The Hedley phosphorus (P) fractionation procedure provides an alternative for characterizing soil P availability without qualifying the component P species. Interpretation of results involving all fractions is complicated and offers little practical utility. The objective of this study was to group P fractions of similar availability into three functional pools according to the plant availability of the P fractions. These grouped pools may simplify practical use of fractionation data and provide insights into differences in availability and cycling of added P in diverse soils. Eight soils in the greenhouse were treated with four levels of P and planted with 14 consecutive crops. Soils were fractionated using a modified Hedley method. The seven fractions were then grouped into three discrete pools of decreasing availability: the readily available, the reversibly available, and the sparingly available. Phosphorus extracted by the iron-impregnated filter paper strips (Strip P) and inorganic P extracted by 0.5 M NaHCO₃ (NaHCO₃-P_i) were readily available in all soils. The reversibly available P fractions differed between the slightly weathered and the highly weathered soils. In the slightly weathered soils, the reversibly available P included the organic P (P_o) (NaHCO₃- and NaOH-P_o), NaOH-P_i, HCl-P, and residual P. In the highly weathered soils, NaOH-P_i and organic P were considered reversibly available. No P fractions appeared to be sparingly available in the three slightly weathered soils. The residual P was the sparingly available P in the highly weathered soils. The results suggest quantitatively that P availability was greater when added to slightly weathered soils than when added to highly weathered soils.

Mozambique includes 10 agroecological zones with distinct climate and cropping systems (INIA, 1980). Agricultural production is more intensively practiced in the central and northern parts of the country where agroecological conditions are favorable. As a result of 16 years of civil war and low funding for agricultural research few assessments of soil nutrient status have been possible. To assess soil nutrient status, four agriculturally promising agroecological zones were selected for a survey. Sixty-two sites were sampled, and composite samples of surface and subsurface soils were taken and analyzed for properties considered critical for the production of food crops. The sample points were recorded with a differential global position system and mapped. The results indicate that most of the soils are low in effective cation exchange capacity, moderate in organic matter, moderate to strongly acidic, moderately weathered, and moderate to high in P sorption capacity. In general, these soils can be classified as having low to moderate fertility. Findings of this study indicate limitations of the existing map of soil resources of Mozambique. The results of this study suggest that additional sampling and soil survey will be required to improve the quality of soil surveys for site-specific applications. A site-specific soil survey is recommended that would provide additional information on soil resources and would greatly improve the current soil map that has an inadequate soil database. Furthermore, calibration and correlation studies are recommended for better decisions on soil nutrient management for ensuring food security.

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.