Adoption of Conservation Agriculture and Its Implications for Household Food Security Among Small-Scale Farmers in Mpumalanga, South Africa

Abstract

Conservation agriculture (CA) is widely promoted as a climate-smart approach to improve productivity and resilience, especially among small-scale farmers who face socioeconomic and climate-related risks that threaten their livelihoods. However, evidence linking CA adoption to household food-security outcomes in South Africa remains limited. This study examines patterns and determinants of CA adoption and assesses its implications for household food security among small-scale farmers in three municipalities of Mpumalanga Province. A quantitative cross-sectional survey was conducted among 391 farmers selected through stratified random sampling. Data were collected using a structured questionnaire and analyzed using descriptive statistics, chi-square tests, Kruskal–Wallis tests, and binary logistic regression. Results show that CA adoption was widespread but largely partial, with most farmers adopting one or two principles rather than the full CA package. Access to CA-related resources and information, household size, livelihood strategy, farm income, and farm size significantly influenced adoption. Higher adoption intensity was consistently associated with improved food-security outcomes, including increased production, lower food-insecurity severity, greater crop diversification, higher likelihood of year-round production, and increased market participation. The study concludes that conservation agriculture can contribute positively to multiple dimensions of household food security when adopted as an integrated system, but partial adoption yields limited benefits. Targeted extension support, improved access to resources, and context-specific interventions are required to enhance sustained and holistic CA adoption among small-scale farmers.

1. Introduction

Agriculture remains central to global poverty reduction and socio-economic development, particularly in developing regions where it serves as a primary source of livelihoods [1]. The agricultural sector is the backbone of the economy in sub-Saharan Africa (SSA), where small-scale farmers constitute approximately 80% of all farmers and produce roughly 69% of the region’s food supply [2]. In South Africa, small-scale farming is an equally important livelihood strategy, especially for populations in rural, resource-constrained areas characterized by high levels of unemployment and poverty [2]. However, this critical sector is increasingly threatened by the combined impacts of climate change and environmental degradation. Southern Africa is highly vulnerable to rising temperatures, erratic rainfall patterns, and more frequent and prolonged droughts, often occurring at rates above the global average [3]. These environmental and climatic threats pose serious risk to the productivity and resilience of small-scale farmers in the region, who already face constraints related to limited access to land, credit, extension services, and modern inputs, as well as the persistent effects of historical marginalization [4,5,6].

Small-scale farming systems in the region rely largely on rainfall and have limited access to advanced irrigation technologies. As a result, they are highly exposed to climatic variability, which threatens their productivity and resilience [2,7]. Consequently, these environmental pressures also pose a significant threat to household food security, defined as a multidimensional concept that encompasses the physical availability, economic access, proper utilization, and long-term stability of food [8]. South Africa continues to face a major food security challenge at the national level. According to the most recent General Household Survey (GHS) Food Security Report (2019, 2022, 2023), an estimated 19.7% of households experienced moderate to severe food insecurity in 2023, up from 15.8% in 2019, while severe food insecurity increased from 6.4% in 2019 to 8.0% in 2023 [9]. The Mpumalanga province, where this study is located, recorded 26.6% of households as moderately to severely food insecure in 2023, well above the national average [9].

Conservation Agriculture (CA) has been widely promoted across SSA as a strategy to enhance resilience to these climatic and structural challenges [10]. It is regarded as a climate-smart agricultural approach based on three core principles: minimum soil disturbance through reduced or no-tillage, permanent organic soil cover using mulching or crop residues, and crop diversification through crop rotations or intercropping [10,11]. Although the conceptual benefits of CA are well established, its effectiveness in small-scale farming contexts remains the subject of ongoing debate. Proponents argue that CA improves soil organic carbon, enhances water infiltration, and strengthens long-term climate resilience [12,13,14,15]. However, empirical evidence suggests a more complex and context-specific reality. Studies indicate that yield improvements associated with CA in SSA are often modest [16,17]. For example, the adoption of reduced tillage alone has been associated with only a 3.7% increase in crop yields compared to conventional practices [18]. In addition, the performance of CA frequently depends on access to complementary inputs. Weed pressure tends to increase under reduced tillage systems, and without the use of herbicides, yield gains may be limited [19,20]. These findings suggest that CA is not a universal solution but rather a system whose outcomes depend on local agroecological conditions as well as social and economic factors.

1.1. Conservation Agriculture and Food Security

The relationship between CA and food security lies in its potential to stabilize crop production and enhance economic access. Evidence from the SSA region suggests that CA can enhance food availability when implemented as a complete system [15,16]. CA also has the potential to improve the access dimension of food security by increasing farm income and reducing production costs [13,21]. A growing body of empirical literature has examined the relationship between CA adoption and food security outcomes across sub-Saharan Africa and beyond. For instance, a multi-country study in southern Africa (Zimbabwe, Malawi, and Mozambique) found mixed impacts of CA on household income and food security, with no significant improvements observed in some contexts, while positive effects were recorded where CA was implemented alongside complementary practices such as improved inputs and better crop management [14]. Similarly, evidence from Ghana indicates that CA adoption can improve household food security, particularly where farmers adopt multiple components of the technology and receive adequate institutional support [22]. These findings suggest that the benefits of CA are highly conditional on implementation intensity and access to complementary resources.

More broadly, studies across eastern and southern Africa report positive effects of CA on crop productivity and, in some cases, household welfare, including income and food security, particularly in countries such as Zambia, Tanzania, and Ethiopia [23,24,25]. However, other analyses highlight that these benefits are often derived from controlled experimental conditions rather than farmer-managed systems, and may not fully translate into real-world small-scale farming contexts [21,26]. Meta-analyses further show that average yield gains from CA in sub-Saharan Africa are relatively modest, reinforcing the view that CA alone may not be sufficient to significantly improve food security in the short term without additional support measures [21].

These mixed findings are particularly relevant for South Africa, where small-scale farming systems differ in important ways from those in other parts of SSA. In contrast to some regions where CA is promoted alongside input support and coordinated extension services, South African small-scale farmers often operate under conditions of fragmented institutional support, high inequality, and competing livelihood priorities [27,28]. Many small-scale farmers in South Africa operate mixed crop-livestock systems, where crop residues are used for livestock feed or household energy [29]. This creates trade-offs that limit the feasibility of maintaining permanent soil cover. Consequently, the soil moisture conservation and weed suppression benefits of CA are reduced. Furthermore, the transition to minimum tillage often leads to increased weed pressure during the early stages of adoption [30]. Resource-constrained farmers may be unable to afford herbicides, resulting in a shift towards labor-intensive manual weeding [31]. This burden frequently falls on family labor and may reduce time available for other productive activities [31]. As a result, CA adoption in South Africa remains low and fragmented, with many farmers adopting only selected components rather than the full system required to achieve optimal food security benefits [32]. Thus, the food security impacts of CA are likely to be mediated not only by biophysical factors but also by broader socio-economic and institutional constraints. This highlights the need for context-specific analyses that examine how CA adoption interacts with local livelihood systems to influence food security outcomes.

1.2. Local Contextual Factors for CA Adoption

In the Mpumalanga Province, CA adoption is shaped by diverse environmental and socio-economic conditions. The province comprises distinct agroecological zones, commonly categorized as the Lowveld, Midveld, and Highveld, which influence both agricultural potential and vulnerability to climate risks [33]. The Lowveld, which falls under the Ehlanzeni district, is generally characterized by low rainfall, high temperatures, and recurrent drought conditions, making water scarcity a key constraint and increasing the relevance of moisture-conserving practices such as conservation agriculture [33]. The ‘Midveld’ represents a transitional zone covering parts of the Ehlanzeni and Gert Sibande districts, with moderate rainfall and mixed farming systems, where both crop and livestock production are practiced under varying levels of climatic uncertainty [33]. In contrast, the Highveld, which extends across parts of the Gert Sibande and Nkangala districts, is relatively cooler and receives higher rainfall, supporting more intensive crop production, although challenges such as soil degradation, erosion, and declining soil fertility remain prevalent [33].

