H. (Eds.), Adaptation of Chickpea in the West Asia and North Africa Region. ICARDA, Aleppo, Syria, pp. 257±263. Silim, S.N., Saxena, M.C., 1993. Adaptation of spring sown chickpea to the Mediterranean basin. I. Response to moisture supply. Field Crops Res. 34, 121±136. Sinclair, T.R., 1986. Water and nitrogen limitations in soybean grain production. I. Model development. Field Crops Res. 15, 125±141. Sinclair, T.R., Ludlow, M.M., 1986. Influence of soil water supply on the plant water balance of four tropical grain legumes. Aust. J. Plant Physiol. 13, 329±341. Sinclair, T.R., Seligman, N.G., 1996. Crop modeling: from infancy to maturity. Agron. J. 88, 698±704. Soltani, A., 1999. Simulating chickpea growth and yield. Ph.D. Thesis. Faculty of Agriculture, Tabriz University, Tabriz, Iran (text in Persian with English abstracts). Soltani, A., Ghassemi-Golezani, K., Rahimzadeh-Khooie, F., Moghaddam, M., 1999. A simple model for chickpea growth and yield. Field Crops Res. 62, 213±224. Soltani, A., Khooie, F.R., Ghassemi-Golezani, K., Moghaddam, M., 2000a. Thresholds for chickpea leaf expansion and transpiration response to soil water deficit. Field Crops Res. 68, 205±210. Soltani, A., Latifi, N., Nasiri, M., 2000b. Evaluation of WGEN for generating long term weather data for crop simulation. Agric. For. Meteorol. 102, 1±12.
A simulation study of chickpea crop response to limited irrigation in a semiarid environment A. Soltania,*, F.R. Khooieb, K. Ghassemi-Golezanib, M. Moghaddamb a
Department of Agronomy, Gorgan University of Agricultural Sciences, Gorgan, Iran b Department of Agronomy, Tabriz University, Tabriz, Iran Accepted 16 October 2000
Abstract In West Asia and North Africa (WANA) including northwest (NW) Iran irrigation is becoming increasingly available and investigation of the effect of limited irrigation (LI) is a research need. Only a few seasons of successful experimentation exist with LI effects. Thus, the objective of this simulation study was to examine potential long-term bene®ts of limited irrigation in NW Iran in terms of grain yield. To do this, a simple, mechanistic chickpea (Cicer arietinum L.) model and 16 years of weather data of Maragheh (NW Iran) were used. Three LI systems with one, two and three irrigations and each with three plant population densities (25, 38 and 50 plants m 2) were simulated. Results showed chickpea crop experiences terminal drought stress that is started at a time between ¯owering and beginning seed growth (BSG). This terminal drought stress severely reduces grain yield by 67%, from 2766 kg ha 1 under full-irrigated conditions to 909 kg ha 1 under rainfed conditions. Grain yield was signi®cantly increased with LI compared to rainfed conditions. Grain yields were reached to 60, 75 and 90% of grain yield simulated under full-irrigated (generally requires ®ve irrigations) conditions. In LI with one irrigation its application at BSG, and in LI with two and three irrigations, application of ®rst irrigation at ¯owering and application of one or two other irrigations when fraction of transpirable soil water dropped to 0.5 in the root zone resulted in higher grain yield. Water use ef®ciency was, also, increased with LI by 28, 39 and 52% for one, two and three irrigations, respectively. In LI systems with two and three irrigations it was required to a higher plant density (38 or 50 plants m 2) to capture and to use applied water more ef®ciently. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Chickpea; Limited irrigation; Terminal drought stress; Model; Simulation
* Corresponding author. Fax: 98-171-2220981/438. E-mail address: [email protected] (A. Soltani).
