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Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies

2022; Linguagem: Inglês

10.1002/9781119874157.ch8

0730-2207

Mark Cooper, Carlos D. Messina, Tom Tang, Carla Gho, Owen Powell, Dean Podlich, Frank Technow, Graeme Hammer,

Plant Virus Research Studies

Genotype-by-environment-by-management (G × E × M) interactions for crop productivity represent both challenges and opportunities for long-term crop improvement. They need to be understood and harnessed to accelerate crop improvement to improve our chances of achieving the targets for sustainable agriculture and global food security that are required to enable sustainable development ( https://sdgs.un.org/goals ). Experimental efforts have emerged to quantify their importance, study their genetic and ecophysiological bases, and support efforts to exploit nascent opportunities. The large and complex G × E × M factorial limits the scope and feasibility for purely experimental approaches at all stages of crop improvement programs. However, iterative experimental modeling approaches, combined with advances in genomics, enviromics, phenomics, simulation, and prediction methodologies, offer a range of opportunities, covering both genomic prediction for breeding and agronomic prediction for enhancing the realization of on-farm productivity targets and managing risk. There has been limited coordination of breeding and agronomic prediction efforts to date. We consider the different perspectives of each area and advances toward their integration. This aims to bring an "end-to-end" perspective to the development of G × E × M prediction methodology for sustainable crop improvement; from the creation of new genotypes in breeding programs to their use in combination with agronomic management strategies within on-farm production systems. We draw on the historical and current efforts to improve yield productivity of maize ( Zea mays L.) for the US Corn Belt as a source of examples. Extensions to and examples from other crops and geographies are also considered. Opportunities for G × E × M prediction to enable circular agriculture strategies, that balance crop productivity and sustainable resource use, are identified as potential targets for the future.