International Journal of Environment and Climate Change

Climate Change Impacts on Coastal Agriculture in Tamil Nadu, India: An Assessment of Sea-Level Rise and Salinity Intrusion

S. Balaselvakumar — 2026-04-21

Background: The coastal districts of Tamil Nadu, spanning approximately 1,076 km of the Bay of Bengal shoreline, are among the most agriculturally productive and climatically vulnerable regions in peninsular India. Accelerating sea-level rise and progressive salinity intrusion now pose existential threats to coastal farming systems, with cascading consequences for food security, rural livelihoods, and ecosystem services.

Objectives: This systematic review synthesises peer-reviewed literature published between 2017 and 2026 to assess the nature, magnitude, and spatial distribution of climate change impacts—specifically sea-level rise (SLR) and salinity intrusion—on agriculture in coastal Tamil Nadu. It further evaluates the current state of adaptation science and identifies critical knowledge gaps.

Methods: Following PRISMA 2020 guidelines, a structured search of six databases (Web of Science, Scopus, Google Scholar, ScienceDirect, PubMed, and CGIAR/ICAR repositories) yielded 2,847 initial records. After systematic screening, 66 studies met full eligibility criteria, of which 47 were included in quantitative synthesis and 28 in meta-analysis.

Results: Satellite altimetry data indicate a mean sea-level rise rate of 3.4–4.1 mm per year along the Tamil Nadu coast since 1993, with ensemble projections estimating 0.4–0.8 m of rise by 2100 under RCP 4.5–8.5 scenarios. Salinity intrusion currently extends 15–40 km inland along major estuaries during the dry season, with electrical conductivity values in affected paddy soils frequently exceeding 8 dS/m—well above the critical threshold for rice (Oryza sativa L.) cultivation. Yield losses in affected areas range from 25% to 70% for rice, 20–55% for groundnut, and up to 80% for banana in severely impacted zones. Districts of Nagapattinam, Tiruvarur, and Ramanathapuram are identified as hotspots of compounded vulnerability.

Conclusions: The convergence of SLR, increased cyclone intensity, and salinisation represents a systemic risk to coastal agricultural sustainability in Tamil Nadu. Evidence-based adaptation strategies, including salt-tolerant variety deployment, managed aquifer recharge, and integrated coastal zone management, are urgently required. Significant research gaps persist in socioeconomic impact quantification and long-term monitoring. This review provides a consolidated evidence base for policymakers, agronomists, and climate scientists.

Plant Growth Regulators in Cucurbitaceae: A Systematic Review of Their Role in Vegetative Growth, Sex Expression, Flowering, Yield and Fruit Quality

Ravneet Kaur — 2026-05-04

The Cucurbitaceae family encompasses some of the world's most economically significant vegetable and fruit crops, including cucumber, melon, watermelon, pumpkin, squash, and bitter gourd. Plant growth regulators (PGRs)—naturally occurring or synthetically applied compounds that modulate plant developmental processes—exert a profound and multifaceted influence on virtually every aspect of cucurbit crop physiology. This review synthesises current knowledge of how major PGR classes, namely auxins, gibberellins, cytokinins, ethylene and ethylene-releasing agents, abscisic acid, and brassinosteroids, regulate vegetative growth, sex expression, flowering, yield, and fruit quality across Cucurbitaceae. The central role of ethylene in determining floral sexuality through the ACC synthase gene network—CsACS2 in cucumber, CmACS7 in melon, CpACO1A in squash, and CitACS4 in watermelon—is examined alongside the promotive role of gibberellins in inducing male flower formation. The practical applications of exogenous ethephon for feminisation, silver compounds for masculinisation, and plant growth retardants for manipulating vegetative architecture are discussed with reference to their agronomic relevance. Parthenocarpy, mediated primarily by auxins, cytokinins, gibberellins, and brassinosteroids, emerges as a key strategy for improving fruit set under suboptimal pollination conditions. The contribution of abscisic acid to non-climacteric fruit ripening in watermelon and the complex hormonal crosstalk governing postharvest quality are also addressed. This review reveals both the scientific advances and the remaining knowledge gaps, and highlights the potential of integrating molecular and applied PGR research to improve cucurbit productivity and quality in the face of climate variability and changing agricultural demands.

Cascading Climate Tipping Points and Compound Extremes: AI-Driven Earth System Digital Twins for Anticipatory Governance

Peter Makieu — 2026-05-12

Cascading climate tipping points and compound extreme events pose urgent and under-characterized threats to Earth system stability. Multiple hazards occurring simultaneously or in rapid succession can reduce effective tipping thresholds by 15–30% compared to single-stressor exposures, narrowing safety margins implied by international temperature targets. Traditional Earth System Models (ESMs) are limited in capturing nonlinear feedbacks and threshold-crossing dynamics. AI-enabled Earth System Digital Twins (ESDTs), data-assimilative, high-resolution, and continuously updated, offer transformative potential to anticipate these risks in near real time.

This systematic review, conducted following PRISMA 2020 guidelines and registered in PROSPERO, synthesizes 287 peer-reviewed studies (2015–2024) across four thematic pillars: (i) cascading climate tipping points, (ii) compound extremes as tipping accelerators, (iii) AI-driven ESDTs, and (iv) anticipatory governance frameworks. At least five tipping elements—including the Greenland Ice Sheet, West Antarctic Ice Sheet, and tropical coral reefs—approach critical thresholds within the Paris Agreement target range, with compound extremes amplifying risks nonlinearly. Current ESDTs lack explicit tipping cascade modules, interpretability standards, and equitable access.

We introduce CASCADE-AI (Cascading Assessment of Systemic Climate Abrupt Dynamics through Artificial Intelligence), linking tipping cascade characterization, compound extreme assessment, physics-AI hybrid digital twin architecture, and governance interfaces. Two critical integration gaps are identified: between compound extreme attribution and tipping characterization, and between ESDT outputs and governance application. Ten prioritized research actions are proposed across near (2025–2028), medium (2028–2032), and long-term (2032–2040+) horizons.

Binder Performance in Biochar Densification: Critical Analysis of Trade-offs between Mechanical and Energy Properties and Implications for Developing Countries

Vonè Beavogui — 2026-05-12

The compaction of biochar into briquette form offers a viable method for improving the energy utilization of leftover biomasses, especially in underdeveloped nations. The intrinsically poor cohesiveness of biochar necessitates the use of binders, whose characteristics and ratios directly affect the mechanical, energy, and environmental attributes of the briquettes. This paper presents a comprehensive analysis of the primary binder types, categorized into organic, mineral, intrinsic, and hybrid classifications, alongside the relevant physicochemical mechanisms, including particle coating, hydrogen bond generation, and thermoplastic phenomena. The investigation indicates that organic binders, including molasses and starch, enhance cohesiveness and augment calorific value, although possess restrictions about their hygroscopicity. Conversely, mineral binders offer substantial mechanical strength at the cost of calorific value. Intrinsic binders, such as lignin, have intriguing possibilities but are contingent upon the conditions of implementation. Hybrid binders present a viable way to enhance these trades-offs. Ultimately, targeted recommendations are presented for Guinea, emphasizing the utilization of local resources and by-products to facilitate the advancement of sustainable solid fuels.

Pharmaceutical Pollutants and Microbial Communities in Freshwater vs Marine Ecosystems

Tuhin Kanti Dutta — 2026-05-11

Introduction: Increased use of pharmaceutical products results in the release of large quantities of these compounds into the environment. This has become a serious concern, especially in the aquatic ecosystem. Wastewater treatment plant, sewage discharge, improper disposal of medicines, agricultural runoff, livestock production and hospital wastes are the major sources of these compounds in the river and oceans. Antibiotics, NSAIDs (non-steroidal anti-inflammatory drugs), and psychotropic drugs are the most commonly detected pharmaceutical products in the aquatic ecosystem. Microorganisms play an important role in nutrient cycling, primary production and breakdown of organic matter in aquatic ecosystems. Pharmaceuticals in the aquatic ecosystems affect the health of the ecosystem.

Method: This review compares the effects of pollutants on freshwater and marine microbial communities and shows how they respond differently. It also compares the impact of different types of pharmaceuticals on freshwater and marine microbial communities.

Results: The Presence of pharmaceuticals in the aquatic ecosystem reduces microbial diversity and disrupts the functioning of microbial communities, which leads to an imbalance in nutrient cycling, primary productivity and often the development of antibiotic resistance. However, the impact of pharmaceuticals is slightly different on freshwater and marine microbial communities. Interestingly, freshwater microbial communities experience more serious impacts. They often lose species that are important to the functioning of the ecosystem. On the other hand, marine microbial communities are generally more resilient because they are more diverse and have developed different adaptive strategies.

Discussion: The increasing presence of pharmaceutical pollutants in aquatic environments poses a significant threat to ecosystem health. These compounds can alter microbial diversity and disrupt their functional activities. Freshwater microbial communities tend to exhibit lower stability, whereas marine communities are relatively more resilient due to differences in environmental conditions, higher microbial diversity, and adaptive mechanisms. Future research should focus on developing effective strategies for the removal of pharmaceutical contaminants to reduce their impact on aquatic ecosystems and minimize the risk of water contamination.

