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
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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
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.
Wambua Alex Mwaniki,
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.
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