Thumbnail Image

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.

Browse

1983 - 19868
Reset filters
Subject: Statistics × Author: KAU × End date: 1986 × Start date: 1983 × Type: Thesis ×
Reset filters

Search Results

Now showing 1 - 8 of 8
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    ThesisItemOpen Access
    Optimum plot size for field experiments on turmeric (Curcuma longa L)
    (Department of Statistics, College of Veterinary & Animal Sciences, Mannuthy, 1984) Gopakumaran Nair, B; KAU; Prabhakaran, P V
    A uniformity trial on turmeric (Curcuma Longa. L.) was conducted at the experimental field of College of Horticulture, Vellanikkara, during the period from June 1983 to January 1984 to assess the nature and magnitude of soil heterogeneity of the experimental field, and to determine the optimum size and shape of experimental plots and blocks in conducting field trials on turmeric by different methods. At the time of harvest, the yield data from 864 plots each of size 0.6m x 0.75m were recorded separately, discarding the external border row.
  • Thumbnail Image
    ThesisItemOpen Access
    Forecasting of rice yield using climatological variables
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1986) Ajitha, T K; KAU; Prabhakaran, P V
    Systematic crop and weather observations on autumn and winter paddy at Pattambi Rice Research Station, during 1949-50 to 1973-74 have been analysed in order to evaluate the effect of different climatic factors on rice yield and to develop suitable prediction models for the preharvest forecasting of rice yield with sufficient degree of precision. The varieties under observation were PTB 1 and PTB 5 in the autumn season and PTB 12 and PTB 20 in the winter season. The crop was raised as rainfed through out the entire period of investigation. The moteorological variables included in the study were total rainfall (mm), number of rainy days, maximum temperature (°C), minimum temperature (°C), maximum humidity (%), minimum humidity (%), total hours of sunshine and wind velocity (km/h).
  • Thumbnail Image
    ThesisItemOpen Access
    Pre-harvest forecasting of sugarcane yield
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1984) Alphi Korath; KAU; Prabhakaran, P V
    Several yield prediction models were tried to examine their suitability for the pre-harvest prediction of yield of two varieties of sugarcane namely CO-997 and CO-62175 in different months of plant growth using biometric characters based on the data collected from the Sugarcane Research Station, Thiruvalla. The methods of multiple regression analysis, path coefficient analysis and principal component analysis were used for the above purpose. Multiple regression analysis using plant biometric characters revealed that cane yield could be predicted on the basis of observations on height of the cane, girth of the cane find estimated total leaf area per cane or area of third leaf from the seventh month after planting onwards with an accuracy in the range of 59*5 to 81.9 per cent. The estimated cane yield when multiplied by the number of canes in the plot will give an advance estimate of the plot yield Linear models with five biometric characters viz., height of the cane, girth of the cane, width of the third leaf determined from the selected plants of each plot and number of canes/tillers and number of leaves determined on a whole plot basis were sufficient to predict the plot yield of the crop as early as in the fifth month of plant growth with an accuracy in the range 68 to 90 per cent. Path analysis revealed that height of the cane and girth of the cane were the t wo important characters contributing towards cane yield in all stages of plant growth. Using the forecasting models fitted with principal components as explanatory variables, yield could effectively be predicted with 81.4. per cent accuracy for variety CO-997 and with 76 per cent accuracy for variety 00-62175 in the Sixth month of plant growth.
  • Thumbnail Image
    ThesisItemOpen Access
    Uniformity trials on colocasia (Colocasia esculenta L.)
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1986) Lizy, M J; KAU; George, K G
    A uniformity trial on colocasia was conducted at the experimental field of the College of Agriculture, Vellayani during the period April – September 1984, to study the nature and magnitude of soil heterogeneity and to estimate the optimum size and shape of plots in conducting field trials on colocasia. The various techniques adopted for achieving these objectives were, productivity contour map, mean squares among strips, serial correlation, heterogeneity index method and maximum curvature method. The biometrical observations such as height, girth, yield number of leaves and leaf area were taken from all plants. Productivity contour map revealed that the field was heterogeneous with regard to soil fertility. The mean squares for the horizontal and vertical arrangements indicated that the fertility was more clear along the length than along the width of the field. The low serial correlation coefficients for both rows and columns established that fertile areas occur in patches. The coefficient of variation increased in plot size. For a given size of the plot, the long and narrow plots yield lower coefficient of variation than square plots. The Smith’s variance law in the form Y = ax-b gave a satisfactory fit to the data. But among all the fitted models the equation Y = a + b/ x1/2 + c/x was found to be the best. Generalisation of Smith’s law in the form Y = ar-g 1c-g 2 also gave a good fit to the data and heterogeneity of rows was found to be significantly more than that of columns. The optimum plot size found out by using Smith’s equation was 12 units (3.34m2). But the optimum plot size computed by using the optimum equation Y = a + b/x1/2 + c/x was 10.87 units (2.93m2). A study of the optimum plot size while considering the cost of experimentation using the Smith’s equation was 1.636m2. In general, it can be recommended that a plot of 2.93m2 as optimum for conducting field trials on colocasia.
  • Thumbnail Image
    ThesisItemOpen Access
    Balanced designs for biological experiments in blocks of natural sizes
    (Department of Statistics, College of Veterinary and Animal Sciences, Mannuthy, 1983) Malika, V; KAU; Surendran, P U
    As a preliminary result we have established Fisher’s inequality associated with a BIB design and generalized it to balanced binary designs with unequal replications and unequal block sizes to balanced n-ary equireplicate designs and also to BIB designs in which one treatment alone is allowed to repeat more than once in a block. Further it is shown that a balanced proper binary design is equireplicate. From existing BIB designs we have constructed balanced binary and ternary designs. A novel method of construction is as follows: Let there be a BIB design with parameters v, b, r, k, λ. From each block form k blocks each of size k+1 with block content as all treatments of the block with one distinct treatment repeated in a block. The resulting design will be a balanced ternary design with parameters v1=v, b1=kb, r1=r(k+1), λ1= λ(k+2). Kroneckor product is applied for the construction of balanced ternary designs by collapsing blocks of a BIB design. We have proved using Kroneckor product, that existence of a resolvable BIB design implies the existence of a proper balanced ternary design and this is an improvement over the results due to Dey (1970). Further it is shown that method of Kroneckor product used for the construction of balanced ternary designs can also be used for the construction of partially balanced ternary designs. Methods have been devised for the construction of balanced ternary designs making use of Finite geometrices and Galois field.