Improving Crop Variety Recommendations for Farmers: An Integrated Approach using Machine Learning, Genetics, and Climate Data
Conference
Format: CPS Abstract - TIES 2024
Keywords: agriculture and rural development, climate change, statistical_genetics
Abstract
Crop variety selection is one of the most important factors influencing on-farm yields. Identifying suitable varieties for farms can be difficult as the relative performance of varieties often varies across environments due to genotype by environment interactions. This research seeks to address this issue by improving the accuracy of variety recommendations using machine learning (ML) methods, single nucleotide polymorphism data, and a decade of climate data to better capture the genotype by environment interaction effect. With this, we look to provide tailored crop variety reccomendations to farmers. In addition, we explore the impact of varying spatial and temporal resolutions on the quality of variety recommendations. We find that ML methods with genomic and climate data can result in significantly increased on-farm yields relative to current methods employed for variety recommendations.