Advances in Hydrological Modelling used in River Basins: A Comprehensive Review of HEC-HMS and SWAT

Pankaj Kumar — 2026-04-27

Water is considered as most valuable resource for human existence on the earth and in order to simulate, optimise, and distribute hydrological data for various purposes in different sectors, hydrological models are very convenient to attain this aim for water resources management and as a decision support tool. A hydrologic model is a simplification of a real-world system that aids in understanding, predicting, and managing water resources. Both the flow and quality of water are commonly studied using hydrologic models. In the present study, two hydrological models are reviewed and studied in view of their effectiveness and performance in water resource allocation and management in river basins. The first model was considered “The Hydrologic Modelling System (HEC-HMS)”, developed by the Hydrologic Engineering Centre of the U.S. Army Corps of Engineers, which is a widely used tool for simulating the precipitation-runoff processes of dendritic watershed systems. Designed to support both natural and engineered hydrologic systems, HEC-HMS is capable of modelling complex rainfall-runoff events and continuous hydrologic processes such as baseflow and snowmelt. The model is versatile and integrates various hydrologic methods for loss estimation, runoff transformation, channel routing, and reservoir modelling. The other model considered in this study was “the Soil and Water Assessment Tool (SWAT)’’ is a comprehensive, semi-distributed, process-based watershed model developed by the USDA to predict the impact of land management practices on water, sediment, and agricultural chemical yields in large and complex watersheds. Designed to operate over long periods and at large spatial scales, SWAT integrates climate, hydrology, soil properties, land use, and management practices to simulate water balance and pollutant transport. The model divides a watershed into multiple sub-basins and hydrologic response units (HRUs), enabling detailed representation of spatial variability in land cover and soil characteristics. SWAT is widely used for evaluating conservation practices, water quality assessments, climate change impacts, and sustainable watershed management strategies.

Climate Resilient Apple Cultivation in India: Agronomic Strategies for Sustainable Yield and Fruit Quality under Changing Environmental Conditions

Kavya Homchyan — 2026-04-27

The Indian landscape of Apple (Malus domestica Borkh) production is experiencing an exceptional change as the traditional temperate production regions are affected by climate change. In addition to warmer winters and variable rainfall patterns, the increasing pest population will continue to impact the sustainability of apple production in the Himalayan foothills, while at the same time, innovations are allowing for apple production in arid, non-traditional areas. This synthesis reviews studies conducted between 2018 and 2025 on climate-resilient agronomic techniques used for producing apples in India, focusing specifically on high-density orchard systems, integration of water and nutrients into the orchard, modification of canopy architecture for optimal light capture, and dynamics of pests under climate variability. As a result of this critical analysis, it appears that while many advancements have been achieved in the development of individual intervention strategies, there still exist large knowledge voids related to how these strategies interact within different agroecological regions. The review also demonstrated that high-density plantations using dwarfing rootstock combined with precision irrigation/mulch result in a 12-14 % increase in water-use efficiency compared to similar production methods while sustaining production levels. Additionally, new low-chill varieties, along with other innovative cultural practices, are enabling expansion of the area for apple production into semi-arid regions. Although the proliferation of exotic planting materials may pose biosecurity risks, there is a disconnect between institutional research and the adoption of technology by farmers; as such, future research must focus on developing a holistic system approach, participant verification of technologies, and developing policy frameworks that enable sustainable intensification while maintaining environmental integrity.

Horticultural Crops in a Warming World: Impacts, Adaptive Strategies and Resilience Pathways

A. S. Akhare — 2026-05-01

Climate change is increasingly recognised as one of the most formidable challenges confronting global agriculture, with horticultural crops—comprising fruits, vegetables, ornamentals, and medicinal plants—particularly vulnerable to its multifaceted impacts. Rising mean temperatures, altered precipitation regimes, elevated atmospheric carbon dioxide concentrations, and the heightened frequency of extreme weather events are collectively reshaping the biophysical environments in which horticultural crops are cultivated. This review synthesises current scientific understanding of how climate warming affects the physiology, productivity, phenology, and quality of horticultural crops, whilst critically examining the adaptive strategies and resilience pathways available to stakeholders at farm, national, and global levels. This review was conducted through a structured search of peer-reviewed academic literature available across multiple bibliographic databases including Web of Science, Scopus, Google Scholar, and PubMed. Key impacts discussed include heat-induced reproductive failure, chilling requirement deficiencies in temperate fruit trees, drought-mediated yield penalties, phenological mismatches, and the expansion of pest and disease pressures under warmer conditions. Adaptive responses encompass genetic improvement, precision horticulture, protected cultivation, deficit irrigation strategies, and agroecological interventions. Resilience pathways are explored through the lenses of digital agriculture, policy and governance frameworks, and systems-level approaches. The review underscores that effective mitigation of climate risks in horticulture demands integrated, multi-scale responses that bridge plant science, agronomy, technology, and socio-economic governance.

Climate-Smart Agriculture Practices in Ghana: Adoption, Challenges, and Opportunities

Darkwah Felix Adu — 2026-05-02

Background & Aim: Climate change poses an existential threat to Ghana's agricultural sector, which employs approximately 45% of the labour force and contributes 18.3% of GDP. Climate-Smart Agriculture (CSA) — encompassing practices that simultaneously enhance productivity, build adaptive capacity, and reduce greenhouse gas emissions — has been promoted as a transformative solution. Yet adoption rates remain uneven, policy implementation is fragmented, and the contribution of rural waste management to CSA has been largely overlooked in the literature. This systematic narrative review synthesises evidence from 2010 to 2025 to assess Climate-Smart Agriculture Practices in Ghana.

Methodology: This study employs a systematic narrative review design following PRISMA 2020 guidelines. Database searches were conducted in Web of Science, Scopus, PubMed, and Google Scholar using Boolean search strings combining terms. Data were extracted using a structured template capturing: author(s), year, study design, sample size, agro-ecological zone, CSA practices studied, adoption metrics, barriers identified, and policy instruments referenced. A thematic coding protocol organised findings under seven themes aligned with the article's section structure. Quantitative findings (adoption rates, effect sizes, willingness-to-pay values) were tabulated. Inter-coder reliability was assessed on a random 20% subsample (Cohen's κ = 0.81, indicating strong agreement).

Results: CSA adoption patterns, identify adoption determinants, evaluate the waste-agriculture interface, and appraise Ghana's policy landscape. Drawing on 35 peer-reviewed studies, institutional reports, and national policy documents, the review finds that (i) labour- and land-intensive CSA practices are broadly adopted (~80% and ~73% of sampled farmers, respectively), while finance-intensive practices lag (~55%), with a 15-percentage-point gender gap disadvantaging women; (ii) extension services multiply adoption probability 2.8-fold and secure land tenure increases long-term CSA investment by 60%; (iii) Ghana generates approximately 13,000 tonnes of municipal solid waste daily, of which 61% is organic and largely untapped as compost feedstock; (iv) microplastic contamination (1,100–2,700 particles kg⁻¹ of organic amendments) constitutes an emerging 'compost quality paradox'; and (v) critical policy coherence gaps persist between agricultural and waste governance instruments.

Conclusion: The review concludes with a six-point research agenda and recommends the creation of an integrated Waste-Agriculture Nexus policy mechanism to bridge institutional silos and unlock Ghana's organic waste potential for sustainable food systems.

Artificial Intelligence for Restoring Land: A Review of Land Degradation Mapping, Climate Change Prediction and Ecosystem Service Valuation

Sayanta Ghosh — 2026-05-07

Land degradation, climate change, and declining ecosystem services are increasingly interconnected challenges that threaten ecological integrity, agricultural productivity, and livelihood security across vulnerable landscapes. At the same time, advances in artificial intelligence (AI), remote sensing, geospatial analytics, and large environmental datasets are reshaping the way these challenges can be assessed, predicted, and managed. This review synthesises peer-reviewed literature published between 2010 and March 2026 on AI-based applications across three closely linked domains: land degradation mapping, climate change prediction, and ecosystem service valuation. It examines the use of machine learning, deep learning, hybrid geospatial models, and multi-source data integration for detecting degradation hotspots, assessing desertification and vegetation stress, forecasting climate-linked risks such as drought, heat stress, and fire, and estimating ecosystem services, including carbon sequestration, soil retention, water regulation, and forage productivity. The reviewed literature shows that AI-based approaches increasingly combine long-term satellite archives, climate datasets, terrain variables, soil attributes, field observations, and socio-environmental data; for example, some studies have used multi-temporal Landsat records of up to 35 years for soil degradation assessment, while others apply AI models to drought, evapotranspiration, wildfire risk, and ecosystem service mapping. The review further evaluates the strengths and limitations of existing approaches in terms of scale, data dependency, transferability, uncertainty, and interpretability. It concludes that AI can substantially strengthen restoration science by linking degradation diagnosis, climate-risk prediction, and ecosystem service assessment within a unified decision-support framework. Therefore, AI should be viewed not merely as a technical tool for environmental analysis but as a strategic enabler for restoration prioritisation, ecosystem resilience, and evidence-based environmental governance in climate-vulnerable landscapes.

Effect of Climate Change on Insect Pest Dynamics in Agro-ecosystems

Avinash Chauhan — 2026-05-07

Climate change is reshaping insect pest dynamics in agro ecosystems through simultaneous shifts in temperature, precipitation patterns, atmospheric CO₂, and the frequency of extreme events. These drivers alter core biological processes—development rates, fecundity, survival, diapause, and dispersal—leading to earlier seasonal activity, increased voltinism, and expanding overwintering and establishment zones. At the same time, plants grown under drought and elevated CO₂ exhibit changes in nutritional quality and defense signalling that cascade to herbivores and their natural enemies, while heatwaves and compound events create nonlinear responses ranging from population collapse to explosive outbreaks. The aggregate result is a heightened and more variable risk landscape for crop damage, with pronounced effects in temperate production regions and along invasion frontiers. This review synthesises recent advances on the mechanisms linking climate drivers to pest population outcomes, highlighting how phenological shifts can decouple pests from hosts and natural enemies, how landscape simplification can amplify climate signals by weakening top-down control, and how biosecurity risks rise as establishment zones and migratory windows expand. We translate these insights into management guidance for climate-ready integrated pest management: climate-informed forecasting that integrates soil and air temperature products with drought and storm indicators; diversified portfolios that combine host resistance, cultural practices, and conservation biocontrol; resistance stewardship calibrated to faster generation turnover; and invasive-species surveillance based on climate-aware risk maps. Key research priorities include multi-stressor field networks that combine warming, drought, and elevated CO₂ with explicit trophic interactions; next-generation phenology and voltinism models that integrate photoperiod and extreme-temperature biology; and landscape-level designs that restore microclimate refugia and stabilise natural-enemy services. By coupling mechanistic understanding with operational decision support and co-designing adaptation strategies with growers, the agricultural sector can reduce pesticide reliance, preserve the durability of control tools, and safeguard productivity under a rapidly changing climate.

