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Subject: Agricultural Statistics and Informatics × Author: KAU × End date: 1997 × Start date: 1997 × Type: Thesis ×
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    ThesisItemOpen Access
    Optimum size of plots In coconut using multivariete techniques
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1997) Kumari Liji, R S; KAU; Gopinathan Unnithan, V K
    This investigation was taken up to determine optimum size of experimental units for coconut using multivariate approach. Observations on yield, female flower production, percentage of buttons set and number of functional leaves from 184 coconut palms for two consecutive years were utilised. These palms belonged to two separate experiments in two locations. All known systematic effects were eliminated from the observations. The trees were arranged in the ascending order of the number of functional leaves of first year of observations. Experimental units of sizes ranging from single tree to ten trees were formed by combining trees adjacent in the list of ordered trees. Blocks of five plots, seven plots and ten plots were also formed by combining adjacent plots. Coefficient of variation in univariate case and determinant of relative dispersion matrix in multivariate case were the measures of variation used. Optimum size of experimental units was determined in univariate case for yield and female flower production in first and second years. Optimum size of plots was determined in multivariate case for the following character combinations. 1) Yield for first and second year 2) Female flower production for first and second years 3) Yield and female flower production for first and second year 4) Yield, female flower production and percentage of buttons set for the first year 5) Yield female flower production and percentage of bottons set for the second year Optimum size of plot was determined by three different criteria viz., (i) that which requires minimal experimental material for a specified precision (ii) that having maximum efficiency and (iii) that which maximises the curvature of the relationship between measure of variation and plot size. Plot size that required minimum number of trees for 5 per cent error was two tree plots except in the univariate case of yield in first year and multivariate case of without blocking for characters sets (4) and (5) for which single tree plots were optimum. In all univariate determinations single tree plots had maximum efficiency. Two tree plots had maximum efficiency in multivariate approach except for characters sets (4) and (5) in the case of no blocking. Four tree plot was optimum by the method of maximum curvature except for characters sets (3), (4) and (5) is multivariate case for which three tree plots were optimum. Though Fair Field Smith's law was a good fit to the relationship between the measure of variation and plot size, Y = a +b/√x+ c/x gave better fit in most of the cases. Two tree plots were recommended for experiments it) established coconut gardens.
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    ThesisItemOpen Access
    Statistical models in growth studies of rabbit
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1997) Manojkumar, K; KAU; George, K C
    An investigation was undertaken in the Kerala Agricultural University Rabbit Research Station, Mannuthy to find a suitable relationship between age and body weight of three different breeds of rabbit viz. Newzealand White, Soviet Chinchilla and Grey Giant and to study the impact of climatic elements, temperature and humidity on body weight. The rabbits were reared under uniform feed formula and identical management practices. The investigation mainly depended on data consisting of weekly body weights of rabbits up to twelve weeks and daily climatological parameters, temperature and humidity. The experiment was conducted during the three time periods (First time period: October to January, Second time period: February to May and Third time period: June to September). Seven mathematical models such as linear, quadratic, von-bertalanffy, exponential, modified exponential, logistic and gompertz were fitted for body weights of individual rabbit as well as average body weights over twelve weeks and these models were compared using coefficient of determination (R2) and standard error of estimate (s). Additive model, Wt = a + b L + c G and Multiplicative model, Wt = a Lb GC were fitted for developing a suitable relationship of average body weights, body lengths and body girths over twelve weeks of the three breeds. Using the average weekly dry bulb temperature and wet bulb temperature, Temperature Humidity Indices [THI = 0.72 (Cdb + Cwb) + 40.6 ] were worked out. Correlation coefficients between average daily weight gain per week and THI were worked out for finding the effect of climatological data on body weight. The investigation was having the following salient features. 1. In the time period, October to January the body weight of Newzealand White is significantly different from that of Soviet Chinchilla and Grey Giant. New Zealand White has lower body weight. But the difference-in body weights between Soviet Chinchilla and Grey Giant was not significant. In the second time period, February to May and in the third time period, June to September the difference in body weights of three breeds were not significant. 2. Von bertalanffy model, Wt = a [1 - b Exp(kt)]3 was the most suitable for ascertaining growth in the three breeds of rabbits on individual basis as well as on the basis of average body weights over twelve weeks. 3. The multiplicative model, Wt = a Lb Gc was obtained as the suitable relationship of body weight, body length and body girth of the three breeds of rabbit. 4. During the periods October to January (Winter) and June to September (Monsoon), temperature and humidity had significant effect on body weight. In the former period body weight will decrease along with increase in temperature and in the later period it will increase along with temperature.
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    ThesisItemOpen Access
    Comparison of transformations used in the analysis of data from agricultural experiments
    (Department of Agricultural Statistics, College of Horticulture, Vellanikkara, 1997) Priya Menon, K; KAU; Prabhakaran, P V
    A study was undertaken to empirically examine the suitability of the various commonly used transformation techniques on the analysis enumerative data relating to agricultural experiments or surveys. The possibility of evolving better transformations for the analysis of data pertaining to certain specific environments was also explored. Data for the study were gathered from the available records of the project on pest surveillance survey on paddy, those on the project on early stage pest control on paddy of Regional Agricultural Research Station, Pattambi and those of the post emergence herbicidal evaluation trial for the control of Pennisetum pedicel/atum of the All India Co-ordinated Research Project on Weed Control, College of Horticulture, Vellanikkara. Comparisons among the various commonly used transformations were made either on the basis of a single criterion viz., Bartlett's chi-square test, Tukey's test of non-additivity, Levene's residual F test or Taylor's power low or on the basis of multiple criteria viz., likelihood method of Box and Cox (1964) or the graphical method of Draper and Hunter (1969). The results of the analysis of the data relating to pest surveillance study on paddy showed that logarithmic transformation was the most desirable in the analysis of data on the counts of all the major types of insects on rice (stem borer, jassid, gall fly, leaf folder, BPH) the only exception being case worm for which a squareroot transformation was indicated. Box-Cox approach undoubtedly emphasised the utility of the logarithmic transformation in analysing data on counts of insects and weeds. The graphical plot of the log likelihood function against the exponent of the power transform had a maximum value around zero for all sets of data indicating the superiority of the logarithmic transformation over the others. The graphical method of Draper and Hunter failed to suggest a unique transformation for all sets of data. However, in most cases, the choice lied between squareroot and logarithmic transformations with a slight superiority for the squareroot transformation. As per the method suggested by Berry (1987) a suitable location parameter 'C' was estimated for the analysis of sets of data involving extreme observations including zero values. The estimated value of the additive constant was found to be approximately 2.8 for all the different sets of data. The analysis of transformated data after incorporating the estimated value of the additive constant to each observation showed slightly better results than the ordinary analysis after incorporating the additive constant 'one' to each datum. An alternative estimate of the parametric constant in the inverse hyperbolic sine squareroot transformation was developed and the resultant estimate produced better results than those by the estimate proposed by Beal (1942). Assuming a non-linear relationship between mean (u) and standard deviation (σ) a new transformation x' = log(x2+k) where x = original observation, k = a parametric constant to be estimated from the data, was derived theoretically. The best estimate (k^) of the parameter k was derived to be ^ ∑σ/μ - n k = ----------- where n is the number of observations. ∑ (1/μ2) This transformation is expected to be useful in the analysis of data when the mean- standard deviation relationship is approximately parabolic. In general, the new transformation was found to be slightly better than the inverse hyperbolic sine squareroot transformation in the analysis of data with disproportionate amount of variability. Rank transformations were also found to be helpful in the analysis of data when there are model violations and were in general helpful for increasing the sensitivity of the F test.