سجاد رحیمی مقدم

Removing the gap between the yield that is currently obtained by farmers and the yield that can be obtained by using the best water, soil, and crop management practices (yield gap) is the key solution to overcoming the nutritional challenge of the growing world population. Wheat has played an important role in the economy and food security of the world over the history. Considering the importance of the wheat crop in food security and human feeding, as well as the importance of Lorestan province in producing wheat crop in Iran, the current research was carried out using the modeling method in order to estimate the yield gap of irrigated wheat agro-ecosystems of Lorestan province.

The current research was conducted in 6 locations in Lorestan province, Iran (Aleshtar, Aligudarz, Borujerd, Khorramabad, Kuhdasht, and Pol-e Dokhtar). To evaluate the APSIM-wheat model for the Chamran cultivar, some independent field experiments were used under different treatments, including planting date, irrigation, and nitrogen regimes. nRMSE, d-index, and MBE indices were used to evaluate the crop model. Then, the management, soil, and climate data of the studied locations were collected. To find the most optimal sowing date, an initial simulation experiment set was performed. After obtaining the optimal sowing date for different locations, the attainable yield (85% of the potential yield) was simulated. Finally, from the difference between attainable yield and actual yield, the total yield gap for the locations was obtained. Also, the contribution of different management practices, including nitrogen, irrigation, and other reducing and limiting factors, was calculated from the total yield gap. In the current research, OriginPro software was used for all statistical analysis and drawing of figures.

The results of the model calibration showed that the APSIM-wheat model was able to simulate the days to flowing, biomass, and grain yield accurately. The normalized root mean square error for days to flowering, biomass, and grain yield was 5.13, 5.29, and 7.87%, respectively. The model validation results of the model also showed that the model simulates the grain yield well (10.3%). In the initial simulation, the best production potential (8433 kg ha-1) was related to the October 15 sowing date. Attainable, water-limited, and nitrogen-limited yields were equal to 7179.2, 6302.7, and 4212.5 kg ha-1 in Lorestan province. Across different locations, the water-limited yield ranged from 1.4722 to 2.7448 kg ha-1, followed by attainable yield (from 6537.5 to 7982.7 kg ha-1) and nitrogen-limited (from 2.2 3850 to 4414.5 kg ha-1). The total yield gap of irrigated wheat in Lorestan province was equal to 4177.5 kg ha-1. The results also showed that, in general, throughout the studied locations and years, the contribution of nitrogen, water, and other reducing and limiting factors in Lorestan province was equal to 69, 22, and 10% of the total yield gap. There were many changes among different locations in terms of the total yield gap in Lorestan province so it varied from 2661.8 kg ha-1 (Pol-e Dokhtar) to 5608.4 kg ha-1 (Aligudarz). In terms of yield gap due to water limitation, the highest amount was related to Pol-e Dokhtar (31%), and the lowest value was related to Khorramabad city (14%). For the yield gap caused by nitrogen limitation, Aligudarz had the largest share (77%), while the lowest share of this limitation was obtained for Khorramabad (59%). The maximum share of the yield gap caused by other limiting and reducing factors was also recorded in Khorramabad (27%), while its lowest value was calculated in Aligudarz and Aleshtar (without restrictions).

In general, the total yield gap of irrigated wheat in agro-ecosystems of Lorestan province is equal to 59%. Also, the results revealed that the largest share of the total yield gap is related to the lack of optimal application of nitrogen management (time of nitrogen application), and if the optimal management of nitrogen fertilizer is applied, the total yield gap will be significantly reduced at all studied locations in Lorestan province.

Drought and heat stress as the most important limiting factors in the production of crops and finally food security, have resulted from changing climate due to human interventions in increasing greenhouse gas emissions. It is also simulated that each 1 ◦C increase in temperature caused a 6% decline in global wheat production. The increase in temperature can further decline grain yield when occurred during the reproductive stage. The simultaneous effects of drought and heat during the growth stages, especially the flowering and grain filling stages, which are the most sensitive, can be more harmful than the individual effects and lead to a significant reduction in yield. The current study aimed at determining the optimal cultivar × sowing date in different climates to coincide the flowering date with optimum climaticconditions (temperature and rainfall) using a simulation modeling approach.

The current study was conducted in eight locations with different climates according to the GYGA (Global Yield Gap Atlas) methods. Dezful and Shushtar had warm and dry climates with high fluctuations in average seasonal temperature, Hamedan and Nahavand had mild and dry climates with medium fluctuations in average seasonal temperature, Khorramabad and Aligodarz had mild and dry climates with high fluctuations in average seasonal temperature and Zanjan and Khodabendeh had cold and dry climates with medium fluctuations in average seasonal temperature. Choosing the study locations was based on both areas under wheat cultivation and the diversity in their climates. In this research, several management methods including 4 sowing dates, 4 initial soil water​​, and 3 cultivars in 8 locations with different climates for 37 years (1980-2016) were investigated. A modified version of the APSIM-Wheat model was used in which a heat stress module could capture the impacts of heat stress on grain number and weight. The simulations were conducted under drought stress alone (rainfed) as well as the simultaneous drought and heat stress.

The average grain yield under drought conditions was simulated at 2.99 tons per hectare, while the yield under simultaneous drought and heat stress was 2.44 tons per hectare, indicating a reduction of approximately 0.5 tons per hectare due to the combined effect of heat and drought stress. The mid-maturity cultivar had the highest yield reduction of 52% while the very early-maturity cultivar showed the lowest yield reduction of 0.16%. The wheat flowering time in cold regions occurred after the completion of flowering in warmer locations. In temperate climates, the occurrence of flowering dates was more extensive than in other locations. Overall, any delay in emergence under rainfed resulted in further yield reduction in all cultivars and locations.

Under warm climates, an early-maturity cultivar along with early sowing could provide better environmental conditions for photosynthesis and plants could escape from terminal drought and heat stresses. In contrast, in cold climates, any delay in flowering date increases yield. In mild regions, using a mid-maturity cultivar resulted in late flowering dates and higher yields in spite of coinciding wheat with heat and drought for a short period of time.

In recent years, enhancing crop grain yield and water use efficiency have become among the important objectives of researchers. Therefore, this study was conducted to identify the optimal combination of cultivar and irrigation in autumn rapeseed. To do this, the current research was carried out using APSIM simulation model on three autumn rapeseed cultivars (Hyola308, Hyola401, and RGS003) and four irrigation treatments (full irrigation, withholding irrigation at flowering stage, withholding irrigation at pod initiation stage, and withholding irrigation at grain filling period) in four counties of Lorestan Province. The results showed that the highest grain yield in Khorramabad, Pol-e Dokhtar, and Kuhdasht was obtained with Hyola401 cultivar and full irrigation (5361, 5378, and 4488 kg.ha-1, respectively), while in Aleshtar, cultivation of Hyola308 cultivar and withholding irrigation in one of the treatments led to the highest yield (2634 kg.ha-1). The results also showed that Hyola401 cultivar and withholding irrigation at flowering stage in Pol-e Dokhtar and Khorramabad and full irrigation in Kuhdasht (0.457, 0.398, and 0.307 kg.m-3, respectively) led to the highest crop water use efficiency in these counties. In Aleshtar, Hyola308 cultivar and withholding irrigation at flowering stage resulted in the highest crop water use efficiency (0.239 kg.m-3). Overall, the results showed that for these rapeseed agro-ecosystems, the air temperature in each county played a key role in choosing the optimal cultivar (i.e. cultivars with higher percentage of fertile pods under water stress conditions) and proper irrigation management.

