Horizon e-Publishing Group (HePG): E-Journals
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Spatial variability map for soil fertility of Sugarcane Research Station Farm, Cuddalore, Tamil Nadu using GIS techniques
No full textA study was conducted during 2023-2024, total of 144 surface soil samples were collected from the Sugarcane Research Station in Cuddalore, Tamil Nadu. GPS coordinates (Latitude °N and Longitude °E) were recorded for each sampling site using a Garmin eTrex Vista HCX GPS. Field maps were created and digitized according to the sampling locations. Soil samples were processed and analyzed for physicochemical properties and fertility parameters. The results showed that the soils were neutral to slightly alkaline and non-saline. Soil fertility groupings revealed low to medium organic carbon, low to medium available nitrogen, medium available phosphorus, medium to high available potassium and medium to high available sulphur. Over 80 % of the soil samples had sufficient levels of Cu, Fe and Mn, based on DTPA extractable micronutrients. However, 58 % of the soil was below the critical level for Zn, while 42 % had sufficient Zn. Nutrient index values indicated low status for organic carbon, available nitrogen and Zn, medium for available phosphorus and adequate levels for available sulphur. For micronutrients, DTPA-Mn was adequate, DTPA-Cu and Fe were high and DTPA-Zn was marginal. Thematic maps showed spatial variability across the station. To sustain soil fertility, deficiencies in areas with poor fertility should be addressed using organic or inorganic amendments. Following soil test-based fertilizer and micronutrient recommendations is essential for improving nutrient availability and maintaining soil health for sustainable crop production
Rural-to-urban migration of agricultural workers in Tamil Nadu: Insights from the PLFS 2020-21
No full textThis study examines the dynamics of rural-to-urban migration among agricultural workers in India, using the latest data from the Periodic Labour Force Survey (PLFS) 2020-21. It aims to identifying key demographic, socioeconomic and employment-related factors influencing the migration decisions of agricultural labourers transitioning from rural to urban areas. Preliminary findings indicate a significant increase in the Labour Force Participation Rate (LFPR) in rural areas, rising from 50.7 % in 2017-18 to 63.7 % in 2020-21, while urban areas saw an increase from 47.6 % to 52.0 % during the same period. This upward trend suggests a heightened engagement of the rural workforce, potentially influencing migration patterns. Logistic regression models are employed to assess the impact of variables such as age, gender, education level, landholding size and access to social security on the likelihood of migration. Understanding these determinants is crucial for policymakers aiming to address the challenges and opportunities presented by the migration of agricultural workers to urban centres
Integrating genetic diversity and biochemical profiling for biofuel-efficient maize genotypes
No full textThis study explores the genetic diversity, biochemical composition and trait associations in maize (Zea mays L.) to assess its potential as a dual-purpose crop for food and biofuel production. Significant variations in lignocellulosic traits among the evaluated genotypes indicate opportunities for enhancing bioethanol yields. A comparative biochemical study reveals the cellulose content in the kernel is 2.15 times higher than in stover, whereas the hemicellulose and lignin content in stover are 4.6 times and 5.3 times higher, respectively, compared to the kernel. The inbred lines DQL 2159, DQL 222-1-1 and DQL 2272 exhibited significantly higher cellulose contents of 37.05 %, 35.98 % and 35.53 %, respectively, along with significantly lower lignin contents of 20.65 %, 22.25 % and 22.53 % in maize stover. Correlation analysis shows that shoot dry weight (rp = 0.29), stalk diameter (rp = 0.33) and plant height (rp = 0.48) are positively associated with biomass yield. Biochemical studies reveal a strong negative correlation (rp = -0.59) between kernel lignin and kernel cellulose content, indicating that higher cellulose leads to lower lignin. This finding is valuable for selecting high-cellulose, low-lignin genotypes. Path coefficient analysis further identifies plant height, number of leaves per plant, stalk diameter and kernel cellulose content as key contributors to grain yield, suggesting that selection for these traits could enhance biofuel production. Identifying desirable traits that enhance biofuel efficiency, such as high cellulose and low lignin content, enables targeted breeding for improved biomass conversion. Cluster analysis revealed that Cluster III exhibited superior performance across the majority of traits evaluated, making it the most promising group for utilization in biofuel breeding programs. Notably, genotypes such as DQL 2037, DQL 2272, DQL 2159 and DQL 222-1-1 emerge as promising candidates for biofuel applications due to their high grain and stover yields, alongside elevated cellulose and hemicellulose content. Collectively, these findings provide a comprehensive framework for targeted breeding strategies aimed at developing high-yielding, biofuel-efficient maize cultivars for climate smart agriculture systems
Biophilic gardens for enhancing urban ecosystems and reconnecting people with nature
