Dayane Cristina Lima,Alejandro Castro Aviles,Ryan Timothy Alpers,Bridget A. McFarland,Shawn Kaeppler,David Ertl,Maria Cinta Romay,Joseph L. Gage,James Holland,Timothy Beissinger,Martin Bohn,Edward Buckler,Jode Edwards,Sherry Flint-Garcia,Candice N. Hirsch,Elizabeth Hood,David C. Hooker,Joseph E. Knoll,Judith M. Kolkman,Sanzhen Liu,John McKay,Richard Minyo,Danilo E. Moreta,Seth C. Murray,Rebecca Nelson,James C. Schnable,Rajandeep S. Sekhon,Maninder P. Singh,Peter Thomison,Addie Thompson,Mitchell Tuinstra,Jason Wallace,Jacob D. Washburn,Teclemariam Weldekidan,Randall J. Wisser,Wenwei Xu,Natalia de Leon
2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project
Maize,Genotype by environment,Phenotype,Variable environments,Grain yield
Objectives This report provides information about the public release of the 2018–2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. Data description Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project’s inception.
2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project

This report provides information about the public release of the 2018–2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions.

Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project’s inception.

Maize (Zea mays subsp. mays L.) plays an important role in the global economy. As a crop, it displays a variety of uses such as food, feed, and fuel. At the same time, and due to its versatility and relevance, maize has been widely studied. The Genomes to Fields (G2F) is a collaborative initiative involving scientists from the public sector that support growers, consumers, and society. G2F researchers generate phenotypic, genotypic, environmental, and metadata datasets to facilitate the understanding of the potential and challenges of maize production in different environments. Individual genotype performances differ across environments, and the magnitude of this difference dictates the importance of the Genotype by Environment (G × E) interaction. Understanding and harnessing G × E interactions improves the efficiency in the use and allocation of resources, and it facilitates the identification of genotypes with higher stability across a range of locations, the identification of locations where the effect of G x E is minimized, and the identification of mechanisms affecting the differential response of phenotypes to variable environments. Furthermore, advances in our understanding of the fundamental components contributing to the differential response of plants to environmental cues will also improve genomic and phenotypic predictabilities for traits of interest. Therefore, this data release provides a unique resource of combined agronomic, phenological, and morphological information to dissect G × E interaction. In the 2018 and 2019 experiments, 1153 publicly available hybrids were evaluated through a network of collaborators in 32 different locations. The main group of hybrids was produced by the cross of doubled-haploid (DH) inbred lines from a collection of three biparental populations that share one parent in common (PHW65) and PHN11, Mo44, and MoG as the alternative parent, to two ex-PVP inbred testers, LH195 in Midwest to Southern locations, and PHT69 in Northern locations.

The 2018 and 2019 datasets are publicly available via CyVerse/iPlant and structured as described in Table 1. Briefly, the datasets included here are: Phenotypic dataset: Phenotypic measurements that follow a standard set of instructions, available in the G2F webpage [1]. Standard traits include days to anthesis, days to silking, ear height, plant height, stand count, stalk lodging, root lodging, grain moisture, test weight, plot weight, and estimated grain yield. Raw data and quality-controlled data are reported. Out of range observations were set to missing following the rules described in the readMe and data description files. Genotypic dataset: Inbred parents of the tested hybrids were genotyped using the Practical Haplotype Graph (PHG) [2, 3]. The data is minimally filtered, allowing the public to perform their own quality control steps prior to using it. The raw sequencing reads are available under BioProject ID PRJNA530187 [4]. The code used to create the genotypic data is also available at https://bitbucket.org/bucklerlab/g2f_2018_phg_genotyping/src/master/. Environmental dataset: WatchDog 2700 weather stations (Spectrum Technologies) were placed at each field site. Data was collected at 30-min intervals from planting through harvest at each location. The geographic locations of the experiments are not identical across years due to crop rotation management practices; thus, the locations of the weather stations vary across years. Each station measured wind speed, direction, and gust; air temperature, dewpoint, relative humidity; soil temperature and moisture; rainfall and solar radiation. Additional measurements taken at selected sites included soil electrical conductivity, ultra-violet light, carbon dioxide, and photosynthetically active radiation. Instructions for weather station maintenance activities including pre-season tasks, field setup, maintenance throughout the growing season, and removal are available in the G2F webpage [5]. Soil dataset: Each field location collected soil samples that represent the experiment field. Collaborators follow instructions available on the G2F webpage for sample collection [5]. Supplemental dataset: Supplemental information consists of metadata (any field-level data collected at planting, in season, and/or at harvest), agronomic information (list of pesticides, nutrients, and irrigation applied), and cooperator list (collaborators responsible for the field locations in 2018 and 2019).

These datasets contain missing data. Missing data includes data not reported by collaborators or erroneous data as determined by data description files. In 2019, some locations had pedigree information set to missing due to packaging problems and only plot number was reported in the phenotypic dataset to reduce misinterpretation.