0378-3774/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 0 ) 0 0 1 4 3 - 8
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1. Introduction Crop simulation models that predict plant growth, water use and yield are being used to understand the response of crops to the dynamics of climate±plant±water systems. These models are also used in crop production to help make decisions that optimize use of available resources (Boote et al., 1996). For example, a number of modeling groups have attempted to optimize water management over long-term historical weather years (Hook, 1994; Aggarwal and Kalra, 1994; O'Leary and Connor, 1998). The crop simulation model assesses alternate management options, allowing the most suitable option to be adopted (Loomis and Connor, 1992, Chapter 13). Chickpea (Cicer arietinum L.) is one of the major pulse crops in West Asia and North Africa (WANA) region. It is cultivated on a large scale in arid and semiarid environments, and has considerable importance as food, feed and fodder. Due to the increasing need for legumes, chickpea is no longer considered a subsistence crop. The upward trend in its trade suggests that the crop is grown increasingly for the market (Saxena et al., 1996). In Iran, chickpea is the most important legume crop, and is sown on more than 50% of total legume area. The crop is predominately rainfed and only 10% is grown with irrigation. Average yields of rainfed chickpea range from 400 to 600 kg ha 1. Irrigated yields are ranged 1000±1500 kg ha 1 (Sadri and Banai, 1996). Most chickpea growing areas of Iran which have cool and cold semiarid climates with terminal drought stress (similar to that of Maragheh, the site chosen for this study) are located in northwest of the country. Nearly 50% of total chickpea production in Iran is concentrated in NW. Terminal drought is a widely occurred pattern of soil water availability in which drought is terminal following the major growth period during which water availability is adequate (Ludlow and Muchow, 1990; Loomis and Connor, 1992). In NW Iran, chickpea is sown from the mid of March to the end of April and grows mainly on stored moisture which is progressively depleted with crop growth. The crop experiences drought stress from late vegetative stage until maturity. The intensity of drought stress varies from year to year, depending on the amount and distribution of rainfall and on spring and early summer temperatures. Thus, large responses in grain yield are expected when one to three irrigation are applied (limited irrigation, LI). In this area irrigation is becoming increasingly available and investigation on the chickpea crop response to LI is a research need and opportunity. However, there are a few seasons of experimentation with LI management so questions remain about its performance and its benefits over seasons with differing weather conditions. Recently, a study was initiated to analyze the biophysical limitations in chickpea production using crop simulation approach in Iran (Soltani, 1999). To do this, a mechanistically based model for chickpea was developed and tested under rainfed and irrigated conditions (Soltani et al., 1999). Hence, the objective of this research was to examine potential long-term benefits of the LI in NW Iran in terms of grain yield. Therefore, this modeling analysis takes the `heuristic' approach recommended by Sinclair and Seligman (1996) whereby a model is used as a tool to explore possible long-term yield consequences of LI.
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2. Material and methods 2.1. The crop model The chickpea crop model of Soltani et al. (1999) was used to simulate chickpea growth and yield. The model is based on a daily time step and simulates crop growth and development as a function of temperature, solar radiation and water availability. Crop phenology is divided into two growth stages (before and after beginning seed growth), the duration of them are predicted based on daily temperature and water deficit. Leaf area development is calculated as functions of expansion and senescence of leaves. These functions are sensitive to temperature and water deficit. Daily biomass production is predicted from leaf area index, light extinction coefficient and radiation use efficiency. Transpiration is calculated as a function of daily biomass production, transpiration efficiency coefficient and daily vapor pressure deficit. Daily biomass production and leaf growth decrease below potential values once the fraction of transpirable soil water (FTSW) declines below threshold values (0.34 for biomass production and 0.48 for leaf expansion, Soltani et al., 2000a). After the beginning of seed growth the accumulated biomass is partitioned into the grains, the rate of which depending on climatic conditions at and after beginning of seed growth. Soil evaporation, soil water drainage, and runoff are also calculated in the soil water balance sub-model. The model uses readily available weather and soil information. The model was tested using independent data from a range of Iran's environmental conditions. In most cases, simulated grain yields were similar to that of observed yields. At this stage the model does not account