The study was conducted across six districts in KwaZulu-Natal province, characterized by progressive agricultural practices, with forestry and sugarcane production serving as substantial contributors to provincial economic output and employment generation. And the six regions were selected based on the presence of emerging forestry commercial farmers engaged in private-sector strategic investment intervention. The geographical scope encompassed Harry Gwala, uGu, Amajuba, uMgungundlovu, uMzinyathi, Zululand, and King Cetshwayo districts as illustrated in Figure 1. These districts were selected due to their significant concentration of emerging farmers involved in commercial forestry and timber production operations.

The forestry sector demonstrated early recognition of the mutual advantages inherent in enterprise and supplier development initiatives, acknowledging that business sustainability necessitated strategic investment in emerging forestry commercial farmers in forestry (Forestry South Africa, 2021). The sector implemented an economic development program aimed at expanding the timber resource base and enhancing the availability of sustainable, competitively priced fiber for pulp and paper processing facilities. These strategic partnerships facilitated comprehensive enterprise development, encompassing access to inputs, technical support, extension services, financial assistance, and market linkages.

Commercial forestry plantations occupy approximately 1.26 million hectares nationally, with over 80% concentrated in Mpumalanga, KwaZulu-Natal, and the Eastern Cape provinces (Department of Agriculture Forestry and Fisheries, 2018). The two dominant forest industry entities, Sappi and Mondi, maintain their primary operations in KwaZulu-Natal, with additional operational presence in Mpumalanga and the Eastern Cape. These enterprises lead the pulp and paper production sector, with their products primarily serving international markets. KwaZulu-Natal’s agricultural productivity is enhanced by annual precipitation exceeding 1,000 mm, predominantly during summer months, combined with fertile soil conditions that position agriculture as a central economic sector, which are conducive environmental factors for forestry production in South Africa.

The province is particularly suited for the cultivation of Eucalyptus spp. and Acacia spp., though Eucalyptus spp. represents the predominant plantation species in South Africa (Laubscher et al., 2022). The country’s primary timber processing facilities are strategically concentrated in KwaZulu-Natal and Mpumalanga (Forestry South Africa, 2021). The agricultural landscape of the province exhibits considerable diversity, reflecting the heterogeneous topographical characteristics of the region.

The Department of Forestry and Fisheries in KwaZulu-Natal province was used to identify the emerging forestry commercial farmers. The provided data included the contact details of the emerging forestry commercial farmers contracted to the private sector. The selection of the emerging forestry commercial farmers was on the basis that, firstly, they had been in a strategic partnership investment intervention with the private sector for over 12 months. Secondly, within the same period, there has been some form of investment by a strategic partner, which also included receiving financial assistance and market access among other things.

The sample size determination was calculated using the 95% confidence interval and 5% margin of error based on the sampling frame of 250 emerging forestry commercial farmers, with each farmer having an equal chance of being selected. Only 135 emerging forestry commercial farmers met the selection criteria of having registered land ownership comprising of 20 ha farm size and being in a strategic investment intervention with the private sector. Participants were either the farm representative or the individual responsible for the farm’s overall management and operations.

The study used a quantitative research method, and questionnaires were used to collect data across 8 of the 11 districts in KwaZulu-Natal. The questionnaire underwent pre-testing among non-participating emerging forestry commercial farmers before conducting actual testing on the farmers identified in the selection criteria. The pre-testing was crucial in refining the questionnaire and verifying the farmers’ understanding of their participation in the strategic intervention with the private sector and to comprehensively capture all necessary information. As the study aimed at gathering an accurate interpretation of practices of the participating emerging forestry commercial farmers in the forestry sector. The ethical considerations were equally considered, as informed consent and confidentiality were undertaken throughout the data collection.

A simple random sampling technique was used for a selection of participants. The sampling technique used ensured each participating member had an equal chance of being selected. This method is considered the most straightforward among probability sampling approaches (Pelser and Pelser, 2021; Hlatshwayo, 2022). Data collection of the emerging forestry commercial farmers was conducted through face-to-face interviews (Neves, 2020).

To enhance the respondent rate of understanding, the use of local language (isiZulu) was adopted to improve response. The research technique comprehended critical areas of the study objective related to participants’ demographic information, contract participation and capacity building, productivity inputs, marketing and credit access, income status, and food security indicators. The approach aided an extensive inspection of participants’ knowledge and perspective toward strategic partnership benefits, while also capturing farmers’ perceptions of success in their agricultural endeavors. This process allowed the researchers to get the participants’ understanding and their attitude toward partnership benefits. The process/method farmers’ perception of being successful.

