Establishment of genomic prediction models combined with weather and soil water information in sugar beet

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Finlay-Wilkinson regression is a popular method for analysing genotype-environment interaction in series of plant breeding and variety trials. The method involves a regression on the environmental mean, computed as the average of all genotype means. The environmental mean indexes the productivity of an environment, which is driven by a wide array of environmental factors. Increasingly, it is becoming feasible to characterize environments explicitly using observable environmental covariates. Hence, there is mounting interest to replace the environmental index with an explicit regression on such observable environmental covariates. This project develops a framework for such methods and implements this for genomic prediction in a commercial sugar beet breeding programme. The focus is on parsimonious models that allow replacing the environmental index by regression on synthetic environmental covariates formed as linear combinations of a larger number of observable environmental covariates. Different method will be employed to derive such synthetic covariates. A roster of environmental covariates for weather conditions and soil water supply will be assessed at ten locations using sensors and used to compute these synthetic covariates, which will be used for genomic prediction of hybrid performance in a sugar beet breeding programme. We will also develop an approach that allows using phenotypic data on the hybrid’s parents to potentially enhance the predictive accuracy for the hybrids.