Volume-10, Issue-12, December 2024

Abstract

Contribution of agroforestry towards ecosystem services is being recognized globally. The benefit people gains from an ecosystem are crucial to community health serving as abridge between nature and society. Forests and agriculture, particularly agroforestry, are some of the vital natural resources for rural and subsistence communities, offering a range of ecosystem services such as food, fodder, fuelwood, timber, medicines and other non-timber forest products. Cultural services are non-material benefits that people derive from ecosystems, contributing to physical health, spiritual enrichment, recreation, ecotourism, cognitive development, and leisure. Cultural services support physical, cultural and intellectual development, including arts, music, and other recreational activities. This study was conducted on people owning and managing Grewia optiva (Bhimal) based agroforestry systems of Garhwal Himalaya in Uttarakhand state of India and mainly focuses on physical health of elderly agroforestry farm owners in the form of physically active hours and reduced screen time. Nowadays, where maintaining health as well as physical activities are considered crucial, this study highlights the role of agroforestry in lifestyle of elderly people. An increase inactive hours and a considerable reduction in screen time have been observed in elderly people from study area as compared to elderly people who were not involved in agroforestry practices. This is an important aspect of agroforestry besides climate change adaptation and mitigation which is yet to be analysed, quantified and studied.

Keywords: Agroforestry, Ecosystem services, Physical activity, Screen time

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Abstract

Orchids exhibit remarkable anatomical adaptations in their roots, enabling them to thrive in diverse ecological conditions. This study investigates the root anatomical features of three orchid species: Polystachya concreta, Liparis viridiflora, and Coelogyne nervosa. Specimens were collected from the Eunoia Orchid Garden, Ambalavayal, and analyzed using histological techniques. Observations revealed significant variations in root structures, including velamen layers, cortical organization, vascular bundle arrangements, and pith characteristics.

Polystachya concreta exhibited a three-layered velamen with hexagonal cortical cells and eight vascular bundles. Liparis viridiflora demonstrated a four-layered velamen, twelve vascular bundles, and pronounced protoxylem. Coelogyne nervosa displayed a six-layered velamen, over eighteen vascular bundles, and a well-developed parenchymatous pith. These anatomical features underscore the adaptive strategies of orchids to different environmental conditions, enhancing water retention, nutrient absorption, and resilience against abiotic stressors.

This comparative analysis highlights the morphological diversity among orchid species and provides insights into their ecological adaptations. The findings contribute to the understanding of orchid biology and support conservation efforts by identifying key anatomical traits essential for survival in varied habitats.

Keywords: Orchidaceae, Root anatomy, Velamen structure, Polystachya concreta, Liparis viridiflora, Coelogyne nervosa, Vascular bundles, Adaptations, Histological analysis, Conservation biology

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Abstract

The increased need for scalable and real-time solutions has initiated the integration of farming with technologies like IoT, hyperspectral imaging, and machine learning. This paper tries to envisage a new theoretical framework supported by the multichannel data developed from the monitoring through camera-based visual observations, hyperspectral image spectroscopic data, and sensor-based soil health data. The model proposed makes use of data fusion techniques and machine learning algorithms to integrate and analyze diverse data streams to provide insights that surpass single-sensor system diagnostic accuracy. This model offers a proactive approach to the management of diseases by addressing some drawbacks of old methods, such as delayed detection and reliance on heavy resources. It improves diagnosis accuracy, reduces detection time by proposing a model that fuses the visual, spectral, and soil data, and provides real-time actionable insights to farmers via a mobile application. Its adaptability across crops and environmental conditions also points to its wide applicability, more so in precision agriculture. On top of this, the alert system works in real-time, hence interventions on time and reducing crop loss, as well as sustainable farming practices through optimized resource usage. The current paper underlines the theoretical basis of the model, but it also presents ways of future validation through pilot studies and field trials. The proposed model can achieve transformative impacts in agricultural productivity, reduced environmental impact, and global food security by leveraging the combined strengths of IoT with advanced imaging technologies.