In addition to climatic variability, land degradation remains a significant challenge in Mpumalanga and across South Africa. Unsustainable agricultural practices, including continuous tillage, residue removal, and limited soil nutrient replenishment, have contributed to declining soil fertility and reduced productivity over time [34]. These challenges are exacerbated by climate change, which intensifies soil erosion, reduces soil moisture availability, and increases the frequency of drought events. As a result, small-scale farmers face declining yields and increasing production risks, reinforcing the need for sustainable land management practices such as CA. Local small-scale farmers also face socio-economic constraints, including limited access to infrastructure, extension services, and input markets, which interact with environmental conditions to shape adoption decisions [28]. Understanding CA adoption and its implications for food security therefore requires careful consideration of these agroecological variations and how environmental risks and institutional support systems interact to influence farmer behavior.

1.3. Problem Statement

Government and non-governmental organizations have invested considerable resources in promoting CA in South Africa [32]. Despite these efforts, sustained adoption among small-scale farmers remains low and is often limited to partial implementation [32]. The barriers to adoption are often multidimensional. Financial constraints limit access to essential inputs such as herbicides and specialized no-till equipment, while under-resourced extension systems restrict the provision of timely and relevant technical support [20,35,36].

In addition, small-scale farmers rarely have routine access to soil testing services and other agronomic diagnostic tools, which limits their ability to observe and quantify the soil health improvements associated with conservation agriculture [37]. In many small-scale farming contexts across the country, soil degradation is widespread, yet farmers often lack the technical support and resources needed to monitor changes in soil fertility and structure over time [38]. As a result, the agronomic benefits of CA, such as improvements in soil organic matter, nutrient cycling, and water retention, may not be immediately visible or measurable at the farm level. Despite this, farmers may still observe indirect outcomes, particularly improvements in crop productivity, yield stability, and resilience under variable climatic conditions [2]. These observable outcomes can play an important role in shaping farmer perceptions and may influence continued adoption, even in the absence of formal soil-based evidence.

However, there is a notable gap in the existing literature. Few studies explicitly link levels of CA adoption to comprehensive household food security outcomes under real, farmer-managed conditions in South Africa. Most research focuses either on agronomic performance or on the socio-economic determinants of adoption. Limited attention has been given to assessing how different levels of adoption, whether partial or full, influence overall household food security. In particular, there is insufficient evidence on how CA affects the key dimensions of food security, namely availability, access, utilization, and stability, among small-scale farming households in the country.

Evidence from other regions highlights both the potential and the limitations of CA in improving food security outcomes, as demonstrated in the previous Section 1.1. These findings reinforce the view that the impacts of CA are highly context-specific and mediated by both biophysical and socio-economic factors. At the same time, broader reviews of CA adoption in sub-Saharan Africa emphasize that outcomes are often heterogeneous and that benefits observed under experimental or supported conditions may not fully translate into farmer-managed systems [39].

These international findings highlight the importance of generating context-specific evidence from South Africa, where small-scale farming systems are shaped by distinct historical, institutional, and socio-economic conditions. Understanding how CA adoption influences household food security within this context is therefore critical for informing policy and extension strategies that are responsive to local realities. Moreover, the coexistence of partial adoption, resource constraints, and limited agronomic feedback mechanisms suggests that the relationship between CA and food security outcomes is unlikely to be linear or uniform across households. Instead, it is shaped by the interaction between adoption intensity, household characteristics, and access to institutional support. This underscores the need for empirical analyses that move beyond isolated assessments of adoption or productivity, and instead examine how different patterns of CA use translate into measurable food security outcomes under real-world conditions. Against this background, this study therefore aims to assess patterns of CA adoption and examine its effects on household food security among small-scale farmers in Mpumalanga. Specifically, it (i) describes patterns of CA adoption, (ii) identifies determinants of adoption, and (iii) assesses the effects of CA adoption on food security outcomes. By integrating adoption analysis with household-level outcomes, this study contributes context-specific evidence relevant for extension programming, policy design, and sustainable agricultural development in Mpumalanga and related contexts.

1.4. Theoretical Framework

This study integrates three complementary frameworks to analyze the complex process of CA adoption and its food security outcomes: The Diffusion of Innovation Theory, the Sustainable Livelihoods Framework, and the Food Security Framework.

The Diffusion of Innovation Theory explains adoption as a process through which individuals evaluate and decide whether to adopt new technologies based on their perceived characteristics [40]. Key attributes influencing adoption include relative advantage, compatibility, complexity, trialability, and observability [40]. The theory assumes that adoption is not only driven by economic rationality but also by access to information, social networks, and communication channels through which innovations are disseminated [40]. In this study, the theory guides the analysis of behavioral and institutional factors influencing adoption, including farmers’ perceptions of CA, their exposure to extension services, and access to information. These elements are reflected in variables capturing extension contact and CA training, which are expected to shape both initial adoption and continued use of CA practices.

The Sustainable Livelihoods Framework situates adoption decisions within the broader context of household assets, vulnerability, and institutional structures [41]. It assumes that households pursue livelihood strategies based on their access to different forms of capital, including human, natural, financial, physical, and social capital, while operating within a context of shocks, trends, and seasonality [41]. The framework further emphasizes that institutional processes and policies mediate access to these resources and shape livelihood outcomes. In this study, it informs the selection of socio-economic variables by linking them to different forms of capital, including human capital (education and household size), natural capital (land tenure and farm size), financial capital (farm income and primary livelihood), and social and physical capital (access to extension services, credit, and CA-related inputs). It provides a basis for analyzing how resource endowments and structural constraints influence both the feasibility of CA adoption and its potential benefits.

The Food Security Framework is used to assess the outcome dimension of the study. It conceptualizes food security as a multidimensional construct comprising availability, access, utilization, and stability [8]. The framework assumes that improvements in agricultural production do not automatically translate into improved food security outcomes, as these depend on broader economic, social, and nutritional factors [8]. In this study, food security is therefore assessed across its four core pillars using established indicators. Availability is measured through changes in farm production, access through measures such as the Household Food Insecurity Access Scale, utilization through indicators such as crop diversification, and stability through consistency in food production and food security status over time. This approach ensures a comprehensive evaluation of how CA adoption affects household well-being beyond production outcomes.

By integrating these frameworks, the study provides a comprehensive analytical lens that captures the interaction between farmer perceptions, household resource constraints, and broader institutional conditions. Together, they enable a more holistic understanding of how CA adoption translates into food security outcomes among small-scale farmers.

2. Materials and Methods

This section presents the methodological approach used in the study. Figure 1 below presents a flow diagram of the research procedure that was followed, which is discussed in detail in subsequent sections.

2.1. Study Area

The study was conducted in the Mpumalanga Province, located in the eastern part of South Africa. The province is characterised by a subtropical climate with warm, wet summers and cool, dry winters, with an average annual rainfall ranging between 600 and 800 mm [42]. Administratively, Mpumalanga is divided into three districts; Ehlanzeni, Gert Sibande, and Nkangala, which are further subdivided into Local Municipalities. This study purposively selected three Local Municipalities (Nkomazi, Bushbuckridge, and Chief Albert Luthuli) across two districts (Ehlanzeni and Gert Sibande) to capture a representative cross-section of the province’s agroecological and small-scale farming systems. The selection of these municipalities was guided by a comparative case design, where sites were chosen to maximise variation in agroecological conditions, farming systems, and institutional contexts that are theoretically relevant to CA adoption. This approach allows for more robust analysis of how contextual factors shape both adoption patterns and food security outcomes.

The Nkomazi Local Municipality, located in the eastern part of Ehlanzeni District, features a dry-subhumid, semi-arid climate [43]. It was selected to represent systems heavily reliant on moisture conservation and irrigation, where small-scale sugarcane, maize, and vegetables dominate [44]. The Bushbuckridge Local Municipality, located in the north-eastern part of Ehlanzeni District, is characterised by semi-arid conditions with high evapotranspiration rates [45]. It was selected to represent highly vulnerable, predominantly rain-fed agricultural systems [44]. The Chief Albert Luthuli Local Municipality, located in the Gert Sibande District, features a more humid climate with higher rainfall [46]. It was selected to represent mixed crop-livestock farming systems facing challenges of land degradation [47]. While Nkangala District forms part of the province, it was excluded from the sampling frame due to its relatively limited concentration of small-scale crop farmers and the predominance of large-scale commercial grain and livestock production systems, which fall outside the scope of this study. The selected municipalities in Ehlanzeni and Gert Sibande therefore provide a more appropriate representation of small-scale, crop-based farming systems relevant to CA adoption. Figure 2 shows the location of the study sites in Mpumalanga province within South Africa.

