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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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  • ThesisItemOpen Access
    Genetic evaluation of leafy mustard (Brassica juncea var. rugosa) for leaf morphological and neutraceutical attributes
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-06) Priyanka; Manoj Raghav
    Leafy Mustard (Brassica juncea var. rugosa) is a popular green vegetable grown in plains and hills of Northern India. It is a rich source of vitamins, minerals and protein. The present investigation carried out with 31 germplasm during November – February 2018 – 2019 at Vegetable Research Center (VRC). Nutraceutical properties of each genotype were determined at G.B.P.U.A & T. Laboratory Pantnagar. The estimation of micronutrient (iron, zinc manganese by atomic absorption spectrophotometer), macronutrient (phosphorus and potassium by spectrophotometer and flame photometer), protein and nitrogen (by Kjeldahl method) were done. Analysis of coefficient of variation showed significant differences among the genotypes for all the traits. The genotype which recorded highest green leaves yield (kg/ha) was PLM-15 (400kg/ha). The phenotype coefficient of variation recorded highest in green leaves yield per plant (g) was PLM-16 (350.00g) and phenotype coefficient of variation recorded highest in green leaves yield (kg/ha) was PLM-15 (400kg/ha). Lowest GCV and PCV value was recorded in days to last leaf harvest. Highest value of heritability was reported for green leaf yield/plot (99.90%). Highest genetic advance was obtained in leaf area (169.99) while highest genetic advance as % of mean was recorded in leaf area (31.37%). All 31 genotypes showed variability in neutraceutical property. Highest protein content (33.38%), nitrogen (5.55%), phosphorus (1400.33 mg/100g), potassium (2770.00 mg/100g), iron (30.56 mg/100g), zinc (5.73 mg/100g) and manganese (4.94 mg/100g) was observed in PLM-16. The 31 genotypes were classified into 6 different clusters based on genetic distance. The analysis showed the highest intra cluster distance in cluster III (451.92) having 5 genotypes and the highest inter cluster distance (20529.11) among cluster V having 1 genotype and cluster VI having one genotype. Thus with these findings, we can state that above genotypes were promising and can be utilized for further improvement programme in leafy mustard. The genotypes which are rich in neutraceutical properties can be used for plant biofortification.
  • ThesisItemOpen Access
    Fuzzy based semantic clustering of news articles
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-10) Priyanka; Joshi, Sanjay
    Text mining is a process that uses data mining approaches to extract valuable information held in the hidden form in textual data. In this paper, a framework for fuzzy clustering of news articles is proposed. These news articles originate on different news portals on the web. The data sets are fetched from two different Indian news portals, The Hindu archive and Times Of India archive. Six data sets are used for implementation and evaluation: 4 news articles Times of India, 150 news articles Times of India, 1000 news articles Times of India, 4 news articles The Hindu, 150 news articles The Hindu, 1000 news articles The Hindu. The fetched data is stored in a central database and then preprocessing reduces the noise. Tokenization is done to split the text content into separate words. Stop words are removed from the text data as they have no significance for cluster discrimination. Then lemmatization technique is applied. Tf-idf is calculated for the data set and saved in the word frequency vector. On these vectors, distance measure or similarity measure function is used to find the similarity between articles. Tf-idf with cosine similarity measure gives semantic similarity between articles. One article may belong to more than one cluster so fuzzy membership values must be generated. The articles are clustered using two clustering algorithms k-means clustering and fuzzy c-means clustering. The similar documents are grouped into same cluster and dissimilar documents are put into different clusters. The proposed framework shows that fuzzy clustering does not restrict each news article to belong exactly to one cluster. Therefore this framework when applied to information retrieval systems or other application systems, system gives better performance and relevance to the users.