4.7 Article

Spatial variability of soil quality within management zones: Homogeneity and purity of delineated zones

期刊

CATENA
卷 209, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.catena.2021.105835

关键词

Soil properties; Random forest; Machine learning; Arid and semiarid regions; Statistical analysis; Geostatistics

资金

  1. National Key Research and Development Program of China [2017YFA0604302, 2018YFA0606500]
  2. German Research Foundation (DFG) [SFB 1070]
  3. DFG Cluster of Excellence 'Machine Learning-New Perspectives for Science', EXC 2064/1 [390727645]

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The study identified management zones through soil quality assessment and found different soil quality grades within each zone. The research provides a framework to investigate the homogeneity of delineated management zones in terms of soil quality.
Fields are the original management zones used in agricultural ecosystems. Uniformity of soil within management zones (MZ) is crucial for sustainable soil management, long-term productivity, and avoiding environmental problems. When considering a new area for agricultural expansion or for improving the efficiency of existing agricultural practices, it is useful to identify homogeneous areas or MZs so that the land can be more sustainably used in the future. One way to identify MZs could be through soil quality assessment. Management zones were determined for an agroecosystem region in southern Iran with an area of 452 km(2), and the homogeneity and purity of delineated zones were examined by soil quality assessment. Soil quality grades were calculated using 421 top-soil samples and two methods: i) the total data set (TDS) and ii) the minimum data set (MDS). The spatial distribution of soil quality grades was mapped using a random forest model. MZs were delineated using a fuzzy kmeans classification algorithm based on the MDS. The random forest model mapped the spatial distribution of the soil quality well (R-2 > 0.871). Among five soil quality grades, three soil quality grades, high (II), moderate (III), and low (IV), were found to cover 90.74 and 93.11% of the total studied area as predicted by the TDS and MDS, respectively. The subsequent classification of the soil quality data into MZs using fuzzy k-means identified two different MZs (p < 0.05). This means that there were heterogeneous soil quality grades in each of the MZs. Consequently, when fuzzy k-means is used to define MZs for the classification of agricultural ecosystems, areas with different soil quality may occur within each MZ. Soil management would be theoretically better if soil quality within management zones were homogenous. This study offers a framework to investigate the homogeneity of delineated MZs in terms of soil quality.

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