A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network
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...
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2025-02-01
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| author | Longpeng Bai Kaiyi Wang Qiusi Zhang Qi Zhang Xiaofeng Wang Shouhui Pan Liyang Zhang Xuliang He Ran Li Dongfeng Zhang Yanyun Han |
| author_facet | Longpeng Bai Kaiyi Wang Qiusi Zhang Qi Zhang Xiaofeng Wang Shouhui Pan Liyang Zhang Xuliang He Ran Li Dongfeng Zhang Yanyun Han |
| author_sort | Longpeng Bai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ef60e0a21ad845c4ba181dfa8bfd4946 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-ef60e0a21ad845c4ba181dfa8bfd49462025-08-20T02:44:33ZengMDPI AGAgriculture2077-04722025-02-0115435810.3390/agriculture15040358A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural NetworkLongpeng Bai0Kaiyi Wang1Qiusi Zhang2Qi Zhang3Xiaofeng Wang4Shouhui Pan5Liyang Zhang6Xuliang He7Ran Li8Dongfeng Zhang9Yanyun Han10Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Hucheng Ring Road 999, Shanghai 201306, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaSDIC Seed Technology Co., Ltd., Beijing 100034, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaThe 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.https://www.mdpi.com/2077-0472/15/4/358small ecological region delineationdeep k-means clustering neural networkgenotype by environment interaction |
| spellingShingle | Longpeng Bai Kaiyi Wang Qiusi Zhang Qi Zhang Xiaofeng Wang Shouhui Pan Liyang Zhang Xuliang He Ran Li Dongfeng Zhang Yanyun Han A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network Agriculture small ecological region delineation deep k-means clustering neural network genotype by environment interaction |
| title | A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network |
| title_full | A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network |
| title_fullStr | A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network |
| title_full_unstemmed | A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network |
| title_short | A Study of Maize Genotype–Environment Interaction Based on Deep K-Means Clustering Neural Network |
| title_sort | study of maize genotype environment interaction based on deep k means clustering neural network |
| topic | small ecological region delineation deep k-means clustering neural network genotype by environment interaction |
| url | https://www.mdpi.com/2077-0472/15/4/358 |
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