Socio-Economic Drivers of Agroforestry in Kaiti Watershed in Makueni County, Kenya
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Date
2025-04
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Kenyatta University
Abstract
Forests and trees are essential resources for sustainable provision of goods and services. They provide social, economic and ecological benefits. However, increasing human population has led to reduction of forest lands for agriculture and settlement. Trees have not only been depleted in reserved forests but also on agricultural lands. Several measures have been formulated to improve forest cover in Kenya. One of such strategies is implementation of Agriculture (Farm Forestry) Rules, 2009. This requires farm owners to set up at least 10 per cent of the land under agroforestry. Existing literature on agroforestry studies focus on factors affecting agroforestry adoption and benefits and challenges in agroforestry. This study aimed at bridging the knowledge gap by examining tree density on farms and determining the relationship between socio-economic characteristics of households and tree density on farms. This study was carried out in Kaiti watershed, Makueni County, Kenya and assessed (1) types of agroforestry practices and tree species diversity (2) tree density on farms (3) socio-economic characteristics of households and (4) determined the relationship between socio-economic characteristics of households and tree density on farms in Kaiti watershed. The study targeted a sample of 100 households. Cluster sampling was used to divide Kaiti watershed into 3 clusters: Machakos Town, Kaiti and Makueni sub-counties. In each sub-county, locations were purposively selected. Further, households were proportionally distributed across locations and simple random sampling was applied where households were randomly selected from each location. Quadrats were used to determine tree species diversity and tree density on farms while questionnaires were used to record socio-economic characteristics of households and benefits and challenges of agroforestry. Data files were prepared in the Microsoft Excel and SPSS version 20 software where descriptive and inferential statistics were used. One-Way ANOVA determined whether the mean of species diversity differed across the study area while Tukey’s Honest Significant Difference (HSD) test ascertained where significant differences in species diversity occurred across the study area and multi linear regression determined the relationship between socio-economic characteristics of households and tree density on farms. The study found 8 agroforestry practices and the most dominant were multipurpose trees on croplands, orchards, trees on pasture and windbreaks. Fifty two tree species were found where 55.8% are indigenous and 44.2% are exotic. Tree species richness range was 2 to 28 and the mean was 6.86. The results of One-Way ANOVA for both Shannon Diversity Index and Simpsons’ Index of Diversity showed significant difference in species diversity in Kaiti watershed with p-value of 0.00023 and 0.00012 respectively. The mean of tree density was 104.5 trees per acre where 54% of farms had less than 40 trees per acre below the recommended number of trees per acre while 46% of farms had more than 40 trees per acre. This study found that household income, land tenure, household farm size, sex of household head, secondary occupation and household age composition (18 to 60 years) had significant influence on tree density on farms with p value of < 0.05 and household income was the most significant. The study recommends sensitization of farmers about importance of trees and suitable tree species for growing in arid and semi-arid areas. Further, supply of certified seedlings close to farmers and at affordable prices would improve tree species diversity and tree density on farms. There is also need for private land ownership for households to promote sense of ownership of trees.
Description
A Thesis Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science (Integrated Watershed Management) in the School of Pure and Applied Sciences of Kenyatta University, April, 2025