Compost is a valuable organic amendment which affords substantive fertility to soils where applied. A common component of compost fertility is cation exchange capacity (CEC), which has traditionally been determined via standard wet chemistry laboratory methods. This research utilized portable X-ray fluorescence (PXRF) spectrometry to evaluate 74 compost samples from the USA and Canada. PXRF elemental data were used for predicting compost CEC via random forest (RF) regression. Comparison between laboratory-determined vs. PXRF predicted CEC produced the following relationships: R²=0.90, RMSE = 5.41 meq 100 g⁻¹ (model calibration) and R²=0.60, RMSE = 8.07 meq 100 g⁻¹ (model validation). A key advantage of this technique is that the same data used for CEC prediction can also yield insight into other compost parameters of interest such as heavy metal content, plant essential nutrient content, salinity, and pH. Taken collectively, the PXRF approach can provide rapid, on-site analysis of compost which was previously not feasible with conventional methods. Our initial study has established the viability of PXRF for compost CEC determination, with further development on a wider array of feedstocks suggested for future study.

Purpose: Soil degradation, exacerbated by agricultural practices that ignore soil spatial variability is a critical threat to sustainable agriculture and food security. Management zones (MZs) delineation is a promising approach that has been used to manage soil spatial variability, and tailored agronomic inputs to optimize crop yield while mitigating soil degradation. Methods: The research was conducted in a 3530 km² catchment located within the Mashhad Plain of Northeast Iran, where arid climate conditions are dominant. Soil physicochemical properties such as texture, bulk density, pH, electrical conductivity (EC), organic carbon, (SOC), cation exchange capacity (CEC), total nitrogen (N), available phosphorous (P), potassium (K), and micronutrients were analysed for 48 samples from the catchment and computed Principal Component Analysis (PCA), ordinary kriging (OK), and fuzzy k-means clustering. Results: Physicochemical properties of the soil samples showed high (nugget/sill (N/S)<0.25), to moderate spatial autocorrelation (N/S< 0.75) except for pH (N/S>0.75). Cluster analysis partitioned the soil properties into two major groups, with PCA indicating that the first six components accounted for 82.9% of the total variance. We delineated seven distinct MZs associated with specific soil textures and elevations but not land uses. The delineated MZs can aid in implementing site-specific management practices, for instance, MZ 1 relatively requires more K fertilization and SOC amendments, while MZ 4 does not. MZs 5 and 6, with EC levels exceeding some cultivated crop tolerance, necessitate cultivating medium to high salt-tolerant crops, using low salt index fertilizers, and high-quality irrigation water. Conclusion: The study shows that management zone delineation can be used in sparsely sampled regions. Additionally, tailored practices can contribute to sustainable agriculture in the region, consequently, mitigating soil degradation and boosting crop productivity, ensuring soil health and food security for future generations. Graphical Abstract