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Grasslands are key elements of the global agricultural system, covering around two-thirds of all agricultural areas and playing an important role in biodiversity conservation, food security, and balancing the carbon cycle. Climate change is a growing challenge for the agricultural sector and may threaten grasslands. To address these challenges, it is vital to conduct in-depth climate studies to understand the vulnerability of grasslands. In this study, machine learning was used to build an advanced model able to evaluate the future impact of climate change on grassland vigour. The objective was to identify the most vulnerable grassland areas, analyse the interaction between climate and grassland performance, and outline management strategies against the detrimental implications of climate change. A Random Forest (RF) regression was used to model the Normalised Difference Vegetation Index (NDVI) using the Standardised Precipitation-Evapotranspiration Index (SPEI). The model explained 76% of the NDVI variability. The foremost significant predictors of grassland vigour are the SPEI with temporal lags of 1, 4, and 12 months. These findings suggest that the vegetative status of grasslands exhibits high sensitivity to short-term drought while also being influenced by the memory of past climatic events over longer periods. Future projections indicate an overall reduction in grassland vigour, mostly in RCP8.5. The results indicate that negative effects will be more pronounced in mountainous regions, which currently host the most vigorous grasslands. Dry lowlands in the north should continue to have the lowest vigour in the future. A substantial reduction in vigour is expected in autumn, with an effect on grassland phenology. The development of grasslands in winter, favoured by increasing temperatures and precipitation, can advance the harvesting of grassland (cutting) and the grazing of livestock. To ensure that vigour is maintained in less favourable zones, adaptation measures will be needed, as well as more efficient management of highlands to provide an adequate level of production.

The increasing gap between water demands and availability is a significant challenge for sustainable water management, particularly in the context of growing irrigation needs driven by climate change. In the Côa region (inner-north Portugal), agriculture plays a vital role in the local economy, ensuring food security and contributing to the conservation of natural resources, though also threatened by climate change. The present study assesses how streamflow in the Côa River can be affected by climate change. The HSPF (Hydrological Simulation Program-FORTRAN) hydrological model was coupled with three global–regional climate model chains to simulate historical monthly and annual streamflow (1986–2015), and to predict future (2040–2099) streamflow under RCP8.5. Irrigation scenarios were subsequently developed considering a potential future increase from 10% to 50% per decade. The evaluation of HSPF performance during the historical period revealed good agreement (R2 > 0.79) between simulated and observed flows. A general decrease in streamflow is found in the future, particularly in 2070–2099, with annual mean streamflow projected to decrease by −30% until 2099. Interannual variability is also expected to increase. Generally, the simulations indicated higher future flows in winter/early spring, whilst they are expected to decrease over the rest of the year, suggesting drought intensification. An increase in water demands for irrigation, potentially rising from 46 hm3·yr−1 (baseline scenario) up to 184 hm3·yr−1 (50% increase per decade) may lead to unsustainable irrigation. Managing these opposite trends poses significant challenges, requiring a comprehensive and integrated approach from stakeholders and policymakers. Strategies should focus on both demand-side and supply-side measures to optimize water use, improve water efficiency, and preserve water availability.

The increasing use of geospatial information from satellites and unmanned aerial vehicles (UAVs) has been contributing to significant growth in the availability of instruments and methodologies for data acquisition and analysis. For better management of vineyards (and most crops), it is crucial to access the spatial-temporal variability. This knowledge throughout the vegetative cycle of any crop is crucial for more efficient management, but in the specific case of viticulture, this knowledge is even more relevant. Some research studies have been carried out in recent years, exploiting the advantage of satellite and UAV data, used individually or in combination, for crop management purposes. However, only a few studies explore the multi-temporal use of these two types of data, isolated or synergistically. This research aims to clearly identify the most suitable data and strategies to be adopted in specific stages of the vineyard phenological cycle. Sentinel-2 data from two vineyard plots, located in the Douro Demarcated Region (Portugal), are compared with UAV multispectral data under three distinct conditions: considering the whole vineyard plot; considering only the grapevine canopy; and considering inter-row areas (excluding all grapevine vegetation). The results show that data from both platforms are able to describe the vineyards’ variability throughout the vegetative growth but at different levels of detail. Sentinel-2 data can be used to map vineyard soil variability, whilst the higher spatial resolution of UAV-based data allows diverse types of applications. In conclusion, it should be noted that, depending on the intended use, each type of data, individually, is capable of providing important information for vineyard management.