Application of Response Surface Methodology for Optimization of Potato Tuber Yield
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 4, July 2015, Pages: 300-304
Received: Jun. 24, 2015;
Accepted: Jul. 2, 2015;
Published: Jul. 14, 2015
Views 4246 Downloads 163
Dennis Kariuki Muriithi, Faculty of Business Studies, Chuka University, Chuka, Kenya
The Author investigates the operating conditions required for optimal production of potato tuber yield in Kenya. This will help potato farmers to safe extra cost of input in potato farming. The potato production process was optimized by the application of factorial design 23 and response surface methodology. The combined effects of water, Nitrogen and Phosphorus mineral nutrients were investigated and optimized using response surface methodology. It was found that the optimum production conditions for the potato tuber yield were 70.04% irrigation water, 124.75Kg/Ha of Nitrogen supplied as urea and 191.04Kg/Ha phosphorus supplied as triple super phosphate. At the optimum condition one can reach to a potato tuber yield of 19.36Kg/plot of 1.8meters by 2.25 meters. Increased productivity of potatoes can improve the livelihood of smallholder potato farmers in Kenya and safe the farmers extra cost of input. Finally, i hope that the approach applied in this study of potatoes can be useful for research on other commodities, leading to a better understanding of overall crop production.
Dennis Kariuki Muriithi,
Application of Response Surface Methodology for Optimization of Potato Tuber Yield, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 4,
2015, pp. 300-304.
Alem G.(1993). Evaluation of tillage practices for soil moisture conservation and maize production in dryland Ethiopia. Agricultural Mechanization in Asia, Africa and Latin America. ;24(3):9–13.
D. K. Muriithi, J. Kihoro and A. Kihoro.(2012). Ordinal logistic regression versus multiple binary logistic regression model for predicting student loan allocation, Journal of Agriculture, Science and Technology 14(1).
D. K. Muriithi, G. G. Njoroge, E. W. Njoroge and O. Mark, (2013). Classification of higher education loans using multinomial logistic regression model, Journal of Mathematical Sciences: Advances and Applications Vol. 22, PP. 1-17.
D .K. Muriithi,A. N. Ngeretha, R. G. Muriungi And E. W. Njoroge.(2014). Analysis of the Fluctuation of Gross Domestic Product In Kenya Using Autoregressive Integrated Moving Average Model. Journal of Statistics: Advances in Theory and Applications.Vol.11, No.1, 2014, PP31-43
G.E.P. Box, W.G. Hunter, J.S. Hunter,(1978). Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, John Wiley, New York.
Giovanilton F. S, Fernando L. C, Andrea L.O.F.(2011). Application of response surface methodology for optimization of biodiesel production by transesterification of soybean oil with ethanol. Fuel Processing Technology, Vol 92: pp 407-413
K.Gathungu.G, N.Aguyoh.J and K.Isutsa Dorcas.(2014). Optimizing Seed Potato (Solanum tuberosum L.) Tuber Yield and Size Distribution through Integrated Irrigation Water, Nitrogen and Phosphorus Mineral Nutrient Application. American Journal of experimental Agriculture, Vol 4(3): pp 349-361
Khuri, A. I. and Cornell, J. A.,(1987). Response Surfaces: Designs and Analyses, Marcel Dekker, New York, NY.
Montgomery, D. C. (2001). Design and Analysis of Experiments, JohnWiley and Sons, New York, NY.
Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M. and Kowalski, S. M. (2004). “Response Surface Methodology: A Retrospective and Literature Survey,” J. Qual. Technol., Vol. 36, pp. 5377.
Koksoy, O., “Dual Response Optimization: The Desirability Approach,” Int. J. Ind. Eng.-Theory, Vol. 12,
Chih-Wei Tsai, Lee-Ing Tong and Chung-Ho Wang.(2010). Optimization of Multiple Responses Using Data Envelopment Analysis and Response Surface Methodology, Tamkang Journal of Science and Engineering, Vol. 13, No. 2, pp.197-203