Keywords: Internet of Things(IoT), Machine learning, Sustainable agriculture, Precision farming, Hyperspectral Imaging Sensors

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Abstract

Pollution of Environment by Toxic metals poses a serious threat to public health, as these metals can accumulate in the environment and be transferred up the Food chain, leading to Harmful Health effects in Animals and Humans. This study was conducted in order to Assess the concentration of some heavy metal levels namely, Lead, Cadmium, Zinc and Chromium in, liver, Heart and Kidney of Cattle, Sheep and Goat slaughtered at Hadejia Central Abattoir, Hadejia Local Government, Jigawa State, Nigeria fresh samples of liver, heart and kidney were collected from hadejia abbattoir, digested and analyzed using Microwave plasma atomic emission spectroscopy. Results obtained were compared with Joint SON/WHO Guidelines. The concentrations of the metals (Cd, Crand Pb) ranged from 0.00±0.00 to 0.02±0.00 mg/kg for Cd, 0.01±0.00 to 0.06±0.00 mg/kg for Cr,0.01±0.00 to 0.02±0.00 mg/kg for Pb, 1.72±0.26 to 3.54±1.23 mg/kg for Zn in the liver of cattle, goat and sheep. Similarly, the concentrations of the metals in the heart of cattle, goat and sheeps were found in the following ranges 0.00±0.00 to 0.01±0.00 mg/kg for Cd, 0.03±0.00 to 0.05±0.00 mg/kg for Cr, 0.01±0.00 to 0.02±0.00 mg/kg for Pband 3.20±0.11 to 3.28±0.05 mg/kg for Zn. Likewise, the concentration ranges of 0.01±0.00 to 0.02±0.00 mg/kg for Cd, 0.01±0.00 to 0.04±0.00 mg/kg for Cr, 0.0±0.00 to 0.03±0.00 mg/kg for Pband 2.35±0.70 to 2.96±0.12 mg/kg. The concentrations of Cd, Pband Crwere lower than the maximum permissible limit of WHO/SON. But zinc levels in all the Analysed organs were above the permissible limit. Therefore liver, heart and kidney of cattle, goat and sheep have been contaminated with zinc. Results from ANOVA indicated no significant difference in heavy metal levels between the Analyzed organs in The Analyzed Animals at pvalue greater than 0.05.

Keywords: Abattoir, Assessment, Heart, Liver and Kidney

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Abstract

RNAi induce variable mutation which the prevalence is very high nowadays, ever diagnose Large Granular Chronic (LGC) patients.

Problem: High prevalence of mortality cases in ITP, MDS, and AML in which the complaint is only nausea/ vomitus, bloated, fatigue or lethargic, and progress to deadly ITP/AML we meet in everyday practice. Anemia and thrombocytopenia progress to death of ITP/AML in 2 weeks-5 months after first time hospitalized to get transfusion and dextrose 5% infusion for the used-up energy/ glycogen in the liver. The laboratory packet should support “bridge therapy” to surgery or invasive procedure, splenectomy and or chemotherapy. Method: Case report and review of my Library recommendation of Google Scholar, ChatGPT, elaborate to ScienceDirect and EBSCOHost MEDLINE full text. Recorded all the Laboratory found in the case report and references.

Result: Case Report and References of Dx/ associated with phases of ITP/MDS/AML.

Discussion: Laboratory of the symptoms in each phase ITP and MDS/AML. Conclusion: Complex biomarker vs. simple laboratory packet in early RNAi induce ITP and AML in the mapping of ITP/AML phase to fight high mortality, stay calm with right nutrition to support the body metabolism of ITP progression to AML/ MDS. High Protein Low Carbohydrate on low Albumin plasma is already built-in in all phases.

Keywords: Hemoglobin, Platelet count, Thrombopoietin, Aplastic Anemia, ITP, Splenectomy

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Abstract

Afield experiment was conducted at Agricultural Research Station, S. D. Agricultural University, Ladol during 2019-20, 2020-21 and 2021-22 for management of root rot disease in soybean. The eight different treatments were evaluated. Based on pooled data of three years, the result revealed that minimum mean disease incidence (2.45%) was observed with seed treatment of Penflufen 13.28 % + Trifloxystrobin 13.28 % FSfound lowest percent disease incidence in throughout the crop season in all three years followed by seed treatment with Thiophanate Methyl 45 % + Pyraclostrobin 5 % FS (2.95%).

Keywords: Soybean, Root rot, Fungicides, Seed treatments and Grain yield

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Abstract

Afield experiment was conducted at Agricultural Research Station, S. D. Agricultural University, Ladol during 2019-20, 2020-21, 2021-22 and 2022-23 for evaluation of different insecticides against sucking pests infesting brinjal. The eleven different treatments were evaluated. There was no any sucking pest infestation in kharif 2019-20. Based on pooled data of three years, Sulfoxaflor 21.8 SC 0.03 percent @ 12.5 ml/10 liter of water recorded minimum whitefly and jassid population (2.79/leaf and 1.21/leaf), no effect on predator and highest yield (286.61 q/ha) followed by Sulfoxaflor 21.8 SC 0.024 percent @ 10 ml/10 liter of water and Cyantraniliprole 10.26 OD 0.0072 percent @ 7.02 ml/10 liter of water.

