Browsing by Author "Mugwe, J. N."

Now showing 1 - 4 of 4
  • Publication
    Publication
    Effects of soil and water conservation techniques on sorghum yield, runoff and soil moisture content in Upper Eastern Kenya
    (African Journal of Science, Technology and Social Siences, 2022-09-26) Omenda, J. A.; Ngetich, K. F.; Kiboi, M. N.; Mucheru-Muna, M. W.; Mugwe, J. N.; Mugendi, D. N.
    Water and nutrients are the main factors limiting grain production in the dry regions of sub-Saharan Africa. Given the onset of global climate change, the effects of drought stress on crop yield becomes more pronounced. Different approaches have been initiated to address this; however, they have been introduced at different times, in isolation, and at varying spatial scales. We evaluated four soil and water conservation technologies (mulching, minimum tillage, tied ridging and MBILI- intercrop) for three cropping seasons (short rains2020, long rains 2021, and short rains 2021) in the dry zones of central highlands of Kenya. The objectives were to determine effects of the technologies on run off, soil moisture content and to assess the influence of the technologies on sorghum yield. Experimental design was a randomized complete block with six treatments replicated four times. At the start of the experiment soil was sampled at 0-20cm and analysed for pH, N, P, K, C, Ca and Mg. Mulch was applied at a rate of 5t ha-1 and runoff sampled. Data were subjected to analysis of variance (ANOVA) using SAS version 9.4 and means separated using Tukey-Kramer Honest Significant Difference Test P≤ 0.05. Runoff, soil moisture and sorghum yield were significantly influenced by mulching. Run off was reduced by 50% (p=0.01) during long rains of 2021 and by 49% during short rains of 2021 under mulching treatment. During short rains of 2020 yield increased by 90% and 77% (p=0.001) under mulching and tied ridges respectively. The study highlights the importance of analyzing selected soil and water conservation technologies under rain fed conditions in response to declining food production with a focus on tied ridges and mulching.
  • Publication
    Publication
    Household's socio-economic factors influencing the level of adaptation to climate variability in the dry zones of Eastern Kenya
    (Elsevier, 2015-11-04) Mugi-Ngenga, E. W.; Mucheru-Muna, M. W.; Mugwe, J. N.; Ngetich, F. K.; Mairura, F. S.; Mugendi, D. N.
    Climate variability has a negative impact on crop productivity and has had an effect on many small-holder farmers in the arid and semi-arid lands (ASALs). Small-holder farmers in Eastern Kenya are faced with the constraint associated with climate variability and have consequently made effort at local level to utilize adaptation techniques in their quest to adapt to climate variability. However, documentation of the factors that influence the level of adaptation to climate variability in the study area is quite limited. Hence, this study aimed at assessing how the household's socio-economic factors influence the level of adaptation to climate variability. The study sites were Tharaka and Kitui-Central sub-Counties in Tharaka-Nithi and Kitui Counties of Eastern Kenya respectively. The data collected included the household demographic and socio-economic characteristics and farmers' adaptation techniques to cope with climate variability. Triangulation approach research design was used to simultaneously collect both quantitative and qualitative data. Primary data was gathered through a household survey. Both random and purposive sampling strategies were employed. Data analysis was done using descriptive and inferential statistics. Multinomial and Binary logistic regression models were used to predict the influence of socioeconomic characteristics on the level of adaptation to climate variability. This was done using variables derived through a data reduction process that employed Principal Component Analysis (PCA). The study considered five strategies as measures of the level of adaptation to climate variability; crop adjustment; crop management; soil fertility management; water harvesting and crop types; boreholes and crop variety. Several factors were found significant in predicting the level of adaptation to climate variability as being either low or medium relative to high. These were average size of land under maize; farming experience; household size; household members involved in farming; education level; age; main occupation and gender of the household head. Household socio economic factors found significant in explaining the level of adaptation should be considered in any efforts that aim to promote adaptation to climate variability in the agricultural sector amongst smallholder farmers.
  • Publication
    Publication
    Indigenous and conventional climate-knowledge for enhanced farmers' adaptation to climate variability in the semi-arid agro-ecologies of Kenya
    (Meru University of Science and Technology, 2021) Mugi-Ngenga, E. W.; Kiboi, M. N.; Mucheru-Muna, M. W.; Mugwe, J. N.; Mairura, F. S.; Mugendi, D. N.; Ngetich, F. K.
    Climate variability is among the main threats to rain-dependent smallholder farming in most sub-Saharan Africa countries. Hence, farmers should make efforts at the local level to utilize indigenous knowledge (IK) combined with conventional knowledge to adapt to climate variability impacts. We assessed; IK used by farmers in climate forecasting, their perceptions of climate variability and adaptation strategies, and their correlation with conventional approaches. We conducted the study in Tharaka South and Kitui Central sub-counties of Kenya. We used the triangulation approach to obtain the quantitative and qualitative data. To select respondents, we used purposive and random sampling strategies combined with the snowballing technique. Observed rainfall and temperature data from 1998 to 2018 were obtained from the Kenya Meteorological Department (KMD). Results showed that there were significant (p<0.05) differences in the use of indigenous indicators such as observation of the behavior of the sky (χ2 = 14.631), moon (χ2 = 7.851), and wind (χ2 = 5.864). The majority of the smallholder farmers (87%) used the change in the behavior of trees as the indigenous indicator in weather forecasting. The most common adaptation strategies (over 80%) used were food storage for future use (88.5%) and change of planting dates (87.5%). The analysis output of conventional data from KMD conformed with the farmers' observations and perception of climate variability over the reference period. Because farmers are still using IK that agrees with conventional knowledge, there is a need to integrate IK with conventional knowledge for use by rain-fed-dependent smallholder farmers in climate forecasting.
  • Publication
    Publication
    Indigenous and conventional climate-knowledge for enhanced farmers' adaptation to climate variability in the semi-arid agro-ecologies of Kenya
    (Elsevier, 2021-12-01) Mugi-Ngenga, E. W.; Kiboi, M. N.; Mucheru-Muna, M. W.; Mugwe, J. N.; Mairura, F. S.; Mugendi, D. N.; Ngetich, F. K.
    Climate variability is among the main threats to rain-dependent smallholder farming in most sub-Saharan Africa countries. Hence, farmers should make efforts at the local level to utilize indigenous knowledge (IK) combined with conventional knowledge to adapt to climate variability impacts. We assessed; IK used by farmers in climate forecasting, their perceptions of climate variability and adaptation strategies, and their correlation with conventional approaches. We conducted the study in Tharaka South and Kitui Central sub-counties of Kenya. We used the triangulation approach to obtain the quantitative and qualitative data. To select respondents, we used purposive and random sampling strategies combined with the snowballing technique. Observed rainfall and temperature data from 1998 to 2018 were obtained from the Kenya Meteorological Department (KMD). Results showed that there were significant (p<0.05) differences in the use of indigenous indicators such as observation of the behavior of the sky (χ2 = 14.631), moon (χ2 = 7.851), and wind (χ2 = 5.864). The majority of the smallholder farmers (87%) used the change in the behavior of trees as the indigenous indicator in weather forecasting. The most common adaptation strategies (over 80%) used were food storage for future use (88.5%) and change of planting dates (87.5%). The analysis output of conventional data from KMD conformed with the farmers' observations and perception of climate variability over the reference period. Because farmers are still using IK that agrees with conventional knowledge, there is a need to integrate IK with conventional knowledge for use by rain-fed-dependent smallholder farmers in climate forecasting.