Search Results (3,527)

This study presents a life cycle assessment (LCA) of drip and flood irrigation systems for wheat production in the semi-arid Central Mashhad Plain, northeastern Iran, following the ISO 14040/14044 framework. The functional unit is defined as 1 ton of wheat grain at the farm gate. Environmental impacts were assessed using the ReCiPe 2016 Midpoint (H) method. Inventory data were sourced from field surveys, the Ecoinvent database, and regional statistics. The results indicate that while drip irrigation reduces water consumption by approximately 35.3% compared with flood irrigation, it is associated with higher environmental burdens in a range of impact categories, notably marine ecotoxicity (+62%), freshwater ecotoxicity (+58%), freshwater eutrophication (+42%), fossil resource scarcity (+41%), human non-carcinogenic toxicity (+38%), and global warming potential (+19%), along with substantial increases in mineral resource scarcity, human carcinogenic toxicity, and terrestrial ecotoxicity. These trade-offs are mainly linked to the material and energy demands of the pressurized irrigation infrastructure (pipes, pumps, and filters). Sensitivity and uncertainty analyses confirm the robustness of the comparative rankings. However, the analysis is limited by the gate-to-gate system boundary and the use of background databases that may not fully reflect region-specific emission factors. These findings highlight the importance of integrated irrigation policies that address both water conservation and the broader environmental implications of irrigation modernization in semi-arid, groundwater-dependent regions. Full article

Although the tasseling stage is a pivotal window for drought stress in maize, the photosynthetic physiological mechanism by which straw mulching compensates for drought-induced impairment relative to conventional rain-fed practice remains unclear. In this study, a two-factor randomized block experiment was conducted with two straw mulching levels (full mulching, FM, 9000 kg ha⁻¹; half mulching, HM, 4500 kg ha⁻¹) and three drought gradients (mild, 60–70% FC; moderate, 50–60% FC; severe, 40–50% FC) during the tasseling stage, with the conventional rain-fed control (CK) being non-mulched and well-watered (80% FC). The results show that only FM under mild drought (FM + LD) maintained the net photosynthetic rate, PSII photochemical efficiency (Fv/Fm), and grain yield at levels not significantly different from the CK (p > 0.05). Under mild drought, photosynthetic inhibition was dominated by stomatal limitation, with FM maintaining effective stomatal regulation. As drought intensified, the limiting factor shifted to non-stomatal limitation; however, FM showed significantly higher Fv/Fm and lower Vj than HM, indicating better PSII preservation. At 24 h after rewatering, donor-side function largely recovered across all treatments, whereas acceptor-side parameters remained elevated except in T1 (FM + LD). Overall, FM under mild drought at the tasseling stage achieved photosynthetic performance and yield comparable to conventional rain-fed management, providing a theoretical basis for water-saving cultivation in semi-arid regions. Full article

While drought and flood are still predominantly described as separate hazards responded to by agricultural producers, research in Saskatchewan, Canada with dryland agricultural producers documents the adaptation synergistically experiencing and responding to compound events of drought and flood through innovative water management techniques. A total of 132 agricultural producers and associated water decision-makers in the Saskatchewan River Basin, a semi-arid dryland basin, provided deep insight into adaptations to the compound risks of climate change through interviews and focus groups. Increasingly in south Saskatchewan, drought and floods are being experienced in the same year, in the same region, often at the same time and even on the same farm. While historically agricultural producers in the southwest experience drought and the southeast flood, over the last few years drought has migrated to the southeast and flood to the southwest. Two lakes have increased in size and volume across the river basin. While interviewees predominantly identified drought as the most extreme due to total loss of production, the impact of floods was identified as particularly damaging to infrastructure. This social science research makes a theoretical contribution to climate adaptation science concerning the experience and response to compound climate impacts of drought and flood by agricultural producers in the semi-arid plains of Saskatchewan. More research focusing on the synergistic experience of compound events and hydrological, socio-economic and institutional advancement of adaptation processes is needed. Full article

