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20101
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Subject: Agricultural Statistics × Author: B.N.Rajeshwari × End date: 2017 × Start date: 2010 ×
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    ThesisItemOpen Access
    Statistiacl Investigation and Interpretataion of replacement series intercropping experiments with mixtures methodology
    (UAS, Dharwad, 2010) B.N.Rajeshwari; A.R.S.Bhat
    Intercropping has been traditional practice in our country. In recent years research into intercropping has attracted attention of the agricultural scientists. Therefore this study has been undertaken to demonstrate the applicability of the mixture experiment in agricultural research. The object of the mixtures methodology is to obtain individual parameters with an interpretation closer to that of parameters in ordinary polynomial response functions. In the methodology used, a relationship is built between crop response and the proportions of crops. Statistical techniques used to analyze data from Mixture Experiments involve fitting Multiple Regression models with the intercept set to zero. One purpose of statistical modeling in a Mixture Experiment is to model the mixing components such that predictions of the response for any mixture component, singly or in combination, can be made empirically. A chickpea based mustard intercropping experiment conducted in Agricultural Research Station (ARS), Gulbarga during the year 2007 and 2008 laid out in RCBD was considered for the study. It is a replacement series intercropping experiment with 3 replications comprising 5 different row proportion treatments along with 2 sole crop treatments and satisfies all the criteria to apply mixtures methodology. About 11 different price combinations were worked out keeping in mind the small and large variations of prices in the market. The results of multiple regressions for all the price ratios showed 6:2 row proportion of chickpea + mustard to be more stable with fluctuations in market prices and proved to be optimum with existing market price when compared with results of usual ANOVA. Mixtures methodology is proved to be advantageous over results of ANOVA in aspects like change in optimum area to be recommended with change in market price, narrow range of confidence interval, prediction of the optimum response of the row ratio not included in the experiment.