<p><strong>Background:</strong> This study aimed to investigate genetic diversity and phylogeographic structure of seven icefish populations from Yunnan freshwater lakes, Southwestern China.<br />
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<strong>Methods: </strong>All 116 COI and 130 Cytb sequences from seven icefish populations were mainly used for genetic diversity, phylogeographic structure and demographic history analyses.<br />
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<strong>Result: </strong>Genetic diversity analysis showed low mtDNA diversity in seven icefish populations. In the COI tree, <em>Neosalanx</em> sp., <em>N. tangkahkeii</em> and <em>N. taihuensis</em> samples formed a monophyletic clade with a 100% bootstrap support rate and the same occurred in the Cytb tree where our sequences, together with those of <em>N. taihuensis</em> and <em>N. tangkahkeii</em>, formed a monophyletic clade with the same high support rate. Haplotype network analysis showed that all haplotypes did not cluster separately. Genetic variation mainly occurred within populations, as revealed by the AMOVA analysis. A Mantel test proved that there was no correlation existing between genetic and geographical distances. Moreover, the entire dataset of icefish populations showed signs of population expansion. Overall, these findings suggest relatively low genetic diversity and no significant phylogeographic structure of icefish in these lakes and also imply that besides <em>N. taihuensis</em>,<em> N. tangkahkeii </em>may inhabit the seven freshwater lakes.</p>


<p>Genetic Diversity and Phylogeographic Structure of Icefishes (Salangidae) from Chinese Freshwater Lakes</p>


<div style="text-align: justify;">Genetic diversity among 54 genotypes of field and garden pea of India were studied using 30 RAPD primers. A total of 168 amplicons were scored, of which 154 were polymorphic revealing 89.3% of polymophism. Out of 30 RAPD primers tested, 22 primers produced clear and reproducible bands. The number of products generated by these primers ranged from 04 to 14 with primer OPU-17 giving maximum (14) and primer OPC-08 giving minimum number of amplicons (04). Genetic similarity estimates based on the binomial data using Jaccard&rsquo;s coefficient ranged from 0.34 (Kaza-2/IC-218991) to 0.89 (IC-209118/IC-209123) with an average similarity index of 0.54 exhibiting considerable diversity among the pea genotypes studied. The dendrogram showed clear pattern of clustering according to the source of germplasm. This study indicated that Himalayan region germplasm exhibited higher diversity.&nbsp;</div>

Diversity analysis of pea genotypes using RAPD markers

<div><strong>Background: </strong>Intercropping and mixed cropping based on two or more different crops have been widely used in Insect Pest Management to manage the population density of insect pests in various crops like maize with soybean, sorghum with soybean and sunflower with soybean. Genotypic diversity is the mixture or combination of various varieties/lines/genotypes of a crop. It decreases the insect-pests population by unfitness of crop to them due to intraspecific diversity such as mixture of susceptible and resistant varieties. Also, it increases the natural enemy population more due to longer period of flowering, availability of wide diverse food, more movement of herbivores <em>etc</em>. This concept has also been used successfully to manage insect-pests in maize, wheat and potato. In India, this connect is used in very small scale in crop. The objective of this study was to determine effects of genotypic diversity by comparing the population densities of major insect pests between monoculture of single variety and mixture of varieties.<br />
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<strong>Methods: </strong>To test the effects of genotypic diversity on soybean insect-pest and their natural enemy population an experiment was laid in<em> kharif </em>2018 and 2019 at the ICAR-Indian Institute of Soybean Research, Indore. All recommended agronomic package of practices were followed for raising of crop. Randomized block design with three replication having plot size of 9 rows of 5 meter row length was used. Totally 20 treatments were chosen for this experiment and out of 20 four treatments namely, T1, T6, T11 and T16 were the treatments of four varietal mixture treatments of almost similar maturity duration and agronomic traits and rest of treatments were of sole varietal treatments. Observations were made on insect-pests from 10 selected plants in each plot on three leaves selected at top, middle and lower portion of plant.<br />
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<strong>Result: </strong>Effects of genotypic diversity in soybean revealed that for whitefly management T11 (NRC-86, JS-335, JS-2098 and RKS-45) was found most effective treatment. In case of mite, thrips and treehoppers management T1 (JS-9560, JS-20-34, MAUS-47 and MAUS-1460) was found most effective treatment. For leafhopper and mealy bug T6 (JS-9305, JS-2029, RVS-2001-4 and Dsb-28-3) was found most effective treatment. While for spittlebug T16 (NRC-37, JS-9752, RSC-1046 and RKS-113) was found most effective treatment. So, genotypic diverse treatments were found more effective than their respective sole varietal treatments.</div>

