Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System

作者：Yifan Yuan, Bo Shi, Russell Shelley Yost, Xiaojun Liu, Yongchao Tian, Yan Peng Zhu, Weixing Cao, Qiang Cao · 发表于：Plants · 年份：2022 · DOI：10.3390/plants11192611 · 被引用次数：40 · 研究领域：Land Use and Ecosystem Services、Soil and Land Suitability Analysis、Soil Geostatistics and Mapping

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. Moreov...