Archives of Current Research International

Mechanical Devices for Seed Cleaning: A Review of Separation Principles, Performance and Emerging Pollution Free Technologies

Kapil Verma, B. P. Mishra — Mon, 21 Sep 2026 00:00:00 +0000

Cleaning converts a threshed, shelled or decorticated lot into a saleable product, and the device chosen depends on the property separating the wanted fraction from the unwanted one. This review organises that evidence by property: aerodynamic devices (terminal velocity), screens (size), indented cylinders (length), spiral separators and inclined drapers (shape and surface texture), gravity tables (density), magnetic and electrostatic separators (roughness and conductivity), and optical sorters (colour). Two findings emerge. First, the aerodynamic route dominates the literature, delivering cleaning efficiencies of 85 to 98 % for air screen and pneumatic units, but at the cost of a fan, a power source and airborne dust, with respirable dust of 3.2 to 10.5 mg per cubic metre and respiratory symptoms in 33 to 47 % of exposed workers - a documented occupational cost of that performance. Second, the friction based inclined draper separator, needing no fan, is almost absent from the primary research literature: no peer reviewed study reports its cleaning efficiency, capacity or governing variables for any oilseed. A device matching air screen performance without the dust burden is technically plausible, but the quantitative basis for designing one for decorticated groundnut the effect of deck angle, belt speed and feed rate on cleaning efficiency and kernel loss has not been established.

Social Media Analytics in Gastrointestinal Cancer Awareness: Trends, Challenges, and Strategic Approaches to Public Trust and Digital Engagement

Isaac Amornortey Yowetu, Juliana Jepkogei — Wed, 23 Sep 2026 00:00:00 +0000

Gastrointestinal cancers are prominent contributors to the global cancer burden, while public understanding of symptoms, risk, screening, diagnosis and survivorship increasingly develops within social media environments. This review critically examines how social media analytics have been used to characterise gastrointestinal cancer awareness, how platform engagement relates to information quality and public trust, and which communication strategies are most defensible for improving digital engagement without equating popularity with public-health value. Peer-reviewed literature was sought across biomedical and multidisciplinary scholarly sources for publications from 2006 to 17 July 2026, supplemented by citation chaining and verification of bibliographic metadata and digital object identifiers. The evidence base is most developed for colorectal cancer, with growing work on gastric, pancreatic and liver cancers and comparatively sparse evidence for several other gastrointestinal malignancies. Across Twitter, Facebook, Instagram, YouTube, TikTok, Bilibili and Douyin, analytic approaches have progressed from hashtag counts and descriptive engagement metrics to thematic coding, quality appraisal and machine-learning-supported topic discovery. Yet the dominant literature remains cross-sectional, platform-specific and dependent on convenience samples such as highly ranked or highly liked posts. High engagement does not consistently indicate accuracy, completeness, actionability, representativeness or trust. Professional sources often achieve better quality scores, whereas patient narratives can generate stronger attention and relational relevance. These findings create a central communication tension: authoritative information may be less socially compelling, while emotionally resonant content may be incomplete or difficult to generalise. Evidence that social media exposure produces sustained screening or other health behaviour remains limited, despite feasibility studies showing that campaigns can move some users from reach to test requests or participation. The review proposes a shift from engagement-centred analytics towards public-value analytics that integrate reach, comprehension, credibility, trust, equity and verifiable behaviour. Future research should prioritise longitudinal, multilingual, cross-platform and intervention designs with transparent sampling, validated outcome measures and linkage to screening or care pathways. Social media can support gastrointestinal cancer awareness, but strategic value depends on evidence-calibrated communication, visible provenance, participatory trust-building and outcome evaluation beyond clicks.

Integrating Artificial Intelligence and Environmental Metagenomics for Ecosystem Monitoring and Management

