The Search for Optimal Weather Window

Modern plant breeding programs collect several data types such as weather, images, and secondary or associated traits besides the main trait (e.g. grain yield). Genotype-by-environment interactions significantly impact the performance of varieties in different environments. In the face of changing climate conditions, there is a need to develop methods able to effectively combine weather information with genotype data and better predict the performance of varieties. In this work, we propose a method to optimize the time-window in the growing season from which weather variables are included to predict traits by integrating the weather data with genomic data and address the various challenges in this problem. We compare our method with several standard machine learning methods using real data.

Look through the poster to learn more about our work! This work was presented at the Conference on Statistical Practice 2022.