Agricultural Research Knowledge
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- Third Regional Coordination meeting ICARDA/Central Asia and the CaucasusAuthor(s): (ICARDA), International Center for Agricultural Research in the Dry Areas (International Center for Agricultural Research in the Dry Areas (ICARDA), 1990-09-30)Date: 1990-09-30Type: Internal ReportStatus: Timeless limited accessThe third ICARDA/Central Asia and the Caucasus Annual Regional Coordination meeting was held in Tashkent, Uzbekistan,27-30 September 1999. The meeting was attended by 53 scientists/heads of NARS of the eight Central Asia and the Caucasus (CAC) Countries,14 scientists from ICARDA, and the representatives of the world bank, Asian Development Bank (ADB), and Global Forum on Agricultural Research (GFAR) . The list of participants is given in Appendix 11. The inaugural session was co-chaired by prof Najmetdin Makhmukhodjaev, Director General of Uzbek Scientific Production Center for Agriculture (USPCA), and Deputy Minister in charge of Research of the Ministry of Agriculture and Water management (MAWM), and Dr. Mahmoud B.Solh, Assistant Director-General, International Cooperation of ICARDA. Also, present in the opening session were Mr. Ison Mustafaev, Deputy Minister of the Ministry of affairs of the Republic of Uzbekistan, Mr. V.N Gnanathurai, Resident Representative, Asian Development (ADB), Dr. Arrigo Di Carlo of the world bank, Tashkent, and Dr. Surendra Beniwal, Regional Coordinator ICARDA's CAC Regional program.
- International Cereals Nurseries 1986/87: List of Cooperators and Distribution of NurseriesAuthor(s): Yau, S. K. (International Center for Agricultural Research in the Dry Areas (ICARDA), 1987-12-31)Date: 1987-12-31Type: Internal ReportStatus: Timeless limited accessThe cereal improvement of ICARDA conducts crop improvement research on Barley, durum wheat, and bread wheat. Within the system of International Agricultural Centers, the consultative Group on International Agricultural Research (CGIAR) gives ICARDA the global mandate for barley improvement and a joint improvement with CIMMYT for wheat improvement in West Asia and North Africa. The prime objective of the International Cereal Nursery system is to disseminate improved germplasm to national plant breeding programs in the ICARDA region and beyond.To enable this objective to be carried out effectively , the feedback of cooperators data to scientists of the national programs and of ICARDA is considered as an important component. Basically the Cereal Improvement distributes 5 types of International Nurseries for the three crops: yield trials,observation nurseries,segregating populations,crossing blocks and special nurseries. Over the last few seasons ,much effort was spent on targetting the germplasm for different environmental areas.A full description of the set-up can be found in the booklet "An Introduction to the International Nurseries systems",which is available from the program.
- First Central Asia/ICARDA Regional Coordination Meeting Aleppo, 13-16 September 1997Author(s): (ICARDA), International Center for Agricultural Research in the Dry Areas (International Center for Agricultural Research in the Dry Areas (ICARDA), 1997-09-16)Date: 1997-09-16Type: Internal ReportStatus: Timeless limited accessThis brief report was prepared with support from Central Asian scientists and contains salient features of the meeting . The meeting was attended by 24 scientists from the five Central Asian Republics which provided an excellent opportunity for them and ICARDA scientists to discuss and finalize collaborative research activities. The meeting also provided an excellent opportunity to jointly develop project proposals for seeking financial resources to initiate these collaborative activities, Furthermore, the meeting at Aleppo provided an opportunity for the scientists of Central Asia to meet ICARDA scientists and familiarise themselves with its research programs and facilities.
- SURE RESULTS Resilient Food and Nutrition Security for Yemen through Agricultural Research Interventions and Capacity Building in the Small Ruminant Dairy and Agriculture Sector: Small Ruminants and Dairy Value Chain Analysis and Upgrading StrategyAuthor(s): Dhehibi, Boubaker; Najjar, Dina; Abeyou, Abeyou; Soula, Rania; Rekik, Mourad (International Center for Agricultural Research in the Dry Areas (ICARDA), 2026-05-31)Date: 2026-05-31Type: Internal ReportStatus: Open accessThis report presents an integrated analysis of the small ruminant dairy value chain across the Taiz, Aden, Lahj, and Abyan Governorates of Yemen, together with a corresponding upgrading strategy and implementation plan. Part I diagnoses the structure, economics, and constraints of the value chain on the basis of primary field data, while Part II translates that diagnosis into a coherent, prioritized program of interventions designed to strengthen the livelihoods and food security of vulnerable populations, including smallholder farmers, women-headed households, youth, and internally displaced persons.
