The present study was done during 2019-20 to assess the current fisheries status of Dal lake in terms of species diversity, trophic status, trends and patterns in fish catch, the lake’s physicochemical properties and its management. Shannon-Wiener index used to calculate the species diversity was 2.08 with the species evenness value of 2.19. The water quality analysis revealed that the lake is highly polluted due to various anthropogenic activities like sewage influx, vegetable cultivation, tourism activities, etc. The lake is dominated by omnivorous fishes followed by herbivores and carnivores. The compound annual growth rate results from 1989 to 2019 for capture fisheries showed the positive growth rate of 0.23. A lack of coordination between the different governing bodies has led the Dal lake to its brim of death. It is recommended to restore by controlling the sewage influx and weed growth. Additionally, the existing pattern of governance and management needs to be modified.
Areas covering native plant and tree species protected on behalf of religious grounds are known as sacred groves. Apart from India, sacred groves occur in various countries including America, Australia, Africa, Asia and Europe. They are not merely patches of forests but are islands in desolated landscapes. Many valuable medicinal plants and wild relatives of cultivated species are residing in sacred groves which have got tremendous roles to play in species or tree improvement programmes. Ancient sacred groves should be treated as possessing “Incomparable Values” according to the National Environment Policy of India. This paper reviewed extensively, analyzed and presented the current status on these invaluable resource pockets in India.
The penetration of internet and subsequent usage of social media, especially among the youth is increasing day by day. In this context, a study was conducted to identify the internet and social media usage by students as well as their mode of accessing professional (fisheries) information through social media. For this study, social media has been classified into two broad categories namely Social networking sites and Instant messaging applications. A pre-tested questionnaire was used to collect data, through online and offline modes, from 223 respondents consisting of undergraduate, Masters and Ph.D.students. The data obtained by the survey was analysed using MS-Excel and SPSS software. Results showed that fisheries professionals spent a significant amount of their time using social media especially Facebook and YouTube with nearly half of them spending >4 hrs / day. ResearchGate (68.5%), Google Scholar (67.5%), YouTube (65.3%), and Facebook (55.2%) were the most preferred / used applications for accessing fisheries related information while WhatsApp (82.1%) and FB Messenger (53%) were the top choices among instant messaging Apps. Though entertainment was prime reason for majority (58.3%) for accessing Facebook, News (49.5%) and professional information (46.6%) are also considered important by almost half the students. In the case of YouTube, it was entertainment (81%), professional information (60%) and news (50.6%), the same reasons but slightly varying degrees. Analysis of tests of significance showed that usage pattern was similar across both male and female students except for FB usage, which females accessed less frequently. The study also documents the list of Facebook pages maintained by various fisheries professional groups.
Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with Analytical Hierarchy Process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF) and support vector machine (AHP-SVM) model. The model performances were assessed through area under the receiver operating characteristic (AUC). AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977) and AHP-GBM (0.975). The study revealed that AHP combined with RF could signi cantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The ndings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.
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