Mitigating Climate Risks in Fruit and Vegetable Production: Physiological, Agronomic and Technological Innovations

A. S. Akhare — 2026-05-09

Climate change represents one of the gravest systemic threats to global food security, with fruit and vegetable production ranking amongst the most climatically sensitive sectors of modern agriculture. Rising temperatures, intensified drought, erratic precipitation patterns, elevated atmospheric CO_₂ concentrations, and heightened frequency of extreme weather events are collectively diminishing yield potential, degrading nutritional quality, and narrowing viable production windows across diverse horticultural systems worldwide. This review synthesises current scientific knowledge on the physiological, agronomic, and technological dimensions of climate risk mitigation in fruit and vegetable production. This review was conducted through systematic searches of peer-reviewed literature using four academic databases: Web of Science, Scopus, PubMed, and Google Scholar. Boolean operators were applied consistently throughout, and the search was restricted to articles published in English between 1996 and 2026. The physiological basis of climate stress responses—including heat shock protein induction, stomatal regulation, reactive oxygen species signalling, and reproductive failure under thermal stress—is examined in relation to the key horticultural crops most threatened by continued warming. Agronomic strategies encompassing deficit irrigation, soil organic matter enhancement, mulching, adjusted planting calendars, and crop diversification are evaluated for their capacity to buffer production systems against climate variability. Furthermore, the review interrogates emerging technological innovations, including precision agriculture enabled by Internet-of-Things sensors and artificial intelligence, protected cultivation under controlled environments, CRISPR-mediated genome editing for stress-tolerant cultivar development, and the application of biostimulants as eco-compatible plant protectants. The evidence synthesised here underscores that no single strategy is sufficient to address the multidimensional challenges posed by a warming climate; rather, an integrated framework combining physiological understanding, adaptive agronomy, and technological advancement is essential for securing the future of horticultural production. Policy coherence, equitable access to innovation, and smallholder inclusion are identified as indispensable social dimensions of effective climate adaptation in horticulture.

Phenomics: Emerging Artificial Intelligence Tool in Crop Improvement

N. Vairam — 2026-05-11

Phenomics, which involves the holistic analysis of plant phenotypes using cutting-edge sensing and data processing technologies, has emerged as an AI-powered technique in plant breeding. Genomics tells us about a plant's genetic potential, whereas phenomics describes the dynamic manifestation of the genes under different environmental conditions. By combining high-throughput phenotyping technologies with AI and machine learning (ML) techniques, complex agronomic traits can be assessed rapidly, accurately and non-destructively, thus speeding up breeding. Advancements in phenotyping technologies, including hyperspectral and multispectral imaging, thermal sensors, LiDAR (Light Detection and Ranging), unmanned aerial vehicles (UAV) and ground robotic systems, provide vast amounts of information about plant growth, physiology, stress, disease, yield potential and other traits. The AI methods such as computer vision and deep learning process the data to discern patterns, forecast traits and detect stress or disease. This helps overcome the phenotyping bottleneck in conventional breeding and improves selection accuracy. AI-based phenomics integrates genomics, phenomics and environmental information to facilitate predictive breeding and mapping of genotype to phenotype to breed climate-adapted, high-yielding varieties. It also helps in precision agriculture for monitoring crops and resource allocation. Phenomics studies are being used in crops like rice, wheat, maize, barley, sorghum and soybean. In India, rice and wheat are extensively studied for high-throughput phenotyping for drought and heat tolerance. Addressing data standardisation and infrastructure issues, phenomics is a disruptive technology, bringing together the genetic potential and field performance for sustainable crop improvement.

Precision-Enhanced Biological Nitrogen Fixation: Transitioning from Natural Symbiosis to High Nitrogen-Fixing Legume Varieties

Diksha Vishwakarma — 2026-05-14

Biological nitrogen fixation (BNF) is a key ecological process that converts atmospheric nitrogen into plant-available forms through symbiosis between legumes and nitrogen-fixing microorganisms. Although essential for sustainable agriculture, its natural efficiency is often constrained by environmental variability, soil nutrient imbalances, and genetic limitations of host plants. Recent technological advancements have led to the development of precision-enhanced BNF systems to overcome these limitations and improve crop productivity.
This review synthesizes findings from multiple recent studies focusing on natural BNF mechanisms, limitations of conventional systems, genetic and microbial innovations, and the integration of precision agriculture tools. The studies employed approaches such as molecular and genomic analyses, evaluation of advanced microbial inoculants, and field-based assessments of site-specific nutrient management to examine their effects on nitrogen fixation and plant performance. The reviewed studies consistently show that integrating genomic-assisted breeding, efficient microbial inoculants, and precision nutrient management significantly enhances nitrogen fixation efficiency, nodulation, and overall legume productivity. However, variability in environmental conditions, challenges in large-scale implementation, and adoption barriers among farmers remain important constraints.

Evaluation of Stress Tolerant Rice Varieties under Rainfed Condition in Dhubri District of Assam, India

Subashi Baruah — 2026-05-13

Agriculture in flood-prone Dhubri district of Assam is highly vulnerable to climate change-induced submergence, necessitating the adoption of climate-resilient rice varieties to sustain productivity and farmer livelihoods. A field experiment was conducted during kharif 2023 and 2024 in NICRA villages (Udmari and Sreegram) under Krishi Vigyan Kendra, Dhubri, Assam, to evaluate the performance of submergence-tolerant rice varieties under rainfed flood-prone conditions. The experiment consisted of three treatments: Ranjit-Sub1, Bahadur-Sub1, and farmers’ variety Ranjit. Results revealed that both stress-tolerant varieties varied significantly (p ≤ 0.05) in improved growth and yield parameters over the farmers’ practice. Ranjit-Sub1 recorded the highest grain yield (4.42 t/ha), followed by Bahadur-Sub1 (4.29 t/ha), compared to 3.66 t/ha in Ranjit. Ranjit-Sub1 showed a yield advantage of 20.9% over the farmers’ variety, while Bahadur-Sub1 recorded 17.3% higher yield. Economic analysis indicated higher gross return (₹93,294.33/ha), net return (₹47,118/ha), and B:C ratio (1.98) in Ranjit-Sub1. The results demonstrate that submergence-tolerant varieties significantly enhance productivity and profitability under flood-affected rainfed ecosystems.

Temporal Variability and Trends of Rainfall, Temperature and Water Balance in Za-Kpota Municipality, Southern Benin, West Africa

Hogouyom Martin Assaba — 2026-04-20

The municipality of Za-Kpota, located in southern Benin, is highly vulnerable to climate variability due to its strong dependence on rain-fed agriculture, which constitutes the main source of livelihood for local populations. This study aims to analyze the spatio-temporal variability of key climatic parameters in this municipality over the period 1993–2022. This study presents a retrospective analysis of long-term climatic data (1993–2022) obtained from the Bohicon synoptic station. The methodological approach is based on the analysis of long-term climatic time series, particularly rainfall and temperature data. Statistical analyses, including trend detection methods and the computation of climatic indices, were applied to characterize the temporal evolution and variability of the studied parameters. The results reveal a bimodal rainfall regime, characterized by two rainy seasons with a cumulative duration of approximately six months. Mean monthly temperatures range from 25.8 °C in August to 29.5 °C in March, with the highest temperatures recorded between February and April. This period is associated with increased evaporation and evapotranspiration, potentially reducing water availability in the study area. In addition, the climatic water balance indicates an overall annual deficit of −171.3 mm, despite significant surpluses observed between May and June (+279.8 mm), and secondary positive balances during September and October. These findings highlight the pronounced variability of climatic conditions in Za-Kpota and their potential impacts on water resources and agricultural systems. The study provides a robust scientific basis for the development of locally adapted climate change strategies and emphasizes the need to integrate climate information into territorial planning and sustainable agricultural development policies.

Monthly Rainfall Prediction Using SARIMA models for 63 Mandals of Anantapur District, Andhra Pradesh, India

N. Ashokkumar — 2026-04-21

Anantapur district lies between 13º40’ to 15º15’N latitude and 76º50’ to 78º30’E longitude. The geographical area of the district is 19,130 km². Anantapur has a semi-arid climate, with hot and dry conditions for most of the year. Monthly rainfall forecasting was done using Seasonal autoregressive moving average (SARIMA) model by testing 267 combinations using SPSS 26 software. Monthly rainfall forecasting was done using Seasonal autoregressive moving average (SARIMA) model by testing 267 combinations using SPSS 26 software. SARIMA models for prediction of monthly rainfall using SPSS 18 software. The best predicted model has been selected based on the maximum coefficient of determination (R²), minimum Bayesian Information Criterion (BIC) and minimum Mean Absolute Error (MAE). In Cluster 1, the SARIMA (0,1,0) (1,1,0) ₁₂ model exhibit strong reliability, notably for Uravakonda, which attained the highest overall R² (0.945) and a comparatively low Normalized BIC (7.656). Within cluster 2, Narpala emerges as the most accurately modeled Mandal (SARIMA (0,0,0) (1,0,1) ₁₂) in this cohort (R² = 0.933), accompanied by the lowest MAE (12.214) and the lowest Normalized BIC (6.671), indicating a high-quality fit. In cluster 3, the highest-performing models within this set are represented. Yellanur attained the best overall outcomes, with an average of 0.944, the lowest MAE at 10.122, and the lowest Normalized BIC at 6.543. Although most Mandals performed well, Hindupur exhibited the lowest accuracy in this cluster. In Cluster 4, SARIMA (0,1,0) (1,1,1)₁₂model exhibit moderate to high consistency. This cluster was led by Kundurpi, whereas Kambadur displayed the lowest fit. Notably, Pamidi achieved a highly efficient fit, evidenced by a low BIC of 7.033, despite its moderate MAE. Cluster 5 contains the most varied results. Chenne Kothapalle is a standout, exhibiting a high score of 0.791 and an exceptionally low normalized BIC of 6.490, which indicates a highly parsimonious and accurate model (SARIMA (0,1,0) (1,1,1)₁₂).

