AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet

Xinwang Dang; Xiaohang Hu; Yahuai Ma; Yanli Li; Wenliang Kan; Xinjiu Dong

doi:10.7717/peerj.16882

AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet

PeerJ. 2024 Feb 22:12:e16882. doi: 10.7717/peerj.16882. eCollection 2024.

Authors

Xinwang Dang¹, Xiaohang Hu^{1

2}, Yahuai Ma^{1

2}, Yanli Li^{1

2}, Wenliang Kan³, Xinjiu Dong⁴

Affiliations

¹ Academy of Modern Agriculture and Ecology Environment, Heilongjiang University, Harbin, Heilongjiang, China.
² National Sugar Improvement Center, Harbin, Heilongjiang, China.
³ Heilongjiang Academy of Agricultural Reclamation Sciences, Qiqihar, Heilongjiang, China.
⁴ Cash Crop Research Institute of Xinjiang Academy of Agricultural Science, Urumqi, Xinjiang, China.

Abstract

Sugar beet, an important sugar crop, contributes significantly to the world's sugar production. However, genotype-environment interactions (GEI) often affect the quality characteristics of sugar beet. Hence, understanding the effects of GEI on sugar beet quality can aid in identifying high-quality genotypes that can adapt to different environments. Traditional variance analysis can only be used to examine the yield of a variety and not its specific adaptability to specific conditions. Therefore, more comprehensive analytical methods are required to evaluate the characteristics of the variety under specific environments. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype × environment interaction (GGE) biplot models can be employed to comprehensively evaluate different varieties and address the drawbacks associated with a single evaluation method. Moreover, these models also allow us to explore new varieties more objectively and comprehensively. In this study, the adaptability and stability of 16 sugar beet varieties, in terms of yield and sugar content, were evaluated using AMMI and GGE biplot analysis in seven pilot projects undertaken in 2022. In the assessment of a small but significant proportion of the total GEI variance for the two qualitative traits (yield and sugar content), 80.58% of the variance was explained by the cumulative contribution of IPC1, IPC2, and IPC3. AMMI and GGE biplots clearly highlighted that KWS4207 (G3) exhibited high and stable quality. They also demonstrated that the experiments in Jalaid Banner (Inner Mongolia) (E7) were the most representative. Together, the results suggested that the comprehensive application of AMMI and GGE biplot analysis allowed for a more comprehensive, scientific, and effective evaluation of sugar beet varieties across different regions. The findings offer a theoretical basis for sugar beet breeding and could guide the rational design of experiments for testing new varieties of sugar beet.

Keywords: AMMI; GGE biplot; Quality; Stability; Sugar beets.

MeSH terms

Ammi*
Beta vulgaris* / genetics
Gene-Environment Interaction
Genotype
Plant Breeding / methods
Sugars

Substances

Sugars

Grants and funding

This work was supported by the National Sugar Industry Technical System Decomposition Project “Research on Sugar Beet Ferti-lization Decision-making Model Based on UAV Remote Sensing Technology” No. CARS-170202; Research on soil improvement and carbon emission in black soil area by returning filtered mud from agricultural waste sugar plant to field, Heilongjiang Department of Ecology and Environment No. HST2022TR003. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.