ABSTRACT Why do we classify? That is, why do we place objects with similar features into groups? A common answer is that classification helps us organize our knowledge. It enables us to better understand the objects being grouped together. Should soils with similar climates be placed together into classes? There are advantages and disadvantages. First, a distinction needs to be made between climate (a soil-forming factor) and soil climate (a measurable soil property). The main advantage pertains to the concepts of differentiating properties (e.g. andic, oxic, vertic, mollic, etc.) and accessory properties (e.g. fertility, infiltration, aeration, toxicities, etc.). In the Soil Taxonomy system, for example, differentiating properties with many accessory properties were placed high within the hierarchy because it created a system that provides taxa which permit users of the system to make the greatest number of important statements. The statements range from those about land use on the short-term time scale (seasons to decades) to statements about soil genesis on the long-term time scale (centuries to millennia). Disadvantages, on the other hand, include the fact that soil climate is dynamic and requires years of moisture and temperature data to capture its variability, especially soil moisture. Still, soil moisture and soil temperature are among the most important soil properties controlling the uses of the soil and long-term genesis of the soil. However, soil climate is often unnecessary for many classification systems to achieve the goals for which the systems were designed, this is especially true for areas without major geographical differences in climate and for systems concerned about their classifications becoming obsolete with climate change. On the other hand, a classification system explicitly containing soil moisture and temperature data in its taxa provides a baseline for assessing the effects of climate change on soil and provides a language discussing those changes.

Abstract. Soil health assessments increasingly rely on indicators to infer soil functions and ecosystem services; however, the extent to which these indicators accurately represent water-related soil processes remains uncertain. This study investigates the relationships between soil properties and provision of water regulation ecosystem services across three contrasting pedo-climatic regions in Austria, Italy, and Tunisia. Using 315 soil profiles, we applied a process-based soil–water model to quantify infiltration, runoff triggering, groundwater recharge, and crop water stress index under representative climatic conditions. We evaluated commonly used soil indicators, including saturated hydraulic conductivity, available water content, bulk density, organic matter content, clay content, saturated soil water content, soil depth, and macroporosity. Pairwise correlation and multiple linear regression analyses were employed to assess interactions between soil properties and soil water balance components. Results show that indicator–process relationships vary considerably across sites and are often non-linear, with specific correlations reflecting local combinations of soil texture, structure, profile development, and climate. For example, in the Marchfeld region (Austria), infiltration exhibited a strong positive correlation with bulk density (r=0.74, p<0.001), while the crop water stress index showed a significant negative correlation with soil depth (r= −0.35, p<0.001). In the Bologna area (Italy), the study also indicated that groundwater recharge was positively correlated with soil macro-porosity (r=0.45, p<0.001), whereas macro-porosity exhibited a strong negative correlation with flux-to-runoff (r= −0.66, p<0.001), underscoring the key role of soil structural characteristics in controlling infiltration–recharge–runoff dynamics. In addition, multiple linear regression models were developed to assess the relevance of the individual soil properties and their interactions in controlling soil water balance components. For instance, the infiltration model for Marchfeld (r=0.79, p<0.001) was highly predictive and incorporated clay content, organic matter, and soil depth. Several widely used indicators exhibited weaker or inconsistent relationships with water-related processes than commonly assumed. For instance, saturated hydraulic conductivity alone was not a robust predictor of infiltration and recharge across sites, whereas soil depth and clay content emerged as recurrent controls, especially when considered jointly. Overall, this study highlights the value of process-based modelling for disentangling soil–climate interactions and cautions against the generalized use of static soil indicators in hydrological and soil health assessments.

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R2), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R2 = 0.57, RMSE = 2.29%), followed by Austin (R2 = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R2 = 0.28, RMSE = 11.28%) and Kingsville (R2 = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions.

