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

Published: 01-11-2020| Version 1 | DOI: 10.17632/d96dsk299d.1
Contributor:
Richard Wanzala

Description

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)

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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. Two Focus group discussions (FGD) was scheduled for two hours in these FCS for twelve days in the October 2020. These FGD sessions per day in each FCS were conducted in succession to avoid borrowing and non-borrowing farmers discussing on issues of the data. The purpose of FGD was capture the spectrum of perceptions of smallholder farmers on the impact of the agricultural credit on coffee productivity. Therefore five questions were framed for FGD participants (SHCFs borrowers and non-borrowers) as follows: (1) what is your perception of agricultural credit on demand for inputs? (2) What is your perception of agricultural credit on demand of labor? (3) What is your perception of agricultural credit on efficiency of coffee production? (4) What is your perception of agricultural credit on returns (profits) on coffee? All the related feedback from FGD were put together per group and themes were generated. In addition, two questions on risk was also asked in order to determine what makes some farmers to take or not to take agricultural credit: “what is your perception of risk of taking agricultural credit in terms of making a loss (RISKL) and loan default (RISKD) ”. For key informants’ interviews to be conducted, a stakeholder mapping exercise was conducted to identify five main coffee value chain players (CVCP). Three key informants were identified in each of these five CVCP making a total of fifteen KIs. The KIs were strategically identified and included in the study especially those in top management who were knowledgeable on the relationship between agricultural credit and coffee productivity. Further, the KIs must have worked for that organization for not less than three years. The feedback obtained from FDG were used to prepare structured questions from KIs. Finally, a survey was conducted on the 174 SHCFs to investigate their perception of the impact of agricultural credit on coffee productivity.