Projecting future changes in potato yield using machine learning techniques: a case study for Prince Edward Island, Canada

Abstract

Accurate prediction of potato yield is essential for informed agricultural decision-making, ensuring food security, and supporting farmers' livelihoods. This is particularly critical in regions like Prince Edward Island (PEI), where potato production is not only a staple of local agriculture but also a cornerstone of the regional economy, accounting for a significant proportion of agricultural revenue and employment. Although machine learning algorithms have been extensively applied in agricultural yield prediction, previous studies have not fully leveraged the potential of capturing both short- and long-term dependencies. This research highlights the efficacy of integrating these temporal dependencies into machine learning models to enhance the accuracy of potato yield predictions. The methodology adopted in this research, including data collection, model selection, and scenario-based projections, can be applied to other regions and crops. Our projections for PEI toward the end of the century indicate a substantial decline in potato yields across different climate scenarios. Under the high-emission SSP5-8.5 scenario, our models predict a potential potato yield reduction of up to 70%. In contrast, the SSP1 and SSP2 scenarios suggest a more moderate decline in potato yield, ranging from 4% to 15%. These findings underscore the urgent need for reducing greenhouse gas emissions to mitigate the adverse impacts on potato production. Furthermore, they highlight the importance of implementing adaptive farming practices to sustain potato yield in the face of climate change.