Version 1
: Received: 25 November 2023 / Approved: 27 November 2023 / Online: 28 November 2023 (01:39:57 CET)
How to cite:
Basirifard, M.; Yoosefdoost, I. An Agent-Based Model for Determining the Agriculture Demand Based on Farmers' Socio-Economic Characteristics. Preprints2023, 2023111628. https://doi.org/10.20944/preprints202311.1628.v1
Basirifard, M.; Yoosefdoost, I. An Agent-Based Model for Determining the Agriculture Demand Based on Farmers' Socio-Economic Characteristics. Preprints 2023, 2023111628. https://doi.org/10.20944/preprints202311.1628.v1
Basirifard, M.; Yoosefdoost, I. An Agent-Based Model for Determining the Agriculture Demand Based on Farmers' Socio-Economic Characteristics. Preprints2023, 2023111628. https://doi.org/10.20944/preprints202311.1628.v1
APA Style
Basirifard, M., & Yoosefdoost, I. (2023). An Agent-Based Model for Determining the Agriculture Demand Based on Farmers' Socio-Economic Characteristics. Preprints. https://doi.org/10.20944/preprints202311.1628.v1
Chicago/Turabian Style
Basirifard, M. and Icen Yoosefdoost. 2023 "An Agent-Based Model for Determining the Agriculture Demand Based on Farmers' Socio-Economic Characteristics" Preprints. https://doi.org/10.20944/preprints202311.1628.v1
Abstract
Modelling and presenting mathematical relationships for human behaviour is one of the most complex issues that researchers have always dealt with. In this article, a bottom-up framework for calculating agricultural needs is presented using the socioeconomic characteristics of farmers (such as education level, age, and dependence on income on agriculture) and how their lands are located concerning each other (interactions between neighbours). The objective function of this framework is to maximize the profit of individual farmers based on the amount of water received. Two scenarios, ABM1 (not considering neighbourhood effects) and ABM2 (all cases of farmers' placement and feeling neighbourhood effects), were investigated. In the first scenario (ABM1), there was a noteworthy reduction in water deficit volumes by approximately 35%, accompanied by a 20% increment in farmers' profits. Interestingly, higher risk-taking tendencies correlated with reduced profit margins. The second scenario (ABM2) underscored the significant role of neighborhood dynamics in cultivating diverse behavioral patterns among farmers, subsequently affecting their profitability. A granular examination revealed that farmers with a higher propensity for risk-taking generally accrued lower profits. Additionally, the study facilitated the calculation of total annual profits and average water consumption for each farmer, offering valuable insights for optimizing water resource management and allocation strategies. These findings are instrumental for planners and water resource managers aiming to promote sustainable agricultural practices and efficient water use.
Keywords
Agriculture Demand; Agricultural Risk; Agent-Based Model; Standard Operating Policy
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