Allium polyrhizum is an important native species in arid and semi-arid regions. This study investigated the responses of A. polyrhizum to drought stress and screened drought-resistant germplasms to provide a basis for the selection of high-quality germplasm resources for degraded grassland restoration. Five A. polyrhizum germplasms were collected from Longquansi Town (LQS), Hongcheng Town (HC), Zhongchuan Town (ZC), and Zhonghe Town (ZH-P and ZH-W) in Lanzhou, Gansu Province. The phenotypic and biochemical indicators were measured under the control treatment (CK) and PEG-6000-simulated drought stress treatments at −0.2, −0.6, −1.0, −1.4, and −1.8 MPa. The results showed that, with increasing drought stress intensity, the root length, shoot height, aboveground biomass, and belowground biomass of the five A. polyrhizum germplasms generally decreased, whereas the contents of soluble protein, soluble sugar, free proline, and malondialdehyde generally increased. The activities of superoxide dismutase, peroxidase, and catalase generally increased initially and then decreased, although the magnitude of these responses varied among germplasms. Two-way ANOVA showed that drought stress and germplasm had highly significant effects on all 11 measured indicators, whereas their interaction had highly significant effects on most indicators. The drought resistance coefficients of the different indicators were correlated to varying degrees. Comprehensive evaluation showed that the drought resistance of the five germplasms was ranked as ZH-W > HC > ZH-P > ZC > LQS. Full article

Water management in Arid and Semi-Arid Regions (ASARs), specifically in the Gulf Cooperation Council (GCC) countries, has historically relied on large-scale, centralized systems that have successfully expanded potable water access. However, their high energy intensity, escalating operating costs, and limited flexibility amid increasing climate variability have raised concerns about their long-term sustainability. In this context, decentralized water systems (DWSs), including rainwater harvesting (RWH), greywater reuse (GWR), and hybrid rainwater–greywater systems (HRGSs), offer promising solutions to reduce pressure on centralized infrastructure, enhance dry-season water availability, and mitigate urban flooding risks. Despite their strategic relevance, a comprehensive sustainability assessment framework tailored to GCC conditions remains insufficiently developed. To address this gap, a systematic review of literature indexed in Scopus, Engineering Village, and Google Scholar was conducted. Thirty studies met the inclusion criteria and were critically analyzed to identify prevailing assessment approaches and recurring sustainability dimensions. Building on these findings, this study proposes a regionally tailored, multi-criteria sustainability framework designed specifically for GCC contexts. The proposed framework integrates five core dimensions, including technical, environmental, economic, social, and political–institutional, comprising 14 indicators and four sub-indicators. To refine and validate the framework, a two-round Delphi technique was conducted. A total of 102 experts from GCC member states were invited, of whom 43 participated in the first round, and 25 completed the second round. The results demonstrated strong consensus regarding the relevance and applicability of the selected indicators, with particular emphasis on technical and environmental dimensions. Notably, the lack of agreement on equal weighting in the first round justified the adoption of a ranking-based weighting approach in the second round, enabling a more realistic representation of expert consensus. The final DWS index, developed using a hierarchical multi-criteria decision analysis (MCDA) approach, integrates criterion weights, indicator weights, and performance scores into a single composite metric. The results indicate that HRGSs achieved the highest overall performance (55.00), followed closely by GWR (54.76) and RWH (54.20). Overall, the proposed framework provides a robust and context-specific tool to support sustainability assessment and inform policy development for DWSs in the GCC region. Full article

Understanding how climate change may alter river discharge in semi-arid regions is essential for sustainable water-resource management. This study assesses future streamflow in the Zat River Basin (High Atlas Mountains, Morocco) using a hybrid framework that combines machine-learning rainfall–runoff modeling, CMIP6 multi-model climate forcing, monthly quantile-mapping post-processing of simulated discharge, and an exploratory temperature-sensitivity assessment. Monthly hydroclimatic observations of precipitation, air temperature, reference evapotranspiration, and discharge were compiled from February 1962 to August 2024. The period 1962–2005 was used for model development, the 2006–2014 window for chronological validation, and the more recent observations for supplementary evaluation of climate-driven simulations. Four algorithms were compared: Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Performance was assessed using NSE, KGE, RMSE, MAE, and R². GBR provided the best validation performance (NSE = 0.71, KGE = 0.80, and R² = 0.72). The selected model was then forced with CMIP6 projections under SSP2-4.5 and SSP5-8.5 to simulate streamflow to 2100. Quantile mapping was applied to the simulated discharge, rather than separately to precipitation, temperature, and reference evapotranspiration. The multi-model ensemble indicates a persistent drying tendency: relative to the historical baseline and without an additional temperature-sensitivity adjustment, mean annual discharge is projected to decline by approximately 12.1% under SSP2-4.5 and 27.3% under SSP5-8.5 by 2081–2100. Under an exploratory sensitivity case using a runoff-temperature-sensitivity coefficient of 0.04 °C⁻¹, the projected declines increase to approximately 23.3% and 44.1%, respectively. Episodic high-flow events nevertheless remain possible, suggesting a shift toward lower mean flows combined with persistent hydrological extremes. Full article

