Socioeconomic Factors Influencing Crop Diversification Among Smallholder Farmers in Bergville, South Africa

Abstract

Crop diversification is a vital strategy for achieving sustainable agriculture and food security, yet adoption rates remain low. This study examined the socioeconomic factors influencing crop diversification among smallholder farmers. A two-stage sampling procedure was employed to elicit data from 161 farmers solely specializing in crop production. A structured questionnaire was used to collect data, analyzed using descriptive statistics. The multiple linear regression and multivariate probit regression models were applied to assess the socioeconomic factors influencing diversification. The results revealed that smallholders primarily focused on vegetable cultivation (87%), followed by cereals (56%) and legumes (43%). Education level, household size, market access, and the perceived benefits of diversification significantly (p < 0.05) influenced diversification decisions. Also, sources of irrigation water, age, marital status, and farm size were key factors in vegetable diversification, while farming experience, farm size, and perceived benefits influenced legume diversification. Only marital status and farming experience were positively linked to cereal crop diversification. Furthermore, 48.4% of farmers practice intercropping, integrating maize with pumpkins or sugar beans, while 33.5% still rely on monoculture, predominantly maize, due to limited resources. These findings highlight the need for policies and extension support to address socioeconomic barriers and encourage a wider adoption of crop diversification strategies.

1. Introduction

Smallholder farming is a vital component of South Africa’s agricultural sector, playing a crucial role in food security, rural livelihoods, and economic development [1,2]. However, smallholder farmers face numerous challenges, including climate variability, soil degradation, limited market access, financial constraints, and inadequate infrastructure [3,4].

Despite the increasing threats posed by climate change, many smallholder farmers continue cultivating the same staple crops, often relying on monoculture systems [5]. These factors increase the vulnerability of smallholder farming systems, thereby reducing agricultural productivity and resilience while exacerbating food insecurity and poverty in rural communities [6]. To enhance resilience and productivity, adaptive strategies such as crop diversification have become increasingly essential [7]. Crop diversification has been recognized as an ecologically sustainable and cost-effective strategy for agricultural intensification. This approach entails the integration of multiple crop species within a farming system through practices such as crop rotation, multiple cropping, or intercropping [8].

According to Barman et al. [8] and Zou et al. [9], this strategy enhances soil fertility, reduces pest and disease prevalence, and minimizes agrochemical inputs by decreasing reliance on nitrogen-based fertilizers, particularly when leguminous crops are incorporated into cropping systems [10]. Furthermore, crop diversification enables farmers to adapt to shifting consumer preferences and market dynamics, potentially increasing profitability, stabilizing income, and enhancing resilience to climatic and economic shocks [11,12,13]. As noted by Shikwambana et al. [14], crop diversification enhances climate resilience in South Africa by reducing the vulnerability of smallholder farmers to climatic shocks such as droughts, irregular rainfall, and temperature extremes. Through the cultivation of diverse crops with various tolerances to water stress, pests, and diseases, farmers can mitigate the risk of total crop failure associated with climate variability. In the South African context, characterized by semi-arid conditions, high interannual rainfall variability, and frequent droughts, diversification allows for more stable income streams and food security.

Moreover, crop diversification benefits the poorest the most and raises farmers out of poverty [15]. While diversification is generally associated with reductions in poverty levels, existing poverty may constrain farmers’ ability to effectively manage multiple crops. Due to limited agricultural resources, resource-poor farmers are likely to prioritize the cultivation of staple crops with high caloric yields to meet basic subsistence needs, potentially limiting their capacity to diversify [16]. From a food security perspective, crop diversification is crucial for sustaining both the quantity and nutritional quality of food production. It contributes to a more stable and balanced food supply, which is particularly vital for vulnerable populations. The inclusion of protein-rich legumes and micronutrient-dense vegetables in smallholder farming systems significantly improves dietary diversity and nutritional outcomes, especially in rural areas where access to affordable, nutrient-rich food is often limited. Despite these well-documented benefits, the adoption of crop diversification remains low among smallholder farmers [17,18].