Variations and drivers of biomass and soil carbon stocks in planted forests across India

Soil color a basic quality of assessing pedogenesis and climatic effects hence, may provide valuable insight into soil properties and may serve as a distinctive and straightforward measurable climatic indicator. In this study, surface horizons samples were sampled at depth 0-30 cm by using an auger in ten provinces of Iraq where the climate conditions are different. Although the different physicochemical properties such as (Soil Texture pH, EC, O.M. CEC, CaCO3) were identified in the lab section, the Munsell Soil Color Book was applied in the field to identify the soil color. The result showed a rise in (pH, O.M., CEC, CaCO3, Fed, Clay) based on the color indices BWR, TR1, and TR2 in sub-humid climate areas; however, under the same climatic conditions, EC and CaSO4.2H2O levels were lower. While Xanthization was more common in semi-arid areas, the processes of Melanization and Runification were more active in humid environments. Multiple regression analysis indicated a statistically significant relationship between BWR and CaCO3, as well as between TR1 and CaSO4.2H2O. No significant relationship was detected between TR2 and the soil properties under investigation. Furthermore, linear regression analysis between climate and the BWR soil color index demonstrated a significant association, with value R² = 0.33 at a significance level of 0.01. Research findings demonstrated that Iraqi soil climate shows direct correlation to the BWR index specifically.

This research introduces a framework for assessing sustainability in agricultural production, enhanced by AI, that merges environmental, agronomic, and management aspects into a cohesive decision-support system. Drawing on data from Karnataka, India, the study examined six critical parameters including crop type, soil pH, rainfall, temperature, soil classification, and nitrogen-phosphorus-potassium (NPK) fertilizer inputs. A Composite Sustainability Indicators (CSI) was created to assess the multifaceted performance of various crops through five normalized sub-index computation: fertilizer efficiency, pH suitability, rainfall adequacy, temperature compatibility, and soil quality. Specific optimal thresholds were established for key crops like wheat, barley, and maize to align with their unique ecological needs. An efficient Life Cycle Assessment (LCA) component calculated energy equivalents (N = 60.6 MJ kg⁻¹, P₂O₅ = 11.1 MJ kg⁻¹, K₂O = 6.7 MJ kg⁻¹) and greenhouse gas emissions (N = 6.6, P₂O₅ = 1.0, K₂O = 0.6 kg CO₂e kg⁻¹) associated with fertilizer usage. An AI-driven regression model (R² = 0.93; MAE = 0.024) was employed to predict CSI under diverse environmental conditions, to facilitate scenario analyses for variations in soil pH (+0.5), rainfall (±100 mm), temperature (±2 °C), and fertilizer rates (±20%). The findings indicated that moderate reductions in fertilizer use improved sustainability scores, whereas climatic variability elicited varied responses based on the crop. Overall, this AI-LCA-enhanced framework offered a data-driven and flexible solution for precision agriculture, melding resource efficiency with environmental responsibility. It fosters sustainable production planning, adaptive management amidst climate variability, and informed policymaking aimed at building resilient agricultural systems.

Global synthesis reveals that climate and soil substrate shape microbial necromass carbon in forest soils

Many factors such as solar radiation, surface air temperature, precipitation, and vegetation cover play an important role in influencing soil temperature. Thus, soil temperature and depth are parameters that mediate and respond to climate change and are related to global warming. Understanding soil temperature dynamics at different depths is essential for assessing the temporal and spatial impacts of climate change on terrestrial systems. Such multi-depth analyses provide insight into how different soil layers respond to warming, which is crucial for agriculture, hydrology, and ecosystem management. In this study, Mann–Kendall Trend Test and Şen’s Innovative Trend Test (using statistical parameters such as Sen's Slope, Z-value, p-value, Tau, and ITA's Slope) were applied to determine the temperature trends at different soil depths (5 cm, 10 cm, 20 cm, 50 cm, and 100 cm) between 1980 and 2022 in Silifke, Mersin, Türkiye. The analyses revealed that soil temperature trends ranged from −0.0119ÅãC to 0.0892ÅãC annually, indicating significant warming in surface layers. When analyzing the monthly changes in soil temperature as a function of depth, it was found that there were significantly increasing trends in all months except January (p < .01 or p < .05). Therefore, it can be said that soil temperatures are also increasing on an annual basis. However, the strongest trend of temperature increase was observed in the summer months (June–August), followed by a significant increase in the fall months (September–November). Furthermore, this increase was more pronounced at near-surface depths (5 cm, 10 cm, and 20 cm). Soil layers near the surface are more affected by global warming or seasonal changes. In contrast, deeper layers exhibit delayed responses to temperature fluctuations. These findings underscore the need for long-term studies to monitor soil–climate feedbacks driven by temperature rise and climate change. Cite this article as: Bolat, İ., Yaman, B., & Şensoy, H. Multi-Depth Soil Temperature Trend Assessment in Silifke, Türkiye: A Case Study from Mersin Province. Forestist, 2025, 75, 0033, doi: 10.5152/forestist.2025.25033.

