The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

Consumption of foodstuff with low contents of saturated fatty acids is considered beneficial for human health. Reducing saturated fatty acid content in oilseed rape (canola) and other oil and protein crops is a relevant breeding aim. The objective of this work was to study the genetic variation and inheritance of saturated fatty acids in two DH populations of oilseed rape, to map QTL and to identify candidate genes. In addition, the correlation to other seed quality traits was studied. To this end, two half-sib DH populations were tested in up to five field environments in north-western Europe and seeds harvested from open-pollinated seeds were analyzed. Genotyping was performed using Illumina Brassica 15 K SNP chip. In both populations, significant effects for the genotypes and for the environments were detected, and heritability ranged from 68 to 89% for the predominant palmitic acid and stearic acid content. Up to 48 QTL for different fatty acids, oil and acid detergent lignin (ADL) content were mapped in the two populations. Co-locating QTL for palmitic acid, stearic acid, the C16/18 fatty acid ratio, the FATB/A ratio, oil and ADL content were identified on different chromosomes. A large number of candidate genes were identified within the vicinity of QTL flanking markers. Identification of several co-locating QTL positions, of associated candidate genes and SNP markers should facilitate oilseed rape breeding for low saturated fatty acid content.