Toward dormant seeding of rainfed chickpea as an adaptation strategy to sustain productivity in response to changing climate

Field Crops Research 247 (2020) 107674

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Toward dormant seeding of rainfed chickpea as an adaptation strategy to sustain productivity in response to changing climate

T

Seyed Reza Amiria,*, Reza Deihimfardb, Hamed Eyni-Nargesehc a

Department of Plant Production, Faculty of Agriculture, Higher Educational Complex of Saravan, P.O. Box 9951634145, Saravan, Iran Department of Agroecology, Environmental Sciences Research Institute, Shahid Beheshti University, G.C., Tehran, Iran c Department of Agronomy and Horticultural Sciences, College of Agriculture, Ahvaz branch, Technical and Vocational University (TVU), Khuzestan, Iran b

ARTICLE INFO

ABSTRACT

Keywords: Cultivar Grain yield Modelling Sowing date Water use efficiency

Dormant seeding management (DSM) is a useful adaptation strategy to sustain chickpea grain yield particularly under climate change. Accordingly, the SSM-Legumes model was employed to evaluate the effects of DSM versus fixed sowing dates (at three levels) and cultivars (early-, mid- and late-maturity) on chickpea production and water use efficiency in eight locations in west and northwest of Iran. Daily climatic data from 1980 to 2010 was collected from the Meteorological Organization of Iran as baseline. Projections of the future climate was accomplished in Miroc5 (Model for Interdisciplinary Research on Climate) GCM for the future of 2040–2070 under RCP4.5 and RCP8.5 emission scenarios using the methodology presented by AgMIP (Agricultural Model Intercomparison and Improvement Project). The results showed that DSM2 (dormant seeding around late February) × a mid-maturity cultivar produced much higher grain yield (1382 kg ha−1) in comparison to other combinations of sowing dates and cultivars in baseline. However, in the future, ILC482 × DSM1 (dormant seeding around 20 December) showed the best performance in terms of grain yield (1350 and 1484 kg ha−1 for RCP4.5 and RCP8.5, respectively. Results also indicated that water use efficiency was much higher in DSM1 and DSM2 (3.6 and 4.6 kg ha−1 mm−1, respectively) compared to the fixed sowing dates in baseline. However, combination of DSM2 × ILC482 in baseline, resulted in 1.3 kg ha−1 mm−1 greater water use efficiency than DSM1 × ILC482. In the future, ILC482 cultivar under DSM1 showed highest water use efficiency (5.2 and 5.8 kg ha−1 mm−1 for RCP4.5 and RCP8.5, respectively). Overall, DSM in combination of a mid-maturity cultivar under climate change could be considered as a suitable adaptation strategy to increase the length of growing season, coincide winter and early-spring rainfalls with the critical period of chickpea growth and consequently increase grain yield and water use efficiency.

1. Introduction

Iran is part of the WANA (West Asia-North Africa) region which is characterized as the area with high growth rate in population, insufficient rainfall, limited arable land and high variability in rainfall and limited water resources (Saxena et al., 1996). Similar to the WANA region, chickpea is the most important legume crops in Iran, with more than half a million ha under cultivation (FAO, 2012). The crop is predominately grown under rainfed conditions with an average yield of about 500 kg ha–1 (FAO, 2012). Farmers in Iran usually sow chickpea in early spring and harvest around July. Under these circumstances, the crop does not coincide fully with winter rainfalls with a low water use efficiency (WUE) and mostly drought and heat stress during reproductive phases (Zyaie et al., 2012). Recently, some farmers tried a type of planting called dormant seeding management (DSM) of chickpea in the area. Under DSM, the seeds remain ungerminated and dormant in the soil until moisture and temperature become suitable for

Chickpea (Cicer arietinum L.) is a main cool-season food legume and a primary source of protein for millions of people in the world. Major producing countries in the world are India, Pakistan and Iran (FAO, 2012). Although it is important as a grain legume for human use, it is also considered for increasing yield of succeeding plants in a rotation and, therefore, contributing to the sustainability and profitability of production systems. Approximately, 90% of the world’s chickpea is grown under rainfed conditions where the plants are grown in a progressively depleting soil water profile and encounters terminal drought stress, a situation in which chickpea productivity is limited (Singh et al., 2014). Hence, chickpea average grain yield is low in the main chickpea producing countries due mainly to insufficient water quantity (Soltani and Sinclair, 2012). ⁎

Corresponding author. E-mail address: [email protected] (S.R. Amiri).

https://doi.org/10.1016/j.fcr.2019.107674 Received 13 January 2019; Received in revised form 14 October 2019; Accepted 30 October 2019 0378-4290/ © 2019 Elsevier B.V. All rights reserved.

