Total Digestible Nutrient Value of forage and concentrate and nutritional characteristics and develop a prediction equation using the chemical composition variables as predictors. Nutrient chemical characteristics data were obtained from 278 forage and 87 feedstuffs. The data included dry and organic matter, crude protein, ether extract, ash, fiber composition, and non-fiber Carbohydrate. Stepwise regression was used to eliminate variables that did not influence variation in the model and used 0.05 as the critical probability level. Data were then randomly divided into two parts; two-thirds of the data was used to estimate the Total Digestible Nutrient, whereas the remaining part was used to validate the estimated Total Digestible Nutrient and was analyzed by multiple linear regressions. Total Digestible Nutrient in forage was negatively correlated with Ether Extract, Acid Detergent Lignin, and Non-fibre Carbohydrate (P<0.01) but positively correlated with Crude Protein (P<0.01), ash, Neutral Detergent Fibre, and Acid Detergent Fiber. Total Digestible Nutrient in feedstuffs was negatively correlated with NFC (P<0.01) but positively correlated with Neutral Detergent Fibre (P<0.01), Acid Detergent Lignin (P<0.01), Ether Extract (P<0.01), Crude Protein (P<0.01), ash, and Acid Detergent Fiber (P<0.01). The results show that the Total Digestible Nutrient content can be accurately estimated starting from the chemical composition. Keywords: total digestible nutrient; forage; concentrate; feed analysis.

Despite a drought- and erosion-tolerant root system, olive trees are vulnerable to abiotic stress due to limited genetic variability. Though some olive cultivars are moderately tolerant to salinity stress, soil salinity is increasing in the semi-arid and arid regions where olive cultivation is common, significantly reducing overall production. In response, breeding programs may rely on proper selection markers for abiotic stresses, including salinity, but these are generally lacking for olive. Here, physiological and biochemical parameters were measured in four Olea europaea genotypes (Frantoio, Leccino, Lecciana, and Oliana) subjected to different intensities of salinity stress (0 mM, 100 mM and 200 mM NaCl). At moderate and high salt concentrations, Na+ exclusion, higher photosynthetic productivity and tissue water content in the tolerant cultivar Frantoio were linked with increased production of polyphenols, with more favorable K+/Na+ values (quercetin and rutin), mitigation of oxidative stress (oleuropein) and increased water absorption (luteolin). In Frantoio and Leccino, a significant change of the proteome repertoire occurred, with overrepresentation of components regulating cellular metabolism, ion transport, redox insult and dissipation of excess photochemical energy. Conversely, Lecciana and Oliana showed increased sensitivity to salinity stress in terms of photosynthetic parameters and elevated internal Na+ concentrations, together with the lowest number of differentially represented proteins. These results highlighted olive germplasm strategies to cope with osmotic stress, suggested a physiological and molecular basis for the augmented responsiveness of tolerant cultivars and identified specific biomarkers as useful targets for future breeding programs.

This study addresses a challenging problem of predicting mean annual precipitation across arid and semi-arid areas in northern Algeria, utilizing deterministic, geostatistical (GS), and machine learning (ML) models. Through the analysis of data spanning nearly five decades and encompassing 150 monitoring stations, the result of Random Forest showed the highest training performance, with R square value (of 0.9524) and the Root Mean Square Error (of 24.98). Elevation emerges as a critical factor, enhancing prediction accuracy in mountainous and complex terrains when used as an auxiliary variable. Cluster analysis further refines our understanding of station distribution and precipitation characteristics, identifying four distinct clusters, each exhibiting unique precipitation patterns and elevation zones. This study helps for a better understanding of precipitation prediction, encouraging the integration of additional variables and the exploration of climate change impacts, thereby contributing to informed environmental management and adaptation strategies across diverse climatic and terrain scenarios.

The natural hazard that has the greatest impact on people and is considered to be the most complicated but least understood is drought. Thus, the projection of drought is extremely necessary to be prepared against possible damage. There are many indices available for the assessment of drought severity, but the result of SPEI is more reliable than any other indices. The objective of the study was to predict drought in the Sirohi district (India) using different meteorological variables like temperature, precipitation, Potential Evapotranspiration (PET), etc. For the projection of drought, the statistical downscaling technique was applied by using observed data and Global Climate Model (GCM) data. Drought years in the period 2020–2099 were identified and the probability of occurrence of the different drought classes has been evaluated. Finally, the relationship between El Niño and droughts was investigated using Sea Surface Temperature (SST) anomaly data. Sirohi district has a 75.0 % chance of drought during El Niño years. The model can predict droughts with a 60 % accuracy using the statistical downscaling technique. From 2019 to 2099, there are 13.75 % chances of moderate droughts and 12.5 % chances of mild droughts. According to this research, there will not be any severe or extreme droughts in the future. This study concludes that the projection of drought can be done accurately by statistical downscaling techniques using GCM data. Results showed that major droughts in the South-West region of Rajasthan occurred during El Niño years. This will help the policymakers to prepare policies, drought contingency plans, and rules according to the drought conditions of the particular region.

