Determinants of rainwater harvesting technology (RWHT) adoption for home gardening in Msinga, KwaZulu-Natal, South Africa

Home gardening is extremely important for resource-poor households that have limited access to production inputs. However, in South Africa attempts to implement home garden programmes often fail to improve food security of the poor due to water scarcity. Rainwater harvesting technology (RWHT) has been used to supplement the conventional water supply systems, but its potential has not been fully exploited. An understanding of the factors influencing the adoption of improved technologies is therefore critical to successful implementation of agricultural development programmes. This study evaluated the determinants of farmers’ decisions to adopt rainwater harvesting technology (RWHT) in rural Msinga, KwaZulu-Natal Province, South Africa, using a binary logistic regression model based on a household survey of 180 rural home gardeners. The result of the logistic regression model showed that gender, age, education, income, social capital, contact with extension agent and perception/attitude towards RWHT are statistically significant in explaining farmers’ adoption of RWHT in the study area. Implications for agricultural and rural development policy were discussed.Keywords: home gardening, rainwater harvesting technology, adoption, logistic regression, South Afric

Vulnerability and Poverty Dynamics in Rural Areas of Eastern Cape Province, South Africa

If the aim of studying poverty is not only improving the well-being of households who are currently poor, but also preventing people from becoming poor in the future, a new forward looking perspective must be adopted. This study analyses determinants of household poverty dynamics in rural areas of the Eastern Cape Province, South Africa using a panel dataset on a representative sample of 300 rural households in the Amathole District Municipality. The result of the study shows a significant flow in and out of poverty, which is a sign of vulnerability. While 63% of the sampled households are poor (ex post), while 48% are vulnerable to becoming poor (ex ante) in future. The result of the probit model indicates that age, level of education and household heads’ occupation, dependency ratio, remittance/diversified income base, exposure to idiosyncratic risks and access to credit are statistically significant in explaining households’ vulnerability to poverty. Implications for policy are discussed.Keywords: Vulnerability, Poverty dynamics, Rural, Household, Expenditure