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Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p > 0.01) were observed for precipitation. This study's methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends. [ABSTRACT FROM AUTHOR]

The 3rd International Electronic Conference on Agronomy (IECAG 2023) was held from October 15 to October 30, 2023. The conference brought together participants from various countries, including students, academics, scientists, and experts in agronomy and related fields. The conference focused on addressing current challenges, exploring innovative solutions, and promoting global collaboration within the agronomy community. The conference included presentations and discussions on a wide range of agronomy research topics, such as disease control, agro-ecology innovation, and digital farming. The conference also featured keynote speeches from distinguished scientists and was supported by various sponsors and media partners. [Extracted from the article]

Enhancing spatial data attributes is crucial for effective basin-scale environmental modelling and improving our understanding and management of precipitation patterns. In this study, we focused on reconstructing homogeneous areal precipitation data in the complex terrain of the Calore River Basin (CRB) in Southern Italy. Until 1869, weather observations in the region were inconsistent, unstandardised, and lacked coordination, but the establishment of meteorological observatories brought a more unified approach to weather monitoring. We relied on the rainfall data obtained from two of these historical observatories: Benevento (1869–present) and Montevergine (1884–present). We utilised a statistical regression framework that considered rainfall measurements and temporal properties from specific locations to reconstruct and visually analyse the evolution patterns of annual mean areal precipitation (MAP) in the CRB from 1869 to 2020. The analysis revealed that mean MAP decreased from 1153 mm yr−1 (1869–1951) to 998 mm yr−1 (1952–2020). This decrease was accompanied by a reduction in interannual variability (from 168 mm yr−1 to 147 mm yr−1 standard deviation), and the difference between the means was significant (p < 0.0001), suggesting a sudden shift in the time-series. These findings provide a basis for CRB water resource management and insights for modelling other complex Mediterranean basins. [ABSTRACT FROM AUTHOR]

Future climate change is expected to significantly alter the growth of vegetation in grassland systems, in terms of length of the growing season, forage production, and climate-altering gas emissions. The main objective of this work was, therefore, to simulate the future impacts of foreseen climate change in the context of two pastoral systems in the central Italian Apennines and test different adaptation strategies to cope with these changes. The PaSim simulation model was, therefore, used for this purpose. After calibration by comparison with observed data of aboveground biomass (AGB) and leaf area index (LAI), simulations were able to produce various future outputs, such as length of growing season, AGB, and greenhouse gas (GHG) emissions, for two time windows (i.e., 2011–2040 and 2041–2070) using 14 global climate models (GCMs) for the generation of future climate data, according to RCP (Representative Concentration Pathways) 4.5 and 8.5 scenarios under business-as-usual management (BaU). As a result of increasing temperatures, the fertilizing effect of CO2, and a similar trend in water content between present and future, simulations showed a lengthening of the season (i.e., mean increase: +8.5 and 14 days under RCP4.5 and RCP8.5, respectively, for the period 2011–2040, +19 and 31.5 days under RCP4.5 and RCP8.5, respectively, for the period 2041–2070) and a rise in forage production (i.e., mean biomass peak increase of the two test sites under BaU: +53.7% and 62.75% for RCP4.5. and RCP8.5, respectively, in the 2011–2040 period, +115.3% and 176.9% in RCP4.5 and RCP8.5 in 2041–2070, respectively,). Subsequently, three different alternative management strategies were tested: a 20% rise in animal stocking rate (+20 GI), a 15% increase in grazing length (+15 GL), and a combination of these two management factors (+20 GI × 15 GL). Simulation results on alternative management strategies suggest that the favorable conditions for forage production could support the increase in animal stocking rate and grazing length of alternative management strategies (i.e., +20 GI, +15 GL, +20 GI × 15 GL). Under future projections, net ecosystem exchange (NEE) and nitrogen oxide (N2O) emissions decreased, whereas methane (CH4) rose. The simulated GHG future changes varied in magnitude according to the different adaptation strategies tested. The development and assessment of adaptation strategies for extensive pastures of the Central Apennines provide a basis for appropriate agricultural policy and optimal land management in response to the ongoing climate change. [ABSTRACT FROM AUTHOR]

Simulating the dynamics of plant species or types in grassland communities remains an open area of research for which the Community Simulation Model (CoSMo) offers novel approaches. The grassland model ModVege was first parameterised based on a functional vegetation typology, in which types "A" and "B" include fast-growing grass species with a phenology-dependent nutrient-capture strategy inherent to fertile grasslands, while the nutrient conservation strategy and late flowering characterise the other types as "b". ModVege was then coupled to the CoSMo rule set to dynamically simulate the relative abundance of plant functional types or individual species, assessed across fertilised and unfertilised, abandoned and mown conditions in a grassland site of the Massif Central of France. While for the simulation of aboveground biomass, model performance is not unambiguously linked to explicit consideration of plant diversity, the simulation of relative abundance for the whole community is satisfactory (relative root mean square error of ~13–25% when simulating functional types and ~28–52% when simulating species). This study extends previous studies by coupling CoSMo, for the first time, to a grassland-specific model and applying it to conditions (long-term observations, extended number of plant species, absence of fertilisation, frequent mowing and abandonment) never investigated before. [ABSTRACT FROM AUTHOR]

Background: Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. Methods and findings: Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm-2 h-1, respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world's land at warning and alert states, respectively. Conclusion: RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected). [ABSTRACT FROM AUTHOR]