Legumes are one of the most important sources of protein, minerals, and calories in the diet of people in developing countries. Chickpea is known as one of the most important legumes influenced by management and genetic factors. Accordingly, the current research aimed to assess the effect of planting density on improving economic yield and yield components of rainfed chickpea cultivars in Khorramabad County at the research farm of Lorestan University in 2023. Factorial arrangement of the treatments was set up as a randomized complete block design with three replications. Factors included plant density in five levels (20, 30, 40, 50, and 60 plants per square meter) and cultivars in three levels (Adel, Saeed, and Mansour). Based on the obtained results, the interaction effect of density and cultivar on the distance of the first secondary branch from the ground, the distance of the first pod from the ground, the number of secondary branches, the number of pods per plant, the number of seeds in a pod, and the number of seeds per plant was significant at the level of 1% and was not on economic and biological yields and 100-seed weight. However, the simple effect of density was significant on economic and biological yields at the level of 5%. Also, the simple effect of cultivar was significant on economic and biological yields at the level of 5%. Based on the mean comparison, the highest economic yield (3390.5 kg/ha) was obtained at a density of 50 plants per square meter and the lowest (2678.3 kg/ha) at a density of 20 plants per square meter showing 26.6% superiority. By comparing the regression between economic yield and different densities for various cultivars, the best economic yield of Adel cultivar (3394 kg/ha) was obtained at a density of 44.98 plants per square meter (approximately 45). Also, the maximum economic yield for the Saeed cultivar (3264 kg/ha) was at a density of 59.83 plants per square meter (approximately 60), and for the Mansour cultivar (3430 kg/ha) at a density of 37.85 plants per square meter (approximately 38). Accordingly, the Mansour cultivar with a density of 40 plants per square meter is recommended for the growers in Khorramabad.

 Legumes are the second food source after cereals, which provide almost a quarter of protein for developing countries. Lentil is recognized as one of important legumes due to its favourable characteristics. The lintile production, like to other crops has been influenced by three factors: management, genetics, and environment. Improving crop production can be achieved by optimizing these factors. Thus, paying attention to the above-mentioned factors can reduce the severe climatic effects on crop production. Among the above-mentioned factors, management strategies e.g., optimal sowing date and using the optimal cultivar are considered to improve crop production. Initial soil moisture, which can affect crop germination, establishment and ultimately growth and yield, is another important strategy. Accordingly, the current research was conducted in order to simulate the effects of cultivar, sowing date, and initial soil moisture on lentil grian yield in different locations of Lorestan province.

 The study locations were Aligudarz, Nurabad, KhorramAbad, and Kuhdasht. Simple Simulation Models-iCrop2 (SSM-iCrop2) was used to simulate the lentil growth and development. The data required to run model included climatic, soil, management, and crop data. Daily long-term climatic data including maximum and minimum temperature, rainfall, and radiation were collected from Iran Meteorological Organization. The soil data included soil depth, soil water content at wilting point, soil water content at field capacity, and saturation water content, which were obtained from different data collection in the Ministry of Agriculture and Agricultural, the Natural Resources Research and Education Centers, and soil laboratories at each location and Food and Agriculture Organization and Global yield Gap Atlas.The management data such as palnt density, tillage, rows distance, and sowing depth were obtained by local experts from the Ministry of Agriculture and Agricultural and the Natural Resources Research and Education Centers at each location. The crop data e.g., the specific genetic coefficients of each cultivar were obtained form Amiri and Deihimfard (2018). The study treatments consisted of four sowing dates (21 January, 12 February, 4 March, and 30 April), two cultivars (early-maturity and late-maturity) and four initial soil moisture (32, 36, 42, and 58 mm). The model was run for 41 years (1980-2020). Initial soil water contents and sowing dates were obtained from a preliminary simulation experiment.

Averaged across all treatments, the highest grain yield was obtained at KhorramAbad (388 kg ha-1) due to higher rainfall during the growing season. In addition, the grain yield difference among the sowing dates ranged from kg ha-1 on 30 April to 364 kg ha-1 on 21 January. The reason for the higher grain yield on 21 January sowing date was the higher cumulative rainfall season (149.3 mm vs. 99.2 mm) and the lower mean temperature (22.9 °C vs. 23.9 °C) during the growing season compared to other sowing dates. Across locations, sowing dates, and cultivars, the highest grain yield (263 kg ha-1) was simulated under initial soil moisture of 58 mm. Also, on average across sowing dates, initial soil moisture contents, and locations, the early-maturity cultivar simulated more grain yield than late-maturity cultivar (405 kg ha-1 vs. 63 kg ha-1) due to the avoidance of drought stress at the end of the growing season. Considering different interactions, on average across locations, the highest grain yield was simulated by 704 kg ha-1 in the combination of early-maturity cultivar, early sowing date (21 January), and initial soil moisture of 58 mm.

 In general, the results indicated that the lentil grain yield varied among the various study treatments (sowing date, cultivar, and initial soil moisture) as well as different locations of Lorestan province. Lentil growers can increase the lentil production in Lorestan province and study locations by using the interaction of early planting date (21 January) ´ early-maturity cultivar (Kimia) ´ higher initial soil moisture (58 mm). It should be noted that the current research was conducted under water-limited condition and it is suggested that researchers focus on other limitimg and reducing factors in the future studies.

Crop simulation models provide the possibility to study the effect of agronomic management practices on agricultural production activities in a given location. The models are mechanistic and process-oriented that are able to simulate different agricultural systems (including arid and semi-arid areas such as Iran) through different sub-models (biological, environmental, managerial, and economic sub-models) connected with the main engine of the models. The APSIM-Barley model can be used to evaluate the management practices on barley crop. The current research was carried out to evaluate the APSIM-Barley model in relation to simulating the growth, development, and yield of three barley cultivars (Azaran, Jolgeh, and Bahman) under different conditions of nitrogen and irrigation supplies.