Applicability of the SARIMA model can be used with fair accuracy for the real time forecasting of monthly rainfall. The results of the SARIMA model will be very useful for planning agriculture and plantations in Anantapur district which is highly dependent of rainfall. Prediction of climatological parameters such as rainfall is very important. Since rainfall if the main source of water availability on the earth. Using time series forecasting model such as SARIMA is a powerful tool to investigate the future rainfall pattern over the regions. This future forecasting may be estimated as policy driven paper to government and local bodies.

Analysis of the Impact of Agroforestry on Rural Economy and Community Welfare in Durg, Chhattisgarh, India

Nishikant Krishan — 2026-04-21

Agroforestry, the integration of trees with crops and livestock, is widely recognized as a sustainable land-use system that enhances productivity, livelihood security, and environmental stability. The present study aimed to evaluate agroforestry practices in Patan Tehsil of Durg District, Chhattisgarh, with a focus on system types, biomass production, carbon storage, and economic returns across different farmer categories. A field-based survey using a stratified random sampling approach was conducted to collect primary data from farmers. The results revealed the dominance of agri-silviculture systems, along with the presence of horti-silviculture and integrated tree–crop–livestock systems. Agroforestry practices contributed to improved farm diversification, enhanced biomass accumulation, and increased carbon stock potential. In addition, integrated systems demonstrated better economic performance and livelihood support compared to conventional practices. Overall, the study highlights the significant role of agroforestry in promoting sustainable agriculture and climate resilience in the region. It also emphasizes the need for clear methodological approaches and standardized procedures for reliable estimation of biomass and carbon stocks.

Simulating Surface Runoff Response to Land Use Change Using ArcSWAT in Jonk Subbasin

Anjali Khandekar — 2026-04-23

Land use and land cover (LULC) changes are widely recognized as key drivers of hydrological variability, particularly in tropical regions where observational data are often limited. The present study evaluates the influence of long-term LULC transformations on surface runoff dynamics in the Jonk sub-basin of the Mahanadi River basin using the Arc Soil and Water Assessment Tool (ArcSWAT). Multi-temporal spatial datasets, including satellite-derived LULC maps, soil information, and a Digital Elevation Model (DEM), were integrated with hydro-meteorological data for the period 1991–2021. The watershed was delineated into 11 sub-watersheds and further discretized into 277 Hydrological Response Units (HRUs) to represent spatial heterogeneity.

Model calibration (1993–2010) and validation (2011–2021) showed strong performance with R² = 0.83 and NSE = 0.82, indicating reliable simulation of streamflow. Analysis of LULC changes revealed a significant expansion of agricultural and built-up areas, accompanied by a decline in forest cover. These changes resulted in increased Curve Number values and reduced infiltration capacity, ultimately leading to a 9–11% increase in mean annual surface runoff. The study highlights the critical role of anthropogenic land transformations in altering watershed hydrology and emphasizes the need for sustainable land management strategies in data-scarce regions.

Soil and Vegetation Responses to Mining Disturbance across Substrates in Tropical Dry Deciduous Forests, Prayagraj, India

Vandita — 2026-04-25

Mining induced landscape degradation poses a critical ecological threat to tropical forests. Grounded in disturbance ecology, this study compares the effects of stone and silica sandstone mining on soil physicochemical properties and vegetation structure to assess whether the ecological responses are generalizable or substrate-specific. Field data were collected along a distance gradient from mining sites, integrating soil analyses with phytosociological assessment. Soil fertility parameters including water holding capacity, total organic carbon, nitrogen, and phosphorus, were consistently reduced near mines and increased with distance, indicating clear disturbance gradients, whereas potassium showed an opposite trend. Soil moisture exhibited site-specific pattern, with higher values near the stone mine likely due to sparse vegetation cover. PERMANOVA confirmed strong zonal structuring of soil properties, with depth effects significant only at the stone site, indicating contrasting vertical recovery dynamics. Vegetation responses mirrored these patterns, with low species richness, reduced diversity, and dominance of pioneer taxa near mines, followed by gradual recovery. However, the silica sandstone site retained sparse woody vegetation and more balance IVI near the mines, suggesting greater structural resilience of plant community. Together, these findings show that substrate type mediates soil-vegetation recovery, influencing both successional trajectories and ecosystem stability. The study advances disturbance ecology by highlighting substrate-dependent recovery pathways and provides a transferable framework for restoration of mining-impacted tropical forests.

Experimental Assessment of the Thermal Efficiency of an Improved Biomass Cookstove Fueled by Charcoal and Wood: Influence of Cooking Pot Size under Water Boiling Test conditions

Drissa Ouedraogo — 2026-04-25

Traditional biomass cookstoves widely used in developing regions exhibit low efficiency and high emissions, and although improved designs show potential, their thermal performance remains insufficiently studied, particularly regarding stove geometry, pot design, and key operational parameters. This study presents an experimental evaluation of the thermal performance of an improved biomass cookstove fueled with charcoal and wood, considering the influence of cooking pot size under standardized Water Boiling Test (WBT) conditions. Two pot sizes (No. 2 and No. 3) were tested during the three conventional WBT phases : cold start, hot start, and simmering. The performance indicators investigated included thermal efficiency, specific fuel consumption, specific boiling time, and fire power. Results obtained with charcoal showed that pot No. 2 provided lower specific fuel consumption, indicating better fuel economy, whereas pot No. 3 exhibited shorter boiling times and higher thermal efficiencies, particularly during hot start and simmering. For wood fuel, pot No. 3 demonstrated the best overall performance, reaching a maximum thermal efficiency of 27.42% compared with 19.29% for pot No. 2. The results further reveal that excessive fire power does not necessarily improve efficiency because high-power phases are associated with significant convective, radiative, and conductive losses. The novelty of this work lies in the combined analysis of fuel type and pot geometry under West African operating conditions. These findings provide useful design guidelines for optimizing improved cookstoves adapted to local domestic cooking practices.

Deciphering Weather Disease Interactions and Epidemiological Drivers of Early Blight in Tomato (Lycopersicum esculentum L.) Caused by Alternaria solani

Jasveer Singh — 2026-04-25

Early blight of tomato, caused by Alternaria solani, is a major disease in tropical and subtropical regions. It survives in soil and plant debris and is strongly influenced by temperature, humidity, and rainfall, which favor its spread and severity, leading to significant yield losses. The present investigation was undertaken to assess the relationship between weather parameters and the development of early blight disease in tomato during the Rabi seasons of 2022–23 and 2023–24. The disease incidence was first observed on 5^th February (90 days after transplanting), progressed rapidly from early to late March, and declined by the first week of April. Correlation analysis indicated that maximum temperature had a significant positive association with percent disease intensity, whereas relative humidity (both maximum and minimum) and rainfall exhibited positive but statistically non-significant relationships during both years of study. Multiple regression analysis revealed that the combined influence of temperature, relative humidity, and rainfall played a significant role in disease progression. The coefficient of determination (R²) ranged from 85.54% to 89.90% in 2022–23 and from 64.15% to 73.23% in 2023–24, suggesting that a substantial proportion of disease variability was explained by these environmental factors. Overall, the findings highlight the importance of meteorological parameters in predicting early blight dynamics and support the development of effective disease forecasting and management strategies.

Assessing Household Adaptive Capacity to Heatwaves in Ouagadougou, Burkina Faso

Jean David Ouezzin Coulibaly — 2026-04-25

Background: Heat waves represent a growing climate risk in Sahelian cities, where households' adaptive capacities remain severely constrained by socioeconomic inequalities and housing conditions.

Aim: This study analyses the adaptation strategies implemented, as well as the levels and determinants of households' adaptive capacity to extreme heat in the city of Ouagadougou.

Study Design: Household survey combined with quantitative statistical analysis.

Place and Duration of Study: The study was conducted at the City of Ouagadougou, Burkina Faso, year 2025.

Methodology: The data come from a survey of 421 households. A composite adaptive capacity index was constructed using principal component analysis (PCA) followed by hierarchical clustering (HAC), supplemented by multiple correspondence analysis (MCA) to identify household profiles.

Results: Adaptation strategies are dominated by behavioural responses, used by more than 80% of households, such as modifying activity schedules, increasing water consumption, or using outdoor spaces for sleeping. Conversely, material strategies remain limited due to economic constraints, as more than 54% of households have less than 100,000 FCFA per month. The analysis highlights that nearly two-thirds of households have a low to moderate adaptive capacity, reflecting high vulnerability to extreme heat events.

Conclusion: The study highlights that adaptation to heat waves in Ouagadougou relies largely on reactive rather than structural strategies, heavily constrained by socioeconomic inequalities. The determinants of adaptive capacity are strongly linked to income, education, age, employment status, and type of neighbourhood.

Application of Analytical Hierarchy Process and Geospatial Techniques for Identifying Suitable Solid Waste Management Sites in Prayagraj City, India

Arjan Sharfuldeen Omar — 2026-04-27

The Identification of suitable sites for solid waste disposal remains a significance challenge in rapidly developing cities due to public health concerns and environmental sustainability. The goal of this study is to identify a suitable area for solid waste disposal of the Prayagraj city using geospatial techniques and analytical hierarchy process (AHP). Seven different criteria such as slope, LULC, lithology, geomorphology, distance from road, distance from railway, distance from river has been obtained and spatial thematic maps were generated in GIS environment. The datasets used for this study were obtained from the ISRO website, OpenStreetMap, and Sentinel-2 imagery. A multi-criteria decision-making approach based on the AHP method was applied to assign weights to all criteria for identifying suitable locations for waste disposal. All the different criteria maps of the study area were generated and overlaid in GIS environment to generate the final suitability map, which was classified into highly suitable, moderate suitable and not suitable area. The result of the study area shows that about 3.87 % of the study area come under most suitable site, 26.45 % come under moderately suitable and 69.68 % come under not suitable area. This study supports the selection of waste disposal sites and provides valuable guidance to municipal authorities and decision makers in addressing environmental issues and improving public health.