The effects of private sector investment interventions on the food security of emerging forestry commercial farmers were analyzed using the Parametric Marginal Treatment Effects (MTE) model. The food security assessment used the internationally accepted food measurement tool: Household Food Insecurity Access Scale (HFIAS).

The “access component of household food insecurity” was assessed using the HFIAS, considering the data supplied in a month (Coates et al., 2007). There are approximately nine questions on this scale that are based on a person’s anxiety and unpredictability about food access. A household’s consumption of high-quality food was the basis for the queries. The survey’s primary goal was to determine if respondents had experienced any difficulties getting food during the previous 30 days. The questions were broken down into three sections to illustrate the extent of food insecurity: question 1, poor quality (questions 2–4), and inadequate consumption (questions 5–9). The participants were asked to indicate if the scenario had happened infrequently or never (one or twice).

Table 2 presents the descriptive statistics of socio-demographic characteristics among the sampled emerging farmers (Mnikathi et al., 2025). The analysis indicates that all respondents (100%) were engaged in forestry production and were in strategic intervention with the private sector. The gender distribution revealed a predominance of male farmers (64%) compared to female farmers (36%), reflecting the typical gender composition in forestry farming in KwaZulu-Natal, South Africa.

The demographic profile indicates a mean age of 56.20 years among respondents, with a significant majority (86%) reporting marital status as married, 7% as divorced, 6% living together like husband and wife, while only 1% identified as single. Educational attainment analysis revealed that 23% of respondents had completed primary education, 22% had attained tertiary education (university or college level), and 17% reported no formal education. This distribution indicates that approximately half of the sample population had received formal education.

Employment status analysis demonstrated that 46% of respondents were employed within their farming operations, while 11% reported unemployment. Regarding access to private sector investment interventions, the distribution was as follows: credit access (41%), market access (37%), and technical support (6%). The mean farming experience among respondents was 16.54 years, indicating substantial agricultural expertise within the sample population.

Figure 2 presents the status of household food insecurity among the sampled emerging forestry commercial farmers, as measured using the Household Food Insecurity Access Scale (HFIAS). The HFIAS was chosen for this study because it is an experience-based tool that effectively captures both the behavioral and psychological dimensions of food insecurity within households. Specifically, it assesses the degree to which households face challenges in accessing sufficient, safe, and nutritious food, while also identifying changes in food security conditions over time (Masa and Sharma, 2015). The analysis of the full sample (n = 121) revealed that a significant majority (97.5%) of these emerging forestry commercial farmers, were experiencing a level of food insecurity, while only 2.5% were classified as food secure. This suggests that, despite their engagement in commercial agriculture, most farmers continue to struggle with consistent and adequate access to food, potentially undermining their health and productivity.

When examining the disaggregated categories of the HFIAS, it was found that 71.1% of the farmers’ households were classified as mildly food insecure, indicating occasional concerns about food availability and the quality of their diet. Meanwhile, 26.4% were moderately food insecure, showing more frequent compromises in food quality and quantity, including reductions in meal sizes or frequency. Interestingly, none of the households fell into the severely food insecure category, meaning there were no cases of farmers going entire days and nights without eating due to lack of food. Only the small minority (2.5%) were considered fully food secure, facing no significant challenges with food access.

This pattern deviates from findings in previous studies (Bhebhe et al., 2023; Ngidi, 2023; Gwacela et al., 2024; Shelembe et al., 2024), which reported a much higher prevalence of severe food insecurity among similar or comparable populations, including smallholder or emerging farmers. The absence of severe food insecurity in this study may suggest that while these farmers are far from being fully food secure, they might still have minimal access to some food sources, possibly through subsistence farming, informal markets, or social networks. Alternatively, it could reflect underreporting or seasonal variation in food access during the survey period.

