Browsing by Author "Muraya, M. M."

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Effects of Agricultural Land Use Practices on Soil Organic Carbon Stocks, Total Nitrogen and Available Phosphorous in Smallholder Farms in Embu County, Kenya
(2022) Osoro, N. N.; Muraya, M. M.; Gathungu, K.
A study was conducted to determine soil organic carbon stocks (SOCs), total nitrogen (TN) and available phosphorous (AP) changes in agricultural land use practices with a focus on maize and coffee based agricultural systems along Kapingazi river catchment in Embu County. Demarcation was done into four agro-ecological zones (AEZ) following the river downstream; Lower Highland Zone 1 LH1; Upper Midland Zone 1, UM1; Upper Midland Zone 2, UM2; Upper Midland Zone 3, UM3. Soil samples were obtained from two depths of 0-25 cm and 25-50 cm across slope positions. The soil organic carbon stocks were high in LH1 at 58.38 kg/m2 whereas UM3 had least amount at 29.48 kg/m2. The total nitrogen was higher in LH1 at 0.27% while least at UM3 with 0.07%. The LH1 had higher mean amount of available phosphorous at 19.44 ppm and least at UM3. The coffee agricultural system had more available phosphorous in LH1 at 23.75 ppm whereas maize had more in UM1, UM2 and UM3. The soil organic carbon stocks, available phosphorous and total nitrogen decreased across the AEZ. The Farm Foot Slope sampling point had high soil organic carbon stocks with the lowest amounts in the Farm Summit sampling point at both depths. The concentration of total nitrogen in coffee was high in all slope positions, whereas, available phosphorous was higher in maize. Therefore, it is concluded that topography and agriculture land use and management practices influence soil nutrient status.
GENERAL OVERVIEW OF SAMPLE SIZE ESTIMATION FOR RANDOMIZED CONTROLLED CLINICAL TRIALS
(Chuka University, 2021) Obare, D. M.; Njoroge, Gladys G.; Muraya, M. M.
Calculation of the minimum sample size needed to meet the primary study objective is a key feature of the design of any clinical trial. The other reason a priori sample size determination is to limit participant harm or loss of clinical benefit to as few study participants as possible. This article generally reviews the basic principles that determine an appropriate sample size and provides methods for its calculation in some simple, yet common, cases. Sample size is closely tied to statistical power, which is the ability of a study to enable detection of a statistically significant difference when there truly is one. A trade-off exists between a feasible sample size and adequate statistical power
IMPACT OF MARKETING CHANNELS, FEED SUPPLEMENTS AND CREDIT ACCESS ON MILK PRODUCTION AMONG SMALLHOLDER DAIRY FARMERS IN CHUKA SUBCOUNTY, KENYA
(Chuka University, 2022) Githae, S. W.; Muraya, M. M.; Munyiri, S. W.
Kenya dairy farming contributes approximately 17% of the Gross Domestic Product. Kenyan milk production has been projected to be between seven to nine litres/cow/day, way below international standards whereby a cow produces 25 to 28 litres/cow/day. Low yield of milk is attributed to lack of adequate information on relationship between age, education level, training, experience, marketing channels, feed supplements, credit access and milk production by the majority of the smallholder dairy farmers. This study determined the relationship between age, education level, training, experience, marketing channels, feed supplements (concentrate and minerals), credit access and milk production among smallholder dairy farmers in Chuka Sub County. The research design was correlational and stratified random sampling technique was used, with 238 respondents from a target population of 7396 farmers. Data was analysed using simple and multiple regression models. The study revealed that age education level, experience, were not significant predictors for milk yield. However, training in dairy farming was significant related to milk production. Inclusion of feed supplements was a significant predictor mineral mix provision; concentrate provision of milk yield. Marketing channels, credit access, were insignificant predictors of milk yield. Concentrate and mineral mix were significantly and positively correlated to milk yield. However, marketing channels and credit access were not significantly correlated to milk yield. The study concludes that feed supplements and mineral mix are important in predicting variations in milk yield.
Mathematical Modeling of Host - Pest Interac- Tions in Stage-Structured Populations: A Case of False Codling Moth [Thaumatotibia Leucotreta ]
(Scitech Research Organisation(SRO)., 2021) Ochwach, J. O.; Okongo, M. O.; Muraya, M. M.
False codling moth (FCM) (Thaumatotibia lucotreta ) is a significant pest due to its potential eco- nomic impact on many susceptible fruits in most temperate regions of the world. Efforts to control the codling moth in the past mostly relied on the use of broad spectrum insecticide sprays, which has resulted in the development of insecticide resistance, and the disruption of the control of secondary pests. Understanding the dynamic of this pest is of great in importance in order to effectively employ the most effective control strategies. In this study, a mathematical model of host-false codling moth interactions is developed and qualitatively analysed using stability theory of system of differential equations. The basic offspring number with respect to FCM free equilibrium is obtain using next generation matrix. The condition for local and global asymptotic stability of FCM free and coexis- tence equilibria are established. The model is analysed numerically and graphically represented to justify the analytical results.
Occurrence of Fungal Foliar Diseases of Tomato in different Agro-Ecological Zones of Kirinyaga County, Kenya
(Farm to Fork Foundation, 2022) Ogolla, F. O.; Onyango, B. O.; Muraya, M. M.