- R4D Brief: Socioeconomic and Environmental Impacts of the Red Palm Weevil in MENA CountriesDate: 2026-08-01Type: BriefStatus: Open accessThis Brief assesses the socioeconomic and environmental impacts of the Red Palm Weevil (RPW) across MENA, emphasizing threats to date palm production, livelihoods, and ecosystems. It quantifies losses from yield declines, palm mortality, and higher control costs, while highlighting trade disruptions that hit smallholders. The brief underscores environmental risks from heavy pesticide use and genetic erosion, advocating sustainable IPM with early detection, stronger quarantine, and regional coordination. It proposes a framework to boost resilience and reduce long-term RPW impacts in MENA date palm systems.
- C4RPWC Socioeconomic Impact and Policy Assessments of the Red Palm Weevil in UAE, Egypt, and Morocco Conceptual FrameworkDate: 2026-06-01Type: Internal ReportStatus: Open accessPrepared under C4RPWC Work Package 1 (Socioeconomics and Policy), this conceptual framework sets out the study’s rationale, methods, and analytical design for evaluating the socioeconomic and policy impacts of the Red Palm Weevil (RPW) in the UAE, Egypt, and Morocco. The framework combines qualitative and quantitative approaches to quantify direct and indirect costs, assess governance and quarantine performance, and identify barriers to IPM adoption. It further specifies data sources, site-selection criteria, and risk–governance indices to inform evidence-based, scalable RPW control policies.
- Consortium for Red Palm Weevil Control Socioeconomic Impact and Policy Assessments of the Red Palm Weevil in UAE, Egypt, and MoroccoDate: 2026-06-01Type: Other (survey)Status: Open accessBaseline socioeconomic survey under C4RPWC (ICARDA-led) assessing RPW impacts on date palm production, quality, income, and water use across UAE and Egypt. It captures farmers’ management practices and challenges to inform effective Good Agricultural Practices and policy innovations for RPW control and improved profitability.
- برنامج التحالف الدولي لمكافحة سوسة النخيل الحمراء C4RPWC)Date: 2026-06-01Type: Other (Survey)Status: Open accessThe Arabic version of the baseline survey under the Consortium for Red Palm Weevil Control, assessing socioeconomic impacts and policy gaps of Red Palm Weevil infestations among date palm farmers and stakeholders in the United Arab Emirates and Egypt to inform Good Agricultural Practices and scaling pathways.
- الآثار الاجتماعية والاقتصادية وتقييم السياسات المتعلقة بسوسة النخيل الحمراء بالإمارات العربية المتحدة ومصر والمغرب : ورقة معلومات الموافقة المستنيرة ، أداة مناقشة المجموعات البؤريةDate: 2026-07-01Type: Working PaperStatus: Open accessThis is the Arabic version of the informed-consent sheet and the FGD instrument for assessing RPW’s socioeconomic and policy impacts in the UAE, Egypt, and Morocco. It targets farmers’ organizations and cooperatives, capturing direct revenue losses and extra costs to inform policy interventions under C4RPWC Work Package 1.
- Socioeconomic Impact and Policy Assessments of the Red Palm Weevil in UAE, Egypt, and Morocco Informed Consent Information Sheet: Focus Groups Discussion (FGD’s) InstrumentDate: 2026-07-01Type: Internal ReportStatus: Open accessThe document is an informed-consent information sheet and focus-group discussion (FGD) instrument for assessing the socioeconomic and policy impacts of the Red Palm Weevil (RPW) in the UAE, Egypt, and Morocco. The target participants are farmers’ organizations and cooperatives. The tool captures both direct economic losses (foregone revenues from dates, offshoots, and by-products) and additional expenditures (removal, burial new plantings, and pesticides). Designed under C4RPWC Work Package 1 (Socioeconomics and Policy), it aims to inform targeted policy interventions to protect date palm livelihoods in the three countries.
