Rajasthan University of Veterinary and Animal Sciences (RAJUVAS) is a state agricultural university located at Bikaner, Rajasthan, India. It was established in 2010 by the Rajasthan University of Veterinary and Animal Sciences Act, 2010 of the Government of Rajasthan, amended in 2013. Its constituent colleges are College of Veterinary and Animal Science, Bikaner (CVAS Bikaner), College of Veterinary and Animal Science, Navania, Vallabhnagar, Udaipur (CVAS Udaipur) and Post Graduate Institute of Veterinary Education and Research (PGIVER). It also has 65 other institutes awarding two-year diplomas.
The aggregation of agricultural waste has resulted in several environmental problems, such as air and soil pollution and the spread of insects and pathogens. To address these issues, experiments have evaluate these wastes as substrates for growing Pleurotus mushrooms at College of AJNKVV, Jabalpur, Madhya Pradesh, which are economically feasible and nutritionally beneficial. This experiment aimed to evaluate the use of rice straw and maize straw, with wheat straw in different ration like 100
Efficient growth of indigenous sheep is vital for productivity and sustainability in arid and semi-arid environments. Sonadi sheep, native to Rajasthan, India, contribute significantly to rural livelihoods; however, genetic evaluations incorporating maternal effects remain limited. This study estimated direct and maternal genetic parameters for average daily gain (ADG), Kleiber ratio (KR), growth efficiency (GE), and relative growth rate (RGR) across pre-weaning (0–3 months), early post-weaning (3–6 months), and late post-weaning (6–12 months) phases in 1,834 lambs using six univariate animal models fitted by restricted maximum likelihood. The model selection was based on the Akaike Information Criterion and statistical stability. Models incorporating direct–maternal covariance produced boundary correlations (ram ≈ − 1) and were excluded from interpretation. Under stable models, direct heritability ranged from 0.09 to 0.28 for ADG, 0.18–0.33 for KR, 0.24–0.31 for GE, and 0.23–0.33 for RGR, indicating moderate additive genetic variation. Maternal influence was more pronounced pre-weaning and declined with age. Sex, lambing season, and dam weight significantly influenced growth performance. Genetic correlations were consistently high (0.96–0.98), and phenotypic correlations were slightly lower (0.91–0.95); however, as KR, GE, and RGR share common body weight components, algebraic dependence warrants cautious interpretation. Pre-weaning ADG was the most practical primary selection criterion, with KR, GE, and RGR serving as complementary indicators. These baseline estimates may support breeding objectives for sustainable meat production in semi-arid regions of India.
Plant diseases are key constraints to agriculture and directly impact on crop yield and food security. Early and reliable diagnosis of the disease is crucial to increase agriculture production and minimize economic loss. This thesis proposes a Hybrid Intelligent Model for Precise Plant Disease Detection and Agricultural Productivity Improvement using deep learning combined with optimization. The model is a fusion of deep learning and optimization processes; in which the convolutional neural networks perform an automated feature extraction, while the classifier and feature selection are learnt by a selected algorithm. Image pre-processing techniques are utilized for enhancing the quality of data, after which a hybrid learning-based model is used to effectively identify diseases in several crops. Experimental results reveal that, In terms of accuracy, precision, recall, F1-score and RMSE the proposed model outperforms traditional image processing methods, machine learning techniques and the individually trained deep.
The present study aimed to investigate genetic parameters and maternal effects for growth performance and feed efficiency traits in Chokla sheep. The dataset comprised 6,785 growth records of Chokla sheep progeny of 499 sires, collected over a period of 47 years (1974–2020) from history-cum-pedigree sheets and databases maintained at the Arid Region Campus of the Central Sheep and Wool Research Institute, Bikaner. Six animal models incorporating different combinations of direct and maternal genetic effects were evaluated to identify the most appropriate model for estimating genetic parameters using Gibbs sampling under a Bayesian framework. Bivariate animal model analysis was performed for estimating correlations based on the best-fitting single-trait models. Traits studied included body weights at birth (BW), 3 (WW), 6 (6W), 9 (9W) and 12 months (YW), along with average daily gain (ADG) and Kleiber ratio (KR) during 0–3 (ADG1/KR1), 3–6 (ADG2/KR2) and 6–12 months (ADG3/KR3). Environmental factors such as period of birth, sex of lamb, season of birth and dam’s weight at lambing significantly influenced most traits, except season of birth on 9W and dam’s weight at lambing on post-weaning ADGs. Based on the deviance information criterion (DIC), the most suitable model was identified. Posterior mean heritability estimates were moderate for most body weight traits (0.381–0.408), except BW (0.151) and WW (0.134). ADG2 showed the highest heritability among ADGs. Maternal genetic effects were highest for birth weight and declined with advancing age. Negative covariance between direct and maternal effects resulted in inflated additive heritability estimates; therefore, total heritability was considered more appropriate for selection response. Genetic correlations among body weights were positive and moderate to high, indicating six-month body weight as a key selection criterion under field conditions.
The current research assessed the impact of various light sources on growth performance, immune response, lymphoid organ histopathology, and economic efficiency in broiler chickens within practical production conditions. A total of 180-day-old Cobb 500 broiler chicks were randomly divided into three treatment groups, each consisting of four replicates of 15 birds, and were raised for 42 days under incandescent (ICD; 60 W), compact fluorescent lamp (CFL; 30 W), and light-emitting diode (LED; 9 W) lighting systems. All birds were kept under uniform feeding and management conditions. Growth performance metrics were recorded on a weekly basis. The immune status was evaluated through serum immunoglobulin (IgA, IgG, IgM), Newcastle disease virus (NDV) antibody titres, and the heterophil-to-lymphocyte (H: L) ratio. A histopathological examination of lymphoid organs was also conducted. The data were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s post-hoc test. Birds raised under LED lighting exhibited significantly (p < 0.05) higher body weight, improved feed conversion ratios, enhanced immunoglobulin levels, higher NDV titres, and a lower H: L ratio in comparison to the ICD and CFL groups. Histopathological findings revealed well-developed lymphoid structures in birds treated with LED lighting. The economic analysis indicated reduced electricity costs and increased net returns associated with LED lighting. In conclusion, various lighting systems affect growth performance, immune response, and economic efficiency in broiler chickens, with LED lighting exhibiting superior overall performance under the conditions of this investigation.