In northeastern Mexico, forage shortages during the cold season limit the availability of high-quality forage for ruminant production systems, as the growth of warm-season C4 forages declines significantly. Forage oats are widely grown to address this seasonal shortage; however, information on the optimal harvest stage and its effect on gas fermentation kinetics of commercially important cultivars under regional semi-arid conditions remains limited. Therefore, this study evaluated the nutritional value and in vitro fermentation parameters of two oat varieties (Chihuahua and Cuauhtémoc) at six phenological stages: tillering (Z2), stem elongation (Z3), stuffing (Z4), milky grain (Z7), doughy grain (Z8), and physiological maturity (Z9). The experiment was carried out using a completely randomized design, with a 2 × 6 factorial arrangement, where the fixed effects corresponded to the oat variety (Chihuahua and Cuauhtémoc), the phenological stage (Z2, Z3, Z4, Z7, Z8, Z9), and their interaction. Crude protein, ash, digestible dry matter, and relative forage value contents decreased (p < 0.05) with increasing phenological stage of the two oat varieties. In contrast, neutral detergent fiber, acid detergent fiber, and hemicellulose contents increased (p < 0.05) with increasing phenological stage of the two oat varieties. Gas production rate, lag phase, and in vitro dry matter digestibility at 72 h decreased (p < 0.05) with increasing phenological stage of the two oat varieties. In contrast, the fast fermentable fraction and total fermentable fraction increased (p < 0.05) with increasing phenological stage in both oat varieties. In conclusion, the nutritional value, gas production, lag phase, and in vitro dry matter digestibility of oats decreased as the phenological stage increased. Full article

Freshwater scarcity in arid and semi-arid regions, coupled with increased population density, leads to greater demand and pressure on aquatic ecosystems and limited groundwater resources. This study, conducted using the PRISMA 2020 methodology, aimed to evaluate the effectiveness and sustainability of hybrid technologies that combine advanced oxidation processes (AOPs) with biological filtration in the treatment of urban and industrial wastewater. A systematic review was conducted between 2015 and 2026 using six main databases (Scopus, Web of Science, ScienceDirect, SpringerLink, PubMed, and Google Scholar). A total of 1248 studies were identified, of which 78 met the eligibility criteria and provided sufficient quantitative data for comparative synthesis. The results showed that hybrid systems, such as ozone biofiltration, Fenton-Membrane Bioreactor (MBR), and photocatalytic biofilm, have higher removal efficiencies for COD (>95%), microorganisms (>90%), and pathogens (>99%), with minimal residual sludge. The environmental assessment also demonstrates the strong potential of these processes when integrated into arid regions such as southwestern Algeria, due to their contribution to conservation of biodiversity and sustainable reuse of treated wastewater. Through this study, our objective is to highlight the role of integrated approaches in circular water management, as well as the urgent need to standardize protocols to assess the magnitude of long-term environmental impacts. Full article

Greenhouse agriculture in semi-arid regions faces persistent challenges from unpredictable thermal variability, frost events, and seasonal drought stress that are difficult to manage without anticipatory climate information. This paper presents the design, implementation, and validation of an end-to-end intelligent greenhouse monitoring and temperature forecasting system deployed in Donovan, San Luis Province, Argentina. The proposed platform integrates a three-layer IoT architecture with cloud-based statistical forecasting to support real-time decision making under semi-arid climatic conditions. The system integrates sensor nodes, a push-MQTT gateway co-located at the Universidad Nacional de San Luis to bypass regional API geo-restrictions, and a cloud application layer. Three forecasting strategies with a six-hour prediction horizon were evaluated: a univariate SARIMA baseline (Model 1), a SARIMAX model using four neighboring meteorological stations as individual exogenous regressors (Model 2), and a SARIMAX model employing a single correlation-weighted synthetic exogenous index (Model 3). The models were assessed using MAE, RMSE, and Diebold–Mariano statistical significance tests. The results show that directly incorporating multiple correlated exogenous variables does not improve forecast accuracy because of multicollinearity, whereas the proposed correlation-weighted synthetic index preserves the spatial predictive signal while reducing model complexity and achieving performance comparable to the baseline overall, with statistically significant improvement during the overnight block. Additional benchmarking against Random Forest, Support Vector Regression, Temporal Convolutional Networks, and Long Short-Term Memory models demonstrates that increasing model complexity does not necessarily translate into improved predictive performance for short-horizon greenhouse temperature forecasting. Together, the proposed IoT architecture and forecasting framework provide an operationally validated solution for intelligent greenhouse monitoring and predictive decision support in resource-constrained semi-arid environments. Full article

