Volume-12, Issue-5, May 2026

Abstract

This study examined fertilizer application practices and their association with nutrient concentrations in irrigation water of the Mahayahay–Kitcharao Small Reservoir Irrigation Project (KSRIP) in Agusan del Norte, Philippines, during the dry season. Nitrogen (N), phosphorus (P), and potassium (K) levels were assessed across upstream, midstream, and downstream locations using a descriptive-comparative design integrating survey data from 64 rice farmers and laboratory water analysis. Survey results showed 78.1% of farmers were ≥50 years old (mean 56.4 years), with average farming experience of 22.3 years; 79.7% were tenants, and 93.8% used only inorganic fertilizers, applied twice per season via broadcast method. Laboratory analysis revealed N concentrations of 0.0075 mg/L (upstream and midstream) and 0.3317 mg/L (downstream). P ranged from 0.1567 to 0.2800 mg/L, and K from 0.7833 to 0.8167 mg/L. Kruskal–Wallis tests showed no significant differences in N, P, or K among sites (p>0.05). All N and P values were within DENR allowable limits for irrigation water (N: 14 mg/L; P: 1 mg/L). Potassium has no established regulatory standard. Despite intensive uniform fertilizer use, dry-season nutrient concentrations remained within regulatory thresholds without significant spatial variation. Continued monitoring and improved fertilizer management are recommended.

Keywords: irrigation water quality, nutrient accumulation, rice farming practices, fertilizer management, agricultural sustainability

References

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https://doi.org/10.1007/s13201-015-0327-1

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Abstract

A field experiment was conducted during the rabi season (November-April) of 2021-22 and 2022-23 at the instructional farm of Uttar Banga Krishi Viswavidyalaya, West Bengal, aimed at evaluating the effects of plant geometry and split nitrogen management on the economic performance of Quality Protein Maize (Zea mays L.). The experimental setup employed a split-plot design with three main plot treatments for plant geometry and five sub-plot treatments for split nitrogen management, with each treatment replicated three times. The main plot treatments included three planting densities and sub-plot treatments contained five different split nitrogen management regimes. Results indicated that among the plant geometry treatments, the PG3 (40 x 20 cm spacing with 125,000 plants ha⁻¹) resulted in the highest cost of cultivation (78,081.49 and 80,865.09 Rs. ha⁻¹), but also achieved the highest gross returns (226,267.9 and 221,563.1 Rs. ha⁻¹), net returns (148,186.38 and 140,998.04 Rs. ha⁻¹), and benefit-cost ratios (2.90 and 2.75) over both years. In contrast, PG1 (60 x 30 cm spacing, 55,555 plants ha⁻¹) showed the lowest economic values, with a cost of cultivation of 73,681.49 and 76,165.09 Rs. ha⁻¹, gross returns of 145,736.7 and 143,451.8 Rs. ha⁻¹, net returns of 72,055.18 and 67,286.66 Rs. ha⁻¹, and a benefit-cost ratio of 1.98 and 1.88 during both years of experimentation. For nitrogen management, the SN5 treatment (10% at basal, 20% at V8, 40% at VT, and 30% at R1) led to significantly higher economic returns, recording gross returns of 193,258.8 and 187,730.6 Rs. ha⁻¹, net returns of 116,961.9 and 108,936.2 Rs. ha⁻¹, and benefit-cost ratios of 2.54 and 2.37. Conversely, the conventional nitrogen management (SN1) yielded the lowest economic outcomes, with gross returns of 173,216.5 and 169,693.3 Rs. ha⁻¹, net returns of 100,329.6 and 94,378.91 Rs. ha⁻¹, and benefit-cost ratios of 2.31 and 2.22 across both years. The study shows that cultivating Quality Protein Maize (QPM) with the VL QPM Hybrid 59, using a dense planting geometry of 40 x 20 cm (125,000 plants ha⁻¹), significantly enhances economic returns for farmers in North Bengal. This setup optimizes land use and productivity. Additionally, a strategic split nitrogen application (10% basal, 20% at V8, 40% at VT, and 30% at R1) aligns nitrogen availability with key growth stages, promoting optimal growth and yield. Together, these practices present an effective agronomic strategy to improve the profitability of maize farming in the region.