for the effects of pests, diseases and soil fertility. Modifications were made to the model. In the original model, potential evapotranspiration was calculated with a modified Penman equation as presented by Sinclair (1986) and Amir and Sinclair (1991), that requires minimum and maximum temperatures and solar radiation data. And, soil evaporation was estimated using a two-stage model used by Amir and Sinclair (1991), and originally proposed by Doraiswamy and Thompson (1982) and O'Leary et al. (1985). Now, potential evapotranspiration is calculated according to Priestly and Taylor model (Priestley and Taylor, 1972; Ritchie, 1972). Inputs required for this model are also minimum and maximum temperatures and solar radiation. Using these three inputs and soil albedo, this model estimates potential evapotranspiration. Soil evaporation from the top 20 cm layer is slightly modified from Ritchie two-stage soil evaporation model (Ritchie, 1972, 1985, 1998) as used by Chapman et al. (1993) in their sunflower model. During Stage 1, water freely evaporates according to the Priestly and Taylor equation depending on crop cover. After a soil type specific amount of water has evaporated, Stage 2 begins. During Stage 2, soil evaporation is dependent on the square root of the number of days since the beginning of the stage. Details for these current methods can be find in Ritchie (1972, 1985, 1998). The procedures used in modified model are as simple as original methods. Another modification was related to root growth. In the original model, the whole soil volume, and consequently the total amount of stored soil water, was made available to the crop at sowing and onwards. To allow for root growth, the model was modified by defining the initial depth of soil water extraction as 200 mm at the full-emergence. Each
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day, up to beginning of seed growth (BSG), when root depth growth stopped, Silim and Saxena (1993), the depth of extractable soil water profile was increased by GRTD
EED 200 DTT TTS TTE
where GRTD is the daily root depth increase in mm per day, EED the effective extraction depth in mm, 200 the initial extraction depth, TTS the thermal time from sowing to BSG (8C day), TTE the thermal time from sowing to full-emergence (8C day) and DTT the thermal time occurred each day (8C per day). The TTE was set at 2308C day and EED (1200 mm) and TTS were as described by Soltani et al. (1999). The amount of irrigation water required to rise the available water to ®eld capacity, calculated by APLDWi
TTSW
ATSW IE
where APLDWi is the amount of irrigation water (mm), TTSW the total transpirable soil water (mm), ATSW the actual transpirable soil water (mm), and IE the irrigation ef®ciency. An evaluation of the chickpea crop model for grain yield predictions under LI conditions was performed using field data collected at the IDARC (Iran Dryland Agriculture Research Center), Maragheh (378240 N, 478160 E and 1476 m asl). The data were taken in 1996 from a chickpea LI experiment. Chickpea (var. Jam and Kaka) were sown April 15 in a randomized complete block design of six irrigation cultivar treatments with four replications. Irrigation treatments were rainfed, one LI at flowering and one LI at BSG. Grain yield was obtained by hand from 3 m2 of each plot. Evaluation of the model for simulating crop evapotranspiration was performed for rainfed conditions, because required data for irrigated conditions were not available. Data were extracted from various chickpea crop experiment reports from Kermanshah (348190 N, 47870 E and 1322 m asl), Maragheh and Tabriz (38850 N, 468170 E and 1361 m asl) (Soltani, 1999) for soil water at planting, rainfall during growing season and soil water at crop harvest. In these semiarid environments deep drainage is negligible and plot preparation methods used in the experiments prevented surface runoff. Crop evapotranspiration then was calculated as soil water at planting rainfall during growth season
soil water at harvest
In both cases, the relevant input requirements were collated to simulate these crops. Daily solar radiation (estimated from sunshine hours), minimum and maximum temperatures and rainfall were available for each case. Date of planting, plant density, and soil water attributes were input from known values for each experiment. The relevant cultivar data were specified as Soltani et al. (1999). Simulated outcomes compared with those measured in experiments by linear regression analysis and calculating root mean square error (RMSE). 2.2. Limited irrigation systems In this simulation study, three LI systems with one, two and three irrigations were evaluated. In NW Iran, terminal drought stress is initiated between flowering to BSG of
