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首页> 外文期刊>Agricultural Water Management >Effects of soil properties, water quality and management practices on pistachio yield in Rafsanjan region, southeast of Iran
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Effects of soil properties, water quality and management practices on pistachio yield in Rafsanjan region, southeast of Iran

机译:土壤特性,水质和管理实践对伊朗东南部的拉斐斯江地区开心素产量的影响

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In recent decades, the quantity and quality of irrigation water have been reduced due to a significant increase in pistachio cultivation and uncontrolled exploitation of groundwater resources as well as reduction in rainfall precipitation. Therefore, agricultural producers, researchers and policy makers need to pay more attention to appropriate land management as an important strategy to achieve greater yield per unit area and to use soil and water resources in an optimal way. So, the present study was conducted to model the relationships between pistachio yield and soil, water and management variables in Rafsanjan region, the southeast of Iran. One hundred and ninety nine mature orchards were selected and sampled in such a way that an acceptable range of soil and water quality and management were included. The data set consisted of a dependent variable (pistachio yield) and 67 independent variables including soil, water and management characteristics. The results of hybrid genetic algorithm-artificial neural network (GA-ANN) showed that the lowest error was related to the case in which the 23 features were used in modeling. Then, stepwise multiple linear regression (MLR) and artificial neural network (ANN) techniques were applied to estimate pistachio yield. The results indicated that MLR could explain only 28% of the pistachio yield variation, whereas its accuracy increased when the data became more homogeneous via geographically dividing the study area into four parts with the highest densities of pistachio orchards. ANN-based model had a 90% accuracy to predict pistachio yield in the study area. Thus, an accurate estimation of pistachio yield could be achieved by reducing the data dimensionality using feature selection techniques and modeling with ANN. As the models were highly sensitive to irrigation water features, special attention should be paid to new irrigation methods and management practices as an effective strategy to minimize water losses and increase water use efficiency.
机译:近几十年来,由于开心果栽培和对地下水资源的不受控制开采的显着增加,灌溉水的数量和质量减少,降雨降雨量。因此,农业生产者,研究人员和决策者需要更多地关注适当的土地管理作为实现每单位面积更高收益率的重要策略,并以最佳的方式使用土壤和水资源。因此,对本研究进行了模拟了伊朗东南部的开发赛季产量和土壤,水和管理变量的关系。选择了一百九十九九成熟的果园,并以这样的方式取样,即包括可接受的土壤和水质和管理。数据集由包括土壤,水和管理特征在内的依赖变量(开发件产量)和67个独立变量。混合遗传算法 - 人工神经网络(GA-ANN)的结果表明,最低误差与23个特征在建模中使用的情况有关。然后,逐步多元线性回归(MLR)和人工神经网络(ANN)技术被应用于估计开发率产量。结果表明,MLR只能解释开场屈服变化的28%,而当数据通过地理上划分研究区域进入具有最高密度的开心果园的四个部分时,其准确性增加。基于ANN的模型具有90%的准确度,可预测研究区域的开心率。因此,可以通过使用特征选择技术和ANN建模来减少数据维度来实现对开发率产量的精确估计。由于模型对灌溉水分高度敏感,应特别注意新的灌溉方法和管理实践,作为最大限度地减少水损失的有效策略,并提高水利用效率。

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