2.2. Research Design and Target Population

A quantitative, cross-sectional research design was employed. This design allows for the collection of data at a single point in time to describe patterns and relationships between CA adoption and household food security among small-scale farmers [49].

The target population comprised registered small-scale crop farmers in the three selected municipalities, obtained from local Department of Agriculture databases. The use of official farmer registers was necessary due to the practical difficulty of constructing a comprehensive sampling frame that includes both registered and unregistered farmers in dispersed rural settings. However, reliance on these databases introduces a methodological limitation, as unregistered smallholder producers are excluded. Consequently, the findings of this study reflect the adoption behaviours and outcomes of registered small-scale farmers and should be interpreted within this scope. From the farmer databases, there were 1577 registered farmers in Nkomazi, 1300 in Chief Albert Luthuli, and 1097 in Bushbuckridge, making up a total target population of 3974 registered farmers. The municipal farmer databases were formally consulted between 10 February and 3 March 2025.

2.3. Sampling Procedure and Sample Size

The sample size was determined using Slovin’s formula, which is commonly applied in survey research where population variability is unknown and a simplified estimation approach is required for large populations [50,51]. The method was considered appropriate given the absence of prior variance estimates for CA adoption within the study population and the need to obtain a representative sample under field constraints. The formula is shown in Equation (1) below:

$$\begin{gathered} n={\displaystyle \frac{N}{1+(N·e^2)}} \\ n={\displaystyle \frac{3974}{1+{(3974\times 0.05}^2)}} \\ n=363 \end{gathered}$$

(1)

whereby:

• $n$ = sample size
• $N$ = population size (3974)
• $e$ = margin of error (0.05)

As per the calculation, a sample size of 363 was obtained and considered adequate for the study. However, to provide a conservative buffer for non-response and to increase precision, a target sample size of 400 was adopted. A stratified random sampling approach was then used with each municipality acting as a stratum to ensure proportional distribution of the sample within the strata. The adopted sample size of 400 was allocated proportionally to each stratum using the stratified sampling formula shown in Equation (2) below:

$$n_h=n({\displaystyle \frac{N_h}{N}})$$

(2)

where

• $n_h$ = sample size for hth stratum
• $N_h$ = Population size for hth stratum
• $N$ = Total population size
• $n$ = total sample size

This yielded sample allocations of Nkomazi = 159, Chief Albert Luthuli = 131, and Bushbuckridge = 110. The registration lists from local department of agriculture offices was used as the sampling frame within each municipality. An online random number generator was used to randomly select the appropriate sample from the list. This was done with the assistance of local agricultural advisors, since the farmer registration database is not publicly available.

2.4. Data Collection and Validation

Primary data for the study were collected using a structured questionnaire instrument developed specifically to address the research objectives (Supplementary File S1). The questionnaire was designed based on an extensive review of relevant literature on conservation agriculture and food security, as well as established survey instruments such as the Household Food Insecurity Access Scale (HFIAS) [52]. The structure of the questionnaire was informed by similar studies conducted among smallholder farmers, ensuring inclusion of variables that capture demographic characteristics, farm production practices, adoption of conservation agriculture (CA) principles, perceived benefits, climate change adaptation measures, and effects on food security.

Face validity of the questionnaire was ensured through a multi-stage review process involving both academic and field-based experts. The instrument was refined in consultation with the study supervisor and subsequently reviewed during institutional approval processes to assess clarity, relevance, and alignment with the study objectives. Agricultural officials familiar with local farming contexts also reviewed the questionnaire to ensure contextual appropriateness for small-scale farmers. Prior to the main survey, the instrument was pre-tested in March 2025 with 20 small-scale farmers from Mbombela Local Municipality (outside the study sites) to evaluate question clarity, sequencing, and completion time. Feedback from the pre-test informed minor revisions to improve comprehension. Data collection for the study was conducted from April to June 2025. Five enumerators, who were fluent in local languages (Siswati, IsiZulu, Xitsonga, Sepulana) and familiar with the farming context of the study area, were recruited and trained with the assistance of the supervisor. The training covered research ethics, effective administration of the questionnaire, and accurate recording of responses. All enumerators signed confidentiality agreements to safeguard participant information.

The questionnaire instrument was administered physically through face-to-face interviews with consenting participants, ensuring that all questions were clearly explained where necessary. The use of trained enumerators and local language facilitation helped minimise misunderstandings and reduce non-response. A total of 400 questionnaires were administered, of which 391 were fully completed and used in the analysis (response rate = 97.8%). The achieved sample by municipality was Nkomazi = 155; Chief Albert Luthuli = 128; Bushbuckridge = 108.

2.5. Measurement of Variables

2.5.1. Conservation Agriculture Adoption

Adoption of each CA practice was coded as 1 = adopted and 0 = not adopted, and the number of CA principles adopted by each respondent was summed from the practices to classify respondents into “None”, “One principle”, “Two principles”, or “All three principles”. The specific CA practices assessed included direct seeding/no-till, planting basins, strip tillage, mulching, cover cropping, living mulches, agroforestry, crop rotation, and intercropping.

2.5.2. Socioeconomic Determinants

The independent variables were grouped into: household characteristics (gender, age, education, household size), farm characteristics (livelihood status, farm size, income), institutional factors (extension and CA-focused support, access to resources), and perceptions (perceived benefits of CA).

2.5.3. Household Food Security

Food security was measured using the Household Food Insecurity Access Scale (HFIAS) developed by Coates et al. [52], which classifies households as food secure, mildly food insecure, moderately food insecure, or severely food insecure. Additional food-security indicators included change in farm production after CA adoption (decreased significantly—increased significantly), crop diversification index (0–3), year-round production (yes/no), and farm produce usage (sell/consume).

2.6. Data Analysis

Data were coded and analysed using the Statistical Package for the Social Sciences (SPSS) version 27.0 (IBM Corp, Armonk, NY, USA). The analysis followed three stages:

2.6.1. CA Adoption Patterns

Descriptive statistics (frequencies and percentages) were used to summarise patterns of adoption for conservation agriculture (CA) principles and specific practices. To assess whether adoption patterns differed significantly across the three municipalities, Pearson’s Chi-square tests were employed. This test is suitable for examining relationships between categorical variables and determining whether observed differences across groups are statistically significant [53]. Given that adoption variables (practice adopted/not adopted) and location are nominal in nature, the Chi-square test provides a robust method for testing independence between these variables. Cramer’s V statistic was computed to assess the strength of associations identified by the Chi-square tests. This measure is appropriate for contingency tables of varying sizes and provides a standardised indication of effect size, enabling interpretation beyond statistical significance [54].

2.6.2. Determinants of CA Adoption

To identify the determinants of CA adoption among small-scale farmers, a binary logistic regression model was used. The logistic regression model was employed because it allows for the estimation of the probability of events in relation to a set of independent variables hypothesised to influence the outcome [55]. Logistic regression is applied to classify respondents into one of two groups based on a binary dependent variable [56]. No assumptions are made regarding the distribution of the independent variables represented by $X$, which may be continuous or categorical [57]. Therefore, $Y$ represents the dichotomous variable which equals 1 if smallholder farmers adopt conservation agriculture (CA) and 0 if they do not. Farmers were considered as adopters if they applied at least one of the CA principles in their farm. The logistic function is shown in Equation (3) below:

$$\mathrm{P}(\mathrm{Y} = 1) = {\displaystyle \frac{1}{1+e-({\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\cdots +{\beta }_n{X}_n)}}$$

(3)

where:

–

P(Y = 1) is the probability of the outcome (adopting conservation agriculture)

–

${\beta }_0$ is the intercept term

–

${\beta }_1$, ${\beta }_2$ …+ ${\beta }_n$ are the coefficients for the predictor variables

To ascertain if there is a significant relationship between the independent variables (demographic, farm-level, institutional, and perceptual) and CA adoption, the model was specified as shown in Equation (4) below:

$$Y={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\cdots +{\beta }_{11}{X}_{11}+\mu$$