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chickpea crop and before these stages water deficit stress is not considerably occurred (Figs. 4 and 5, see Section 3.2). Therefore, first irrigation in LI systems was applied at flowering or BSG. Because of the better conditions for crop growth with LI, each LI system with three plant population densities (PPD) was simulated. Hence, considering LI with one irrigation, there were six treatments as: one irrigation at flowering with 25, 38, and 50 plants m 2, and one irrigation at BSG with 25, 38, and 50 plants m 2. In LI systems with two or three irrigations, the time of first irrigation was considered as LI with one irrigation, but second (in LI with two and three irrigations) and third (in the case of LI with three irrigations) irrigations were applied when the simulated available soil water dropped to 50% in the root zone. LI systems with two or three irrigations were also simulated with three PPD as mentioned above for LI with one irrigation. For comparison, simulations were run for rainfed conditions with 25 plants m 2 and full-irrigated conditions with 50 plants m 2. Under irrigated conditions, water was applied to fill the soil of the root zone to field capacity when the simulated available soil water dropped to 50% in the root zone. An application efficiency of 100% was used for full-irrigated and LI systems. 2.3. Simulations Maragheh was selected as a site to compare the LI systems. Maragheh is one of the most important chickpea-producing regions in NW Iran, and IDARC is located in this city. For running the model, maximum temperature, minimum temperature, precipitation and solar radiation data are required. Precipitation and maximum and minimum temperatures data were available for 16 years (1980±1995). Solar radiation data were calculated from sunshine hours and extraterrestrial radiation as outlined by Doorenbos and Pruitt (1977) or were generated by a synthesis program (WGEN, Richarsdon and Wright, 1984; Pickering et al., 1994; Soltani et al., 2000b). With estimated solar radiation data, a complete 16-years data set of daily precipitation, maximum and minimum temperatures and solar radiation data was available. The model was run for each treatment in each of the 16 seasons by inputting the daily weather data. For all treatments sowing date was considered 1 April (usual sowing date of the region). The soil and cultivar were same for all treatments. The soil that was chosen was sandy loam (typical of the location) with a total plant available water of 158 mm, a depth of 120 cm and a curve number of 75 (for calculation of run-off, only under rainfed treatment). Available soil water at sowing date was calculated for each year by running the soil water balance sub-model of the model from October 1 of the pervious year until sowing date. The chickpea variety Jam (typical) was chosen for simulations. The inputs of this variety was same as Soltani et al. (1999). Daily estimates of the soil water and various crop state variables were calculated. The results presented here focus on the final status of the crop at maturity. Grain yield is presented with 14% moisture content. Applied-water use efficiency was calculated for each year as crop yield obtained with LI (or full irrigation) minus rainfed yield divided by irrigation applied water. Means of variables for treatments of each LI system and rainfed and full-irrigated treatments were compared using Duncan's multiple range test P 0:05. Analysis of variance was performed on log transformed
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data using a model as Xij M Yi Tj Eij where Xij is observation, M the mean, Yi the effect of year, Tj the effect of treatment and Eij the residual effects. 3. Results 3.1. Crop model evaluation A plot of simulated versus observed crop yield is presented in Fig. 1. Simulated grain yield varied from 0.79 to 1.40 t ha 1 and measured yields from 0.77 to 1.35 t ha 1, and the means (S.D.) were 1.11 (0.22) t ha 1 and 1.07 (0.20) t ha 1, respectively. The model generally provided good estimates of crop yield with RMSE of 0.05 t ha 1 which is 5% of the observed mean grain yield. Fig. 2 shows simulated and observed crop evapotranspiration for rainfed conditions. While observed crop evapotranspiration ranged from 120 to 330 mm, simulated crop evapotranspiration varied from 165 to 300 mm. Mean (S.D.) of observed and simulated crop evapotranspiration were 236 (57) and 238 (59), respectively. In spite of some overprediction and under-prediction of crop evapotranspiration in the range 120±250 and 250±330 mm, respectively, the model acceptably simulated crop evapotranspiration. Model accounted for 64% of variations in crop evapotranspiration with an RMSE of 37 mm which is 16% of the observed mean crop evapotranspiration. 3.2. Climate and the time when terminal drought stress is initiated Mean monthly maximum temperature, minimum temperature, solar radiation and the monthly precipitation of Maragheh are presented in Fig. 3. In NW Iran, rainfall occurs in autumn, winter and early spring when temperatures are low. From March onwards the
Fig. 1. Simulated versus observed grain yields for six limited irrigation cultivar treatments, squares: cultivar Jam, triangles: cultivar Kaka. The solid line is 1:1 line and the broken line is the regression.
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Fig. 2. Simulated versus observed crop evapotranspiration. The solid line is 1:1 line and the broken line is the regression.
Fig. 3. Long term (16-year) means of monthly maximum and minimum temperatures (a) and solar radiation (pluses) and monthly precipitation (squares) (b) of Maragheh, NW Iran.
