Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. Data description: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

Objectives: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. Data description: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

High air temperatures during the crop growing season can reduce harvestable yields in major agronomic crops worldwide. Repeated and prolonged high night air temperature stress may compromise plant growth and yield. Crop varieties with improved heat tolerance traits as well as crop management strategies at the farm scale are thus needed for climate change mitigation. Crop yield is especially sensitive to night-time warming trends. Current studies are mostly directed to the elevated night-time air temperature and its impact on crop growth and yield, but less attention is given to the understanding of night-time soil temperature management. Delivering irrigation water through drip early evening may reduce soil temperature and thus improve plant growth. In addition, corn growers typically use high-stature varieties that inevitably incur excessive respiratory carbon loss from roots and transpiration water loss under high night temperature conditions. The main objective of this study was to see if root-zone soil temperature can be reduced through drip irrigation applied at night-time, vs. daytime, using three corn hybrids of different above-ground architecture in Uvalde, TX where day and night temperatures during corn growing season are above U.S. averages. The experiment was conducted in 2014. Our results suggested that delivering well-water at night-time through drip irrigation reduced root-zone soil temperature by 0.6 °C, increase root length five folds, plant height 2%, and marginally increased grain yield by 10%. However, irrigation timing did not significantly affect leaf chlorophyll level and kernel crude protein, phosphorous, fat and starch concentrations. Different from our hypothesis, the shorter, more compact corn hybrid did not exhibit a higher yield and growth as compared with taller hybrids. As adjusting irrigation timing would not incur an extra cost for farmers, the finding reported here had immediate practical implications for farm scale adaptation to hot environments.

Fumonisin mycotoxins are a persistent challenge to human and livestock health in tropical and sub-tropical maize cropping systems, and more efficient methods are needed to reduce their presence in food systems. We constructed a novel, low-cost device for sorting grain, the “DropSort”, and tested its effectiveness on both plastic kernel models and fumonisin-contaminated maize. Sorting plastic kernels of known size and shape enabled us to optimize the sorting performance of the DropSort. The device sorted maize into three distinct fractions as measured by bulk density and 100-kernel weight. The level of fumonisin was lower in the heaviest fractions of maize compared to the unsorted samples. Based on correlations among fumonisin and bulk characteristics of each fraction, we found that light fraction 100-kernel weight could be an inexpensive proxy for unsorted fumonisin concentration. Single kernel analysis revealed significant relationships among kernel fumonisin content and physical characteristics that could prove useful for future sorting efforts. The availability of a low-cost device (materials~USD 300) that can be used to reduce fumonisin in maize could improve food safety in resource-limited contexts in which fumonisin contamination remains a pressing challenge.