Wed, 23 Sep 2026 00:00:00 +0000

Artificial Intelligence (AI) is driving a significant transformation in microbiology and environmental metagenomics, shifting the field from a descriptive framework towards a predictive and systems-level understanding. The advent of metagenomics has enabled direct analysis of microbial communities from environmental samples, overcoming limitations of culture-dependent approaches. However, rapid advances in high-throughput sequencing have generated unprecedented volumes of complex data, creating a critical need for advanced computational tools. In this context, AI has emerged as an essential approach for extracting meaningful biological insights. AI-based methods have enhanced microbial community analysis by enabling deeper understanding of community dynamics and functional interactions. Models such as cNODE predict community shifts based on initial species configurations, while Graph Neural Network approaches, including MicrobeGNN, estimate steady-state community structures using genomic relationships. Machine learning algorithms such as Random Forest are also widely applied to identify keystone species essential for ecosystem stability. In environmental DNA (eDNA) and metagenomic data analysis, machine learning improves tasks such as metagenome binning through tools like VAMB and SemiBin, while DeepMAsEd and ResMiCo detect assembly errors without reference genomes. Additionally, Natural Language Processing-based models such as DeepMicrobes and BERTax interpret DNA as structured language for accurate taxonomic classification. AI also contributes to predicting microbial functions, particularly in bioremediation, by identifying organisms capable of degrading pollutants using techniques such as Random Forest and Support Vector Machines. Reinforcement learning frameworks like SPAM-DFBA further model microbial metabolism as a decision-making system. In pathogen tracking, AI supports outbreak detection and source attribution, with applications including prediction of Salmonella enterica origins and real-time surveillance systems such as HealthMap. Future developments include microbial foundation models, tools like AlphaFold 3 and Evo, and digital twin systems, although challenges such as limited interpretability remain.

Responsible Artificial Intelligence in Tax Administration and Public Financial Management: Transparency, Procedural Fairness and Accountability

Sat, 26 Sep 2026 00:00:00 +0000

Artificial intelligence is moving from peripheral experimentation to consequential use in tax administration and public financial management, where models can prioritise audits, detect anomalous transactions, screen procurement, support accounting and assurance, and structure interactions with taxpayers and suppliers. These applications may improve consistency, targeting and analytical capacity, but they also change how public power is exercised. This critical narrative review examines the conditions under which artificial intelligence can be used responsibly in revenue and public-finance functions, with particular attention to transparency, procedural fairness and accountability. The review integrates public-administration research, tax-compliance scholarship, public-sector accounting and auditing literature, machine-learning studies in tax and procurement, responsible-artificial-intelligence research, and authoritative governance instruments. The evidence is strongest for the predictive and screening value of machine learning in bounded tasks, including audit selection and procurement-risk detection. Evidence is substantially weaker for long-term organisational effects, distributional fairness, legitimacy, contestability and accountability outcomes. Three recurrent findings emerge. First, transparency must be differentiated into system, process and decision-level transparency; indiscriminate technical disclosure is neither necessary nor always compatible with enforcement integrity, but meaningful notice, documentation and reasons are essential. Second, procedural fairness cannot be reduced to model accuracy or the presence of a human reviewer. It depends on data provenance, label quality, consistency, individualised consideration, reason-giving, effective challenge and correction. Third, accountability requires an institutional chain of responsibility spanning authorisation, design, procurement, deployment, monitoring, appeal and independent assurance. Human oversight is useful only when reviewers have competence, time, authority and evidence that intervention improves decisions. The review proposes a risk-tiered Fiscal Artificial Intelligence Assurance Chain that links public-purpose justification to data and model assurance, operational controls, outward-facing explanations, redress and continuous audit. Responsible deployment therefore requires treating artificial intelligence not merely as a technical efficiency tool but as public administrative infrastructure whose legitimacy depends on demonstrable legality, fairness, traceability and correctability.

Lepcha Animism as an Indian Knowledge System: Cultural-ecological Perspectives on Sustainability

Shristi Kala Chhetri — Tue, 29 Sep 2026 00:00:00 +0000

Indigenous knowledge systems frequently organise ecological understanding through relationships among land, living beings, social institutions, and cultural practice. This narrative review examines Lepcha animism as a culturally embedded knowledge system and evaluates, with explicit evidentiary caution, its relevance to contemporary sustainability debates. A structured narrative search was conducted in July 2026 through scholarly web search, PubMed, journal and publisher databases, and academic repositories using combinations of terms related to Lepcha knowledge, sacred landscapes, Sikkim ethnobiology, animism, biocultural conservation, disaster-risk reduction, and environmental education. Peer-reviewed scholarship was prioritised; classic ethnographies were retained for historical and cosmological description, while official and community documentation was used only for contemporary heritage and ritual details. The synthesis indicates that Lepcha traditions link sacred geography, ritual specialists, oral narratives, seasonal ceremonies, and customary ethics within a relational understanding of human-environment interaction. Empirical studies from Sikkim further demonstrate that local knowledge can contain verifiable information relevant to medicinal plants, bird identification, and resource preferences. These findings support the value of Indigenous knowledge for place-based conservation and education, but they do not establish that ritual or sacredness automatically produces conservation, agricultural sustainability, or disaster resilience. The review therefore distinguishes documented cultural meanings from plausible social-ecological mechanisms and from measured environmental outcomes. Lepcha knowledge is best understood as a dynamic biocultural system whose sustainability relevance depends on community authority, context, continued transmission, and appropriate dialogue with scientific knowledge rather than on romanticised assumptions about Indigenous practice.

