Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China.

Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semi-arid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near-infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in salt-affected and sodicity-affected soils.

Assessment of soil fertility and nutrient management strategies in calcareous soils of Khuzestan province: a case study using the Nutrient Index Value method.

Soil fertility (SF) is a crucial factor that directly impacts the performance and quality of crop production. To investigate the SF status in agricultural lands of winter wheat in Khuzestan province, 811 samples were collected from the soil surface (0-25 cm). Eleven soil properties, i.e., electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (TN), calcium carbonate equivalent (CCE), available phosphorus (Pav), exchangeable potassium (Kex), iron (Fe), copper (Cu), zinc (Zn), manganese (Mn), and soil pH, were measured in the samples. The Nutrient Index Value (NIV) was calculated based on wheat nutritional requirements. The results indicated that 100%, 93%, and 74% of the study areas for CCE, pH, and EC fell into the low, moderate, and moderate to high NIV classes, respectively. Also, 25% of the area is classified as low fertility (NIV < 1.67), 75% falls under medium fertility (1.67 < NIV value < 2.33), and none in high fertility (NIV value > 2.33). Assessment of the mean wheat yield (AWY) and its comparison with NIV showed that the highest yield was in the Ramhormoz region (5200 kg.ha-1), while the lowest yield was in the Hendijan region (3000 kg.ha-1) with the lowest EC rate in the study area. Elevated levels of salinity and CCE in soils had the most negative impact on irrigated WY, while Pav, TN, and Mn availability showed significant effects on crop production. Therefore, implementing SF management practices is essential for both quantitative and qualitative improvement in irrigated wheat production in Khuzestan province.

Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard.

This study aims to compare three popular machine learning (ML) algorithms including random forest (RF), boosting regression tree (BRT), and multinomial logistic regression (MnLR) for spatial prediction of groundwater quality classes and mapping it for salinity hazard. Three hundred eighty-six groundwater samples were collected from an agriculturally intensive area in Fars Province, Iran, and nine hydro-chemical parameters were defined and interpreted. Variance inflation factor and Pearson's correlations were used to check collinearity between variables. Thereinafter, the performance of ML models was evaluated by statistical indices, namely, overall accuracy (OA) and Kappa index obtained from the confusion matrix. The results showed that the RF model was more accurate than other models with the slight difference. Moreover, the analysis of relative importance also indicated that sodium adsorption ratio (SAR) and pH have the most impact parameters in explaining groundwater quality classes, respectively. In this research, applied ML algorithms along with the hydro-chemical parameters affecting the quality of ground water can lead to produce spatial distribution maps with high accuracy for managing irrigation practice.