Abstract
Smallholder dairy farmers occupy high potential areas of Kenya and are a source of manure, crops and milk. There is need to use other means of characterising smallholder dairy farmers as they mostly practice mixed farming. The objective of this paper is to use cluster analysis method to characterize the smallholder dairy farmers with added farmer and activity data variables. Clusters of 336 farmers in this study were derived using 28 key variables. This paper demonstrates how to conduct farmer assessments for climate change adaptation activities, climate smart technologies implementation using knowledge of key farmer variables and their distribution in the smallholder dairy farmers of Nandi County, Kenya. This paper demonstrates the importance of integrating agricultural information for smallholder dairy farmers to machine models to characterize the groups and observe the natural groupings. This allows for policy managers to know the key characteristics and how to use them in policy implementation especially in designing climate change adaptation programs factoring education and training of farmers as demonstrated in this paper that they are practicing many activities on their farms. Key words: Cluster analysis, smallholder dairy farmers, farm utilisation, climate change adaptation.
Highlights
Sub-Saharan Africa (SSA) has the fastest growth in agriculture and the greatest level of agricultural imports compared to other global regions (Livingston et al., 2011)
Cluster analysis was used to classify the smallholder farmers and examine how key variables of acreage for grazing, total acreage, education level, number of dairy cattle in the households and manure management affect their labour practices and major income categories and classify them (Table 2). This agrees with observations from other studies on smallholders where such variables were enumerated (Nyambo et al, 2019)
These clusters when using discrete variables showed focus areas such as the low education level of farmers and they were majorly male dominated. These clusters had total low acreage, as well as areas available for grazing, the farmers had less labour and high dairy livestock numbers. This finding agrees with Chibanda et al (2009), Tittonell et al (2010) and van Averbeke and Mohamed (2006) whose studies found that cluster analysis gave the key variables of note to define the components for practice change
Summary
Sub-Saharan Africa (SSA) has the fastest growth in agriculture and the greatest level of agricultural imports compared to other global regions (Livingston et al., 2011).
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