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No full textChapter 2 of training manual "ISO 22000/HACCP for fish processing establishments"Not AvailableNot Availabl
SIMULATING THE EFFECT OF INCREASED AREA UNDER DIRECT SEEDED RICE AND CANAL WATER SUPPLY ON GROUND WATER TABLE UNDER CLIMATE CHANGE IN FIVE SELECTED CENTRAL PUNJAB DISTRICTS, INDIA
No full textPhD Research ArticleThe groundwater table has been critically declined in the Central Punjab districts of Moga, Barnala, Patiala, Sangrur, and Ludhiana, India in past years due to its major rice-wheat cropping system and excessive extraction of groundwater for irrigation purposes. Devising appropriate management strategies for improving groundwater table in those districts are of paramount importance. In the present study, the groundwater table variation was estimated for the RCP4.5 scenario using bias-corrected climatic data derived from IITM-RegCM4 based six climate model ensembles and validated SWAT-GMS model under six definitive management strategies involving more area under DSR and enhancement of canal water supply were formulated for the study districts. A significant rise by 4 - 6.8 cm year-1 in the groundwater table was simulated at p<0.05 under strategy-IV to strategy-VI for Moga district, 3.1-8.4 cm year-1 for Barnala district under strategy-I to Strategy-VI, 2.9 - 4.8 cm year-1 for Sangrur district under strategy-V to strategy-VI, 4.9 – 7.1 cm year-1 for Patiala district under strategy-III to strategy-VI and 7.6 – 10.8 cm year-1 under strategy-IV to strategy-VI for Ludhiana district was simulated during 2022-2030. It was observed that increasing the area under the DSR planting method of rice by 25 to 50 % besides augmentation of canal water supply by 20 to 30 % would result in arresting the decline in the water table by a minimum of 2.9 cm to a maximum of 10.8 cm per year in central Punjab, India. Thus, the adoption of the DSR cultivation method along with the augmentation of canal irrigation would be the most effective approach for arresting the declining water table and leading to the attainment of sustainability in groundwater usage in the region
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No full textNot AvailableAsparagus adscendens Roxb. also known as “safed musli” or “shatavari” is a medicinal plant
commonly found in South Asian countries. Shatavari is effective for the treatment of gastric ul cers, renal stones, bronchitis, diabetes, diabetic neuropathy, irritable bowel syndrome, alcohol
withdrawal and has reported immunostimulatory effects. In this study, the adjuvant potential of
Shatavarin-IV saponin against Staphylococcus aureus bacterin in mice was investigated.
Shatavarin-IV was evaluated for its toxicity and immunomodulatory potential against S. aureus
bacterin in mice. Cellular and humoral immune responses were assessed. Shatavarin-IV was
isolated from the fruit extract of Asparagus adscendens. The confirmation of the isolated molecule
as Shatavarin-IV was done via TLC-based comparison with the standard molecule. Further, the
structure was confirmed by using extensive spectroscopic analyses and comparing the observed
data with literature reports. It was found safe up to the dose of 0.1 mg in the mice model.
Shatavarin-IV adjuvant elicited IgG and IgG2b responses at the dose of 40 μg against S. aureus
bacterin. However, the cell-mediated immune response was lesser as compared with the com mercial Quil-A saponin . We demonstrated that Shatavarin-IV saponin adjuvant produced an
optimum humoral immune response against S. aureus bacterin. These results highlight the po tential of Shatavarin-IV as an adjuvant in a combination adjuvant in vaccine formulations for
induction of potent immune response.Not Availabl
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No full textThe Indian Council of Agricultural Research (ICAR), the apex body for co-ordinating, guiding and managing research and education in agriculture, and World Bank have come together to announce the first International Conference on 'Blended Learning Ecosystem for Higher Education in Agriculture' in India under the National Agricultural Higher Education Project (NAHEP). The three-day event to be held from March 21-23 in New Delhi will be hosted by ICAR - Indian Agricultural Statistics Research Institute (IASRI), which is a multi-partner global forum to support collaboration for development of state-of-the-art blended education system for higher agricultural education. The aim of this conference is to facilitate the development of a global ecosystem of partners from academia, industry, government, and multilateral and bilateral organizations who would provide critical insights towards design and full-scale implementation of all aspects of the Resilient Agricultural Education System (RAES) under National Agricultural Higher Education Project (NAHEP), that is, learning management system, content repository, and system-wide capacity building. Apart from the engaging discussions the three-day event will also showcase an exhibition on the diverse range of services and offerings in the field of agriculture education and blended learning. Furthermore, these insights would be utilized towards strengthening the quality of the full-scale implementation of this development initiative. The amalgamation of leading practices as identified during the conference would serve as a knowledge, to be utilized by various education systems to adapt and emulate in their own countries. This would strengthen the collaboration among multiple implementing agencies working in the domain of digital education and blended learning ecosystem and provide strategies for making this digital transformation sustainable. The digital initiatives are focused to empower the students/faculties and administration in one way or the other and transform the agricultural education system across the country. The agricultural education system is evolving for the sake of betterment, as this generation of students are not born to be confined to the limits of simple learning; their curiosity is vast and cannot be catered with the legacy education systems. We would like to thank each and every one of the participants who have contributed directly and indirectly for the success of this International Conference on Blended Learning Ecosystem for Higher Education in Agriculture. We would also like to convey our thanks to World Bank, Staff of ICAR Headquarters, Project Implementation Unit of NAHEP, ICAR - IASRI and special thanks to the team from EY LLP, PwC and Pavilion and Interiors for their technical inputs and support in organization and management of this International Conference.Not Availabl