Keywords: Brinjal, Jassids, Whiteflies, LLB, Predator, Insecticides

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Abstract

Horticulture crops play significant role in improving the productivity of land, generating employment, enhancing exports, improving economic conditions of the farmers and entrepreneurs and providing food and nutritional security to the people. For better management of the existing crops and to bring more area under horticulture crops, updated and accurate database is necessary for systematic planning and decision making. Remote sensing (RS) is an advanced tool that aids ingathering and updating information to develop scientific management plans. Many types of sensors namely microwave radiometers, laser meters, magnetic sensors and cameras collect electromagnetic information to derive accurate, large-scale information about the Earth'ssurface and atmosphere. Because these data and images are digital, they can easily be quantified and manipulated using computers. RScan be used in efforts to reduce the risk and minimize damage. The same data can be analyzed indifferent ways for different applications. A number of studies were aiming at identification of crop, area estimation, disease and pest identification, etc. using satellite data in horticulture. The potential use of RStechniques in Horticulture is briefly reviewed in order to exploit the available techniques for efficient crop management.

Keywords: Remote sensing, Crop acreage estimation, Crop growth monitoring, Crop stress detection, Yield assessment, Weather forecasting

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Abstract

The present study was conducted on evaluation of different fungicides, antibiotics and antagonists against bacterial blight disease under field conditions. Among them, combi fungicide streptomycin sulphate 18.75 w/v + oxytetracycline 2 w/v (200ppm)+ trifloxystrobin 25 + tebuconazole 50 (75WG) at 0.03 percent was found significantly superior and most effective for the control of bacterial blight and recorded minimum disease intensity (26.28%), the highest grain yield (5.63 kg/plot), highest straw yield (7.08kg/plot) and the highest test grain weight (27.43g) which was statistically at par with streptomycin sulphate 18.75 w/v + oxytetracycline 2 w/v (200ppm) + azoxystrobin 18.2 + difenconazole 11.4 SCat 0.03 per cent. Followed by streptomycin sulphate 90 w/v + tetracycline hydrochloride 10 w/v at 1000ppm and copper oxychloride 50 WP at 1.25 per cent, respectively.

Keywords: Rice, Bacterial Blight, Fungicides, Antibiotics, Bioagents

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Abstract

Dragonfruit (Hylocereus spp.) is a vibrant tropical fruit gaining attention for its unique appearance, nutritional value, and market potential. Its color serves as a critical determinant of consumer preference, reflecting fruit quality and maturity. This study aimed to quantify the HSV (hue, saturation, and value) color indices of dragonfruit at different maturity stages—unmature, mature, and overmature—using advanced image processing techniques. Images were collected under controlled conditions, preprocessed for segmentation, and analyzed using MATLAB R2023a. The HSV parameters were calculated for 400 samples, revealing distinct variations in color attributes across developmental stages. Mature dragonfruit exhibited the highest hue values, indicating peak coloration, while overmature fruits showed a decline due to potential discoloration. Saturation values were most vivid in mature fruits, signifying optimal pigmentation, whereas unmature fruits displayed subdued colors. Brightness progressively increased with maturity but slightly decreased in overmature samples. Combined HSV indices provided a robust metric for differentiating between maturity stages, with the highest values observed at the mature stage. These findings underscore the utility of HSV color indices as reliable indicators for maturity classification, contributing to quality control, automated sorting, and improved postharvest management.

Keywords: Dragonfruit, HSV color indices, Image processing, Color analysis

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Abstract

This paper encompasses three different machine learning models that we built to help Nepali farmers in selecting ideal crops for their land, using the right fertilizers, and predicting plant diseases. We tried about five models each for crop recommendation and fertilizer recommendation and a single model for plant disease prediction. We chose “Decision Trees” for both our Crop Recommendation and Fertilizer Recommendation and “Convolutional Neural Networks (CNN)” for Plant Disease Prediction. All models achieved over 95% accuracy. Our GitHub repository houses all the code, making it accessible for future researchers and ML developers working on related tasks. (https://github.com/anamgiri/uunchai).

