Frontiers in plant science · 2026
Multi-environment evaluations reveal that peanut (Arachis hypogaea L.) quality traits are equally determined by genotype and environment.
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Jiang Xiao, Zhao Jianxin, Xu Jing, Yin Dongmei, Xie Hongfeng, Chen Na, Pan Lijuan, Yin Xiangzhen, Ma Junqing, Zhao Xuhong, Chi Xiaoyuan
Аннотация
BACKGROUND: Peanut (Arachis hypogaea L.) is a widely planted cash and oil crop, and regional and meteorological factors, as the most critical environmental factors, play crucial roles in peanut kernel quality traits. To explore the relationship between peanut quality traits and environmental factors, this study dissected the quality traits and the genotype-by-environment interaction factors using seven varieties across multiple environments.
METHODS: Analysis of variance (ANOVA) was used to partition the variance of kernel quality traits, correlation analysis was used to examine the associations between quality traits and environmental factors, and genotype plus genotype-by-environment (GGE) biplot analysis was used to evaluate the association between genotypes and multiple environments.
RESULTS: ANOVA showed that environmental factors were the largest source of variance among the three quality traits. Correlation analysis showed that long sunshine duration, high accumulated temperature, and large mean daily temperature difference were beneficial to the accumulation of oil and protein. Oil content showed an overall trend of increasing with latitude; protein content performed best in the Huang-Huai-Hai and Yangtze River Basin production areas; and sucrose content showed an overall trend of decreasing with latitude and longitude. GGE analysis further showed that HY33 achieved optimal oil content with strong multi-environment stability, while HY21 had superior protein content and the most stable sucrose performance across multi-environments.
CONCLUSIONS: This study provides theoretical guidance for adjusting planting arrangements in different regions according to different climatic conditions and peanut growing periods, which is of great significance as a guide for improving peanut quality.
METHODS: Analysis of variance (ANOVA) was used to partition the variance of kernel quality traits, correlation analysis was used to examine the associations between quality traits and environmental factors, and genotype plus genotype-by-environment (GGE) biplot analysis was used to evaluate the association between genotypes and multiple environments.
RESULTS: ANOVA showed that environmental factors were the largest source of variance among the three quality traits. Correlation analysis showed that long sunshine duration, high accumulated temperature, and large mean daily temperature difference were beneficial to the accumulation of oil and protein. Oil content showed an overall trend of increasing with latitude; protein content performed best in the Huang-Huai-Hai and Yangtze River Basin production areas; and sucrose content showed an overall trend of decreasing with latitude and longitude. GGE analysis further showed that HY33 achieved optimal oil content with strong multi-environment stability, while HY21 had superior protein content and the most stable sucrose performance across multi-environments.
CONCLUSIONS: This study provides theoretical guidance for adjusting planting arrangements in different regions according to different climatic conditions and peanut growing periods, which is of great significance as a guide for improving peanut quality.