The objective of the study was to determine the factors that influence the perceptions of smallholder farmers towards the adoption of digital technologies. A purposively selected sample of 250 smallholder farmers who were cross-sectionally surveyed from Port St Johns and Ingquza Hill Local Municipalities in South Africa was used in the study. The Technology Acceptance Model (TAM) and the Attention, Desire, and Action (AIDA) model were used to analyse the data. The results showed that digital technologies were perceived to be expensive, cause a digital divide, and discouraged the use of Indigenous Knowledge even though they increased production. Positive perception towards digital technologies was associated with cattle rearing, with extreme negative perception for sheep and goat rearing. Educational level, employment status, monthly income, household size, being part of a cooperative (1% level), age, and source of income (10% level) were significant factors affecting smallholder farmers’ perceptions of digital technologies. In conclusion, there are economic, social justice, and traditional perceptions towards digital technologies by smallholder farmers, with socio-economic factors affecting the perceptions. The study recommends providing low-cost digital technologies that promote Indigenous Knowledge, which should target the youth and young farmers with less education in small households who are full-time farmers with moderate-to-high incomes and are part of farmer groups/organisations.

Purpose: The purpose of this paper is to analyze food security status and its determinants among households in the Eastern Cape Province of South Africa. Design/methodology/approach: The analysis is based on the General Household Survey which was conducted in 2014 where 3,033 households were sampled from the province. Specifically, this study examines the determinants of food security proxy by dietary diversity (24-hour recall) using descriptive statistics, Poisson regression. A frequency count of food groups consumed household dietary diversity score was used as the explained variable. Findings: The descriptive analysis shows that, although 61.7 percent of households in the study area have a high dietary diversity score, however, food group giving micronutrients are less consumed as food groups having cereals (maize), beef, sugar and oil was mostly consumed. Results on the marginal effect of Poisson regression indicate that household head characteristics (age, gender, education, marital status, and employment status), pension receiving households and geographical location significantly influence household dietary diversity. Originality/value: This study advocates for the intensification of rural development and food security programs, formal and informal education for household heads, female empowerment and dietary enlightenment for households in order to promote the consumption of diverse diets and more healthful food groups.

The major concern of most African countries, including Nigeria, in recent times is how to increase food production because of food insecurity issues, which by extension, is a major contributing factor to the prevalence of poverty. Therefore, adoption of conservation agricultural practices is regarded as a pathway to drive the achievement of food and nutrition security, as well as the needed optimal performance in the agri-food sector. Reportedly, scaling up of the limited adoption of these practices could be facilitated through kinship ties, peer influence, and social networks that govern mutual interactions among individuals; therefore, this motivated the study. Using cross-sectional data obtained from 350 sample units selected from South-Western Nigeria through a multistage sampling technique, this study applied descriptive statistical tools and cross-tabulation techniques to profile the sampled subjects while count outcome models were used to investigate the factors driving counts of conservative agriculture (CA) adoption. Similarly, a marginal treatment effects (MTEs) model (parametric approach) using local IV estimator was applied to examine the effects of CA adoption on the outcome (log of farmers’ farm income). Additionally, appropriate measures of fit tests statistics were used to test the reliabilities of the fitted models. Findings revealed that farmers’ years of farming experience (p < 0.1), frequency of extension visits (p < 0.05), and social capital viz-a-viz density of social group memberships (p < 0.05) significantly determined the count of CA practices adopted with varying degrees by smallholder farmers. Although, social capital expressed in terms of membership of occupational group and diversity of social group members also had a positive influence on the count of CA practices adopted but not significant owing largely to the “information gaps” about agricultural technologies in the study area. However, the statistical tests of the MTEs indicated that the treatment effects differed significantly across the covariates and it also varied significantly with unobserved heterogeneity. The policy relevant treatment effect estimates also revealed that different policy scenarios could increase or decrease CA adoption, depending on which individuals it induces to attract the expected spread and exposure.

Globally, climate change remains one of the most pressing challenges, and it is also an obstacle to the fundamental achievement of the Sustainable Development Goals. The purpose of the study was to examine the determinants of small-scale farmers’ participation in social capital networks to enhance adoption of climate change adaptation strategies. Multistage and purposive sampling were used to carry out the study. A cross-sectional research design was used to carry out the study and structured questionnaires were used as a data collection tool. The data collected were analyzed using descriptive statistics, the Binary Logit model, and the Ordered Logit model. The findings of the study show that gender, household size, age, marital status, education, and employment status affect a farmer’s participation in social capital networks. The results also show that household size, employment status, and income level affect the extent of participation in social networks. As the paper is the first to look at the determinants of the participation of farmers in social capital networks in Eastern Cape, the results are of paramount importance to policy formulators in order to formulate policies that will encourage farmers to join localized farmer-based social capital networks to adopt climate change adaption measures.

This study aimed to identify and assess smallholder farmers' awareness and perceptions of digital technologies in the Eastern Cape, South Africa. The study used a cross-sectional survey of a purposively selected sample of 250 smallholder farmers from Port St Johns (PSJ) and Ingquza Hill (IH) Local Municipalities. The Likert scale and a perceptive index were used to analyse the data. The study found that 57.4% and 74.7% of smallholder farmers were aware of digital technologies. Fifty-six percent to 70.6%of smallholder farmers were aware of a combination of smartphones, radios, and TV. There was indifference to the reliability of digital technologies as indicated by 42.7% to 52.9%, with 97.8% to 98.1% highlighting the need to improve awareness of digital technologies, mainly through awareness campaigns and information days as indicated by 51.6% from PSJ and farmer training as highlighted by 50.6% from IH. About 76.4% of the smallholder farmers did not have adequate knowledge of digital technologies, while 54.4% did not find them user-friendly. However, 88.0% indicated that it made farming easier, is labour saving (69.6%), improves agricultural production (81.2%), improves access to information (77.6%), easier access to extension (65.6%), and will increase farm output (71.6%). However, digital technologies were expensive (78.8%), caused a digital divide (69.2%), and required specific skills (81.0%). There was a negative perception towards digital technologies by 69.6% of the smallholder farmers. The study concluded that negative perception is mainly influenced by cost, skill requirement, lack of knowledge, and difficulty in use. There was awareness of less complicated digital technologies. Recommendations include promoting digital technologies through farmer groups or associations such as farmer field schools. Awareness campaigns through extension officers can be used to promote digital technologies to smallholder farmers.