Abstract

Classification of fields into management units based on soil variability and fertility is important for spatial crop planning. The present study was conducted in Chukanagallu subwatershed (97 km²), Koppal district of Northern Karnataka Plateau, India to map the soil fertility management units and to analyse the suitability of soil for different crops. Random forest regression and classification algorithms were used to map the differentiating characteristics of soil series (soil depth, coarse fragments and soil colour), physicochemical properties (pH, EC and OC) and fertility parameters (P₂O₅, K₂O, S, Fe, Mn, Zn, Cu, B). Random forest model performed well for the prediction of fertility parameters (R² = 44–73%) and physicochemical properties (R² = 39–83%) compared to soil depth and coarse fragments (R² = 17–18%). Predicted soil fertility parameters and physicochemical properties were used for the delineation of different homogenous fertility management units. Soil series characteristics and fertility parameters were also evaluated using a multi-criteria approach for suitability of soil for cotton, groundnut and rice cultivation and the results showed that major area of subwatershed is moderately suitable for the cultivation of cotton, rice and groundnut. The management units derived from DSM approach were symmetrical in production potential and requires similar management aspects which are useful for appropriate planning of management strategies such as crop selections and nutrient management to achieve sustainable production.

摘要

根据土壤变异性和肥力将田地划分为管理单元对于空间作物规划很重要。本研究在印度北卡纳塔克高原 Koppal 区的 Chukanagallu 流域（97 km ²）进行，以绘制土壤肥力管理单元图并分析土壤对不同作物的适宜性。随机森林回归和分类算法用于绘制土壤系列（土壤深度、粗碎块和土壤颜色）、理化性质（pH、EC和OC）和肥力参数（P ₂ O ₅、K ₂ O、S ）的差异化特征, 铁, 锰, 锌, 铜, B)。随机森林模型对生育力参数的预测表现良好（R ²= 44–73%) 和理化性质 ( R ² = 39–83%) 与土壤深度和粗碎块 ( R ²= 17–18%）。预测的土壤肥力参数和理化性质用于划分不同的同质肥力管理单元。采用多标准方法对土壤系列特征和肥力参数进行棉花、花生和水稻种植适宜性评价，结果表明流域主要区域适宜棉花、水稻和花生种植。源自 DSM 方法的管理单位在生产潜力方面是对称的，并且需要类似的管理方面，这有助于适当规划管理策略，例如作物选择和养分管理，以实现可持续生产。