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Climate And Agronomy, Not Genetics, Underpin Recent Maize Yield Gains In Favorable Environments, Gonzalo Rizzo, Juan Pablo Monzon, Fatima Amor Tenorio, Réka Howard, Kenneth G. Cassman, Patricio Grassini
Climate And Agronomy, Not Genetics, Underpin Recent Maize Yield Gains In Favorable Environments, Gonzalo Rizzo, Juan Pablo Monzon, Fatima Amor Tenorio, Réka Howard, Kenneth G. Cassman, Patricio Grassini
Department of Agronomy and Horticulture: Faculty Publications
Quantitative understanding of factors driving yield increases of major food crops is essential for effective prioritization of research and development. Yet previous estimates had limitations in distinguishing among contributing factors such as changing climate and new agronomic and genetic technologies. Here, we distinguished the separate contribution of these factors to yield advance using an extensive database collected from the largest irrigated maize-production domain in the world located in Nebraska (United States) during the 2005-to-2018 period. We found that 48% of the yield gain was associated with a decadal climate trend, 39% with agronomic improvements, and, by difference, only 13% with …
Utilizing Random Regression Models For Genomic Prediction Of A Longitudinal Trait Derived From High‐Throughput Phenotyping, Malachy T. Campbell, Harkamal Walia, Gota Morota
Utilizing Random Regression Models For Genomic Prediction Of A Longitudinal Trait Derived From High‐Throughput Phenotyping, Malachy T. Campbell, Harkamal Walia, Gota Morota
Department of Agronomy and Horticulture: Faculty Publications
The accessibility of high‐throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study was to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic …