2016
DOI: 10.21511/ee.07(2).2016.4
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Operational challenges faced by smallholder farmers: a case of Ethekwini Metropolitan in South Africa

Abstract: The study is aimed at establishing the impact of access to capital, access to markets, access to information and access to technology on competitiveness of smallholder farmers on the market in Ethekwini Metropolitan in South Africa. A questionnaire is used as a research instrument. A total of 100 participants was sampled from a population of 485 smallholder farmers in Ethekwini Metropolitan. From the sample, 21% were male and 79% were female. All respondents were all from the African race. The study reveales t… Show more

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Cited by 14 publications
(14 citation statements)
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“…About 38% of the farmers have farm sizes between 0.51 and 1.0 ha, followed by 36% of farmers who have farm sizes between 0.25 and 0.51 ha; only 3% have farm sizes of greater than 2 ha per household. These findings correspond with previous studies which reported that most of the South African smallholder farmers own less than 2 ha of land [3,24,58]. These findings confirmed that most of the South African smallholder farming community, which is dominated by resource-poor black farmers and who are mainly farming on former homeland areas, own about 13% of the total agricultural land [47,49,55,58].…”
Section: Total Area Ownedsupporting
confidence: 91%
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“…About 38% of the farmers have farm sizes between 0.51 and 1.0 ha, followed by 36% of farmers who have farm sizes between 0.25 and 0.51 ha; only 3% have farm sizes of greater than 2 ha per household. These findings correspond with previous studies which reported that most of the South African smallholder farmers own less than 2 ha of land [3,24,58]. These findings confirmed that most of the South African smallholder farming community, which is dominated by resource-poor black farmers and who are mainly farming on former homeland areas, own about 13% of the total agricultural land [47,49,55,58].…”
Section: Total Area Ownedsupporting
confidence: 91%
“…Binary logistic regression model with a dependent variable (adoption of SAPs) against demographic, socio-economic and farm characteristics as explanatory variables was used to analyze the factors influencing the adoption of SAPs. The major demographic, socio-economic and farm characteristics factors that influenced adoption of SAPs in this study area were hypothesized to be age, gender, education, occupation, off-farm income, on-farm income, ownership of farm equipment, ownership of livestock, access to hired labor, awareness of SAPs, access to land, access to credit and access to extension services based on literature [3,17,24,[32][33][34].…”
Section: Explanatory Variables For a Binary Logistic Regression Modelmentioning
confidence: 99%
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“…Furthermore, the paucity of data limits the ability to draw generalized lessons about the impacts, while well-demonstrated benefits of PIM is of paramount importance to convince reluctant farmers to take additional roles [21]. This is particularly the case in smallholder schemes of developing countries, where farmers are poorly resourced and their access to relevant information is cumbersome [22][23][24]. However, more than 80% of farmers are smallholders in Sub-Saharan Africa, so plugging the gap is crucial [25].…”
Section: Introductionmentioning
confidence: 99%