Climate Aridity and the Geographical Shift of Olive Trees in a Mediterranean Northern Region

Soil quality under rotational and conventional grazing in Mediterranean areas at desertification risk

Rotational grazing (RG) could be a valid alternative to continuous grazing (CG) in Mediterranean extensive pastures to fight land degradation. This study aimed to compare soil quality under RG and CG management, in paired RG-CG Portuguese pasture areas under strong aridity stress, with RG sites converted from CG management in 2018. Soils were sampled in 2022, at 10 cm depth, over 71 ha of RG and 37 ha of CG pastures, subdivided in 16 and 10 sampling plots, respectively. In each plot, five soil samples were taken to provide one composite sample. Physico-chemical and microbial indicators of soil quality were measured immediately after. Principal Component Analysis (PCA) and Redundancy analysis (RDA) showed a clear separation between RG and CG sites with significantly higher level of soil organic carbon (27%-67%), total nitrogen (67%-77%), cation exchange capacity (9%-36%), and, at a minor extent, water holding capacity (6%-17%), in the RG plots respect to CG plots. No significant difference was found for soil bulk density, microbial and fungal biomass and microbial diversity between sites with different management, although the latter was positively correlated with CG sites in the RDA analysis. Soil organic carbon was significantly correlated to the most relevant physico-chemical parameters for nutrient cycling and water balance and to microbial fungal biomass and N-mineralization, confirming the central role of soil organic carbon for soil health. Results support RG management as an effective choice to improve soil quality in areas under desertification risk.

Future shifts in climatic conditions promoting northward expansion of the Mediterranean climate in the circum-Mediterranean region

In the upcoming decades, precipitation and temperature patterns are expected to shift in the Mediterranean basin due to global warming, potentially having an influence on the environment and the economy in the area. Using monthly precipitation and temperature data from 15 global climate models (GCMs) developed as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), the Mediterranean Climate Envelop (MCE), as defined by Daget's (1977) criteria, is projected under two climate change scenarios: SSP2-4.5 and SSP5-8.5, and for two future periods: 2050s and 2070s. According to the findings, the MCE is expected to expand by 3.51 and 4.93% in the 2050s under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, in comparison to the current state. This expansion is expected to reach 5.28 and 9.87% for SSP2-4.5 and SSP5-8.5, respectively, in 2070s. For both situations and durations, MCE contraction would be minor, however, at less than 1%. More than 99% of the present MCE would stay stable proportionately. The northern Mediterranean region is mostly concerned by the MCE's expansion. The SSP2-4.5 scenario predicts that by the 2070s, expansion zones will occupy 674,183 km2, with 64% of the area located in Southern Europe and 36% in Western Asia. In SSP5-8.5 scenario, this area is expected to be significantly larger, estimated to be approximately 1,256,881 km2; 67% in Southern Europe and 33% in Western Asia.

Optimizing Agroecological Measures for Climate-Resilient Olive Farming in the Mediterranean

In order to evaluate the potential of climate change mitigation measures on soil physiochemical properties, an experiment based on the application of five agroecological practices such as the addition of composted olive-mill wastes, recycling pruning residue, cover crops, organic insect manure, and reduced soil tillage, solely or combined, was conducted over two years (2020 to 2022) in a 48-year-old olive plantation. The results showed significant increases in soil water content during the spring and summer periods for the combined treatment (compost + pruning residue + cover crops) (ALL) compared to the control (CONT) by 41.6% and 51.3%, respectively. Also, ALL expressed the highest soil organic matter (4.33%) compared to CONT (1.65%) at 0-10 cm soil depth. When comparing soil nutrient contents, ALL (37.86 mg kg-1) and cover crops (COVER) (37.21 mg kg-1) had significant increases in soil nitrate compared to CONT (22.90 mg kg-1), the lowest one. Concerning exchangeable potassium, ALL (169.7 mg kg-1) and compost (COMP) (168.7 mg kg-1) were higher than CONT (117.93 mg kg-1) at the 0-10 cm soil depth and had, respectively an increase of 100.9% and 60.7% in calcium content compared to CONT. Over the experimental period, the implementation of the five agroecological management practices resulted in enhanced soil fertility. In a long-term Mediterranean context, this study suggests that these sustainable practices would significantly benefit farmers by improving agroecosystem services, reducing reliance on synthetic fertilizers, optimizing irrigation water use, and ultimately contributing towards a circular economy.

Framing socioecological complexity: The long-term evolution of multiple dimensions of desertification risk in Italy

Desertification risk depends on the interplay of biophysical and socioeconomic drivers, among which climate change, soil depletion, landscape modifications, and biodiversity decline are key factors of change in Southern Europe. The present study introduces a diachronic analysis of desertification risk in Italy adopting a multidimensional approach based on four dimensions (ecological, economic, demographic, and administrative) assessed at three dates (1961, 1991, and 2011). These risk components were evaluated separately in Southern Italy, a formerly affected region (sensu United Nations Convention to Combat Desertification), and Northern/Central Italy, a nonaffected region in the country. All risk measures document how the divide between affected and nonaffected regions in Italy has gradually reduced. Because of local warming and rising human pressure, Northern Italy has recently displayed a level of desertification risk close to those observed in Southern Italy over the last 30 years. These results suggest a thorough revision of the national classification of risky areas, that may inform more specific mitigation and adaptation policies responding effectively to recent socioenvironmental trends and local (economic) dynamics. The intrinsic system's evolution observed at both regional and national level in Italy may be generalized to a broader European context. Our work finally documents the appropriateness of a multidimensional definition of desertification risk grounded on the joint analysis of ecological, demographic, economic, and administrative indicators. A comprehensive knowledge of socioeconomic patterns and processes of change contributes to more precise scenario modeling and design of integrated strategies mitigating desertification risk.

Predicting Site Energy Usage Intensity Using Machine Learning Models

Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind's daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site's energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption. Recently, a rising number of researchers have applied machine learning in various fields, such as wind turbine performance prediction, energy consumption prediction, thermal behavior analysis, and more. In this research study, using data publicly made available by the Women in Data Science (WiDS) Datathon 2022 (contains data on building characteristics and information collected by sensors), after appropriate data preparation, we experimented four main machine learning methods (random forest (RF), gradient boost decision tree (GBDT), support vector regressor (SVR), and decision tree for regression (DT)). The most performant model was selected using evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE). The reported results proved the robustness of the proposed concept in capturing the insight and hidden patterns in the dataset, and effectively predicting the energy usage of buildings.