G. B. Pant University of Agriculture, also known as Pantnagar University, is the first agricultural university of India. It was inaugurated by Jawahar Lal Nehru on 17 November 1960 as the "Uttar Pradesh Agricultural University" (UPAU). Later the name was changed to "Govind Ballabh Pant University of Agriculture and Technology" in 1972 in memory of the first Chief Minister of Uttar Pradesh, statesman and Bharat Ratna recipient Pandit Govind Ballabh Pant. The University lies in the campus-town of Pantnagar in Kichha Tehseel and in the district of Udham Singh Nagar, Uttarakhand. The university is regarded as the harbinger of Green Revolution in India. B.
Natural coatings composed of biopolymers are an environmentally friendly and effective method that can help preserve fruit after harvest and maintain quality. This review explains how coating materials influence film structure, gas and water vapor permeability, adhesion to fruit surfaces, and sensory quality. The role of active components such as antimicrobials, antioxidants, probiotics, and ethylene scavengers in controlling decay, enzymatic browning, and textural softening is discussed. Recent advances, including composite and nano-enhanced coatings, multilayer systems, and probiotic or nano-encapsulated formulations, are highlighted for their potential to improve mechanical strength, stability, and targeted release of functional compounds. Methods of coating application (dipping, spraying, electrospraying, and layer-by-layer techniques) and key formulation factors affecting uniformity and commercial scalability are also reviewed. Finally, this review outlines major constraints, including variable performance across fruit types, sensory changes, regulatory concerns, and the need for pilot- to industrial-scale validation. Overall, biopolymer-based edible coatings offer a promising strategy for extending shelf life and preserving the nutritional and market quality of fresh fruits in a sustainable way.
Efficient energy use and carbon management are central to promoting sustainable agricultural practices. A two-year field experiment was conducted to compare direct and residual effect of soil applied Zn, B, S, and foliar applied Zn and B on system productivity, profitability, energy budgeting, and carbon efficiency in a cluster bean-mustard cropping system. Thirteen treatments, comprising soil and foliar applications of Zn, B, and S at varying levels, were evaluated in a randomized block design with three replications. Direct and residual effect of soil application of 4.0 kg Zn ha-1, 1.5 kg B ha-1, and 60 kg S ha-1 significantly improved system productivity, carbon budgeting, and energy indices of the two-year cluster bean-mustard system. The combined foliar application of Zn and B also significantly improved net energy output, energy profitability, and human energy profitability, while reducing specific energy. Maximum carbon output during I and II year (9031.42 kg ha-1, 8617.64 kg ha-1) and carbon sustainability index were achieved with soil-applied 1.5 kg B ha-1 and 0.25% foliar Zn application. Economic analysis revealed that the 4.0 kg Zn ha-1 treatment consistently achieved the highest gross returns, net returns, and benefit-cost ratio across both years. Overall, the strategic application of Zn, B, and S improved energy and carbon efficiencies, yield attributes, and profitability in cluster bean-mustard cropping, offering a sustainable and climate-resilient model for nutrient-depleted soils in India.
Shiga toxin-producing Escherichia coli (STEC) and Enteropathogenic E. coli (EPEC) are important foodborne pathogens posing significant public health threats. This cross-sectional study investigated the prevalence, virulence profiles, serotypes, cytotoxicity, and antimicrobial resistance profiles of STEC and EPEC from milk and milk products in Uttarakhand, Northern India. A total of 680 samples (260 raw milk and 420 milk product samples) were collected from dairy farms, milk shops, collection centers, and street vendors over 9 months and screened for E. coli using conventional and molecular methods. Multiplex PCR targeting stx1, stx2, eaeA, and hlyA genes was employed to identify virulent isolates, which were further serotyped, evaluated for cytotoxicity on Vero cells, and tested for antibiotic susceptibility against 19 antibiotics. Resistant isolates were screened for tetA, tetB, sul1, and CITM genes by PCR. E. coli was detected in 28.82% of samples, with higher prevalence in raw milk (31.15%) than milk products (27.38%). Among isolates, 39.8% harbored at least one virulence gene, with stx1 being most prevalent. Serotyping revealed 22 O-serogroups, predominantly O18, O111, O120, O126, and O17. All stx-positive isolates showed cytopathic effects in Vero cells, enhanced after ciprofloxacin induction. High resistance was observed against ampicillin, tetracycline, oxytetracycline, cephalothin, and sulphonamides, while imipenem, gentamicin, and nalidixic acid were most effective. Among multidrug-resistant isolates, 95% carried tetB, sul1, or CITM genes, while tetA was absent. The study confirms the presence of virulent and multidrug-resistant STEC and EPEC in milk and dairy products, highlighting the need for improved hygiene, judicious antimicrobial use, and regular monitoring to mitigate food safety and zoonotic risks.