The study of land use change dynamics in developing countries is particularly important, as it contributes to sustainable land management and the more efficient use of natural resources. Southern Iran, which includes the provinces of Bushehr, Fars, Kerman, Sistan and Baluchestan, and Hormozgan, provides a valuable case study due to its diverse land uses and varying climatic conditions. It is hypothesized that land use changes between 2000 and 2022 in this region have significantly impacted the trends of soil temperature (ST) and soil volumetric water content (SWV), especially in areas where natural covers such as forests and shrublands have been converted to agricultural or barren lands. Trend analysis using the Mann-Kendall Z test and Sen’s slope estimator revealed a negative trend in ST across 62.60% of the study area, encompassing most parts of Sistan and Baluchestan, Kerman, Hormozgan, and southeastern Fars. In these regions, soil volumetric water content showed a positive and statistically significant trend. It can be attributed to an increase in sudden and intense rainfall and seasonal precipitation patterns. In 34.79% of the study area, an increasing trend in ST was observed, particularly in Bushehr and sporadically in parts of Fars Province. Similarly, the trend analysis of soil climate variables across different land uses indicated that soil volumetric water content increased by 85.36% in barren lands and by 66.36% in grasslands. In contrast, negative trends were found in forests (94.85%), shrublands (72.79%), and agricultural lands (82.24%). The main reason for this declining trend is the conversion of forests and shrublands to agricultural land. The trend of ST showed a decrease of 69.23% in barren land use, 94.85% in forest use, and 56.73% in grassland use. In these land uses, trees and dense vegetation block direct sunlight, which helps keep ST lower in these areas. In contrast, an increasing trend was observed in shrublands (63.48%) and agricultural lands (82%). Given the rapid pace of land use change, forecasting and analyzing satellite imagery represents a valuable approach for supporting environmental management strategies. Such forecasts provide deeper insights into potential future changes and inform proactive measures to mitigate their adverse impacts.

ABSTRACTUnderstanding the evolution of permafrost extent and active layer thickness (ALT) surrounding Antarctica is critical to global climate change and ecosystem transformations in the polar regions. However, due to the remoteness and harsh environment of Antarctica, most studies lack long‐term and a regional perspective on the variations of ALT in Antarctica, resulting in hindering accurate assessment of ALT dynamics. In this study, based on MODIS land surface temperature (LST) and soil climate station data, we used the Stefan model to reconstruct ALT in the ice‐free area of the McMurdo Dry Valleys (MDV) in East Antarctica from 2003 to 2022. The modeled ALT was verified against ground observations showing a good correlation (R) of 0.72 (p < 0.001), with an RMSE of 12.66 cm. The results indicate that the ALT exhibits a decreasing trend from coastal to inland, ranging from a maximum of 60 cm near the coastal area to zero in the polar plateau. Furthermore, within the inland valleys, deeper ALT values are mainly distributed in the lower elevation areas, reaching up to 60 cm at the lowest altitudes. During the period from 2003 to 2022, the interannual variability in ALT was notable, especially in coastal areas, with a maximum amplitude close to 30 cm in the years 2012 and 2016. Our study proved that the Stefan model with parameters estimated by MODIS LST and soil climate station data has good potential to reconstruct large‐scale ALT in the ice‐free area of Antarctica.

