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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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2006 - 20093
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Subject: Agricultural Statistics and Informatics × Author: KAU × End date: 2009 × Start date: 2005 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Nonlinear models for major crops of Kerala
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2007) Joshy, C G; KAU; Krishnan, S
    Nonlinear modelling techniques are the most suited tools for describing any time series phenomenon. Among the various nonlinear models in vogue monomolecular, logistic, gompertz and mixed-influence models find a prominent place. With this idea the agricultural scenario of Kerala was measured through the three important descriptors namely area, production and productivity of the major crops viz; coconut, rubber, paddy, pepper, tapioca, cashew and banana for all the districts and the state as such. Monomolecular model was the most apt model in most of the cases. The data sets were further explored based on the carrying capacity achieved by 2002-03 coupled with intrinsic growth rate. When none of the nonlinear models were found satisfactory either simple linear regression model or quadratic model was tried to explore the nature of trend. Coconut production was found to have reached its near maximum in all the districts where it was a major crop but the productivity figures gave a warning note for increasing the productivity. Rubber was found to be one of the most gifted crops, which was not devoid of proper attention. Even with this stature, production of rubber can be improved through uniform management practices. Usually nonlinear and quadratic models aptly describe a time series data on crop production. It is astonishing that simple linear regression model aptly described the paddy production in the state. The regressive value of the regression coefficients indicated that paddy production in the state is facing extinction.Paddy production in the state has at least to be protected. The lack of fit of most of the nonlinear models and even quadratic models to the data of pepper production indicate the various devastating hazards that the crop faced with. These contrasting features bring out the fact that pepper cultivation be not allowed to be toyed with. The area specific crops like cashew, cardamom, coffee and banana be made nonspecific through innovative technologies. A concerted effort with valid stresses specific to each crop will make the agricultural scenario bright.
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    ThesisItemOpen Access
    Interaction effect under ammi model
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2006) Eldho, Varghese; KAU; Krishnan, S
    The study of interaction is one of the major objectives of most of agricultural experiments. Conceptually this is done based on regression technique. Among the interactions studied, two factor interaction derives its importance as it is the simplest of the interactions. The joint regression technique is employed to study the G x E interaction. The regression techniques are having the assumption of additivity of effects. When there is departure from these assumption the joint regression technique fails. Additive Main effects and Multiplicative Interaction studies have helped a lot at this juncture. Raju (2002) derived a more comprehensive measure of interaction based on AMMI model. This was achieved using the spectral decomposition of the relevant interaction matrix which enabled the study of interaction with the same precision as that of studying the main effects. Biplots formulations of interaction effects based on the PCA vector scores are the most simplest and explicit representation of interaction. The study of interaction based on spectral decomposition has been illustrated using the secondary data on the biometric, chemical and qualitative characters from the projects “Development of a bimodal phasic management system to improve both quantity and quality in Kacholam (Kaempferia galanga)” and “Development of a bimodal phasic management system to improve both quantity and quality in Njavara (Oriza Sativa)”. The DMRT tests for each level of the factors viz., calcium and source were carried out for the parameters viz., percentage content of phosphorus in rhizome, percentage content of potassium in rhizome and North – South foliage spread. In all these characters no specific interaction effect could be sorted out. These interactions when studied based on the factor analytical technique revealed that source II and second level of calcium had the highest positive interaction as regards the percentage content of phosphorus; source III and third level of calcium for percentage content of potassium and source II and third level of calcium for North – South foliage spread. When the order of the interaction matrix was high as in the case of the second experiment, DMRT tests failed to highlight the appropriate interactive effect in the characters viz., grain yield, percentage content of nitrogen in grain, percentage content of phosphorus in grain, percentage content of phosphorus in straw and percentage content of potassium in straw. The study based on the factor analytical technique revealed that the treatments T15, T8, T3, T1 and T4 respectively had the highest interactive effect with Payyanur for the above said characters where as for Badagara they were T3, T14, T4, T5 and T8 .
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    ThesisItemOpen Access
    Changing scenario of Kerala agriculture- an overview
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 2009) Unnikrishnan, T; KAU; Ajitha T K
    The present investigations on “Changing scenario of Kerala agriculture – an overview” was carried out in the Department of Agricultural Statistics, College of Horticulture, Vellanikkara during 2006 – ’09. The secondary data on area, production, productivity and price of major crops of Kerala viz; coconut, rubber, paddy(season wise), pepper, cashew, arecanut, coffee, tapioca and banana collected from the Directorate of Economics and Statistics for the period from 1952-53 to 2006-07 were used for the analysis. The main objectives of the study included assessment of trend and growth rates of area, production, productivity and price, testing of the cointegrated movement of price and respective area of each crop, identification of the best ARIMA(Auto Regressive Integrated Moving Average) model for prediction of area, production, productivity and price and comparison of predictability of forecasting models developed by different techniques. Modified P-Gan’s method helped to understand whether the growth rate in crop production was mainly due to area or productivity. The series of prices and areas of respective crops could be co-integrated and the regression models evolved through this technique resulted in moderately high values of predictability. ARIMA models were superior to other models developed achieving a maximum value of R2 = 99.8% for the prediction of area of rubber with a very low value of MAFPE = 1.23%. Excellent parsimonious forecasting equations could be generated using the ARIMA technique for all the crops studied. The general findings of the study showed that there was a shift in area from food crops to non-food crops. The production of major food crops, rice and tapioca reached at negative growth rates due to the declining trend of their areas. But production rate of banana has increased due to increase in both area and yield. Among cash crops, both area and productivity growths influenced the production rates. The major cash crops coconut, arecanut and pepper showed positive growth rates. Compared to food crops, cash crops in general showed better growth trends in production. Negative growth rate in the production of cashewnut was due to the decline in area. Among plantation crops, rubber and coffee attained a high production growth rate due to the combined growth of area and productivity. The highest production growth rate and area growth rate were recorded by rubber among all the crops studied.