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plant growth after frost and low temperatures period in winter (Amiri and Deihimfard, 2018). The seeds germinate and emerge with the onset of warming during March or even earlier thanks to climate change exploiting late-winter rainfalls, decreasing frost risk stress at seedling stage, increasing water use efficiency, avoiding terminal heat stress, escaping terminal drought stress and ultimately increasing the length of growing season and grain yield. Over the past decade, the field crops have been affected by climate change, more frequent droughts and more erratic precipitation trends. Accordingly, the goal of climate change risk assessment is to recognize adaptation strategies for achieving sustainable development in a particular area (Luo et al., 2009). Such adaptation strategies included adapted cultivars, changes in common sowing windows and rates, modern cropping rotations and introduction of new crops (White et al., 2011). Amongst the adaptation options, planting date × cultivar is the most important option to tackle the negative impacts of climate change particularly rising temperature in the dry areas (White et al., 2011), Under future climatic conditions, cultivars with different maturity rates need to be investigated under earlier and later planting date than currently applied in terms of grain yield (Soltani and Sinclair, 2012a,b). For instance, Singh et al. (2014) concluded that climate change by 2050 increased the yield of chickpea by 17–25% at the cooler sites and decreased yield by 7–16% at the warmer sites in India as compared to the yields under baseline climate. They also reported that yield was lower for long maturity cultivars because these cultivars were exposed to warmer temperatures as well as soil water depletion later in the season. Mohammed et al. (2017) in their study to identify the best crop management options to increase productivity of chickpea using CROPGROchickpea model concluded that early sowing increased chickpea grain yield in northeastern Ethiopia under the present and future climate conditions. Furthermore, selection of appropriate cultivars was an important strategy to increase chickpea productivity. Crop models have been shown to be the most suitable tools to investigate the effect of management practices on crop yield (RahimiMoghaddam et al., 2018; Amiri et al., 2016; Deihimfard et al., 2015; Jones et al., 2003), the effect of genotypes in combination of sowing windows on crop productivity (Liu et al., 2013; Zheng et al., 2012) and the impacts of climate change on grain yield of field crops (Deihimfard et al., 2018; Eyni-Nargeseh et al., 2019; Asseng et al., 2011; Lobell et al., 2015; Zheng et al., 2012). In contrast, traditional experimentations have been proved to be costly and time consuming and crop simulation models have the potential to substantially eliminate the cost and time of field experiments necessary for adequate evaluation of genotypes, environment and management interactions. Amiri et al. (2016) in Khorasan-Razavi province in Iran using the SSM- Legume model found that a sowing date of 2–3 weeks earlier (early and mid February) than the typical time practiced by farmers decreased the risk of chickpea yield failure by 52% and resulted in stable yields under arid and semi-arid environments. Dettori et al. (2017) in southern Sardinia in Italy studied the impacts of climate change on productivity of durum wheat using CERES-Wheat model and concluded that adaptation options such as the selection of appropriate genotypes would be a useful strategy to tackle the negative impacts of climate change. In an another study in the northwest of Iran, Amiri and Deihimfard (2018) using SSMlegumes model concluded that sowing lentil under DSM was superior over the other sowing dates because it not only shortened the frost risk to seedlings but also provided optimal conditions for the growth and use of rainfall and soil moisture. To date, little is known about the role of DSM as an adaptation option to climate change for improving chickpea grain yield over a long-term period. In addition, DSM in combination with better adapted cultivars might boost chickpea WUE and grain yield under current agroecosystems. Accordingly, the main objective of the current study was to evaluate DSM as an adaptation option to climate change along with adapted cultivars to enhance grain yield in the chickpea-growing areas of northwestern Iran.

2. Materials and methods 2.1. Description of the SSM-Legume model The SSM-Legumes model accounts for leaf area development as a function of temperature which can be restricted by inadequate nitrogen and soil water. The leaf area index of the crop is used to intercept solar radiation, which in turn is used to calculate crop growth as a function of radiation use efficiency (Sinclair et al., 2014). Radiation use efficiency, like leaf extension, is decreased proportional to soil water content (Soltani and Sinclair, 2012b). Thus, daily growth was calculated based on incident solar radiation, leaf area index, and soil water content. Finally, daily seed growth was calculated as a fraction of total dry matter, considering a linear increase in harvest index. For calculation of the soil water balance, the entire soil volume occupied by roots is considered as a single compartment for calculating the reservoir for crop available soil water. The initial depth of soil water extraction at plant emergence is set equal to 200 mm. Subsequently, the depth of extractable soil water profile is increased steadily per biological day. The final depth of soil water extraction is limited either by phenological development, the maximum rooting depth capacity of the crop, or by chemical and physical barriers in the soil. After beginning of seed growth (BSG), increases in rooting depth were terminated. Further details of SSM-Legume model are presented by Soltani and Sinclair (2012a). 2.2. Calibration and validation of the SSM-Legume model The model was calibrated using measured data from a two-year field experiment conducted in the 2011 and 2012 growing seasons. The experiment was a factorial arrangement based on a randomized complete block design with four replications conducted at the research field of Khorramabad (33.29 N, 48.22E), Iran. Three cultivars with different maturity (early, medium and late-maturity) (Bivanij, ILC482 and Hashem, respectively) along with three sowing dates (21 December as DSM, 20 March and 4 April) were included in the study. The field data were applied to estimate the required cultivar-specific parameters for model (Table 1). For model calibration, the difference between observed and simulated values was minimized by using a trial and error procedure. Relevant parameters that influenced days to flowering, dry matter and grain yield the most, were adjusted. The procedure was iterated until the closest match between the model simulated and observed values was obtained for all treatments (Bhatia et al., 2008). The validation of the model was done using independent observed data from field experiments carried out in a wide range of treatments and environments (Table 2) with high variability in rainfall and temperature. In these experiments, standard agronomic practices for weed, insect control and optimum fertilization were applied to avoid biotic and abiotic stresses. Data obtained from the field experiments included crop development, day to flowering, dry matter over growing season and grain yield. The following indices were calculated to measure the Table 1 Parameters of three cultivars obtained in model calibration.

