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 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.

Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter.

Major and trace elements in soils originate from natural processes and different anthropogenic activities which are difficult to discriminate. On a 17-ha impacted site in northern France, two industrial sources of soil contamination were xidentified: a former iron foundry and a current secondary lead smelter. To discriminate and map natural and anthropogenic sources of major and trace elements on this site, the rarely applied MULTISPATI-principal component analysis (PCA) method was used. Using a 20-m × 20-m grid, 247 topsoil horizons were sampled and analysed with a field-portable X-ray fluorescence analyser for screening soil contamination. The study site was heavily contaminated with Pb and, to a lesser degree, with Sn. Summary statistics and enrichment factors allowed the differentiation of the main lithogenic or anthropogenic origin of the elements. The MULTISPATI-PCA method, which explained 73.9 % of the variability with the three first factors, evidenced strong spatial structures. Those spatial structures were attributed to different natural and artificial processes in the study area. The first axis can be interpreted as a lithogenic effect. Axes 2 and 3 reflect the two different contamination sources. Pb, Sn and S originated from the secondary lead smelter while Fe and Ca were mainly derived from the old iron foundry activity and the old railway built with foundry sand. This study demonstrated that the MULTISPATI-PCA method can be successfully used to investigate multicontaminated sites to discriminate the various sources of contamination.