- Impact of Climate Change Adaptation Strategies on Household Food Security: Empirical Evidence from TunisiaAuthor(s): Souissi, Asma; Ouerghemmi, Hassen; Soula, Rania; Dhehibi, Boubaker; Abeyou, Abeyou; Frija, Aymen (Economic Research Forum (ERF))Date: 2026-05-01Type: Conference PaperStatus: Open accessUnder the global challenge of climate change (CC), farmers’ coping strategies have long been a focus for policymakers and researchers. The scientific community has engaged in foresight scenarios and modelling, as well as studies tracing links between CC, biodiversity, food security, and livelihoods. In drylands, a key impact of climate change is the “meteorological-drought pathway,” which affects food production and rural food security. In North Africa, studies show Tunisia is drought-prone. Increasing droughts have seriously affected food production in arid areas. Climate change, food security concerns, and erratic rainfall are straining agriculture, highlighting the need for more resilient food systems. Tunisia receives national and international support to counter climate change. The country is committed to helping smallholder farmers adopt climate-smart innovations. However, there is limited documentation on how these coping strategies affect farmers’ livelihoods and vulnerability to food insecurity. Thus, direct links between climate impacts and coping strategies, especially regarding food security, remain largely unexplored. This study examines how adopting individual and combined climate change coping strategies affects smallholder farmers' vulnerability to food insecurity in two Tunisian dryland regions, Zaghouan and Kairouan. The paper aims to construct a resilience index that integrates coping strategies into the resilience discussion by linking the role of resilience capacity and coping strategies with a resilience capacity index. In this study, we utilize cross-sectional quantitative and qualitative data collected from primary and secondary sources through surveys of 670 farm households in two Tunisian dryland regions. We develop a conceptual framework that identifies the components and linkages of the meteorological drought pathway, alongside coping strategies adopted by smallholder farmers. Building on the established meteorological drought framework, which has advanced empirical understanding of environmental stressors, we assess the agricultural drought pathway in these regions. This analysis provides insights into spatial and temporal drought patterns, enabling classification of households by resilience capacity and drought intensity. To measure resilience, we apply the FAO’s RIMA-II framework (2016), using the Resilience Capacity Index (RCI) and Resilience Structure Matrix (RSM). Our approach follows a two-stage procedure: first, factor analysis constructs resilience pillars; second, a Multiple Indicators Multiple Causes (MIMIC) model estimates the RCI and examines its relationship with food security outcomes. Structural Equation Modeling (SEM) is employed to explore complex interactions among these pillars. This study anticipates demonstrating that smallholder farmers’ coping strategies are closely linked to the intensity of drought events, with resilience capacity playing a critical moderating role in reducing the negative impacts of climate shocks on food security. Key factors such as income and food access, asset ownership, access to basic services, adaptive capacity, and social safety nets are expected to significantly enhance farm households’ resilience to food insecurity. Overall, the integration of resilience and coping strategies is projected to improve both food security and the well-being of smallholder farmers in Tunisia’s dryland regions. The findings will provide evidence-based guidance for policymakers to promote diversified and context-specific adaptation strategies that enhance food security in arid and semi-arid landscapes. Specifically, the results will support the design and prioritization of targeted public-private interventions, including investments in research and development, improved dissemination of climate-smart agricultural practices, and strengthened collaboration among farmers, extension services, and research institutions. By identifying the most effective coping strategies, the study will inform policies that not only improve household food security but also build community-level resilience against future climate-related shocks. This approach aligns with the broader regional goals of sustainable agricultural development and poverty reduction, offering actionable pathways to support vulnerable smallholder farmers in Tunisia and similar MENA contexts. This study combines spatial analysis with resilience measurement to offer a novel framework for assessing drought impacts on smallholder farmers. It provides evidence on how coping strategies linked to the meteorological drought pathway help mitigate risks to livelihoods and food security. By highlighting resilience’s role in food security, the research informs scalable interventions in dryland contexts. Focusing on smallholder farmers as end-users, it delivers practical policy insights on the effectiveness of climate-smart coping strategies.