The MTE model estimation was fitted through local IV and separate approach estimators regarding parametric assumptions. However, the local IV was favored due to the model’s performance. The output from this estimation, as shown in Table 3, highlights the impact of private sector investment interventions on the food security of emerging forestry commercial farmers. Likewise, the differences in the average outcomes across the fitted covariates could be inferred directly from the first panel of the output as indicated by β₀ (untreated). However, there is a need to account for the strong exogeneity assumption, which is required on the fitted covariates. Therefore, the second panel of the output with β₁— β₀ (treated) in Table 3 explains the observed differences in treatment effects across covariate values, which also indicates treatment status and covariate interactions. Also, since the dependent variable, HFIAS, increases with food insecurity severity, positive coefficients indicate the possibility of more severe food insecurities, and negative coefficients indicate otherwise. The dependent variable is HFIAS presented as follows in the table ***, **, *. Indicate significance at 1, 5, and 10% level, respectively.

For the untreated farmers, the results showed that the training period and training satisfaction are positively and significantly influenced HFIAS. This means that as the training period increases, the food insecurity status of farmers increases. The possible explanation can be that farmers do not receive adequate training as the number of years increases. This also led them to be dissatisfied with the training that they were receiving and negatively affected their food security status. However, the results were not statistically significant on the treated farmers, which means that it is difficult to confidently infer that it is the actual effects of the training period and training satisfaction that increased food insecurity if the exogeneity assumption is not observed as required on the fitted covariates. Many factors affect emerging farmers; therefore, it is important to account for the strong exogeneity assumption (Olawuyi and Mushunje, 2019).

The results showed that education positively and significantly influences HFIAS of treated and untreated farmers. This means that by holding other factors constant, the educational status of farmers leads to increased food insecurity. This could be attributed to the fact that most of the farmers in this study had primary schooling; therefore, they did not have much knowledge on the adoption of the private sector investment interventions associated with modern commercial forestry, which will improve their food security status. When involved in commercial forestry farmers need to adopt modern forestry practices to achieve sustainable yields from their timber. Emerging forestry commercial farmers must have knowledge to comprehend adopted technology and modern forest management practices to improve mean annual increment. The results were in line with Kara and Kithu (2020), who found that the household heads who had attained primary education and those who had attained secondary education were food insecure and experienced hunger. The authors clarified that this was caused by a lack of knowledge about professions and employment opportunities, a lack of funds to start a business, a skills mismatch that hindered the transfer from education to income, and negative views toward agricultural and informal employment. However, the results were contrary to Mutisya et al. (2016) who found that the probability of being food insecure decreased by an increase in the average years of schooling for a given household.

The results revealed that the farm association has a positive and significant influence on the food insecurity situation of treated emerging farmers. This means that, holding other factors constant, the involvement of emerging farmers in the in-farm association led to increase in their food insecurity status. The results imply that farmers who participated in farmers’ associations did not adopt private sector investment interventions, which affected their food security status. The results contradicted the findings of Nugusse et al. (2013), who found that cooperative societies were key determinants of household food security in the study areas. The authors justified that the expansion of cooperative societies was an important tool to minimize the food insecurity problem in the country. Gebremichael (2014) also reported that being a member of a farm association contributed significantly toward improving the standards of living of the rural residents, enabling them to participate in various economic activities, which helped in the promotion of food security.

For the parametric joint normal assumption using local IV, Figure 3 shows the MTE curve plot, as well as the associated confidence intervals for the treated and untreated farmers. This will permit making necessary inferences about the common support. In this case, the downward sloping of the estimated MTE plot is observed, with relatively high treatment effects at the beginning of the U_D distribution (addressing propensity not to be treated), which eventually declines to negative effects at the right end of the distribution. This pattern of slope (downward) is in tandem with the Roy model, which predicts a positive selection on unobservable benefits. The results show that farmers who are most inclined/least resistant to adopt private sector investment interventions (those with low values of U_D) had high food security, positive marginal treatment effects. By contrast, farmers with particularly high values of U_D who are most reluctant to adopt private sector investment interventions showed negative values, indicating that they had high food insecurity.

The study is based on data collected in the KZN province of South Africa and may not be applicable as a general representation of emerging forestry commercial farmers on a national and international scale. There is a need for similar studies to be conducted across all nine provinces of South Africa. The study used quantitative research to identify correlations, which often reduces complex social phenomena to numerical data. The limitations of this research method are that it potentially loses the richness and complexity of the real-world situations, and its findings may not be easily transferable to other populations or contexts, especially if the sample is not representative of the broader audience. This study specifically examined the impact of private sector strategic interventions on emerging forestry commercial farmers; however, future research could expand to include other types of farmers, such as smallholder and subsistence farmers. Additionally, this study focused on a single food security indicator, the HFIAS. The study proposes that future studies should incorporate the nutritional aspects of food security as well.