Soil organic matter (SOM) decomposition is a critical biogeochemical process that regulates the carbon cycle, nutrient availability, and agricultural sustainability of cropland systems. Recent progress in multi-omics and microbial network analyses has provided us with a better understanding of the decomposition process at different spatial and temporal scales. Climate factors, such as temperature and seasonal variations in moisture, play a critical role in microbial activity and enzyme kinetics, and their impacts are mediated by soil physical and chemical properties. Soil mineralogy, texture, and structure create different soil microenvironments, affecting the connectivity of microbial habitats, substrate availability, and protective mechanisms of organic matter. Moreover, different microbial groups (bacteria, fungi, and archaea) contribute differently to the decomposition of plant residues and SOM. Recent findings suggest the paramount importance of living microbial communities as well as necromass in forming soil organic carbon pools. Microbial functional traits such as carbon use efficiency, dormancy, and stress tolerance are essential drivers of decomposition in the soil. Furthermore, the role of microbial necromass, alongside live microbial communities, in the formation and stabilization of persistent SOM fractions is increasingly recognized. Based on this microbial perspective, feedback between local microbial processes and landscape-scale carbon dynamics illustrates the cross-scale interactions that drive agricultural productivity and regulate soil climate. Understanding these dynamics also highlights the potential for incorporating microbial functioning into sustainable agricultural management, which offers promising avenues for increasing carbon sequestration without jeopardizing soil nutrient cycling. This review explores current developments in intricate relationships between climate, soil characteristics, and microbial communities determining SOM decomposition, serving as a promising resource in organic fertilization and regenerative agriculture. Specifically, we examine how nutrient availability, pH, and oxygen levels critically influence these microbial contributions to SOM stability and turnover.

Winter climate change is outpacing our conceptual understanding of how winter conditions regulate soil biogeochemical cycling and ultimately impact vital ecosystem services like soil carbon and nutrient retention. In seasonally snow-covered ecosystems like northern temperate forests, increasingly inconsistent winters lead to less precipitation falling as snow, frequent midwinter snow melting, and the loss of a stable, insulative snowpack. These changes leave soils vulnerable to freezing, freeze/thaw cycling, and increasing dry/wet cycles from added snowmelt and rainwater. To uncover how these new winter soil climate conditions alter soil biogeochemistry, we introduce the DeFR❆ST (Determining Forest Responses to Snowmelt Treatments) experiment, a novel approach where we melt snow insitu throughout the winter and monitor changes to soil climate, gas exchange, and biogeochemical cycling. We installed DeFR❆ST in a New England temperate forest, an ecosystem that is part of the most significant global carbon sink and is also in the epicenter of winter climate change in the US. Experimental snow melting drove soil moisture fluctuations in addition to deep and persistent soil freezing. In turn, soils in melted plots exhibited blocked gas diffusion and lower soil oxygen availability. Oxygen limitation may have driven shifts in soil processes from high redox potential metabolisms like aerobic decomposition and nutrient mineralization towards low redox potential metabolisms like iron reduction and the dissolution of iron and carbon from organo-mineral associations. As these changes snowball, altered soil properties and shifts in soil microbial community structure and function could reshape forest biogeochemical cycling, both in these forests and more broadly across seasonally snow-covered ecosystems.

Abstract Understanding soil moisture (SM) dynamics is crucial for environmental and agricultural applications. While satellite‐based SM products provide extensive coverage, their coarse spatial resolution often fails to capture local SM variability. This study presents a multimodal network (MMNet) that integrates remote sensing and weather data to downscale Soil Moisture Active Passive (SMAP) Level‐4 surface SM. We evaluated the performance of MMNet by comparing it with in situ SM observations from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) under three scenarios. The results showed that (a) MMNet trained with on‐site data provided accurate SM estimates over time in withheld years; (b) MMNet demonstrated spatial transferability, capturing SM dynamics in regions with sparse or no in situ measurements; and (c) the integration of snapshot and time‐series data was crucial for maintaining the model's accuracy and generalizability across diverse scenarios. The downscaled SM maps demonstrated its potential for producing high‐resolution temporally and spatially continuous SM estimates, which could further support a broad range of environmental and agricultural applications.

