Analysing the extent of agricultural digitalisation adoption between proactive land reform farmers in Gauteng and Mpumalanga provinces

<p dir="ltr">The objective of the study was to compare the key determinants of digitalisation in both provinces between Proactive Land Acquisition Strategy (PLAS) farmers in Gauteng and Mpumalanga. These two provinces are characterised by different farming systems, infrastructure, and institutional support. Three dimensions of digitalisation were explored, and these included automation, data management, and value chain optimisation. Secondary data was obtained from a structured survey of 200 PLAS beneficiaries (70 in Gauteng and 130 in Mpumalanga). A digitalisation index, adapted from previous studies, was applied to categorise farmers as either “digitalised” or “non-digitalized.” To assess the extent of adoption, measures of central tendency and frequency tables were used to summarise the data, providing both the socioeconomic background of the farmers and the operational characteristics of their farms. A t-test compared mean differences in the levels of digitalisation, while the Mann-Whitney U test examined mean rank differences in farmers’ perceptions of digitalisation dimensions across provinces. A binary logistic regression model identified determinants of digitalisation in both provinces. Data analysis was carried out using the STATA software package (version 17 for Windows, 2021). Microsoft Excel (Windows 11 version, 2021) was also used for data processing, graph and table generation, and supplementary statistical analysis.</p><p dir="ltr">The results revealed a statistically significant difference in digitalisation between provinces. Overall, 55% of farmers were classified as digitalised, with Mpumalanga showing higher adoption (65%) compared to Gauteng (36%). Mpumalanga farmers also displayed stronger perceptions of data management tools (p < 0.01), automation (p < 0.05), and value chain optimisation technologies (p < 0.10; p < 0.01). Regression analysis further showed that age, farming experience, and province were significant predictors at the 1% level, while extension visits and signal strength were significant at the 5% level, and farming type at the 10% level.</p><p><br></p>