No full textThe rapid growth of cities and the shift toward highly urbanized lifestyles have distanced humanity from its innate connection with nature. This disconnection has taken a toll on mental health, physical well-being and overall quality of life. Biophilic gardens emerge as a revolutionary approach to reconnect people with nature by weaving natural elements into the fabric of urban environments. Rooted in the biophilia hypothesis, it emphasizes the profound psychological and physiological benefits of integrating features like greenery, natural light and organic forms into built spaces. This review explores the evolution of biophilic gardens, distinguishing it from sustainable design while underscoring its unique focus on nurturing human-nature relationships. Practical strategies, from incorporating green walls and water features in buildings to creating biodiverse urban landscapes, are discussed alongside compelling real-world examples. Projects like Singapore’s Khoo Teck Puat Hospital and Milan’s Bosco Verticale demonstrate how Biophilic gardens can transform spaces into vibrant, health-promoting ecosystems, enhancing well-being, productivity and environmental resilience. While the benefits are undeniable, challenges such as costs, maintenance and scalability remain hurdles. Looking ahead, the integration of smart technologies, biomimicry and regenerative practices could unlock new possibilities. Biophilic gardens offers a hopeful vision for the future, where cities become havens of harmony between humans and nature, fostering healthier, more sustainable communities
Interaction of compact varieties, nitrogen levels and deficit sub-surface drip irrigation on growth and yield of cotton under high-density planting system
No full textA field experiment was conducted during the 2024-25 summer and winter seasons at Wetland Farm, TNAU, Coimbatore, to optimize irrigation and nitrogen management for cotton under high-density planting systems (HDPS) in semi-arid tropics. The trial, designed as a split-split plot with three irrigation levels in the main plots (1.0, 0.8 and 0.6 ETc), two varieties in the sub-plots (CO 17, VPT 2) and three nitrogen management strategies in the sub-sub plots (Control, 100 % RDN through granular urea, 50 % RDN through granular urea + 20 % N through Nano urea @ 25 DAS + 20 % N through Nano urea @ 45 DAS + 10 % N through Nano urea @ 65 DAS), each replicated three times. Results revealed that the 1.0 ETc irrigation regime significantly enhanced plant height, bolls per plant and seed cotton yield. Compact variety CO 17 showed superior performance in growth and yield under a high-density planting system compared to VPT 2. Regarding nitrogen management, application of 50 % RDN through granular urea + 20 % N through Nano urea @ 25 DAS + 20 % N through Nano urea @ 45 DAS + 10 % N through Nano urea @ 65 DAS significantly increased the seed cotton yield. For high-density cotton cultivation in semi-arid regions, the CO 17 variety under 1.0 ETc sub-surface drip irrigation, combined with the nitrogen strategy, is recommended to maximize productivity and profitability. This approach offers a sustainable framework for improving cotton yields in water and nutrient-constrained environments
Development of a psychometric scale for measuring farmers\u27 attitudes toward crop residue management practices
No full textThis research presents the Attitude Scale for Crop Residue Management (AS-CRM), a validated tool designed to measure farmers\u27 psychological tendencies toward crop residue management practices. Addressing the pressing need for reliable instruments in agricultural sustainability research, the AS-CRM was developed using Likert’s summated rating scale method through a structured three-stage process: item generation, item selection and statistical validation. An initial pool of 70 statements was created through a literature review and expert consultations. Following expert evaluation, 45 items were selected and administered to 60 farmers in a non-sample area for item analysis. Based on t-test values (≥ 2.05), 20 items were retained for the final scale. The instrument demonstrated high reliability (Guttman Split-Half Coefficient = 0.947) and strong content validity confirmed by expert judgment. The final 20 item scale is scored on a five-point continuum ranging from strongly agree to strongly disagree. Key findings highlighted positive attitudes in areas related to environmental awareness and soil health benefits, whereas scepticism remained around labour intensity and economic feasibility. The AS-CRM offers a robust framework for understanding farmer perceptions, facilitating the design of targeted behavioural interventions and policy measures. By providing a standardized assessment mechanism, it supports evidence-based decision making for promoting sustainable crop residue management practices and advancing climate-resilient agriculture
Daily solar power prediction using machine learning: A model wise comparative study
No full textSolar energy produced by photovoltaic panels is a vital energy source that offers numerous benefits to both the environment and society. However, meteorological variables such as solar irradiation, weather patterns, precipitation and climate conditions present significant challenges to seamless energy integration into the power grid. Accurate forecasting is essential to maintain supply-demand balance, optimize energy storage and ensure grid stability. This study leverages machine learning (ML) techniques to predict solar power generation and address renewable energy integration challenges. Nine ML models were employed, including linear regression, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), support vector machines (SVM), random forest (RF), decision tree, gradient boosting machine (GBM), light gradient boosting (LGBM) and extreme gradient boosting (XGBM). Inputs such as irradiance, humidity, minimum temperature, maximum temperature and surface pressure were used to train these models. The model performances were evaluated using metrics like root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results highlighted ANN as the most effective model, achieving an RMSE of 274.84 kWh, MAE of 245.93 kWh and a MAPE of 5.26 %. This research contributes to the existing literature by addressing the relatively unexplored application of multiple machine learning models for predicting energy output from photovoltaic systems. A key novelty of this study is its ability to achieve accurate solar power forecasts using a limited dataset from a newly installed solar power plant, unlike many existing studies that rely on large volumes of data. Additionally, it explores the solar power production potential of Namakkal district in Tamil Nadu, India-a region with limited prior research