Crop failure due to drought periods may affect crop yield due to climatic variation, and natural disasters may affect soil health in the agricultural environment. Factors that impact soil health, such as filtering the natural water quality level. Forecasting tools are used to identify future trends in the soil robotic sensor to improve the accuracy of crop yield. In this paper, we study the real-time data monitoring for accurate prediction to help traders to better decisions. The best-fit Proximal Policy Optimization (PPO)model is chosen by finding a specific location to crop the yield in the environment. The output of such an interpretable model could improve valuable forecasting for plant growth. This approach can achieve 96% accuracy in solving a complex problem. This computational result is cost-effective and intelligent farming for predicting yield and resource allocation in farming. Precision agriculture for early detection, for planning a model for long-term food security, and for real-time agricultural decision making. To forecast crop yield monitoring of health care parameters for environmental factors, Remote sensing, and robotic soil sensors for real-time data. Crop specification for land location to reduce the impact of soil climate change variability. Agriculture is a critical sector for rising food demand and soil health parameters such as pH, moisture content, and nutrient concentration. The study of PPO stands for Proximal Policy Optimization, a powerful tool for smart agricultural farming.

Biogeochemical processes within and across ecosystems are core to understand the functioning of terrestrial ecosystems, i.e., forests and agroecosystems, in particular under changing environmental conditions. Measurements are necessary at multiple scales, e.g., for forests at soil, forest floor, tree, canopy, and forest ecosystem scales, using methodology from many different disciplines. Data should be available in high temporal resolution, preferentially for long time periods, to quantify and understand short-term responses to environmental drivers and management, but also to detect and identify long-term responses to climate change. The SwissFluxNet is a network of six long-term research sites in Switzerland with ecosystem-scale eddy-covariance (EC) measurements of biosphere-atmosphere greenhouse gas (GHG) exchange (i.e., CO2, H2O vapor, CH4, N2O; Fig. 1). The Swiss FluxNet offers exactly these opportunities, namely long-term, high-temporal resolution GHG flux data and serves as a research platform for many other studies and research programs. It covers the major land-use types in Switzerland: forest (mixed deciduous: Lägeren, CH-Lae; evergreen: Davos, CH-Dav), grassland (Chamau, CH-Cha; Früebüel, CH-Fru; Alp Weissenstein, CH-Aws), and cropland (Oensingen, Ch-Oe2), and is complemented by project-based flux stations which run for 2-4 years (currently, two below-canopy stations in the two forest sites, one young forest plantation, and two cropland sites). Thus, including data of 2024, we provide 129 site-years of continuous GHG flux measurements to the scientific community (19-28 years per site, and continuously growing…), since all data are open access and have been downloaded from FLUXNET and ICOS over 35'250 times between November 2016 and December 2024. In the talk, we will focus on the two forest sites, Davos and Lägeren. At Davos, above-canopy EC flux measurements of CO2 and H2O vapor started in 1997; measurements of above-canopy CH4 and N2O fluxes were carried out between 2016 and 2023 and complemented by below-canopy flux measurements of CO2 and H2O vapor (since 2021) as well as of CH4 (since 2023). Data on forest floor CO2, N2O and CH4 fluxes, measured automatically by chambers, tree phenology, sap flow and stem diameter changes are available for many years as well. Since 2019, Davos is an ICOS RI Class 1 Ecosystem station, where highest standards apply. At Lägeren, EC flux measurements of CO2 and H2O vapor are available since 2004, complemented by below-canopy flux measurements of CO2 and H2O vapor since 2014 as well as by phenology, soil respiration and tree ecophysiology measurements. At both sites, meteorological (above and within the canopy) as well as soil climate variables (soil profiles, 0 to 60/80 cm soil depth) are recorded continuously as well. Thus, measurements from multiple scales for long time periods allow studying short- and long-term responses to changing environments at both forest sites. We will provide selected highlights from almost 50 site-years of measurements, about the short-term responses of forests to weather extremes such as drought and heatwaves as well as about the long-term carbon sink behaviour of both forests and their vulnerability. Results based on machine learning approaches about the environmental and biological drivers of GHG fluxes at multiple scales will be presented along with their temporal contributions within and across years. Disentangling the role of climate vs. nitrogen (N) deposition for water-use efficiency of both tree species beech and spruce as well as linking tree to forest responses across scales will be discussed. Our experiences clearly demonstrate that using long-term, highly equipped EC sites as research platforms for additional research projects, nesting research programs within large-scale research infrastructure networks, and sharing data openly following FAIR principles, pays out, for one’s own curiosity, for career development of the next generation scientists, for policy advice and science at large!