 In order to evaluate the APSIM-Barley model regarding simulating and quantifying the phenological stages, biomass, and grain yield of different barley cultivars, some independent experiment datasets were used. For crop model calibration, an experiment was conducted in a factorial split plot design at the Agricultural Research Center of Hamedan in 2019. The main factor consisted of three irrigation levels (30-40, 60-70, and 90-100% of the field capacity) and sub-factors included cultivar levels (Azran, Jolgeh, and Bahman as early, mid, and late cultivars, respectively) and three nitrogen levels (zero, medium, and optimum). For model validation, another series of independent datasets in different years consisting of published articles and research projects were used. For assessing the efficiency of the crop model and comparing the simulated and measured values, R2, nRMSE, d-index, and MBE indices were used and OriginPro software was considerd for all statistical analysis and drawing of figures.

 The simulation results of APSIM-Barley model in the calibration step showed that nRMSE for days to flowering and maturity was 2.43 and 2.4%, respectively.  Also, under full water and nitrogen conditions, nRMSE for the leaf area index of Azaran, Jolgeh and Bahman cultivars was 13.5, 14.1, and 10.7%, respectively, and under severe water and nitrogen stresses, it was 16.5, 18.6, and 26.5% respectively. At this step, and nRMSE and d-index for biomass and grain yield were 24.9% and 0.98 and 15.2% and 0.96, respectively.  In model validation step, nRMSE, d-index, R2, MBE were 15.02%, 0.96, 0.82 and 0.34 t ha-1, respectively, for grain yield.

In general, the results showed that the APSIM-Barley model was able to simulate the growth, development, and grain yield of different barley cultivars with acceptable accuracy under different water and nitrogen management conditions in different years and regions. Therefore, the APSIM-Barley model can be used as a reliable tool in future studies such as climate change assessment, yield gap analysis, agricultural zoning, and etc. by other researchers.

Due to the lack of water resources and excessive evaporation in the country, it is necessary to have a detailed irrigation program and a suitable management method. The present research was conducted to investigate the effect of superabsorbent and mulch in Khorramabad in July 2022 in a factorial combination with a completely randomized design in three replications. The first experimental factor was irrigation water treatment in 4 levels including irrigation that provided 100% water requirement (I100), 80% of crop water requirement (I80), 60% of crop water requirement (I60), and 40% of crop water requirement (I40). The second factor included different corrective materials including plant mulch (M), superabsorbent (S), and control treatment (I). The results showed the maximum amount of wet and dry yield and crop height was related to I100-M treatment, i.e. 100 percent water requirement and compost corrective material, which were 89.52 tons per hectare, 29.42 tons per hectare, and 2.27 meters. The maximum wet and biological productivity for I40-S was calculated as 14.24 kg of wet matter per cubic meter of water and 4.75 kg of dry matter per cubic meter of water. The lowest wet and dry yields were related to I40-M, which decreased the yield of the control treatment by 6.5 percent and 0.9 percent. The lowest productivity was related to the I100-S treatment, which was calculated as 3.13 kilograms per cubic meter of water for biological productivity and 9.14 kilograms per cubic meter of water for wet weight productivity. In general, mulch had a better performance in the treatments where the water stress was low, but when the water stress increased, the performance of the mulch treatments decreased. In the superabsorbent matter, the treatments with complete irrigation or with less stress, yield decreased, but the treatments with increased stress showed better results than most of the corrective materials and the control treatment.

After soybean and oil palm, rapeseed has the third place in supplying vegetable oil in the world, so that it accounts for 14.7% of the total production of vegetable oil. The current research was carried out using modeling approach in order to simulate the replacement of wheat cultivation with rapeseed cultivation in terms of water and economic productivity in four locations (Aleshtar, Khorramabad, Pol-e Dokhtar, and Kuhdasht) in Lorestan province. APSIM model was used to simulate the growth and yield of wheat and rapeseed crops. The model validation results showed that it accurately simulates wheat and rapeseed grain yield with nRMSE of 8.6%. The results showed that wheat cultivation (3524.4 kg) had a higher grain yield than rapeseed cultivation (2750.2 kg). In addition, wheat cultivation system (1.45 kg m-3) compared with rapeseed cultivation (1.15 kg m-3) had higher water productivity. However, the difference between these two cultivation systems in terms of irrigation productivity was not considerable (0.11 kg m3). Also, economic productivity and net income of rapeseed cultivation system were 0.220 million tomans per cubic meter and 59.9 million tomans per hectare, respectively, while they were 0.014 million tomans per cubic meter and 41.1 million tomans per hectare, respectively, for wheat cultivation system. In general, the results approved that moving from wheat cultivation to rapeseed cultivation can be environmentally and economically sustainable in the agro-ecosystems of Lorestan province, especially in Khorramabad county.

Climate changes and indiscriminate harvesting as well as overgrazing of different Iranian rangelands have caused the destruction and reduction of vegetation in the rangelands. This has been especially severe for medicinal plants, in the last decade. By identifying the plant communities and analyzing the ecological nature of each species, it is possible to plan for optimal management based on ecological principles for the present and the future. Based on the above-mentioned issues, the current research was conducted regarding the ecological evaluation of cultivating medicinal plants in different rangelands of Lorestan Province through analytic hierarchy process (AHP) approach and ArcGIS software in order to restore the rangelands and protect the medicinal plants.

The studied area was the rangelands class 1 of Lorestan Province in the west of Iran. Required data included soil, long-term climatic (1970-2020), and topographic data. Climatic and topographical data were obtained from the WorldClim database. Soil data were gathered by the International Soil Reference and Information Centre (ISRIC). Studied medicinal plants were Asafoetida, Galbanum, Liquorice, Keluss, and Borago Oxtongue flower. The judgment of 15 experts was used for pairwise comparisons and weighting of ecological data for each medicinal plant using the Delphi method, and the weight of each of the options and criteria was calculated in Expert Choice11 software. Then, to match the environmental requirements of the medicinal plants with the characteristics of the rangelands, the natural breaks –(Jenks) classification method was employed in ArcGIS software.

The results showed the major role of annual mean temperature with a weighting coefficient of 0.309 in cultivating medicinal plants, followed by elevation and annual cumulative rainfall (0.212). Also, among the studied factors, the lowest weight belonged to soil organic carbon percentage (0.057). For annual mean temperature, more than 53% of rangelands in Lorestan Province experienced a temperature between 10 and 15 °C. The rangelands received an average of 350 mm. More than 60% of the rangelands were located at an elevation between 2000 and 3000 m. For slope, more than 54% of rangelands had a slope between 25 and 60%. The soil of Lorestan rangelands had a heavy texture, so that more than 75% of the rangelands had clay loam soil texture. More than 59% of the rangelands had soil organic carbon of 2-5%. Also, more than 74% of the rangelands had a pH between 7.7 and 7.9. Also, the rangelands were divided into three classes (very good, good, and medium) in terms of potential and having suitable ecological conditions for growing Asafoetida, Galbanum, Liquorice, Keluss, and Borago Oxtongue flowers. Ninety-eight, 97, and 94% of the rangelands were placed in the very good and good categories for cultivating Asafoetida, Galbanum, and Keluss, respectively. However, these values were 26 and 27% for planting Liquorice and Borago Oxtongue flower, respectively.

