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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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1994 - 19965
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Subject: Agricultural Statistics and Informatics × Author: KAU × End date: 1996 × Start date: 1994 ×
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Now showing 1 - 5 of 5
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    ThesisItemOpen Access
    Construction of a composite sow inded and study of its effects due to sire, parity and season in pigs
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1995) Gini, Varghese; KAU; George, K C
    An investigation was done for the constructions of composite sow index based on the data collected from sow cards of pigs maintained at the University Pig Breeding Farm, Mannuthy, with the additional objectives of studying the effect of sire, parity and season on this index and also to suggest for culling the uneconomic animals based on this index. Data were collected from 255 pigs selected under the first parity for the characters age at farrowing , post weaning conception period, litter size at birth, average weight of a piglet at birth, litter size at weaning and average weight of a piglet at weaning. The data were collected for the subsequent parities also for the above mentioned characters, from among the 255 sows selected. Three different types of selection indices were worked out viz. phenotypic index based on one main character and one auxiliary character, phenotypic index based on one main character and two auxiliary characters and a composite sow index. While comparing the phenotypic indices, it was found that the indices based on the characters litter size at weaning and average weight of a piglet at weaning were the most contributing characters along with age at farrowing and post weaning conception period. The variances of the composite sow index was less than that of the other two indices for all the five parities. Hence the composite sow index was selected as the most efficient index. Therefore, the best 25 animals were sorted out for each parity based on the composite sow index and used for further analysis. The best sow-sire pairs under each parity were identified by comparing the ranks of the three types of indices coming within the first 25. The seasonal effect on various characters considered was also tested by classifying the best ranking 25 sow – sire pairs into these seasons namely, winter season, summer season and rainy season under each parity. The average index under each season was compared by using the analysis of variance and it was found that there is no seasonal influence on any of the six contributing characters. The sows repeatedly coming under most of the parities were sorted out from the best 25 sows selected based on the composite sow index. The average values for the index and also for all the contributing characters under different parities were compared with the normal values of a standard sow and 07/160 was selected as the best sow. Similarly, 01/182 was selected as the best sire and 07/160-01/182 was chosen as the best sow-sire pair. An attempt was done to find out the best parity also. For this the sows came under at least for the first three parities were sorted out and their mean index values were compared using the analysis of variance test. No significant difference was observed for any of the parities. Being the most efficient index, the standard value for the composite sow index should be around six. Hence it can be concluded that the sows showing an index value less than 6 can be culled and nearer or greater than 6 can be retained for further breeding .
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    ThesisItemOpen Access
    Optimality of block designs used in one way elimination of heterogeneity
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1995) Somy Kuriakose; KAU; Krishnan, S
    Block designs are usually used in experiments where it is important to eliminate heterogeneity at least in one direction. From the class of designs it is desired to choose a design which will estimate the elementary treatment contrasts with maximum precision. The optimality criteria are based on the dispersion matrix of all possible elementary contrasts. The A-optimality criterion based on the information matrix was derived. Usually for comparing test treatments with a control RBD is used with the control treatment replicated in all blocks. The same objective could be achieved by using Balanced Treatment Incomplete Block Designs (BTIBD). BTIBD was found to be more efficient than RBD with the control treatment replicated in all blocks. Optimalities of BTIBD were also examined. When a BTIBD was augumented with certain number of blocks, such that the augmented blocks contains only the test treatments the resulting design was found to be E-optimal.
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    ThesisItemOpen Access
    Time series modelling and forecasting of the yield of cashew (Anacardium occidentale L) in Kerala
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1996) Mini, K G; KAU; Graceamma Kurien
    The present investigations, time series modeling and forecasting of the yield of cashew in kerala was undertaken with the following objectives. 1. To formulate a suitable model for the forecast of production of cashew crop in Kerala 2. To work out the major determinants of yield variations. For this purpose, secondary data were collected from the Directorate of economics and statistics, Government of Kerala , Thiruvananthapuram for a period of thirtyseven years starting from the year 1956-57. The data on average, production , productivity price of raw cashew kernel and annual rainfall were collected. The stochastic models viz. Box -Jenkins model, distributed lad model, log normal diffusion model and markov chain model were tried on the time series. Univariate ARIMA models of all the variables were considered separately, Diagnosis checking was done to ascertain the adequacy of the model. Then the fitted models were used to obtain the sample period and post sample period forecasts. To judge the forecasting ability of the model the Mean absolute percentage error (MAPE)was calculated. The results showed that the univariate ARIMA models offered a good technique for predicting the magnitude of all the variables. Cross correlation analysis of the series was done with yield as the dependent variable and area price and rainfall as the independent variables. But the results were not in favour of trying a transfer function model. Distributed lag models of varying types involving selected exogeneous variables were developed. The area response models had lagged area, price risk, lagged price and lagged rainfall as the explanatory variables while yield response function. The result of the analysis clearly indicated that area was not responsive to prices. Cashew growers are least sensitive to price movements and they prefer to grow the crop in all types of soil due to its wide adaptability and ease of management. The coefficient of determination of all functions were relatively high indicating that the proposed models were satisfactory in describing yield and acreage fluctuations. The log normal diffusion model was fitted to the data on production of cashew in Kerala. It was found that the model gave a satisfactory fit to the data. Yield forecasts for the period from 1997 to 1999 were obtained using the model . A four state Markov chain model was used to represent the time series distribution of production. The four states of the model were identified based on the qualities of the series and a transition probability matrix was calculated. Equilibrium probabilities were estimated. It was found that the yields reached equilibrium position after twenty years. The steady state probabilities were estimated and used to forecast the production .
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    ThesisItemOpen Access
    Comparison of alternate methods for the control of experimental error in perennial crops
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1994) Seena, C; KAU; Prabhakaran, P V
    The feasibility of using certain novel devices for the control of error in experiments on perennial crops was examined on the basis of actual experimental data and the resulting efficiency gain evaluated. A considerable amount of reduction in error variance was achieved by the application of analysis of covariance with suitable functions of pre-experimental yield as concomitant variable. Application of quadratic covariance resulted a substantial gain of precision in the analysis of data on coconut. Nearest neighbourhood analysis (NNA) resulted in a significant improvement of precision in the analysis of data in most of the experiments. Double covariance analysis involving suitable functions of pre-experimental yield and NN variable as covariates resulted in further reduction of experimental error. Pearce’s iterative NN procedure was found to be the best alternative method for reduction of error over the coventional method of stratification. A plot of eight trees was found to be optimum for conducting yield trails on coconut and cashew. The percentage of genetic variability to the total phenotypic variability in the yields of cashew, coconut and cocoa was estimated to be 77.7, 83.4 and 45.4 respectively. The result called for the use of calibration of the plots and choice of appropriate concomitant variables for the reduction of experimental error in designing experiments on perennial crops.
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    ThesisItemOpen Access
    Analysis of auto correlated data in groups of experiments
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1994) Premi, T C; KAU; Gopinathan Unnithan, V K
    Analysis of variance model for the groups of experiments needs modification, when observations are taken repeatedly on the same experimental units owing to the autocorrelated nature of error terms. A model which takes the dependence of error terms into consideration was evolved for dealing such situations. But estimation of parameters using least square principle and their tests of significance not straight forward. Therefore numerical solutions using iterative technique was employed for estimation of parameters of the model. The newly developed procedure was compared to the widely used analysis of the split-plot setup and the comparative advantage of the new method was established. The new methodology along with the widely used analysis of the split – plot set up were illustrated using two different sets of data. The superiority of the new method over the split –plot analysis was demonstrated in both sets of data.