- Climate and soil moisture variability and cropland exposure in Western Yemen: a spatiotemporal analysis using satellite and reanalysis time series from 2000 to 2024Author(s): Ha, Tuyen V.; Govind, Ajit; Thi, Nguyen Quang (Springer (part of Springer Nature), 2026-02-18)Date: 2026-02-18Type: Journal ArticleStatus: Timeless limited accessClimate and soil moisture variability can cause a significant impact on agriculture and vegetation ecosystems. In dryland regions, crops are primarily produced in a rainfed system, making them particularly vulnerable to climate and soil moisture stress. Understanding the spatial and temporal dynamics of climate variability and its impact on soil moisture and cropland is crucial for enhancing agricultural resilience in vulnerable dryland regions. This study presents a detailed analysis of climate and soil moisture anomalies, as well as their associated cropland exposure, in western Yemen from 2000 to 2024, utilising monthly standardised indices and satellite-derived vegetation data. The findings revealed a significant transition toward warmer and drier conditions over the study period. Seasonal trend analysis indicated that precipitation and soil moisture have declined significantly in summer, autumn, and winter. Notably, temperatures have increased consistently across all seasons over the study period, with a cumulative rise of ~ 1.5 °C, and record highs were observed in 2023 and 2024. Cropland vegetation showed the strongest response to soil moisture variability during the summer periods, with a lag time of approximately 2.8 months. In contrast, winter correlations were slightly lower, with the longest lag observed for precipitation at 3.5 months, highlighting seasonal differences in crop vegetation sensitivity to hydroclimatic drivers. These findings underline the urgent need for climate-resilient agricultural strategies and sustainable water management to safeguard food security under the intensifying climate crisis in arid and semi-arid environments.
- How armed conflict shapes environmental trends in Yemen?Date: 2026-02-14Type: Journal ArticleStatus: Timeless limited accessThe ongoing conflict in Yemen has resulted in widespread socio-economic disruption and environmental degradation, with more than half of the population requiring humanitarian aid. In such conflict-affected contexts, direct field assessments are often unfeasible, making remote sensing an essential tool for environmental monitoring. This study integrates multi-temporal satellite observations, including the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Precipitation Measurement Version 6 (GPM v6), and the Tropical Rainfall Measuring Mission (TRMM). It also uses station-based and gauge-corrected climate products, such as the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and TerraClimate, along with atmospheric reanalysis data from the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5). These datasets were integrated to evaluate the impacts of prolonged armed conflict on vegetation dynamics, water stress, and climate variability across Yemen from 2001 to 2024. We examined national-scale spatiotemporal patterns using annual trends, breakpoints, and spatial comparisons between pre-conflict and conflict periods. The escalation of the civil war during 2011-2015 coincides with a marked decline in the aridity index and the Normalized Difference Vegetation Index (NDVI), accompanied by an increase in potential evapotranspiration (PET). These changes suggest conflict-driven impacts on vegetation and water balance, likely resulting from agricultural disruption. From 2016 onward, a partial recovery in NDVI and the aridity index, alongside reduced PET, indicates the onset of environmental stabilization following the initial conflict shock. Although precipitation shows no persistent long-term trend, it displays pronounced interannual variability. Surprisingly, agricultural areas in Yemen have increased between 2010 and 2020 over some regions despite the ongoing conflict, which may have forced populations to settle in new areas, converting previously unused or marginal land into farmland. Overall, the findings reveal the complex interactions between conflict, climate variability, and ecosystem resilience, underscoring the value of satellite monitoring in politically inaccessible regions.
- Seasonal agricultural vulnerability in semi-arid Morocco: combining remote sensing and farmer knowledge to inform climate adaptationAuthor(s): Ivan Alvarez, Cesar; Govind, Ajit; Bollas, Anna Muñoz; Waha, Katharina; Labbaci, Adnane (Springer (part of Springer Nature), 2026-03-19)Date: 2026-03-19Type: Journal ArticleStatus: Open accessAgricultural systems in semi-arid regions are increasingly exposed to climate variability, yet the drivers of seasonal vulnerability remain insufficiently understood. This study examines whether perceived vulnerability during winter and summer cropping seasons is shaped by distinct mechanisms—climatic exposure during the wet season and adaptive capacity during the dry season. Using Morocco as a representative case for North African agriculture, we integrate long-term Earth observation indicators of environmental variability (precipitation, temperature, and NDVI) with survey data from 3,591 smallholder farmers and apply machine learning classification models evaluated under spatially explicit cross-validation. Model performance was benchmarked against multinomial logistic regression and majority-class baselines. While standard cross-validation yielded optimistic estimates, spatial cross-validation produced more conservative and policy-relevant results. Under spatial validation, Random Forest and XGBoost consistently outperformed simpler models, with macro F1 scores ranging from approximately 0.53 for overall vulnerability to over 0.70 for seasonal outcomes. Comparative experiments showed that survey-based predictors explain a larger share of perceived vulnerability than Earth observation indicators alone, while their combination provides complementary gains, particularly for winter vulnerability. Explainable model analysis revealed clear seasonal contrasts: winter vulnerability was dominated by hydroclimatic variability and soil moisture conditions, whereas summer vulnerability was more strongly shaped by adaptive capacity, including groundwater access, irrigation practices, and climate information use. The proposed framework offers a transferable approach for assessing climate vulnerability and informing targeted adaptation strategies in semi-arid farming systems.