Optimizing Irrigation Scheduling and Nitrogen Management for Improved Quality, Nutrient Uptake and Moisture Dynamics in Summer Fodder Sorghum (Sorghum bicolor L. Moench)

G. Madhavan — 2026-04-27

Sorghum bicolor is a climate-resilient, dual-purpose crop vital for food and fodder security in arid regions, yet in India, its productivity is constrained by inadequate quality feed and suboptimal irrigation and nitrogen management, which are critical determinants of forage yield and nutritional quality. A field experiment was conducted during the summer season of 2021 at the Integrated Farming System Research Farm, Junagadh Agricultural University, Junagadh on clayey soil to evaluate the effects of irrigation scheduling and nitrogen levels on crude protein, fibre yield, nutrient uptake, and water use efficiency (WUE) of fodder sorghum. The experiment employed a split-plot design with three irrigation schedules (Irrigation water/cumulative pan evaporation (IW/CPE) ratios: 0.6, 0.8 and 1.0) and four nitrogen levels (60, 80, 100 and 120 kg N ha^-1), each replicated four times. We hypothesized the data will give the chance to recognize the proper combination of irrigation and nitrogen that improve fodder quality and resource use efficiency. Findings showed that irrigation at 1.0 IW/CPE ratio notably increased crude protein content and yield, crude fibre yield, NPK content and uptake, as well as water consumption. However, the highest WUE was detected at the 0.6 IW/CPE ratio. Applying 120 kg N ha^-1 resulted in high crude protein content and yield, crude fibre content and yield, PK content, N content and uptake, while also improving WUE. Irrigation schedules did not significantly impact crude fibre content or soil availability of N, P₂O₅ and K₂O. The study revealed that the interaction effects between irrigation schedules and nitrogen levels on crude protein yield, crude fibre yield, NPK uptake and WUE was significant. The study recommends that irrigation at an IW/CPE ratio of 0.8 combined with 120 kg N ha⁻¹ (I₂N₄) significantly improved fodder quality and WUE of summer sorghum supporting better livestock productivity.

Establishment Potential of Seed Balls in Forest Ecosystems in Tamil Nadu, India

S. K. Rajkishore — 2026-04-27

Background: Forest degradation and limited success of natural regeneration necessitate cost-effective restoration approaches, with direct seeding emerging as a scalable alternative to conventional planting methods. Seed ball technology offers potential to enhance seed germination and establishment under harsh field conditions, yet its effectiveness in tropical forest ecosystems remains inadequately understood.

Aims: The study aims to assess establishment potential of seed balls in different forest ecosystems.

Study Design: Laboratory standardization and then field experiments.

Place and Duration of Study: Laboratory work and field experiments were conducted in Athikadavu, Pethikuttai forests of Karamadai and Sirumugai forest ranges in Western Tamil Nadu for South-West and North-East monsoon seasons respectively.

Methodology: Laboratory experiments were done to determine the germination potential of the selected tree and grass species by use of sand tray, roll towel and pot culture. Depending on the laboratory standardization, seed balls were made with optimized medium of red soil and vermicompost (2:1 ratio) that was found to be effective in terms of seed ball size, weight, fragmentation and dissolution behavior. After this, field experiments were conducted with the seven species.

Findings: Laboratory findings showed that there was increased germination in grass species which included Apluda mutica (80%), Eriocoma hymenoides (85%) and lower germination in tree species which included Albizia amara (25%). Nevertheless, very low germination in the forest conditions was observed during field observations. In South-West monsoon, germination was restricted to 27 percent, but a small increase (to 89 percent) was seen during North-East monsoon. Moreover, grass species fared better than tree species and only Albizia amara and Acacia chundra established significantly as far as tree species were concerned. Overall, the study revealed that seed balls had maximum germination potential of about 10% under forest ecosystems.

Conclusion: Seasonal effects, as well as fungal infection, pest incidence and environmental conditions, had significant impacts on seed ball establishment. More research to enhance seed ball technology to achieve successful mass restoration of forests is needed.

Determinants of Climate‑smart Agritech Adoption among Smallholder Farmers in Northeast Nigeria

Kwaji Barka Peter — 2026-04-28

Smallholder farmers in sub-Saharan Africa, particularly in Nigeria, face significant climate risks, and while climate-smart agriculture can enhance resilience and productivity, its adoption remains limited due to socio-economic, institutional, and access constraints. This study aimed to assess the awareness, adoption, and socio‑economic and gender determinants of selected climate‑smart agritech practices among smallholder farmers, and to examine how entrepreneurial orientation and resilience shape farmers’ adaptive capacity to climate change. The study was conducted in Adamawa State, Northeast Nigeria, across 36 farming communities in nine local government areas, covering four agricultural zones. Data collection was undertaken during the 2023/2024 agricultural season. A multistage sampling technique was used to select 543 arable smallholder farmers. Primary data were collected through face‑to‑face interviews using a semi‑structured questionnaire administered with Open Data Kit (ODK). The study assessed adoption of climate‑smart practices including intercropping, certified seeds, organic fertilizers, agroforestry, conservative tillage, gender‑inclusive practices, and use of weather information. Descriptive statistics were used to estimate awareness and adoption rates, while chi‑square tests were applied to examine associations between adoption and socio‑economic variables (gender, age, marital status, and education) at a 95% confidence level (P =.05). Intercropping recorded the highest awareness (83%) and adoption (78%), followed by organic fertilizer (75%) and certified seeds (70%). Adoption of agroforestry (27%) and weather information services (34%) was very low. Chi‑square analysis showed that education significantly influenced adoption of agroforestry and weather information (P =.05), while gender, age, marital status, and education were all significantly associated with adoption of certified seeds. Entrepreneurial orientation findings revealed strong innovativeness and proactiveness, with 49% of farmers open to trying new practices, though risk aversion constrained investment in resource‑intensive CSA practices. Only 53% of farmers reported preparedness to absorb climate‑related shocks, with notable gender and spatial disparities. The study concludes that while smallholder farmers demonstrate high innovative potential, adoption of climate‑smart agritech is constrained by risk aversion, gendered inequalities, and uneven access to education and services. Targeted, gender‑responsive, and location‑specific interventions are required to strengthen adaptive capacity and climate resilience among smallholder farmers.

Knowledge Level and Adoption Pattern of Climate Smart Technologies in NICRA Adopted Village in the Western Agro-climatic Zone of Punjab, India

Manpreet Jaidka — 2026-04-30

Background: Climate smart agriculture technologies including the management of crop residues without resorting to burning, foliar application of potassium nitrate, the adoption of direct-seeded rice (DSR), and the cultivation of short-duration paddy varieties were systematically demonstrated as part of the initiative. These interventions were complemented by a series of structured awareness campaigns and capacity-building programmes designed to enhance farmers’ knowledge, promote sustainable agronomic practices, and encourage the adoption of environmentally resilient farming systems. Collectively, these efforts aimed to mitigate adverse environmental impacts while improving productivity and resource-use efficiency within the agricultural sector.

Aims: The present survey-based investigation was carried out with an objective to have an idea about the impact of various extension activities carried out under NICRA project in the context of the knowledge level and the adoption pattern of climate smart technologies (CSTs).

Study Design: The study was carried out during the year 2024-25 using a pre-designed interview cum questionnaire schedule by randomly contacting the beneficiaries (70) and non-beneficiaries (40) of NICRA project in the adopted village.

Methodology: The test to check the knowledge level constituted 35 queries which included a list of questions pertaining to CSTs. The answers to the question were quantified by giving 2 score to full knowledge, 1 score for partial knowledge and zero score for no knowledge. The correlation matrix between knowledge level and independent variables was used to identify the deriving factors behind the preference of a given technology by the beneficiaries.

Results: The data showed that beneficiary farmers reported highest percentage of farmers in the category of high knowledge level (44.3%) i.e., 31 followed by medium knowledge level category (32.9%) i.e., 23. On the contrary, non-beneficiary farmers recorded highest percentage in low knowledge level (17) followed by medium category (12). Among beneficiaries recorded 50, 47.1 and 71.4% in high category in terms of technology wise understanding, respectively. Adoption pattern depicted that 32.9% (23) beneficiaries follow alternate wetting and drying in comparison to 22.5% (09) among non-beneficiaries. Understanding about the CSTs depicted highly significant positive correlation with education (0.817), farming experience (0.359) and attending the training programmes (0.519) and significantly positive correlation with age of the respondents (0.262).

Conclusion: Education level, training component, land holding and access to the credit depicted positive relation with the adoption of technologies making these factors as best fitting determinants.

Assessment of Land Use and Land Cover Dynamics in the Cavally River Watershed, Western Côte d’Ivoire

Gue Franck Achille — 2026-04-30

Rapid population growth and expanding mining activities, especially in Africa and Côte d’Ivoire, are driving deforestation and land-use changes, threatening biodiversity and ecosystem services. In areas like Zouan-Hounien, intensive gold mining has replaced agriculture, leading to significant environmental degradation and vegetation loss.

This study aims to evaluate the spatiotemporal dynamics of land use and land cover within a sub-watershed of the Cavally River. To achieve this objective, four Landsat satellite images were acquired from the U.S. Geological Survey website. The dataset includes Landsat Thematic Mapper (TM5) imagery from 1985, Landsat Enhanced Thematic Mapper Plus (ETM+) imagery from 2000, and Landsat 8–9 Operational Land Imager (OLI) imagery from 2015 and 2025.

The methodological approach relied on the application of remote sensing techniques. Image preprocessing and processing workflows enabled the production of land-cover maps for each reference year. Five major land-cover classes were identified from the Landsat images: dense vegetation, degraded vegetation, agricultural areas, settlements and bare soils, and water bodies.

