Determining the Optimal Cropping Pattern with Emphasis on the Interaction between Risk and Profitability: Farmlands of Dehgolan Plain

Document Type : Research Article-en

Authors

M. Ghasabi ¹

M. Haji-Rahimi ¹

H. Ghaderzadeh ¹

R. Shankayi ²

¹ Department of Agricultural Economics, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

² Department of Agricultural Economics, Faculty of Agriculture, University of Tehran, Tehran, Iran

https://doi.org/10.22067/jead.2024.90686.1312

Abstract

Abstract
Risk is an undeniable factor in agricultural activities, and its neglect can lead to inefficient resource allocation in the sector. Various theories and mathematical programming models have been developed to assist decision-making in cropping pattern management under risk conditions. This study aimed to determine the optimal cropping pattern for Dehgolan Plain, Iran, using data from 2014 to 2023. A linear programming model was employed to maximize farmers' gross income, and the results were compared with those from a Quadratic Programming Model and the Minimization of Total Absolute Deviation (MOTAD) model, both incorporating risk minimization. The findings revealed that risk factors can significantly influence cropping patterns. Under the highest level of risk, the profit-maximizing cropping pattern included only cucumber, alfalfa, and canola, indicating a preference for higher gross-income crops despite their greater water requirements. However, when risk was incorporated into the model, the cultivated area of wheat and barley increased compared to the risk-neutral scenario. This shift reflects a tendency toward lower water-requirement crops, even at the cost of reduced gross income. These results highlight the necessity of balancing income maximization and risk management for more sustainable cropping pattern.

Keywords

Cropping pattern

Linear programming model

MOTAD model

Quadratic programming model

Risk model

Subjects

• Agricultural Managment

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©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

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