The rapid increase in the integration of wireless sensor networks within the Internet of Things (IoT) ecosystem has led to crucial difficulties in providing reliable, energy-efficient, and secure communication. Most of the traditional intrusion detection models face few struggles in mitigating these challenges due to limited scalability and centralized processing. Therefore, this paper proposes a lightweight federated learning-based energy-aware (LF-LEA) model to overcome all the existing issues. The proposed model is an integration of federated learning, bio-inspired optimization, and energy-aware routing for decentralized environments. For local intrusion detection at edge nodes, the proposed model uses a lightweight convolutional neural network to transmit only the model updates rather than raw data, and this ensures the user's data privacy. The integration of the Levy flight and lotus effect mechanisms enhances the exploration and exploitation balance for improved intrusion detection accuracy. Furthermore, the S-LEACH-based routing protocol is incorporated to ensure secure and energy-efficient communication between nodes and base stations. Two benchmark datasets are used to validate the proposed model. The experimental results demonstrated that the proposed model achieves a higher accuracy of 98.60%, a precision of 98.32%, and a packet delivery ratio of 92.7%. In addition, the proposed model achieves a minimum communication delay and false alarm rate. Furthermore, the statistical Wilcoxon rank-sum test is conducted to confirm the effectiveness and consistency of the proposed model across diverse evaluation metrics. The overall result demonstrates that the proposed model ensures privacy preservation, scalability, and energy efficiency, making it a robust model for real-time intrusion detection in IoT-enabled applications, including smart cold storage monitoring systems, industrial automation, and environmental sensing networks.
Eight QTLs linked to Botrytis grey mould resistance in chickpea were identified, enabling marker-assisted selection and candidate gene discovery for the development of resistant cultivars. Botrytis grey mould (BGM), caused by Botrytis cinerea Pers. ex. Fr., is a destructive disease limiting chickpea production globally. To control this disease, breeding for the development of resistant chickpea cultivars is one of the most economic and effective method. Hence, identification and mapping of resistance genes/quantitative trait loci (QTL(s)) are critical. However, there are limited reports on QTL mapping and the identification of candidate genes for BGM resistance in chickpea. The present study employed bulked segregant analysis and whole-genome sequencing (BSA-seq) approach for the detection of candidate genomic regions controlling BGM resistance. A total of eight QTLs were mapped with three on chromosome 1 (BGM1.1, qBGM1.2, qBGM1.3), one on chromosome 3 (qBGM3.1), two each on chromosome 4 (qBGM4.1, qBGM4.2) and chromosome 7 (qBGM7.1, and qBGM7.2). Based on high ΔSNP-index and G' values, qtlBGM4.2 (2.36 Mb region) was selected for subsequent validation by KASP primers. Linkage mapping with KASP primers derived from six polymorphic SNPs with high read depth identified a 506.16 kb genomic region equaling 0.84 cM on the genetic map linked with BGM resistance. The results presented herein provide insights for further cloning and functional analysis of candidate genes (Leucine-rich repeat extension-like kinases, rust resistance kinase Lr10, and F-box proteins) related to BGM resistance. Additionally, BGM-specific KASP markers developed in this study will be highly useful for marker-assisted selection (MAS) to accelerate chickpea breeding programmes for BGM resistance.
