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The objective of this study was to estimate the date production according to soil characteristics by two methods of multivariate regression and artificial neural networks. First, 90 groves with a wide range of soil characteristics but with almost the same climatic conditions and management were selected in five date-producing provinces of Iran. Then, a control profile was dug in each soil and the taken samples were sent to the laboratory for the designated physico-chemical analyses. Modeling was performed to estimate the quantity of production using soil variables with the highest correlation with yield. Obtained results revealed that some variables namely soil salinity, exchangeable sodium percentage, gypsum, lime, gravel, available potassium and phosphorus have highest correlation with the date yield. Comparison of two applied models indicated that both models are able to effectively estimate the date yield. The value of evaluation criteria consists of normalized root mean square error (NRMSE), coefficient of determination (R2) and coefficient of residual mass (CRM) for the artificial neural network model performance was better than the regression model. But, the average value of the predicted yield in the regression was more similar to the actual yield. Furthermore, breaking the data into different regions and creating multivariate regression with one or two important soil variables showed that the regression method can predict the quantity of date yield with acceptable accuracy. Consequently, due to the simplicity of implementation and application of multivariate regression model compared to artificial neural network and its acceptable accuracy, this model can be preferred.

Estimating Quantity of Date Yield Using Soil Properties by Regression and Artificial Neural Network