Spain is one of the largest wine producers in the world, with Extremadura (south-west Spain) being its second-largest producing region after Castilla La Mancha. Within Extremadura, the most traditional and productive viticulture region is the Tierra de Barros, which boasts an annual production of 3×10⁶ litres. However, no soil erosion assessment has been undertaken in any vineyard in the region to ascertain environmental sustainability. Therefore, the Improved Stock Unearthing Method (ISUM) and the Revised Universal Soil Loss Equation (RUSLE) were applied to assess the long-term soil erosion rates. Both methods were applied using an experimental plot (2.8 m × 148.5 m) encompassing 99 paired vines in a 20-year-old vineyard under a tillage management system and on bare soils throughout the year. The ISUM and RUSLE found total soil mobilization values of 45.7 Mg ha⁻¹ yr⁻¹ and 17.4 Mg ha⁻¹ yr⁻¹, respectively, a difference of about 5 times. Mapping techniques showed that soil surface declined to an average of −6.2 cm, with maximum values of −28 cm. The highest values of soil depletion were mainly observed in the upper part and the form of linear features following the hillslope direction. On the other hand, under the vines, the soil surface level showed accumulations of up to +2.37 cm due to tillage practices. Our study demonstrated the potential of high soil erosion rates occurring in conventional vineyards managed with tillage in the inter-row areas and herbicides under the vines within the Tierra de Barros. Also, we demonstrated the elevated differences in soil mobilisation rates using the ISUM and RUSLE. Therefore, further research must be conducted in other vineyards to determine the suitability of the models for assessing soil erosion rates. Undoubtedly, soil conservation measures must be designed and applied immediately due to high erosion rates.

Acquiring comprehensive insights into soil properties at various spatial scales is paramount for effective land management, especially within small catchment areas that often serve as vital pastured landscapes. These regions, characterized by the intricate interplay of agroforestry systems and livestock grazing, face a pressing challenge: mitigating soil degradation while optimizing land productivity. This study aimed to analyze the spatial distribution of eight topsoil (0–5 cm) properties (clay, silt, sand, pH, cation exchange capacity, available potassium, total nitrogen, and soil organic matter) in a small grazed catchment. Four machine learning algorithms—Random Forest (RF), Support Vector Machines (SVM), Cubist, and K-Nearest Neighbors (kNN)—were used. The Boruta algorithm was employed to reduce the dimensionality of environmental covariates. The model’s accuracy was assessed using the Concordance Correlation Coefficient (CCC) and Root Mean Square Error (RMSE). Additionally, uncertainty in predicted maps was quantified and assessed. The results revealed variations in predictive model performance for soil properties. Specifically, kNN excelled for clay, silt, and sand content, while RF performed well for soil pH, CEC, and TN. Cubist and SVM achieved accuracy in predicting AK and SOM, respectively. Clay, silt, CEC, and TN yielded favourable predictions, closely aligning with observations. Conversely, sand content, soil pH, AK, and SOM predictions were slightly less accurate, highlighting areas for improvement. Boruta algorithm streamlined covariate selection, reducing 23 covariates to 10 for clay and 4 for soil pH and AK prediction, enhancing model efficiency. Our study revealed spatial uncertainty patterns mirroring property distributions, with higher uncertainty in areas with elevated content. Model accuracy varied by confidence levels, performing best at intermediate levels and showing increased uncertainty at extremes. These findings offer insights into model capabilities and guide future research in soil property prediction. In conclusion, these results urge more research in small watersheds for soil and territorial management.

To determine which interpolation technique is the most suitable for each case study is an essential task for a correct soil mapping, particularly in studies performed at a regional scale. So, our main goal was to identify the most accurate method for mapping 12 soil variables at three different depth intervals: 0–5, 5–10 and >10 cm. For doing that, we have compared nine interpolation methods (deterministic and geostatistical), drawing soil maps of the Spanish region of Extremadura (41,635 km² in size) from more than 400 sampling sites in total (e.g., more than 500 for pH for the depth of 0–5 cm). We used the coefficient of determination (R²), the mean error (ME) and the root mean square error (RMSE) as statistical parameters to assess the accuracy of each interpolation method. The results indicated that the most accurate method varied depending on the property and depth of study. In soil properties such as clay, EBK (Empirical Bayesian Kriging) was the most accurate for 0–5 cm layer (R² = 0.767 and RMSE = 3.318). However, for 5–10 cm in depth, it was the IDW (Inverse Distance Weighted) method with R² and RMSE values of 0.689 and 5.131, respectively. In other properties such as pH, the CRS (Completely Regularized Spline) method was the best for 0–5 cm in depth (R² = 0.834 and RMSE = 0.333), while EBK was the best for predicting values below 10 cm (R² = 0.825 and RMSE = 0.399). According to our findings, we concluded that it is necessary to choose the most accurate interpolation method for a proper soil mapping.

Chestnut cultivation is a type of traditional centennial exploitation in southern areas of Extremadura. Currently, chestnut is in danger of extinction due to the effects of climate change, some diseases ( e.g. Cryphonectria parasitica ) and land mismanagement. The high temporal variability of rainfall leads to soil erosion in chestnut cultivation. New forms of management are proposed to try, among other things, to reduce soil losses. This study quantifies soil losses in areas under tree canopies and open areas considering two different age plantations; 1990s and 1960s. To achieve the proposed goal 18 erosion plots were installed as paired plots under tree canopies and open areas in both plantations. The total amount of rainfall per event, tree cover, bare soil, runoff coefficient and sediment concentration were also measured in every plot. The results showed that the highest percentage of bare soil (> 45%) coincides with the period of greatest tree cover (> 75%). The open areas and the youngest plantation showed soil losses higher than the areas under the tree and the oldest plantation. In addition, soil losses increase as higher percentages of bare soil are recorded. We conclude that the size of the trees and a better soil stability in older plantations help reduce soil losses.

Water is a key strategic resource, particularly in Mediterranean climate-type areas with impermeable rocks and shallow soils like Southwestern Spain. The region of Extremadura is commonly known by its large surface occupied by big dams (30% of water dammed in Spain) although this theoretical abundance of water does not hide other problems of use. In this study, we have interviewed 132 people from the municipality of Arroyo de San Serván in order to know what the problems related to water use are, especially those that concern local people the most. Regarding the use of water at home, 90% of interviewees spend less than 60 EUR per month for water and their mean degree of satisfaction about the service is 3.7 out of 7. The reason for this low value can be the excessive content of calcium and bad taste according to 82.1%. Therefore, 64.2% of people do not usually drink water from the tap. Around two thirds of these local people usually buy water in the supermarket or drink filtered water. Concerning agricultural activities, local people gave great importance to irrigation as a source of employment (5.6/7) and inputs (4.5/7), although their satisfaction decreases about the current price of water for agriculture (0.02 EUR m⁻³). In addition, they are really worried about the overuse of fertilizers and herbicides (5.4/7) and they think about the necessity of taking measures to reduce these problems (6.1/7) as well as to reduce some management problems such as supply cuts. In the last few years, private (swimming pools) and public leisure facilities (swimming pool and spa) have been built in spite of not being considered important by local people (3.6–4.0/7). Nevertheless, about 60% of them consider these common facilities very positive in terms of employment, tourism attractions and entertainment for local people.

Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.