Bioclimatic modeling of Tulipa fosteriana and Tulipa ingens: Predicting the effects of climate change on the distribution of endangered wild tulips
No full textBioclimatic modeling is an essential tool for predicting species distributions under changing environmental conditions. T. fosteriana and T. ingens, rare and endemic tulip species in Uzbekistan, are currently facing increasing threats from habitat loss and climate change. Understanding their potential range under current and future climate scenarios is crucial for conservation planning. The present study employed Maximum Entropy (MaxEnt) modeling to assess the habitat suitability of T. fosteriana and T. ingens using occurrence data from field surveys, herbarium records and biodiversity databases. Environmental predictors included climatic, soil and topographical variables. Model accuracy was evaluated using the Area Under the Curve (AUC) and future habitat projections were generated under Ssp126 (moderate emissions) and Ssp585 (high emissions) scenarios for 2041-2060. The results suggest that T. fosteriana may expand its range, particularly in the Hissar and Bobotog mountain ranges, while T. ingens is projected to suffer severe habitat reduction, losing over 90 % of its suitable areas under the high-emission scenario. The most influential environmental variables were precipitation in the coldest quarter and depth to bedrock, highlighting the role of moisture availability and soil structure in habitat suitability. High AUC values (above 0.98) confirm model robustness. These findings emphasize the contrasting responses of the two species to climate change. While T. fosteriana may benefit from rising temperatures, T. ingens is at high risk of habitat loss, requiring urgent conservation efforts. This study provides valuable insights for biodiversity management in Central Asia, highlighting the need for protected areas, in-situ conservation and potential ex-situ preservation strategies
Gene characterization and computational identification of potential phytochemicals against non-small cell lung carcinoma
No full textNon-small cell lung carcinoma (NSCLC) accounts for about 85 % of lung cancer cases and is frequently linked to mutations in genes like EGFR, ALK and BRAF, which play a role in tumor resistance and growth. It is crucial to develop innovative therapeutic strategies that target NSCLC-related genes with significant mutations and poor prognostic outcomes. Numerous phytochemicals derived from plants offer promising alternatives for targeting key NSCLC-related genes due to their potential anticancer effects. Phytochemicals from neem (Azadirachta indica), turmeric (Curcuma longa), green tea (Camellia sinensis), grapes (Vitis vinifera) and red spider lily (Lycoris radiata) were examined. Compounds with strong binding affinities were identified through molecular docking and virtual screening and their pharmacokinetic properties were assessed using ADMET profiling. Computational tools such as cBioPortal and GEPIA2 were utilized to analyze gene selection and expression, while BIOVIA discovery studio was used to visualize protein-ligand interactions. Among the phytochemicals screened, meliantriol and riboflavin stood out as promising candidates due to their high binding affinities and favorable ADMET profiles. Riboflavin effectively targeted LMNB2, while meliantriol showed strong interactions with PCLO, highlighting their potential to interfere with cancerous pathways. Phytochemicals also demonstrated mechanisms such as the suppression of signaling pathways, induction of mitochondrial apoptosis and inhibition of EGFR. This comprehensive approach highlights the potential of natural compounds in addressing drug resistance and tumor heterogeneity in NSCLC, paving the way for novel, plant-based therapies. Future research will involve molecular dynamics simulations and in vitro validation to confirm these findings
Policy issues in commodity futures trading: A critical review
No full textCommodity futures trading plays a vital role in today\u27s financial markets by facilitating price discovery and price risk management. However, in India, commodity futures trading faces several challenges, including issues related to trading, price discovery, price risk management, hedging, speculation and the regulatory environment. These challenges hinder the sustainable growth of the futures markets in the country. The paper specifically aims to critically review the challenges faced by commodity futures trading and the resulting consequences for stakeholders. The review is expected to provide insights into potential policy measures that could improve the efficiency and effectiveness of commodity futures markets in India. Additionally, price discovery in Indian commodity futures is often influenced by external factors, including global price fluctuations, making it difficult for market participants to rely solely on domestic futures contracts. By analysing these issues, the study offers insights into policy interventions and regulatory frameworks that could enhance market efficiency, encourage participation and improve the overall functioning of commodity futures trading in India. This study also serves as a valuable resource for policymakers and stakeholders, supporting more informed decision-making and aiding in the development of an optimal regulatory approach. Addressing these challenges is essential for fostering a transparent and well-functioning commodity futures market that benefits the stakeholders. Strengthening regulatory mechanisms and improving market infrastructure can significantly enhance the long-term growth and sustainability of the sector