- Spatiotemporal Patterns of Vegetation Stress and Hydroclimatic Responses in the CWANA Region (2000–2024)Author(s): Ha, Tuyen V.; Govind, Ajit; Thi, Nguyen Quang; Tran, Khuong H.; Hai, Nguyen Khac; Huy, Nguyen Q. (Springer (part of Springer Nature), 2026-03-17)Date: 2026-03-17Type: Journal ArticleStatus: Timeless limited accessUnderstanding the characteristics of vegetation stress and their responses to hydroclimatic variability is critical for assessing ecosystem resilience and predicting ecological trajectories under ongoing global climate change. The CWANA region is among the most significant vegetation ecosystems, with a massive expanse of drylands. This study provides a detailed analysis of the spatiotemporal characteristics of vegetation stress and its influencing factors (precipitation, temperature, and soil moisture) in the region, using monthly MODIS-based vegetation condition index time series from 2000 to 2024. The findings of this study revealed that nearly 59% of the vegetated areas experienced 10–20 stress events, particularly observed in Kazakhstan and Ethiopia. The most prolonged stress events lasted nearly 17 months and were recorded in parts of Pakistan and the Horn of Africa. Almost 43% of major stress events occurred in the early 2000s, whereas recent years have witnessed non-stressed conditions. Despite several major stress occurrences, the vegetation in the study region exhibited an overall positive trend over the study period. Turkey and Pakistan experienced among the strongest greening trends. A stronger positive trend in vegetation was primarily observed in forests (mixed and evergreen) and cropland (an increase of around 1.5% per year). In contrast, grassland and shrubland exhibited a lower trend. Among the influencing factors, soil moisture accounts for the largest share of significant partial correlation coefficients (32.4% of the study area), followed by temperature. When stratified by land-cover, elevation, and aridity classes, the influence of soil moisture is strongest at lower elevations, in arid and semi-arid environments, and across rainfed croplands and mixed forest systems, where it accounts for roughly 35–36% of the total influence. This study provides critical insights into the spatiotemporal patterns of vegetation stress in relation to climate and soil moisture under ongoing global climate change and the expansion of drylands.
- Utilization of MOLUSCE tool and GEE cloud to predict the variation of LST through LULC, NDBI and NDVI in Tollygunge-Panchannagram Basin of KolkataAuthor(s): Singha, Chiranjit; Sahoo, Satiprasad; Swain, Kishore Chandra; Govind, Ajit; Al-Quraishi, Ayad M. Fadhil; Singh, Surajit (Springer Nature [academic journals on nature.com] (Fully open access journals), 2026-01-03)Date: 2026-01-03Type: Journal ArticleStatus: Open accessKolkata, a metropolitan city in India with over 15 million residents, has experienced a steady rise in urban heat islands over recent decades. This study investigates the spatial and temporal dynamics of land use/land cover (LULC) and their impact on land surface temperature (LST) in Tollygunge-Panchannagram (TP) basin of Kolkata using Landsat data from 2000, 2010, and 2020 within the Google Earth Engine (GEE) environment. Additionally, it projects LST for 2030 based on LULC, the Normalized Difference Built-up Index (NDBI), and the Normalized Difference Vegetation Index (NDVI). The prediction employed the Methods of Land Use Change Evaluation (MOLUSCE) plug-in in QGIS, which integrates Artificial Neural Networks with Cellular Automata (CA-ANN). CMIP5-based RCP 4.5 climate data were incorporated to simulate future climate conditions and forecast LST for 2030. The findings revealed that the maximum LST increased from 36.15 °C in 2000 to 47.65 °C in 2020, showing a strong positive association between NDBI and LST. Elevated temperatures were primarily concentrated in the western and northwestern sectors, driven by urban expansion, industrial growth, and infrastructure development. According to the CA-ANN MOLUSCE analysis, built-up areas expanded by 6.31% between 2000 and 2020 and are anticipated to rise by another 8.1% by 2030, leading to further reductions in vegetation, open spaces, and water bodies. These spatiotemporal shifts, resulting from rapid urbanization, have significantly altered the city’s microclimate and reduced the extent of water-related land cover. The study further emphasizes the necessity of implementing sustainable Urban Heat Island (UHI) management strategies through increased green cover and water body restoration.