Agripreneurship as the New Agricultural Extension Frontier: Incubation, Digital Platforms and Rural Enterprise Development

Wed, 30 Sep 2026 00:00:00 +0000

Agricultural extension is being asked to solve a problem that conventional technology-transfer models were not designed to address: how to help rural actors convert knowledge, technologies and market opportunities into viable, adaptive enterprises. This critical narrative review examines agripreneurship as a possible new frontier for agricultural extension, with particular attention to business incubation, digital platforms and rural enterprise development. Literature published from 2009 to 27 July 2026 was considered, alongside necessary foundational material, using multidisciplinary scholarly sources and critical appraisal of conceptual, observational, experimental and programme-evaluation evidence. The synthesis indicates that extension has progressively moved towards pluralistic innovation-system roles involving brokerage, network facilitation and capability development, creating an institutional basis for enterprise-oriented advisory work. Incubators and accelerators can add mentoring, finance readiness, legitimacy and network access, but their apparent performance is highly heterogeneous and agriculture-specific evaluations frequently lack credible counterfactuals or long post-graduation follow-up. Digital advisory services more consistently improve information, knowledge and selected practices than final productivity or profit outcomes, while platform models can connect advisory, market and financial functions but also introduce exclusion, dependency, data-governance and bargaining-power risks. Evidence on rural enterprise development is similarly uneven: youth and women can gain skills and opportunity access, yet selection effects, resource constraints and institutional barriers complicate claims that agripreneurship alone generates durable employment or inclusive rural transformation. The review therefore proposes an enterprise innovation-broker model of extension in which technical advice, venture capability, incubation, market and finance intermediation, and digital governance are treated as complementary rather than substitutable functions. The field now requires stronger causal designs, longitudinal venture tracking, distributional metrics and evaluation of hybrid public-private architectures. Agripreneurship is best interpreted not as a replacement for extension, but as an extension frontier whose legitimacy depends on demonstrated additionality, inclusion and public-value accountability.

Utilization of Pulse Milling By-Products for Food Purposes: A Review

Chandan Solanki, Deepika Goswami, D. Mridula, R. K. Vishwakarma — Wed, 30 Sep 2026 00:00:00 +0000

Pulse milling generates considerable quantities of by-products, including broken grains, powder, husk, germ, and mixed fractions such as chuni-bhusi. Although these materials have traditionally been used as animal feed or farm fertiliser, the reviewed evidence indicates that they contain nutritionally and functionally valuable components that may support higher-value food applications. Pulse husks and bran fractions provide protein, dietary fibre, minerals, and bioactive constituents, including phenolic compounds, flavonoids, and tannins. Their composition varies among pulse species, milling fractions, and processing conditions, making fraction-specific characterisation important before food use. Recovery and separation technologies can isolate edible cotyledon-rich material and other useful fractions from milling streams. Reported applications include traditional foods such as papad and deep-fried snacks, as well as bakery and extruded products. Pulse-derived fibre may partially replace refined wheat flour, although excessive incorporation can adversely affect dough rheology, texture, appearance, and sensory acceptability. The review also highlights fermentation and other processing approaches as potential means of modifying nutritional, antinutritional, bioactive, and techno-functional properties. Overall, pulse-milling by-products represent underutilised resources with potential for food valorisation, provided that appropriate recovery, stabilisation, compositional assessment, processing, quality control, and safety evaluation are applied to identify suitable fractions and incorporation levels for safe and acceptable human consumption.

On-farm Performance of Pusa Basmati 1847 under Frontline Demonstrations Compared with Farmer Practice Using Pusa Basmati 1509 across Seven Districts of Uttarakhand