Keywords: Machine learning, Algorithms, Nepal, Agriculture, Plant disease, fertilizers, crop, recommendation, Plant Disease Prediction Nepal, Decision Tree, Random Forest, Convolutional Neural Networks (CNN), Deep Learning

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Abstract

This technical note outlines a systematic approach to baseline quantification for ARR (Afforestation, Reforestation, and Revegetation) carbon finance projects using advanced remote sensing (RS) and GIS methodologies. This approach particularly addresses India'sfragmented landscapes, aiming to integrate small and marginal farmers into carbon finance markets, thus enhancing agroforestry potential and providing additional income generation. The challenges in meeting common practice criteria and additionality, as per VERRA/Gold Standard methodologies, are also discussed, offering recommendations to improve inclusivity and applicability.

Keywords: Baseline, Carbon Finance, Remote Sensing, GIS, LULC, Afforestation

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Abstract

Water is a finite and vital resource, making its efficient utilization particularly critical in irrigation, especially during the summer months when water scarcity is most acute. Summer okra (Abelmoschus esculentus), a key vegetable crop in India, depends on precise irrigation scheduling to ensure optimal yields. To address this need, afield experiment was conducted over the summer seasons of 2023 and 2024 at Vasantrao Naik Marathwada Krishi Vidyapeeth (VNMKV), Parbhani, situated in the semi-arid Marathwada region of Maharashtra, India. The study utilized a weighing-type lysimeter to estimate crop coefficient (Kc) values for okra, which are crucial for determining accurate irrigation schedules. In 2023, the Kcvalues for the okra crop were recorded as follows across different growth stages: initial stage (12 to 14 MW) – 0.63, development stage (15 to 18 MW) – 1.05, mid-season stage (19 to 23 MW) – 1.42, and late season stage (24 to 26 MW) – 0.76. In 2024, the Kcvalues were slightly different: initial stage (13 to 15 MW) – 0.60, development stage (16 to 19 MW) – 0.99, mid-season stage (20 to 24 MW) – 1.33, and late season stage (25 to 26 MW) – 0.75. The seasonal water requirement for okra was calculated to be 579.18 mm in 2023 and 529.38 mm in 2024. These Kcestimates provide valuable insights for optimizing irrigation management, facilitating more accurate water demand predictions and resource planning. The study'sfindings contribute to improving water use efficiency in the Marathwada region, where conserving water is vital for sustainable agricultural practices.

Keywords: Crop coefficient, FAO-56 Penman Monteith method, lysimeter, okra, reference evapotranspiration, crop evapotranspiration, water requirement

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Abstract

In non-parametric algorithms such as k-nearest neighbour the fundamental predicaments are the larger storage and computational requirements. Moreover, the effectiveness of classification task affected significantly due to uneven distribution of training data. To overcome the drawbacks of lazy learner likek-nearest neighbour classifier, the scope of training set reduction by editing and condensing the training set is explored in this research work. Additionally, the reduction of training set is carried out by hybrid techniques of training set reduction namely TSR-FkNN (Elbow method) and TRS-FkNN (Silhouette value) in optimized way to achieve improvement of classification performance.

Keywords: Machine Learning, k-NN, Hybrid method

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Abstract

Maize is a major predominant crop during the Rabi season in the Visakhapatnam district of Andhra Pradesh. Traditionally, farmers grow maize by ploughing fields after paddy harvest and sowing seeds behind the plough or by dibbling, which leads to loss of residual soil moisture and delayed sowing. To conserve this crucial moisture and enable timely sowing, the zero-tillage practice was introduced by the DAATTC, Visakhapatnam, in collaboration with the Department of Agriculture. This method involves sowing maize directly into unploughed fields using a manually operated ‘Peg Marker,’ eliminating the need for tillage. On-Farm Demonstrations (OFDs) were organized across 16 locations in the district during Rabi 2021-22 and Rabi 2022-23. Zero-tillage maize recorded a significant yield advantage, with an average increase of 9.4% (6701 kg/ha vs. 6125 kg/ha in conventional tillage). Economically, the practice was highly beneficial, reducing the cost of cultivation by approximately 11.1% (Rs. 3,500/ha) due to savings on tillage and irrigation. Combined with a 7-10 day earlier harvest that fetched a premium market price (Rs. 14/kg vs. Rs. 12/kg), zero-tillage resulted in a 40.0% higher net income (Rs. 64,401/ha vs. Rs. 42,000/ha) and a superior cost-benefit ratio (3.30 vs. 1.33). The study concludes that zero-tillage maize is a feasible, profitable, and moisture-conserving technology for rice fallows in the region.

Keywords: Zero tillage, Maize, Peg Marker, On-Farm Demonstrations, Rice fallow, Economics, Residual moisture

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