Because of human dependence on sustainable food production, it is needed to adopt agricultural production to climate change, and their consequences on associated socioeconomic issues. Crop modelling potentially can contribute to global food and nutrition security. Therefore, in this context DSSAT (decision support system for agro-technology transfer) was validated for predicting growth and yield of wheat under a diverse semi-arid climate (2 years with diverse climates) and different irrigation strategies, planting methods, and nitrogen rates. The irrigation strategies were ordinary furrow irrigation (OFI) and variable alternate furrow irrigation (VAFI), and the planting methods were on-ridge planting (ORP) and in-furrow planting (IFP) methods. The fertilizer levels were 0 (N0), 150 (N1) and 300 (N2) kg N ha −1 . Results indicated that water stress and inappropriate weather especially during the stem elongation influences the grain yield remarkably without noticeable effect on straw yield. Furthermore, VAFI strategy did not impose any limitation on top dry matter N concentration in both years. Calibration of DSSAT showed that the model underestimated the actual evapotranspiration at the end of growing season (during spring with high temperature) and resulted in overestimating the soil water content at depths under 10 cm during this period of time. Since the potential transpiration has an important role in calculating the effect of water stress factor, the model overestimated slightly the maximum leaf area index (LAI) and consequently biomass and yield in water stress condition; however, overall, model could statistically simulate LAI (NRMSE = 0.3, d = 0.96, R 2 = 0.95), total dry matter (NRMSE = 0.07, d = 0.99, and R 2 = 96) and grain yield (NRMSE = 0.13, d = 0.96, and R 2 = 90). Model also could reasonably simulate the nitrogen components including the grain N concentration (NRMSE = 0.05, d = 0.95), above-ground nitrogen uptake (NRMSE = 0.11, d = 0.99), and soil nitrate content (NRMSE = 0.23, d = 0.86). However, evaluating the model with data set from 2015 to 2016 indicated that the model could not simulate wheat yield and nitrogen components in validation year due to strong effects of diverse weather condition and water stress on grain yield and crop development.

中文翻译：

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不同半干旱气候和农艺实践中的冬小麦产量和 DSSAT 模型评估

由于人类对可持续粮食生产的依赖，需要采用农业生产来应对气候变化及其对相关社会经济问题的影响。作物建模可能有助于全球粮食和营养安全。因此，在这种情况下，DSSAT（农业技术转让决策支持系统）被验证可用于预测不同半干旱气候（2 年，不同气候）和不同灌溉策略、种植方法和氮肥率下小麦的生长和产量. 灌溉策略为普通沟灌（OFI）和可变交替沟灌（VAFI），种植方式为垄上种植（ORP）和沟内种植（IFP）。肥料水平为0 (N0)、150 (N1) 和300 (N2) kg N ha -1 。结果表明，水分胁迫和不适宜的天气尤其是在茎秆伸长期间对粮食产量影响显着，对秸秆产量影响不显着。此外，VAFI 策略在这两年都没有对顶部干物质 N 浓度施加任何限制。DSSAT 的标定表明，该模型低估了生长季末（春季高温）的实际蒸散量，导致高估了这段时间 10 cm 以下深度的土壤含水量。由于潜在蒸腾作用在计算水分胁迫因子的影响中具有重要作用，模型略微高估了最大叶面积指数（LAI），从而导致水分胁迫条件下的生物量和产量；然而，总体而言，模型可以在统计上模拟 LAI（NRMSE = 0.3，d = 0。96，R 2 = 0.95）、总干物质（NRMSE = 0.07、d = 0.99 和 R 2 = 96）和谷物产量（NRMSE = 0.13、d = 0.96 和 R 2 = 90）。模型还可以合理模拟谷物氮含量（NRMSE = 0.05，d = 0.95）、地上氮吸收量（NRMSE = 0.11，d = 0.99）和土壤硝酸盐含量（NRMSE = 0.23，d = 0.86）等氮成分）。然而，使用 2015 年至 2016 年的数据集对该模型进行评估表明，由于不同天气条件和水分胁迫对粮食产量和作物发育的强烈影响，该模型无法模拟验证年度的小麦产量和氮成分。05, d = 0.95)、地上氮吸收量 (NRMSE = 0.11, d = 0.99) 和土壤硝酸盐含量 (NRMSE = 0.23, d = 0.86)。然而，使用 2015 年至 2016 年的数据集对该模型进行评估表明，由于不同天气条件和水分胁迫对粮食产量和作物发育的强烈影响，该模型无法模拟验证年度的小麦产量和氮成分。05, d = 0.95)、地上氮吸收量 (NRMSE = 0.11, d = 0.99) 和土壤硝酸盐含量 (NRMSE = 0.23, d = 0.86)。然而，使用 2015 年至 2016 年的数据集对该模型进行评估表明，由于不同天气条件和水分胁迫对粮食产量和作物发育的强烈影响，该模型无法模拟验证年度的小麦产量和氮成分。