Area estimates derived from the classification results show a substantial decline in dense vegetation, decreasing from 226.49 km² (48.74%) in 1985 to 40.96 km² (8.81%) in 2025. This pronounced loss of forest cover is largely attributable to mining activities and rapid population growth, which have intensified anthropogenic pressures on the environment. Concurrently, agricultural land expanded considerably, from 39.79 km² (8.56%) in 1985 to 116.19 km² (25%) of the total area of the sub-watershed in 2025, reflecting increasing demand for arable land.

Effect of Organic Substrates on Vermicompost and Compost Quality and Multiplication Rate of Earthworms

Akshay Kumar — 2026-04-30

Vermicomposting utilizes earthworms to recycle organic wastes from urban, industrial, and agricultural sources into valuable organic fertilizers. During vermicompost preparation, organic waste products are decomposed through the collaborative activity of beneficial microbes and earthworms. The present study aimed to investigate the effects of various organic substrates on the quality of vermicompost, compost, and the multiplication rate of earthworms. The organic waste substrates, water hyacinth, paddy straw, cow dung, and kitchen waste, were converted into nutrient-rich vermicompost using the earthworm species Eisenia fetida and compost through the composting process. The results indicated that the highest recovery percentage 54.20%, increased nitrogen content 1.80%, and the shortest maturation time was observed in treatment T₁ (water hyacinth + paddy straw + cow dung @ 2:1:1). Higher phosphorus and potassium content were recorded in T₄ treatment (WH+CD @ 3:1). However, the highest earthworm multiplication rate and the lowest bulk density of vermicompost were recorded in treatment T₃ (paddy straw + cow dung @ 3:1). The total N content across the organic substrates ranged from 0.67% to 2.20%. The highest total N content (2.20%) was recorded in KW, while the lowest (0.67%) was observed in CD. KW showed the highest level of total P (0.54%) content, followed by WH (0.49%), CD (0.40%), and PS (0.20%). The total K content was greatest in WH (3.68%), followed by KW (2.30%), PS (1.89%), and CD (1.29%). Furthermore, the total S content was highest in CD (0.72%), followed by KW (0.54%), WH (0.39%), and PS (0.36%). The organic substrates' carbon-to-nitrogen (C: N) ratio exhibited considerable variation, ranging from 18:1 to 49:1. The combinations of water hyacinth, paddy straw and cow dung, were found to be effective organic substrates for producing high-quality vermicompost.

Integrated Geo-environmental Assessment of Heavy Metal Contamination in a Municipal Landfill System: A Case Study from Aluu, Obio-Akpor Local Government Area

E. E. Esonanjor — 2026-04-30

Background: Unregulated dumpsites driven by poor waste management infrastructure facilitate the release and leaching of persistent, toxic heavy metals into soils and groundwater, posing significant long-term environmental and public health risks, particularly in regions such as Nigeria’s Niger Delta.

Aim: This study evaluates the extent, spatial distribution, and environmental risk of heavy metal contamination in soils at the Aluu municipal landfill, Obio-Akpor Local Government Area, Rivers State, Nigeria a rapidly urbanizing Niger Delta community characterised by high groundwater vulnerability and inadequate solid waste management infrastructure.

Study Design: A field-based, cross-sectional analytical study integrating geochemical assessment, spatial analysis, and human health risk evaluation.

Place and Duration of Study: Aluu municipal landfill, Obio-Akpor, Rivers State, Nigeria; sampling and laboratory analysis were conducted over a six-month study period.

Methodology: Sixteen soil samples were collected from eight locations representing four contamination zones landfill core (A), near-field (B), far-field (C), and control/background (F) at depths of 1.0 m and 1.5 m. Samples were analyzed for arsenic (As), nickel (Ni), chromium (Cr), cadmium (Cd), and lead (Pb) using atomic absorption spectrophotometry (AAS) following aqua regia digestion (ISO 11466:1995). Contamination levels were quantified using the Contamination Factor (CF), Geo-accumulation Index (Igeo), and Pollution Load Index (PLI), calculated relative to locally derived geochemical background concentrations from control sites. Spatial distribution patterns were mapped using Surfer 20.0, and human health risks were assessed using the USEPA hazard index (HI) approach for ingestion, dermal, and inhalation exposure pathways.

Results: All measured heavy metal concentrations remained below WHO and DPR/NESREA regulatory thresholds for example, maximum arsenic (0.112 ppm) and lead (1.040 ppm) were well within permissible limits indicating no immediate regulatory exceedance. However, when assessed against the very low local geochemical background concentrations (As: 0.004 ppm; Ni: 0.020 ppm; Cr: 0.012 ppm; Cd: 0.039 ppm; Pb: 0.042 ppm), pollution indices revealed significant anthropogenic enrichment. CF values indicated very high contamination for arsenic (CF = 5.25) and considerable contamination for nickel (CF = 3.90) and lead (CF = 4.43). Igeo values classified soils as moderately polluted for arsenic (Igeo = 1.81), lead (Igeo = 1.56), and nickel (Igeo = 1.38). The overall PLI of 3.17 indicated considerable multi-metal pollution across the study area. It is important to note that these elevated index values reflect the degree of anthropogenic departure from natural background conditions not proximity to health-based regulatory limits and therefore serve as sensitive early-warning indicators of progressive contamination. Spatial analysis revealed a pronounced arsenic hotspot within the landfill core and a distinct lead anomaly in the far-field zone (C2: Pb = 1.039 ppm), suggesting multiple contamination sources and complex transport pathways. The high permeability of the Benin Formation was identified as a key hydrogeological factor enhancing contaminant mobility. Despite the evidence of anthropogenic enrichment, cumulative human health risk indices remained low (HI < 0.1 for all receptors), indicating no significant non-carcinogenic health risk under current exposure conditions.

Conclusion: Although current heavy metal concentrations are within regulatory limits and associated health risks are low, the significant anthropogenic enrichment above natural background levels as evidenced by elevated CF, Igeo, and PLI values and the spatial variability of contamination patterns highlight the urgent need for continuous environmental monitoring, source control, and improved waste management strategies at the Aluu landfill to prevent future environmental degradation in this vulnerable Niger Delta setting.

Food Wastage: A Threat to the Environment

Priyanka Bhati — 2026-05-02

The global supply chain in food system is one of the most complex and interconnected systems on the planet. It is also one of the most unsustainable supply chain. Food “waste” refers to food that is fit for consumption but consciously discarded at the retail or consumption phases. Poor management of food waste causes the loss of natural resources, human health issues, pollution of rivers and seas, the generation of methane emissions from dumps and landfills, and a missed opportunity to recover valuable energy, organic matter, nutrients and water contained in food waste. And the majority of food, almost 50kgs per capita food is wasted through household consumption. This study is done with a sample size of 55 respondents to study their knowledge about the harmful effects of food wastage management, which is identified as 77.4 percent, and their practices followed in regard to food disposal, and lastly the awareness level among respondents about the initiatives taken by government organizations to minimize food waste, which is found to be 57.3 percent. The findings suggest a need for more comprehensive awareness programs that not only promote initiatives but also educate individuals about legal provisions and global efforts related to food waste reduction. This study will help to provide a base for further initiatives that can be taken to minimise food wastage and alter the consumption pattern so that there would be sustainable use of resources.

Optimization of In-vitro Androgenesis in Cereals: Role of Temperature Conditions and Iron Availability

D. S. Zhaparova — 2026-05-04

Background and Aims: Sorghum (Sorghum bicolor L.) is a climate-resilient cereal crop widely valued for its high productivity and adaptability to drought, heat, and low-fertility soils, making it crucial for sustainable agriculture and food security. The aim of this study was to optimize in vitro anther culture conditions in sorghum (Sorghum bicolor L.) by evaluating the effects of different iron sources and temperature regimes on callus induction, embryogenic response, and plant regeneration efficiency. The influence of two iron formulations, Fe-EDTA and ferric citrate, on anther growth, viability, callus induction, and morphogenetic response was assessed using Gamborg B5 agar medium. In addition, the effects of optimal, elevated, and reduced temperature conditions on sorghum growth dynamics were analyzed. In the context of global climate change and the increasing demand for stress-tolerant cereal genotypes, the development of effective protocols for haploid and doubled haploid production is of critical importance for modern breeding programs.

Study Design: Experimental in vitro study using a randomized design with iron source and temperature as treatment factors.

Place and Duration of Study: The study was conducted at the Plant Biotechnology Laboratory, Satbayev University, from May 2025 to March 2026.

Methodology: Sorghum anthers were cultured under controlled in vitro conditions on induction media supplemented with Fe-EDTA or Fe-citrate as iron sources. Cultures were maintained under standardized photoperiod and temperature regimes, including optimal (25–34°C), heat stress (>34°C), and cold stress (≤20°C) conditions. Callus induction frequency, embryogenic response, plant regeneration efficiency, and relative growth indices were recorded and statistically analyzed.

Results: The type of iron source significantly influenced in vitro response. Fe-EDTA supplementation resulted in the highest callus induction frequency (78.4 ± 2.3%), compared to Fe-citrate (61.2 ± 1.8%). Plant regeneration efficiency was also greater under Fe-EDTA treatment (42.7 ± 1.5%) than under Fe-citrate (28.9 ± 1.3%). Under optimal temperature conditions (25–34°C), sorghum exhibited the highest relative growth index (100 ± 3.1%), while heat stress (>34°C) reduced growth to 72.5 ± 2.7%, and cold stress (≤20°C) to 54.8 ± 2.1%, confirming the strong inhibitory effect of temperature stress on sorghum growth.

Conclusion: The findings demonstrate that both iron formulation and temperature regime are critical determinants of successful in vitro androgenesis in sorghum. Fe-EDTA was identified as the most effective iron source for enhancing callus induction and plant regeneration, while optimal temperature conditions maximized growth performance. These results contribute to the refinement of cereal androgenesis protocols and may improve doubled haploid production efficiency for climate-resilient breeding strategies.