Tomato production is characterized by inconsistent quality and yields due to biotic constraints such as fungal foliar diseases. Information on the occur- rence of the diseases in different agro-ecological zones of Kenya is scanty. This study assessed the occurrence of early blight, late blight and Septoria spot diseases in tomato farms in five agro-ecological zones (AEZ) of Kirinyaga County in Kenya (UM2, UM3, UM4, LM3 and LM4) using cross sectional survey method. Macro plots were systematically established diagonally in tomato farms and were used to assess disease incidence and severity. Inci- dences and severity data were subjected to analysis of variance (ANOVA) using Kruskal Wallis is H test at α = 0.05. Median comparison was performed using Steel Dwass Critchlow Fligner with bonferroni adjustment in Statistical Analysis Software (SAS) version 9.4. Incidences and severity of early blight, late blight and Septoria spot in tomato farms were significantly different (p<0.05) among the villages and agro-ecological zones. Incidence of early blight ranged from 35.7% to 76.65% with severity ranging from 17.15% to 50.87%. The incidence of Septoria spot ranged from 23.56% to 93.42% with severity ranging from 16.67% to 44.44%. The incidence of late blight ranged from 33.33% to 86.63% with severity ranging from16.67% to 33.33%. The inci- dence of early blight was significantly higher in AEZ UM3 (Median = 75%), the incidence of Septoria spot was significantly higher in AEZ LM4 (Median = 83.33%) while the incidence of late blight was significantly higher in AEZ UM3 (Median= 50%). The severity of early blight was significantly higher in AEZ UM3 (Median = 38.89%), Septoria spot was significantly severe in AEZ LM4 (Median = 40.28%) while late blight was significantly higher in AEZ UM4 (Median = 32.72%). It can be concluded that the incidences and severity of the three foliar fungal diseases of tomato differed in different AEZ of Kirinyaga County. These findings serve as a baseline study and can be used to enlighten farmers on tomato fungal diseases in the area. However, there is a need for studies to evaluate predisposing factors and to determine the economic impact of foliar fungal diseases of tomatoes in Kirinyaga County.
SELECTION OF OPTIMAL FEATURES IN STATISTICAL MODELLING
(Chuka University, 2021) Gachoki, P. K.; Njoroge, G. G.; Muraya, M. M.
In statistical modelling, selection of optimal features entails making a selection of relevant predictor variables to be used in development of statistical models. Most modelling studies have focused on construction of statistical models skipping out or failing to put on record the process of selection of best features which is an integral part of statistical modeling. This failure might lead to use of duplicated features, features that are less relevant or other that have low variance in addition to random features which could result to poor performing prediction models. This study seeks to discuss how feature selection can be done as a pre-requisite for statistical modeling. Some of the methods used in selection of best features include; forward selection, backward elimination, recursive elimination, entropy selection, variance threshold elimination, chi-square statistics, tree based selection, feature importance and correlation matrix with heat maps. This study is vital to researchers building statistical models since use of optimal features in statistical modeling would lead to high performing statistical models.
Tomato Cultivation and Farmers’ Knowledge on Selected Foliar Fungal Diseases in Agro- Ecological Zones of Kirinyaga County, Kenya
(Asian Journal of Agricultural and Horticultural Research, 2022) Ogolla, F. O.; Onyango, B.; Muraya, M. M.
Diseases are hindrance to tomato production in Kirinyaga County, Kenya. However, information on farmer’s knowledge about tomato diseases to warrant pesticide usage is scanty. Further, there is information gap on disease predisposing factor such as varietal choice and seed source. This study assessed the tomato farmers’ socio characteristic, varieties grown, seed source and knowledge of selected foliar fungal disease among tomato farmers in agro-ecological zones (AEZs) of Kirinyaga County. A cross sectional survey design that in cooperated purposive sampling and snowballing approaches was adopted in the study. Data were collected from 120 tomato farmers using structured questionnaires. A chi square (Ӽ2) test was used to examine the association between different variables at α= 0.05 using SAS version 9.4. No significant association (p > 0.05) was observed between gender of farmers and AEZ. Nonetheless, there were more men (83.33%) than women (16.67%). Terminator F1 variety was popular among farmers (25%). No significance (p > 0.05) association was observed between source of tomato planting material and AEZs. However, Agrovet was a popular seed source among farmers (40%). The reasons for choosing a particular tomato variety was significantly (p < 0.05) associated with the AEZ with 40.83% of farmers preferring tomato varieties with good marketability traits such as fruit size. Farmers’ knowledge of causative agent of early blight, late blight and Septoria leaf spot was significantly (p < 0.05) associated with AEZs. The source of farmer’s knowledge on tomato foliar fungal diseases was not significantly (p > 0.05) associated with AEZ. However, farming experiences was a popular source of knowledge (51.67%) among farmers. Inability of some farmers to identify tomato diseases negates the efforts on disease management in tomato production in Kirinyaga County. Therefore, measures such as coordinated education on tomato diseases is necessary to empower farmers on disease causes and identification to enhance disease management and improve tomato yields in Kirinyaga County in Kenya.