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

Ana J P Carcedo,Carlos D Messina,Geoffrey P Morris,Ignacio A Ciampitti,Jane Lingenfelser,Paula Demarco,Laura Mayor

doi:10.3389/fpls.2022.768610

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.

Highlights

Sorghum (Sorghum bicolor L.) crop improvement during the last six decades has been associated with targeted changes in genotype (G), management practices (M), and environment (E), with crop productivity considered as the outcome of a complex GxExM interaction (Duvick and Cassman, 1999; Assefa and Staggenborg, 2010; Ciampitti et al, 2020)
Relevant grain sorghum environments were classified for the United States (US) Great Plains region using a classical approach integrating relevant soil, weather, and field data
Four outcomes are worth highlighting from this study: (i) the defined patterns assisted in explaining the basis for the observed G x E interaction for yield, (ii) knowledge of the spatial and temporal distribution of the most frequent patterns can help defining sites for evaluation trials, design of breeding programs, future target traits, and exploring innovations linked to crop management, (iii) the validation of the patterns via a sensitivity analysis with an independent dataset is a novel approach that should be included in future environtyping framework, and (iv) clustering low frequency patterns to explore the most relevant environments demonstrated to be a valid strategy to create a classification easier to be applied by breeders, agronomists, and farmers, without introducing any noticeable trade-off

Summary

Introduction

Sorghum (Sorghum bicolor L.) crop improvement during the last six decades has been associated with targeted changes in genotype (G), management practices (M), and environment (E), with crop productivity considered as the outcome of a complex GxExM interaction (Duvick and Cassman, 1999; Assefa and Staggenborg, 2010; Ciampitti et al, 2020). In the presence of G x E, the effectiveness of genotype evaluation is highly influenced by (i) the ability to discriminate genotypes within an environment, (ii) the representativeness of this environment within the target population of environments (TPE), and (iii) its repeatability (Yan et al, 2011). Breeding strategies designed to exploit components of genetic variation associated with GxE interactions need to characterize the TPE. Environmental characterization is a critical step in defining the TPE suitable for genotype evaluation upon the trait targets of the breeding program

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