2

Parameter

Bivanij

ILC482

Hashem

Photoperiod sensitivity coefficient Maximum stem node number (node d−1) Maximum increase of harvest index rate per day at linear stage of its increase Grain nitrogen concentration (mg g−1) Biological day between plant emergence and flower appearance (day) Biological day between first flower and first pod (day) Biological day between first pod and initiate seed filling (day)

0.1 0.42 0.02

0.11 0.51 0.02

0.11 0.52 0.02

0.043 25

0.043 23

0.043 28

12

9

8

4

3

5

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Table 2 Independent data sets used for model validation. Location Kermanshah

Hamedan

Sannadaj

Urmia Khorramabad

Season

Latitude and longitude

Annual cumulative rainfall (mm)

Annual mean temperature (0C)

Experimental design and treatments

Cropping system

2013

34.43 N 47.00 E

142

13

Randomized complete block design /sowing date

2008

34.43 N 47.00 E

120

14

Randomized complete block design/ Genotype

2005-2006

34.43 N 47.00 E 34.43 N 47.00 E

15 16 17

Randomized complete block design/ Genotype

2001-2002

163 133 101

Irrigation and rainfed Irrigation and rainfed Rainfed

2006-2007 2006-2007 2009 1999 2007 2002 2006

34.43 N 34.43 N 34.43 N 34.43 N 34.43 N 34.43 N 34.43 N

E E E E E E E

207 210 208 119 207 234 252

15 18 15 16 17 16 17

2005-2006 2006

34.52 N 49.00 E 34.52 N 49.00 E

156 113

2007

34.52 N 49.00 E

1999 2010

Factorial experiment based on randomized complete block design/Genotype × plant density Randomized complete block design/ Genotype Randomized complete block design/ Genotype Randomized complete block design/ Genotype Randomized complete block design/ Genotype Randomized complete block design/ Genotype Randomized complete block design/ Genotype Factorial experiment based on randomized complete block design/Genotype × sowing date

Irrigation

14 16

Randomized complete block design/ sowing date Randomized complete block design/ Genotype

142

14

Randomized complete block design/ Genotype

Rainfed Irrigation and rainfed Rainfed

35.40 N 46.99 E 35.40 N 46.99 E

171 226

13 14

Randomized complete block design/ Genotype Factorial experiment based on randomized complete block design/Genotype × planting date × plant density

Rainfed Rainfed

2007-2008

37.53 N 45.30 E

179

14

Randomized complete block design/ Genotype

Rainfed

2004-2005 2004-2005

33.48 N 48.25 E 33.48 N 48.25 E

133 101

16 17

Randomized complete block design/ Genotype Factorial experiment based on randomized complete block design/Genotype × planting date

Rainfed Irrigation and rainfed

47.00 47.00 47.00 47.00 47.00 47.00 47.00

differences between observed and simulated data (Huang et al., 2009):

RRMSE=

EF= 1

100

n i= 1

(Si

n (Si i= 1 n ( i= 1

region, this method generates climate scenarios by modifying historically-observed climate data using the simulated absolute change in minimum and maximum air temperature and relative change in precipitation in the climate model (Ruane et al., 2013). Projections of the future climate was accomplished in Miroc5 (Model for Interdisciplinary Research on Climate) GCM (Watanabe et al., 2010) for future 2040–2070 under RCP4.5 and RCP8.5 emission scenarios using the methodology presented by AgMIP (Hudson and Ruane, 2013). The Miroc5 GCM was used because it displayed the greatest accuracy for projection of the climate data of the study locations compared with the other GCMs (Ghahreman et al., 2015).

Oi)2

n

(1)

Oi) 2 Oi) 2

Irrigation Irrigation Rainfed Rainfed Irrigation Rainfed Rainfed

(2)

In these equations O and S are the observed and simulated data, respectively, ō is the mean of observed data, and n is the number of observations. The relative root mean square error (RRMSE) assesses a value in percent for the model simulation accuracy. Model precision boosts as RRMSE value approach zero. While the greatest value of EF (model efficiency) is one, a positive value indicates that the simulated values describe the trend in the measured data better than the mean of the observations (Brisson et al., 2002).

2.4. Simulation scenarios Long-term factorial simulation treatments consisted of three cultivars, six sowing dates, eight locations and two future emission scenarios (RCP4.5 and RCP8.5) over a span of 30 years (a total of ∼8640 simulation experiments). A CO2 concentration of 360 ppm was used for the baseline simulations of 1980–2010 whereas 499 and 571 ppm were considered for RCP4.5 and RCP8.5, respectively under future 2040–2070. The cultivars were Hashem (a late-maturity cultivar), ILC482 (a mid-maturity cultivar) and Bivanij (an early-maturity cultivar). These cultivars were predominant in the northwest of Iran. Sowing dates included two DSM (called as DSM1 and DSM2) and four fixed sowing dates. The four fixed sowing dates included: 6 March (as early sowing), 21 March and 4 April (as mid sowing) and 21 April (as late sowing). Under the fixed sowing dates, all simulations were started with the same initial soil water conditions as described below. In the calculation of the soil water balance in the chickpea model,

