Due to the pressure on water resources, especially in agricultural sectors, evaluation of the effective strategies on decrease in water consumption has an applicable and important role in field water management. In this research, the effectiveness rate of the 7 climatic parameters on the water requirement of barley (amount of potential evapotranspiration of barley (PETB) during the growth period) using the random forest algorithm (RF), Bayesian multiple linear regression (BR) and cross-correlation function (CCF) was investigated and prioritized. The results of this research can help the managers to control effective climatic variables on PETB. In this paper, the climatic data series of 8 stations with different climate conditions (with two replicates for each climate condition) in Iran during 1968–2017 was used. The results showed the linear regression of simulated PETB using AquaCrop model and predicted PETB using the RF and BR models had no difference with perfect reliable in 0.05 significant levels (T-Statistics varies from 0.010–0.103 and 0.000–0.842 in BR and RF models, respectively, and the R-square (R²) between simulated and predicted PETB were significant in 0.01 levels at all stations (R² varies from 0.843–0.996 and 0.930–0.999 in BR and RF models, respectively; therefore, the RF and BR models had a good accuracy to predict PETB based on the climatic parameters and the accuracy of RF was more than BR model. The results indicated that based on the all statistical methods the wind speed had the most impact on the PETB (at 62.5% of stations) and based on the RF, BR and CCF methods the average of minimum temperature, the average of sunshine and the average of precipitation had the lowest impact on the PETB, respectively.

由于水资源的压力，尤其是农业部门的水资源压力，评估减少水消耗的有效策略在田间水资源管理中具有可应用的重要作用。在这项研究中，使用随机森林算法（RF），贝叶斯多元线性回归（BR）和回归法对7个气候参数对大麦需水量（大麦在生长期间的潜在蒸散量）的有效率。对互相关函数（CCF）进行了研究并确定了优先级。这项研究的结果可以帮助管理者控制PETB上的有效气候变量。在本文中，使用了1968-2017年伊朗8个不同气候条件的站台的气候数据系列（每种气候条件有两个副本）。在所有站点上，模拟和预测的PETB之间的R ²均具有0.01的显着水平（在BR和RF模型中R ^2分别从0.843–0.996和0.930–0.999变化；因此，RF和BR模型具有良好的预测PETB的准确性根据气候参数，RF的准确性优于BR模型，结果表明，基于所有统计方法，风速对PETB的影响最大（占站点的62.5％），基于RF，BR CCF方法的最低温度平均值，日照平均值和降水平均值对PETB的影响最小。