(4)

where

• $Y$ = CA adoption (1 = adopter, 0 = non-adopter)
• $X_1$ = Gender of respondent (0 = Male, 1 = Female)
• $X_2$ = Age group (0 = 18–27, 1 = 28–37, 2 = 38–47, 3 = 48–57, 4 = >57)
• $X_3$ = Level of education (0 = No school, 1 = Primary, 2 = Secondary, 3 = ABET, 4 = Tertiary)
• $X_4$ = Household size (0 = <3, 1 = 3–6, 2 = 7–10, 3 = >10)
• $X_5$ = Primary livelihood status (0 = Subsistence farmer, 1 = Farmer with off-farm income, 2 = Self-employed farmer)
• $X_6$ = Farm size (0 = <1 ha, 1 = 1–5 ha, 2 = 6–10 ha, 3 = 11–15 ha, 4 = 16–20 ha)
• $X_7$ = Annual farm income (0 = ≤R10,000, 1 = R11,000–R20,000, 2 = R21,000–R30,000, 3 = R31,000–R40,000, 4 = R41,000–R50,000, 5 = >R50,000)
• $X_8$ = CA perceived as beneficial (0 = No, 1 = Yes)
• $X_9$ = Adequate access to CA resources/information (1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly agree)
• $X_{10}$ = Received extension support in the last/current season (0 = No, 1 = Yes)
• $X_{11}$ = Received CA-focused extension support (0 = No, 1 = Yes)
• ${\beta }_0$ = Constant (intercept)
• ${\beta }_1-{\beta }_{11}$ = Coefficients of explanatory variables
• $\mu$ = Error term

Model fitness was evaluated using multiple complementary indicators: The Omnibus test of model coefficients (to assess overall model significance), Cox & Snell and Nagelkerke R² (to estimate explanatory power), and the Hosmer-Lemeshow test (to assess goodness-of-fit between observed and predicted values). Interpretation of results was based on odds ratios [Exp(β)], which provide an intuitive measure of how changes in predictor variables influence the likelihood of adoption. It should be noted that the proportion of non-adopters in the sample was relatively small compared to adopters. While the overall sample size was sufficient, this imbalance may affect the stability and precision of coefficient estimates, particularly for categories with low representation. As such, the regression results should be interpreted with caution.

2.6.3. Effects of CA on Household Food Security

The effects of conservation agriculture (CA) adoption on household food security were assessed across the four key dimensions: availability, access, utilisation, and stability, using indicators aligned with established food security frameworks. The analysis focused on the relationship between CA adoption intensity (measured by the number of CA principles adopted) and selected indicators within each dimension.

Food availability was assessed using self-reported changes in farm production following the adoption of CA practices, measured on a five-point scale ranging from “significantly decreased” to “significantly increased.” Cross-tabulations and Pearson’s Chi-square tests were used to examine the association between CA adoption intensity and production change categories. The Chi-square test is appropriate in this context as both variables are categorical, allowing for the assessment of statistically significant differences in production outcomes across adoption levels. However, it is important to note that this indicator reflects farmers’ perceptions rather than objectively measured yield data and may therefore be subject to recall bias and subjective interpretation.

Food access was evaluated using the Household Food Insecurity Access Scale (HFIAS), with households categorised into food security levels (food secure, mildly food insecure, moderately food insecure, and severely food insecure). Pearson’s Chi-square tests were used to assess the association between CA adoption intensity and HFIAS categories, as both variables are categorical.

Food utilisation was assessed using the Crop Diversification Index (CDI), which was categorised into levels of diversification. The association between CA adoption intensity and CDI categories was tested using Pearson’s Chi-square test, which is appropriate for evaluating relationships between categorical variables representing farming practices and dietary diversity proxies.

Food stability was assessed using two indicators: (i) year-round food production and produce usage (categorical variables), analysed using Pearson’s Chi-square tests; and (ii) the mean HFIAS frequency score, an ordinal variable reflecting the frequency of food insecurity experiences. Differences in mean HFIAS frequency scores across CA adoption levels were tested using the Kruskal-Wallis H test. This non-parametric test is appropriate for comparing more than two independent groups when the dependent variable is ordinal or does not meet normality assumptions [58]. Cramer’s V statistic was computed for all Chi-square analyses to assess the strength of associations, providing a standardised measure of effect size.

2.7. Ethical Considerations

This study was conducted in accordance with the ethical standards of the University of Mpumalanga and international guidelines for research involving human participants. Ethical clearance was obtained prior to data collection (protocol: UMP/NKAMBULE201739895/SAS/PhD/2024/01), and all respondents were provided with an information leaflet and consent form explaining the purpose, objectives, and procedures of the study. Participation was entirely voluntary, and participants were informed of their right to withdraw at any stage without penalty. Informed consent was obtained in writing before the administration of the questionnaire. Sensitive information relating to household food security and income was handled with discretion, and participants could decline to answer any question they were uncomfortable with. The study posed no foreseeable physical, psychological, or social risks to participants and was intended purely for academic purposes.

3. Results

This section presents the results of the study, starting with the socioeconomic characteristics of respondents to provide contextual background, and subsequently outlining the findings related to each research objective.

3.1. Socioeconomic Characteristics of Respondents

The socioeconomic characteristics of respondents based on the sample of 391 farmers are shown in

Table 1. Socioeconomic characteristics of the respondents.

Characteristic	Category	Nkomazi n (%)	Chief Albert n (%)	Bushbuckridge n (%)	Total n (%)
Gender	Male	75 (48.4)	62 (48.4)	45 (41.7)	182 (46.5)
	Female	80 (51.6)	66 (51.6)	63 (58.3)	209 (53.5)
Age group	18–27	1 (0.6)	5 (3.9)	4 (3.7)	10 (2.6)
	28–37	2 (1.3)	9 (7.0)	4 (3.3)	15 (3.8)
	38–47	32 (20.6)	19 (14.8)	16 (14.8)	67 (17.1)
	48–57	52 (33.5)	44 (34.4)	26 (24.1)	122 (31.2)
	>57	68 (43.9)	51 (39.8)	58 (53.7)	177 (45.3)
Marital status	Single	50 (32.3)	69 (53.9)	54 (50.0)	173 (44.2)
	Married	63 (40.6)	34 (26.6)	37 (34.3)	134 (34.3)
	Divorced	5 (3.2)	16 (12.5)	4 (3.7)	25 (6.4)
	Separated	18 (11.6)	5 (3.9)	4 (3.7)	27 (6.9)
	Widowed	19 (12.3)	4 (3.1)	9 (8.3)	32 (8.2)
Highest level of education	No school	46 (29.7)	20 (15.6)	12 (11.1)	78 (19.9)
	Primary	50 (32.3)	19 (14.8)	28 (25.9)	97 (24.8)
	Secondary	25 (16.1)	59 (46.1)	50 (46.3)	134 (34.3)
	ABET	14 (9.0)	6 (4.7)	6 (5.6)	26 (6.6)
	Tertiary	20 (12.9)	24 (18.8)	12 (11.1)	56 (14.3)
Household size	<3	16 (10.3)	23 (18.0)	20 (18.5)	59 (15.1)
	3–6	66 (42.6)	71 (55.5)	50 (46.3)	187 (47.8)
	7–10	53 (34.2)	31 (24.2)	32 (29.6)	116 (29.7)
	>10	20 (12.9)	3 (2.3)	6 (5.6)	29 (7.4)
Primary livelihood status	Subsistence farmer	89 (57.4)	27 (21.1)	57 (52.8)	173 (44.2)
	Farmer with off-farm income	49 (31.6)	69 (53.9)	12 (11.1)	130 (33.2)
	Self-employed farmer	17 (11.0)	32 (25.0)	39 (36.1)	88 (22.5)

. These were analysed to contextualise the respondents’ farming circumstances and support interpretation of subsequent results. Overall, 46.5% of respondents were male and 53.5% were female, with women forming the majority in all three municipalities. This reflects the well-documented prominence of women in small-scale farming in South Africa, where they play central roles in household food production but continue to face gendered constraints in access to land, credit, and extension services [59,60,61,62]. These constraints make it difficult for them to adopt practices that require supporting resources and technical knowledge, particularly for woman-headed households [62]. Respondents were predominantly older farmers, with 45.3% aged above 57 years, followed by those aged 48–57 years (31.2%) and 38–47 years (17.1%). Younger farmers (18–37 years) were sparsely represented. This pattern is consistent with broader challenges of youth disengagement from agriculture, often linked to perceptions of farming as a low-return and labour-intensive livelihood [63]. The aging farming population has implications for innovation and resilience, as older farmers tend to be more risk-averse and less likely to adopt new technologies such as conservation agriculture [35,64].