Physiological and Morpho-Yield Characterization of Wheat (Triticum aestivum L.) Genotypes for Terminal Heat Stress Tolerance

Bhupnesh Sharma — 2026-05-04

Terminal heat stress is a major constraint for taking a profitable wheat crop by small and marginal farmers in the Indo-Gangetic Plains of south Asia. Hence, breeders remain in constant search for heat-tolerant genotypes. The present study was conducted on 15 wheat genotypes (G1 to G15). This study was done to find out high-yielding wheat genotypes that perform stably under terminal heat stress as well as to conclude an easy phenotyping trait for this objective. The crop was grown under two environments viz., timely (17^thNovember, 2020) and late sown (24^thDecember, 2020) with three replications. The physiological traits like Normalized difference vegetation index (NDVI), canopy temperature, and above-ground biomass; and morphological (at 80, 100, and 110 DAS) and yield attributing characters viz., days to heading, days to anthesis, days to maturity, grain yield (g/m²) and Heat susceptibility index (HSI) at harvesting stage. Among the genotypes, genotype G12 (657.33 g/m²) and G14 (653.33 g/m²) performed better under the timely sown (TS) condition while genotypes G13 (486.00 g/m²) and G14 (507.33 g/m²) under the late sown (LS) condition in terms of having higher NDVI, biomass, yield, and its attributes. Heat susceptibility index (HSI) was used to assess heat tolerance. The genotype G14 was found to be highly tolerant with lower HSI under LS condition whereas G6 was highly sensitive based on highest HSI.

Climate-Smart Agriculture and Carbon Emissions: Panel Data Evidence on Policy Effectiveness in Sub-Saharan Africa (2000-2024)

Peter Makieu — 2026-05-08

Agriculture in Sub-Saharan Africa accounts for approximately 59 percent of total national greenhouse gas emissions- the highest proportional share globally- and these emissions grew by 35 percent between 2000 and 2021. This study investigates whether climate-smart agriculture policies have resulted in a reduction in per-capita carbon dioxide (CO₂) emissions across 15 Sub-Saharan African countries from 2000 to 2024, with a particular focus on the period following the 2015 Paris Agreement. A balanced panel dataset of 375 country-year observations was constructed from the World Bank Development Indicators, the Food and Agriculture Organization statistical database (FAOSTAT), the International Monetary Fund, and the NASA POWER climate database. Pooled ordinary least squares, fixed effects, and random effects panel models were estimated. The Hausman specification test confirmed the fixed effects estimator as preferred.

All models were corrected for confirmed heteroskedasticity and serial autocorrelation using country-clustered robust standard errors. Panel cointegration tests confirmed a long-run equilibrium structure. The primary finding is that agricultural productivity- the climate-smart agriculture proxy- exerts a significant negative effect on per-capita CO₂emissions. The fixed effects elasticity of -2.236 indicates that a 10 percent improvement in agricultural productivity is associated with approximately 22 percent lower per-capita emissions, controlling for temperature, rainfall, and agricultural land expansion, which increases them. A non-linear income-emission relationship was confirmed, consistent with a modified Environmental Kuznets Curve pattern. The climate-smart agriculture CO₂correlation strengthened considerably after 2015, suggesting that nationally determined contribution commitments are beginning to generate measurable emission outcomes. Policy simulations indicate that scaling climate-smart agriculture investment to recommended agricultural budget targets could achieve aggregate emission reductions of 12-18 percent by 2030.

Adaptation to Climate Variability by Maize Farmers in Mezam Division in the North West Region of Cameroon

Ngwa Kester Azibo — 2026-05-09

Background: Climate variability remains an issue of global concern. The unprecedented hike in global temperature in the last decades have increased drought and flood events, with significant agricultural impacts especially on farmers whose livelihoods depend largely on rain-fed agriculture. Despite maize contribution to livelihoods especially in developing countries, the growing research on climate variability effects on crop yields has not sufficiently identified and quantified strategies that promote its production.

Aim: This study identifies maize farmers’ adaptation strategies to climate variability in Mezam Division, in the North West region of Cameroon. Specifically, the study, (1) Identifies farm strategies adopted by maize farmers to climate variability, and (2) quantifies the contribution of adopted strategies on maize yields.

Study Design: The study adopted a quantitative cross sectional design

Place and Duration of Study: This study was carried out in Mezam Division, North West Region of Cameroon, between March and May 2025.

Methodology: Data was collected using face-to-face interviews from 370 randomly selected maize farmers from 12 maize growing communities in four sub divisions in Mezam Division, using a pre-tested structured questionnaire.

Results: Results on adaptation strategies revealed that cultivating early maturing varieties, use of integrated pest management, and increased chemical fertilizer application were the most significant strategies adopted by farmers (P = .000). Binary Logistic Regression revealed that irrigation practices (B = 0.721, P = .07), crop rotation (B = 0.637, P = .06) and agroforestry practices (B = 0.447, P = .07) were the key strategies promoting maize yields under climate variability conditions.

Conclusion: Farmers in the study area are successfully adopting strategies which are increasing maize yields under climate variability. Climate variability strategies promoted by relevant actors in the region should include endogenous successful strategies to reduce the effects of climate variability on crops like maize. Identifying gender differentiate strategies will boost policy interventions towards climate variability adaptation.

Identification and Characterization of Pollution Plumes from Illegal Dumps Using Electrical Resistivity Tomography in the City of Ouagadougou, Burkina Faso, West Africa

Abdoul Aziz Tarpaga — 2026-05-11

Illegal dumps have a major impact on the environment, particularly through groundwater pollution caused by leachate in the form of a pollution plume. Indeed, the pollution plume consists of various ionic pollutants, notably dissolved trace metals, whose presence in the soil or groundwater alters the electrical characteristics of the environment. The objective of this study is to characterize the pollution plume from the illegal dumpsites at Goundrin and Yamtenga in the municipality of Ouagadougou (Burkina Faso) using the electrical resistivity tomography method. This method allows for the determination of the medium’s electrical parameters (electrical resistivity and conductivity), a parameter that indicates a material’s ability to resist the flow of electric current. The application of this method revealed the presence of a pollution plume by defining its geometry (lateral extent, depth, and thickness). Indeed, the electrical resistivity tomography profiles positioned to intersect the direction of groundwater flow indicate the presence of resistivity anomalies with very low electrical resistivity values ranging from 3 to 15 Ωm. These anomalies correspond to a pollution plume that reaches depths of approximately 20 m in some areas, with a thickness of 2 to 20 m and a lateral extent of 5 to 90 m. These results also made it possible to identify clay zones acting as a barrier that slows the migration of the pollution plume in the direction of groundwater flow while limiting its infiltration into the groundwater aquifer.

Three-Dimensional Convolutional Neural Network-based Forecasting of Wildfire Spread from Satellite Fire Detections and Environmental Context Layers

Joshua Lee — 2026-05-11

Wildfires are increasingly causing severe economic losses and ecological damage, highlighting the need for accurate predictive models to support effective risk mitigation and resource management. This study developed and evaluated a three-dimensional convolutional neural network framework for short-term wildfire spread forecasting using satellite-derived fire detections and environmental context layers from Canada during the 2023 wildfire season. Moderate Resolution Imaging Spectroradiometer active fire detections were obtained from NASA FIRMS and preprocessed into daily raster layers at 1 km resolution. Fire-affected areas were estimated from fire radiative power, rasterised onto a national grid, clustered using DBSCAN in space-time coordinates, and converted into fixed-size 160 x 160 x T tensors. Static environmental layers, including land cover and elevation, were incorporated as contextual predictors for model training. The model output consisted of normalised probabilistic fire-spread maps, which were compared with ground-truth binary fire masks. Qualitative evaluation showed that the model captured major spatial patterns of fire progression in representative fire-event clusters. Quantitative evaluation using binary cross-entropy loss, Dice coefficient, and Intersection over Union indicated low pixel-wise prediction error and high spatial overlap for most test clusters; the Dice coefficient was concentrated in the 0.8-1.0 range for 50 clusters, and IoU values were concentrated in high-overlap ranges, with only a small subset of difficult cases showing poor overlap. These findings support the feasibility of three-dimensional convolutional neural networks for forecasting wildfire spread from preprocessed satellite and environmental datasets. The framework contributes a computational remote sensing proof-of-concept for transforming satellite fire detections and environmental context layers into spatially explicit short-term wildfire prediction outputs, providing a baseline for future benchmarking and refinement of deep learning-based wildfire forecasting systems.

Land Cover Changes in Wetlands and Surface Temperature Variation in the Wouri Estuary, Cameroon

M. E. Awuh — 2026-05-12

Wetlands play a major role in maintaining a stable climate. Nevertheless, their global decline driven by both natural and anthropogenic factors poses a serious threat to the environment. This research aims to investigate the impact of land cover changes in wetlands on Land Surface Temperature (LST) between 1985 and 2025 in Douala IV within the Wouri Estuary. Using Geographic Information Systems (GIS) and remote sensing, land surface biophysical properties were compared to sub-pixel thermal changes derived from Landsat imagery of 1985, 2005, and 2025. Pearson’s Product Moment Correlation Analysis was utilized to assess the association between urban expansion and wetland thermal variation. The results indicated a drastic reduction in wetlands to just 2.0 km², representing a 74% loss from 2005 and a 69% loss from 1985, averaging a loss of 0.28 sq. km annually. Wetland destruction in Douala occurred through multiple, distinct pathways. Built-up areas directly replaced 1.7 km², expanding water bodies converted 1.8 sq. km, and vegetated areas encroached upon 1.1 km². Exposed surfaces contributed essentially nothing (0.0 km²) to wetland loss. LST distribution revealed a significant warming trend, with overall values ranging from 21.1°C to 29.1°C. A strong negative correlation (R² = 0.8788) was observed, indicating that wetland cover explains 87.88% of the temperature variation. The linear regression model (y = -1.3377x + 35.777) indicates that for every 1 km² increase in wetland area, the mean LST decreases by 1.34°C. Ultimately, the severe wetland reduction of 4.5 km² between 1985 and 2025 contributed to an approximate 6°C increase in local land surface temperature. Given the continuous depletion of wetlands and the persistent threat to the environment, it's essential to identify and implement strong strategies to manage the remaining wetland.

