Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System
Soil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluste...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/11/19/2611 |
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| author | Yifan Yuan Bo Shi Russell Yost Xiaojun Liu Yongchao Tian Yan Zhu Weixing Cao Qiang Cao |
| author_facet | Yifan Yuan Bo Shi Russell Yost Xiaojun Liu Yongchao Tian Yan Zhu Weixing Cao Qiang Cao |
| author_sort | Yifan Yuan |
| collection | DOAJ |
| description | Soil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluster analysis algorithms. However, these clustering methods have been used to delineate MZs independent of the spatial dependence of soil variables. Thus, the accuracy of the clustering results has been limited. In this study, six soil variables (soil pH, total nitrogen, organic matter, available phosphorus, available potassium, and soil apparent electrical conductivity) were used to characterize the spatial variability within a representative village in Suining County, Jiangsu Province, China. Two variable reduction techniques (PCA, multivariate spatial analysis based on Moran’s index; MULTISPATI-PCA) and three different clustering algorithms (fuzzy C-means clustering, iterative self-organizing data analysis techniques algorithm, and Gaussian mixture model; GMM) were used to optimize the MZ delineation. Different clustering model composites were evaluated using yield data collected after the wheat harvest in 2020. The results indicated that the variable reduction technologies in conjunction with clustering algorithms provided better performance in MZ delineation, with average silhouette coefficient (ASC) and variance reduction (VR) of 0.48–0.57, and 13.35–23.13%, respectively. Moreover, the MULTISPATI-PCA approach was more conducive to identifying variables requiring MZ delineation than traditional PCA methods. Combining MULTISPATI-PCA and the GMM algorithm yielded the greatest VR and ASC values in this study. These results can guide the optimization of MZ delineation in intensive agricultural systems, thus enabling more precise nutrient management. |
| first_indexed | 2024-03-09T21:17:06Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-09T21:17:06Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
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| series | Plants |
| spelling | doaj.art-9e1b2a1888a64ed7a42d379c9203d4982023-11-23T21:30:21ZengMDPI AGPlants2223-77472022-10-011119261110.3390/plants11192611Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming SystemYifan Yuan0Bo Shi1Russell Yost2Xiaojun Liu3Yongchao Tian4Yan Zhu5Weixing Cao6Qiang Cao7National Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Tropical Plant and Soil Science, University of Hawai’i at Manoa, Honolulu, HI 96822, USANational Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, ChinaSoil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluster analysis algorithms. However, these clustering methods have been used to delineate MZs independent of the spatial dependence of soil variables. Thus, the accuracy of the clustering results has been limited. In this study, six soil variables (soil pH, total nitrogen, organic matter, available phosphorus, available potassium, and soil apparent electrical conductivity) were used to characterize the spatial variability within a representative village in Suining County, Jiangsu Province, China. Two variable reduction techniques (PCA, multivariate spatial analysis based on Moran’s index; MULTISPATI-PCA) and three different clustering algorithms (fuzzy C-means clustering, iterative self-organizing data analysis techniques algorithm, and Gaussian mixture model; GMM) were used to optimize the MZ delineation. Different clustering model composites were evaluated using yield data collected after the wheat harvest in 2020. The results indicated that the variable reduction technologies in conjunction with clustering algorithms provided better performance in MZ delineation, with average silhouette coefficient (ASC) and variance reduction (VR) of 0.48–0.57, and 13.35–23.13%, respectively. Moreover, the MULTISPATI-PCA approach was more conducive to identifying variables requiring MZ delineation than traditional PCA methods. Combining MULTISPATI-PCA and the GMM algorithm yielded the greatest VR and ASC values in this study. These results can guide the optimization of MZ delineation in intensive agricultural systems, thus enabling more precise nutrient management.https://www.mdpi.com/2223-7747/11/19/2611soil variableMULTISPATI-PCAGaussian mixture modelmanagement zoneclustering model composites |
| spellingShingle | Yifan Yuan Bo Shi Russell Yost Xiaojun Liu Yongchao Tian Yan Zhu Weixing Cao Qiang Cao Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System Plants soil variable MULTISPATI-PCA Gaussian mixture model management zone clustering model composites |
| title | Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System |
| title_full | Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System |
| title_fullStr | Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System |
| title_full_unstemmed | Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System |
| title_short | Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System |
| title_sort | optimization of management zone delineation for precision crop management in an intensive farming system |
| topic | soil variable MULTISPATI-PCA Gaussian mixture model management zone clustering model composites |
| url | https://www.mdpi.com/2223-7747/11/19/2611 |
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