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Kerala Agricultural University, Thrissur

The history of agricultural education in Kerala can be traced back to the year 1896 when a scheme was evolved in the erstwhile Travancore State to train a few young men in scientific agriculture at the Demonstration Farm, Karamana, Thiruvananthapuram, presently, the Cropping Systems Research Centre under Kerala Agricultural University. Agriculture was introduced as an optional subject in the middle school classes in the State in 1922 when an Agricultural Middle School was started at Aluva, Ernakulam District. The popularity and usefulness of this school led to the starting of similar institutions at Kottarakkara and Konni in 1928 and 1931 respectively. Agriculture was later introduced as an optional subject for Intermediate Course in 1953. In 1955, the erstwhile Government of Travancore-Cochin started the Agricultural College and Research Institute at Vellayani, Thiruvananthapuram and the College of Veterinary and Animal Sciences at Mannuthy, Thrissur for imparting higher education in agricultural and veterinary sciences, respectively. These institutions were brought under the direct administrative control of the Department of Agriculture and the Department of Animal Husbandry, respectively. With the formation of Kerala State in 1956, these two colleges were affiliated to the University of Kerala. The post-graduate programmes leading to M.Sc. (Ag), M.V.Sc. and Ph.D. degrees were started in 1961, 1962 and 1965 respectively. On the recommendation of the Second National Education Commission (1964-66) headed by Dr. D.S. Kothari, the then Chairman of the University Grants Commission, one Agricultural University in each State was established. The State Agricultural Universities (SAUs) were established in India as an integral part of the National Agricultural Research System to give the much needed impetus to Agriculture Education and Research in the Country. As a result the Kerala Agricultural University (KAU) was established on 24th February 1971 by virtue of the Act 33 of 1971 and started functioning on 1st February 1972. The Kerala Agricultural University is the 15th in the series of the SAUs. In accordance with the provisions of KAU Act of 1971, the Agricultural College and Research Institute at Vellayani, and the College of Veterinary and Animal Sciences, Mannuthy, were brought under the Kerala Agricultural University. In addition, twenty one agricultural and animal husbandry research stations were also transferred to the KAU for taking up research and extension programmes on various crops, animals, birds, etc. During 2011, Kerala Agricultural University was trifurcated into Kerala Veterinary and Animal Sciences University (KVASU), Kerala University of Fisheries and Ocean Studies (KUFOS) and Kerala Agricultural University (KAU). Now the University has seven colleges (four Agriculture, one Agricultural Engineering, one Forestry, one Co-operation Banking & Management), six RARSs, seven KVKs, 15 Research Stations and 16 Research and Extension Units under the faculties of Agriculture, Agricultural Engineering and Forestry. In addition, one Academy on Climate Change Adaptation and one Institute of Agricultural Technology offering M.Sc. (Integrated) Climate Change Adaptation and Diploma in Agricultural Sciences respectively are also functioning in Kerala Agricultural University.

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2011 - 201922020 - 20211
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Subject: Agricultural Biotechnology × Author: KAU × Start date: 2010 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Metagenomic analysis of bacterial diversity in the rice rhizosphere of kole lands of Thrissur
    (Department of Plant Biotechnology, College of Agriculture, Vellanikkara, 2021) Athira Krishnan, L R; KAU; Girija, D
    Kole wetlands of Kerala are a complex ecological system and are known for the higher productivity of rice. The Kole lands remain submerged under flood water for about six months in a year and this seasonal alteration gives it both terrestrial and water related properties which determine the ecosystem structure. Though several studies have been conducted for exploring the diversity of fishes, birds, flora, butterflies, etc., in Kole lands, no systematic studies have been made on rhizosphere microbial diversity. This study was intended to analyze the bacterial diversity in the rice rhizosphere ecosystem of Kole lands of Thrissur. Rhizosphere soil samples were collected from three locations of Kole lands of Thrissur viz. Puzhakkal (Pzk), Mullassery (Mls) and Cherpu (Chr) and analyzed for physico-chemical and biological properties. Culturable microflora was enumerated using serial dilution plate method for bacteria, fungi, actinomycetes, N- fixers, P, K and Zn-solubilizers. Twenty four predominant bacterial isolates were purified and screened for PGP activities including production of IAA and ammonia and phosphate solubilization. The bacterial diversity of the rhizosphere samples was analyzed by metagenomic library construction and sequencing of V3-V4 regions of the 16S rRNA gene, using Next Generation Sequencing (NGS) technology. The sequences thus obtained were analyzed for the Operational Taxonomic Units (OTUs) using MEGAN and MG- RAST server. The analysis of physico-chemical parameters showed a comparatively low pH in all the samples. An extreme low pH can reduce the availability of major and secondary nutrients in the soil. The sample Pzk showed higher content of organic C. Culturable microflora and microbial biomass C analysis also showed a slight increase in the sample Pzk. The soil organic C content and microbial biomass C are reported to be positively correlated. The microbial biomass C is the measure of the weight of the organisms present.The predominant bacterial phyla in the rice rhizosphere of Kole lands of Thrissur included Proteobacteria, Chloroflexi, Acidobacteria, Actinobacteria, Bacteroidetes and Nitrospirae. The bacterial population was found higher in the sample Puzhakkal and comparatively lower in the sample Chr. Phylum Proteobacteria was found to be the most predominant bacterial phylum in Pzk while, Chloroflexi was more predominant in Mls and Chr. The classes Acidobacteria and Ktedonobacteria were found dominant in the samples Mls and Chr and the Pzk sample was dominated by Acidobacteria and Deltaproteobacteria. The phylum level bacterial diversity was found highest in the sample Chr with 21 phyla while the genus level bacterial diversity was highest in the sample Mls. The abundance of genera Desulfobacca, Thermoanaerobaculum, Thioalkalispira, Anaerolinea, Ktedonobacter, Gemmatimonas, Puedolabrys, Sulfuricurvum, Syntrophobacter, Haliangium, Geobacter and Syntrophorhabdus was observed in the Kole land rice rhizosphere samples. Many of these genera are involved in geo-cycling of nutrients like Fe, S and Mn and a few are used in waste water treatment. The species- level bacterial diversity was found to be highest in the sample Mls as indicated by the Chao1 and observed species indices. The predominant archaeal phyla in the rice rhizosphere of Kole lands of Thrissur included Euryarchaeota, Crenarchaeota and Thaumarchaeota. Archaea are still an under- detected and little-studied part of the soil, so their full influence on global biogeochemical cycles remains largely unexplored. This study has thrown light on the diversity of bacterial and archaebacterial communities in the peculiar ecosystem of Kole lands of Thrissur. Many of the biofertilizer organisms like Azospirillum, Paenibacillus, Cellulosimicrobium and biocontrol agents like Bacillus and Pseudomonas could be detected, which could be cultured and used as potential acid tolerant biofertilizers and PGPR. Many of the ‘Unclassified’ genera could be novel bacteria and more research is needed to identify their taxonomic position and functional role in the ecosystem.
