Agriculture productions play significant roles in economic development. Extreme weather events, especially drought under climate change conditions, can affect future crop production. Nowadays, researchers are trying to apply modeling approaches for estimating future changes on amounts of crop yields. This study employed pooled panel data to simulate the most effective meteorological drought indices, economic and meteorological variables on rainfed wheat yield. The observation period was 1990–2016 for several meteorological data, besides SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) drought indices in monthly and yearly scales. The available economic variables during the study period were yearly guaranteed wheat prices (Rial/kg) and area under cultivation (ha). In this research, first, the most effective variables were selected according to the efficiency criteria and stepwise regression. Then by using pooled panel data, a relation was estimated between yield and the independent variables. Finally, with future downscaled variables, the amount of wheat yield was determined for the next 20 years (2019–2038). The GFDL- ESM2M and MIROC5 models under RCP45 and RCP85 were run, and MIROC5 under RCP45 was selected as the best model, for the evaluation period. The results revealed that guaranteed wheat prices, yearly precipitation and sunshine hours, the area under cultivation, and SPI of October were identified as the most effective variables on wheat yield through the Panel model. By using the projection weather variables and the pooled panel model, we achieved that the amount of rainfed wheat yield would be increased over two next decades at Mashhad, Sabzevar, and Torbat H. locations.

农业生产在经济发展中起着重要作用。极端天气事件，尤其是气候变化条件下的干旱，可能会影响未来的作物产量。如今，研究人员正在尝试应用建模方法来估计未来农作物产量的变化。这项研究使用汇总的面板数据来模拟最有效的气象干旱指数，雨育小麦单产的经济和气象变量。除SPI（标准降水指数）和SPEI（标准降水蒸散指数）干旱指数的月度和年度尺度外，观测期为1990年至2016年。研究期间可用的经济变量是年度保证小麦价格（里亚尔/公斤）和耕地面积（公顷）。在这项研究中，首先，根据效率标准和逐步回归选择最有效的变量。然后，通过使用合并的面板数据，估计产量和自变量之间的关系。最后，利用未来缩减的变量，确定了未来20年（2019-2038年）的小麦产量。在评估期间，运行了RCP45和RCP85下的GFDL-ESM2M和MIROC5模型，并选择了RCP45下的MIROC5作为最佳模型。结果表明，通过Panel模型，保证的小麦价格，年降水量和日照时间，耕种面积以及10月的SPI被确定为最有效的小麦产量变量。通过使用投影天气变量和汇总面板模型，