Overall, the rangelands of Lorestan Province had a good potential for growing Asafoetida, Galbanum, and Keluss, and they had a medium potential for cultivating Liquorice and Borago Oxtongue flowers. However, the suitability of medicinal plants cultivation varied based on the geographical position of the rangelands, and the recommendation for cultivating each medicinal plant should be considered according to each geographical zone.

Chickpea is one of the important legumes in West Asia and North Africa. It is also one of the important legumes in rainfed agricultural systems in these regions. Although Iran has the fifth rank in terms of under chickpea cultivation, it ranks 47th in terms of average grain yield per hectare (400 kg/ha). In dry areas of Iran, chickpea yield is highly dependent on the amount of rainfall and its seasonal distribution. Therefore, annual changes in rainfall make it difficult to employ a unitary agricultural management that leads to maximum crop yield with a predictable trend over the years. Chickpea is the most important legume crop in Iran, accounting for over 50% of cultivated legume lands, and is primarily planted under rainfed conditions. Chickpea production is significantly lower than other crops.

Simulation of the potential yield of rainfed chickpea was carried out  using the SSM-Chickpea model and rainfed chickpea yield resulting from farmers' crop management practices, including planting date, plant density, and nitrogen in 12 main chickpea growing regions were evaluated. These experiments consisted of 9 independent field experiments. In the current research, the model was evaluated based on the comparison of measured and simulated values for grain yield. Statistical indices including determination coefficients (R2) and normalized root mean square error (nRMS) were used.

The simulation with model SSM-Chickpea showed that the average potential rainfed chickpea yield was 1200 kg/ha. The simulation results also indicated that the optimal planting date for all regions was mid-December, as dormant seeding management, led to an increase in grain yield of about 10% in Urmia and 60% in Khorramabad compared with the spring sowing date. Optimal plant density was 20 plants/m2 resulted in a 27% increase in grain yield in Islamabad-e Gharb. Regarding the plant nutrition, the results showed that using 20 kg/ha as a starter fertilizer led to up to a 10% increase in grain yield in the Eqlid and Khorramabad. In all studied regions cv. Bivanij had 23% greater grain yield comparing ILC 482 and a large part of chickpea grain yield potential depented on agricultural management practices in general and planting date, specifically.

 Water is considered as one of the main factor driving agricultural activities. It is predicted more than 67% of the world's population will suffer from water shortages in their habitats that by 2050. Therefore, maximizing water efficiency is the best solution to deal with the problem. Water productivity is an important factor in evaluating the efficiency of irrigated and rainfed systems. Sugar beet is one of the strategic crops in Iran which its area under cultivation has been decreased in the last two decades, especially in Khorasan Razavi Province. The high water requirement of sugar beet and the problem of water shortage during spring and summer are the most important reasons for the decrease in the area under cultivation of this crop. Given the crisis in water supply for crop production, increasing water productivity in irrigated agroecosystems can be considered as a suitable strategy to increase the area under cultivation and yield of crops. In recent years, different studies have been conducted to increase the water productivity of different crops using simulation models.

 In this study, root yield and water productivity of sugar beet were evaluated in response to three different sowing date treatments (20 February, 21 March, and 21 April) and four irrigation interval treatments (10 days, 12 days, 14 days and treatment with model according to the crop water demand) in Neyshabur, Sabzevar, Quchan, and Torbat Jam locations. For this purpose, the SUCROS simulation model and long-term meteorological data were used to estimate the growth and production of sugar beet. In addition, the trend of sugar beet daily water demand as well as changes in soil water in different treatments during the growing season was extracted and analyzed.

 The results showed that the highest yield of sugar beet (63.80 t ha-1) was obtained in Sabzevar by sowing on 20 February and irrigation with 10 days intervals while in Torbat Jam, Neyshabur, and Quchan, the maximum root yield (66.44, 109.38 and 112.15 t ha-1, respectively) was achieved by sowing on 20 February and irrigation based on crop water demand.  In contrast, sowing sugar beet on 21 April with 14 days of irrigation intervals in all studied locations resulted in the lowest yield among different treatments. Water productivity comparison in different locations showed that in Sabzevar, Torbat Jam, and Neyshabur, the treatment of the 20 February sowing date and irrigation based on crop water demand resulted in maximum water productivity (4.08, 4.52, and 8.10 kg m-3, respectively) and in Quchan, the treatment of 20 February sowing date and 14 days irrigation interval with 7.70 kg m-3 led to the maximum water productivity. The results also indicated that in Sabzevar, the lowest water productivity was obtained by sowing sugar beet on 21 April and irrigation with 14 days intervals, and in Torbat Jam, Neyshabur and Quchan, the minimum water productivity was simulated by sowing date of 21 April and 10 days irrigation intervals. Evaluation of the relationship between the length of growth season (as a criterion of sowing date) with sugar beet yield and water productivity in all locations showed that there were significant relationships among these variables and the length of growth season at 1% of probability level, which indicated the importance of early cultivation in increasing the sugar beet production and water productivity in Razavi Khorasan Province.

The findings of this study suggest that modifying the sowing date, in comparison to adjusting irrigation intervals, may have a more significant impact on enhancing sugar beet yield and water productivity. Additionally, in warmer locations like Sabzevar and Torbat Jam, exploring alternative options such as autumn sugar beet cultivation is advisable. In light of the constraints posed by limited water resources, it is recommended that researchers prioritize investigations into water productivity rather than solely focusing on maximizing crop yield.

Acknowledgments:

The authors are grateful to Dr. Reza Deihimfard from Shahid Beheshti University, Iran for providing valuable comments and his advice on methodology.

Iran is located in the dry belt of the earth and its rainfall is one third of the global average. Therefore, proper management of water resources is necessary, especially in the agricultural sector. For this purpose, in 2022, a research was conducted with the aim of investigating the effect of several soil amendments in Khorram Abad region, in the research farm of Lorestan University. The experiment was factorial with a randomized complete block design in three replications. Treatments were irrigation water at 4 levels of I1=100%, I2=80%, I3=60%, and I4=40% of the water requirement and different soil moisture amendments including vermicompost (C) 6 t/ha, biochar (B) 1.5 t/ha, superabsorbent (S) 63 kg/ha, and organic mulch (M) 7.5 t/ha and the control treatment (I). Results showed that the highest productivity, biological yield, and plant height were related to I1-C treatment, which were 126.71 t/ha, 46.27 t/ha and 2.35 meters, respectively. The highest water productivity and biological productivity at the probability level of 5% was I2-C, which was calculated as 16.79 kg of fresh fodder/m3 of irrigation water and 5.9 kg of dry matter/m3 water. In general, vermicompost and biochar also increased the fresh and biological yield, height, and water productivity of corn. The use of mulch in 100% and 80% treatments had better effect, but with the increase in water stress (i.e. I3 and I4), effect of mulch decreased (5.3% and 1% relative to the control). Superabsorbent in I100, I80, I60 treatments showed lower effect (9.3%,7.2%, 3% less fresh weight than the control, respectively). However, with increasing stress, I4 had better results (7.6% higher fresh yield than the control). Therefore, in Khorram Abad region, the amount of 6 t/ha of vermicompost and 80% of the water requirement applied by drip tape irrigation for fodder corn is recommended to increase the production rate while saving 20% of water consumption.