Smallholder farmers have historically employed diverse cropping systems, utilizing indigenous knowledge to cultivate a variety of crops for household consumption, soil health, and risk mitigation. These practices were tailored to local agroecological conditions and aimed at enhancing resilience. A systematic review highlights that crop diversification, including the use of indigenous and drought-tolerant crop varieties, has been a key strategy for smallholder farmers in South Africa to adapt to climate change impacts on their livelihoods [19]. However, the green revolution introduced high-yielding varieties and modern agricultural techniques, leading many smallholders to adopt monoculture practices [20]. This shift was driven by government incentives and market demands, resulting in reduced crop diversity and increased reliance on a narrow range of staple or cash crops. Research indicates that the promotion of high-yield varieties often led to the displacement of traditional crops, impacting biodiversity and resilience [21]. In response to climate change, market volatility, and declining soil fertility, there has been a renewed interest in crop diversification among smallholder farmers. Integrating drought-resistant and climate-resilient species, as well as reintroducing traditional crops, has been observed as a strategy to enhance income, food security, and resilience. A study in Zimbabwe demonstrates that crop diversification significantly improves crop productivity, household income, food security, and nutrition, underscoring its role as a viable climate-smart agriculture practice [22].

Socio-economic factors, including limited access to land, water, credit, and agricultural technologies, along with policy and institutional barriers, such as insufficient government support and weak market linkages, further constrain the success of crop diversification efforts [23,24]. Inadequate extension services and a lack of technical knowledge hinder the effective implementation of diversification strategies [25,26]. While existing studies have examined the role of crop diversification in mitigating climate risks and enhancing agricultural productivity, most research has primarily focused on large-scale commercial farms. Consequently, there is a lack of empirical data on the determinants and effectiveness of crop diversification practices among smallholder farmers in South Africa. A comprehensive investigation is required to assess the socioeconomic factors influencing crop diversification practices and identify strategies to enhance their adoption, particularly within South Africa’s diverse agro-ecological zones.

This empirical study employed a survey-based research methodology to assess the socioeconomic factors influencing crop diversification among smallholder farmers in South Africa. To achieve this objective, the study examined predominant crops and cropping systems, assessed farmers’ knowledge of diversification benefits and training attendance, understanding, and knowledge utilization, and analyzed the socio-economic factors influencing their crop diversification decisions. The study’s findings are expected to provide valuable empirical insights to bridge existing knowledge gaps, support policymakers in developing targeted interventions, and strengthen agricultural extension services. The findings will also contribute to a more profound understanding of how socioeconomic factors influence crop diversification among resource-constrained farmers in rural communities of the Global South. Furthermore, this study is in alignment with the Sustainable Development Goals (SDG 1 and 2), which are aimed at eradicating hunger, poverty, and malnutrition by 2030, as well as SDG 12 and 13, which promote responsible production and address climate action. The study will provide a roadmap on key gaps that need to be addressed to promote sustainable and climate-resilient farming practices that enhance food security, income stability, and rural livelihoods in smallholder farming systems.

Theoretical Framework for the Study

This study is underpinned by two complementary theoretical approaches: the Theory of Planned Behaviour (TPB) and the Sustainable Livelihood Framework (SLF). These frameworks provide a comprehensive analytical approach to examining the socioeconomic factors of crop diversification decisions among smallholder farmers in Bergville, South Africa. The theory of planned behaviours posits that an individual’s intention to perform a specific behaviour, such as adopting crop diversification, is shaped by three core components: attitudes, subjective norms, and perceived behavioural control. In the context of smallholder farming, attitudes reflect farmers’ beliefs about the benefits of diversification, such as enhanced food security or income stability. Subjective norms involve social pressures or community expectations, including the influence of farmer cooperatives or local institutions like the Farmers Support Group. Perceived behavioural control is especially relevant in Bergville, where access to land, water, markets, and extension nlservices affects farmers’ ability to diversify. These three factors collectively shape behavioural intentions, which are strong predictors of actual diversification practices.

Complementing the TPB, the Sustainable Livelihood Framework provides a holistic view of the resources and vulnerabilities that shape smallholder farmers’ livelihood strategies. The SLF identifies five key asset categories, namely human, social, natural, physical, and financial capital, which interact with external factors such as policies, institutions, and climate variability. In Bergville, access to education (human capital), land and water (natural capital), farming tools (physical capital), and income or credit (financial capital) all influence a farmer’s capacity to adopt crop diversification. The framework also emphasizes vulnerability contexts, such as climate shocks, market fluctuations, or policy gaps that may constrain or motivate diversification choices. By integrating TPB and SLF, this study acknowledges that crop diversification is not only a rational decision influenced by individual beliefs and intentions but also a livelihood strategy shaped by broader structural and resource-based constraints. This dual-theoretical approach allows for a deeper analysis of how socioeconomic factors collectively determine the extent and nature of crop diversification among smallholder farmers in Bergville.