Mon, 21 Sep 2026 00:00:00 +0000

Frontline demonstrations (FLDs) provide an opportunity to evaluate improved crop technologies under farmer-field conditions. This study assessed an FLD technology package centred on the short-duration Basmati rice variety Pusa Basmati 1847 in comparison with farmer practice using Pusa Basmati 1509 during Kharif 2025 across seven Krishi Vigyan Kendras in Uttarakhand. Five demonstrations were organised through each KVK, giving 35 farmer-participatory demonstrations in total. Agronomic traits, grain and straw yields, cultivation costs, and monetary returns were summarised descriptively. Across the seven district summaries, Pusa Basmati 1847 FLDs averaged 41.3 q ha⁻¹ grain yield and 47.0 q ha⁻¹ straw yield, compared with 37.3 and 42.5 q ha⁻¹, respectively, for the Pusa Basmati 1509 farmer-practice summary. The descriptive grain-yield advantage was 10.6%. Mean net return from the Pusa Basmati 1847 FLDs was Rs 57,808 ha⁻¹ with a mean benefit:cost ratio of 2.33, compared with Rs 49,053 ha⁻¹ and 2.11 under farmer practice. District-level grain yield ranged from 24.9 to 50.9 q ha⁻¹, indicating substantial spatial variation in field performance. Because the FLD combined Pusa Basmati 1847 with recommended production practices, the comparison represents a technology-package assessment rather than an isolated varietal effect. The results support further multi-season, paired on-farm evaluation of this package under the diverse production environments of Uttarakhand.

Assessment of Perceptions of Climatic Variability among Dairy Farmers: An Initiative towards Climate Resilient Dairy Farming System in Jharkhand, India

Mon, 21 Sep 2026 00:00:00 +0000

Several studies conducted in Jharkhand have clearly indicated changes in climatic patterns, with the most prominent conditions being hot and dry weather, scorching temperatures and heat waves. These alterations affect dairy animals directly or indirectly and pose challenges for sustainable dairy husbandry. Therefore, to achieve climate-resilient dairy farming systems, it is of foremost importance to understand dairy farmers’ perceptions to gauge their responses to this changing situation. In this context, the present study was carried out to assess perceptions of climatic variability among 240 dairy farmers from all three agro-climatic regions of Jharkhand. Data were collected through personal interviews using a pre-structured interview schedule.

The results indicated that nearly 55% and 52% of farmers showed a medium level of perception of climate change parameters and the climate change phenomenon, respectively. The majority of farmers perceived an increase in the length of hot periods (82%) and the summer season (71%), erratic rainfall (54%) and extreme annual weather conditions (64%), and a decrease in the length of the rainy season (57%), whereas the majority did not know about changes in annual temperature and annual rainfall. Most farmers strongly agreed that environmental temperature was increasing (52%) and precipitation was decreasing (49%), whereas most agreed that the climate is really changing (67%) and that significant changes in weather patterns are causing a serious problem that needs immediate attention (44%). Farmers’ perception was found to be significantly correlated with age, farming experience, landholding, annual income and SES, and was significantly affected by education and gender at the 1% level of significance. The medium level of perception of climate change in the present study indicates a need for future emphasis on organising camps and training programmes on climate change issues by the state animal husbandry departments, SAUs/SVUs, KVKs and NGOs.

Comparative Analysis of Multiple Gridded Precipitation Datasets for Assessing Their Reliability across Various Physiographic Zones in Odisha, India

Wed, 23 Sep 2026 00:00:00 +0000

Aims: To evaluate and intercompare six prominent gridded precipitation products—IMD (gauge-based interpolated), CHIRPS, NASA-POWER, PERSIANN-CDR (satellite-derived), ERA5, and ERA5-Land (reanalysis)—against IMD rain gauge observations across four physiographically diverse districts of Odisha, India, and to assess the effectiveness of linear scaling bias correction for agroclimatic and hydrological applications.

Study Design: A comparative validation study was conducted using daily precipitation data for 2000–2023. Both raw and linear scaling bias-corrected datasets were analysed to evaluate the impact of bias correction on product performance.

Place and Duration of Study: The study covered Keonjhar (northern plateau), Sambalpur (central tableland), Koraput (Eastern Ghats), and Khordha (coastal plain), Odisha, India, over a 24-year period (2000–2023).

Methodology: Product performance was evaluated against IMD rain gauge observations using RMSE, NRMSE, MAE, Pearson correlation coefficient (r), R², index of agreement (d), and Kling–Gupta Efficiency (KGE). Heatmaps and Taylor diagrams were used for comparative visualisation.

Results: Considerable spatial variation was observed among products and regions. NASA-POWER showed the best overall performance, particularly in Khordha (r = 0.87; KGE = 0.83) and Koraput (r = 0.87; KGE = 0.87), with relatively low RMSE values (5.2–9.5). Keonjhar showed poor agreement across all products (r = 0.15–0.32), indicating challenges in representing rainfall over complex terrain. Linear scaling generally improved most datasets without substantially reducing statistical skill.

