Integrated modelling approaches for sustainable agri-economic growth and environmental improvement: Examples from Canada, Greece, and Ireland

TL;DR

This paper presents integrated optimization models for sustainable agriculture that address water scarcity, water quality, and environmental impacts across Canada, Greece, and Ireland, offering practical solutions for balancing economic and environmental goals.

Contribution

It introduces novel, case-specific optimization approaches using Python tools to address complex agricultural problems, providing practical, adaptable solutions for sustainability.

Findings

Optimized water use in Greece's irrigation systems.

Improved water quality management in Ontario, Canada.

Reduced emissions and resource degradation in Irish animal farming.

Abstract

Complex agricultural problems concern many countries, as the economic motives are increasingly higher, and at the same time the consequences from the irrational resources use and emissions are becoming more evident. In this work we study three of the most common agricultural problems and model them through optimization techniques, showing ways to assess conflicting objectives together as a system and provide overall optimum solutions. The studied problems refer to: i) a water-scarce area with overexploited surface and groundwater resources due to over-pumping for irrigation (Central Greece), ii) a water-abundant area with issues of water quality deterioration caused by agriculture (Southern Ontario, Canada), iii) and a case of intensified agriculture based on animal farming that causes issues of water, soil quality degradation, and increased greenhouse gases emissions (Central Ireland). Linear, non-linear, and Goal Programming optimization techniques have been developed and applied for each case to maximize farmers welfare, make a less intensive use of environmental resources, and control the emission of pollutants. The proposed approaches and their solutions are novel applications for each case-study, compared to the existing literature and practice. Furthermore, they provide useful insights for most countries facing similar problems, they are easily applicable, and developed and solved in publicly available tools such as Python.