Journal of Scientific Research and Reports, ISSN: 2320-0227,Vol.: 9, Issue.: 1
Perceptions and Truism of Climate Variability within Smallholder Farming Communities in Meru County, Kenya
Mwoga Muthee1*, Joy Obando2 and Fuchaka Waswa3 1Department of Environment and Community Development, Kenyatta University, Nairobi, Kenya. 2Department of Geography, Kenyatta University, Nairobi, Kenya. 3Department of Agricultural Resources Management, Kenyatta University, Nairobi, Kenya.
Mwoga Muthee1*, Joy Obando2 and Fuchaka Waswa3
1Department of Environment and Community Development, Kenyatta University, Nairobi, Kenya.
2Department of Geography, Kenyatta University, Nairobi, Kenya.
3Department of Agricultural Resources Management, Kenyatta University, Nairobi, Kenya.
(1) Leszek Labedzki, Institute of Technology and Life Sciences, Kujawsko-Pomorski Research Centre, Poland.
(1) Job N. Nmadu, Federal University of Technology, Nigeria.
(2) Anonymous, Cadi Ayyad University, Morocco.
(3) J. J. Dukiya, Feral University of Technology, Minna, Nigeria.
Complete Peer review History: http://sciencedomain.org/review-history/11509
Aims: To assess actual and perceived climate variability within smallholder communities in Meru county.
Study Design: A cross-section survey to obtain data on farmers’ perceptions and using Long term hydro-climatic records.
Place and Duration of the Study: Targeted smallholder farmers in the seven major sub-agro ecological zones of Meru County. Rainfall and stream flow records ranging between 1976 and 2011 were used. Household survey and focused groups discussions were conducted in August 2010.
Methodology: A stratified random sampling was used in 7 sub agroecological zones, each zone representing a stratum. Structured questionnaire was administered to 275 household heads. Focus group discussions were undertaken to understand the community perspective on climate variability. Data was analysed employing descriptive statistics. Using rainfall data from 3 stations and 1 river gauge, seasonal rainfall and stream flow anomalies were computed. ANOVA was used to determine significant mean differences across represented sub-agroecological zone.
Results: The key indicator of climate variability was variations in rainfall. In the low highland 1, coefficient of variability in rainfall amount for first season was 0.43 and 0.26 for second season. For the upper midland 2 and in the transition zone with upper midland 3 the coefficient of variability for first season was 0.36 and 0.37 respectively. As such the first season was the main determinant of annual agricultural productivity in both upper midland and low highland agro-ecological zones. February and September had highest (0.44) Stream flow coefficient of variability. Majority (91.6%) of respondents concurred that there was climate variability, an indication of the awareness level.
Conclusion: Responses were pegged on perceived forms of climate variability. There was divergence in observed and perceived climate variability parameters necessitating integration of farmers’ and scientific approaches in mitigation against effects of climate variability. Planning for effective agricultural productivity needs to be seasonal and agro-ecological zone specific to counter temporal and spatial variations.
Agroecological zone; rainfall variability; smallholder farmers’ responses.
DOI : 10.9734/JSRR/2016/20239Review History Comments
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