Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains

Abstract

In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers’ risk from year-to-year environmental variation in India’s wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.

Environment Characterization in Sorghum (Sorghum bicolor L.) by Modeling Water-Deficit and Heat Patterns in the Great Plains Region, United States

“…This scenario seems more reasonable when the crop is exposed to low intensity of drought for long periods ( Ben Haj Salah and Tardieu, 1997 ), but not to a severe stress when this condition can cause damage at the cellular level losing the ability of the plant to fully recover from this stressor ( Prasad et al, 2019 ). Nevertheless, this approach has been widely tested and its conclusions validated in sorghum, maize, mungbean, wheat, chickpea, rice, and pea ( Chenu et al, 2011 , 2013 ; Sadras et al, 2012 ; Chauhan et al, 2013 ; Kholová et al, 2013 ; Chauhan and Rachaputi, 2014 ; Cooper et al, 2014 ; Hammer et al, 2014 ; Harrison et al, 2014 ; Heinemann et al, 2015 , 2019 ; Lobell et al, 2015 ; Seyoum et al, 2017 , 2018 ; Crespo-Herrera et al, 2021 ). Using the same method in maize ( Zea mays L.) in the US Corn Belt, Cooper et al (2014) identified relevant water patterns and characterized their spatial distribution for the region.…”

Effect of Flowering Time-Related Genes on Biomass, Harvest Index, and Grain Yield in CIMMYT Elite Spring Bread Wheat

“…CENEB, near Ciudad Obregon, Mexico, is the key research and breeding site for the CIMMYT Global Wheat Program. Multi-year testing and controlled irrigation to create a simulation of diverse selection environments at CENEB have delivered novel high-yield potential germplasms worldwide [45], and research has shown that the conditions at this site represent major CIMMYT target regions for wheat breeding in the developing world [46][47][48].…”

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

“…In plant breeding research, mostly based on the selection of bestevaluated genotypes in a certain experimental network, this approach discriminates which genetic and non-genetic factors affect adaptative responses and yield performance. Thus, a conscious and well-conducted environmental characterization is crucial to bridge the results obtained in some experimental networks and expectations for the target population of environments (TPEs) of the breeding program (e.g., Chenu et al, 2011;Heinemann et al, 2015Heinemann et al, , 2019Crespo-Herrera et al, 2021).…”