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Fig. 4. Simulated FTSW during chickpea growing season for a year with highest simulated rainfed yield (1995, dotted line) and for a year with lowest simulated rainfed yield (1989, solid line) in Maragheh, NW Iran. Arrows show the time of flowering.
region experiences increasing radiation and a rapid rise in temperature. The growing season of chickpea is started from the April (month 4) when minimum temperature rises to above 2.58C. Thus, the crop may experiences drought stress from the flowering onwards. Fig. 4 shows simulated dynamics of FTSW (Sinclair and Ludlow, 1986) during chickpea growing season in a year with the highest simulated yield (1995) and in a year with the lowest simulated yield (1989) under rainfed conditions. The FTSW lower than 0.34 and 0.48 can adversely affect photosynthesis and leaf growth of chickpea, respectively (Soltani et al., 1999, 2000a). As presented in Fig. 4, during the 1/2±2/3 of growing season, the crop did not experienced drought stress seriously, but thereafter, the crop experiences an increasing water deficit stress. In 1989, terminal drought stress was begun at flowering, while in 1995 terminal drought stress started about 12 days after flowering (i.e. at BSG phase). Therefore, in this environment, terminal drought stress is started at a time between flowering till BSG of the chickpea crop. Fig. 5 confirms this
Fig. 5. Cumulative probability of days to beginning terminal drought stress (from flowering as an origin) under rainfed conditions of Maragheh, NW Iran.
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fact, by showing the cumulative probability of days to beginning terminal drought stress (from flowering as an origin). In this figure, numbers mean after flowering. For example, 2 shows that terminal drought stress is started 2 days after flowering. Median of days to beginning drought stress was 4 days after flowering (with a mean of 5.1 days), and the probability of the occurrence of terminal drought stress 1 week or more after flowering is 0.3 (30%). In 100% of years terminal drought stress is occurred at a time between flowering and BSG. 3.3. Limited irrigation effects Grain yields are 909 and 2766 kg ha 1 with coefficient of variations (CV: standard deviation/mean) of 29 and 4% under rainfed and full-irrigated conditions, respectively. Rainfed yield is 33% of irrigated yield with a CV of seven times greater, showing that terminal drought stress in this semiarid environment can greatly reduce chickpea yield, but greatly increases grain yield variability (Table 1). Results of the effect of one LI on simulated grain yield (YLD), crop evapotranspiration (ET), water use efficiency (WUE), applied irrigation water (APLDW), and applied-water use efficiency (EAW) are shown in Table 1. The highest grain yield is obtained with LI application at BSG and the differences between PPDs were not significant. Because of the lower seed requirement and lower yield variability, PPD of 25 plants m 2 combined with LI at BSG may be considered as the best irrigation management option (the median of flowering-BSG interval was 12 days (data not shown)). This treatment increases simulated grain yield 81%, in respect to rainfed conditions. If grain yield to be considered as a product of ET and WUE, the increase of grain yield is a result of the rising in the both components. LI at BSG compared to LI at flowering requires more irrigation water, due to lower FTSW at BSG. For the best option, 124 mm irrigation water is required, assuming an irrigation efficiency of 100%. EAW is dependent on APLDW and is increased from F1 to IRR through P1. Table 1 Effect of LI with one irrigation at flowering (F1) or beginning of seed growth (pod filling, P1) on grain yield (YLD, kg ha 1 ), CV of grain yield, crop evapotranspiration (ET, mm), water use efficiency (WUE, kg ha 1 mm 1), applied irrigation water (APLDW, mm) and applied-water use efficiency (EAW, kg ha 1 mm 1). Rainfed (RFD) and full-irrigated (IRR) variables are included for comparison. Numbers in parenthesis show plant density. The numbers with different letters indicate significant differences at 5% level of probability Treatment
YLD
CV
ET
RFD F1 (25) F1 (38) F1 (50) P1 (25) P1 (38) P1 (50) IRR
909 d 1442 c 1503 c 1524 c 1648 b 1701 b 1721 b 2766 a
29 16 19 19 18 19 20 4
228 296 305 310 323 334 338 441
g f ef de cd bc b a
WUE
APLDW
EAW
3.99 4.90 4.93 4.93 5.12 5.10 5.10 6.31
0 68 d 74 c 78 c 124 b 127 b 128 b 325 a
0 7.84 8.01 7.94 5.99 6.25 6.34 5.71
c b b b b b b a
a a a bc bc b c