2.3. Description of study locations and future climate scenarios Eight locations in northwestern Iran were studied (Fig. 1). Longterm (1980–2010) daily weather data including solar radiation (MJ m−2 d−1), precipitation (mm), and maximum and minimum temperatures (˚C) were collected for each location from its climatological station (Fig. 2). These climatic data were used as the basis for future climatic scenario projection. Future climatic change scenarios were employed using the delta scenario of the CMIP5 General Circulation Model (GCM) and the climate scenario generation tools in R as reported in the Agricultural Model Intercomparison and Improvement Project (AgMIP) (Araya et al., 2015; Hudson and Ruane, 2013; R Core Team, 2013). For a given 3

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Fig. 1. Geographical position of the selected weather stations in the study locations.

the volumetric transpirable soil water and the maximum rooting depth were considered as 0.13 m3 m−3 and 1000 mm as no extensive soil data were available. The farmers in the study locations usually apply a light irrigation at sowing to establish the seedlings uniformly (Parsa et al., 2012), provided that there is no rainfall that time. Accordingly, the initial soil water was assumed to be near 70% of the volumetric

extractable soil water at sowing in all fixed sowing dates except for DSMs. For simulation of DSMs, the switch of sowing date in SSM-Legume model was turned off so that the model would not sow the chickpea on a fixed date. Accordingly, the possible planting date under DSMs, occurred whenever the initial soil water on the top soil layer (200 mm)

Fig. 2. Long-term (1980–2010) monthly cumulative rainfall (columns) and maximum (red line) and minimum (blue line) temperatures of meteorological stations at study locations. 4

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filled to the volumetric transpirable soil water (0.13 × 200) and the temperature of the layer was above the base of 4.5 °C. However, when the germination event did not occur up to maximum 60 days after midDecember (starting time of DSM1 by farmers) due to lower temperatures than the base temperature, sowing time was considered as around late February (starting time of DSM2 by farmers). WUE was calculated as simulated grain yield (kg ha−1) divided by the simulated seasonal evapotranspiration (mm). Other management practices including sowing density, tillage, fertilization and pest management were considered fixed in the model as practiced by farmers in the study locations.

to flowering ranged from 67 to 81 days for all cultivars as against observed values between 64 and 79 days. Model evaluation results indicated that the model could also predict chickpea dry matter and grain yield at different locations and seasons reasonably well. The observed and simulated dry matter and grain yield at these locations are presented in Fig. 3. The simulated chickpea grain yield ranged from 422 to 3600 kg ha−1 while its measured values ranged from 230 to 3050 kg ha-1 for all cultivars in various locations and years with different climates. The difference between observed and simulated values (RRMSE) for dry matter and grain yield were obtained from 13 to 10% and 14–16%, while these values for model efficiency (EF) ranged from 0.92 to 0.89, and 0.91 to 0.89, respectively indicating that the SSM-legume model was able to simulate the growth and grain yield of chickpea reasonably well for almost all sites, years and cultivars.

2.5. Statistical analysis All the multi-year simulation output data of crop grain yields were analyzed using analysis of variance (ANOVA). The study used a randomized complete block design (RCBD) in a factorial arrangement. In all analyses, simulation years were considered as replications. The analysis was done using SAS v 9.1.2 (SAS, 2003) software.

3.2. Grain yield at baseline and projected climate change There was a large variability in grain yield at baseline depending upon sowing date and cultivar for all study locations. The average grain yield of the entire province was 780 kg ha−1 (Fig. 4). At a given location, the response of grain yield to sowing date and cultivar was significant (P < 0.05) (Table 3); yield ranged from 91 to 2105 kg ha−1depending on the cultivar, sowing date and season. The highest and lowest average grain yield was observed in Tabriz (1374 kg ha−1) and Sanandaj (400 kg ha−1), respectively. Under fixed sowing dates at baseline, the best performance was obtained for 6 March with the average grain yield of 781 kg ha−1 compared with other fixed sowing days. Any delay in sowing from 6 March till 21 April resulted in a decreased grain yield of -0.8% per day. When averaged across all locations, seasons and cultivars, however, DSM1 and DSM2 were superior to the all fixed sowing dates with the average grain yield of 897 and 1054 kg ha−1, respectively. The interaction of cultivars with the sowing dates and DSMs

3. Results 3.1. Model evaluation Fig. 3 illustrates the results of model evaluation for three cultivars of Bivanij, ILC482 and Hashem using the large number of experimental data collected in a wide range of environments and management practices (Table 2). Evaluation results indicated that the model predicted phonological development (i.e. days to flowering) reasonably well (Fig. 3C, F and I). For instance, the RRMSE and EF values for days to flowering were 7%, 0.61 for Bivanij, 9% and 0.89 for ILC482 and 6% and 0.77 for Hashem. In addition, the coefficient of determination (R2) values for regression between observed and simulated days to flowering ranged from 0.95 to 0.86 for all cultivars. The averaged predicted days

Fig. 3. Simulated versus measured grain yield, dry matter and days to flowering for Bivanij (A, B, C), Hashem (D, E, F) and ILC482 (G, H, I) cultivars in different locations and seasons. Refer to Table 2 for data sets used for model evaluation. Continuous line: 1 to1 line; dashed line: regression line. 5

