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Grasslands of Mediterranean oak savannas supply numerous ecosystem services and are key for the rural development of extensive regions. Their phenology and overall productivity are significantly influenced by water availability. As a result, the considerable year-to-year fluctuations in production are tied to the variable nature of the Mediterranean climate. This study aimed to assess the grasslands net primary production (NPP) in a region of Southern Spain for a period of 17-year (2001-2018). The spatiotemporal variations and linkages to water availability, with a focus on practical on-farm management were examined. A Light-Use Efficiency model to estimate NPP was combined with the Surface Energy Balance System (SEBS) models to derive evapotranspiration and the anomalies of relative evapotranspiration, used as a proxy of water stress. Both models were adapted to the particular structure of these savanna-type systems and applied integrating meteorological information and MODIS satellite data. The findings yielded valuable insights into how these grasslands respond to drought conditions in the region. During the major droughts of 2004/2005 and 2011/2012, the reduction in aerial biomass production was 42% and 67%, respectively. The study pinpointed the most productive area, characterized by low slopes and moderate tree cover. The biomass production time series classification identified four distinct trends in the region, all displaying shifted relationships with similar slopes between production and anomalies of relative evapotranspiration. However, a seasonal and monthly analysis was necessary to explain the behavior of years with unusual relationships. In addition to the known importance of spring for annual grassland production in this area, the seasonal analysis highlighted the significance of autumn, particularly with spring water deficits. The most productive years exhibited favorable conditions in both spring and autumn. The proposed methodology to characterize grassland productivity in relation to water availability can be a useful tool for farmers. When combined with forecast data, it could assist in determining the optimal level of management intensification, leading to an adjustment of their stocking rate.
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Muñoz-Gómez et al. (2024) studied this question.
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