Application of Response Surface Methodology for Determining Optimal Factors in Maximization of Maize Grain Yield and Total Microbial Count in Long Term Agricultural Experiment, Kenya
Science Journal of Applied Mathematics and Statistics
Volume 5, Issue 6, December 2017, Pages: 200-209
Received: Sep. 20, 2017; Accepted: Oct. 8, 2017; Published: Nov. 11, 2017
Views 1822      Downloads 79
Authors
Wambua Alex Mwaniki, Department of Planning and Statistics, Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya
Koske Joseph, Department of Mathematics and Computer Science, Moi University, Eldoret, Kenya
Mutiso John, Department of Mathematics and Computer Science, Moi University, Eldoret, Kenya
Mulinge Wellington, Kenya Agricultural and Livestock Research Organization, Nairobi, Kenya
Kibunja Catherine, Kenya Agricultural and Livestock Research Organization, Nairobi, Kenya
Eboi Bramuel, Department of Planning and Statistics, Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya
Article Tools
Follow on us
Abstract
The Agriculture sector is the main stay of the Kenyan economic development contributing over 70% of the Gross Domestic Product (GDP). The sector is faced with numerous challenges leading to frequent and recurrent food shortages. Declining maize grain yield is one among the major challenges that call for urgent interventions to address the looming food crisis in the country. Maize play a big role in the Kenyan food security and in most case lack of the same is taken to mean food insecurity. It is due the importance attached to the crop that a Long Term Agricultural Experiments (LTAE) was set up specifically to research on the Maize grain yield. Many paper published on the LTAE in the country are only single factors analysis and lack the application of Response Surface Methodology (RSM) approaches in solving challenges facing the low and declining maize grain yield (y1), total microbe population (y2) a crucial component of Soil Organic Matter (SOM) and their optimization. The focus of this paper therefore is the application of RSM in maize grain yield and total microbial population optimization. Specifically, the paper determined the most significant factors for maize grain yield and total microbial population (bacteria, fungi, actinomycetes, rhizobia), (screening phase of the paper), constructed of an efficient and appropriate experimental design for evaluating the optimal settings of maize yield and total microbial population count and determined univariate optimal settings for maize grain yield and total microbial population. The primary data was summarized from LTAE in National Agricultural Research Laboratories (NARL) in Kabete under the Kenya Agriculture and Livestock Research Organization (KALRO) and secondary data imputed for experimental points falling outside the set field experimental design points. Two treatment factors were identified as the most significant treatment factors (Farm Yard Manure (FYM) and Nitrogen and Phosphorus (NP)) at their low levels and Circumscribed Central Composite Design (CCCD) with two star points as the most efficient design. CCCD passed most optimal criteria of DAET. Univariately, optimal setting for maize grain yield was realized at 3.8x103 kg/ha and that of the total microbial population at 3.6x106 count. The study confirmed that it was possible to optimize the input treatment factor that lead to the optimization of both maize grain yield and maintaining maximal total microbial population count at its optimal levels.
Keywords
Response Surface Methodology, Long Term Agricultural Experiments, Univariate Optimization, Circumscribed Central Composite Design
To cite this article
Wambua Alex Mwaniki, Koske Joseph, Mutiso John, Mulinge Wellington, Kibunja Catherine, Eboi Bramuel, Application of Response Surface Methodology for Determining Optimal Factors in Maximization of Maize Grain Yield and Total Microbial Count in Long Term Agricultural Experiment, Kenya, Science Journal of Applied Mathematics and Statistics. Vol. 5, No. 6, 2017, pp. 200-209. doi: 10.11648/j.sjams.20170506.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Box, G., & Wilson, K. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13 (1), 1-45.
[2]
Cheng, S.-W., & Wu, C. F. (2001). Factor screening and response surface exploration. Statistica Sinaca, 553-604.
[3]
Cochran, W. G., & Cox, G. M. (1950). Experimental Designs. New York: Wiley.
[4]
Edmondson, R. N. (1991). Agricultural response surface experiments based on four level-factorial designs. Biometrics, 1435-1448.
[5]
Food and Agriculture Organization for United Nation. (2012). The state of food insecurity in the world. Rome: United Nations.
[6]
Food and Agriculture for United Nation. (2014). The state of food insecurity in the world. Rome: United Nation.
[7]
Food and Agriculture Organization for United Nation. (2013). The state of food and agriculture. Rome: United Nation.
[8]
Food and Agriculture Organization of the United Nation. (2015). The State of Food and Agriculture Report 2015: Social protection and agriculture: breaking the cycle of rural poverty. Rome: United Nation.
[9]
George, E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for experinters: Design, innovation and discovery 2nd edition.
[10]
Greenland, D. (1994). Soil science and sustainable land management. Soil science and sustainable land management in the tropics, 1-15.
[11]
Hill, W. J., & Hunter, W. G. (1966). A review of response surface methodology-a literature survey. Techno Metric, 8, 571-590.
[12]
Kariuki, J. G. (2011). The future of agriculture in Africa (The Pardee Paper No. 15). Boston: Boston University.
[13]
Kenya National Bureau of Statistics. (2016). Economic survey. Nairobi: Government of Kenya.
[14]
Kibunja, C. N. (2007). Nutrient dynamics and soil microbial diversity (Unpublished PhD Thesis). Nairobi: University of Nairobi.
[15]
Koning, N., & Smaling, E. (2005). Environmental crisis or "lie of land?" The debate on soil degradation in Africa. Land Use Policy, 22, 3-11.
[16]
Lee, J.-K., Park, I., Choo, S., Kim, S., Choi, Y., & Choo, J. (2013). Optimization of medium composition for alpha-galactosidase production by Antarctica bacterial isolate, Bacillus sp. LX-1 using response surface methodology. African Journal of Microbiology Research, 7(27), 3494-3500.
[17]
Maat, H. (2011). The history and future of agricultural experiments. NJAS - WageningenJournal of Life Sciences, 57, 187-195.
[18]
Mead, R., & Pike, D. J. (1975). A review of response surface methodology from a biometric point of view. Biometric, 803-915.
[19]
Montgomery, D. C. (2009). In Design and anlysis of experiments. New York: John Wiley & Sons.
[20]
Montgomery, D. C. (2009). Design and analysis of experiments. New York: John Wiley & Sons.
[21]
Myer, R. H., & Montgomery, D. C. (2002). Response surface methodology: process and product optimization using designed experiments, second edn,. Boston: PSN-Kent.
[22]
Myer, R. H., Khuri, A. I., & Carter, W. H. (1989). Response surface methodology. Technometrics, 31(2), 137-157.
[23]
Raissi, S., & Farsani, R. (2009). Statistical Process Optimization Through Multi-Response Surface Methodology. World Academy of Science, Engineering and Technology, 267–271.
[24]
Richards, P. (1989). Farmers also experiment: a neglected intellectual resource in African science. Quality and Reliability Engineering International, 3, 227-240.
[25]
Su, C.-T., & Yeh, C.-J. (2011). Optimization of the CU wire bonding process for IC assembly using Taguchi methods. Microelectronics reliability, 53-59.
[26]
Wadsworth, H. M. (1997). Handbook of statistical methods for engineers and scientists. (2nd, Ed.) New York: McGraw - Hill Professional.
[27]
Wass, J. (2010). First steps in experimental design-the screening experiment. Journal of validation technology, 49-57.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186