The evolution of land-use types in mining cities is critical for balancing resource exploitation and ecological sustainability. Taking Jungar Banner as a case study, this study uses remote sensing imagery and auxiliary data (2010–2025) to examine spatiotemporal land-use changes, applies the OPGD model to detect driving factors, and integrates a Markov chain with an optimized NEGM-MOP-PLUS model to project 2030 land-use patterns under multiple scenarios. Results show that grassland shrank markedly, while cropland and built-up land expanded—the latter reaching 395.75 km² by 2025. After 2020, core mining areas became more contiguous, while peripheral zones showed increased fragmentation, with built-up land expansion becoming the dominant trend. Driving forces shifted from natural constraints to anthropogenic dominance: natural factors prevailed in 2010, mining impacts took the lead by 2015, and a mining–precipitation dual-core structure emerged by 2020. Future projections indicate continued grassland and bare land reduction, alongside water and built-up land expansion across all scenarios. Among them, the CDS, which balances economic and ecological objectives, is identified as the optimal spatial planning direction based on ecosystem service value (ESV) assessment. These findings offer practical guidance for managing land-use transitions in arid and semi-arid resource-based mining regions. Full article

Sesame (Sesamum indicum L.) cv. BRS Anahí is a promising crop for agricultural diversification in the Brazilian semi-arid region, although saline irrigation can limit its development and productivity. The objective of this study was to evaluate the physiological and productive responses of sesame irrigated with brackish water under different fertilization treatments. The experiment was conducted in a greenhouse at the Federal University of Ceará, using a completely randomized design in a 4 × 2 factorial scheme, with four replications. These corresponded to four fertilization treatments (F1: HomeBiogas liquid biofertilizer from CAGECE; F2: HomeBiogas from food waste; F3: shrimp biofertilizer; and F4: mineral fertilization—NPK) and two irrigation water salinity levels (0.8 and 3.0 dS m⁻¹). Irrigation with 3.0 dS m⁻¹ significantly reduced the analyzed variables; however, the intensity of these effects varied according to the fertilization treatment. Treatment F1 promoted greater physiological activity under saline conditions, partially mitigating saline stress. At low salinity, treatments F1 and F3 showed productivity performance similar to F4, while at high salinity, F3 was less effective. The results highlight the potential of the selected biofertilizers to improve sesame performance under saline irrigation. Full article

Traditional evaluations of ecological restoration in mining lands have long been dominated by above ground vegetation metrics, such as coverage and community diversity. Yet in arid and semi-arid mining regions, soil hydrology recovery is key, as soil organic carbon (SOC) and bulk density (BD) are critical factors affecting soil hydrology. However, their thresholds for water holding capacity and infiltration remain unclear. Therefore, this study determined the SOC and BD thresholds and evaluated the hydrological trends across them. The study was in a Loess Plateau coal reclamation area. Five restoration types and one reference forest were selected, and soil samples were collected from the 0–10 cm layer. Redundancy analysis (RDA), partial least squares structural equation modeling (PLS–SEM), and the threshold regression model were used for analysis. Results show the following: (1) Vegetation indirectly controls soil hydrology via soil structure and chemical properties (RDA: 76.04%). BD limited water holding capacity (path coefficient: −0.908), while chemical properties promoted infiltration (path coefficient: 0.397). (2) Soil hydrological recovery exhibits non-linear threshold responses. The SOC thresholds for water holding capacity and infiltration were 9.52 g/kg and 5.93 g/kg, respectively, while the BD thresholds were 0.93 g/cm³ and 1.03 g/cm³. The reclamation of soil hydrological functions in mining lands follows a two-stage mechanism. First, control by carbon accumulation, then follow by structural dominance. During the early stage, soil organic carbon (SOC) buildup quickly boosts hydrological performance by promoting aggregate formation. However, once the system nears its functional threshold, simply adding more carbon gives limited returns. At this stage, breaking through physical constraints becomes vital to overcoming the bottleneck. Above-ground metrics alone cannot capture below-ground ecosystem functional trajectories. Consequently, a dynamic strategy centered on SOC and BD is proposed. Full article