Conclusion: NASA-POWER, particularly after bias correction, emerged as the most reliable dataset for agroclimatic and hydrological applications in Odisha. The findings highlight the importance of physiography-specific validation, bias correction, and multi-product ensembles to reduce rainfall estimation uncertainty.

Supervisory Practices of Instructional Leaders: A Qualitative Study on Effectiveness and Ineffectiveness

Girlie J. Yabo — Wed, 23 Sep 2026 00:00:00 +0000

This qualitative phenomenological study explored how teachers and instructional leaders at Payao National High School experience supervisory practices, with an emphasis on those perceived as effective or ineffective and their influence on teacher motivation, professional growth, and instructional performance. The study was conducted at Payao National High School in Payao District, Schools Division of Zamboanga Sibugay, Philippines, in 2026. Ten participants representing teaching and instructional leadership roles were purposively selected. Data were generated through open-ended, semi-structured qualitative prompts, small-group discussions, and document review. Data were analysed using Braun and Clarke’s thematic analysis and Moustakas’ phenomenological procedures, with triangulation and member checking to strengthen credibility. Four central themes emerged: supportive and development-oriented supervision; evaluative and compliance-based supervision; the influence of supervision on teacher motivation and professional growth; and contextual constraints affecting supervisory practices. Participants described classroom observation, lesson-plan review, monitoring by walking, coaching, technical assistance, and post-observation feedback as common practices. Supervision was viewed as effective when feedback was timely, specific, respectful, and development-focused, but ineffective when it was inconsistent, overly rigid, unclear, or reduced to compliance. Time limitations, heavy workloads, scheduling conflicts, high teacher-to-supervisor ratios, resistance to feedback, and limited resources constrained implementation. Participants recommended consistent supervision, a positive school culture, open communication, teacher input, and structured supervision plans. Instructional supervision is most constructive when it combines accountability with coaching, trust, professional dialogue, and follow-through. Consistency, contextual support, and actionable feedback are critical to sustaining teacher engagement and instructional improvement.

Fruit Bagging as a Preharvest Intervention Improves Postharvest Quality and Antioxidant Stability in Dashehari Mango (Mangifera indica L.)

Swatantra Yadav, V. K. Tripathi, A. K. Trivedi — Thu, 24 Sep 2026 00:00:00 +0000

Preharvest fruit bagging is an effective, eco-friendly orchard practice for improving fruit quality and extending postharvest life, yet its influence on the biochemical and antioxidant machinery of mango remains inadequately understood. The present two-year study (2023–24 and 2024–25) evaluated the effect of different coloured preharvest fruit bags on the postharvest quality and storage life of mango (Mangifera indica L.) cv. Dashehari. Fruits were bagged at the egg-size stage with green (T₂), red (T₃), brown (T₄), and white (T₅) bags or left unbagged as the control (T₁), harvested at physiological maturity, and evaluated at 0, 3, 6, 9, and 12 days after harvest for physicochemical, biochemical, antioxidant, and oxidative stress-related attributes. Among the treatments, green bagging (T₂) was most effective in preserving fruit quality during storage. At 12 days after harvest, T₂ recorded the highest total flavonoid content (26.21 mg 100 g⁻¹), antioxidant activity (24.63%), total phenolic content (87.76 mg 100 g⁻¹), catalase activity (7.73 μg min⁻¹ g⁻¹), peroxidase activity (0.42 μg min⁻¹ g⁻¹), and superoxide dismutase activity (72.78 units min⁻¹ g⁻¹), while recording the lowest physiological loss in weight (15.79%), lipid peroxidation, and superoxide radical accumulation. Green bagging also delayed fruit softening and senescence by suppressing polygalacturonase and pectin methylesterase activities, thereby maintaining firmness for longer. These findings indicate that green-coloured preharvest bagging enhances antioxidant defence, minimises oxidative deterioration, and improves the biochemical quality and storability of mango fruits, offering a practical, low-cost strategy for extending marketable shelf life and reducing postharvest losses in cv. Dashehari mango.

Development and Quality Evaluation of Cooked Chicken Sausages with Partial Replacement of Broiler Chicken Meat by Gizzard

M. Anna Anandh, G. Gawdaman, R. K. Kanimozhi, M. Prema, R. Karthick — Thu, 24 Sep 2026 00:00:00 +0000

Aims: To evaluate the effect of replacing broiler chicken skeletal meat with chicken gizzard at different levels on the physicochemical, processing, proximate and sensory characteristics of cooked chicken sausages.

Study Design: Treatment-based experimental study with six independent batches for each formulation.