Keywords: quality protein maize, plant geometry, split nitrogen application, economics.

References

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Abstract

Pomegranate (Punica granatum L.) peel, a byproduct of juice extraction, is rich in phenolics, flavonoids, and tannins. This study aimed to optimize the extraction of these bioactive compounds from pomegranate peel extract (PPE). It also evaluated PPE's efficacy in inhibiting lipid peroxidation in a high-unsaturated-lipid system. Total phenolic content (TPC) and DPPH scavenging activity (DPPH•-SA) were used as indicators of antioxidant potential. Extraction was optimized using Response Surface Methodology (RSM) with a Box–Behnken Design (BBD) to assess the effects of temperature, time, and ethanol concentration. The optimal conditions were set at 65°C for 60 min using 70% ethanol, yielding a TPC = 231.8 mg GAE/g dry weight. The DPPH•-SA of PPE was 91.8%, with an IC₅₀ = 16.92 µg/mL. The antioxidant performance of PPE was validated in refined, bleached, deionized sunflower oil (SFO) using the Rancimat assay. PPE at 250 µg/g extended the SFO induction period from 8.32 to 15.90 h. This resulted in a Relative Stability Index = 1.91 and a Free Radical Scavenging = 91.11%. These findings demonstrate that PPE's antioxidant activity is comparable to synthetic antioxidants like TBHQ and BHT. This makes it a sustainable alternative to synthetic antioxidants and a valuable option for food and healthcare applications.

Keywords: Pomegranate peel extract, Agro-industrial waste, Valorization, Polyphenols, Response Surface Methodology (RSM), Sustainable food preservation.

References

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Abstract

A field experiment was conducted during the kharif season of 2025 at Krishi Vigyan Kendra (KVK), Anini, Dibang Valley, Arunachal Pradesh, to evaluate the effect of different levels of neem-coated urea (NCU) on productivity of finger millet (Eleusine coracana L. Gaertn.) cv. VL Mandua 379. The experiment was laid out in a Randomized Block Design with seven treatments and three replications. Data were analysed via analysis of variance (ANOVA), and treatment effects were evaluated at 5% level of significance. Application of NCU significantly influenced all yield parameters. The treatment T₂ (100% NCU) recorded superior performance in yield attributes, including number of seeds per finger (428) [F(6,12) = 4.80, p < 0.05], test weight (3.20 g), finger weight (7.77 g) and finger length (9.77 cm). Similarly, biological yield (7.37 t ha-¹) and economical yield (2.85 t ha-¹) were significantly higher under T₂ compared to other treatments. Harvest index also differed significantly among treatments [F(6,12) = 19.77, p < 0.001]. In conclusion, using 100% neem-coated urea increases yield, economical quality and profitability of finger millet. Such a result advocated the use of NCU as a means to improve nitrogen use efficiency in agriculture, although more multi-location testing is desirable.

Keywords: Finger millet, Neem-coated urea, yield.

References

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Abstract

Agriculture is intrinsically linked to climate variability, and India, with its vast agrarian base and climate-sensitive agricultural practices depending mostly on seasonal rains, is among the most vulnerable countries globally. Rising temperatures, shifting rainfall patterns, increased frequency of extreme weather fluctuations, and soil and groundwater depletion pose serious threats to agricultural productivity, farmer livelihoods, and national food security. Climate-Smart Agriculture (CSA) has emerged as an integrated approach to simultaneously address these complex challenges by enhancing productivity, building resilience, and mitigating greenhouse gas emissions wherever feasible. In India, CSA involves a combination of traditional knowledge and modern scientific innovations tailored to the diverse agro-climatic regions. Practices such as conservation agriculture, agroforestry, climate-resilient crop varieties, integrated farming systems, and efficient water management techniques are gaining increasing popularity. Initiatives like the National Innovations on Climate Resilient Agriculture (NICRA), the National Mission for Sustainable Agriculture (NMSA), and state-led programs underscore India's strategic focus on climate-resilient farming. However, despite proactive policies, the transition towards CSA faces significant hurdles. Socio-economic constraints, small landholdings, limited access to finance, technological gaps, and gender disparities continue to impede widespread adoption, particularly among smallholder and marginal farmers who form the backbone of Indian agriculture. A concerted effort across government, academia, private sector, and farming communities will be pivotal to realizing the full potential of Climate-Smart Agriculture in India.

