Search Results (89)

Tropical peatlands in Indonesia are increasingly degraded by conventional oil palm practices involving drainage and chemical fertilizers. This study evaluates AeroHydro Culture (AHC), a method applying microbe-enriched organic media aboveground, as a sustainable alternative that maintains high groundwater levels while supporting plant productivity. Field trials were conducted at two sites: a managed plantation in Siak and a degraded, abandoned plantation in Pulang Pisau. Ten months after treatment, AHC plots showed development of aerial-like lateral roots, improved chlorophyll levels, and increased arbuscular mycorrhizae colonization (from 0–46% to 22–73% in Siak, and 1.7–20% to 16–60% in Pulang Pisau). In Siak, AHC significantly increased IAA-producing and proteolytic bacteria in the 0–25 cm soil layer and raised oil palm yield by 36% over controls. This yield benefit was sustained in 2025, five years after the initial application. In Pulang Pisau, AHC also enhanced microbial abundance and promoted growth in the native species Shorea balangeran, suggesting its potential for reforestation. Drone imagery confirmed visible long-term differences in canopy color, supporting lasting physiological improvement. These results demonstrate that AHC promotes plant–microbe symbiosis, enhances nutrient acquisition, and sustains oil palm yield under saturated conditions. AHC offers a promising strategy for peatland rehabilitation where ecological recovery and agricultural productivity must be achieved in parallel. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed during three years in a hydro-agricultural area of the Alqueva irrigation system (Portugal) with Mediterranean climate conditions. The sampling campaigns included collecting water samples from eight irrigation hydrants, analyzed four times yearly. The analysis incorporated meteorological data and indices (precipitation, temperature, and drought conditions) alongside chemical parameters, using multivariate statistics (factor analysis and cluster analysis) to identify key water quality drivers. Additionally, machine learning models (Random Forest regression and Gradient Boosting machine) were employed to predict electrical conductivity (ECw), sodium adsorption ratio (SAR), and pH based on chemical and climatic variables. Water quality evaluation showed a prevalence of a slight to moderate soil sodification risk. The factor analysis outcome was a three-factor model related to salinity, sodicity, and climate. The cluster analysis revealed a grouping pattern led by year and followed by stage, pointing to the influence of inter-annual climate irregularity. Variations in water quality from the reservoirs to the distribution network were not substantial. The Random Forest algorithm showed superior predictive accuracy, particularly for ECw and SAR, confirming its potential for the reliable forecasting of irrigation water quality. This research emphasizes the importance of integrating time-sensitive monitoring with data-driven predictions of water quality to support sustainable water resources management in agriculture. This integrated approach offers a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments. Full article

Diffuse agricultural pollution is a leading contributor to surface water degradation, particularly in regions undergoing rapid land use change and agricultural intensification. In many developing countries, conventional assessment approaches fall short of capturing the spatial complexity and cumulative nature of multiple environmental drivers that influence surface water vulnerability. This study addresses this gap by introducing the Integral Index of Vulnerability to Diffuse Contamination (IIVDC), a spatially explicit, multi-criteria framework that combines the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS). The IIVDC integrates six key indicators—slope, soil erodibility, land use, runoff potential, hydrological connectivity, and observed water quality—weighted through expert elicitation and mapped at high spatial resolution. The methodology was applied to the Guachal River watershed in Valle del Cauca, Colombia, where agricultural pressures are pronounced. Results indicate that 33.0% of the watershed exhibits high vulnerability and 4.3% very high vulnerability, with critical zones aligned with steep slopes, limited vegetation cover, and strong hydrological connectivity to cultivated areas. By accounting for both biophysical attributes and pollutant transport pathways, the IIVDC offers a replicable tool for prioritizing land management interventions. Beyond its technical application, the IIVDC contributes to sustainability by enabling evidence-based decision-making for water resource protection and land use planning. It supports integrated, spatially targeted actions that can reduce long-term contamination risks, guide sustainable agricultural practices, and improve institutional capacity for watershed governance. The approach is particularly suited for contexts where data are limited but spatial planning is essential. Future refinement should consider dynamic water quality monitoring and validation across contrasting hydro-climatic regions to enhance transferability. Full article

Western Kazakhstan is susceptible to desertification, with surface water resource scarcity constraining agricultural development. Groundwater has substantial potential as a reliable and secure alternative to other water resources, particularly for irrigation, which is required to ensure food security. Eight aquifer segments with an exploitable potential of 0.24 km³/year have been identified for the integrated assessment of groundwater’s suitability for irrigation. The assessment criteria included hydro-chemical groundwater characteristics and irrigated land soil-reclamation conditions. The primary objectives of this study were to assess the groundwater quality for irrigation and to develop a practical operation scheme for rational groundwater use in water-saving irrigation technologies and optimize agricultural crop cultivation. Approximately 90% of the groundwater in these aquifer segments was found to be suitable for irrigation, with a total amount of 6520 thousand m³/day and a salinity of up to 1 g/L, and an additional 12,971 thousand m³/day had a water salinity of up to 3 g/L. Only approximately 10% had TDS values above 3 g/L and up to 6.5 g/L, categorized as conditionally suitable for restricted customized agricultural crop irrigation. Irrigated land development by complex soil desalination agro-reclamation operations enabled the use of brackish water for irrigation. The integrated analysis allowed the development of drip irrigation and sprinkling system irrigation schemes that gradually replaced wasteful surface irrigation. The irrigated land prospective area recommended for groundwater irrigation development is 653 km², with the further restructuring of cultivated areas, reducing the number of annual grasses and grain crops and increasing the number of vegetables, potatoes, and perennial grasses. Full article

