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A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network

Author

Listed:
  • Longpeng Bai

    (Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Hucheng Ring Road 999, Shanghai 201306, China
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Kaiyi Wang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Innovation Center for Digital Seed Industry, Beijing 100097, China)

  • Qiusi Zhang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Qi Zhang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Xiaofeng Wang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Shouhui Pan

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Beijing PAIDE Science and Technology Development Co., Ltd., Beijing 100097, China)

  • Liyang Zhang

    (SDIC Seed Technology Co., Ltd., Beijing 100034, China)

  • Xuliang He

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Ran Li

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    College of Modern Agriculture and Ecological Environment, Heilongjiang University, Harbin 150080, China)

  • Dongfeng Zhang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Yanyun Han

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G × E) interaction, expressed as P = G + E + G × E. Thus, studying G × E interactions is essential for phenotypic research. Traditional methods of crop phenotypes and adaptability based on G × E interaction analysis, based on large ecological regions, fail to account for year-to-year environmental changes and the blurring of region boundaries, leading to inaccurate insights into the relationship between genotypes and environmental factors. To address these issues, this study divided the research area into small ecological regions through the clustering of meteorological data, providing a more accurate framework for studying G × E interactions in maize. To ascertain the optimal method for ecological region delineation, the yield variance (SYV), the Davies–Bouldin Index (DBI), and the Silhouette Index (SI) were used to evaluate and compare the performance of the K-Means, Autoencoder K-Means (Ae-KM), and Deep K-Means Clustering Neural Network (DKMCNN) methodologies. The DKMCNN surpassed other methodologies and was selected for delineation. Based on this delineation result, the interactions between genotypes and the environment on maize were investigated and clarified using genome-wide association analysis (GWAS) and analysis of variance (ANOVA). Ultimately, through the analysis of maize field trial data from 2020 to 2021, we identified up to 108 single-nucleotide polymorphisms (SNPs) in 2020 and 153 SNPs in 2021 that exerted significant effects on maize yield and exhibited strong correlations with environmental factors, including temperature, cumulative precipitation, and cumulative sunshine duration.

Suggested Citation

  • Longpeng Bai & Kaiyi Wang & Qiusi Zhang & Qi Zhang & Xiaofeng Wang & Shouhui Pan & Liyang Zhang & Xuliang He & Ran Li & Dongfeng Zhang & Yanyun Han, 2025. "A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network," Agriculture, MDPI, vol. 15(4), pages 1-21, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:4:p:358-:d:1585887
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    References listed on IDEAS

    as
    1. Marijn Velde & Francesco Tubiello & Anton Vrieling & Fayçal Bouraoui, 2012. "Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data," Climatic Change, Springer, vol. 113(3), pages 751-765, August.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    3. Xiang Zhou & Peter Carbonetto & Matthew Stephens, 2013. "Polygenic Modeling with Bayesian Sparse Linear Mixed Models," PLOS Genetics, Public Library of Science, vol. 9(2), pages 1-14, February.
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