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<p>​Effects of Genotypic Diversity on Insect-pests of Soybean</p>


<div style="text-align:justify">Modern intensive monocropping system is becoming unproductive, pest ridden, loss incurring, and resource depleting and hence turning out to be untenable. Lack of discrimination in input use by farm managers and overall poor management are accelerating degradation and desertification processes, and loss of biodiversity particularly add in endangered regions. &nbsp;While, properly designed diverse and strategically located tree/seasonal crop based land use systems can contribute to ecologic services by mitigation land degradation, climate change and desertification, while conserving natural resources and biodiversity. Besides providing, diverse goods and services, on-farm diversity can be considered as an adoptive strategy in areas climatically and biologically vulnerable. Being a potential C sink, tree based land use systems can also mitigate negative impacts of CO2 emission. As green belt/bank these have an important role in maintaining air, water and soil quality in mining and industrial areas. The so-called cellulosic feedstocks can be grown on marginal land, enhance soil fertility by promoting the growth of various soil organisms, and provide a climate- friendly source of energy. There are plenty of underexploited but potential plant species which can find niche in the existing agroecosystems with substantial addition to total production while contributing sufficiently to ecological services. These needs to be identified and useful models are developed for specific situations.</div>

On-farm crop diversity for sustainability and resilience in farming - A review

<p><strong>Background: </strong>Mango quality is crucial for economic viability, waste reduction and public health, yet traditional assessment methods often suffer from subjectivity, inefficiency and can be destructive. Existing deep learning approaches, specifically Convolutional Neural Networks (CNNs), achieve higher accuracies but are unable to generalise across diverse datasets because they cannot capture contextual features.<br />
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<strong>Methods: </strong>To address these limitations, this research explores the application of machine learning algorithms, particularly deep learning techniques such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), for an objective and efficient mango classification task. The research work concentrated on two publicly available datasets <em>viz</em>. Mango Ripeness Dataset, Mango Classification Dataset based on Mango. A comparative study of these datasets is performed to analyse their performance when fed to variants of CNNs, such as ResNet, MobileNet and ShuffleNet. By leveraging image analysis and feature extraction, this study aims to study the behaviour of different variants of the Convolutional Neural Network on different datasets related to mangoes.<br />
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<strong>Result: </strong>Comparative experiments with CNN-ViT fusion achieve superior accuracy (98.19% on the Mango Ripeness Dataset and 100% on the Mango Classification Dataset), consistently. Additional ablation studies and cross-dataset validation confirm the robustness and scalability of the approach. This work establishes a reproducible framework for automated mango quality evaluation, paving the way for practical deployment in agricultural quality control and supply chain management.</p>


<p>Vit-CNN Fusion for Robust Mango Quality Evaluation based on Classification Across Multiple Public Datasets</p>