The control of rainfall extremes is essential in the design of hydro-agricultural works, as their performance depends on it. This study aims to determine the best-fit probability model suited to current climatic conditions in the Mono-Couffo basin in Benin. To this end, daily rainfall data from six rainfall stations from 1981 to 2021 were used. The application of the Decision Support System (DSS) with graphical and numerical performance criteria (such as RMSE, SD, and CC represented by the Taylor diagram; AIC and BIC) made it possible to identify the best distribution class and then to select the most suitable distribution for this basin. The results indicate that class C distributions, characterized by regular variations, are the most appropriate for the modeling maximum annual daily precipitation at all stations (78% of cases). Of these, the Inverse Gamma distribution proved to be the most suitable, although its estimation errors ranged from 16.47 mm/d at Aplahoué to 39.80 mm/d at Grand-Popo. The second most appropriate distribution is the Log-Pearson Type III. The use of the Inverse Gamma distribution is, therefore, recommended for hydro-agricultural development studies in the Mono-Couffo basin. Full article

The hydrological dynamics of Raipur are profoundly influenced by the intricate interplay between surface and groundwater systems, driven by changes in land use, climatic conditions, and human activities such as agriculture and industry. This research investigated the interdependencies between the Kharun River and groundwater systems, essential for understanding water security in the face of escalating demands and rapid urbanization. Through meticulous monitoring and analysis of approximately 70 bore wells, nine river sampling sites, and 13 groundwater samples from dug wells, alongside rigorous adherence to established sampling protocols, this study delved into the seasonal variations and influences on water quantity and quality. Statistical methodologies, stable isotope analyses, and Gibbs diagrams were employed to unravel the complexities governing water resource dynamics and interactions. Notably, correlation analysis revealed significant associations between various water quality parameters, indicating anthropogenic influences on groundwater chemistry. Cluster analysis aided in understanding hydro-chemical processes, while stable isotope examinations further elucidated the sources and interactions of groundwater and surface water. Results indicate the urgent need for sustainable water management strategies tailored to the region’s evolving socio-environmental landscape, considering escalating urbanization and agricultural activities. This integrated approach, combining analytical methods and statistical techniques, offers a holistic understanding of water resource dynamics essential for effective governance and sustainable development. Full article

This paper compares the use of renewable energy sources (RESs) in the EU, focusing on their application in the transport, cooling and heating sectors and electricity production. It examines the current situation in the EU and assesses to what extent their laws and policies have contributed to the transition to renewable energy sources. This study focuses on several forms of renewable energy, including solar, wind, hydro energy, and biomass energy. The analysis is based on several variables, including production, consumption, and environmental impact. The findings of the analysis highlight the contrasts and similarities between the two nations and illuminate the best strategies for advancing sustainable energy development. This study also analyzes the main renewable energy strategies and policies to understand which factors support or hinder the transition to sustainable and clean energy. The analysis underscores the importance of optimizing energy efficiency and achieving long-term savings using RESs. Additionally, this study emphasises the role of RESs in enhancing energy security and promoting economic growth through technological innovation. This study also highlights the potential of RESs to reduce emissions in industry, transport, and agriculture, thereby contributing to environmental sustainability. The results show that the use of renewable energy sources (RESs) in the European Union has increased in the analyzed period. The results of this study can be useful to government agencies, businesses, academia, and the public interested in supporting the development of sustainable energy in the EU. Full article

Due to growing water demands and changing hydro-meteorological variables brought on by climate change, drought is becoming an increasingly serious climate concern. The Al-Baha region of Saudi Arabia is the subject of this study because it is susceptible to both agricultural and meteorological droughts. This study investigates how climate change affects patterns of drought in Al-Baha by analyzing four drought indices (Agricultural Standardized Precipitation Index (aSPI), the Standardized Precipitation Index (SPI), the Rainfall Deficiency Index (RDI), and the Effective Reconnaissance Drought Index (eRDI)) for the years 1991–2022. Analysis of rainfall data was carried out to classify drought events according to their duration, frequency, and severity. Results showed that severe droughts occurred in 2009, 2010, 2012, 2016, and 2022, with 2010 being the worst year. Results also indicated a notable decrease in precipitation, which has resulted in extended dry spells. Several indices indicate that this tendency has significant ramifications for agriculture, particularly in areas where farming is a major economic activity. In addition, the possible occurrence of hydrological drought was also observed based on the negative values for the Reservoir Storage Index (RSI) in Al-Baha. Projections for the future under two Representative Concentration Pathways (RCPs) showed notable variations in temperature and precipitation. Both the RCP4.5 (low emission) and the RCP8.5 (high emission) projection scenarios indicate that drought conditions will likely worsen further. Depending on the emission scenario, it is projected to show a temperature increase of 1–2 °C, whereas the variability in precipitation projections indicates significant uncertainty, with a reduction change in the range of 1.2–27% between 2050 and 2100. The findings highlight the urgent need for proactive adaptation strategies, effective water resource management, and the development of sophisticated drought prediction tools. Addressing these challenges is crucial for sustaining agriculture and managing water scarcity in Saudi Arabia in the face of increasing drought risk. Full article