This study elucidates soil–climate regulatory mechanisms on regional health baselines in China and hydrogeochemical roles in cardiovascular biomarker differentiation. Utilizing data from 26,759 healthy adult samples across 286 Chinese cities/counties, seven core factors were identified via Pearson correlation analysis from 25 indicators, including longitude (X1, r = −0.192, p = 0.009), elevation (X3, r = 0.377, p = 0.001), and precipitation (X7, r = −0.200, p = 0.006). Ridge regression analysis (R2 = 0.714) was subsequently applied to simulate predicted values for 2232 cities/counties. The synergistic effects of soil calcium sulfate content and salinity (X25) on serum cardiac troponin I (cTnI) reference values were rigorously validated, explaining 25.5% of regional cTnI elevation (ΔR2 = 0.183). The findings demonstrate that precipitation leaching and groundwater recharge processes collectively drive a 25.5% elevation in cTnI levels in northwestern regions (e.g., Nagqu, Tibet: altitude > 4500 m, annual sunshine > 3000 h) compared to southeastern areas. To mitigate salinity transport dynamics, optimization strategies targeting soil cation exchange capacity (X18/X19) were proposed, providing a theoretical foundation for designing gradient water treatment schemes in high-calcium-sulfate zones (CaSO4 > 150 mg/L). Crucially, regression equations derived from the predictive model enable the construction of a geographically stratified reference framework for cTnI in Chinese adults, with spatial analysis delineating its latitudinal (R2 = 0.83) and longitudinal (R2 = 0.88) distribution patterns. We propose targeted strategies optimizing soil cation exchange capacity to mitigate sulfate transport in groundwater, informing geographically tailored water treatment and cardiovascular disease prevention efforts. Our findings provide localized empirical evidence critical for refining WHO drinking water sulfate guidelines, demonstrating direct integration of hydrogeochemistry, water quality management, and public health.

ABSTRACT Aim Introduced species can establish in climates outside of their native niche and undergo ‘niche shifts’. However, studies of niche shifts generally rely on above‐ground climate data, neglecting the potential buffering effect of ground‐level or soil climates. Location Global. Time Period Present. Major Taxa Studied Formicidae. Methods Here, we investigated the impact of soil temperatures on niche shifts in 95 introduced ant species using both ordination and hypervolume‐based approaches. We compared niche shifts using air temperature and soil temperature. Results Overall, between 65.2% and 82% of species (depending on the metric) exhibited smaller niche shifts when considering soil temperature, with varying levels of correlation between air‐ and soil‐temperature niche shifts across species (Correlation coefficient range: 0.56–0.73). Furthermore, air and soil climate conditions were generally more uncoupled than expected at random. This suggests that species use microrefugia and that this may explain the lower levels of niche shifts observed when using microclimatic conditions. Ecological traits, nesting type, forest cover and spatial spread did not consistently impact the differences across metrics in soil temperature buffering of niche shifts among species. This highlights the need for experimental microclimatic research to explore species differences in air‐ versus ground‐climatic niche shifts. Main Conclusions We overall highlight the importance of incorporating ecologically relevant microclimatic data, particularly for small, ground‐dwelling organisms like ants. This study emphasises the ongoing need for a nuanced understanding of the intricate interplay between air and soil temperatures in the context of niche dynamics. Ultimately, soil‐level datasets may improve habitat suitability models, leading to more accurate predictions of establishment success for introduced species.

