Climate change and evolving growing conditions present new challenges for breeding. It is important to take local environmental conditions into account. An international team led by the IPK Leibniz Institute of Plant Genetics and Crop Plant Research has used AI and big data to develop a method of determining which winter wheat varieties are best suited to specific locations. The study's results have been published in the journal Genome Biology.
The interaction between genotype and environmental conditions is crucial for a plant's performance and yield. For instance, a wheat variety may produce a high yield in one location but perform poorly in another with distinct environmental conditions. Therefore, the environment affects the performance of the genotype.
Given the increasing diversification of cultivation environments, it is crucial, in the context of climate change, to provide varieties tailored to specific local conditions. The research team, therefore, focused on modeling the interactions between genotype and environment as precisely as possible. This is essential for accurately predicting yields in specific locations.
First, the scientists analyzed large amounts of data on winter wheat. Grain yield data from over 13,200 genotypes (lines and hybrids) grown and tested at 31 locations in Central Europe between 2010 and 2022 were collected for this purpose. This phenotypic data was then combined with genomic data (approximately 10,000 genetic markers) and environmental information, such as daily temperature and precipitation.
The researchers built and compared different prediction models, including statistical and deep learning approaches. They used the best model to forecast wheat line performance across 117 environments and to identify varieties suited to specific conditions.
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