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.

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.

The Journal for Transdisciplinary Research (TD) is a journal dedicated to promoting the concept of transdisciplinary research. It is aimed at creating knowledge with the collaboration of the formal sectors of the natural and human sciences as base. Over and above this, the intention is to focus on levels of knowledge that are to be found at grassroots level. It is important to explore this knowledge because it becomes the groundwork for further knowledge, which is often not confined to a fixed formal discipline or area of knowledge. The editorial team of the journal welcomes contributions of a theoretical nature, as well as material based on empirical research. Preference is given to southern Africa as a region of focus. However, should submitted articles show the potential for making a contribution towards our understanding of science beyond the confines of single disciplines, it would enjoy favourable consideration.

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.

Literature suggests that there are high levels of commercialisation by even the poorest and smallest landholders within the horticulture subsector in most rural Africa. Thus, the notion of poor commercialisation among smallholder farmers is now being challenged. Against this background,this paper estimated factors that influence commercialisation by smallholder cabbage farmers in the Eastern Cape Province of South Africa. The study used a cross-sectional survey of120 smallholder cabbage farmers obtained through a multistage sampling procedure. A Cragg double-hurdle model was applied to analyse the factors that influence the commercialisation decision and intensity of commercialisation. In the first stage, the result of the probit regression model revealed that gender, age, access to informal credit and area planted were significant factors towards influencing smallholder cabbage farmers’ commercialisation decisions. In the second stage, the results of the truncated regression model revealed that family size and area planted were the key factors determining the intensity of commercialisation. Hence, the attempts to improve smallholder farmers’ cabbage commercialisation should be guided by these significant explanatory variables in the study area, given the low commercialisation index revealed.