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The adoption of green roofs is an effective practice for mitigating environmental issues in urban areas caused by extreme weather conditions. However, certain design aspects of green roofs, such as the characterization of the physical properties of their substrates, need a better understanding. This study proposes a simple method for estimating two hydraulic properties of green roof substrates based on the evolution of moisture during drying periods, or drydowns, where evaporative processes dominate: the weighted-mean diffusivity and the saturated hydraulic conductivity. Soil moisture was monitored using 12 in situ sensors from 2015 to 2020 in a study involving six different green roof plots composed of various mixtures of demolition-recycled aggregates and organic substrates. A universal parameterization for determining water diffusivity in soils was applied to estimate the weighted-mean hydraulic diffusivity. As a by-product, the saturated hydraulic conductivity was estimated from the evaluated diffusivity and the measured water retention data. The median values obtained for D ¯ and k s range from 14.5 to 29.9 cm2d−1 and from 22 to 361 cmd−1, respectively. These values fall within the ranges reported by other research groups using direct measurement methods and supports the validity of Brutsaert's model for green roof substrates. Furthermore, an increase in D ¯ and a decrease in k s were observed as the percentage of recycled aggregates in the substrates increased, which could be considered for design purposes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Bituminaria bituminosa var. albomarginata, known as Tedera, is a promising forage for Mediterranean climates. An improved variety named LANZA® has been developed. Previous research suggests that soil water saturation in heavy-textured soils might affect its initial development. Competition from grasses could also compromise its successful establishment and persistence. We investigated the effects of soils with contrasting textures (loamy sand vs. clay) with a high soil water content and the competition from Lolium multiflorum on the development of LANZA® in a pot experiment. Dry mass (DM) production of LANZA® was strongly reduced (86%) when grown with L. multiflorum. LANZA® shoot DM was 60% higher than the total shoot DM production of the mixture of LANZA® and L. multiflorum. Soil type did not significantly affect the shoot and total root DM. However, a 44% reduction of the DM of thin roots and slower development was observed in clay soils, which may indicate a preference for light-textured soils. This study provides further information on the factors limiting the establishment and persistence of LANZA®. Future research should confirm these results at field scale and investigate measures aimed at reducing early competition in monocultures and functional complementarity with partner species in mixtures to successfully establish LANZA®. [ABSTRACT FROM AUTHOR]

The agricultural use of saline table olive processing wastewater enables the implementation of closed water cycles in this socioeconomically important industry for rural southern Spain and relieves environmental, economic, and legal burdens. To allow growers to evaluate and guarantee adequate long-term soil and plant conditions when irrigating with such regenerated wastewaters, efficient soil monitoring strategies are needed. Field-scale monitoring with electromagnetic induction sensing, after one (2013) and five years (2017) of irrigation with regenerated wastewater with average electrical conductivity (EC) near 6 dS m−1 in an olive orchard in southern Spain, showed accumulation of highly conductive material in the subsoil in relation to local topography and soil characteristics. Laboratory analysis of the soil water revealed strongly varying patterns of EC during the growing season and across the olive grove, which were attributed to dilution and concentration effects due to rainfall and evaporation, respectively. Visual inspection and leaf analyses revealed no negative effects on the olive trees. Apparent electrical conductivity (ECa), measured in between the tree rows in 2013, showed a linear relationship with surface soil EC1:5 under the drippers and allowed identification of areas with high ECa in the low elevation zones of the farm, due to the presence of shallow perched saline water tables. A second ECa measurement in 2017 showed similar spatial ECa patterns and was used to estimate the distribution of soil EC across the soil profile using inversion software, although no unique field-wide relationships with soil properties could be inferred, possibly as a consequence of spatially variable soil clay and water contents, due to the influence of the topography. Despite the implementation of a more conservative irrigation strategy since 2015, results showed that the salinity has increased since 2013 in about 15% of the study area, with the largest increments in the deepest horizons. [ABSTRACT FROM AUTHOR]

Grasslands are of key importance for the provision of ecosystem services (ES). Suitable management is essential to guarantee their persistence and functionality. There is a growing interest in innovations such as new technologies aimed at facilitating and improving the management of grasslands while increasing their provision of ES. The uptake of innovations by farmers is a complex process, and relevant socio-economic or technological factors that are crucial to farmers are often overlooked. This information can be useful for increasing the adoption of these innovations through the design of public policies to facilitate them. This paper analyses the relevance of the main innovations that can be applied to the management of the grasslands of Dehesa farms for the farmers and the factors that might affect this relevance. Through questionaries, we gathered information on the relevance that farmers give to the selected innovations and analysed it by cumulative link models. The results show that innovations aimed at increasing the biomass production of grasslands and resilience such as the use of seed mixtures and the use of forage drought-resistant species are considered highly relevant by Dehesa farmers. However, high-tech innovations such as GPS collars were poorly rated which could denote low applicability to the context of Dehesas or the existence of barriers hindering the adoption but also a need for further development and better information on their potential. Characteristics of the farmer and farm such as age, education level, and stocking rate seem to be related to the relevance given to some of the innovations. These results provide insightful information for the implementation and research of relevant grassland-related innovations in the context of Mediterranean Dehesa/Montado systems, as well as for the design of policies supporting them. [ABSTRACT FROM AUTHOR]

Understanding the natural and anthropogenic drivers that influence erosion and sediment transport is a key prerequisite for adequate management of river basins, where, especially in tropical catchments, there are few direct measurements or modeling studies. Therefore, this study analyzed the effect of human-induced land-use changes and natural ENSO (El Niño-Southern Oscillation) related changes in rainfall patterns on soil erosion and catchment-scale sediment dynamics with the SEDD (Sediment Delivery Distributed) model. In the 393 km2 Tonusco river basin, representative of tropical, mountainous conditions, daily rainfall data were used to quantify changes in rainfall erosivity and satellite images for the evaluation of cover factor changes between 1977 and 2015. The final model combined soil loss, calculated by RUSLE, with a sediment routing-based delivery ratio, that was calibrated and validated with data from the sediment load recorded at the basin outlet. The results detected a great reduction of the vegetation cover in the catchment during the last decade of from 79.5 to 29.5%, and the influence of important runoff and erosion events linked to La Niña episodes. Soil erosion rates were locally very high, of over 120 Mg ha−1yr−1, and sediment yields were estimated at the range of 6.17–8.23 Mg ha−1yr−1. [ABSTRACT FROM AUTHOR]

Drought is an important natural hazard that is expected to increase in frequency and intensity as a consequence of climate change. This study aimed to evaluate the impact of future changes in the temperature and precipitation regime of Spain on agricultural droughts, using novel static and dynamic drought indices. Statistically downscaled climate change scenarios from the model HadGEM2-CC, under the scenario representative concentration pathway 8.5 (RCP8.5), were used at a total of 374 sites for the period 2006 to 2100. The evolution of static and dynamic drought stress indices over time show clearly how drought frequency, duration and intensity increase over time. Values of static and dynamic drought indices increase over time, with more frequent occurrences of maximum index values equal to 1, especially towards the end of the century (2071–2100). Spatially, the increase occurs over almost the entire area, except in the more humid northern Spain, and in areas that are already dry at present, which are located in southeast Spain and in the Ebro valley. This study confirms the potential of static and dynamic indices for monitoring and prediction of drought stress. [ABSTRACT FROM AUTHOR]

The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools). [ABSTRACT FROM AUTHOR]