<div><strong>Background: </strong>The variety of indigenous duck germplasm contribute maximum to the poultry industry in India, besides chicken population. In southern part of India, the available duck genetic resource, particularly Arni ducks (comprises of Sanyasi and Keeri ducks) of Tamil Nadu, has its own characteristics with innate potentiality of higher productivity without any input system of management. Genetic characterization and diversity of indigenous duck genetic resources has not been properly studied. In the present study, the genetic diversity of Arni ducks with other indigenous and exotic duck germplasm were analysed with microsatellite markers.&nbsp;<br />
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<strong>Methods: </strong>Genomic DNA was isolated from the blood samples of six duck populations. Molecular characterization was carried out with duck specific FAO recommended microsatellite markers. The genotyping of ducks was done based on the size of 4324 PCR amplicons of 23 microsatellite loci, which were subjected to capillary electrophoresis using automatic sequencer.<br />
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<strong>Result: </strong>A total of 222 alleles in six duck populations across 23 microsatellite loci with a mean of 9.65&plusmn;0.95 alleles were found. Kuttanad duck variety had the highest number of alleles (139) followed by Sanyasi (136), Keeri (129), Muscovy (118), Assam (91) and White Pekin (78) ducks. The mean observed number of alleles was 6.04&plusmn;0.59, 5.91&plusmn;0.76, 5.61&plusmn;3.17, 5.13&plusmn;0.44, 3.96&plusmn;0.76 and 3.39&plusmn;0.40 in Kuttanad, Sanyasi, Keeri, Muscovy, Assam and White Pekin ducks respectively. The overall mean polymorphism information content (PIC) values among the six duck populations was 0.6269. &nbsp;In most of the duck populations, the mean PIC value was more than 0.5 except in Assam (0.4815) and White Pekin (0.3725) ducks. The observed heterozygosity was the highest in Keeri ducks (0.5217) and lowest in White Pekin ducks (0.2766), while, the mean expected heterozygosity was the highest in Sanyasi (0.5628) and lowest in White Pekin (0.4038) ducks. The variations in the observed and expected number of alleles, differences in PIC of various microsatellite loci might be attributed to the genetic variability of the duck populations, number and type of microsatellite primers utilised for analysis and the genetic diversity of the duck breeds under study. Higher FST value indicates the substantial degree of breed differentiation among the studied duck populations.</div>


<div>Genetic Diversity of Six Duck Populations in India</div>


<div><strong>Background: </strong>Agriculture plays a vital role in the economic development of many countries worldwide. The integration of cost-effective artificial intelligence (AI) solutions into farming practices has the potential to significantly enhance productivity and improve the livelihood of farmers. One of the major challenges in automating agricultural tasks such as pesticide spraying on fruit trees, is the lack of sufficient labeled datasets. This limitation hinders the development of robust AI models for accurate tree detection, segmentation and spraying. A well-structured and diverse database is therefore essential for training effective AI models that can support precision agriculture.<br />
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<strong>Methods: </strong>In this work, we propose the systematic collection of aerial images of mango orchards and the creation of mango fruit image database captured using a DJI Air 2S UAV (Unmanned Aerial Vehicle). We also proposed image processing algorithms to divide, classify and annotate the images. These images are collected from different Indian farms where high numbers of mango orchards are found. The database contains 3917 mango tree images, which were manually sorted after collection. The images, which are large in size, are divided into smaller frames to make further image processing tasks easy and then those frames are classified based on the tree availability using different image processing techniques such as HSV, tree contours, contour area and eccentricity threshold. The count we got is 69607 frames with the presence of trees and 8713 frames with the absence of trees. Later, image processing algorithm was designed to annotate tree positions in images in (You Only Look Once) YOLO format within a specified directory structure, with tasks such as identifying tree positions based on color thresholds and drawing bounding boxes around them.<br />
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<strong>Results: </strong>A total of 58,608 frames were annotated using the YOLO format. Frames that did not contain mango trees were excluded. Annotation was performed through user-interaction-based methods to ensure high-quality labeling. As a result, all annotated images are confirmed to contain mango trees or parts of mango trees only. These images are accessible to anyone interested in the research in related domains. Through this paper, we are publishing our UAV-captured original mango orchard image dataset and its auto-annotated image dataset for the Image processing, AI, ML and DL based UAVs application developments.</div>


<p>Mango Orchard Aerial Image Dataset for Research on Fruit Trees with UAVs in the Precision Agriculture</p>


