Applying fertilizers to soil in a site-specific way that maximizes yields and minimizes environmental damage is an important goal. Developing soil management zones (MZs) is a suitable method for achieving sustainable agricultural production.
Riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, we used machine learning to predict soil properties in the forests.
Riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, we used machine learning to predict soil properties in the forests.
Riparian buffers are important for several services such as providing high water quality, nutrient recycling, and buffering agricultural production. Accordingly, in this research, we used machine learning to predict soil properties in the forests.
we tested the predictive power of three different ML models, the random forest, the support vector machine, and extreme gradient boosting coupled with the remote sensing data covariates to estimate soil texture data in Germany.
Sixteen models were used for describing the PSD. Results indicated that the models had acceptable accuracy for describing the PSD curves. Importantly, the Jaky model, with only one fitting parameter was able to accurately describe PSD. Therefore, the parameter P in the Jaky model was used for spatial mapping of the PSD curve using DSM techniques.
Sixteen models were used for describing the PSD. Results indicated that the models had acceptable accuracy for describing the PSD curves. Importantly, the Jaky model, with only one fitting parameter was able to accurately describe PSD. Therefore, the parameter P in the Jaky model was used for spatial mapping of the PSD curve using DSM techniques.
We use five machine learning techniques-Random Forest, M5P, Reduced Error Pruning Tree, Gaussian Processes, and Pace Regression-under two scenarios to predict soil erodibility. All five algorithms show a positive correlation between the soil erodibility factor and silt, sand, fine sand, bulk density, and infiltration.
Vis-NIR spectroscopic data was combined with DEM-derived topographic data and remote sensing data using an RF model hybridized with a particle swarm optimization algorithm for predicting the spatial variability of soil surficial clay contents, EC, and CCE for the agriculturally-intensive region of Kurdistan, Iran.