Random forest, an efficient smart technique for analyzing the influence of soil properties on pistachio yield

Pistachio is one of the most important and valuable orchard products in Iran and some other places in the world. Because it is adaptable to adverse environmental conditions, especially drought and salinity, more land is being used for pistachios. In an increasingly resource constrained world, producers, researchers and policy makers need to clearly identify suitable land to optimize production. The present study modelled the relationship between pistachio yield and soil variables by regression, linear and non-linear (MLR and NMLR), feed forward back propagation artificial neural network (FFBP-ANN), adaptive neural fuzzy inference system (ANFIS) and random forest (RF) models in areas under pistachio production from Iran. For this purpose, 124 pistachio orchards were selected and sampled in Kerman, Fars, Khorasan Razavi, Isfahan and East Azarbaijan provinces of Iran. The results indicated that MLR and NMLR could explain 72 and 77% of the pistachio yield variation, respectively, whereas prediction accuracy increased when the data of pistachio orchards were entered in intelligent models: ANFIS, ANN-based and RF to 86, 92 and 96%, respectively. The RF model was found to be most parsimonious. As the models were highly sensitive to gravel, electrical conductivity, exchangeable sodium, CaCO3, gypsum, and available phosphorus and potassium concentrations, special attention should be paid to the measurement and management of these properties.