Comparison of the ability of inverse demand function and artificial neural network to predict crop prices, Case Study of Qazvin Plain Irrigation Network

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Document Type : Research Paper

Authors

1 Faculty member, water eng. group, Imam Khomeini International University

2 Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran.

Abstract

The occurrence of climate change and its impact on water resources such as reduction of surface water flows has led to vulnerability and instability of farmers' livelihoods. Currently, Qazvin plain is one of the regions of the country that is facing water crisis and severe water shortage.The reduction of water allocated from Taleghan Dam to Qazvin plain has led farmers to take unauthorized harvesting of water from wells in order to provide their livelihoods (Hosseini and Mazandarani Zadeh, 2022). Despite farmers draining groundwater aquifers, most of their livelihoods still face problems due to crop price fluctuations. Therefore, in this study, with the aim of providing livelihood to farmers and preserving groundwater resources, the price of agricultural products has been predicted using Inverse demand function, Artificial Neural Network and nonlinear regression methods. The Inverse demand function and Artificial Neural Network methods were used to predict the price of products without guaranteed purchase price and Artificial Neural Network and nonlinear regression were used to predict the price of products with guaranteed purchase price including wheat, barley, sugar beet and rapeseed.

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History

  • Receive Date: 23 December 2023
  • Revise Date: 30 July 2024
  • Accept Date: 05 August 2024
  • Publish Date: 05 August 2024

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