GIS, Remote Sensing and AHP-Based Groundwater Potential Zone Mapping in the Wainganga River Basin, Madhya Pradesh, India

Pushplata Aherwar — 2026-05-13

Background: Groundwater is one of the most important natural resources for domestic, agricultural, and industrial purposes. Due to increasing population, urbanization, and irregular rainfall patterns, groundwater resources are under severe stress in many parts of India. Due to explosive population growth, there has been a regular increase in water demand. This has led to overexploitation of water, particularly groundwater in different parts of the country, transforming them into water dark zones.

Aim: The present study focuses on the identification of groundwater potential zones in the Wainganga River Basin of Madhya Pradesh using Geographic Information System (GIS), Remote Sensing, and Analytical Hierarchy Process (AHP) techniques.

Method: Nine thematic layers, namely geomorphology, geology, lineament density, soil, slope, drainage density, rainfall, land use/land cover (LULC), and elevation, were prepared and integrated through weighted overlay analysis. The weights of the thematic layers were assigned using Saaty’s AHP method. The consistency ratio (CR) obtained was 0.039, which indicates acceptable consistency in weight assignment.

Results: The results classified the study area into five groundwater potential zones: very poor, poor, moderate, good, and very good. The moderate groundwater potential zone occupied the largest portion of the study area (47.55%), followed by poor (20.87%), good (14.20%), very poor (12.79%), and very good (3.64%) zones. The eastern and western parts of the basin showed better groundwater potential due to favourable conditions such as forest cover, dense lineament networks, suitable geology, and higher rainfall.

Conclusion: The study demonstrates that the integrated GIS, Remote Sensing, and AHP approach is effective for groundwater resource assessment, planning, and sustainable management.

Effect of Fertigation on Flowering and Physico-chemical Responses of Guava in the Lower Shiwalik Foothills of Punjab, India

Avaninder Preet Kalsi — 2026-05-14

Background and Aim: Guava (Psidium guajava L.), often referred to as the ‘Apple of Tropics,’ is a vital fruit crop in tropical and sub-tropical regions worldwide. This study focused on a sub-mountainous, undulating terrain with high cultivation costs and no permanent water sources, aiming to optimize fertilizer efficiency, enhance fruit yield and quality, and conserve water for future generations. Drip irrigation recognized as effective method for delivering water and nutrients, results in reduced water usage while, improving fruit quality. The research investigated the effects of fertigation on the phenological and physico-chemical responses of guava cv. Allahabad Safeda in the lower Shiwalik belt of Punjab.

Study Design: The experimental trial utilized a simple randomized block design with ten different treatments replicated thrice.

Place and Duration of Study: The research was conducted at Fruit Research Fram, Dr D R Bhumbla Regional Research Station, Ballowal Saunkhri, Balachaur, SBS Nagar, Punjab. The trial was conducted for duration of nine months.

Methodology: The experiment comprised of 10 different treatment viz. T₁- I₁F₁ (100% of PE + 100% RDF), T₂– I₁F₂ (100% of PE + 75% RDF), T₃– I₁F₃ (100% of PE + 50% RDF), T₄– I₂F₁ (80% of PE + 100% RDF), T₅- I₂F₂ (80% of PE + 75% RDF), T₆- I₂F₃ (80% of PE + 50% RDF), T₇– I₃F₁ (60% of PE + 100% RDF), T₈- I₃F₂ (60% of PE + 75% RDF), T₉- I₃F₃ (60% of PE + 50% RDF) and T₀- control (no fertigation) and replicated thrice. The data were recorded and analyzed as per standard procedures.

Results: The results showed significant improvements in phenological traits viz., date of initiation & period of vegetative bud emergence, date of initiation & period of flower bud initiation, date of initiation & period of peak flowering, date of initiation & period of fruit set; and date of initiation & period of fruit maturity. Additionally, the fruit's physico-chemical properties were also enhanced, yielding higher Vitamin C and pectin content, along with lower titratable acidity.

Conclusion: This work underscores the importance of efficient water and nutrient management for sustainable guava production and improved nutritional security in agriculturally challenged areas.

Habitat-Specific Foraging Patterns of Wild Bees on Flowering Plants in an Arid Landscape of Jodhpur, Rajasthan, India

Purnima Pathak — 2026-05-16

Background: Wild bee foraging behaviour in the arid landscapes of Jodhpur is shaped by habitat heterogeneity, floral resource availability and extreme desert climatic conditions, influencing pollination dynamics across natural and managed ecosystems.

Aims: This study quantified habitat-specific foraging activity of wild bees across urban, peri-urban, scrubland and agricultural habitats of Jodhpur, Rajasthan, to evaluate variation in visitation frequency, visit duration and foraging consistency.

Study Design: Observational field study with standardized focal plant method across multiple habitat types.

Place and Duration of Study: In and around Jodhpur city, Rajasthan, India. January to March, 2026.

Methodology: Thirteen flowering host plant species were monitored across four habitat types. Each plant-bee combination was observed during five sessions during morning (08:00-10.00 h) and evening (16.00-18.00). Visit frequency (visits/ 5 min) and mean visit duration (s) were. recorded and CV was calculated as (SD/mean) 100 to assess foraging consistency. Bee species were identified using standard morphological keys and literature.

Result: Seven bee species from four genera (Apis, Ceratina, Lasioglossum and Nomia) were recorded on 13 host plants across nine families. Visitation rates ranged from 1 to 70 visits/5 min and visit duration from 2.8 ± 1.48 s to 77.0 ± 7.74 s. Agricultural habitats showed the highest bee richness and visitation rates, while scrubland had the lowest. CV ranged from 7.28% to 55.75% indicating variable foraging consistency among plant-bee interactions.

Conclusion: Wild bees play a critical ecological role in arid agroecosystems of Jodhpur, with agricultural habitats supporting the greatest foraging diversity and intensity. Habitat-sensitive pollinator conservation strategies, particularly preserving peri-urban and scrubland patches, are essential for sustaining diverse wild bee communities in arid India.

Assessing Climate Change Vulnerability for Soybean-based Cropping Systems: Evidence from Bhopal and Ujjain Districts of Madhya Pradesh, India

Shruti Mishra — 2026-05-18

Background and Aims: Soybean, being an important crop of the Madhya Pradesh state, is continuously facing a threat due to climate change. Thus, this study was done to assess the season-wise vulnerability of soybean-based cropping systems to climate change in Madhya Pradesh, India’

Methodology and Study Area: The study utilised an index-based approach grounded in the Intergovernmental Panel on Climate Change framework. Two dominant cropping systems—Soybean–Wheat (Bhopal district) and Soybean–Wheat/Chickpea (Ujjain district)—were selected based on the proportion of soybean area under cultivation. The methodology of vulnerability was conceptualised as a function of exposure, sensitivity, and adaptive capacity, and a composite vulnerability index was constructed using normalised indicators and statistically derived weights.

Results: The results indicate that temperature is the major contributor to exposure in both districts, with higher exposure observed during the Rabi season. Sensitivity was primarily driven by availability-related factors such as landholding size, irrigation access, and cropping intensity, while experience and dependency played a relatively minor role. Adaptive capacity was largely influenced by institutional support, scientific knowledge, and income diversification. Comparative analysis revealed that Ujjain exhibited relatively higher exposure, whereas Bhopal demonstrated stronger adaptive capacity. Despite moderate exposure levels, both districts were categorised as less vulnerable.

Conclusion: The findings highlight that enhanced adaptive capacity effectively offsets climatic risks, thereby strengthening the resilience of soybean-based cropping systems. The study underscores the importance of improving access to climate-resilient technologies, extension services, and institutional support to further reduce vulnerability and ensure sustainable agricultural development.

A Discrete Preference Report on Population and Development Issues in Sub-Saharan Africa: A Mixed-methods Analysis of Environmental Illiteracy, Sexual Health Risks and Substance Abuse among Youth

Kenneth Yongabi Anchang — 2026-05-19

Background and Aim: Sub-Saharan Africa (SSA) possesses a, uniquely, youthful population approximately 65%, compared to other continents, yet the potential for a demographic dividend is jeopardized by a complex set of interconnected public health challenges. While a high level of environmental illiteracy, sexual health risks, and substance abuse have often been studied in isolation, this study the following specific objectives are pursued: To assess the level of environmental literacy among SSA youth and evaluate its contribution to environmental pollution and biodiversity loss. To analyze emerging trends in sexual and reproductive health, including the prevalence of transactional sex and shifts in contraceptive use, and to explore associated clinical trends. To investigate the health literacy, perceptions, and key drivers of substance abuse and drug addiction among the youth population. To synthesize the findings from these three domains to propose a holistic framework for integrated public health intervention and policy formulation.

Study Design: We employed a convergent mixed-methods design, this research collected integrated quantitative and qualitative sample size of seventy -two respondents (n=72) data from youth across 24 Sub Saharan African countries to analyze the synergistic interactions between these domains.

Methods: This study employed a convergent mixed-methods design to investigate the interlinked nature of environmental illiteracy, sexual health risks, and substance abuse among youth in Sub-Saharan Africa (SSA). The design is consistent with a syndemic framework, which requires understanding how these challenges cluster and interact under shared socioeconomic pressures

Results: The findings reveal a critical disconnect between environmental awareness (90%) and harmful practices, largely attributed to economic despair and externalized responsibility. Economic pressure was the primary driver (88%) of transactional sex, which was strongly linked to substance use, with 22% of engaged youth participating to support a substance habit. Substance abuse was normalized (89%) and driven by peer pressure (95%), mental health challenges (90%), and economic despair (82%). Thematic analysis identified key syndemic pathways, including a cycle of economic desperation and mental health strain.

Conclusion: The study concludes that these challenges are not discrete but form an interconnected syndemic fueled by poverty and systemic failures. Consequently, standalone interventions are inherently ineffective. We propose the Integrated Youth Resilience and Empowerment (I-YRE) Framework, advocating for multi-sectoral strategies that combine environmental education, green entrepreneurship, co-located health services, and participatory governance to holistically build youth resilience and achieve sustainable development.