## 4 Discussion  
### Characterization of Nutrient Variation and Recommendations for Zoning Management  
In our research, we utilized geostatistics, merging the techniques of GS+ and GIS to tailor-fit various models. From these, the interpolation model was chosen based on each model's optimal parameters, aiming to augment interpolation precision. Through collecting and assessing soil samples, we determined the concentrations of diverse nutrient indicators. We then employed the geostatistics analysis module for a spatial variation evaluation, defining its spatial autocorrelation and significance via the global [PERSON]'s I index. As a result, we generated illustrative maps of soil nutrient distribution and LISA clustering. These maps shed light on the inherent relationships and spatial variation attributes of distinct soil nutrients within the study domain. The mature wheat was collected on the quadrats at the sampling points in the next year, and the correlation between its yield and each nutrient index was calculated. Principal component analysis (PCA) was used to determine the minimum data set (MDS) of soil nutrient evaluation, and the soil quality index (SQ) was calculated by linear scoring method to explore the key factors restricting soil quality in the region. For Fengtai County, the span of soil pH is 165 m with a coefficient of variation standing at 8.22%. This, in comparison to other nutrient metrics, is the lowest. The nugget-to-sill ratio for soil pH rests at 92.6%. Drawing from these statistics, we deduce that the area's soil pH is predominantly shaped by stochastic elements like human interventions. There's a pronounced lack of spatial continuity, with weak spatial autocorrelation over limited extents. This causes the soil pH to manifest in a patchy, clustered distribution. On a broader note, the central sector of the surveyed region has predominantly acidic soil. Conversely, the soils in the northern and southern segments lean towards being neutral or alkaline. This spatial pH differentiation may have significant ties to the distribution of soil organic matter [32]. Soil organic matter is a pivotal determinant in steering the spatial dispersion of pH values [33]. Within the components of organic matter, the content of humic acid stands out as a crucial pointer for soil pH; notably, humic acid has the propensity to reduce soil pH [34]. Over-reliance on nitrogenous fertilizers can acidify the terrain, bringing about a pH drop [35]. Hence, for the acidic terrains in the central low-value cluster, it is prudent to  
Figure 9: Distribution of soil quality index (SQI) levels in the study area. embrace judicious fertilization strategies [36] and fitting cropping patterns. Incorporating an optimal quantity of substances like biochar or limestone can also serve to modulate soil pH [37], ushering in enhanced amelioration effects. In Fengtai County, the soil demonstrates a pronounced correlation between organic matter, total nitrogen, and alkali-hydrolyzable nitrogen, with their spatial distribution patterns being notably alike. Their respective nugget ratios stand at 89.1%, 98.8%, and 99.8%. When compared to other indicators, their range of variabilities is relatively vast with values of 462 m, 415 m, and 777 m. The global Moran's \(I\) index values for these elements are notably high, listed as 0.682, 0.783, and 0.876. This indicates that across a broad spectrum, their spatial differences are predominantly affected by random determinants, like human activities or fertilization methods. Their distribution appears concentrated, forming a patch-like appearance on nutrient maps. This may be attributed to organic matter and total nitrogen content being related to the distribution of nitrogen fertilizer [38]. The diverse agricultural practices among farmers result in the uneven distribution of these elements within the county. Notably, the variation coefficients for these factors are smaller when juxtaposed against other indicators, with values of 21.04%, 20.53%, and 19.45%.