Authors : David Umolo; George. O. Edeki

Volume/Issue : Volume 8 - 2023, Issue 11 - November

Google Scholar : http://tinyurl.com/45z75jk5

Scribd : http://tinyurl.com/3fw3p662

DOI : https://doi.org/10.5281/zenodo.10469382

Abstract : This work investigates the impact of temperature and rainfall on the yield of cocoa using an experimental dataset from the Cocoa Research Institute of Nigeria, Ibadan (n=213) with a blocking factor having 4 levels. The data were analyzed using exploratory data analysis and the response surface methodology. The exploratory data analysis relationship/distributional plot shows that there exists significant negative relationship between the yield of cocoa and the predictors (temperature and rainfall). The estimated boxplot with respect to blocking factor indicates that there is presence of outlier in the yield of cocoa with majority of the yields measured below 500kg over the period of study. Results from the response surface models without blocking indicate that all the estimated models were statistically significant with all the lack of fit test estimated to be insignificant (an indication of good fit). On the basis of incorporated blocking factor to the experiment, we observed that all models which range from first order to second order outperformed those without blocking factor by considering the estimated adjusted R 2 . The blocking factors incorporated into the experiment were found to be statistically significant with all contour plots on the basis of the Eigen analysis suggesting insignificant lack of fit. This implies that incorporating blocking factor helped minimize the sum of squared error and in turn improved the precision.This study recommends that CRIN and other cocoa farmers should learn to adopt newly developed techniques that could militate against the impact of weather change being experienced.

Keywords : Response surface model, cocoa yield, rainfall, temperature, Eigen-analysis, contour, lack of fit.