Keywords: Climate-Smart Agriculture, climate resilience, sustainable agriculture, India, food security, adaptation, mitigation.

References

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Abstract

New techniques and farm information need to reach farmers. Although there are a number of communication technologies available, video is the most effective medium to convey information in an attractive way and also effectively supports educational efforts. The present study aims to develop and standardize audio-visual aid on natural farming and find its effectiveness in generation of awareness about natural farming among farmers of Gujarat State. The prepared video film was of 18 minutes and 10 seconds duration. Names and acknowledgments were highlighted at the end of the visual script. The panel of twenty experts standardized the audio-visual aid on natural farming. Audio-visual aid was evaluated on a three-point scale i.e., most appropriate, appropriate, and not appropriate on 19 varied criteria which included audio, visual and overall presentation of audio-visual aid. The overall weighted mean score for audio aspect of audio-visual aid was found to be 2.77; the overall weighted mean score for visual aspect and presentation of audio-visual aid was found to be 2.82 each.

Keywords: Development, Standardization, Audio-Visual Aid (Video), Natural Farming, Farmers.

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Abstract

This study examines the Conseil de Concertation des Organisations d'Appui aux Initiatives de Base (CCOAIB), a national umbrella civil society organization in Rwanda, tracing its evolution from 1987 to 2025. Employing a qualitative, instrumental case study design based on Robert K. Yin's approach, the study draws on a systematic review of national policy frameworks, institutional reports, and CCOAIB's archival and program documents. Thematic analysis was applied to trace patterns of adaptation, strategic positioning, and impact. Findings show that CCOAIB transitioned from a humanitarian actor to a recognized policy partner by institutionalizing complementary state-civil society relations. Key achievements include training 8,255 smallholder farmers (52% women) in climate-resilient practices, creating 442 green jobs (82% women, 57% youth), establishing 16 farmers' networks, and influencing the national agriculture budget to reach nearly 13% in 2013–14. The organization consulted over 120 NGOs to shape Rwanda's NGO Law No. 058/2024 and implemented participatory Imihigo in 12 districts, engaging 13,482 farmers in policy planning. CCOAIB strengthened 42 CSOs, trained 316 local leaders and 100 Master Farmers, and mobilized over 300 million RWF through a donor roundtable. The findings demonstrate that umbrella CSOs can effectively complement state-led development while maintaining constructive accountability roles within guided civic spaces.

Keywords: Civil Society Organizations (CSOs), Rwanda, CCOAIB, State-Society Relations, Umbrella Organizations, Participatory Governance, Policy Advocacy, Capacity Building.

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Abstract

Honey is globally revered as a complex bioactive matrix with potent therapeutic and antimicrobial virtues. However, its chemical integrity is increasingly threatened by escalating environmental degradation. This study aimed to evaluate the concentrations of cadmium (Cd) and lead (Pb) in honey samples taken from four environments: industrial, highway, agricultural, and rural, and to investigate their relationship to hydrogen peroxide (H₂O₂) production as an indicator of its antimicrobial activity. A clear pollution variation was observed, with honey samples from industrial environments recording the highest levels of cadmium (0.041 ± 0.004 mg/kg) and lead (0.12 ± 0.009 mg/kg), followed by honey samples from highway environments, and then agricultural environments. Rural honey recorded the lowest concentrations (cadmium: 0.010 ± 0.001 mg/kg; lead: 0.028 ± 0.003 mg/kg). Hydrogen peroxide (H₂O₂) production varied significantly with different levels of contamination and dilution ratios (25%, 50%, and 75%), peaking at a 50% dilution. Rural honey region exhibited the highest enzyme activity (42.8 ± 3.1 µg/g/h), while industrial honey region showed the lowest (28.6 ± 2.2 µg/g/h). A strong inverse correlation was found between heavy metal concentrations and hydrogen peroxide production, suggesting that cadmium and lead may inhibit glucose oxidase activity, thereby reducing honey's antimicrobial efficacy. These results highlight that honey from less polluted environments has superior biological properties, and emphasize the need for continuous monitoring of heavy metal pollution to ensure product safety and therapeutic quality.