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No full textNot AvailableThe study was aimed to investigate the efficacy of a phytochemical gallic acid (GA) in preventing the
pathomorphological alterations induced by cisplatin (CP) in testicular tissue of Wistar albino rats. One
hundred and eight Wistar albino rats were equally divided into six groups. Group I served as normal
control, Group II received single dose of intraperitoneal injection of CP at 7.5 mg/kg bw, Group III
received GA at 75 mg/kg bw for 45 days, Group IV was treated with GA daily for 15 days prior to CP
injection and discontinued post CP injection, Group V received CP injection and concurrently received
GA for 45 days post CP injection and Group VI was treated with GA for 15 days prior to CP injection
and continued for 45 days post CP injection. The testis samples collected on 7th, 14th, 28th and 45th day of
post CP injection were subjected for histopathological examination to study the sequential
pathomorphological changes. CP administration produced moderate congestion and interstitial oedema,
severe seminiferous tubular atrophy, tubular cell degeneration and necrosis. The Gallic acid
supplemented groups showed significant improvement in CP induced pathological changes. The pre +
concurrent GA supplementation (Group VI) produced much earlier improvement in CP induced
pathological changes than only pre and only concurrent GA supplementation. It was concluded that, GA
supplementation have protective role against CP induced testicular toxicity.Not Availabl
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No full textNot AvailableClimate change and the growing population are major challenges in the global agriculture scenario. High-quality crop genotypes are essential to counter the challenges. In plant breeding, phenotypic trait measurement is necessary to develop improved crop varieties. Plant phenotyping refers to studying the plant's morphological and physiological characteristics. Plant phenotypic traits like the number of spikes/panicle in cereal crops and senescence quantification play an important role in assessing functional plant biology, growth analysis, and net primary production. However, conventional plant phenotyping is time-consuming, labor-intensive, and error-prone. Computer vision-based techniques have emerged as an efficient method for non-invasive and non-destructive plant phenotyping over the last two decades. Therefore to measure these traits in high-throughput and non-destructive way, computer vision-based methodologies are proposed. For recognition and counting of number of spikes from visual images of wheat plant, a deep learning-based encoder-decoder network is developed. The precision, accuracy, and robustness (F1-score) of the approach for spike recognition are found as 98.97%, 98.07%, and 98.97%, respectively. For spike counting, the average precision, accuracy, and robustness are 98%, 93%, and 97%, respectively. The performance of the approach demonstrates that the encoder-decoder network-based approach is effective and robust for spike detection and counting. For senescence quantification, machine learning-based approach has been proposed which segments the wheat plant into different senescence and greenness classes. Six machine learning-based classifiers: decision tree, random forest, KNN, gradient boosting, naïve Bayes, and artificial neural network (ANN) are trained to segment the senescence portion from wheat plants. All the classifiers performed well, but ANN outperformed with 97.28% accuracy. After senescence segmentation, percentage of senescence area is also calculated. A GUI-based desktop application, m—Senescencica has been developed, which processes the input images and generates output for senescence percentage, plant height, and plant area.Not Availabl
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No full textChapter 13 of training manual "ISO 22000/HACCP for fish processing establishments"Not AvailableNot Availabl
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No full textNot AvailableParasitic infestations and their control programmes are one among the challenges to be considered the most significant in aquaculture. A parasitic infestation was studied elaborately in Asian Seabass, Lates calcarifer juveniles with clinical signs, post-mortem findings, morphological and molecular identifications. In addition, those fish were also treated with emamectin benzoate (EMB) @ 50 μg kg−1 of fish body weight (BW) d−1 for 10 consecutive days under the controlled wet lab facility by feeding through the medicated feed at 4% BW. Results showed that the parasitic prevalence, parasitic intensity (PI) and mortality were 45.5%, 8.17 ± 0.15 per fish and 40% over a period of one week in that existing cage culture. The parasite was identified as a crustacean bloodsucker, anchor worm Lernaea sp. and EMB was found to be 100% effective with significant
reduction in PI over a period of 10 days with improved survival rate of 90% against the untreated group. Infested but treated group revealed substantial haematological improvement in parameters such as RBC, WBC, Hb, PCV, large lymphocytes, small lymphocytes and total lymphocytes (P < 0.01). Similarly, comparative histopathology of vital organs also revealed no discernible lesions between the healthy and treated fish juvenile as compared to that of infested untreated group. Hence, EMB can be used to control the Lernaea sp. infestation in Asian Seabass.ICA
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No full textNot AvailableAccurate and timely price information and forecasting help in making efficient plans and strategies. Non-linearity and non-stationarity behaviour of
price data create problems in price forecasting. In this paper, variational mode decomposition (VMD) based optimised genetic algorithm (GA) hybrid
machine learning (ML) models have been proposed. The VMD algorithm is employed to decompose the price data into intrinsic mode functions
(IMFs) which is further forecasted using ML models namely support vector regression (SVR) and random forest (RF). The practical use of the SVR
and RF models is limited because the accuracy of ML models heavily depends on a proper setting of hyper-parameters. Therefore, these model
hyper-parameters are optimized using GA. Further, the forecasted values of IMFs through the GA optimised SVR and RF are aggregated for the final
forecast. The results of the proposed model are benchmarked with the comparative models. The proposed VMD-GA-RF and VMD-GA-SVR models
are tested on the weekly onion price of the Delhi and Nashik market. The results clearly demonstrate that the combination of VMD and GA optimized
models can improve the performance of the prediction of the dataset.Not Availabl