An understanding of factors influencing smallholder farmers’ livestock ownership at the household level is vital in formulating pro-poor livestock production policies and technologies. Hence, this study examined factors that influence livestock ownership of smallholder farmers. The data was collected randomly from three purposively selected study areas in the OR Tambo District (King Sabata Dalindyebo, Port St Johns and Ingquza Hill local municipalities) in the Eastern Cape Province of South Africa using a cross-sectional survey of 650 households. A multivariate probit model (MPM) was used to estimate correlates of livestock species ownership at the household level. Results indicated that education, age, household income, marital status, religion, rainfall, gender, household size and employment status influence livestock species ownership at the household level. Therefore, efforts to promote livestock ownership and production should be guided by these significant explanatory variables in the study area. Interdependence among species was also noted (cattle and sheep; goats and pigs; sheep and pigs; cattle and goats; goats and sheep), suggesting complementarity among the different types of livestock species. This complementarity among the species can possibly be explained by functional diversity generic with multi-species livestock farming which is worth supporting to enhance biodiversity conservation, climate change mitigation, rural resource use efficiency and socio-economic sustainability at the household level.

Energy plays critical direct and indirect roles in the livelihoods of human beings. Despite its relevance, the literature highlights that a majority of rural households are energy insecure and very much rely on unclean energy sources that compromise their health. Against this background, this paper estimates the determinants of energy security at the household level based on evidence from Melani-inland and Hamburg-coastal communities in the rural Eastern Cape Province of South Africa. The study used the multidimensional energy poverty index (MEPI) to calculate the energy security status of households in the two communities while the Tobit regression model was used to investigate the main factors affecting household energy security. The results indicated that in both communities, households were energy secure. However, access to additional energy measures was low, which could affect their energy security if the primary source (ESKOM electricity) is interrupted. Tobit regression results revealed that factors like marital status, household size, electricity affordability and source of income are the most critical factors influencing the energy security status of households in the study areas. Therefore, the paper recommends policy options that improve access of rural households to additional measures on energy and affordability of electricity.

Food is one of the essential areas of service delivery required for a household’s wellbeing and development. Against this background, the literature suggests a high level of food insecurity among rural households in South Africa. Thus far, this study has investigated drivers of household-level food insecurity. Data were collected from 283 randomly selected respondents from Melani and Hamburg communities using a structured questionnaire. An ordered logit regression model was used to investigate drivers of household food insecurity. Descriptive statistics results revealed that most of the respondents from the study areas were food secure to mildly food secure (Melani-inland −73%; Hamburg-coastal −77.5%). The findings suggest that overall, the two communities were food secure. Econometrics results further revealed that factors like gender, household size, energy security, access to credit, farming, land ownership, and being able to afford to pay for water influence food insecurity of households in Melani and Hamburg. Therefore, to reduce household food insecurity, the study recommends that priority should be given to these socio-economic and institutional factors. This could be through improving gender and cultural policies which restrict women from generating income, training households about credit usage, and family planning to limit household sizes.

Maize plays a significant role among rural farming households in South Africa. The study therefore estimated the drivers of maize cultivars’ selection choices among rural farming households focusing on the commonly grown cultivars from the study area (Landrace and genetically modifies [GM] maize). A cross-sectional survey of 650 respondents randomly selected from Port St Johns and King Sabata Dalindyebo Local Municipalities of the Eastern Cape Province of South Africa were used. Descriptive results revealed that a significant number of the respondents from the study area (65%) were growing Landrace maize cultivars, followed by GM maize (31%), improved OPVs (3%) and conventional hybrids (1%). Multivariate Probit regression estimates show that GM maize cultivars’ selection is positively influenced by the amount of rainfall (1% level), household size (5% level), education (1% level), size of arable land (10% level) and access to cell phones (5% level), whilst negatively influenced by the employment status (5%). Conversely, the selection of Landrace maize cultivars is negatively influenced by the of amount rainfall (1% level), education (1% level), income (10% level), access to cell phone (10% level) and radio (10% level) whilst positively influenced by the number of livestock (5% level). The study therefore argues that GM maize cultivars may be meaningfully promoted in high rainfall areas, focusing on arable land sizes and targeted awareness campaigns. The promotion of Landrace maize cultivars may be targeted in low rainfall areas in a mixed farming setting to enhance the complementarity of maize and livestock.

In an effort to address the decline in maize productivity, the government of Zimbabwe in 2016/17 endorsed a special program for input support named command agriculture scheme (CAS). Against this background, the study questioned the beneficiaries’ technical efficiency and factors that influence farmers to gravitate towards the frontier using Chegutu and Zvimba districts of Zimbabwe as case studies. The study used a cross-sectional survey of 240 households randomly selected through a three-stage multiple-sampling procedure. The single-stage modelling stochastic frontier approach was applied to assess technical efficiency of A1 smallholder command agriculture maize farmers. The study revealed that A1 smallholder command agriculture maize farmers in Chegutu and Zvimba districts were technically efficient at 85% and 94%, respectively. The major determinants of technical efficiency were basal fertilizer, labour, area allocated to maize production and topdressing fertilizer which all indicated a positive relationship. The main determinants of technical inefficiency were age, maize farming experience, level of education, marital status, occupation status and other sources of income. Results further revealed that farmers from Chegutu district had increasing returns to scale (1.43) while farmers from Zvimba district had decreasing returns to scale (0.54). The study therefore argues that despite the observed high technical efficiencies, Chegutu farmers could bridge their 15% gap between the observed output and the frontier output by focusing more on input usage with increasing returns to scale while Zvimba farmers could bridge their 6% gap by focusing more on socio-economic drivers of technical inefficiency given their decreasing returns to scale.