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Subject: Agricultural Statistics and Informatics × End date: 1994 × Start date: 1994 ×
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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.