Place and Duration of Study: Department of Livestock Products Technology, Veterinary College and Research Institute, Orathanadu, Thanjavur, Tamil Nadu, India, March to May 2026.

Methodology: Fresh broiler chicken meat and chicken gizzard were used for the preparation of cooked chicken sausages. Chicken meat was replaced with gizzard at 0, 25, 50 and 100%, designated as C, T1, T2 and T3, respectively. The sausages were evaluated for pH, emulsion stability, product yield, diameter shrinkage, drip loss, expressible moisture, moisture retention, fat retention, juiciness, proximate composition and sensory characteristics.

Results: Increasing gizzard replacement increased pH, diameter shrinkage, drip loss and expressible moisture, while emulsion stability, product yield, moisture retention, fat retention and juiciness decreased. The pH increased from 6.08 ± 0.02 in C to 6.25 ± 0.03 in T3, whereas product yield decreased from 93.62 ± 0.42 to 88.56 ± 0.51%. Moisture content decreased from 65.24 ± 0.46 to 61.72 ± 0.44%, while protein, fat and ash contents increased from 16.42 ± 0.21, 8.64 ± 0.18 and 2.18 ± 0.05% to 17.62 ± 0.25, 9.48 ± 0.22 and 2.39 ± 0.06%, respectively. Sensory scores for appearance and colour, flavour, juiciness, tenderness, binding and overall acceptability declined with increasing gizzard replacement. T1 remained statistically comparable to C for all sensory attributes.

Conclusion: Chicken gizzard was suitable as a partial replacement for broiler chicken meat in cooked chicken sausages. Among the levels tested, 25% replacement provided the most suitable combination of processing, physicochemical and sensory characteristics.

An Analysis of Social Participation and Extension Contact among Farmers under Public, Private and NGO Extension Systems in Telangana, India

Fri, 25 Sep 2026 00:00:00 +0000

Social participation and extension contact are important factors influencing farmers’ access to agricultural information, interaction with extension personnel and participation in agricultural development activities. The present study was undertaken to analyse the social participation and extension contact of farmers under public, private and NGO extension systems in Telangana. The study examines the social participation and extension contact of farmers across three extension service systems in Telangana, namely the public, private and non-governmental organisation (NGO) extension service systems. An ex-post facto research design was adopted, with a total sample of 135 farmers selected for the study.The analysis found that the majority of farmers under the public extension service system, i.e., 62.22 per cent, had a medium level of extension contact; 42.22 per cent were in the “Not a member in any organisation” category. The majority of farmers under the private extension system, i.e., 40.00 per cent, had a medium level of extension contact; 57.78 per cent were in the “Not a member in any organisation” category. The majority of farmers under the NGO extension system, i.e., 42.22 per cent, had a low level of extension contact; 57.78 per cent were in the “Not a member in any organisation” category. Improved coordination among public, private and NGO extension agencies can further enhance farmers’ social participation and access to extension services.

Information Management Behaviour of Farmers in Agricultural Extension Service Systems in Telangana

Fri, 25 Sep 2026 00:00:00 +0000

Agriculture is an information-intensive sector in which timely, reliable and relevant information plays an important role in improving farmers’ decision-making and farm management practices. The study aimed to assess and compare the information management behaviour of farmers associated with public, private and NGO agricultural extension service systems in Telangana. Farmers require information on crop production technologies, input availability, pest and disease management, weather conditions, markets, government programmes, credit facilities and emerging agricultural innovations. Information management behaviour refers to the ways in which farmers seek, access, select, process, evaluate, store and utilise information to address their agricultural needs. Effective information management enables farmers to identify relevant information from different sources, assess its usefulness and apply it appropriately in farm decision-making. The study focused on the information management behaviour of farmers across three extension service systems in Telangana, namely public, private and NGO extension service systems. An Ex-post facto research design was adopted, with a total sample of 135 farmers. The analysis showed that, in the public extension service system, 55.56% of farmers had a medium level of information management behaviour, followed by high (24.44%) and low (20.00%) levels. In the private extension service system, 48.88% had a medium level, followed by low (33.33%) and high (17.79%) levels. In the NGO extension service system, 48.89% had a medium level, followed by low (35.55%) and high (15.56%) levels.

