Data-Driven Decisions for Accelerated Plant Breeding

Abstract

The success of plant breeding is critical to meet planetary challenges of food and water security for the world’s growing population. Plant breeders need to continuously develop new sustainable varieties with stable high yields despite changing climate with high resource use efficiency and pest/disease tolerance. In the current era of Breeding 4.0 where specific parts in the genome can be targeted, technological advances along with the data revolution greatly enhance the capacity of researchers to develop sustainable systems around the world. The evolution of breeding over the years correlates with the advancements in data analytics, and Breeding 4.0 uses prescriptive analytics to make informed decisions. The success of these modern breeding initiatives depends on intentional and standardized data management which not only ensures harmonization of multidimensional data (like genomics, phenotypic, and environment) from an organization but also facilitates community integration for sharing of resources. In modern digital agriculture, machine learning technologies will continue to play an important role in advancing research and product development within the agricultural industry. In the next phase of Breeding 5.0, unleashing the power of integrated technologies and big data will enable crop production systems to identify genotypes with an optimized phenotype for each different environment.