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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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1982 - 198917
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Subject: Statistics × End date: 1989 × Start date: 1982 × Type: Thesis ×
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Now showing 1 - 9 of 17
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    ThesisItemOpen Access
    Forecasting models for crop yield in cashew (anacahdium occident ale l.)
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1987) Usha, Menon R; KAU; George, K C
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    ThesisItemOpen Access
    Comparative study of genotype environment interactions in sesame
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1989) Mini, C J; KAU; George, K C
    The present study has been conducted to choose a consistent variety for all the regions and all seasons in the light of genotype-environment interaction with the following objectives. (i) to evaluate the existing techniques available for studying GE interaction in sesame (ii) to develop new concepts and methods to solve some problems peculiar to crop sesame like non-linearity of interactions, non-orthogonality of data and different patterns of genotype-environment (GE) interactions that are encountered while studying the stability of varieties simultaneously for several traits.
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    ThesisItemOpen Access
    Statistical approach on the pattern of development of shank length in ducks
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1989) Sunanda, C; KAU; George, K C
    The present investigation entitled ‘‘statistical approach on the pattern of development of shank length in Ducks’’ has been undertaken to study the following objectives. 1. To examine the pattern of development of shank length in two breeds of ducks in University Duck Farm, Mannuthy. 2. To compare them (a) between genetic group (b) between males and females within each genetic groups (c) between males of genetic group (d) between females of genetic group and 3. To fit appropriate growth curves for prediction of body weight through shank length at different stages of growth. For this purpose shank length and body weights on 14 males and 25 females of Desi ducklings and 26 males and 26 females of White Pekin (WP) ducklings were utilized. The ducklings were reared for twelve weeks in Kerala Agricultural University Duck Farm, Mannuthy under uniform feed formula and identical management practices. In the day old and the twelfth week of age uniformity could be seen in the mean shank length of the four groups. But at the fourth and eighth week of age, mean shank length of the four groups was not uniform. Upto ninth week of age, Desi females had higher shank length than the other three groups except at the fifth week. But during the fifth, tenth, eleventh and twelfth week of age Desi males had the highest shank length. The least shank length was always observed for WP males. During the entire period, Desi ducklings had higher shank length than WP duckling. The growth pattern of body weight was not uniform in the four groups except the initial body weight. High correlation was found between the body weight and shank length. It revealed that shank length can be made a criterion for selection for higher body weight. The method of comparison of growth rates recommended by Rao (1958) was found unsuitable for the present study. Among the functional relationships worked out linear, exponential and second degree equations were found to be unsuitable for fitting shank length as a function of age. Modified exponential, logistic, Gompertz and Von-Bertalanffy equations were found to be suitable for fitting shank length over a period of time. Among these four, Gompertz was found to be the best fit. The second best fitted equation was logistic. Graphs of the best fitted equations ie. Gompertz and logistic were drawn for all the six group along with the observed values. This also confirms above result. Among the two functional relations ie. linear and exponential used for predicting body weight from shank length, exponential was found to be most suitable.
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    ThesisItemOpen Access
    Milk marketing in the organised sector- a programming approach to optimisation of collection and distribution
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1987) Asokan, M V; KAU; Ravindranathan, N
    Two milk collection and one distribution route were taken for suggesting a suitable transportation model for optimizing the cost of collection and distribution of milk in dairy plants. Three Vehicle Scheduling Models, viz. saving model (model 1) suggested by Clarke and Wright (1964) λ model (model II) and ∏ model (model III) suggested by Gaskel (1967) were used in this study. Since there was high variation in supply of milk by each society to chilling plant, median and third quartile values of daily supply of milk of two selected months for each season was taken as expected availability of milk. Maximum distance that can be travelled by a truck in a route was calculated by considering the time. Morning and evening routes were formed with median and third quartile values as expected availability of milk in each season. Routes obtained in all cases indicated that routes formed by model 1 were the best. In the case of distribution of milk routes obtained by the model I was found to be the best. Using traveling salesman problem technique, an attempt was made to check the optimality of the routes obtained by each model and found that the routes were not optimum in most of the cases. Refinement method suggested by Holmes and Parker (1976) was tried out for knowing whether any further improvement is possible in model I. In certain cases better routes could be achieved. From this study, it is suggested that for the route formation in dairy plants for collection and distribution of milk, three techniques, viz. Clarke and Wright method (model I). Refinement method and traveling Slaesman problem technique should be used in the order stated. Forty four dairy co-operative societies were considered in the analysis of performance rating and grading of societies. Seven parameters were taken and subjective weights were given to each of them. Total score for each society was calculated and based on it the societies were graded as A, B, C and D.
