Identifying Soil and Plant Nutrition Factors Affecting Yield in Irrigated Mature Pistachio Orchards

ABSTRACT

The main objective of this study was to evaluate the potential use of a hybrid Genetic Algorithm-Artificial Neural Network (GA–ANN) method for predicting pistachio yield and for identifying the determinant factors affecting pistachio yield in Rafsanjan region, Iran. A total of 142 pistachio orchards were selected randomly and soil samples were taken at three depths. Besides, water samples and leaves from branches without fruit were taken in each sampling point. Management information and pistachio yields were achieved by completing a questionnaire. Primarily, 58 variables affecting pistachio yield were measured, and then 26 out of them were selected by minimizing mean square error (MSE) using a feature selection (FS) method. The results showed that the accuracy of the method was acceptable. Furthermore, the sensitivity analysis showed that the main determinant features affecting the pistachio yield were the irrigation water amount, leaf phosphorus, soil soluble magnesium, electrical conductivity (EC), and leaf nitrogen.

KEYWORDS:

• Crop yield prediction
• feature selection
• non-linear modeling
• pistachio production optimization

Acknowledgments

Laboratory facilities provided by Vali-e-Asr University of Rafsanjan and Soil Lab. of Pistachio Research Center of Iran are highly acknowledged. Besides, the authors are grateful to Ehsan Mehrabi Kermani and KU Writing Center service for editing the English text.