The present research was conducted to simulate the impact of climate change on irrigated barley in Lorestan and Hamadan provinces. For this purpose, nine regions including Aligudarz, Borujerd, Khorramabad, Kuhdasht, Pol-e Dokhtar, Hamedan, Malayer, Nahavand, and Kabudarahang were selected in the two provinces. The APSIM-barley model was employed to simulate the growth and development of irrigated barley. Firstly, the APSIM-barley model was evaluated using two independent field experiments. The first experiment was conducted in Khorramabad to calibrate the crop model; while the second experiment was done in Hamedan to validate the crop model. The future climate was projected using the AgMIP methodology under two scenarios (RCP4.5 and RCP8.5) for the period 2040-2070. The results of the model validation showed that the crop model was able to simulate barley yield and biomass with nRMSE of 16.4% and 13.3%, respectively. Additionally, the results indicated that on average across the study locations, barley grain yield would decrease by 3.8% and 5.7% under RCP4.5 and RCP8.5, respectively. However, in Pol-e Dokhtar, barley grain yield is projected to increase by 1.3% and 4.8% under RCP4.5 and RCP8.5, respectively. Based on these findings, adaptation strategies should be considered in the future to prevent the reduction of irrigated barley yield in the studied provinces.

A proper understanding of crop yield and corresponding water consumption is important to reach the sustainable agriculture. Water footprint (WF) is one of the indicators for describing water consumption and water use efficiency so that its accurate estimation in different climates and cropping systems (i.e. rainfed and irrigated) could be effective in managing water resources. WF consists of four components included green water, blue water, gray water, and white water. The magnitude of WF and its components is notably affected by deferent factors such as soil type, climate, and water management practices. Accordingly, the current study aimed to assess water footprint and its components in various irrigated and rainfed wheat agro-ecosystems of Iran. In the current study, water footprints and its components (blue, green, gray, and white waters) were simulated for irrigated and rainfed wheat agro-ecosystems in six locations in Iran (Ardebil, Hamedan, Sanandaj, Tabriz, Urmia, and Zanjan) during the 37-year period (1980-2016) using APSIM (Agricultural Production Systems sIMulator) crop model. To do this, four inputs for the crop model including crop characteristics (i.e. genetic coefficients), climatic data (i.e. maximum and minimum temperatures, rainfall, and solar radiation), soil characteristics (i.e. soil water content at wilting point and field capacity, saturation water content and bulk density), and management practices (i.e. sowing date, nitrogen fertilizer, irrigation, and etc.) were gathered to run the crop model. Outputs from the model were used to estimate WF components. The outputs included grain yield, cumulative net irrigation, and evapotranspiration in irrigated and rainfed systems, respectively. The results showed that grain yield and total WF were 2.4 t ha-1 and 1498 m3 t-1 for rainfed, and 5.7 t ha-1 and 1393 m3 t-1 for irrigated systems. The magnitude of WF was not only associated with type of cropping system (i.e. irrigated and rainfed) but also strongly region-dependent. Shifting from rainfed wheat cultivation to irrigated one increased WF of +15.2, +14.6, +28.1 % for Tabriz, Urmia, and Zanjan and decreased WF of -5.94, -9.03, and -9.12 % for Ardebil, Hamedan, and Sanandaj, respectively. In irrigated wheat systems, the shrare of green, blue, gray, and white waters in total WF simulated by 32.3, 24.2, 20.9, and 22.6 %, respectively. However, in rainfed wheat systems, the shrare of green and gray waters in total WF estimated by 90.3 and 9.60 %, respectively. The findings of the current research showed that shifting from irrigated to rainfed cultivation of wheat in some areas of northwestern Iran (i.e. Ardebil, Hamedan, and Sanandaj) could lead to a significant reduction in blue water consumption (less irrigation) and subsequently increase sustainability in these areas.

Canola is one of the most important oilseed crops all over the world. This oilseed crop is mainly utilized for its high oil content (with about 40–45% oil). However, in recent years, the area under cultivation of canola has decreased due to water scarcity. Applying drought-tolerant cultivars with high water use efficiency can help to develop the area under cultivation of canola and increase canola production. Therefore, the current study was conducted to assess the water use efficiency of spring canola cultivars in warm and temperate climates.

This study investigated different cultivars as a strategy for increasing canola production and improving its water use efficiency under different climate types in Khuzestan and Lorestan provinces. For this purpose, four locations including Khoramabad and Kuhdasht in Lorestan Province as semi-arid climate regions and Dezful, and Shushtar in Khuzestan Province as hot and arid climate regions were selected. Daily long-term climatic data (including minimum and maximum temperatures, rainfall, and global radiation) were collected for these locations from Iran Meteorological Organization. In this study, Hyola308 (early-maturity), Hyola401 (mid-maturity), and RGS003 (late-maturity) cultivars were used. In order to simulate the growth and yield of canola in different locations, the APSIM-Canola model was employed. OriginPro 9.1 software was used for all statistical analyses and the generation of figures.

The results showed that grain yield, biomass, water use efficiency, grain weight, actual evapotranspiration, the average temperature during the canola growth period, and the length of the canola growth period were substantially affected by cultivar and region (climate type). According to the results, the highest grain yield and water use efficiency (3037 kg ha-1 and 6.9 kg mm-1 ha-1, respectively) were achieved for the mid-maturity cultivar (Hyola401). Furthermore, simulation results revealed that temperate and semi-arid regions compared to hot and arid regions increased grain yield, biomass and water use efficiency by 2507 kg ha-1, 10100 kg ha-1, and 2.7 kg mm-1 ha-1, respectively. Khorramabad × Hyola401 treatment had the highest water use efficiency, grain yield, and biomass (9 kg mm-1 ha-1, 4954, and 17943 kg ha-1, respectively) due to lower the average temperature during the canola growth period (10.9 ° C) and higher the length of the canola growth period (2424.9 day), while the lowest amount of these traits was recorded in Dezful × Hyola308 treatment (5 kg mm-1 ha-1, 1369, and 5514 kg ha-1, respectively).