Keywords: Bee honey, Heavy metals, H₂O₂, Environmental pollution.

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Abstract

The identification of suitable land resources is critical for promoting sustainable horticulture in ecologically fragile regions such as Meghalaya, India. This study presents a GIS-based land suitability assessment for dragon fruit cultivation by integrating soil, climatic, and topographic parameters within the FAO Land Evaluation Framework. Key variables, including slope, elevation, rainfall, temperature, soil pH, drainage, texture, stoniness, and soil depth, were analyzed using geospatial techniques. Thematic layers were generated from multiple data sources, including digital soil maps, Soil Health Card data, India Meteorological Department rainfall records, and MODIS-derived temperature datasets, and were standardized and integrated in a GIS environment for spatial analysis.

A parametric evaluation approach was employed, wherein each land characteristic was assigned a limitation score (0–4) based on its deviation from optimal crop requirements. The overall suitability was determined using the most limiting factor method. The results reveal that 17.14% of the total assessed area is highly suitable (S1), 16.37% moderately suitable (S2), and 66.49% marginally suitable (S3) for dragon fruit cultivation. The predominance of marginally suitable land is mainly attributed to steep slopes and higher elevations prevalent across the state.

District-level analysis indicates that West Khasi Hills, East Khasi Hills, East Jaintia Hills, and Ri-Bhoi offer the most favorable conditions for cultivation, while several other districts are constrained by terrain and soil limitations. At the block level, Mairang, Umsning, Jirang, Kharkutta, and Umling emerge as priority areas for expansion. The study highlights that a substantial extent of culturable wastelands and fallow lands can be effectively utilized for dragon fruit cultivation with appropriate management interventions. Overall, the findings provide a scientific basis for sustainable land-use planning, horticultural diversification, and improved livelihood opportunities in Meghalaya.

Keywords: Dragon fruit, FAO framework, Geospatial analysis, Land suitability, Soil parameters.

References

1. Das, P. T., & Maheshwari, T. (2025). Geospatial assessment of temperate fruit sites for sustainable horticultural diversification in Meghalaya. Indian Journal of Soil Conservation, 53(2).
2. Wakchaure, G. C., Kumar, S., Meena, K. K., Rane, J., & Pathak, H. (2021). Dragon fruit cultivation in India: Scope, constraints and policy issues. ICAR-National Institute of Abiotic Stress Management.
3. Yadav, A., Dhakar, M. K., Arunachalam, A., Jha, S., Garg, S., Gangwar, N., Handa, A. K., Alam, B., & Kadam, D. (2023). A review on the scope of adoption of underutilized climate smart dragon fruit (Hylocereus spp.) cultivation. Erwerbs-Obstbau. Springer Science and Business Media Deutschland GmbH.
4. Advancing Northeast. (2021). Dragon fruit cultivation guide. 

https://www.advancingnortheast.in/wp-content/uploads/2021/10/Dragon-Fruit-PP-converted-1.pdf

1. Ali, A., Dhillon, A., Sharma, N., Chandana, M. R., M, M., Vishwakarma, P. K., & Chavda, D. (2024). Exploring dragon fruit in India: From taxonomy to nutritional benefits and sustainable cultivation practices. Applied Fruit Science, 66(4), 1641–1655. Springer Science and Business Media Deutschland GmbH.
2. Raut, U. A., Thakare, C. D., Bharad, S. G., More, P. G., & Mahalle, S. P. (2022). Nutrient management studies on vegetative growth performance of dragon fruits. Progressive Horticulture, 54(1), 57–61.
3. Advancing Northeast. (n.d.). Dragon fruit in Northeast India. https://www.advancingnortheast.in/horticulture/dragon-fruit/
4. Chandra Sharma, S., Mittal, R., Sharma, A., & Verma, V. (2021). Dragon fruit: A promising crop with a growing food market that can provide profitable returns to farmers. International Journal of Agricultural Science and Research.
5. Das, P. T., Lakiang, T., & Saikia, B. (2022). Soil fertility mapping using GIS in Meghalaya Plateau. International Journal of Current Microbiology and Applied Sciences, 11(3), 71–79.
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8. Hosen, M. B., Islam, M. R., Tahera-Tun-Humayra, U., Sharker, R., Kader, Z., Aziz, M. T., Miah, M., Hasan, M., Pervin, R., Hossain, M. A., & Tofiquzzaman, M. (2025). Assessing land suitability for dragon fruit cultivation in Bangladesh: A GIS-based AHP approach. Smart Agricultural Technology, 12, Article 101241.
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https://agriplexindia.com/blogs/featured/dragon-fruit-cultivation-in-india-crop-management-fertilization-pest-control-guide.

