Environmental context of phenotypic plasticity in flowering time in sorghum and rice

Authors: GUO, TINGTING - Huazhong Agricultural University; WEI, JIALU - Iowa State University; Li, Xianran; YU, JIANMING - Iowa State University

Submitted to: Journal of Experimental Botany
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/17/2023
Publication Date: 11/6/2023
Citation: Guo, T., Wei, J., Li, X., Yu, J. 2023. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. Journal of Experimental Botany. 75(3):1004-1015. https://doi.org/10.1093/jxb/erad398.
DOI: https://doi.org/10.1093/jxb/erad398

Interpretive Summary: Conducting a multi-environment trial (MET) is essential to parse out the effect of genotype, environment, and their interaction on phenotypic plasticity. Meanwhile, how to design a MET to efficiently capture the environmental effect, which is a major contributor to phenotypic variation, has not been studied. For crops growing in natural field, environment is typically defined by testing sites and growing seasons. This study, with empirical METs across large geographical regions from two crops, tested potential strategies on optimizing environment for achieving the goal of predicting phenotypic plasticity properties through genomic prediction. The results showed both environment sample size and environmental mean range of the subset affected the consistency for prediction accuracy.

Technical Abstract: Phenotypic plasticity, which quantifies the outcome of how genotypes respond to varied environmental conditions, is an important topic in biology and critical for precision agriculture and sustainable agriculture. However, how to improve the design of multi-environment trials to study phenotypic plasticity in crops remains a challenge. Here, with two genetic populations in sorghum [Sorghum bicolor (L.) Moench] and rice (Oryza sativa L.) tested in a large geographical region, we examined the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to optimize the study design. Both environment sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental means or a large sample size resulted in parameter estimation consistent with the overall pattern. Furthermore, high prediction accuracy was obtained for reaction norms of untested genotypes using genomic prediction models built from tested genotypes under the subsets of environments with either a large range or sample size. To further examine testing site optimization within a geographic region, we generated 1,428 and 1,674 simulated environments for these two genetics populations to analyze the variability of environmental index values. The sites generating a wide range of environmental index values should be given priority in site selection to breed varieties with boarder environmental adaptability. By leveraging environmental data, genomics, and data analytics, prioritizing the testing sites to achieve a large environmental range can help study phenotypic plasticity and breed crops with different performance expectations.