The giant Calotropis, Calotropis gigantea This versatile plant is a member of the Asclepiadaceae family, which has long been recognized for its significant medicinal properties. You may find this plant all over India. It is generally known as arka in Hindi. One such plant that has been blessed with the best natural resources and age-old wisdom for its prudent application is Calotropis gigantea. It is found in most part of world with warm climate in sandy, alkaline and dry soils. Calotropis is harvested because of its medicinal properties. Hindus worship this plant. This is used for flavoring. Numerous chemical compounds, such as cardiac glycosides, flavonoids, terpenoids, alkaloids, tannins, and resins, have been extracted from this plant. The herb has been used to treat a variety of ailments, including piles, leprosy, ulcers, and tumors. Just a handful of the documented pharmacological activities include analgesic, antipyretic, pregnancy prevention, CNS, anti-inflammatory, procoagulant, anti-diarrheal, free radical scavenging, antimicrobial, anti-tumor, antifungal, antitussive, and antifeedant properties.

Vineyard decline is one of the main challenges viticulture now faces, especially in the current context of climate change. While decline is often attributed to grapevine trunk diseases and climate change, it also involves a myriad of other interconnected factors, including agricultural practices, whose role in decline remains unclear. This study explored for the first time how various planting conditions and agricultural practices implemented over an extended period of time affect the intensity and dynamics of decline. We analysed a large dataset obtained from 107 plots in the Cognac wine-growing region in western France from 1988 to 2018. The dataset included data on soil and climate conditions, genetic material at planting, annual agricultural practices, and information on annual mortality and yield. This study comprised six steps: dataset building, characterisation of decline categories, selection of explanatory variables to be studied, creation of plot trajectories based on agricultural practices, linking of decline categories and plot trajectories, and analysis of plot characteristics and practices. Using data from 43 plots and 58 variables, we showed that vineyard plots can be classified into three stages of decline depending on the dynamics and intensity of mortality and yield dynamics. We then established 11 trajectories over the plot lifespans (16 to 30 years). A detailed analysis of these trajectories revealed that plots associated with certain trajectories were more subject to decline, which helped us identify strategies that could limit or even prevent decline. We concluded that preventing decline requires adopting an integrated approach from planting to grubbing up, finding the right match between genetic material and soil–climate conditions, ensuring optimal water management, and managing potentially competing vegetation, like cover cropping. By identifying key practices over the vineyard lifespan, this study opens up new avenues for preventing decline by putting the focus on vigour management, water and mineral status of the plant.

Earthworms are a key faunal group in agricultural soils, but little is known on how farming systems affect their communities across wide climatic gradients and how farming system choice might mediate earthworms' exposure to climate conditions. Here, we studied arable soil earthworm communities on wheat fields across a European climatic gradient, covering nine pedo-climatic zones, from Mediterranean to Boreal (S to N) and from Lusitanian to Pannonian (W to E). In each zone, 20-25 wheat fields under conventional or organic farming were sampled. Community metrics (total abundance, fresh mass, and species richness and composition) were combined with data on climate conditions, soil properties, and field management and analyzed with mixed models. There were no statistically discernible differences between organic and conventional farming for any of the community metrics. The effects of refined arable management factors were also not detected, except for an elevated proportion of subsurface-feeding earthworms when crop residues were incorporated. Soil properties were not significantly associated with earthworm community variations, which in the case of soil texture was likely due to low variation in the data. Pedo-climatic zone was an overridingly important factor in explaining the variation in community metrics. The Boreal zone had the highest mean total abundance (179 individuals m-2) and fresh mass (86 g m-2) of earthworms while the southernmost Mediterranean zones had the lowest metrics (<1 individual m-2 and <1 g m-2). Within each field, species richness was low across the zones, with the highest values being recorded at the Nemoral and North Atlantic zones (mean of 2-3 species per field) and declining from there toward north and south. No litter-dwelling species were found in the southernmost, Mediterranean zones. These regional trends were discernibly related to climate, with the community metrics declining with the increasing mean annual temperature. The current continent-wide warming of Europe and related increase of severe and rapid onsetting droughts will likely deteriorate the living conditions of earthworms, particularly in southern Europe. The lack of interaction between the pedo-climatic zone and the farming system in our data for any of the earthworm community metrics may indicate limited opportunities for alleviating the negative effects of a warming climate in cereal field soils of Europe.