[dataset] The Perception of Smallholder Farmers on the Impact of the Agricultural Credit on Coffee Productivity

The sampling frame was a list of SHCFs who obtained credit from Commodity Fund in 2007/2008 financial year in Kiambu County. It was thought after twenty-three years (2007/2008 to 2019/2020), the farmers would be in a position to give a valid perception on the impact that the agricultural credit had had on their coffee productivity. Further, the study only considered farmers who borrowed credit between KShs. 100,000 and KShs. 1 million. This is because those farmers who had more than 1 million had coffee farms between 4 to 8 acres and maintained their own farming records. This reduced the number of SHCFs from 3,589 to 87. The FCS of these SHCFs were identified from CF database and this formed a basis of random sampling 87 farmers who did not borrow credit for the study period – either from CF or other formal financial institutions. Thus the total sample size was 174. The summary of the data is as follows with Theme 1 to Theme 4, Risk perception and Regressand are binary.

Theme 1 (demand for inputs) had six response variables: payment of leasing land (FDI1); buying of land (FDI2); accessing both printed and electronic information (FDI3); acquisition of agrochemicals and fertilizers (FDI4); acquisition of tree seedlings (FDI5); acquisition of manure (FDI6).
Theme 2 (demand for labor) had five variables: increased use of child labor on the coffee farm (FDL1); increased use of labor from other members of your family apart from children on the farm (FDL2); increased use of hired labour (FDL3); increased use of ox-plough (FDL4); and increased use of tractor (FDL5).
Theme 3 (Efficiency of production) had five variables: increased use of optimal combination of inputs (FIE1); increase in area of farming of coffee (FIE2); replacement of old trees with improved varieties (FIE3); increased access to extension services (FIE4); and increase of the cost of labour (FIE5)
Theme 4 (returns) had five variables: annual profit per acre (FRT1); increase in numbers of shares for farmers in SACCO (FRT2); increase in farmers’ wealth (FRT3); and investing in other business (FRT4).
Risk perception: had two response variables: risk of making loss (RISKL) and risk of loan default (RISKD)
Regressand: impact of agricultural credit on coffee productivity (FPOR)