Abstract

The traditional educational system has changed from teacher-centred, library-based instruction to dynamic, technology-driven learning settings due to the rapid development of digital technologies. The use of online education was further hastened by the COVID-19 pandemic, which also made artificial intelligence (AI) a key factor in the transformation of teaching and learning methodologies. The present study adopts a qualitative and descriptive methodology based on the analysis of recent scholarly articles, reports, and empirical studies related to AI applications in education. The study synthesizes literature focusing on AI-driven language learning tools, research writing platforms, and video-making and editing technologies. Secondary data from peer-reviewed journals and academic databases were critically examined. The results indicate that AI replaces passive learning models with collaborative and individualized approaches by fusing intelligent components with learning strategies to produce interactive and adaptive educational experiences. This review study examines how AI can improve interactive learning through platforms for creating and editing videos, research writing tools, and language learning tools. Learner engagement and academic performance are enhanced by applications like AI-powered tutoring systems and adaptive platforms, which offer individualized curriculum, real-time feedback, interactive assessments, and content creation. The paper synthesizes existing literature to highlight how AI-driven technologies foster student–teacher collaboration, promote self-directed learning, and bridge methodological gaps in education. It concludes that AI has significant potential to revolutionize formal and informal education by making learning more engaging, efficient, and learner-centred.

Keywords: Learning, Artificial Intelligence, AI Tools, Interactive Learning, Educational Technology.

References

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https://doi.org/10.3390/su131810424

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Abstract

The present study focused on the development and characterization of protein-based edible films using soy protein isolate (SPI), mung bean protein (MBP), and their blends with corn starch (CS). A total of six formulations were prepared, namely SPI, MBP, corn starch–soy protein isolate blends (CSSP 40:60 and CSSP 50:50), and corn starch–mung bean protein blends (CSMBP 40:60 and CSMBP 50:50). The films were developed using the casting method with sorbitol as a plasticizer. The prepared films were evaluated for their physical properties including thickness, solubility, transparency, colour parameters (L*, a*, b*), mechanical properties such as tensile strength, and structural characteristics using Fourier-transform infrared spectroscopy (FTIR). The results revealed a significant (p ≤ 0.01) variation among all film formulations in terms of physical, optical, and mechanical properties. The thickness of the films ranged from 0.196 mm to 0.263 mm, with SPI-based films exhibiting the highest thickness (0.263 mm), while MBP films showed the lowest thickness (0.196 mm). Blended films displayed intermediate thickness values, indicating that incorporation of starch influenced film structure. Solubility values varied from 31.34% to 31.48%, with CSMBP 50:50 (31.48%) and CSSP 50:50 (31.34%) exhibiting significantly higher solubility compared to other formulations, suggesting improved interaction and dispersion of components in blended systems. Transparency also differed significantly among the films, ranging from 0.436 to 1.66. The highest transparency was observed in CSSP 40:60 (1.66), indicating better light transmittance and a more uniform film matrix, whereas MBP films exhibited the lowest transparency (0.436), reflecting higher opacity. Colour analysis demonstrated notable differences, with L* values ranging from 64.36 to 84.56. CSSP 40:60 films showed the highest lightness, while MBP and CSMBP 40:60 films appeared darker. The a* values ranged from 3.623 to 7.516, with MBP films exhibiting higher redness, whereas SPI films showed lower values. The b* values ranged from 4.356 to 14.196, indicating that MBP incorporation increased yellowness in the films. Mechanical analysis indicated significant differences (p ≤ 0.01) in tensile strength among formulations. The highest tensile strength was recorded for CSMBP 40:60 (4.093 MPa), followed by MBP (3.170 MPa), while SPI films showed the lowest value (1.24 MPa). Blended films generally exhibited improved mechanical performance due to enhanced intermolecular interactions between protein and starch components. FTIR analysis confirmed the presence of characteristic functional groups in all films. Strong O–H and N–H stretching bands around 3273–3278 cm⁻¹ indicated hydrogen bonding, while Amide I and II bands confirmed the protein structure. Additional peaks in starch-blended films verified the contribution of polysaccharides and improved compatibility within the matrix. Overall, the results suggest that blending proteins with corn starch significantly enhances the functional properties of edible films. Among all formulations, CSMBP 40:60 demonstrated superior mechanical strength, while CSSP 40:60 showed excellent optical properties, indicating their potential application in biodegradable and sustainable food packaging systems.