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Fig. 4. Averaged long term simulated grain yield and water use efficiency (WUE) by location, sowing date and cultivar at baseline. (Error bars show range of grain yield difference among years).

ha−1 grain yield (Fig. 4). Averaged across locations, seasons, sowing dates and cultivars, chickpea grain yield increased +4 and +6% in the future for RCP4.5 and RCP8.5, respectively compared to baseline (Fig. 5). However, the increase was significant amongst various fixed sowing dates and DSMs (P < 0.05) (Table 3). In 6 March as the best fixed sowing date, grain yield increased +3 and +4% for RCP4.5 and RCP8.5, respectively in comparison to baseline. With delaying in fixed sowing date, however, grain yield decreased in the future at -6 and -16% for RCP4.5 and

changed the results substantially at baseline. Under DSM1, cultivars of ILC482, Bivanij and Hashem, produced grain yield of 1200, 936 and 550 kg ha−1, respectively (Fig. 4). Similar trend was observed under DSM2 so that the highest and lowest grain yield was observed for ILC482 (1382 kg ha−1) and Hashem (686 kg ha−1), respectively meaning the different response of cultivars to both dormant seeding treatments. Among fixed sowing dates, the best performance was obtained for ILC482 cultivar on 6 March with 1045 kg ha−1 grain yield and the least one simulated for Hashem cultivar on 21 April with 392 kg Table 3 Analysis of variance (MS) for simulated grain yield and WUE of chickpea. S.O.V

df

Grain yield

WUE

Replication Location Scenario Cultivar Sowing date Location × Scenario Location × Cultivar Scenario × Cultivar Location × Scenario × Cultivar Location × Sowing date Scenario × Sowing date Location × Scenario × Sowing date Cultivar × Sowing date Location × Cultivar × Sowing date Scenario × Cultivar × Sowing date Location × Scenario × Cultivar × Sowing date Error

29 7 2 2 5 14 14 4 28 35 10 70 10 70 20 140 12493

14528744** 218079925** 613581** 326975946** 65645679** 691088** 1319354** 802958** 64588ns 1914252** 1653170** 86162ns 6376662** 224340** 86524ns 53152ns 68475

180.41** 7813.96** 1526.28** 16569.53** 1417.54** 2022.15** 2099.31** 1845.10** 2028.81** 2046.62** 2113.01** 2027.37** 1608.40** 2041.22** 2108.07** 2021.88** 3.04

6

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Fig. 5. Simulated grain yield in the future compared with baseline for RCP4.5 (blue columns) and RCP8.5 (red columns) in different locations, sowing dates and cultivars. Each bar chart is the mean of 30-year simulations. (Error bars show range of grain yield difference among years).

RCP8.5, respectively compared to the baseline. It should be noted that the increase in grain yield in the future was much more for DSM1 (+12 and +24% for RCP4.5 and RCP8.5, respectively) than the fixed sowing dates. Under DSM2, however, the grain yield decreased in the future at -13 and -17% for RCP4.5 and RCP8.5, respectively. Considering the performance of cultivars under future scenarios, ILC482 showed highest grain yield (1350 and 1484 kg ha−1 for RCP4.5 and RCP8.5, respectively) and Hashem produced the lowest one (600 and 655 kg ha−1 for RCP4.5 and RCP8.5, respectively) both under DSM1. Considering fixed sowing dates, ILC482 also had the highest performance in terms of grain yield in 6 March (1107 and 1170 kg ha−1 for RCP4.5 and RCP8.5, respectively) compared with other fixed sowing dates and any delay in sowing from 6 March resulted in reduced grain yield (Fig. 5).

water use efficiency was much higher in DSM1 and DSM2 (3.6 and 4.6 kg ha−1 mm−1, respectively) compared to the fixed sowing dates (Fig. 4). The interactions between sowing dates and cultivars on water use efficiency were significant (P < 0.05) (Table 3). Under DSM1, the average water use efficiency simulated were 4.7, 3.7 and 2.2 kg ha−1 mm−1 for ILC482, Bivanij and Hashem, respectively while in DSM2, the values were much higher as 6, 4.7 and 3 kg ha−1 mm−1 for ILC482, Bivanij and Hashem, respectively (Fig. 4). Under fixed sowing dates, the highest water use efficiency was simulated for ILC482 cultivar on 6 March with 5 kg ha−1 mm−1 and the lowest one obtained for Hashem cultivar on 21 April with 2 kg ha−1 mm−1 (Fig. 4). Averaged across all scenarios in the future, water use efficiency increased in all fixed sowing dates. The highest increase simulated for 6 March with 3.8 and 4 kg ha−1 mm−1 and the lowest one observed for 21 April with 3.6 and 3.4 kg ha−1 mm−1 for RCP4.5 and RCP8.5, respectively. However, the changes in water use efficiency was significant for DSMs (P < 0.05) (Table 3). For instance, in DSM1, water use efficiency increased at +11 and +25% for RCP4.5 and RCP8.5, respectively in comparison to baseline while the value decreased in DSM2 at -13 and -15% for RCP4.5 and RCP8.5, respectively (Fig. 6). Similar results were obtained under future conditions when interaction of cultivar × DSM was considered. Accordingly, ILC482 cultivar showed highest water use efficiency under DSM1 (5.2 and 5.8 kg ha−1 mm−1 for RCP4.5 and RCP8.5, respectively) and the lowest one under DSM2 was observed for Hashem cultivar with 2.3 and 2.6 kg ha−1 mm−1 for RCP4.5 and RCP8.5, respectively. It is worth noting that ILC482 also showed good performance in terms of water use

