Dataset Information

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

ABSTRACT:

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.

SUBMITTER: Lima DC

PROVIDER: S-EPMC10502993 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

Lima Dayane Cristina DC Aviles Alejandro Castro AC Alpers Ryan Timothy RT Perkins Alden A Schoemaker Dylan L DL Costa Martin M Michel Kathryn J KJ Kaeppler Shawn S Ertl David D Romay Maria Cinta MC Romay Maria Cinta MC Gage Joseph L JL Holland James J Beissinger Timothy T Bohn Martin M Buckler Edward E Edwards Jode J Flint-Garcia Sherry S Gore Michael A MA Hirsch Candice N CN Knoll Joseph E JE McKay John J Minyo Richard R Murray Seth C SC Schnable James J Sekhon Rajandeep S RS Singh Maninder P MP Sparks Erin E EE Thomison Peter P Thompson Addie A Tuinstra Mitchell M Wallace Jason J Washburn Jacob D JD Weldekidan Teclemariam T Xu Wenwei W de Leon Natalia N

BMC research notes 20230914 1

<h4>Objectives</h4>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, environment ...[more]

PMID: 37710302

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Project description:Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Project description:BackgroundThe dataset presented here is an achievement of the H2020 European project "Integrated Pest Management of the Invasive Japanese Beetle, Popilliajaponica (IPM-Popillia)". This project addresses the challenge of a new risk to plant health in Europe, the invasion of the Japanese beetle, Popilliajaponica (Newman, 1838) (Coleoptera, Rutelidae) and provides an environmentally friendly IPM Toolbox to control the expanding pest populations across Europe. This study aims to present the records of terrestrial arthropod diversity with a special focus on four groups belonging to Carabids and Staphylinid beetles (Coleoptera), Opiliones and Anisolabididae (Dermaptera), collected with the potential to be used as biocontrol agents against P.japonica in future Integrated Pest Management programmes. A thorough sampling programme was conducted in maize and pasture fields in two Islands of the Azores (São Miguel and São Jorge) in the summer of 2022.New informationWe provided an inventory of the arthropods recorded in two Azorean agroecosystems (maize and pasture fields) from São Miguel and São Jorge Islands. A total of ten maize and ten pasture fields were sampled and a total of 360 pitfall traps were installed, 216 in São Miguel and 144 in São Jorge, for seven consecutive days in August and September of 2022.We collected 18559 specimens belonging to the phylum Arthropoda, four classes, twelve orders, twenty-six families and forty morphospecies (two identified at the family level as carabid and Staphylinid larvae and 38 identified at the species level). We identified 38 taxa at the species level (n = 18281). Of the 38 identified taxa, 18 species were predators, 15 were plant feeders and five were omnivores. The 18 predators belong to the following families: 10 species were Carabidae, two Staphylinidae, one Anisolabididae, one Chrysopidae, one Leiobunidae, one Nabidae, one Phalangiidae and one Scathophagidae. Concerning the origin of the predators, we recorded five native species: two Carabidae, one Leiobunidae, one Scathophagidae and one Nabidae. The other 13 predator species were introduced or indeterminate.

Dataset Information

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

Objectives

Data description

Publications

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

Similar Datasets

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