Effect of Input Usage on Wheat Yield: An Application of Artificial Neural Networks (ANN)

Abstract

This study aimed to investigate the effects of inputs such as pesticides, fertilizers, seeds, labor and machine use on wheat yield. The data used in the study were obtained from 177 wheat producers in Diyarbakir Province through a questionnaire, and Artificial Neural Networks (ANN) were used in the analysis of the data. According to the results, the average wheat yield is 5482.03 kg ha(-1), and 294.75 kg of seeds, 550.73 kg of fertilizer, 3.59 hours of machinery, 5.37 hours of labor and 2662.43 cc of pesticides were used per hectare for wheat production. According to the results of the ANN analysis, the relative importance of inputs affecting wheat yield was quantified, with the use of pesticides and fertilizer having the most significant impacts. Specifically, the sensitivity coefficients for pesticide use and fertilizer use were found to be 0.23 and 0.14, respectively. These coefficients represent the relative change in wheat yield per unit change in the input parameters.