Secondary school education was the most common level attained (34.3%), followed by primary schooling (24.8%) and no formal schooling (19.9%). Education is a key factor influencing access to information and understanding of agricultural innovations, with higher literacy often associated with greater adoption of new practices [5,32]. However, research also suggests that farmers with limited formal education can still adopt conservation agriculture when supported through participatory and tailored extension approaches [39,65]. Most households consisted of 3–6 members (47.8%), followed by 7–10 members (29.7%), while 15.1% had fewer than three members. Household size affects labour availability and consumption demands. Larger households may benefit from greater labour supply for farm activities, especially for labour-intensive practices during the early stages of conservation agriculture [66,67], while also facing higher food consumption pressures [68]. Smaller households may experience labour shortages that influence their ability to adopt or sustain farming innovations [69].

Subsistence farming was the dominant livelihood strategy (44.2%), while 33.2% combined farming with off-farm income and 22.5% were self-employed farmers. Subsistence farmers often face resource constraints and may be more risk-averse in adopting new practices such as conservation agriculture [4,5]. Households with off-farm income, however, may have more flexibility to invest in new technologies, reflecting diversification as an important resilience strategy [39,62]. Most farmers had 6–10 years of experience (38.4%), followed by 1–5 years (24.0%) and 11–15 years (20.7%). Experience shapes perceptions of risk and openness to innovation. More experienced farmers may rely on established practices and be reluctant to adopt conservation agriculture due to perceived labour or input requirements [70]. Conversely, newer farmers may be more receptive to innovation when adequate extension support is available [65]. This mix of experienced and new farmers suggests opportunities for peer learning and mentorship.

Farm sizes were generally small, with most respondents cultivating 6–10 ha (30.7%) or 1–5 ha (29.7%), while 14.3% had plots smaller than 1 ha. Such small landholdings are typical among South African smallholders and may constrain diversification and limit the scale of conservation agriculture implementation [35]. Nonetheless, previous research notes that farm viability depends not only on size but also on productivity and market orientation [4,71]. Annual farm income varied considerably, as shown in Table 1. The largest share (37.1%) earned more than R50,000 per year, while 21.7% earned ≤R10,000, with others distributed across intermediate categories. Income levels influence farmers’ ability to invest in improved technologies. Higher-income farmers are more likely to afford inputs and equipment that support conservation agriculture [13], while low-income farmers may face financial constraints that hinder adoption despite recognising potential benefits [69]. Table 1 also shows that communal tenure dominated (62.7%), followed by land ownership (13.3%), family land (13.3%), and leased land (10.7%). Communal tenure is common among South African smallholders but often limits long-term investment in land improvements due to insecure ownership [4,72]. Farmers on leased or family land may also hesitate to invest in practices requiring sustained commitment, while landowners tend to adopt innovations more readily [10,36].

3.2. Adoption Pattern of Conservation Agriculture

3.2.1. Adoption of Specific CA Practices

Across the sample, 56 respondents reported not using any of the specified CA practices, resulting in an effective sample of 335 for this variable. The adoption levels of individual conservation agriculture practices are illustrated in Figure 3. Crop rotation was the most widely adopted practice (86.9%), followed by intercropping (71.9%) and cover cropping (51.3%). Strip tillage was used by 43.9% of farmers, mulching by 31.3%, and planting basins by 28.1%. Adoption of agroforestry was relatively low (11.6%), while direct seeding/no-till (3.9%) and living mulches (0.6%) had the lowest uptake. Non-adoption rates were particularly high for living mulches (99.4%) and direct seeding (96.1%).

3.2.2. Adoption of CA Principles

Figure 4 presents the distribution of respondents according to the number of conservation agriculture principles adopted (Minimum soil disturbance, permanent soil cover, and crop diversification). As shown in the figure, 40.7% of respondents adopted two principles, 32.5% adopted all three, 12.5% adopted one, and 14.3% adopted none.

3.2.3. Combinations of CA Principles Adopted

The frequency and percentage distribution of the different combinations of conservation agriculture principles adopted are summarised in

Table 2. Combination of Conservation Agriculture principles adopted among respondents.

Adopted Principles	Frequency	Percent (%)
Minimum Soil Disturbance (MSD) only	2	0.5
Permanent Soil Cover only	3	0.8
Crop Diversification only	44	11.3
MSD + Permanent Soil Cover	7	1.8
MSD + Crop Diversification	51	13.0
Permanent Soil Cover + Crop Diversification	101	25.8
MSD + Permanent Soil Cover + Crop Diversification	127	32.5
None	56	14.3
Total	391	100.0

. The most common combination of principles was adoption of all three principles (32.5%), followed by permanent soil cover plus crop diversification (25.8%), and minimum soil disturbance plus crop diversification (13.0%). Single-principle adoption was dominated by crop diversification alone (11.3%). Minimum soil disturbance only (0.5%) and permanent soil cover only (0.8%) were the least common combinations.

3.2.4. Adoption of Specific Practices by Area

Adoption of several CA practices differed significantly across the municipalities, as shown in

Table 3. Specific Conservation Agriculture practices adopted by area.

Practice	Bushbuckridge (%)	Nkomazi (%)	Chief Albert Luthuli (%)	Chi2	p-Value
Direct seeding/no-till	3.1	3.5	5.2	0.664	0.717
Planting basins	11.3	51.4	10.4	66.586	<0.001
Strip tillage	47.4	50.7	30.2	10.466	0.005
Mulching	54.6	31.0	8.3	48.091	<0.001
Cover cropping	20.6	52.1	81.3	71.058	<0.001
Living mulches	0.0	0.0	2.1	5.009	0.082
Agroforestry	6.2	20.4	4.2	18.666	<0.001
Crop rotation	80.4	90.1	88.5	5.112	0.078
Intercropping	64.9	83.1	62.5	15.346	<0.001

. Planting basins were most prevalent in Nkomazi (51.4%), compared to lower uptake in Bushbuckridge (11.3%) and Chief Albert Luthuli (10.4%), with a strong chi-square association (χ² = 66.586, p < 0.001). Strip tillage adoption also varied significantly (p = 0.005), highest in Nkomazi (50.7%) and Bushbuckridge (47.4%). Mulching adoption was highest in Bushbuckridge (54.6%), while cover cropping was most common in Chief Albert Luthuli (81.3%). Agroforestry was concentrated in Nkomazi (20.4%). Significant associations were also found for intercropping (p < 0.001). By contrast, crop rotation, direct seeding/no-till, and living mulches showed no significant area-based differences.

3.3. The Determinants of Conservation Agriculture Adoption

Binary logistic regression was analysis conducted to identify the predictors of conservation agriculture (CA) adoption using household characteristics, farm characteristics, institutional factors, and perceptions as independent variables. The model performed well, with the Omnibus Test of Model Coefficients showing statistical significance (χ² = 187.501, df = 12, p < 0.001), indicating that the model reliably differentiated adopters from non-adopters. The Cox & Snell R² (0.381) and Nagelkerke R² (0.675) suggested that the model explained between 38.1% and 67.5% of the variation in CA adoption, while the non-significant Hosmer-Lemeshow test (χ² = 3.680, p = 0.885) indicated a good model fit. The full logistic regression results for the determinants of conservation agriculture adoption are reported in

Table 4. Logistic Regression results on the determinants of Conservation Agriculture adoption.