Mapping vegetation alliances is essential for understanding ecological patterns and supporting sustainable land management in arid and semi-arid areas. However, traditional remote sensing typically only distinguishes grassland boundaries or broad subclasses, failing to differentiate specific vegetation alliances. Furthermore, while traditional vegetation mapping relies heavily on field surveys, manual interpretation, and expert knowledge, this labor-intensive approach hinders efficient large-scale mapping. This study proposes an efficient method for vegetation mapping by integrating field survey data with multi-source and multi-temporal remote sensing variables using deep learning. A series of deep neural network models was designed to systematically characterize and leverage spectral signatures, climatic factors, and topographic habitat features for fine-grained classification of 32 vegetation alliances in Xinjiang, a typical arid to semi-arid region. The resulting vegetation map achieved an alliance-level classification accuracy of 0.5125 on an independent test set, with the five most dominant alliances: Stipa spp. desert steppe, Stipa spp. steppe, Poa spp. meadow, and Anabasis spp. desert, accounting for over 10.45% of the total area of Xinjiang. Compared with traditional approaches, this method significantly improves mapping efficiency and offers a scalable solution for large-area, updatable vegetation classification. The approach provides a valuable reference for ecological assessment and dynamic vegetation monitoring in arid and semi-arid regions. Full article

Drought is a complex and periodic issue that has a significant impact on agriculture and water resources, particularly in semi-arid areas. This study evaluated meteorological and agricultural drought conditions in the Thanjavur district by combining rainfall data with remote-sensing methods. Meteorological drought was analyzed using 33 years of rainfall data and the Standardized Precipitation Index (SPI) (1992–2024) using 20 rainfall stations for the data available between 1992 and 2024. The spatial variation in rainfall was analyzed using Kriging interpolation in GIS. Agricultural droughts were analyzed using the Normalized Difference Vegetation Index (NDVI) and Vegetation Con0dition Index (VCI) using multi-temporal Landsat satellite images (Landsat 5 and Landsat 8). Land Use and Land Cover (LULC) classification was included to determine drought vulnerability across different land types. The NDVI and VCI indices showed an intensification of agricultural drought in 2010. The results demonstrated temporal and spatial differences in drought conditions for the years 1992, 1997, 2004, 2009, 2014, 2019, and 2024 and indicated that the region experienced periodic severe drought conditions of 3%, 3%, 3%, 8%, 19%, 9%, and 11% in the study area, respectively. During the drought period, the vegetation indices showed a strong sensitivity of agricultural areas to changes in rainfall, and low NDVI and VCI values indicated increased vegetation stress. Meteorological and agricultural droughts were integrated using the Analytical Hierarchy Process (AHP) method by combining various indicators to analyze the drought condition across the Thanjavur district. The multiple criteria decision-making (MCDM) method uses pairwise comparisons of various factors, such as giving high importance to SPI and rainfall, followed by vegetation indices and LULC. The consistency ratio validated the reliability of the weighting term. This method shows that combining meteorological and remote sensing indicators advances a robust framework for monitoring and assessing droughts. Conceptual droughts illustrate how meteorological droughts are associated with the development of agricultural droughts. The results of this study can be adopted for effective drought management, irrigation planning, and sustainable agricultural practices in this region. Full article

Background/Objectives: Dehydrins (DHNs) are late embryogenesis abundant proteins that play protective roles under water-deficit conditions; however, their organization and function remain unexplored in sesame (Sesamum indicum), an important oilseed crop frequently cultivated in arid and semi-arid regions. This study aimed to identify and characterize the DHN gene family in sesame and evaluate the expression of its members under drought stress. Methods: Genome-wide identification was performed using the Dehydrin domain HMM profile, followed by phylogenetic analysis, conserved motif and gene structure characterization, synteny analysis, promoter cis-element profiling, and secondary/tertiary structure prediction. Transcriptional responses were profiled by qPCR in two sesame cultivars (drought-sensitive and drought-tolerant) under PEG-induced osmotic stress at germination and seedling stages. Results: Four DHN genes were identified, spanning three phylogenetic subfamilies (I–III) and three DHN subclasses: SK_n (SiDHN1/2), Y_nK_n (SiDHN3), and Y_nSK_n (SiDHN4). SiDHN1 and SiDHN2 likely arose from a segmental duplication, and a single conserved syntenic pair was found between SiDHN4 and olive (Olea europaea). Secondary structure predictions uncovered contrasting structural propensities: SiDHN1/2 are predicted to be α-helix-rich, partially ordered proteins (41–44% predicted α-helix), whereas SiDHN3/4 are predicted to be predominantly intrinsically disordered (~75–80% random coil). SiDHN3 was consistently upregulated across all conditions (1.84–9.12-fold), while SiDHN4 exhibited strong genotype-specific induction of 9.39-fold exclusively in the drought-tolerant cultivar during germination. Conclusions: The sesame DHN family achieves functional breadth through structural diversification—an ordered–disordered continuum mirrored by divergent expression programming—rather than through numerical expansion. SiDHN3 and SiDHN4 are identified as primary candidates for drought tolerance improvement in sesame. Full article