3.3. Water use efficiency at baseline and future climate change The simulation results showed a large variability in water use efficiency at baseline for all study locations, sowing dates, DSMs and cultivars. The average water use efficiency of all study locations was 3.6 kg ha−1 mm−1 (Fig. 4) and ranged from 0.5 to 9.6 kg ha−1 mm−1 depending upon sowing date, season and cultivar. The highest and lowest average water use efficiency simulated for Tabriz and Sanandaj were 6.4 and 1.7 kg ha−1 mm−1, respectively (Fig. 4). On average, the highest and lowest water use efficiency for fixed sowing dates at baseline obtained in 6 March and 21 April as 3.5 and 3.3 kg ha−1 mm−1, respectively. When sowing date delayed up to 21 April, the water use efficiency decreased -6%. In contrast, the average 7

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Fig. 6. Simulated water use efficiency (WUE) in the future compared with baseline for RCP4.5 (blue columns) and RCP8.5 (red columns) in different locations, sowing dates and cultivars. Each bar chart is the mean of 30-year simulations. (Error bars show range of WUE difference among years).

efficiency at fixed sowing date of 6 March (5.1 and 5.5 kg ha−1 mm−1 for RCP4.5 and RCP8.5, respectively) relative to other fixed sowing dates.

d) in Tabriz than Sanandaj which consequently resulted in increasing grain yield. Results of the current study also showed that both dormant seeding managements were superior to the all fixed sowing dates in terms of grain yield at baseline. This is mainly due to the fact that, days from emergence to maturity was 50 d longer (Data not shown) and rainfalls was 75 mm higher (Table. 4) in DSMs than the average of all fixed sowing dates (96 d and 80 mm). On the other hand, grain yield in DSM2 (except Khorramabad) was 15% higher than DSM1 largely because of having optimum temperature during growing season compared with DSM1 (13 and 6 °C, respectively). Greater grain yield (72 kg ha−1) was obtained in DSM1 than DSM2 in Khorramabad as the temperature during growing season of DSM1 and DSM2 was 10 and 15 °C, respectively (Table 4). The optimum temperature for chickpea is ranged between 10 to 30 °C (Soltani and Sinclair, 2011). Therefore, the temperatures out of this range can led to decreasing plant growth and ultimately grain yield. Results also indicated that grain yield was further increased when DSMs were accompanied with a mid-maturity cultivar at baseline. In other words, DSM2 × ILC482 produced much higher grain yield when compared with other combinations of sowing dates and cultivars (Fig. 4). The main reason behind this issue is that an early to midmaturity cultivars in combination with DSM2 could escape from higher temperatures and drought stress at the end of growth season in late spring and early summer as well as efficient use of rainfall over growing season. As an example, ILC482 under DSM2, although had a bit shorter days to maturity (124 d) than Hashem (131 d) (Data not shown) but resulted in more grain yield as its flowering stage was not coincided

4. Discussion 4.1. Dormant seeding of chickpea is superior to fixed sowing dates under both baseline and future climate change in terms of grain yield The evaluation of the SSM-legume model with the experimental data collected at a wide range of locations and seasons (Table 2) revealed that the model predicted dry matter, days to flowering and grain yield of chickpea reasonably well. This indicates that the model could be applied for investigating various management practices in chickpea agroecosystems under all parts of the WANA region which has similar characteristics to the study locations. The robustness of the SSM-Legumes model has been demonstrated in several previous studies over different environments for various legume species including assessment of the benefits of altered soybean drought traits across the United States (Sinclair et al., 2010), adaptation of chickpea to different latitudes of India (Vadez et al., 2013), evaluation of irrigation scenarios to improve performance of bean in the south west of France (Marrou et al., 2014) and assessment of dormant seeding in rainfed lentil in northwestern Iran (Amiri and Deihimfard, 2018) There was large variability among the study locations. For instance, simulated grain yield was highest in Tabriz (374 kg/ha) and lowest in Sanandaj (400 kg/ha) mainly owing to the longer days to maturity (15 8

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Table 4 The rainfall and temperature during growing season under different sowing dates and cultivars of chickpea from 1980 to 2010 in all study locations. Rainfall (mm) Location Ardabil Hamedan Kermanshah Khorramabad Urmia Sanandaj Tabriz Zanjan

Temperature (˚C)

Cultivar

DSM1

DSM2

6 Mar

21 Mar

4 Apr

21 Apr

DSM1

DSM2

6 Mar

21 Mar

4 Apr

21 Apr

Bivanij ILC482 Hashem Bivanij ILC482 Hashem Bivanij ILC482 Hashem Bivanij ILC482 Hashem Bivanij ILC482 Hashem Bivanij ILC482 Hashem Bivanij ILC482 Hashem Bivanij ILC482 Hashem

137 136 137 156 156 156 208 207 208 246 246 246 164 162 163 226 226 226 140 139 141 152 152 152

