Substantial economic losses have occurred because of unacceptable concentrations of deoxynivalenol (DON) in wheat. Accurate predictions of DON in mature grain at wheat heading are needed to make decisions on whether a control strategy is needed. Our objective was to identify important weather variables, and their timing, for predicting concentrations of DON in mature grain at wheat heading. We measured the concentration of DON in 399 farm fields in southern Ontario, Canada, from 1996 to 2000. DON varied in field samples from undetectable to over 29 μg g-1. Weather variables, such as daily rainfall, daily minimum and maximum air temperatures, and hourly relative humidity, were estimated for each field from nearby weather stations and were normalized to the date of 50% head emergence. Stepwise multiple regression procedures determined the most important weather variables and their timing around heading. DON was responsive to weather in three critical periods around heading. In the first period, 4 to 7 days before heading, DON generally increased with the number of days with >5 mm of rain and decreased with the number of days of <10°C. In the second period, 3 to 6 days after heading, DON increased with the number of days of rain >3 mm and decreased with days exceeding 32°C. In the third period, 7 to 10 days after heading, DON increased with number of days with >3 mm of rain. A relationship between relative humidity and DON was not detected. Overall, 73% of the variation in the concentration of DON was explained by using weather from all three critical periods. Concentrations of DON <2.0 μg g-1 were predicted best; in fact, concentrations of DON of <1.0 μg g-1 were predicted correctly on over 89% of the fields used to train the model.

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements.

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High-dimensional and high throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

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