Postharvest Melatonin Treatment Modulates Antioxidant Defense System and Delays Ripening in Mango (Mangifera indica L.) cv. Dashehari

Kripa Shankar Yadav, A. K. Trivedi, V. K. Tripathi — Sat, 26 Sep 2026 00:00:00 +0000

Mango (Mangifera indica L.) cv. 'Dashehari' is a highly esteemed climacteric fruit whose commercial value is constrained by a short postharvest shelf life owing to rapid ripening and senescence under ambient storage conditions. This study investigated the effectiveness of postharvest melatonin dip treatments (0, 50, 100, 150, and 200 µM) in regulating biochemical, enzymatic, and quality attributes of 'Dashehari' mangoes stored at ambient temperature (27±2°C, 75±2% RH) for 12 days. Fruits treated with melatonin exhibited a marked delay in ripening-associated changes, including reduced weight loss and better retention of firmness, indicating enhanced structural integrity throughout storage. Among the concentrations tested, 200 µM melatonin was most effective, significantly slowing the accumulation of total soluble solids and sugars, reflecting suppressed carbohydrate catabolism, while maintaining higher titratable acidity. This treatment also conserved bioactive compounds, evidenced by greater retention of total phenolics and flavonoids compared with untreated fruit. Additionally, melatonin-treated fruits displayed pronounced upregulation of antioxidant enzymes, catalase and peroxidase, indicating strengthened enzymatic defence against oxidative stress and reactive oxygen species accumulation during senescence. These physiological and biochemical modulations collectively contributed to delayed ripening, improved shelf life, and superior nutritional quality retention. The results demonstrate that 200 µM melatonin dip treatment is an effective, economical, and eco-friendly postharvest management strategy for extending shelf life and preserving the quality of 'Dashehari' mango under ambient storage conditions, with potential application for other climacteric fruits requiring extended postharvest longevity and reduced reliance on synthetic preservation methods.

Screening of Promising Rice Genotypes against Asian Rice Gall Midge (Orseolia oryzae) Biotype-4

V. Ramya Sri, C. Sandhya Rani, P. Udaya Babu, K. Madhu Kumar, A. P. Padmakumari — Sat, 26 Sep 2026 00:00:00 +0000

Asian rice gall midge, Orseolia oryzae (Wood-Mason) is an important constraint in endemic rice-growing areas and identification of resistant genotypes under field conditions is important for resistance breeding. This study evaluated 36 rice genotypes together with six checks against biotype-4 under natural field infestation at the Agricultural Research Station, Ragolu, during the 2024 and 2025 kharif seasons. An augmented block design was used and silver shoot incidence was recorded at 30, 50 and 65 days after transplanting. Resistance reactions were classified using the Standard Evaluation System based on observations at 65 days after transplanting. Gall midge incidence increased with crop stage and differed between seasons. In the pooled analysis, six entries were highly resistant, two resistant, four moderately resistant, 13 moderately susceptible, 14 susceptible and three highly susceptible. Among the 36 test genotypes, eight showed low pooled damage: RGL-7001, RGL 7002, RGL-7003, RGL-7004, RGL-7005, RGL 7008, RGL 7036 and MR 1523. Four of these genotypes were highly resistant and four were moderately resistant in the pooled assessment. Significant differences among entries were also detected in the augmented block analysis. The observed variation indicates useful phenotypic diversity in gall midge response and identifies promising material for further validation across locations and gall midge populations before use as donor parents in resistance-breeding programmes.

Effect of Integrated Nutrient Management on Crop Growth and Yield of Broad Bean (Vicia faba L.) cv. Pusa Udit

Gajala, G. K. Singh, Yatendra Singh — Wed, 30 Sep 2026 00:00:00 +0000

Integrated nutrient management (INM) can combine mineral, organic and biological nutrient sources to support broad bean production. A field experiment was conducted during the Rabi season of 2025-26 at the Agricultural Research Farm, Department of Horticulture, Raja Balwant Singh College, Bichpuri, Agra (U.P.), to study the effect of integrated nutrient management (INM) on the growth, yield and economics of broad bean (Vicia faba L.) cv. Pusa Udit. The experiment was laid out in a randomised block design with three replications and comprised eight treatments, viz., T0 (absolute control), T1 (100% RDF @ 50:60:50 kg N:P:K ha⁻¹), T2 (100% RDF + Rhizobium as seed treatment @ 10 g inoculant kg⁻¹ seed), T3 (100% RDF + Rhizobium as soil treatment @ 2.5 kg ha⁻¹), T4 (75% RDF + 25% RDF through FYM + Rhizobium as soil treatment @ 2.5 kg ha⁻¹), T5 (75% RDF + 25% RDF through FYM + Rhizobium as seed treatment @ 10 g inoculant kg⁻¹ seed), T6 (50% RDF + 25% RDF through FYM + Rhizobium as soil treatment @ 2.5 kg ha⁻¹) and T7 (50% RDF + 25% RDF through FYM + Rhizobium as seed treatment @ 10 g inoculant kg⁻¹ seed). The results revealed that treatment T5 was significantly superior to the other treatments for plant height, number of branches and leaves per plant, earliness in flowering, pod length and width, number of pods per plant, seeds per pod, fresh pod weight and total fresh pod yield (107.23 q ha⁻¹). Treatment T5 also produced the highest gross income (₹2,14,460 ha⁻¹), net income (₹1,62,900 ha⁻¹) and benefit-cost ratio (3.16).

