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.

This study sought to develop an index for agricultural digitalization by applying composite confirmatory analysis (CCA). Another aim was to determine the factors that affect the development of digitalization in PLAS farms. Data on the indicators of the three dimensions of digitalization were collected from 300 Proactive Land Acquisition Strategy (PLAS) farms in South Africa using semi-structured questionnaires. Confirmatory composite analysis (CCA) was employed to reduce the items into three digitalization dimensions and ultimately to a digitalization index. Standardized digitalization index scores were extracted and fitted to a linear regression model to determine the factors affecting digitalization. The results revealed that the model shows practical validity and can be used to measure digitalization as measures of fit (geodesic distance, standardized root mean square residual, and squared Euclidean distance) were all below their respective 95%quantiles of bootstrap discrepancies (HI95 values). Therefore, digitalization is an emergent variable that can be measured using CCA. The average level of digitalization in PLAS farms was 0.02 and varied significantly across provinces. Although farmers have attempted to digitalise their farms, there are still minimal levels of digitalization in PLAS farms. The results further reveal different digitalization patterns. As judged by the estimated weights of various dimensions of digitalization, the use of digital technologies to collect, store, analyse, and disseminate (CSAD) farm-related data contributed more towards the digitalization index. The second most important component of digitalization was automation digitalization. In contrast, value chain digitalization was the least significant contributor. The factors that significantly influence digitalization were age, gender, farm type, network type, and cellular data type. Since PLAS farmers have not embraced much digitalization, it is important to focus on awareness and capacity building. A balanced approach to digitalization would benefit PLAS farms by ensuring that strategies to integrate digital solutions within the value chain are developed. To foster and support the digitalization in PLAS farms, policymakers and stakeholders should tailor their strategies to fit specific socioeconomic factors.

(2016). Agriculture in a Developmental State: Finding the Nexus for Development(al) Agricultural Economists. Agrekon: Vol. 55, No. 4, pp. 303-330.

Climate change necessitates that farmers explore alternative renewable energy sources to meet their immediate energy needs and reduce carbon emissions. The study aimed to determine the potential for energy transition in proactive land acquisition strategy (PLAS) farms in South Africa and identify factors associated with energy transition. Data from 1956 PLAS farms across the country were collected and analysed. Results showed that about 58% of the farmers relied on grid electricity, mainly supplied by Eskom. Only 13% and 0.3% of farmers used solar and wind as renewable energy sources, respectively. These findings are important for policy and practice because the low uptake of solar and wind energy among PLAS farmers highlights a missed opportunity to advance climate-smart agriculture, reduce production costs, and enhance energy resilience in rural farming systems. Moreover, there is a need for renewable-energy subsidies, clearer policy incentives, and improved rural energy infrastructure. A further disaggregation of solar-powered PLAS farms by province revealed the Northern and Western Cape as the most promising regions for solar energy development. At the same time, the Eastern Cape and KwaZulu-Natal had relatively low potential. Farmers who had no electricity at all accounted for 26%, indicating low-hanging fruit for the Just Energy Transition (JET) strategy. The logistic regression results suggest that factors such as gender, farm size, education level, whether a farmer is receiving recap, and whether Eskom supplies electricity significantly influence the use of solar energy. Therefore, just transition policies should target these factors to ensure efficient and effective implementation.

The fall armyworm, Spodoptera frugiperda, arrived during the 2016/2017 cropping season in Limpopo, South Africa and it continues to cause devastating damage on maize in this region. The present study investigated knowledge, perception and management of S. frugiperda by smallholder farmers at Dzindi and Tshiombo irrigation schemes in this region. The study was conducted in 16 focus group discussion interviews, 12 at Dzindi and four at Tshiombo irrigation scheme. Although farmers failed to identify S. frugiperda eggs and pupal stages, they positively identified the pest by its larval stage and refer to it by several local names. All farmers relied extensively on insecticides for S. frugiperda control. With the exception of the Dzindi irrigation scheme farmers, who have adopted Bt maize, very few farmers have adopted alternative methods to chemical control such as mechanical control, physical control (crushing egg and handpicking of larvae), biological control, cultural practices (such as Push-Pull Technology), possible indigenous knowledge, or the use of monitoring tools, for the management of S. frugiperda. The study provided valuable information for designing sustainable management practices to control S. frugiperda on maize in smallholder farming communities.