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Table 2 Effect of LI with two irrigation first at flowering (F2) or beginning of seed growth (pod filling, P2) and second irrigation when simulated FTSW dropped to 0.5 in the root zone on grain yield (YLD, kg ha 1), CV of grain yield, crop evapotranspiration (ET, mm), water use efficiency (WUE, kg ha 1 mm 1), applied irrigation water (APLDW, mm) and applied-water use efficiency (EAW, kg ha 1 mm 1). Rainfed (RFD) and full-irrigated (IRR) variables are included for comparison. Numbers in parenthesis show plant density. The numbers with different letters indicate significant differences at 5% level of probability Treatment
YLD
CV
ET
RFD F2 (25) F2 (38) F2 (50) P2 (25) P2 (38) P2 (50) IRR
909 f 1909 cd 2041 bc 2101 b 1731 e 1853 de 1902 d 2766 a
29 12 14 14 16 17 19 4
228 352 370 378 329 347 355 441
f d bc b e d cd a
WUE
APLDW
EAW
3.99 5.45 5.54 5.58 5.28 5.34 5.36 6.31
0 144 150 154 200 203 205 325
0 6.96 7.59 7.74 4.14 4.68 4.87 5.71
e bcd bc b d cd bcd a
cd cd c b b b a
b a a e d d c
For LI with two irrigations, the highest grain yields are obtained with first irrigation at flowering and second irrigation when the simulated available soil water dropped to 50% FTSW 0:5 in the root zone, using PPDs of 38 or 50 plants m 2 (Table 2). In these treatments, grain yields increased 125 and 131% compared to rainfed conditions. Grain yield increase is a result of increase in ET and WUE. For the best options, 150 and 154 mm irrigation water are required (sum of two irrigations) with an irrigation efficiency of 100%. The median of interval between these two irrigations was 10 days (data not shown). PPD affects efficiency of applied water. An increase in the efficiency is observed with greater PPD. The efficiency in F2 (50) is 2 and 11% higher than that of F2 (38) and F2 (25), respectively. For LI with three irrigations, application of first irrigation at flowering and using PPD of 38 or 50 plants m 2 resulted in the highest grain yields (2431 and 2554 kg ha 1) compared to rainfed conditions with about 64% lower yield variation (Table 3). In these Table 3 Effect of LI with three irrigation first at flowering (F3) or beginning of seed growth (pod filling, P3) and second and third irrigations when simulated FTSW dropped to 0.5 in the root zone on grain yield (YLD, kg ha 1), CV of grain yield, crop evapotranspiration (ET, mm), water use efficiency (WUE, kg ha 1 mm 1), applied irrigation water (APLDW, mm) and applied-water use efficiency (EAW, kg ha 1 mm 1). Rainfed (RFD) and full-irrigated (IRR) variables are included for comparison. Numbers in parenthesis show plant density. The numbers with different letters indicate significant differences at 5% level of probability Treatment
YLD
CV
ET
RFD F3 (25) F3 (38) F3 (50) P3 (25) P3 (38) P3 (50) IRR
909 f 2133 c 2431 b 2554 b 1731 e 1854 de 1904 d 2766 a
29 7 10 11 16 17 18 4
228 372 406 422 329 348 355 441
f c b b e d d a
WUE
APLDW
EAW
3.99 5.80 6.01 6.08 5.28 5.34 5.36 6.31
0 222 228 232 203 215 226 325
0 5.57 6.70 7.10 4.06 4.40 4.42 5.71
e c bc ab d d d a
bc bc b d cd bc a
b a a c c c b
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treatments grain yields are increased 167 and 181% compared to rainfed treatment. The increase of grain yield is a result of increasing WUE and ET. WUE in the best treatments (F3 (38) and F3 (50)) increased by about 50% compared to rainfed conditions. Amount of irrigation water in F3 (38) and F3 (50) were 228 and 232 mm without a significant difference. The significantly lower grain yield in F3 (25) compared to F3 (38) and F3 (50) is interesting, showing that in LI with three irrigations a higher PPD is necessary to capture available resources completely. Also, EAW is increased from 5.57 kg ha 1 mm 1 in F3 (25) to 6.70 and 7.10 kg ha 1 mm 1 in F3 (38) and F3 (50), respectively. In LI with first irrigation at BSG (P3 treatments), APLDW is similar to LI with two irrigations (first at BSG), suggested that third irrigation is not necessary in the most of the years. Average irrigation water per irrigation for F3 (38) and F3 (50) are 76 and 77 mm. The median of interval between first and second and second and third irrigation was 9 or 10 days in 80% of years (data not shown). 