Predictor	B	SE	Wald	df	Sig.	Exp(β)	95% CI for Exp(β)
							Lower	Upper
Gender: Male	0.130	0.478	0.074	1	0.786	1.139	0.446	2.909
Age	0.031	0.208	0.023	1	0.880	1.032	0.686	1.552
Level of education	0.222	0.207	1.149	1	0.284	1.248	0.832	1.872
Household size	0.607	0.292	4.331	1	0.037 *	1.835	1.036	3.251
Primary livelihood status (Overall)			8.310	2	0.016 *
Subsistence farmer	−0.023	0.730	0.001	1	0.975	0.977	0.234	4.087
Farmer with off-farm income	−1.711	0.818	4.372	1	0.037 *	0.181	0.036	0.898
Farm size	−0.863	0.339	6.474	1	0.011 *	0.422	0.217	0.820
Annual farm income	0.624	0.183	11.683	1	<0.001 *	1.867	1.305	2.670
Perceives CA as beneficial: No	−3.317	0.602	30.376	1	<0.001 *	0.036	0.011	0.118
Access to CA resources/information	1.860	0.405	21.053	1	<0.001 *	6.422	2.902	14.213
Received extension support: No	0.750	0.544	1.897	1	0.168	2.116	0.728	6.152
Received CA-focused extension support: No	−1.280	0.902	2.014	1	0.156	0.278	0.047	1.629
Constant	−0.417	1.632	0.065	1	0.798	0.659
Cox & Snell R2	0.381
Nagelkerke R2	0.675
Hosmer-Lemeshow test	Chi-square 3.680 df 8 Sig 0.885

* Represents statistical significance at 0.05. Note: Reference categories are: Gender = Female; Primary livelihood status = Self-employed farmer; Perceives CA as beneficial = Yes; Extension support = Yes; CA-focused extension support = Yes.

.

Results showed that several variables significantly influenced CA adoption. Household size had a positive and significant effect (β = 0.607, p = 0.037), indicating that larger households were more likely to adopt CA, with each additional household member increasing the odds of adoption by a factor of 1.835. Primary livelihood status was also significant overall (p = 0.016). Relative to self-employed farmers, households with off-farm income were significantly less likely to adopt CA (β = −1.711, p = 0.037, Exp(β) = 0.181), while subsistence farmers did not differ significantly. Farm size had a significant negative association with adoption (β = −0.863, p = 0.011), with each additional hectare reducing the odds of adoption by a factor of 0.422, suggesting higher uptake among smaller farms. In contrast, annual farm income was positively associated with CA adoption (β = 0.624, p < 0.001), with higher income increasing the odds of adoption by 1.867 times.

Perceptions and institutional factors also played important roles. Perceiving CA as beneficial was highly significant (β = −3.317, p < 0.001), though unexpectedly associated with lower odds of adoption (Exp(β) = 0.036), indicating that positive perceptions alone did not translate into practice. Access to CA resources and information showed a strong positive effect (β = 1.860, p < 0.001, Exp(β) = 6.422), making it one of the most influential predictors in the model. By contrast, gender, age, education, general extension support, and CA-focused extension support were not statistically significant predictors of CA adoption.

3.4. The Effects of Conservation Agriculture Adoption of Household Food Security

3.4.1. Food Availability

Respondents’ reported changes in farm production following conservation agriculture adoption are shown in

Table 5. Respondents’ reported change in farm production after adopting Conservation Agriculture practices.

	Frequency	Percent	Valid Percent
Decreased	15	3.8	4.5
Same	157	40.2	46.7
Increased	101	25.8	30.1
Increased significantly	63	16.1	18.8
Total	336	85.9	100.0
Missing	55	14.1
Total	391	100.0

. Analysis of changes in farm production among CA adopters showed that 46.7% reported no change, while 30.1% experienced an increase and 18.8% a significant increase in production; only 4.5% reported decreases.

A chi-square test showed a significant association between the number of CA principles adopted and production changes (p < 0.001), with a moderate effect strength (Cramer’s V = 0.370). The relationship between the number of conservation agriculture principles adopted and changes in farm production is presented in

Table 6. Relationship between number of CA principles adopted and reported changes in farm production after adoption.

No. of CA Principles Adopted	Decreased		Same		Increased		Increased Significantly		Total
	n	%	n	%	n	%	n	%	n	%
One principle	5	10.2	40	81.6	2	4.1	2	4.1	49	100.0
Two principles	7	4.4	90	56.6	48	30.2	14	8.8	159	100.0
All three principles	3	2.4	26	20.5	51	40.2	47	37.0	127	100.0
Total	15	4.5	156	46.6	101	30.1	63	18.8	335	100.0

Pearson Chi-Square = 91.551, df = 6, p < 0.001. Cramer’s V = 0.370, p < 0.001.

. Farmers adopting only one principle largely fell into the “same” category, while those adopting two principles reported more increases. The most favourable outcomes were among three-principle adopters, where 40.2% reported increases and 37.0% significant increases.

3.4.2. Food Access

Descriptive statistics for the Household Food Insecurity Access Scale (HFIAS) scores are presented in

Table 7. Descriptive Statistics for HFIAS score.

	n	Minimum	Maximum	Mean	Std. Deviation
HFIAS Total Score (0–27)	391	0	27	9.82	5.686

. The Household Food Insecurity Access Scale (HFIAS) scores ranged from 0 to 27, with a mean of 9.82 (SD = 5.686).

Consistent with these scores, 13.3% of respondents were food secure, 17.6% mildly food insecure, 45.3% moderately food insecure, and 23.8% severely food insecure. The distribution of respondents across HFIAS food insecurity categories is illustrated in Figure 5.

A significant relationship was also found between CA adoption intensity and HFIAS categories (p < 0.001, Cramer’s V = 0.337).

Table 8. Relationship between number of Conservation agriculture principles adopted and HFIAS food insecurity category.

No. of CA Principles Adopted	Food Secure		Mildly Insecure		Moderately Insecure		Severely Insecure		Total
	n	%	n	%	n	%	n	%	n	%
None	3	5.4	2	3.6	16	28.6	35	62.5	56	100.0
One principle	1	2.0	2	4.1	25	51.0	21	42.9	49	100.0
Two principles	14	8.8	23	14.5	97	61.0	25	15.7	159	100.0
All three principles	34	26.8	42	33.1	39	30.7	12	9.4	127	100.0
Total	52	13.3	69	17.6	177	45.3	93	23.8	391	100.0

Pearson Chi-Square = 133.058, df = 9, p < 0.001. Cramer’s V = 0.337, p < 0.001.

illustrates the relationship between the number of conservation agriculture principles adopted and household food insecurity categories. Non-adopters experienced the highest food insecurity (62.5% severely insecure). In contrast, among three-principle adopters, 26.8% were food secure and only 9.4% severely insecure.

3.4.3. Food Utilisation

The Crop Diversification Index (CDI) ranged from 0 to 3, with a mean of 1.46 (SD = 0.870). The distribution of respondents by Crop Diversification Index is shown in Figure 6. Nearly half of respondents (47.6%) had a CDI score of 2, while only 7.9% reached the maximum score of 3.

A chi-square test showed a strong, significant association between CA adoption intensity and CDI (p < 0.001, Cramer’s V = 0.526). The association between conservation agriculture adoption intensity and the Crop Diversification Index is presented in

Table 9. Relationship between number of Conservation Agriculture principles adopted and Crop diversification index.

No. of CA Principles Adopted	CDI = 0		CDI = 1		CDI = 2		CDI = 3		Total
	n	%	n	%	n	%	n	%	n	%
None	56	100.0	0	0.0	0	0.0	0	0.0	56	100.0
One principle	5	10.2	20	40.8	24	49.0	0	0.0	49	100.0
Two principles	7	4.4	48	30.2	91	57.2	13	8.2	159	100.0
All three principles	0	0.0	38	29.9	71	55.9	18	14.2	127	100.0
Total	68	17.4	106	27.1	186	47.6	31	7.9	391	100.0

Pearson Chi-Square = 324.428, df = 9, p < 0.001. Cramer’s V = 0.526, p < 0.001.

. All non-adopters scored 0, whereas CDI increased noticeably with each additional CA principle adopted, with the highest diversification observed among three-principle adopters, 14.2% of whom achieved the maximum CDI = 3.

3.4.4. Food Stability

Figure 7 shows the distribution of respondents with respect to year-round crop production. Year-round crop production was reported by 67.8% of respondents, with 32.2% not producing throughout the year.

A chi-square test showed a significant relationship between CA adoption intensity and year-round production (p < 0.001, Cramer’s V = 0.252). The relationship between the number of conservation agriculture principles adopted and year-round crop production is shown in Figure 8. Only half of non-adopters produced year-round, compared to 82.7% among those adopting all three principles.

The distribution of respondents according to farm produce usage is shown in Figure 9. The results show that 52.9% of the respondents consumed most of their produce, while 47.1% sold it.

