Horticulture Commons^™

2020-2021 Field Seasons Of Maize Gxe Project Within The Genomes To Fields Initiative, Dayane Cristina Lima, Alejandro Castro Aviles, Ryan Timothy Alpers, Alden Perkins, Dylan L. Schoemaker, Martin Costa, Kathryn J. Michel, Shawn Kaeppler, David Ertl, Maria Cinta Romay, Joseph L. Gage, James Holland, Timothy Beissinger, Martin Bohn, Edward Buckler, Jode Edwards, Sherry Flint-Garcia, Michael A. Gore, Candice N. Hirsch, Joseph E. Knoll, John Mckay, Richard Minyo, Seth C. Murray, James Schnable, Rajandeep S. Sekhon, Maninder P. Singh, Erin E. Sparks, Peter Thomison, Addie Thompson, Mitchell Tuinstra, Jason Wallace, Jacob D. Washburn, Teclemariam Weldekidan, Wenwei Xu, Natalia De Leon

Department of Agronomy and Horticulture: Faculty Publications

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 …

Genomes To Fields 2022 Maize Genotype By Environment Prediction Competition, Dayane Cristina Lima, Jacob D. Washburn, José Ignacio Varela, Qiuyue Chen, Joseph L. Gage, Maria Cinta Romay, James Holland, David Ertl, Marco Lopez-Cruz, Fernando M. Aguate, Gustavo De Los Campos, Shawn Kaeppler, Timothy Beissinger, Martin Bohn, Edward Buckler, Jode Edwards, Sherry Flint-Garcia, Michael A. Gore, Candice N. Hirsch, Joseph E. Knoll, John Mckay, Richard Minyo, Seth C. Murray, Osler A. Ortez, James C. Schnable, Rajandeep S. Sekhon, Maninder P. Singh, Erin E. Sparks, Addie Thompson, Mitchell Tuinstra, Jason Wallace, Teclemariam Weldekidan, Wenwei Xu, Natalia De Leon

Department of Agronomy and Horticulture: Faculty Publications

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 …

2018–2019 Field Seasons Of The Maize Genomes To Fields (G2f) G X E Project, Dayane Dayane 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 Xu31, Natalia De Leon

Department of Agronomy and Horticulture: Faculty Publications

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. …

A Leaf-Level Spectral Library To Support High-Throughput Plant Phenotyping: Predictive Accuracy And Model Transfer, Nuwan K. Wijewardane, Huichun Zhang, Jinliang Yang, James C. Schnable, Daniel P. Schachtman, Yufeng Ge

Department of Agronomy and Horticulture: Faculty Publications

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets …

On Correlation Between Canopy Vegetation And Growth Indexes Of Maize Varieties With Different Nitrogen Efficiencies, Xia Zhao, Shuaili Wang, Tao Wen, Jiamin Xu, Bao Huang, Shufeng Yan, Gangqiang Gao, Yali Zhao, Hongping Li, Jiangfang Qiao, Jinliang Yang, Lianhai Wu, Hongwei Wang, Tianxue Liu, Xinyuan Mu

Department of Agronomy and Horticulture: Faculty Publications

Studying the canopy spectral reflection characteristics of different N-efficient maize varieties and analyzing the relationship between their growth indicators and spectral vegetation indices can help the breeding and application of N-efficient maize varieties. To achieve the optimal management of N fertilizer resources, developing N-efficient maize varieties is necessary. In this research, maize varieties, i.e., the low-N-efficient (Zhengdan 958, ZD958), the high-N efficient (Xianyu 335, XY335), the double-high varieties (Qiule 368, QL368), and the double inefficient-type varieties (Yudan 606 YD606), were used as materials. Results indicate that nitrogen fertilization significantly increased the vegetation indices NDVI, GNDVI, GOSAVI, and RVI of maize …