Best Linear Unbiased Predictions of Environmental Effects on Grain Yield in Maize Variety Trials of Different Maturity Groups

Abstract

Development of new cultivars and agronomic improvements are key factors of increasing in future grain yield in maize grown in environments affected by climate change. Assessment of value for cultivation and use (VCU) reflects the results of latest breeding efforts showing yield trends, whereby external environmental covariates were rarely used. This study aimed to analyze several environmental effects including stress degree days (SDD) on grain yields in Croatian VCU trials in three maturity groups using linear mixed model for the estimation of fixed and random effects. Best linear unbiased predictions (BLUPs) of location-year interaction showed no pattern among maturity groups. SDD showed mostly non-significant coefficients of regression on location BLUPs for yield. Analyzing location BLUPs, it was shown that the effect became consistently stronger with later maturity, either positive or negative. The effects of management might play more critical role in maize phenology and yield formation compared with climate change, at least in suboptimum growing conditions often found in Southeast Europe. To facilitate more robust predictions of the crop improvement, the traditional forked approach dealing with G×E by breeders and E×M by agronomists should be integrated to GxExM framework, to assess the full gradient of com-binations forming the adaptation landscape.