113 112 114 113 113 113 142 142 142 163 163 163 132 132 132 161 161 161 118 117 119 123 123 124

101 100 101 94 94 94 114 114 114 133 133 133 118 117 118 133 133 133 108 106 108 109 109 109

87 87 88 76 76 77 83 83 83 101 101 101 101 99 101 104 104 104 95 94 95 94 94 95

71 70 71 53 53 53 53 53 53 63 63 63 78 77 78 71 71 71 79 78 80 77 77 77

56 55 57 34 35 34 33 33 33 36 36 36 57 57 58 46 46 46 59 58 59 55 55 56

6.01 5.79 6.31 5.66 5.77 5.95 7.97 8.03 8.32 9.48 9.52 9.85 6.34 6.19 6.49 6.71 6.76 7.17 7.14 6.82 7.59 5.78 5.75 6.05

10.90 10.69 11.23 12.06 12.10 12.27 13.61 13.60 13.87 14.71 14.77 15.08 12.33 11.92 12.49 12.68 12.77 13.14 13.60 13.27 14.13 11.85 11.80 12.01

13.91 11.70 12.18 13.11 13.05 13.34 14.55 14.61 15.01 15.70 15.73 16.20 13.38 13.18 13.52 13.81 13.59 14.31 14.83 14.51 15.25 12.87 12.85 13.08

13.10 12.91 13.51 14.62 14.72 14.79 16.06 16.12 16.52 17.19 17.23 17.76 14.92 14.74 15.08 15.34 15.35 15.86 16.49 16.18 16.87 14.39 14.35 14.56

14.26 14.10 14.32 16.04 16.14 16.21 17.65 17.70 18.05 18.89 18.86 19.08 16.27 16.08 16.44 16.90 16.93 17.31 17.94 17.73 18.37 15.78 15.78 15.90

15.32 15.18 15.47 17.97 18.10 17.99 20.05 19.98 20.10 21.51 21.17 21.53 18.01 17.86 18.06 19.31 19.30 19.38 20.09 19.78 20.33 17.55 17.54 17.67

with higher temperatures and drought stress at the end of growing season. In addition, the average cumulative rainfall in DSM2 for both ILC482 and Hashem was 134 mm (Table 4). It is also worth noting that an early maturity cultivar (i.e. Bivanij) expected to have higher performance in terms of escaping end-season heat and drought when accompanied with DSM2, however, produced less grain yield compared to mid-maturiry cultivars because of having 6 days shorter growth season than mid-maturiry cultivars (Data not shown) and consequently less photosynthesis and biomass production. Overall, our results showed that DSM2 × mid-maturity cultivar is needed for achieving maximum yield at baseline. Under future scenarios (both RCPs), grain yield increased for all locations (except Tabriz and Urmia), DSM1 and cultivars (except Hashem) compared with baseline (Fig. 5). This could be highly associated with the increasing CO2 concentration to 571 ppm which neutralized the negative effects of high temperature (Fig. 7) during the growing season and consequently increased grain yield. The amount of increase, however, was higher for RCP8.5 than RCP4.5. Under DSM1, the average cumulative rainfall over growing season in all study locations except Tabriz and Urmia increased 15 mm in comparison to baseline for both RCP4.5 and RCP8.5, respectively (Fig. 8). However, in Tabriz and Urmia, the average cumulative rainfall decreased 6 mm in comparison to baseline for RCP4.5 and RCP8.5, respectively (Fig. 8) which led to decreasing grain yield (Fig. 5). In addition, grain yield of both dormant seedings (DSM1 and DSM2) were greater than the all fixed sowing dates for both RCP4.5 and RCP8.5 largely because days to maturity extended as 47 and 52 d longer (Data not shown) and the amount of rainfall increased as 76 and 85 mm (Fig. 8) in DSMs than fixed sowing dates for RCP4.5 and RCP8.5, respectively. Under future scenarios, the grain yield of DSM1 was much higher than DSM2 because of higher days to maturity (31 d more) for both RCP4.5 and RCP8.5 scenarios. Furthermore, the amount of rainfall increased 47 mm for both RCP4.5 and RCP8.5 scenarios. The interaction between sowing dates and cultivars was significant under future condition (P < 0.05) (Table 3) so that, DSM1 × ILC482 produced the highest grain yield (1417 kg ha−1, average of both RCPs) while DSM1 × Hashem resulted in the lowest grain yield (620 kg ha-1, average of both RCPs, Fig. 5). Under DSM1 × ILC482, days to maturity

