David Ertl
YOU?
Author Swipe
View article: Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients
Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients Open
Understanding genotype‐by‐environment (G × E) interactions that underlie phenotypic variation, when observed for complex traits in multi‐environment trials, is important for biological discovery and for crop improvement. The regression‐on‐…
View article: Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems
Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems Open
Maize (Zea mays L.) is the world's most productive grain crop and a cornerstone of global food supply. However, in temperate agricultural systems, maize exhibits 2 key anomalies. First, as a tropical species, maize cannot be planted in the…
View article: Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates
Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates Open
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of …
View article: Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates
Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates Open
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of …
View article: Applications, Benefits, and Challenges of Genome Edited Crops
Applications, Benefits, and Challenges of Genome Edited Crops Open
The tools of genome editing were described more than a decade ago as promising ways to accelerate crop improvement in addition to applications for human and animal health. Now, a decade later, we are seeing applications of genome editing a…
View article: 72 The AG2pi Vision for Resources in Agricultural Genomics and Phenomics: How Asas Can Contribute
72 The AG2pi Vision for Resources in Agricultural Genomics and Phenomics: How Asas Can Contribute Open
To meet the challenges of an increasing global population and multiple environmental stressors on agricultural production, it is essential to better understand how genotype (G) and environment (E) influence phenotype for traits of economic…
View article: 175 Building the Tools to Solve the Genome to Phenome Puzzle in Agriculture
175 Building the Tools to Solve the Genome to Phenome Puzzle in Agriculture Open
The USDA-NIFA is developing a vision for agricultural genomics to phenomics research (AG2P) through two programs. In 2017, the Functional Annotation of Animal Genomes (FAANG) request for proposals was released, which has provided over $9 M…
View article: Genomes to Fields 2022 Maize Genotype by Environment Prediction Competition
Genomes to Fields 2022 Maize Genotype by Environment Prediction Competition Open
Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (G x E) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize G x E project field trials, leveraging the datasets previously …
View article: 2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project
2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project Open
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 acros…
View article: 2020-2021 Field Seasons of Maize G x E Project within Maize Genomes to Fields Initiative
2020-2021 Field Seasons of Maize G x E Project within Maize Genomes to Fields Initiative Open
Objectives: This release note describes the Maize G x E project datasets within the Genomes to Fields (G2F) Initiative. The Maize G x E project aims to understand genotype by environment (G × E) interactions and use the information collect…
View article: Current Challenges and Future of Agricultural Genomes to Phenomes in the U.S.
Current Challenges and Future of Agricultural Genomes to Phenomes in the U.S. Open
Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (…
View article: Yield prediction through integration of genetic, environment, and management data through deep learning
Yield prediction through integration of genetic, environment, and management data through deep learning Open
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. T…
View article: Community Perspectives: Genome to Phenome in Agricultural Sciences
Community Perspectives: Genome to Phenome in Agricultural Sciences Open
The Agricultural Genome to Phenome Initiative (AG2PI) project was launched in 2020 with support from the United States Department of Agriculture National Institute of Food and Agriculture. The goal of AG2PI is to engage and prepare the sci…
View article: Erratum to: Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds
Erratum to: Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds Open
Relative utility of agronomic, phenological, and morphological traits for assessing genotype
View article: Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning
Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning Open
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. T…
View article: Ten simple rules to ruin a collaborative environment
Ten simple rules to ruin a collaborative environment Open
Trigger warning: Here, you will find a bit of satire, written from the not-so-funny, real experiences of the authors who have been involved in “team science” collaboratives. The material presented below covers topics that readers may find …
View article: Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project
Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project Open
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…
View article: The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment
The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment Open
High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments a…
View article: Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds
Relative utility of agronomic, phenological, and morphological traits for assessing genotype‐by‐environment interaction in maize inbreds Open
Plant breeders face the challenge of genotype × environment interaction (G × E) in comprehensively breeding for expanded geographic regions. An improved understanding of G × E sensitivity of traits and the environmental features that effec…
View article: Assessing requirements and concerns of potential users of automated driving services progressed by Internet of Things using a co-designer approach
Assessing requirements and concerns of potential users of automated driving services progressed by Internet of Things using a co-designer approach Open
Road vehicles are becoming increasingly automated and connected due to rapid technological progress and digitalization trends. Vehicle connectivity might improve automated driving (AD) in various ways. While the benefits of convergence of …