Climate-smart agricultural technological innovations have the potential to reduce climate change impacts on agriculture. Due to several barriers, their current rate of adoption and diffusion is low. Public-private partnerships (PPPs) have the potential to facilitate technology development and dissemination to smallholder farmers. The aim of this paper is to examine the role of the water efficient maize for Africa (WEMA) project in enhancing technology adoption by smallholder farmers in South Africa. This study explores how PPPs enhance technology adoption and highlights the challenges faced within PPPs using WEMA case. A critical analysis, involving iterative process helped to construct a comprehensive narrative. We found that disputed outcomes, stakeholder concerns, shortage of seeds, disinclination of local companies to market new seeds, upkeep of previous relationships, contractual arrangements and high level of expertise and skills required from farmers were the main factors that affected the efficacy and impact of WEMA on the targeted output and beneficiaries.

Climate change represents a serious threat to African agriculture, consequently leading to water scarcity and climate variability. These challenges negatively impact agricultural production. Climate-smart Agriculture (CSA) technologies, such as drought-tolerant seed varieties (DTSVs), can provide a solution. However, effective adoption and use of these technologies within smallholder communities is not straight forward. This study investigated the factors determining adoption of CSA technologies by exploring the farmers’ characteristics, contextual factors, and considered additional factors extracted from farmers’ perceptions of CSA technology-specific attributes. The study was carried out in Limpopo Province, South Africa. Quantitative and qualitative data from farmers’ cross-sectional survey (n=196) and focused group discussions (n=5), was subjected to descriptive and inferential statistical analysis. Factor analysis reduced 19 identified CSA technologies specific attributes to 5 factors that were used in the multinomial logistic regression model. Results show a range of drivers and barriers influencing DTSVs adoption. The adoption of DTSVs by sampled smallholder farmers were influenced by training and demonstration; knowledge and benefits related to DTSVs; necessary requirements like receiving tractor services on time, knowledge on better dates of DTSVs and weather information; enabling factors like additional training on DTSVs and information including knowledge about other CSA technologies other than DTSVs, gender, marital status and credit access. These results have policy implications for various stakeholders which reinforce multi-actor approach to climate change adaptation and building of functional institutions, enhancing training of smallholder farmers, improving on access to sufficient demonstrations, climate change information and credit support amongst other support. Key words: Climate smart agriculture, smallholder farmers, drought-tolerant seeds technology adoption, multinomial logit regression. Climate change represents a serious threat to African agriculture, consequently leading to water scarcity and climate variability. These challenges negatively impact agricultural production. Climate-smart Agriculture (CSA) technologies, such as drought-tolerant seed varieties (DTSVs), can provide a solution. However, effective adoption and use of these technologies within smallholder communities is not straight forward. This study investigated the factors determining adoption of CSA technologies by exploring the farmers’ characteristics, contextual factors, and considered additional factors extracted from farmers’ perceptions of CSA technology-specific attributes. The study was carried out in Limpopo Province, South Africa. Quantitative and qualitative data from farmers’ cross-sectional survey (n=196) and focused group discussions (n=5), was subjected to descriptive and inferential statistical analysis. Factor analysis reduced 19 identified CSA technologies specific attributes to 5 factors that were used in the multinomial logistic regression model. Results show a range of drivers and barriers influencing DTSVs adoption. The adoption of DTSVs by sampled smallholder farmers were influenced by training and demonstration; knowledge and benefits related to DTSVs; necessary requirements like receiving tractor services on time, knowledge on better dates of DTSVs and weather information; enabling factors like additional training on DTSVs and information including knowledge about other CSA technologies other than DTSVs, gender, marital status and credit access. These results have policy implications for various stakeholders which reinforce multi-actor approach to climate change adaptation and building of functional institutions, enhancing training of smallholder farmers, improving on access to sufficient demonstrations, climate change information and credit support amongst other support. Key words: Climate smart agriculture, smallholder farmers, drought-tolerant seeds technology adoption, multinomial logit regression.

Agriculture contributes to the South African economy, but this sector is highly vulnerable to climate change risks. Smallholder maize farmers are specifically susceptible to climate change impacts. The maize crop plays a crucial role in the country’s food security as is considered a staple food and feed. The study aimed at examining the socioeconomic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture in the Limpopo Province, South Africa. It was conducted in three different areas due to their specific agro-ecological zones. A multipurpose research design was used to gather data, and multistage random sampling was used to choose the study areas. Subsequently, 209 purposefully selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive results revealed that 81%, 67%, and 63% farmers in Ga-Makanye, Gabaza, and Giyani were willing to adopt CSA. Using the double-hurdle model, the t-test was significant at 1%, Prob > chi2 = 0. 0000, indicating a good model. At a 5% confidence level, education, crop diversification, and information about climate-smart agriculture (CSA) positively influenced adoption, while household size and agricultural experience negatively influenced it. It is recommended that the Department of Agriculture, Land Reform, and Rural Development provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.

The creation and deployment of digital technologies throughout the agro-food system contribute to achieving Sustainable Development Goal nine. However, various digitalization studies offer limited insight into the policy issues and solutions around emerging technologies and big data systems in agriculture. There is a need for an additional understanding of how agricultural policymaking should respond to the usage of digital technologies in the agri-food sector. Furthermore, evidence is limited on how existing agricultural government programs such as land reform can be linked with digitalization policy. This systematic review of literature sought to determine the transformation that is needed in the political and economic environment for the digital revolution to take place within South African land reform agriculture. A thematic analysis of data sampled from ProQuest Central, Scopus, Dimensions, and Google Scholar reveals five areas of intervention from agricultural policymaking. The digital revolution in agriculture can be brought about by transforming digital infrastructure, data interoperability and governance, digital markets, the compatibility of government incentives with the private sector, and the digital cultural landscape. This essay contributes to agricultural policy and decision-making dialogues that pay attention to digital technologies and land reform programs in South Africa.

Digital technologies disrupt agricultural value chains, thereby facilitating enhanced connectivity among various players. This contributes to the development of resilience and sustainability within farms. This study examined behavioural factors that influence farmers’ intentions to adopt value chain digital technologies available in South Africa. A simple random sample of 100 Eastern Cape PLAS farms was chosen. Data was collected using a semi-structured questionnaire. The data was analysed in R and STATA statistical packages using the unified theory of acceptance and use of technology (UTAUT) and structural equation modelling (SEM). The results indicated a significant relationship between performance and effort expectancy and the intention to adopt value technologies. Furthermore, male farmers had a higher expectation of performance and effort. Prioritising performance demonstrations and trainings on the utilisation of these technologies is crucial in efforts to enhance their adoption. This contributes to the existing body of literature on the deployment of value chain technologies.