Evaluation of the Most Significant Treatment Factors for Maize Grain Yields and Total Microbial Count in Long Term Agricultural Experiment (LTAE), Kenya
Wambua Alex Mwaniki,
Koske Joseph,
Mutiso John,
Mulinge Wellington,
Kibunja Catherine,
Eboi Bramuel
Issue:
Volume 5, Issue 6, December 2017
Pages:
188-199
Received:
18 September 2017
Accepted:
8 October 2017
Published:
11 November 2017
Abstract: Agriculture and its related economic activities form the main livelihood for Kenya population. The sector faces numerous challenges that have led to food insecurity in the country. Maize production plays a significant role in the country’ economic development contributing significantly to the national overall Gross Domestic Product (GDP). Declining maize grain yield is one of the major challenges that require interventions to avert the looming food crisis. To address the challenge various Long Term Agricultural Experiments (LTAE) and studies on soil fertility maintainance options have been developed. However, such studies have explored only single factors at a time with limited application of robust statistical application. Statistical procedures could offer best set of few treatment factors that explain the maize grain yields in LTAEs in Kenya and beyond. The focus of this paper was the application of robust statistical methods in obtaining set of minimum treatment factors that could be used in the determination maize grain yield in LTAE. Specifically, the paper sought to describe the trend in maize grain yield over the experimental period, characterize the input factors for maize grain yield and to determine the most significant treatment factors for maize grain yield and total microbial population count (bacteria, fungi, actinomycetes, rhizobia). The primary data was summarized from LTAE in National Agricultural Research Laboratories (NARL), 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 isolated (Farm Yard Manure (FYM) and Nitrogen and Phosphorus (NP)) at their low factor levels as the most significant treatment factor in maximizing the maize grain yield and total microbial population count. It was possible to select a minimum set of treatment factors in LTAE that are critical in predicting the maize grain yield.
Abstract: Agriculture and its related economic activities form the main livelihood for Kenya population. The sector faces numerous challenges that have led to food insecurity in the country. Maize production plays a significant role in the country’ economic development contributing significantly to the national overall Gross Domestic Product (GDP). Declining...
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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
Wambua Alex Mwaniki,
Koske Joseph,
Mutiso John,
Mulinge Wellington,
Kibunja Catherine,
Eboi Bramuel
Issue:
Volume 5, Issue 6, December 2017
Pages:
200-209
Received:
20 September 2017
Accepted:
8 October 2017
Published:
11 November 2017
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.
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 f...
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Determinants that Lead Drivers into Traffic Accidents: A Case of Arba Minch City, South Ethiopia
Issue:
Volume 5, Issue 6, December 2017
Pages:
210-215
Received:
29 August 2017
Accepted:
14 September 2017
Published:
7 December 2017
Abstract: This study attempted to assess the factors that lead drivers into traffic accidents at Arba Minch City. 200 drivers were selected using stratified random sampling method and the data have been collected using structured questioners. From sampled drivers 62% of the drivers were involved in one or more accidents. Poisson regression model was the appropriate one compared to the negative binomial regression model for the data. From Poisson regression analysis variables like driver experience, driving after alcohol use, having more licenses, speedy driving, and number of punishments were the causes that lead drivers into traffic accidents in the study area. Road safety professionals should target these factors in their efforts to reduce the occurrence of traffic accidents.
Abstract: This study attempted to assess the factors that lead drivers into traffic accidents at Arba Minch City. 200 drivers were selected using stratified random sampling method and the data have been collected using structured questioners. From sampled drivers 62% of the drivers were involved in one or more accidents. Poisson regression model was the appr...
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