Genetic Diversity Studies of Pigeonpea Germplasm by Heat Map Approach

M. Ramesh, A. Srinivas — Wed, 30 Sep 2026 00:00:00 +0000

Assessment of genetic diversity is essential for the identification of promising parents for pigeonpea improvement. The present investigation was undertaken to evaluate variability, characterise the germplasm, and assess genetic diversity among pigeonpea genotypes using a heat map-based hierarchical clustering approach. Twenty pigeonpea genotypes were evaluated in a Randomised Block Design (RBD) with two replications during Kharif 2016 at the Regional Agricultural Research Station, Palem. Considerable variability was observed for all the quantitative traits studied, indicating the presence of substantial phenotypic diversity among the germplasm. Based on hierarchical clustering, the twenty genotypes were grouped into two major clusters, with Cluster I comprising nine genotypes (GMU-2, GMU-5, GMU-13, GMU-18, GMU-9, GMU-6, GMU-10, GMU-19, and GMU-14) and Cluster II consisting of eleven genotypes (GMU-15, GMU-11, GMU-20, GMU-12, GMU-17, GMU-3, GMU-8, GMU-1, GMU-16, GMU-4, and GMU-7). The heat map effectively depicted variation in trait expression and phenotypic relationships among the genotypes. Genotypes GMU-10 (1746 kg ha⁻¹) and GMU-9 (1729 kg ha⁻¹) exhibited superior seed yield and were grouped in Cluster I, whereas GMU-4 (1523 kg ha⁻¹), a relatively high-yielding and medium-duration genotype, was placed in Cluster II, indicating that superior-performing genotypes were distributed across different clusters. The occurrence of high-yielding genotypes in separate clusters suggests the presence of broad genetic diversity, making them valuable candidates for hybridisation programmes. Crosses involving genetically divergent genotypes from different clusters are expected to enhance recombination, generate greater variability, and facilitate the development of superior pigeonpea cultivars with improved yield and desirable agronomic traits.

Cross Validation of Machine Learning Models for Paddy Yield Prediction

Anusha B Rao, P. Subrahmanya V. Bhat, S. G. Yamagar — Thu, 01 Oct 2026 00:00:00 +0000

Crop-yield regression models support precision agricultural decision-making and regional food-security assessment. This study evaluates the generalisation performance of seven machine-learning algorithms—Linear Regression, Ridge Regression, Lasso Regression, K-Nearest Neighbours (KNN), Decision Tree, Random Forest and Gradient Boosting—against a mean-prediction baseline using an audited dataset of 99 complete observations with seven soil and environmental predictors: nitrogen, phosphorus, potassium, temperature, humidity, pH and rainfall. A leakage-safe pipeline incorporating fold-specific standard scaling was rigorously evaluated using repeated 5-fold cross-validation across 25 validation folds, complemented by Nadeau-Bengio corrected resampled t-tests and permutation feature importance. The mean-prediction baseline attained the lowest overall prediction errors (MAE 0.8121 ± 0.1439, RMSE 1.0317 ± 0.2082, R² -0.0800 ± 0.0941). Among the machine-learning models, Random Forest achieved the lowest errors, with an MAE of 0.8172 ± 0.1241 and an RMSE of 1.0556 ± 0.1785, followed by KNN and Gradient Boosting. Linear, Ridge and Lasso regression showed slightly higher errors, whereas Decision Tree yielded the highest error. Corrected t-tests found no statistically significant differences between Random Forest and the baseline, KNN or Gradient Boosting. An 80:20 holdout evaluation of Random Forest produced an MAE of 0.9057, an RMSE of 1.3106 and an R² of -0.3011. Cross-validated permutation importance identified temperature as the most influential predictor, followed by rainfall. Overall, the findings highlight the essential role of mean baselines and leakage-safe validation in evaluating agricultural predictive models.