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

Abstract

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolor L.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water–heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006–2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.

Breeding crops for drought-affected environments and improved climate resilience

“…Advances in proximal and remote sensor methodology for spatial and temporal measurements of important environmental variables have enabled refinements in the level of resolution and deconvolution of some of the combinations of abiotic and biotic environmental variables that contribute to GxE interactions throughout the crop lifecycle ( Figure 4 ; Cooper et al, 2014a , 2014b , 2020 ; Costa-Neto et al, 2021 ; Gage et al, 2021 ; Li et al, 2021 ; Smith et al, 2021a ; Washburn et al, 2021 ; Diepenbrock et al, 2022 ; Messina et al, 2022a , 2022c ; Piepho, 2022 ). To target breeding efforts for stresses such as drought, investigations have been undertaken to quantify the occurrences of repeatable GxE interactions for yield and the contributions from traits contributing to improved yield stability across drought-affected environments ( Figures 2–5 ; Chapman et al, 2000 , 2003 ; Löffler et al, 2005 ; Chenu et al, 2011 ; Blum, 2011a ; Kholová et al, 2013 ; Cooper et al, 2014a ; Messina et al, 2015 , 2022a , 2022c ; Carcedo et al, 2022 ). Today the availability of many drought-specific environmental predictors has created new opportunities for their incorporation within prediction models to account for repeatable components of the total GxE interaction variance for a TPE ( Boer et al, 2007 ; Heslot et al, 2014 ; Jarquín et al, 2014 ; Millet et al, 2019 ; Messina et al, 2018 ; Costa-Neto et al, 2021 ; Crossa et al, 2021 ; Gage et al, 2021 ; Li et al, 2021 ; Resende et al, 2021 ; Washburn et al, 2021 ; Diepenbrock et al, 2022 ; Piepho, 2022 ).…”

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques

“…While in 2019 most of the flowering period in ME1 had a high vapor pressure deficit ( Figure 4A ) and maximum temperatures between 34.5°C and °C 39.3°C ( Supplementary Figure S15A ), 2020 had milder temperatures and a smaller vapor pressure deficit. This approach can leverage plant ecophysiology knowledge aiding to identify the main sources of the genotype-environment interaction to select stress-resilient hybrids ( Costa-Neto and Fritsche-Neto, 2021 ; Resende et al., 2021 ; Carcedo et al., 2022 ).…”

Plant Breeding in the face of climate change

“…In this case, weights are estimated from the relative frequency of sampled environments over the expected frequencies of the environment types that comprise the mixture of environments of the TPE. Frequency of environments could be defined based on climatology or more sophisticated methods (Chapman et al 2000; Löffler et al 2005; Kholová et al 2013; Ramirez Villegas et al, 2020; Cooper & Messina, 2021; Carcedo et al 2022), such as crop growth models (CGM, Jones et al 2003; Holsworth et al, 2014). CGMs are functions that approximate the phenotypic function, where the function F represents perfect knowledge of the observable phenotype as determined by nature, the function Q represents the phenotypes that are predictable based on our current scientific knowledge and phenotyping systems, and H is the function that represents what we don’t know, it is not knowable/predictable (Day 1976), or we do not want to include in the model.…”

“…In this case, weights are estimated from the relative frequency of sampled environments over the expected frequencies of the environment types that comprise the mixture of environments of the TPE. Frequency of environments could be defined based on climatology or more sophisticated methods (Chapman et al 2000;Löffler et al 2005;Kholová et al 2013;Ramirez Villegas et al, 2020;Carcedo et al 2022), such as crop growth models (CGM, Jones et al 2003;Holsworth et al, 2014). CGMs are functions that approximate the phenotypic function,…”