Implementing result-based agri-environmental payments by means of modelling

From a theoretical point of view, result-based agri-environmental payments are clearly preferable to action-based payments. However, they suffer from two major practical disadvantages: costs of measuring the results and payment uncertainty for the participating farmers. In this paper, we propose an alternative design to overcome these two disadvantages by means of modelling (instead of measuring) the results. We describe the concept of model-informed result-based agri-environmental payments (MIRBAP), including a hypothetical example of payments for the protection and enhancement of soil functions. We offer a comprehensive discussion of the relative advantages and disadvantages of MIRBAP, showing that it not only unites most of the advantages of result-based and action-based schemes, but also adds two new advantages: the potential to address trade-offs among multiple policy objectives and management for long-term environmental effects. We argue that MIRBAP would be a valuable addition to the agri-environmental policy toolbox and a reflection of recent advancements in agri-environmental modelling

Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders) and landslides (mixing up mineral soil horizons on slopes)

The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest Predictions

ISSN:1687-7667ISSN:1687-767

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches - Fig 4

<p>RMSE boxplots of repeated cross-validation (a₁-e₁) and development of the mean RMSE of rpeated cross-validation during the predictor selection process (a₂-e₂). a) RF, b) ANN, c) MARS, d) BRT, e) SVM. In a₁-e₁: “all” refers to the all-predictor model, “1”refers to the 10bestPR model, “2” to the sFS model, and “3” to the 3stepFS model. In a₂-e₂: The star refers to the mean RMSE of the best individual predictor model of step 1, black points refer to added predictors and the resulting mean RMSE during step 2, and grey points refer to the mean RMSE after step 3. The dashed line represents the mean RMSE of the all-predictor model.</p

Predictors derived from the landsat image.

<p>Predictors derived from the landsat image.</p

Overview of selected predictors.

<p>Overview of selected predictors.</p