The results indicated that expanding canola cultivation in temperate regions can be used to boost canola production in Iran and to improve the sustainability of canola cultivation agroecosystems. Also, using a mid-maturity cultivar such as Hyola401 in both temperate and hot climate conditions can increase water use efficiency and sustainability of canola production agroecosystems through higher production per water consumption.

Despite genetic advances, the emergence of limiting factors such as climate, soil and crop management has led to a significant gap in many areas between farmers harvest performance and genetic yield potential. Knowing the limiting factors and proper planning and determining strategies to increase the adaptation of crops to these possible changes, causes the appropriate response of crops to these changes. For this purpose, the present study was conducted to investigate the effect of planting date and cultivar on yield and experimental physiological traits as a split plot in the form of a randomized complete block design with three replications in the crop year 2020-2021 in the Faculty of Agriculture, Lorestan University. The main factor included the four planting dates of 6 October, 21 October, 5 November, 20 November and the secondary factor of the cultivar, which included Chamran 2, Sirvan and Mihan. The results of analysis of variance showed that the simple effect of planting date on all studied traits was significant at one percent probability level and the simple effects of cultivar on photosynthesis rate, grain yield at one percent probability level and mesophilic conductance, stomatal conductance at five percent probability level were significant. Planting date was 6 October due to early sowing and occurrence of growth stages with temperatures lower than plant tolerance and planting date was 20 November due to delay in sowing time reduced seed yield. In general, the appropriate planting date, while increasing the optimal conditions for wheat cultivars, the growth and physiological characteristics of the plant, including photosynthetic components, provide favorable conditions for better plant growth and ultimately increase grain yield. In this study, this planting date of 5 November and Mihan cultivar is recommended as the best planting date and cultivar for Khorramabad region and similar climatic regions, respectively.

In order to determine the phenological differences of some improved rice cultivars in Iran for applying in crop simulation models, an experiment has been conducted in the research farm of the Rice Research Institute of Iran (Rasht) in 2020 as a randomized complete block with three replications. The experimental treatment consist of six rice cultivars (Rash, Anam, Gohar, SA1, SA6 and M7). Results show that the highest development rate can be observed in development rate in juvenile phase and grain filling phase in Anam cultivar. The minimum and maximum time required to start emergence with 3 and 6 days are in Anam and Gohar cultivars, respectively. The maximum time required to achieve maximum flowering and physiological maturity is obtained with 71 and 103 days in Gohar cultivar. The highest flowering period with 19 and 20 days is obtained in late maturing Rash and Gohar cultivars, respectively. The highest growth degree days (GDD) in beginning of grain filling to maturity stage is observed with 401 GDD for M7 cultivar. The highest growth-day for pre-flowering with 1208 GDD belongs to Gohar cultivar. The highest harvest index is obtained with 50.91% in Gohar cultivar. The results also show that the single grain weight under ideal growing conditions with 0.030 g is observed in Gohar and M7 cultivars. The highest plant height belongs to cultivar M7 with 150 cm and the highest total nitrogen uptake is observed in the plant at maturity of Anam cultivar. Overall, the estimated genetic coefficients in different models differ between cultivars and the coefficients vary in the range defined in the model for different groups of maturity. To accurately calculate the genetic coefficients, it is suggested that this experiment should be repeated over several years and in different ecosystems under rice cultivation.

Introduction:

Improving crop yields can meet the projected demand in developed and developing countries as a key and promising solution. To increase dryland wheat production in arid and semi-arid regions such as Iran, researchers and crop breeders need to understand the drought pattern (in terms of time and intensity) occurring in wheat agro-systems because depending on the time and intensity of drought stress, drought stress impacts on different processes and genes of crops. To accurately study the drought pattern of dryland wheat and different cultivars adapted to these conditions, researchers need to design multi-environmental field experiments, which are time-consuming and costly. Under these circumstances, some essential information is limited in the field experiments, especially when done for only a few years, and basically does not indicate fluctuations in the target environments. In contrast, the modeling and simulation approach is a useful method for comprehensive environmental impact assessment. 

Materials and Methods:

This study was conducted to identify and study different drought patterns in 8 locations including Ardebil, Khalkhal, Maragheh, Marand, Meshginshahr, Sarab, Tabriz, and Urmia in the northwest of Iran. APSIM model was used to simulate the growth and development of dryland wheat in the study locations. The long-term climatic included minimum and maximum temperatures, rainfall, and radiation (1980 to 2016) collected from the Meteorological Organization of Iran. These data were used as input to the crop growth simulation model. Water supply and demand index was used to determine different drought patterns. This index is obtained by dividing soil water content to plant water demand (WSDR). After calculating the water supply and demand index for each year and region, the CLARA clustering method was used to classify and group the index. The number of groups obtained by CLARA method was equal to the type of drought patterns of dryland wheat. 

Results and Discussion:

The results showed 4 different drought patterns during the dryland wheat growing season in the study locations.  Drought pattern 1 showed no drought stress or mild drought stress throughout the growing season of dryland wheat. Under drought pattern 2, drought stress started before flowering and ended during seed filling. For drought pattern 3, water stress started from the time of germination but removed during the vegetative growth period. Drought pattern 4 started in the early stages of growth and continued until the end of the dryland wheat growing season. The frequency of occurrence of 1, 2, 3, and 4 drought patterns was 27.8%, 26%, 8% and 38.2%, respectively. On average across locations, transpiration was 160, 137.1, 31, and 86.7 mm for 1, 2, 3, and 4 drought patterns during the wheat growing season. Also, a significant difference was observed among different drought patterns in terms of biomass that biomass under drought patterns 1, 2, 3, and 4 was equal to 13411, 10076, 2097 and, 6435 kg ha-1, respectively. Grain yield under drought pattern types 1, 2, 3 and 4 were 3738.7, 2949.7, 694.6, and 1456.2 kg ha-1, respectively. The highest percentage of light and mild drought patterns (1 and 2) was related to Marand region with 100% and Sarab region experienced more severe drought patterns (3 and 4) than other regions (88.2%). Under different locations and drought patterns, on average across years, the highest grain yield was simulated for Marand under drought pattern 1 (4759 kg ha-1) and the lowest was recorded for Urmia under drought pattern 3 (208.8 kg ha-1). 

Conclusion:

In general, the simulation results showed that 4 different types of drought patterns were observed during the dryland wheat growing period in the study locations. The frequency of occurrence of 4 drought patterns was various in the study locations and the grain yield varied under the drought patterns. However, the highest occurrence was related to drought pattern 4, under which drought stress begun in the early stages of growth and lasted until the end of the dryland wheat growing season. Accordingly, it is suggested to consider various strategies such as using cultivars with short maturity period to avoid drought stress at the end of the season and using cultivars with high resistance to drought stress (especially in the flowering period).