Keywords: Edible film, soy protein isolate, mung bean protein, physical properties.

References

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35. Miller, L. M., Bourassa, M. W., & Smith, R. J. (2013). FTIR spectroscopic imaging of protein aggregation in living cells. *Biochimica et Biophysica Acta (BBA) - Biomembranes, 1828*(10), 2339–2346.

Abstract

Rice (Oryza sativa L.) is the primary staple food for a large proportion of the global population, particularly in Asia, where it accounts for nearly 90% of global rice production and consumption. In India, rice has been cultivated on about 46 million hectares. However, productivity and resource-use efficiency remain sub-optimal due to conventional practices characterised by excessive use of fertilisers, water, pesticides and labour, resulting in increased production costs and environmental degradation. Nitrogen use efficiency in rice rarely exceeds 30–40% under blanket fertiliser recommendations, leading to significant nutrient losses and pollution. Precision farming-based site-specific management offers a sustainable alternative by integrating data-driven tools for nutrient, water, weed and crop management. Site-specific nutrient management using tools such as Leaf Colour Chart, SPAD meter, Green Seeker, Nutrient Expert and Rice Crop Manager synchronises nutrient supply with crop demand, reducing nitrogen use by 15–30% while improving grain yield by 10–25%. Precision water management practices, including alternate wetting and drying, sensor-based irrigation and micro irrigation systems, achieve 20–40% savings in irrigation water without yield penalties, while improving water productivity and reducing methane emissions. Precision weed and disease management using sensors, remote sensing and uncrewed aerial vehicles enables early detection and site-specific interventions, resulting in 40–45% reductions in pesticide use with stable yields. Precision mechanisation technologies, such as laser land levelling, mechanised establishment, and autonomous harvesting, further enhance crop uniformity and operational efficiency. Despite adoption constraints related to cost, technical complexity, awareness, and fragmented landholdings, this review highlights precision farming as a sustainable pathway to enhance the productivity, profitability, and environmental sustainability of rice-based systems.

Keywords: Micro-irrigation, Site-specific nutrient management, LCC, SSNM, Productivity.

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Abstract

The antagonistic activity of Purpureocillium sp. at different concentrations (2, 4, 6, 8, 10 and 12 g/l) and carbofuran (2 g/l) against root-knot nematode (Meloidogyne sp.) was evaluated. Among the tested concentrations, Purpureocillium sp. at 12 g/l resulted in the highest juvenile mortality (65.90%) and also showed considerable egg hatching inhibition (36.54%) compared to the control. The findings suggest that Purpureocillium sp. has potential as a biocontrol agent against root-knot nematodes.

Keywords: Purpureocillium, Root-knot nematode, Egg hatching inhibition, Juvenile mortality.

References

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Abstract

Agriculture remains one of the most critical sectors for ensuring food security, economic development, and sustainable resource utilization. The increasing demand for agricultural products requires farmers and policymakers to optimize crop production while minimizing resource consumption. This study presents a comprehensive data-driven analysis of an Agriculture and Farming Dataset obtained from Kaggle. The dataset consists of 50 farm records containing information regarding crop type, farm area, irrigation methods, fertilizer usage, pesticide usage, soil type, seasonal variations, crop yield, and water consumption.