SummaryTree mortality and forest dieback episodes are increasing due to drought and heat stress. Nevertheless, a comprehensive understanding of mechanisms enabling trees to withstand and survive droughts remains lacking. Our study investigated basal area increment (BAI), and δ13C- derived intrinsic water-use-efficiency (iWUE), to elucidate beech resilience across four healthy stands in Italy with varying climates and water availability. Additionally, fist-order autocorrelation (AR1) analysis was performed to detect early warning signals for potential tree dieback risks during extreme drought events.Results reveal a negative link between BAI and vapour pressure deficit (VPD), especially in southern latitudes. After the 2003 drought, BAI decreased at the northern site, with an increase in δ13C andiWUE, indicating conservative water-use. Conversely, the southern sites showed increased BAI andiWUE, likely influenced by rising CO2and improved water availability. In contrast, the central site sustained higher transpiration rates due to higher soil water holding capacity (SWHC). Despite varied responses, most sites exhibited reduced resilience to future extreme events, indicated by increased AR1.Temperature significantly affected beechiWUE and BAI in northern Italy, while VPD strongly influenced the southern latitudes. The observed increase in BAI andiWUE in southern regions might be attributed to an acclimation response.

AbstractNowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP‐AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web‐based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S‐DSS LANDSUPPORT platform, consisting of a free web‐based smart Geospatial CyberInfrastructure containing 15 macro‐tools (and more than 100 elementary tools), co‐designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web‐GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling ‘on‐the‐fly’ in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land.

Mud volcanoes in northwestern Colombia are used by local people as inexpensive recreation and wellness centres, especially for pelotherapy. However, the potential therapeutic value of the muds and thermal waters released by these volcanoes has not been fully investigated. The main objective of this research is to determine the potential therapeutic value of the solid and liquid fractions of the muds sampled from nine mud volcanoes located along the northwestern Caribbean coast of Colombia through geochemical and mineralogical analysis. An evaluation of the granulometric characteristics was also carried out. The presence of a hypertonic saline environment created by secondary volcanic processes is indicated by the thermal fluid fraction. The mineral composition of the muds is dominated by quartz, phyllosilicates (kaolinite, smectite, illite and chlorite), feldspar, carbonates (dolomite, calcite) and a significant amorphous component. Based on the composition of the clay fraction, three distinct groups can be distinguished: (i) kaolinite-rich, (ii) illite-rich, and (iii) chlorite-rich. These muds are suitable for use by the local population due to the extremely low levels of contaminants harmful to human health, such as potentially toxic elements, nitrates, nitrites or fluorides. The results of this study suggest that the muds and thermal waters released by these mud volcanoes have potential therapeutic value. Further research is needed to confirm these findings and to investigate the specific mechanisms by which these muds and waters can be used in medical practice.

In some areas of southern Italy, the change in land use over the last 4–5 decades has increased pressure on land and water resources and caused different forms of soil degradation. In order to mitigate the magnitude of soil erosion, different strategies that include construction of flood control structures and reforestation programs have been done in several areas. However, quantifying the effectiveness of these strategies is difficult in absence of direct measurements of soil erosion. To cover this information gap, the use of distributed numerical models coupled with measurements of the radionuclide cesium-137 (137Cs) offers a good alternative to the classic experimental sites (plot, catchments) that, on the contrary, require long term datasets to produce reliable estimates of soil loss. In this paper, measurements of 137Cs in a floodplain area are firstly described for a representative Calabrian catchment as an example to reconstruct the trend of soil deposition rates during the last six decades. These measurements have been integrated with estimates of soil loss obtained with the Revised Universal Soil Loss Equation (RUSLE) model for which land use maps of different periods are available. The final comparison between estimates of soil erosion provided by the RUSLE at catchment scale and sedimentation rates derived from 137Cs measurements on depositional areas allowed interesting information on the trend of soil erosion and deposition rates in these areas to be obtained.

The growing interest in soil health and sustainable agriculture has emerged as a paramount element in addressing the multifaceted challenges facing modern agriculture [...]