- Revolutionizing wheat crop disease prediction: A novel framework on integrating nature-inspired random forest optimization and explainable artificial intelligence (XAI) in MoroccoDate: 2026-04-14Type: Journal ArticleStatus: Open accessPrecision agriculture has substantial hurdles in wheat crop disease because of the complicated environmental variability. This study presents a novel method by fusing Random Forest models optimized using nature-inspired algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Differential Evolution (DE), and Honey Badger Algorithm (HBA) with explainable artificial intelligence (XAI). Nine important climatic and biophysical factors are assessed, such as precipitation (Pr), maximum and minimum temperatures (Tmax and Tmin), the Green Chlorophyll Index (CGI), the Normalized Difference Chlorophyll Index (NDCI), the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2), the Normalized Difference Vegetation Index (NDVI), soil organic carbon (SOC), and total nitrogen (TN). Multicollinearity analysis and Boruta were used to evaluate feature importance. SSP2-4.5 and SSP5-8.5 scenarios from CMIP6 were used to predict climate projections using GEE (1990–2030) across three GCMs (EC-Earth3, NorESM2-LM, and MIROC6). Blight, rust, fusarium wilt, and powdery mildew disease predictions were verified using 10-fold cross-validation and field data observed by farmers. Based on current research, the best AUC for powdery mildew illness (AUC = 0.946) was represented by RF-GWO, the highest AUC for rust (AUC = 0.897) was recorded by the RF-DE model, and the highest AUC values for blight (AUC = 0.833) and fusarium wilt (AUC = 0.836) diseases were created by RF-GA. RF-GA performs the best on average. The method's importance for sustainable agriculture and SDG accomplishment was supported by the XAI interpretation, which identified temperature and precipitation as major disease-promoting factors. Furthermore, it is a cutting-edge decision-support system that transforms Morocco's wheat disease management by fusing XAI with cutting-edge nature-inspired random forest optimization (NIRFO).
- Spatial assessment of rice straw yield under future climate pathways using interpretable stacked machine learning modelsAuthor(s): Sahoo, Satiprasad; Singha, Chiranjit; Govind, Ajit (Springer (part of Springer Nature))Date: 2026-08-04Type: Journal ArticleStatus: Open accessThis study develops a hybrid stacked ensemble (SE) machine learning framework integrated with CMIP6 climate projections to generate high-resolution spatial predictions of rice straw yield in Eastern India. The SE approach combines Cubist, Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splines (MARS), and Support Vector Machine (SVM), and was validated using 1,780 farmer-reported field observations. Among the models, SE-MARS achieved the highest predictive accuracy (R² = 0.775). Current straw yields were estimated to range from 0.10 to 6.78 t/ha, while future projections under SSP2-4.5 and SSP5-8.5 scenarios indicate yields reaching 4.75–8.85 t/ha in Bankura and parts of Birbhum. Feature importance analysis identified precipitation (pr) as the dominant predictor (Boruta score = 34.58; Sobol first-order ≈ 0.40; total effect ≈ 0.45), whereas available water capacity showed comparatively lower influence (13.32). SHAP results further confirmed soil moisture, precipitation, elevation, and soil temperature as key controlling factors. These findings demonstrate the robustness of the SE framework for climate-resilient straw yield assessment and sustainable residue management planning.
