Irrigation Sciences and Engineering

Irrigation Sciences and Engineering

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

Document Type : Research Paper

Authors
Seyed Marzieh Hhosseini 1
Hamed Mazandarani Zadeh 2
1 PhD sudent in Water Resources Engineering, Dept. of Water Science and Engineering, Imam Khomeini International University, Qazvin, Iran.
2 Associated professor, Dept. of Water Science and Engineering, Imam Khomeini International University, Qazvin, Iran.
10.22055/jise.2024.45614.2109
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 Iran 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 (ANN) and nonlinear regression methods. The inverse demand function and ANN methods were used to predict the price of products without guaranteed purchase price and ANN and nonlinear regression were used to predict the price of products with guaranteed purchase price including wheat, barley, sugar beet and rapeseed.
Keywords
Artificial Neural Network
Equilibrium
Guaranteed purchase
Inverse demand function
Supply
Subjects
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Irrigation Sciences and Engineering
Volume 48, Issue 2
Summer 2025
Pages 35-47

  • Receive Date 29 December 2023
  • Revise Date 02 August 2024
  • Accept Date 05 August 2024
  • Publish Date 23 August 2025