## 1 Introduction
The online visible and near-infrared (vis-NIR) spectroscopy sensors are methodical in depicting water-holding capacity zones for site-specific irrigation [16]. It also has a strong relationship with moisture content, clay content, and the plasticity index. The increase in agricultural mechanization and the need to maximize agricultural yields globally have influenced soil compaction [17]. Soil compaction is a substantial form of soil degradation [18] caused by compression from machinery traffic, stock trampling, and humans [19; 20], and these also affect trees and shrubs [21]. Soil compaction takes place when soil milasys its natural resistance to the movement of machinery [22] and presents a major problem, resulting in inadequate rooting systems, poor yields [23], low soil fertility, and increased soil erosion [24]. Soil compaction reduces soil porosity, increases soil density and penetration resistance, decreases infiltration, degrades the soil structure, and limits root growth [25; 26; 27]. Soil compaction is a resultant side effect of land-use intensification. Moreover, increased compaction increases nitrova oxide (N\({}_{2}\)O) emissions [28]. Shear stress refers to the force that causes the deformation of materials by slippage along a plane parallel to the imposed stress, which may occur in solids or liquids, and the resultant shear is related to the downslope movement of the earth materials. Soil shear stress depends on soil characteristics, granular composition, humus content, humidity, root density, and the degree of vegetation cover in the case of vegetated areas. Researchers have proven that shear stress induces an ambiguous pore system with vertical and horizontal soil displacement, decreases permeability, and causes excess surface runoff during strong rainfall [29]. Shear stress affects soil structure by drastically reducing macro-porosity and saturated hydraulic conductivity in topsoil [30]. An increase in wheel slip and traction force results in shear stress rising, which may steer the detachment of a weak topsoil layer exposed to erosion and surface runoff [31]. An effective perspective for managing agricultural land variability is the implementation of MZ. An agricultural management zone depicts an area or region of an agricultural field that is inconsistent with the rest of the field and can be managed with a specific, single-rate management practice to maximize yields and environmental impacts [14; 32]. The agricultural management zone aims to ensure that no information vital for crop man agement is lost [33]. Under fast-changing climatic conditions, the management zone is prone to uncertainty, especially in situations where knowledge of agronomy and climate are not incorporated [14]. Artificial intelligence (AI) and machine learning (ML) methods are extensively used to model complex relationships in agriculture [34; 35; 36; 37; 38], including soil properties. [PERSON] et al. [39] compared four ML methods, namely, partial least squares regression, least squares support vector machines (SVM), extreme learning machines, and the Cubist regression model employed for the prognosis of soil organic matter and pH. Five machine learning models, including multilayer perceptron and support vector regression, were developed by [PERSON] et al. [40] for the estimation of soil salinity. [PERSON] et al. [41] found random forest and multiple linear regression (MLR) methods to be accurate tools for approximating soil aggregate stability. [PERSON] et al. [42] compared ten machine learning techniques and reported the generalized regression neural network model as an effective method for the evaluation of soil nutrient content. [PERSON] et al. [43] used different machine learning algorithms and stated that random forests, improved by the polarimetric decomposition of parameters, outperformed support vector regression and gradient boosting regression trees. Another promising machine learning approach is Gaussian process regression (GPR).