Figure 10 illustrates the relationship between conservation agriculture adoption intensity and farm produce usage. A significant association was found between CA adoption and produce usage (p = 0.011, Cramer’s V = 0.168), with non-adopters more consumption-oriented (71.4% consumed), while three-principle adopters were more market-oriented (52.8% sold).

Finally, analysis of the Mean HFIAS frequency scores (as a stability proxy) showed significant differences across adoption categories (Kruskal-Wallis H = 36.87, p < 0.001). The differences in mean HFIAS frequency scores across conservation agriculture adoption categories are presented in

Table 10. Relationship between the number of Conservation Agriculture principles adopted and the Mean HFIAS frequency score.

CA Adoption Status	n	Mean Score	SD	Mean Rank
None	53	2.04	0.289	205.99
One principle	48	2.07	0.309	218.70
Two principles	145	1.94	0.377	179.50
All three principles	101	1.66	0.507	128.08

Note: The Mean HFIAS frequency score was calculated as a proxy for food stability, based on the HFIAS frequency of occurrence responses (1 = rarely, 2 = sometimes, 3 = often). Kruskal-Wallis H = 36.872, df = 3, p < 0.001.

. Non-adopters had the highest mean score (2.04), indicating more frequent food insecurity experiences, whereas three-principle adopters had the lowest (1.66).

4. Discussion

4.1. Adoption Patterns of Conservation Agriculture

This section discusses the patterns of conservation agriculture adoption observed in the study, interpreting the results in relation to established evidence and the study’s theoretical framework.

4.1.1. Adoption of Specific CA Practices

The prominence of crop rotation and intercropping reflects farmers’ reliance on practices already embedded in traditional African farming systems, consistent with literature noting that diversification-based CA components are often easier to integrate into existing farming systems [10,13,35]. From a Diffusion of Innovation perspective, these practices exhibit high compatibility with existing farming systems and low perceived complexity, facilitating their uptake. By contrast, the extremely low uptake of direct seeding/no-till and living mulches suggests barriers linked to the need for specialised machinery, higher input requirements, and limited familiarity with these techniques among small-scale farmers [10,32]. These practices are often perceived as more complex and less trialable, which may slow their diffusion. The near-universal non-adoption of living mulches further suggests constraints in knowledge and resource access within the South African small-scale farming context, particularly in resource-constrained municipalities such as Bushbuckridge, where limited inputs and high climatic variability may restrict experimentation with such practices.

4.1.2. Adoption of CA Principles

The partial uptake of CA, where most farmers adopt one or two principles rather than the full package, is consistent with evidence showing that small-scale farmers frequently integrate CA incrementally due to financial, labour, and institutional constraints [26,73]. This pattern reinforces the notion that CA adoption is not a binary outcome but a progressive process, where farmers selectively adopt components based on perceived benefits and feasibility. While the fact that nearly one-third adopted all three principles is encouraging, the proportion of non-adopters underscores persisting challenges such as risk aversion, insecure land tenure, and limited technical support [67,74]. Importantly, the findings suggest that full (multi-principle) adoption remains limited, despite evidence of its stronger association with improved outcomes, highlighting a critical gap between awareness and comprehensive implementation. This pattern of incremental uptake directly reflects the trialability and complexity components of the Diffusion of Innovation Theory, where farmers adopt innovations in stages to reduce risk and uncertainty [40]. At the same time, the persistence of partial adoption suggests constraints within the livelihood asset base, as conceptualized by the Sustainable Livelihoods Framework, where limited access to financial, physical, and institutional resources restricts the ability to fully implement all CA principles [41].

4.1.3. Combinations of CA Principles

The preference for combining crop diversification with either permanent soil cover or minimum soil disturbance reflects the relative ease of adopting crop-based strategies compared to soil management practices [75,76]. Crop diversification fits well within customary cropping calendars and requires fewer specialised inputs, explaining its dominance in both single-principle and combination categories [35]. The very low uptake of minimum soil disturbance alone aligns with literature highlighting constraints related to mechanisation access and the labour intensity of manual no-till among small-scale farmers [77,78]. This pattern further illustrates how farmers prioritise practices with immediate and observable benefits, consistent with the Diffusion of Innovation Theory’s emphasis on relative advantage and observability in shaping adoption behaviour.

4.1.4. Area-Based Variations in Practice Adoption

The significant differences in adoption across municipalities highlight the role of agroecological conditions, resource endowments, and institutional support structures. Nkomazi’s high adoption of planting basins aligns with findings that such practices are often promoted in semi-arid regions to enhance water harvesting [79,80], particularly in systems reliant on irrigation and moisture conservation. Bushbuckridge’s stronger adoption of mulching reflects adaptation strategies in a highly vulnerable, predominantly rain-fed system characterised by high evapotranspiration and limited resource availability [61,62]. In contrast, the high prevalence of cover cropping in Chief Albert Luthuli corresponds with more favourable rainfall conditions that support biomass production and integration of crop-livestock systems [63,65,81,82]. These patterns emphasise that CA adoption is highly context-specific, shaped by local climatic conditions, farming systems, and institutional priorities, and reinforces the need for location-specific extension strategies tailored to diverse agroecological environments.

Despite the observed uptake of individual practices, the persistence of partial adoption suggests the presence of practical and structural constraints that limit full implementation of conservation agriculture. In the study context, these constraints likely include labour demands associated with residue management and manual minimum tillage, limited access to appropriate machinery, and competition between crop residues and livestock feed, particularly in mixed farming systems such as Chief Albert Luthuli. In more resource-constrained municipalities such as Bushbuckridge, high climatic variability, limited input access, and financial constraints may further restrict the ability of farmers to adopt multiple CA principles simultaneously. Additionally, the delayed or uncertain benefits associated with some CA practices may discourage full adoption, particularly among risk-averse small-scale farmers who prioritise immediate livelihood needs. These findings suggest that while awareness of CA may be increasing, the transition from partial to full adoption is still limited, highlighting the need for more context-specific and practically oriented support mechanisms.

4.2. The Determinants of Conservation Agriculture Adoption

This section discusses the significant predictors identified in the logistic regression model and interprets the findings in relation to existing literature and the study’s theoretical framework.

4.2.1. Household Size

The positive association between household size and CA adoption reflects the labour-intensive nature of CA, particularly in early implementation stages when weed management and residue handling require substantial labour inputs [66,83,84]. From a Sustainable Livelihoods Framework perspective, this highlights the role of human capital in enabling adoption. Similar findings in Southern Ethiopia and Northern Namibia show that larger households adopt CA more readily due to greater labour availability [85,86]. However, contrasting findings from Lesotho demonstrate that in some contexts, training and income override labour constraints [87], highlighting the context-specific nature of labour dynamics in CA adoption.

4.2.2. Primary Livelihood Status

The negative influence of off-farm income on CA adoption aligns with evidence that households prioritising non-farm work have less incentive to invest labour and resources into CA [35,88]. This reflects a trade-off in livelihood strategies, where engagement in off-farm activities may reduce attention to on-farm innovation. Within the Sustainable Livelihoods Framework, this finding illustrates how diversification of livelihood strategies can both enable and constrain agricultural investment. In this study, the stronger likelihood of adoption among farming-based households reflects the direct relevance of CA benefits to their core livelihood activities.

4.2.3. Farm Size

The negative association between farm size and adoption contrasts with common findings that larger farms have greater capacity to adopt new technologies [89,90,91]. However, recent evidence from Ethiopia, Malawi, and Kenya suggests that smaller farms may adopt CA as a strategy to cope with land scarcity and limited inputs [69,85,92]. This finding may reflect the specific context of the study area, where smaller farms, particularly in resource-constrained municipalities such as Bushbuckridge, may rely more heavily on soil-conserving practices to sustain productivity. It also suggests that CA may be perceived as a resource-saving strategy rather than a scale-dependent innovation.

4.2.4. Annual Farm Income

The positive effect of income is consistent with literature showing that higher income enhances capacity to purchase inputs, manage risks, and access complementary services needed for CA implementation [93,94]. From a Sustainable Livelihoods perspective, this reflects the importance of financial capital in enabling adoption. Income also improves access to credit, training, and innovation platforms [95,96], and supports the sustained implementation of CA practices over time [19,97,98]. These findings highlight the central role of economic capacity in facilitating both initial adoption and long-term continuation.