were 152 and 149 d for RCP4.5 and RCP8.5, respectively while the values for DSM1 × Hashem were simulated 158 and 155 d for RCP4.5 and RCP8.5, respectively. However, for the longer cycle cultivar (i.e. Hashem), the chickpea stands experienced warmer temperatures over growing season (14 and 15 °C for RCP4.5 and RCP8.5, respectively) while in the shorter cycle cultivar, the crops exposed the bit cooler temperatures over growing season (10 and 11 °C for RCP4.5 and RCP8.5, respectively). Also, for the longer cycle cultivar × DSM1, chickpea canopy coincided with soil water depletion during the critical grain-set and grain-filling phases at the end of growth season. Overall, our results showed that DSM1 × mid-maturity cultivar would be needed for achieving maximum grain yield for both future scenarios. Mohammed et al. (2017) in Northeastern Ethiopia showed that early sowing × mid-maturity cultivars is important in areas where high temperature is a major crop production constraint in the present and future climate conditions. They also reported that high temperatures could affect chickpea productivity by altering its development and growth stages. On the other hand, Soltani and Sinclair (2012a) concluded that under future condition, earlier maturity through shorter vegetative period alone or in combination with longer grain filling period resulted in increased grain yield (13–18%) in chickpea producing regions of Iran. 4.2. Water use efficiency can be boosted in chickpea agroecosystems by adopting dormant seeding under both baseline and projected climate Findings of the present study at baseline, indicated that water use efficiency was much higher under DSMs compared with fixed sowing dates (Fig. 4). In addition, there was a big difference between DSM2 and DSM1 so that water use efficiency was 1 kg ha−1 mm−1 higher in DSM2 than DSM1 mainly because of 157 kg ha-1 higher grain yield and 25 mm lower seasonal evapotranspiration (Data not shown). When the interaction of sowing date and cultivar was considered, the simulation results showed that water use efficiency under DSM2 × ILC482 was 1.3 kg ha−1 mm−1 greater than DSM1 × ILC482. This greater water use efficiency for DSM2 × ILC482 was resulted from lower seasonal evapotranspiration (20 mm lower than DSM1 × ILC482) (Data not shown) which was mainly due to shorter days to maturity (36 9

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Fig. 7. Absolute mean temperature during growing season in the future (2040–2070) compared to the baseline. The temperatures simulated for different sowing dates and cultivars under RCP4.5 (blue columns) and RCP8.5 (red columns) in all study locations. Each bar chart is the mean of 30-year simulations.

d) in DSM2 compared with DSM1 (Data not shown). On the other hands, the average temperature over growing season was much closer to optimal in DSM2 (13 °C) than DSM1 (6 °C) (Table 4) which led to increased grain yield and water use efficiency. Under future climate change, water use efficiency was 0.2 and 0.4 kg ha−1 mm−1 higher in DSM1 than DSM2 for RCP4.5 and RCP8.5, respectively (Fig. 6). As mentioned earlier higher water use efficiency in DSM1 in the future was largely resulted from 45 and 143 kg ha-1 higher grain yield for DSM1 compared with DSM2 for RCP4.5 and RCP8.5, respectively (Fig. 5). Almost similar results were obtained when the impact of cultivar was included. DSMs × cultivars were better than fixed sowing dates × cultivars. The highest water use efficiency (5.5 kg ha−1 mm−1) obtained in DSM1 × ILC482 largely due to better environmental conditions such as average temperature over growth season (8.5 °C), higher cumulative rainfall (184 mm) (Fig. 8) which resulted in superior grain yield (1417 kg ha−1) and lower evapotranspiration (249 mm) in both scenarios (Data not shown). Our results showed that in the future conditions, water use efficiency could be boosted up to 5.2 and 5.8 kg ha−1 mm for RCP4.5 and RCP8.5, respectively by applying dormant seeding management in combination with a mid-maturity cultivar as an optimal adaption option. However, it is worth mentioning that these values are too close to the minimum value of water use efficiency reported by FAO for pulse crops under rainfed conditions worldwide (2-16 kg ha−1 mm−1) (Oweis et al., 2004; Oweis and Hachum, 2006) meaning that further investigations are needed to increase water use efficiency by improving fertilization management, plant density and identifying better adapted germplasms to the study locations which are similar to WANA regions. Other

researchers reported almost similar results by assessing water use efficiency under climate change. For example, Gholipoor and Soltani (2009) in evaluating the effect of climate change on chickpea production in ICARDA concluded that spring sowing dates will have the lowest grain yield in the future due to declining rainfall and increasing terminal drought stress. They also suggested that dormant sowing can increase grain yield and water use efficiency through both increasing water availability for chickpea in early stages and lower vapor pressure deficit over growth season. Amiri and Deihimfard (2018) also indicated that in the arid and semi-arid environments of Iran, DSM systems and planting of short- cycle lentil cultivar could save water and increased lentil water use efficiency. In an another study, Hajjarpoor et al. (2014) in simulating the effect of climate change on production of rainfed chickpea in the North and West of Iran concluded that chickpea grain yield and water use efficiency would be raised between 37–89% and 35–81%, respectively in rainfed conditions. Therefore, higher WUE is important in the future climate because water availability will be lower. 5. Conclusion The results of model evaluation showed that the SSM-legume model can be used to predict chickpea production limitation efficiently in baseline and in the future. The results also revealed that dormant seeding managements (DSMs) as an adaptation strategy enabled chickpea to better exploit rainfall during growing season when combined with mid-maturity cultivar through both water availability in early stages and lower vapor pressure deficit. However, in future, the combination of DSM1 (dormant seeding around 20 December) and 10

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Fig. 8. Averaged changes in annual cumulative rainfall during growing season under different sowing dates and cultivars of chickpea of 2040–2070 for RCP4.5 (blue columns) and RCP8.5 (red columns) in all study locations. Each bar chart is the mean of 30-year simulations.

(ILC482) mid-maturity cultivar led to increase grain yield and water use efficiency 36 and 15%, respectively in comparison to baseline.

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