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    ThesisItemOpen Access
    Pattern of development of shank length in chicken
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1982) Indirabai, T K; KAU; Surendran, P U
    Shank length and body weight measurements on 30 male and 30 female chicks from each of White Cornish (WC) and White Plymouth Rock (WR) breed were utilized to study the pattern of development of shank length in chicken. The birds were reared for eight weeks in Kerala Agricultural University Poultry Farm under uniform management. Upto the end of three weeks uniformity could be seen in the pattern of growth of shank length of the four groups. Thereafter WR male had a lead over the rest. The growth pattern of body weight was not uniform in the groups from the beginning. At the end of eight weeks the growth pattern was found to differ between sexes and breeds. Uniformity in growth rates was found in females of the two genetic groups as also between WC male and WR female. All the other pairs were heterogeneous. High correlation between body weight and shank length revealed that longer shank length can be made a criterion for selection for higher body weight. Shank length at the end of the first week was found to be most suitable for this purpose. Shank length has high positive correlation with age. The method of comparison of growth rates recommended by Rao (1958) was found unsuitable for the present study. Among the functional forms examined Modified Exponential, Gompertz and Logistic were found to be unsuitable for expressing shank length as a function of age. Most suitable patterns for expressing shank as a function of age in weeks were found to be linear and exponential. Among these two exponential turned out to be better than the other.
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    ThesisItemOpen Access
    Comparative study of lactation curves in cattle
    (Department of Statistics, College of Veterinary & Animal Sciences, Mannuthy, 1985) Mathew Sebastian; KAU; George, K C
    An investigation, based on 174 normal lactation records of 93 Jersey crossbred cows and 90 normal Isolation records of 55 Brown Swiss crossbred cows belonged to the University Livestock Farm, Mannuthy, was undertaken : (1) to compare the relative efficiency of various isolation curve models and to select the best one (ii) to compare the two genetic groups based on order of Isolation and season of calving and (iii) to develop equations for predicting total milk yield from part yields. Records upto the 4th Isolation were included in the study. The observations spread over a period of six years from 1978 to 1983. The year was delinested into dry, rainy and moderate seasons.
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    ThesisItemOpen Access
    Assessment of genetic divergence by factor analysis in groundnut (Arachis hypogaea L.)
    (Department of Statistics, College of Veterinary & Animal Sciences, Mannuthy, 1986) Muralidharan, K; KAU; Saraswathi, P
    Factor analysis, Principal component analysis, discriminent analysis, and cluster analysis were carried out with a multivariate data on 30 characters of 62 bunch type groundnut varieties grown in upland during khariff 1982 and rice fallows during summer 1982. Vegetative, reproductive and growth factors were identified as the causative factors of genetic divergence in both the environments. A height factor was also found to work with rice fallows. The characters which were most amenable to change due to selection in these factors were identified. They were not found to agree with the results obtained from discriminant analysis. When factor loadings were estimated from principal components, clustering of characters were found identical to those obtained from factor analysis.
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    ThesisItemOpen Access
    Factor analysis of genetic divergence in sesame
    (Department of Statistics, College of Veterinary & Animal Sciences, Mannuthy, 1988) Tes, P Mathew; KAU; Saraswathi, P
    Sesame is an important annual oil seed crop grown in India. It is grown in a very limited area of 1453 hecters in Kerala. The lack of high yielding varieties suitable to the seasons in different regions was the main factor limiting the productivity of sesame in our State. The genetically divergent parents will produce better segregants in the hybridisation programme. The present study was undertaken to delineate the underlying causes of divergence in the sesame plants using the factor analytic methods.
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    ThesisItemOpen Access
    Comparison of different techniques for the estimation of genotype-environment interaction
    (Department of Statistics, College of Veterinary & Animal Sciences, Mannuthy, 1984) Laly John, C; KAU; Gopinathan Unnithan, V K
    The genotypic stability analyses of Eberhart and Russell (1966), Perkins and Jinks (1968), Freeman and Perkins (1971), Wricke (1966) and Shukla (1972) were studied in detail. The mistakes in the analysis of variance of Perkins and Jinks (1968) were corrected. The first three analyses which used the theory of regression explains a large part of the genotypic environment interaction. On the otherhand, when the regression cannot explain a large part of the genotype - environment interaction, Wrioke's ecovalence ratio and Shukla's stability variance could satisfactorily be used.