The management of transboundary aquifers across the Americas faces significant challenges, especially as climate change and population growth intensify groundwater stress. Groundwater use has increased to support domestic, industrial, and agricultural demands but has been extracted through unregulated withdrawals, leading to the severe degradation of aquifer health and transboundary frictions. This study focuses on how hydro-diplomacy can accelerate the adaptive governance of shared groundwater resources in three key regions: Canada–USA, USA–Mexico, and Mexico–Guatemala–Belize. We utilized a mixed methodology by integrating a transect approach, borrowed from ecology, into the field of geopolitics. To compare the hydro-diplomatic relations and groundwater governance across a continental gradient in the Americas, we conducted a literature review and employed the TWINS conflict–cooperation matrix to evaluate governance frameworks and hydro-diplomatic interactions across time. Our findings demonstrate that hydro-diplomacy plays a pivotal role in expediting agreements, fostering transboundary data sharing, and supporting participatory governance models. In particular, the presence of supranational bodies such as the International Joint Commission (IJC) between Canada and the USA has been effective in maintaining long-term collaboration through social learning and technical cooperation. Meanwhile, in regions like Mexico–Guatemala–Belize, the absence of robust institutions has hindered progress, with limited financial and knowledge-sharing networks. This study highlights the need for improved cross-border cooperation mechanisms and the establishment of common monitoring protocols to better manage aquifer resources under the pressures of climate change. The results support the development of more adaptive transboundary groundwater management strategies aligned with Sustainable Development Goal (SDG) 6.5.2 and call for broader geopolitical cooperation to address the complexities of groundwater governance. Full article

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected. Full article

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. Full article

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure–activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area. Full article

The valorisation of sewage sludge for sustainable agricultural use and biofuel production proposes an effective and beneficial management of sewage sludge in a closed-loop cycle. The management of sewage sludge biowaste is a rising problem due to increasing waste storage expenses. In this sense, the use of circular economy principles in sewage sludge management creates opportunities to develop new technologies for processing. The biorefinery model allows the application of wasteless technologies via sewage sludge valorisation in terms of agricultural use and biofuel production, especially with the hydrothermal carbonisation method. Applying hydrothermal carbonisation in the treatment of biosolid sewage sludge has numerous benefits due to processing highly hydrated organic waste into carbon hydro char, a high-quality solid biofuel. The direct use of sewage sludge in the soil does not allow for full use of its functional properties. However, the hydrothermal carbonisation of sewage sludge results in biocarbon pellets, making it a viable approach. This work also discusses the barriers (legal, chemical, biological, and technical) and possibilities related to sewage sludge biorefining processes. Full article

In regions where drought has become a common occurrence for most of the year and where agriculture is the main economic activity, the development of hydro-agricultural systems has made it possible to improve water management. Despite this, the intensification of agriculture combined with climate change leads to a potential decrease in water quality and water management practices are essential to improve agro-environmental sustainability. The aim of this study was to assess the water quality for irrigation and potential ecological status of the reservoir (using support chemical parameters). The results showed biological oxygen demand values above the maximum stipulated for an excellent ecological potential in all sampling periods except April 2018 and December 2020 (with the highest values of 10 mg L⁻¹ O₂ in dry periods). Most of the total nitrogen concentrations (TN) surpassed those stipulated for a good ecological potential (0.96 ≤ TN ≤ 2.44 mg L⁻¹ N). In fact, TN and total suspended solids were the main parameters used for water classification. From the perspective of irrigation and according to FAO guidelines regarding infiltration rate, these waters presented light to moderate levels of restrictions. Thus, the results revealed that the decrease in the water quality status and its possible impact on the soil infiltration rate can be related, in part, to the meteorological conditions and to the intensive agricultural practices developed around the drainage basin. Despite that, as the Lage reservoir is part of Brinches–Enxoé hydraulic circuit, the water recirculation is also an important factor that may have affected the results obtained. Furthermore, the experimental design, integrating ecological status, irrigation parameters, and the impact on soil systems; using the same parameters from different perspectives; allowed us to have a global idea of water contamination and its impact on agroecosystems, improving river basin management processes. Full article