4.2.5. Perception of CA Benefits

The negative coefficient, despite strong significance, indicates a counter-intuitive relationship. This likely reflects the complexity of perception-adoption dynamics. While farmers may recognise the potential benefits of CA, constraints such as labour demands, input shortages, residue-livestock competition, and tenure limitations may prevent actual adoption [35,99,100]. Social desirability bias and programme targeting may also contribute, where awareness campaigns increase perceived benefits among non-adopters without enabling practical implementation [101]. This finding presents a deviation from the Diffusion of Innovation Theory, which posits that perceived relative advantage should positively influence adoption decisions [40]. The negative association observed in this study suggests that awareness of CA benefits does not automatically translate into adoption. This divergence highlights the importance of structural and institutional constraints, which may prevent farmers from acting on positive perceptions. In this regard, the finding reinforces the Sustainable Livelihoods Framework, which emphasizes that access to assets and enabling conditions ultimately mediates the ability to adopt innovations [41]. These findings reinforce the importance of aligning awareness with practical support and enabling conditions.

4.2.6. Access to CA Resources and Information

The strong positive effect of access to CA resources and information aligns with extensive evidence that knowledge, training, and input access are critical drivers of adoption. From a Diffusion of Innovation perspective, this reflects the importance of communication channels and knowledge transfer in accelerating adoption. Tufa et al. [102] show that across Malawi, Zambia, and Zimbabwe, hands-on training significantly increases adoption by improving farmers’ technical competence. Access to resources reduces financial and logistical constraints, particularly in municipalities such as Nkomazi where irrigation-based systems may facilitate access to inputs and support services. These findings highlight that scaling CA requires integrated extension systems that combine knowledge dissemination, resource provision, and sustained technical support.

Collectively, these findings provide empirical support for both theoretical frameworks underpinning the study. From a diffusion perspective, factors such as access to information and perceived benefits reinforce the role of relative advantage and knowledge dissemination in shaping adoption decisions [40]. However, the significance of resource-related variables, such as income and access to inputs, highlights that adoption is not solely a function of information and perception. Instead, it is strongly mediated by the availability of livelihood assets, as emphasized by the Sustainable Livelihoods Framework [41]. This suggests that while awareness is necessary, it is insufficient in the absence of enabling economic and institutional conditions.

Several variables, including gender, age, education level, and general extension support, did not show statistically significant effects. This suggests that, in the study context, resource-based and institutional factors may outweigh demographic characteristics, reinforcing the context-specific nature of CA adoption dynamics [65,69]. The lack of significance of variables such as age and education level partially challenges conventional diffusion-based assumptions that farmer characteristics are primary drivers of adoption. Instead, the findings suggest that in this context, structural constraints and resource access may outweigh socio-demographic factors, indicating a more complex adoption environment than that predicted by classical diffusion models.

4.3. The Effects of Conservation Agriculture Adoption on Household Food Security

This section discusses the effects of conservation agriculture on the four dimensions of household food security.

4.3.1. Food Availability

The observed production improvements among multi-principle adopters align with evidence that CA is most effective when implemented holistically, as the three principles reinforce each other through improved soil structure, moisture retention, and pest regulation [10,13,103]. However, it is important to note that these production outcomes are based on self-reported perceptions, which may not fully correspond to objectively measured yield changes. As such, the results should be interpreted as indicative of perceived improvements rather than definitive evidence of productivity gains. The stronger outcomes among full adopters also reinforce the importance of adoption intensity, suggesting that partial adoption may limit the realisation of CA benefits. These findings further support the relative advantage principle of the Diffusion of Innovation Theory, as the observable productivity gains among multi-principle adopters reinforce the perceived benefits of full CA adoption and can accelerate wider diffusion.

4.3.2. Food Access

The association between CA adoption intensity and HFIAS categories suggests that integrated CA adoption can contribute to improved food access by stabilising production and reducing vulnerability to seasonal shortages [104,105]. However, the persistence of moderate to severe food insecurity highlights that CA alone is insufficient to address broader structural constraints, including market access, income instability, and credit limitations [106]. This reinforces the need to situate CA within broader livelihood and rural development strategies.

4.3.3. Food Utilisation

The relationship between CA adoption and crop diversification reflects the role of diversified production systems in enhancing dietary quality and resilience. The higher diversification among multi-principle adopters reinforces the importance of integrated CA practices in supporting improved utilisation outcomes [107].

4.3.4. Food Stability

The association between CA adoption and year-round production is consistent with evidence that CA enhances water-use efficiency and soil moisture retention, enabling continuous production through different seasons [12,13,108]. Similarly, higher market participation among multi-principle adopters suggests that more comprehensive adoption may contribute to surplus production and income diversification [69]. The reduction in HFIAS frequency scores among these farmers further supports the role of CA in enhancing stability. However, these benefits appear to be most pronounced among full adopters, again underscoring the importance of moving beyond partial implementation.

5. Conclusions and Recommendations

5.1. Conclusions

The study assessed conservation agriculture (CA) adoption patterns, the determinants of adoption, and the association between CA adoption and household food security among registered small-scale farmers in Mpumalanga. The findings indicate that CA is practiced in the study area, although adoption remains largely partial, with most farmers implementing one or two principles rather than the full set. Adoption patterns were shaped by a combination of socio-economic, institutional, and perceptual factors, with household size, livelihood strategy, farm income, perceived benefits of CA, and access to CA-related resources and information emerging as significant predictors.

The study further shows that higher levels of CA adoption are associated with more favourable outcomes across multiple dimensions of household food security. In particular, multi-principle adopters reported improved production outcomes, lower food insecurity scores, greater crop diversification, higher likelihood of year-round production, and increased market participation compared to partial or non-adopters. These findings suggest that the benefits of CA may be cumulative, reinforcing the importance of integrating minimum soil disturbance, permanent soil cover, and diversified cropping practices.

Despite these contributions, the findings should be interpreted with caution. The cross-sectional design of the study limits causal inference, as data were collected at a single point in time. In addition, key variables such as changes in production and food security experiences were based on self-reported perceptions rather than objective measurements. The study also measured the number of CA principles adopted without capturing the quality or intensity of implementation, which may mask variation within adoption categories. Furthermore, the sample was limited to registered small-scale farmers, potentially excluding unregistered producers. These limitations suggest that the results should be viewed as indicative rather than definitive, and future research should incorporate longitudinal and mixed-method approaches to better capture long-term adoption dynamics and impacts.

Notwithstanding these limitations, the study contributes to the literature by highlighting the importance of adoption intensity, demonstrating that partial adoption may limit the realisation of CA benefits. This provides a more nuanced understanding of CA adoption beyond binary classifications and underscores the need to consider how different combinations of practices influence outcomes. Overall, the findings suggest that CA has the potential to contribute to improved food security when implemented in an integrated and context-specific manner. However, its effectiveness remains contingent on addressing broader structural constraints, including limited market access, input shortages, labour demands, and climate variability.

5.2. Recommendations

The study’s findings point to the need for coordinated and targeted actions across multiple stakeholders to strengthen conservation agriculture (CA) adoption and its potential contribution to household food security. Policymakers should prioritise the development of CA-supportive policies and resource allocation, including subsidising cover-crop seeds, facilitating access to small-scale farming-appropriate implements (such as jab planters or ripper tines), and integrating CA into local agricultural development plans.

Extension services should play a central role by providing regular, practical training through demonstration plots, farmer field schools, and mentorship programmes, ensuring that farmers understand not only the principles but also the effective implementation of CA practices. Community-based organisations and cooperatives can further support adoption by facilitating group-based input purchases, shared equipment access, and peer learning platforms. Development agencies and NGOs should prioritise resource-constrained households by providing starter packs and targeted support to reduce initial adoption barriers.

Given that integrated adoption of all three CA principles is associated with more favourable food security outcomes, interventions should emphasise progressive and holistic adoption pathways. For example, extension officers can support farmers in transitioning from single-principle adoption to more integrated systems through phased implementation strategies. Strengthening market linkages and improving access to market information, input supply chains, and storage infrastructure can further enhance food stability and income generation, particularly for farmers achieving higher levels of adoption. Gender-responsive programming remains essential, including targeted support for women farmers in areas such as land access, training, and participation in farmer groups. Finally, future research should prioritise longitudinal and experimental approaches, incorporating objective biophysical measurements, to better assess the long-term impacts of CA adoption and strengthen the evidence base for policy and practice.