In order to investigate the effect of planting date and cultivar on yield components and physiological traits, split plot was performed in a randomized complete block design with three replications in 2020-2021years at the University of Lorestan. The main factor consisted of four cultivation dates 6 October, 21 October, 5 November, 21 November and the sub-agent of the figure, including Chamran, Sirvan, Mihan. The results of analysis of variance showed that simple effect of planting date on all traits in one percentage probability level and simple effects of cultivar on grain yield, harvest index, number of spikes per square meter, 1000 grain weight and dry matter remobilization at the probability level of a Percent was significant. Planting date on 6 October due to early culture and developmental stages with lower temperatures of plant tolerance and planting date 21 November due to delay during planting and limiting the production of tillers per plant and, as a result of reducing the number of spikes per square meter, grain yield decreased. The highest grain yield with 6976 kg ha-1 was related to the planting date of 5 November and the Mihan cultivar. In general, the history of cultivation, while increasing the optimal conditions for wheat cultivars, provide growth and physiological characteristics of the plant, including photosynthetic components, provide favorable conditions for the more favorable growth of the plant and ultimately increase the event. In the recent study, the Date of Planting 5 November and Mihan cultivar is recommended as the best planting date and cultivar for Khorramabad region and similar climate areas.

Today, rapid population growth and economic development have increased demand for food, and climate change has affected food security worldwide. Climate change processes, including increasing the atmospheric carbon dioxide concentration, rising temperature, and fluctuation of precipitation, could directly affect agricultural products. Climate change also causes drought, which indirectly influences agricultural systems as water is the most important for grain yield and its quality. Arid and semi-arid regions are limited in terms of water resources and they are the most fragile regions faced with drought caused by climate change. Khuzestan province is one of the hot and arid regions in Iran which its agricultural crops (especially maize) are very sensitive to climate change. Irrigation schedules and various cultivars can be considered as the adaptation strategies according to the climate change conditions. In agricultural ecosystems, water consumption should be reduced, and grain yield should be increased as much as possible. Optimizing water consumption by improving water use efficiency (WUE) is essential for achieving agricultural sustainability in arid and semi-arid regions. Accordingly, modelling approach has been considered as a time-saving and low-cost way to study the effects of climate change and different treatments. The current study was conducted to investigate the effects of different irrigation management practices on maize grain yield and WUE under climate change conditions in order to optimize water consumption and WUE by using modeling approach.

The current study was carried out in several locations of Khuzestan province, including Dezful, Izeh, Bostan, and Ahwaz. The long-term climatic data of the studied locations were collected from the Iran Meteorological Organization. These data included minimum and maximum temperatures (°C), rainfall (mm), and solar radiation (MJ m-2) from 1980 to 2010. Angstrom equation was used for calculating the radiation based on sunshine hours. The climatic data were modified using WeatherMan software embedded in the DSSAT package. The future climate of Khuzestan province (2040-2070) was predicted by the MIROC5 general circulation model under the RCP4.5 climate scenario and using AgMIP methodology. According to the previous studies, the MIROC5 climatic model showed the highest accuracy in predicting the future climatic data of Khuzestan province. Two adaptation strategies, including cultivar and irrigation regime, were considered to mitigate the negative effects of climate change. The cultivars consisted of SC704 (late-maturity) and SC206 (mid-maturity), which had the highest area under cultivation in Khuzestan province. Irrigation regimes included three levels: 12-time irrigation (as farmers’ common practice), 10–time irrigation, and 14-time irrigation per growing season.

The results of the current study indicated that climate change had negative effects on maize grain yield as well as positive effects on average temperature during the growing season, evapotranspiration, and corn water use efficiency across the whole province. The results showed that the average grain yield and corn WUE in Khuzestan province in 2050 under the RCP4.5 scenario was -2% and -5.7%, respectively, compared to the baseline. In addition, mean temperature during the growing season and evaporation and transpiration increased by +12.6% and + 0.9% compared to the baseline. The results also showed that with the application of an optimal amount of irrigation regime (10-time irrigation), an increase in WUE and decrease in evapotranspiration were observed, which resulted in acceptable grain yield. Results also portrayed that applying the optimal irrigation level (10-time) along with a late maturity cultivar (SC704) showed the best performance in terms of grain yield (9433.9 kgha-1) and WUE (19.57 kgha-1 mm-1) in the province Khuzestan.

The results illustrated that by 2050, the average grain yield and WUE were reduced compared to the baseline period. However, the mean temperature and evapotranspiration over the growing season were increased. Totally, the results of the current study revealed that an optimal irrigation level 10 and suitable cultivar SC704 could mitigate the negative impacts of climate change on maize in the agroecosystems of Khuzestan province.

 Heat stress is one of the most important threats and concerns for maize production, which mostly occurs in hot and dry areas. Heat stress reduces grain yield and the plant's photosynthesis rate and increases transpiration. Maize is very sensitive to heat stress and extreme temperatures at the flowering stage because extreme temperatures decrease pollen germination ability, and thus, decrease grain yield. However, there are some strategies to prevent the maize flowering stage from being exposed to heat stress. Careful management practices including Adjusting the sowing time and cultivar can be considered as useful strategies to deal with heat stress. Crop simulation models can be used to investigate these practices. Therefore, the present study was carried out to evaluate the risk of heat stress (frequency and intensity of heat) on grain maize of Iran and evaluate the risk window for grain maize using the modeling approach.

 In order to evaluate the risk of heat stress in maize agroecosystems of Iran, a simulation experiment was designed in five regions (Iranshahr, Dezful, Parsabad, Kermanshah, and Kerman), three sowing times (common: farmers sowing time in each region; late: 20 days after common sowing time; early: 20 days before common sowing time), and two cultivars (SC704 and SC260 as late- and early-maturity cultivars, respectively). To do this, the long-term climatic data of each region including minimum and maximum temperatures, rainfall, and radiation were collected from Iran Meteorological Organization. These data were applied as inputs of the crop simulation model. In this study, the APSIM model was employed to simulate the growth and development of the maize plant. In order to assess the risk of heat stress on grain maize, three dimensions including the critical stage of grain maize to extreme temperatures (flowering), frequency of extreme temperatures at the critical stage, and intensity of extreme temperatures at the critical stage were evaluated. Furthermore, the risk window for maize flowering in each region was equal to the first day of the year with a temperature of over 36 °C until the last day of the year with a temperature above 36 °C.

 The highest risk window of extreme temperatures was recorded in Iranshahr County (183 days) as a hot and dry region and the lowest risk window was simulated in Parsabad (14 days) as a semi-arid and temperate region. Moreover, the percentage of the number of maize flowering days with temperatures above 36 °C and the mean maximum temperature during the flowering period were 63.5% and 37.09 °C, respectively. This issue reduced the grain yield of maize in Iran so that the grain yield was simulated 6196.5 kg ha-1. However, in the spring season, the early sowing time and the early-maturity cultivar decreased the percentage of the number of maize flowering days with temperatures above 36 °C (37.2%) and mean maximum temperature during the flowering period (35.1 °C) and increased grain yield (7486.9 kg ha-1). Overall, in the summer, the percentage of the number of maize flowering days with temperatures above 36 °C and mean maximum temperature during the flowering period were decreased 38.9% and 35.3 °C, respectively, and grain yield was boosted to 7743.6 kg ha-1 under the combination of late sowing time and late-maturity cultivar.