4. Discussion Model evaluation made here for LI conditions and crop evapotranspiration, and previous model testing (Soltani et al., 1999) show that the model is able to simulate well the crop yield and evapotranspiration. Mean simulated yield and crop evapotranspiration and the associated standard deviations (S.D.) were remarkably close to values for measured ones. None of the simulated means are significantly different from the measured means at 95% confidence level. Likewise, there was a significant P 0:05 relationship between measured and simulated yield and evapotranspiration (R2 values in Figs. 1 and 2). In semiarid environment of NW Iran, chickpea crop experiences terminal drought stress which is started at a time between flowering and BSG in 100% of years, because of the high temperatures and ever-increasing evaporative demand. This terminal drought stress reduces grain yield by 67%, from 2766 kg ha 1 under full-irrigated conditions to 909 kg ha 1 under rainfed conditions. Silim and Saxena (1993) working in north of Syria with 14 chickpea cultivars during three seasons, showed terminal drought stress reduces yield by 61%, which is consistent with result of this study. Grain yield is significantly increased with LI compared to rainfed conditions. These increases were 739, 1162 and 1584 kg ha 1 for LI with one, two and three irrigations, respectively. Grain yields were reached to 60, 75 and 90% of grain yield obtained with full-irrigated (generally with five irrigations) conditions. These results indicate a positive response of chickpea grain yield to irrigation and are consistent with findings of Farah et al. (1988), Saxena (1987), Silim and Saxena (1993). The increase of grain yield was a result of increasing ET and WUE, suggesting that irrigation increases the WUE of chickpea which is in agreement with results of Khana-Chopra and Sinha (1987), Saxena (1984, 1987) and Silim and Saxena (1993). For example, Silim and Saxena (1993) reported that WUE is increased with irrigation. In their study, mean WUE of 14 cultivars of chickpea under rainfed conditions was 1.56 kg ha 1 mm 1 that was increased to 4.74 kg ha 1 mm 1 under full-irrigated conditions.
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Within each LI system, PPD increases efficiency of LI and this increase is more evidence with greater number of LI. In comparison of PPD of 50 plants m 2 with 25 and 38 plants m 2, these increases were 3.5, 6.5 and 16.5% for LI with one, two and three irrigations, respectively. It seems that higher PPD causes a more rapid canopy closure and enhances transpiration at the expense of direct soil evaporation (Ludlow and Muchow, 1990; Loomis and Connor, 1992); hence, applied water is used more efficiently. In conclusion, the results of this study showed that: 1. The chickpea crop model developed by Soltani et al. (1999) and modi®ed here can be used to optimizing irrigation management of chickpea. 2. In NW Iran, chickpea crop experiences terminal drought stress that is started at a time between flowering and BSG and this stress severely reduces grain yield by 67% compared to irrigated conditions. 3. Grain yield shows a large response to LI. In LI with one irrigation, the best results are obtained when first irrigation is applied at BSG and with this irrigation grain yield is increased 81% compared to rainfed conditions. In LI with two irrigations, the application of first irrigation at flowering (and second irrigation when available soil water dropped to 50% in the root zone) and using a PPD of 38 or 50 plants m 2 were the best options. In LI with three irrigations, the application of first irrigation at flowering was also the best option, combined to a PPD of 38 or 50 plants m 2. At end it should be noted that 16 years of simulation is probably a minimum in a simulation study. Further, simulation data are still artificial and only serve to compare scenarios on a longer-term basis. The artificial data do not necessary reflects the real yield variability of 16 years of field experiment. Acknowledgements The authors are thankful for suggestions provided by referees that resulted in significant improvements. References Aggarwal, P.K., Kalra, N., 1994. Analyzing the limitation set by climatic factors, genotype, and water and nitrogen availability on productivity of wheat. II. Climatically potential yield and management strategies. Field Crops Res. 38, 93±103. Amir, J., Sinclair, T.R., 1991. A model of water limitation on spring wheat growth and yield. Field Crops Res. 29, 59±69. Boote, K.J., Jones, J.W., Pickering, N.B., 1996. Potential uses and limitations of crop models. Agron. J. 88, 704± 716. Chapman, S.C., Hammer, G.L., Meinke, H., 1993. A sunflower simulation model. I. Model development. Agron. J. 85, 725±735. Doorenbos, J., Pruitt, W.O., 1977. Guidelines for predicting crop water requirements, 2nd Edition. FAO Irrigation and Drainage Paper 24. FAO, Rome. Doraiswamy, P.C., Thompson, D.R., 1982. A crop moisture stress index for large areas and its application in the prediction of spring wheat phenology. Agric. Meteorol. 27, 1±15.
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