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Toward a Better Understanding of Genotype × Environment × Management Interactions-A Global Wheat Initiative Agronomic Research Strategy.

ABSTRACT: The Wheat Initiative (WI) and the WI Expert Working Group (EWG) for Agronomy (www.wheatinitiative.org) were formed with a collective goal to "coordinate global wheat research efforts to increase wheat production, quality, and sustainability to advance food security and safety under changing climate conditions." The Agronomy EWG is responsive to the WI's research need, "A knowledge exchange strategy to ensure uptake of innovations on farm and to update scientists on changing field realities." The Agronomy EWG aims to consolidate global expertise for agronomy with a focus on wheat production systems. The overarching approach is to develop and adopt a systems-agronomy framework relevant to any wheat production system. It first establishes the scale of current yield gaps, identifies defensible benchmarks, and takes a holistic approach to understand and overcome exploitable yield gaps to complement genetic increases in potential yield. New opportunities to increase productivity will be sought by exploiting future Genotype × Environment × Management synergies in different wheat systems. To identify research gaps and opportunities for collaboration among different wheat producing regions, the EWG compiled a comprehensive database of currently funded wheat agronomy research (n = 782) in countries representing a large proportion of the wheat grown in the world. The yield gap analysis and research database positions the EWG to influence priorities for wheat agronomy research in member countries that would facilitate collaborations, minimize duplication, and maximize the global impact on wheat production systems. This paper outlines a vision for a global WI agronomic research strategy and discusses activities to date. The focus of the WI-EWG is to transform the agronomic research approach in wheat cropping systems, which will be applicable to other crop species.

SUBMITTER: Beres BL

PROVIDER: S-EPMC7308648 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Toward a Better Understanding of Genotype × Environment × Management Interactions-A Global Wheat Initiative Agronomic Research Strategy.

Beres Brian L BL Hatfield Jerry L JL Kirkegaard John A JA Eigenbrode Sanford D SD Pan William L WL Lollato Romulo P RP Hunt James R JR Strydhorst Sheri S Porker Kenton K Lyon Drew D Ransom Joel J Wiersma Jochum J

Frontiers in plant science 20200616

The Wheat Initiative (WI) and the WI Expert Working Group (EWG) for Agronomy (www.wheatinitiative.org) were formed with a collective goal to "coordinate global wheat research efforts to increase wheat production, quality, and sustainability to advance food security and safety under changing climate conditions." The Agronomy EWG is responsive to the WI's research need, "A knowledge exchange strategy to ensure uptake of innovations on farm and to update scientists on changing field realities." The ...[more]

PMID: 32612624

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Project description:Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Dataset Information

Toward a Better Understanding of Genotype × Environment × Management Interactions-A Global Wheat Initiative Agronomic Research Strategy.

Publications

Toward a Better Understanding of Genotype × Environment × Management Interactions-A Global Wheat Initiative Agronomic Research Strategy.

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