- Groundwater resilience and recharge enhancement policy for the East Kolkata WetlandsAuthor(s): Maity, Subrata; Sahoo, Satiprasad; Saha, Somnath; Govind, Ajit (IWA Publishing)Date: 2026-08-17Type: Journal ArticleStatus: Open accessEnhancing groundwater resilience in ecologically sensitive wetlands requires reliable identification of recharge-prone areas to support evidence-based management. This study develops an integrated geospatial framework for delineating groundwater recharge potential zones (GWRPZ) across the East Kolkata Wetlands (EKW), a Ramsar-listed urban wetland. Fifteen geo-environmental conditioning factors were integrated with three multi-criteria decision-making models, TOPSIS, VIKOR, and EDAS, using a statistically optimized pixel-based sampling strategy based on Cochran’s formula (n = 16,468). Model performance was evaluated using ROC-AUC, coefficient of determination (R²), sensitivity analysis, and bathymetric-based validation. EDAS achieved the highest predictive performance (ROC-AUC = 0.939; R² = -0.803), identifying 36.60% of the wetland as having high recharge potential. Bathymetric-based validation confirmed a significant relationship between water depth and recharge potential (R² = 0.451), supporting the reliability of spatial predictions. Sensitivity analysis identified NDWI, NDVI, canal density, and lithology as dominant positive controls, whereas rainfall and groundwater storage showed negative responses associated with urbanization and altered hydrological processes. The framework supports targeted recharge enhancement through Managed Aquifer Recharge (MAR) and Aquifer Storage and Recovery (ASR) while providing a transferable decision-support approach for wetland restoration, climate adaptation, sustainable groundwater governance, and advancing SDGs 6, 13, and 15.
- Delineation of spring potential zones (SPZs) using multi-criteria decision making (MCDM), frequency ratio (FR), and weights of evidence (WofE) methodsAuthor(s): Mohanty, Saidutta; Sahoo, Satiprasad; Dhar, Anirban; Kar, Amlanjyoti; Al-Quraishi, Ayad M. Fadhil (Springer (part of Springer Nature) (Springer Open Choice Hybrid Journals), 2026-04-14)Date: 2026-04-14Type: Journal ArticleStatus: Timeless limited accessThe apparent demand for phreatic water necessitates the construction of potential zones as a long-term strategy for groundwater identification, conservation, and management. An approach for delineating potential spring zones utilizing the Multi-Criteria Decision Making (MCDM), Frequency Ratio (FR), and Weights of Evidence (WofE) techniques are demonstrated for the Sikkim region. A spring potential map is created using the 17 influencing layers, namely Elevation, Normalized Difference Vegetation Index, Land-use/ Land Cover, Slope, Average Annual Rainfall, Aspect, Geomorphology, Drainage Density, Soil, Lithology, Stream Power Index, Curvature, Terrain Ruggedness Index, Distance to Road, Plan Curvature, Distance to River, and Profile Curvature. The overlay weighted sum approach merges all thematic feature maps to build a spring potential map. The map is categorized into three sub-divisions, namely (a) Poor, (b) Moderate, and (c) Good. A total of 202 springs are scattered over the study area, with 141 springs (70%) considered for training purposes and 63 springs (30%) considered to validate groundwater spring potential zones. The validation is done for < 5 lpm and > 5 lpm discharge using the FR and WofE methods. The SPI value ranges from 0.13 to 0.27 for MCDM, 3.06 to 49 for FR (< 5 lpm discharge), 24.78 to 96.16 for WofE (< 5 lpm discharge), 3.21 to 47.40 for FR (> 5 lpm discharge) and 15.35 to 105.20 for WofE (> 5 lpm discharge). The accomplishment of MCDM, FR, and WofE was appraised by dealing with the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC). The MCDM method utilizing the training dataset had an AUC value of 0.776 and a validation dataset of 0.767. The AUC value of the FR (< 5 lpm) method is 0.939, WofE (< 5 lpm) is 0.968, FR (> 5 lpm) is 0.895, and WofE (> 5 lpm) is 0.879. Sensitivity analyses have been carried out for MCDM, FR, and WofE, which signifies the importance and contribution of each layer in spring potential maps. The adopted methods can be implemented in different locations with or without modification. This work also shows that the “Poor” zone is visible in the northern portion, while the “Good” zone surrounds the southern portion and Weights of evidence provides the best result among all methods used to identify SPZs.