The research employs descriptive analytics, exploratory data analysis (EDA), statistical correlation analysis, and agricultural productivity assessment techniques to identify relationships among farming inputs and crop outputs. Results reveal substantial variations in yield across different crop categories, irrigation systems, and resource utilization patterns. Carrot and tomato crops demonstrate the highest average productivity in this dataset, while maize and cotton exhibit comparatively lower yields. Correlation analysis indicates weak-to-moderate relationships among farming variables, suggesting that agricultural productivity is influenced by multiple interacting factors. Due to the limited sample size (n=50 farms, with 3-7 farms per crop type), these findings should be considered preliminary and require validation with larger datasets. The findings provide insights for precision agriculture, sustainable farming practices, and agricultural decision support systems.

Keywords: Agriculture Analytics, Precision Farming, Crop Yield Prediction, Data Mining, Resource Optimization, Smart Agriculture, Agricultural Informatics.

References

1. Zhang, X., Wang, Y., & Liu, J. (2020). Precision agriculture and smart farming technologies. Computers and Electronics in Agriculture, *172*, 105-118.
2. Sharma, R., & Kumar, P. (2021). Machine learning approaches for crop yield prediction. Agricultural Informatics Journal, *14*(2), 45-58.
3. Ahmed, S., Khan, M., & Ali, T. (2022). IoT-based smart irrigation systems for sustainable agriculture. Sensors, *22*(7), 2514.
4. Patel, D., Singh, R., & Verma, K. (2023). Agricultural data mining and yield analytics. International Journal of Data Science in Agriculture, *8*(1), 11-29.
5. Wang, H., Li, Y., & Zhao, Q. (2024). Sustainable resource optimization in precision farming. Sustainability, *16*(4), 1752.
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Abstract

Smart farming technologies are transforming modern agriculture by integrating sensor networks, environmental monitoring systems, and data analytics to enhance crop productivity and resource efficiency. This research presents a comprehensive exploratory analysis of the Smart Farming Data 2024 (SF24) dataset. The dataset contains 2,200 observations and 23 attributes, including soil nutrients, climatic conditions, soil moisture, irrigation characteristics, fertilizer usage, pest pressure, crop density, growth stages, and water-use efficiency metrics.

The study aims to investigate relationships among environmental factors, soil properties, and agricultural productivity indicators to support intelligent crop recommendation systems. Descriptive statistics, exploratory data analysis (EDA), and agricultural performance evaluation are employed to derive actionable insights. Results indicate that nutrient availability, rainfall, humidity, soil moisture, and irrigation management significantly influence crop suitability and resource efficiency. The findings demonstrate the potential of smart farming analytics for precision agriculture, sustainable resource utilization, and decision-support systems. This study presents an exploratory analysis of the SF24 dataset; predictive crop recommendation models are not implemented in this paper.

Keywords: Smart Farming, Precision Agriculture, Crop Recommendation, Agricultural Analytics, Machine Learning, Sustainable Farming, IoT Agriculture.

References

1. Zhang, X., Wang, Y., & Liu, J. (2020). Precision Agriculture Technologies and Applications. Computers and Electronics in Agriculture, 172, 105-118.
2. Sharma, R., & Kumar, P. (2021). Machine Learning Approaches for Crop Recommendation Systems. Agricultural Informatics Journal, 14(2), 45-58.
3. Ahmed, S., Khan, M., & Ali, T. (2022). IoT-Based Smart Irrigation Systems for Sustainable Agriculture. Sensors, 22(7), 2514.
4. Patel, D., Singh, R., & Verma, K. (2023). Agricultural Big Data Analytics for Sustainable Farming. Journal of Agricultural Data Science, 8(1), 11-29.
5. Wang, H., Li, Y., & Zhao, Q. (2024). Artificial Intelligence in Precision Agriculture. Sustainability, 16(4), 1752.
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9. Smart Farming Data 2024 (SF24) Dataset. Kaggle. Available at: [Please insert full URL] (Accessed: Date)
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