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    ThesisItemOpen Access
    Molecular marker development for cassava mosaic disease resistance using bioinformatics tools
    (Department of Plant Biotechnology, College of Agriculture, Vellayani, 2015) Ambu, Vijayan; KAU; Sreekumar, J
    The study entitled “Molecular marker development for cassava mosaic disease resistance using bioinformatics tools” was conducted at ICAR-CTCRI, Sreekariyam, Thiruvananthapuram during October 2104 to October 2015. The objectives of the study included development and evaluation of various SNP and SSR prediction pipelines, computational prediction and characterization of SNP and SSR in cassava, verification of SNP and SSR markers for cassava mosaic disease (CMD) resistant and susceptible breeding lines. The preliminary data set for the identification of SSR and SNP markers was obtained from the EST section of NCBI and the cassava transcript sequences from the Phytozome. A total of 120461 sequences was classified into 20 cultivars. The dataset was reduced to 14336 sequences after several pre-processing and screening steps. The resulting sequences were assembled and aligned using CAP3 and 2088 contigs were obtained. SNPs and SSRs were predicted from these datasets using respective prediction tools. The SNP prediction tools such as QualitySNP and AutoSNP were compared for their performance. Analysis was performed to identify the tool with the ability to annotate and identify more viable nonsynonymous and synonymous SNPs. The SSR prediction tools such as MISA and SSRIT was compared for their performance. Analysis was performed to identify the tool having the ability to predict more viable SSRs and the ability to classify them as mono, di, tri, tetra, penta, hexa and poly SSRs. Using QualitySNP, thirty nonsynonymous SNPs and twenty-six synonymous SNPs were identified. Using MISA, n 217 mono SSRs, 132 di SSRs, 139 tri SSRs, 3 tetra SSRs, 1 penta SSRs, 3 hexa SSRs and 42 complex SSRs were identified. Five sequences from identified SNPs and SSRs which have high hit percentage were selected for validation and primer designing for CMD resistant genes. These primers were validated using 5 resistant and 5 susceptible cassava varieties. Among the 10 primers after validation in wet lab, one SNP (SNP896) and one SSR (SSR 2063) primer was able to clearly differentiate between the resistant and susceptible varieties which can be used as potential markers in the breeding program for screening CMD resistance in cassava.
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    ThesisItemOpen Access
    Production technology for organic watermelon
    (College of Agriculture, Vellayani, 2011) Sheeba, B S; KAU; Sajitha, Rani T
    The present investigation was carried out at College of Agriculture, Vellayani to find out a suitable date of planting and the effect of different doses of FYM on the growth, yield and economics of organic watermelon from October 2010 to March 2011. The experiment was laid out in Split plot design with four replications. The Main plot treatments consisted of five dates of planting (D1- October 15th, D2- November 1st, D3- November 15th , D4- December 1st and D5- December 15th. The sub plot treatment consisted of four levels of nutrients viz. (T1-5 kg FYM plant-1, T2-4 kg FYM plant-1, T3-3 kg FYM plant-1 and T4-2 kg FYM plant-1 + 7:2.5:2.5 g NPK plant-1 [control] - POP Recommended dose of KAU). Plants sown on earlier planting date recorded significantly more number of branches, maximum vine length, female flowers plant-1, total fruit plant-1, total fruit ha-1, marketable fruit plant-1, marketable fruit ha-1, marketable yield ha-1, average fruit weight, fruit diameter, flesh thickness and fruit girth compared to the later planting dates. Flower opening was significantly influenced by planting dates. The earlier planting took maximum days to first harvest and maximum crop duration compared to later planting dates. Quality attributes like total sugar and non-reducing sugar were significantly higher for earlier planting dates. Uptake of N and K was more in earlier planting dates compared to that of later planting dates. Gross return, Net return and B: C ratio of water melon were recorded higher for earlier planting dates. The highest level of nutrient (5 kg FYM plant -1) recorded maximum days to first harvest and took more crop duration. Maximum number of branches plant-1, maximum vine length , total fruit plant-1, marketable fruit ha-1, marketable yield ha-1, average fruit weight, fruit length, fruit diameter, flesh thickness and fruit girth was also recorded by the highest level of nutrient ( 5 kg FYM plant -1) compared to the lower levels of nutrients. Uptake of N, P and K was also significantly more in plots receiving highest level of nutrient. The available N, P and K content in soil was also significantly more in plots receiving highest level of nutrient. The highest Gross return, Net return, B: C ratios for watermelon were also recorded by the highest level of nutrient (5 kg FYM plant -1).