 The results showed that grain maize is currently cultivated by farmers under high-risk conditions of heat stress. In order to reduce the risk and increase grain yield, farmers in each region should apply the optimal sowing times and cultivars according to the growing season.

To study the effect of arbuscular mycorrhizal fungi on morphological and physiological characteristics of maize in different levels of phosphorus fertilizer, a greenhouse experiment as factorial arranged in completely randomized design with three replications was carried out at Islamic Azad University Miyaneh Branch, Iran. The experimental treatments consisted of mycorrhizal inoculation with Glomus mosseae (inoculation and non-inoculation) and four levels of phosphorus chemical fertilizer (control, 13, 26, and 39 kg P per ha). Results showed that the highest values of wet and dry biomass (224.08 and 172.47 g Plant-1, respectively) were belonged to mycorrhizal inoculation in 39 kg P per ha. Also, the lowest values of wet and dry biomass (120.74 and 95 g Plant-1, respectively) were belonged to mycorrhizal non-inoculation at control level of phosphorus fertilizer. The highest values length, weight and number of ears (23.22 cm, 116.32 g and 4.25, respectively) were related to mycorrhizal inoculation in 39 kg P per ha. Totally, the result indicated which 39 kg P per ha at mycorrhizal non-inoculation had not only higher wet and dry biomass than 26 kg P per ha at mycorrhizal inoculation but also it had lower wet and dry biomass than 26 kg P per ha at mycorrhizal inoculation. Generally, the results of this research shows the increasing amount of biomass, chlorophyll and leaf phosphor concentration with improving in morphological and physiological characteristics of maize due to use of arbuscular mycorrhizal fungi. Also, absorption of phosphor and plant yield were increased by applying a low amount of phosphor fertilizer.

Rising temperature as consequences of climate change could affect on the various processes such as photosynthesis, respiration, partitioning of assimilates and ultimately grain yield of agricultural crops. The present experiment was conducted to simulate the impacts of climate change on maize grain yield under two future climate change scenarios (RCP4.5 and RCP8.5) using APSIM model in three counties of Kermanshah province (Kermanshah, Kangavar and Eslamabad Gharb). Future climate data were projected by using long-term climatic data for the baseline period of 1980-2010 and AgMIP methodology for the mid-future (2040-2070). The results showed that, on average, the mean temperature increased up to 11 and 23 percent under RCP4.5 and RCP8.5 scenarios when compared to the baseline, respectively. The maximum and minimum grain yields in the baseline were obtained in Kangavar (14399 kg.ha-1) and Kermanshah (7741 kg.ha-1), respectively. Simulation results also showed that average temperature during growing season rised (0.5 and 2 ˚C under RCP4.5 and RCP8.5, respectively) and length of growing season decreased (5 and 6 percent under RCP4.5 and RCP8.5, respectively) which resulted in a reduction in maize grain yield from 69 to 90 percent under RCP4.5 and RCP8.5, respectively. Results illustrated that under climate change conditions, flowering of maize may coincide with high temperatures and this may reduce maize grain yield, therefore, it can be suggested that changing the sowing dates in the target regions prevents the coincidence of the critical flowering stage with heat stress.

Heat stress caused by climate change will become a restriction for maize production in the future (Cairns et al., 2013). Damage of heat stress is very strong when is occurred in a critical stages of plant growth (particularly in the flowering phase) (Teixeira et al., 2013). Non-coincidentally of flowering stage with high temperature can be reduced the negative effects of heat stress, especially in climate change conditions (Zheng et al., 2012). In this theme, a useful change in the management practices such as early planting dates (Zheng et al., 2012; Liu et al., 2013) could be considered to avoid heat stress and reduce production risk, especially climate change situations. Khuzestan province in terms of grain maize has the highest cultivated area in Iran (Anonymous, 2013). Based on this and according to the impact of climate change on reducing maize yield in the Khuzestan province (Abbas Torki et al., 2011), this study tries to assess the risk of heat stress in grain maize at the Khuzestan province under rising temperature conditions caused by climate change.
This research was conducted in six locations of Khuzestan Province to investigate the risk assessment due to heat stress in maize in the future climate change. Accordingly, the future climate in the study areas was generated using long-term (1980-2009) climate data of the baseline (included minimum and maximum temperatures, rainfall and global radiation) and AgMIP technique under two climate scenarios (RCP4.5 and RCP8.5) for the future period of 2040 -2069. Long-term simulation experiments consisted of three sowing dates (3st February, 19st February and 5th March), six locations (Ahwaz, Behbahan, Dezful, Izeh, Ramhormoz and Shushtar), two future climate scenarios (RCP4.5 and RCP8.5) in 30 years. In total, around 1620 simulation experiments were carried out. To assess the risk of heat stress on maize, it was considered the time (phase), frequency and intensity of maize threshold temperatures in its sensitive phenological phases. To this end, flowering and grain formation phases of maize were noted as the most sensitive to high temperature stress. In this study, APSIM crop model was used for simulation of maize growth and yield. The OriginPro 9.1 and R software were used to draw figures and perform statistical analyses.
Results indicated that the average temperature during the growing season in the Khuzestan province was increased under RCP4.5 and RCP8.5 (8.5 and 34.57 percent, respectively) in comparison to the baseline (27.2 °C). The highest temperature rise was obtained in the Ramhormoz (27.2 °C) on 5th March under RCP8.5. Also, the highest temperature rise during the growing season under RCP4.5 obtained in Shushtar (27.2 °C) on 19st February. In the baseline, on average, grain yield and the number of grains/m-2 in the Khuzestan province were obtained 8.8 t ha-1 and 2305.7. These values in 2050 were 8.5 and 8.7 t ha-1 and 2227.3 and 2254.3 grains/m-2 for RCP4.5 and RCP8.5, respectively. When average across sowing dates, locations and periods, the cumulative probability function for economic yield, non-economic yield and zero yield were 45.4, 13.5 and 41.2 %, respectively for common sowing dates. Under, earlier sowing date (3 February) the cumulative probability for economic yield was higher than the other sowing dates both in future and the baseline periods (65.2 percent).
Overall, the results of this study showed that common sowing date which is used by most farmers (19st February) in Khuzestan province was not optimal for both current and future periods while the early sowing date (3st February) in most locations could be considered as an effective adaptation strategy to reduce the amount of extreme temperatures risk in future and to increase grain yield under the current conditions.