<div>The study was undertaken to document the elasmobranch diversity and their abundance along Wadge Bank. Species were collected fortnightly during June 2015 to May 2016 from three landing centres viz., Chinnamuttom,Colachel and Vizhinjam of Wadge Bank. A total of 1,575 specimens were collected during the period and 44 species were identified belonging to 8 orders, 13 families and 25 genera. Among the recorded \families,Carcharhinidae is the most dominant family with 12 species. The Colachel landing centre was rich in diversity with 43 species followed by Chinnamuttom 39 species and Vizhinjam26 species. The highest Shannon Weiner diversity (H&rsquo; value) was observed at Colachel (4.17) followed by Chinnamuttom (4.11) and Vizhinjam (3.76). The Margalef&rsquo;s species richness (&lsquo;d&rsquo;) value was assessed at Colachel (4.55) followed by Chinnamuttom (4.01) and Vizhinjam (2.91). The Pielou&rsquo;s evenness (J&rsquo;) estimated was 0.7786, 0.7700 and 0.8005respectively, for Chinnamuttom, Colachal and Vizhinjam. The highest taxonomic diversity value was observed at Colachel (60.33) and the lowest during at Vizhinjam (54.08). Among the three landing centres studied, the total phylogenetic diversity (sPhi+) was found to be the lowest at Vizhinjam (940) and highest at Colachel landing centre (1720). Bray Curtis similarities measure was also calculated, fish communities were separated into several clusters based on seasons. The conservation of elasmobranchs and the management measures to be followed along the Wadge Bank is also discussed.</div>


<div>Elasmobranch diversity, conservation and management along Wadge Bank, South India</div>


<div><strong>Background: </strong>Livestock&nbsp;genetic diversity studies focus on their within and diversity, breed history, adaptive variations, ancestral information, site of domestication and parentage testing and assess the genetic uniformity, admixture or subdivision, inbreeding, or introgression in the population which is helpful in breed formation and their sustainable utilization.<br />
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<div style="text-align:justify"><strong>Methods:</strong> The present research work was conducted during the year 2016-17 at National Bureau of Animal Genetics Resources, Karnal-132001. STR data of 25 markers on 1237 random samples of 27 goat populations was used for analysis. The genetic diversity analysis of new population &nbsp;<em>viz</em>: Narayanpatna, Raighar, Kalahandi, Malkangiri of Odisha state and Rohilkhandi (UK) and their association studies with other Indian goat breeds was performed.<br />
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<strong>Result: </strong>It was found that used markers are highly polymorphic- and the studied breeds/population showed great diversity and distributed mostly on the basis of physio-geographical condition and type of production but among new populations diversity was least which might be due to exchange of animal for breeding purposes. The studied new goat populations were well differentiated from other goat breeds which might be due to physio-geographical condition and breeding practices, so these may be considered as separate breeds/populations. In conclusion, the results showed high level of conserved genetic diversity in the Indian goat breeds. The smaller and isolated new population showed less diversity and a higher inbreeding level as compared to registered breeds.</div>


<div>Genetic Diversity Analysis among 27 Indian Goat Populations using Microsatellite Markers</div>


<div><strong>Background:</strong> The estimation of genetic diversity in pigeonpea is crucial for desiging breeding programmes and germplasm conservation. Morphological studies alone do not provide sufficient information to understand genetic diversity. Molecular analysis using SSRs can provide additional information on genetic diversity that can be used for selection of diverse parents.<br />
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<strong>Methods:</strong> The experimental material for present study consisted of 50 diverse genotypes of pigeonpea. The fifty genotypes were planted in Randomized Complete Block Design consisted of three replications during the <em>kharif</em> 2020-21. The estimation of morphological genetic diversity was done by Mahalanobis D<sup>2</sup> statistics. The molecular genetic diversity analysis was done by using 30 molecular markers in same genotypes.<br />
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<strong>Result:</strong> The assessment of morphological diversity revealed that the fifty pigeonpea genotypes were grouped into four different clusters with cluster I as the largest cluster (33 genotypes). The molecular markers differentiated the 50 genotypes in five major clusters with cluster II as the largest cluster (24 genotypes). The results of present study suggested that morphological and molecular diversity in pigeonpea is different. On the basis of genetic distance, the genotypes RVSA 2014-1 and PA 406 were found most genetically distant and may be used in hybridization programme to create diverse progenies.</div>
