Grasslands are at risk of degradation due to unsustainable management practices and climate change. Here, we review the principal soil quality indicators (SQIs) to evaluate the sustainability of different grassland management practices globally. We discuss the importance of SQI assessment and the Soil Quality Minimum Dataset (MDS) specifically in the context of grasslands. We then review two potential solutions, the first of which is adopting grazing management, whereby sustainable grazing management plans (GMPs) offer great potential. The other solution is the development and adoption of novel grassland species, which may improve either drought resistance or infiltration rates, erosion and flooding. Sustainable grassland soil management can promote ecosystem service delivery and improve the resilience of the entire grassland ecosystem to anthropogenic change.

Land use change (LUC) is identified as one of the main drivers of soil erosion in the Mediterranean. However, very little information exists regarding the relationship between land use and erosion over longer time periods and on regional scales. We quantified the LUC in Southern Spain between 1956 and 2018, examining its effect on soil erosion and assessing the mitigation role of the permanent grassland (PG). The land use influence on erosion is represented by the RUSLE's C-factor, which was modelled using the Monte Carlo Method (MCM) based on historical LUC. Moreover, future LUC scenarios by 2038 were developed by binary logistic model (scFS) and by a complete conversion of PG to cropland (scPC), permanent crop (scPP) and forest and natural (scFP). Historically, Southern Spain has experienced an impressive intensification of its agricultural system. While soil loss variation is noted within the classes, no big variation is observed in cumulative erosion on a regional scale. The underlying reasons for this resilience are multifold, but mainly attributed to the fact that a small fraction of the total surface (20%), dominates total erosion (67%). The C-factor decrease in this area displays a LUC towards forest and natural area, suggesting an agriculture abandonment. On the other hand, the agricultural intensification that has taken place in the remainder of the area, contributes much less to overall soil erosion. Future LUC scenarios illustrate the importance of PG for erosion mitigation. scFS scenario does not project major changes. However, scCP and scPP, show an abrupt increase in regional erosion by 13% and 14%, while scFP shows a negligible reduction of erosion close to 0%. This allows to quantify the erosion mitigation offered by maintaining the PG and should be taken into account for future agricultural policy.

Grasslands are at risk of degradation due to unsustainable management practices and climate change. Sustainable grassland soil management can promote ecosystem service delivery and improve the resilience of the entire grassland ecosystem to anthropogenic change. Here, we re-view the principal soil quality indicators (SQIs) and how they have been used to evaluate the sustainability of different grassland management practices globally. We then discuss sustainable grazing management practices, before reviewing some novel grassland species which may im-prove grassland resilience with relevance for grassland management in Europe and the UK. We also give an overview of current sustainable grassland management methods and their assessment at field scale. From this, we suggest that sustainable Grazing Management Plans (GMPs), together with the testing of drought-resistant grass species and appropriate SQIs monitoring, is key to increasing resilience of grassland ecosystems to anthropogenic change.

Vegetation indices are widely used to assess vegetation dynamics. The Normalized Vegetation Index (NDVI) is the most widely used metric in agriculture, frequently as a proxy for different physiological and agronomical aspects, such as crop yield or biomass, crop density, or drought stress. Much effort has therefore been directed to NDVI forecasting, which is usually correlated with precipitation. However, in Mediterranean and arid climates, the relationship is more complex due to prolonged dry periods and sparse precipitation events. In this study, we forecast the NDVI 7 and 30 days ahead for Mediterranean permanent grasslands using a machine learning Random Forest (RF) model for the period from 2015 to 2022. The model compares two soil moisture products as predictors: simulated soil moisture values based on in situ soil moisture sensor observations and remote sensing-derived observations of Soil Water Index (SWI) values. We further analyzed the anomalies of the predicted NDVI using the z-score. The results show that both products can be used as reliable predictors for permanent grasslands in Mediterranean areas. Predictions at 7 days are more accurate and better forecast the negative effect of drought on vegetation dynamics than those at 30 days. This study shows the potential of using a simple methodology and readily available data to predict the grassland growth dynamics in the Mediterranean area.

Permanent grassland (PG) covers about 30% of the cultivated area of Europe, and 5 x 108 ha of them are in the Mediterranean zones. Climate change and prolonged drought seasons can expose PG’s soils to bare conditions for a long time, raising the land degradation risk, and diminishing food delivery to cattle. The objective of this study is to measure the response of soil moisture to a reduction in rainfall in different grass species. A rainfall exclusion experiment was set up to intercept 30% of the natural rainfall in Cordoba, Spain. Soil moisture was measured in 18 plots of 2 m2, of which 9 were under exclusion and 9 under natural rainfall. We used a Drill and Drop Sentek soil moisture probe, measuring between 0 – 30 cm depth, from 1/11/21 until 27/01/22. We also measured phenological plant response.  Average soil moisture was clearly lower in the reduced rainfall plots. These results will contribute to understand the effect of future climate on soil moisture, grass response and agricultural food security in the region. In particular, it will help farmers to select the species that best resist drought conditions.

Vegetation indexes are widely used as a proxy of vegetation status, they are often used to monitor and assess qualitatively and quantitatively the growing season. The Normalized Vegetation Index (NDVI) is the most widely used in agriculture, frequently as a proxy for different physiological and agronomical aspects, such as drought stress and crop yield losses evaluation. NDVI forecast is usually correlated to precipitation however, in Mediterranean and arid climates, it is not well correlated due to prolonged dry periods and sparse precipitation events. In this study, we forecast Mediterranean permanent grassland NDVI at 7 and 30 days ahead using machine learning and two soil moisture products as predictors, simulated soil moisture values and satellite-based Soil Water Index (SWI) values. Results show that both products can be used as reliable predictors of permanent grassland in Mediterranean areas. Predictions at 7 days are more accurate and better forecast the negative effect of drought on vegetation dynamics than 30 days. This study shows the potential of using a simple methodology and readily available data to predict the grassland growth dynamic in the Mediterranean area .