Based on a synthesis of the reviewed literature, a conceptual framework (Figure 1) was developed to illustrate the interactions between the variables measured in the study and how these interrelationships influence crop diversification among smallholder crop farmers. The framework demonstrates how the socioeconomic characteristics of smallholder farmers, such as age, education level, marital status, household size, farming experience, and farm size, can directly affect their crop diversification decisions. Farmers’ perceptions of the benefits of crop diversification, along with their access to key resources such as water, markets, land, and seeds, interact with their socioeconomic profiles and are expected to influence their diversification choices. Ultimately, increased crop diversification in smallholder farmers’ agricultural practices is anticipated to contribute to enhanced food security, income stability, improved livelihoods, poverty reduction, increased climate resilience, reduced crop failure, decreased reliance on agrochemical inputs, and improved soil fertility.

2. Materials and Methods

2.1. Description of the Study Area

The study was conducted in Bergville, located within the Okhahlamba Municipality of KwaZulu–Natal Province, South Africa (Figure 2). This site was chosen due to its agro-ecological zone, which is both ecologically diverse and agriculturally significant. Bergville forms part of South Africa’s maize production belt and supports a range of agricultural activities, including livestock grazing, maize cultivation, and horticulture. It is centrally located at approximately 28°43′49.74″ S and 29°21′4.20″ E, with an altitude ranging from 1200 to 1800 m above sea level. The region experiences an average annual precipitation of 700 mm to 1200 mm, with winter minimum temperatures ranging from 2 °C to 5 °C (occasionally accompanied by frost), and summer maximum temperatures (December to February) ranging from 25 °C to 30 °C. This climatic variability, combined with its semi-arid conditions, makes Bergville a valuable location for studying agricultural and ecological dynamics, where rainfall and temperature fluctuations have a significant impact on local livelihoods and biodiversity. Water sources in the region primarily include constructed pits, earth dams, and rainwater harvesting systems. Most smallholder farmers in Bergville rely on rainfall for irrigation, cultivating maize, vegetables, and potatoes as predominant crops. Common cropping systems include monoculture, crop rotation, and intercropping [27]. The soils are generally clay-loamy, with a high buffering capacity against pH fluctuations [28].

2.2. Sampling Procedure and Sample Size

The study employed a quantitative research approach, incorporating a descriptive survey research design consistent with the methodology outlined by Kamarudeen [29]. As noted by Bless et al. [30], descriptive and quantitative research is instrumental in exploring beliefs, attitudes, and emerging patterns within a population. The study’s target population included all farmers who were members of the Farmers’ Support Group (FSG) at the time of the research, with eligibility limited to smallholder farmers specializing in crop production. The FSG’s criteria were to register all resource-constrained smallholder farmers in the region with diverse farming practices and socio-economic backgrounds, ensuring inclusivity and support for a broad range of agricultural activities. A two-stage sampling procedure was employed to select respondents for the study. In the first stage, purposive sampling was used to select smallholder crop farmers from the Farmers’ Support Group. In the second stage, a simple random sampling method was employed to select participants from this group, ensuring adequate representation and preserving the diversity among the smallholder crop farmers.

The total population of smallholder crop farmers comprised 269 individuals. Slovin’s formula was used to determine the appropriate sample size for the study population. This method ensures the statistical neutrality of the selected samples. The formula was applied using a 95% confidence level and a 5% margin of error. Based on the computation, 161 smallholder farmers were randomly selected from five villages (Mlimeleni, Ezimbovini, KwanoKopela, Eqeleni, and Busingatha) in Bergville to participate in the study.

The following calculation illustrates the method used to determine the suitable sample size.

$$\mathrm{N}={\displaystyle \frac{\mathrm{N}}{1+{\mathrm{N}\mathrm{e}}^2}}=\mathrm{n}={\displaystyle \frac{269}{1+269{\left(0.05\right)}^2}}=161$$

where n = sample size (161); N = population size of smallholder farmers (269); e = desired margin of error (0.05).

2.3. Data Collection and Analysis

2.3.1. Baseline Survey

A structured questionnaire was developed as the primary survey instrument to elicit data for the study. Prior to the data collection process, four enumerators were trained to assist with data collection. Informed consent was obtained from all participating farmers, and participation was voluntary. Data were collected through face-to-face interviews using structured questionnaires, adhering to all protocols and ethical principles outlined in the Declaration of Helsinki. The questionnaire was face and content validated by field experts in the field of agronomy to ensure relevance and applicability prior to data collection. This evaluation assessed its structure and relevance and examined whether the included variables were reasonable and clear. In addition, a reliability assessment was conducted through a pre-test to determine the instrument’s stability and consistency in measuring the intended variables. The pre-test method was applied to 10 smallholder farmers from a village not included in the study. A reliability coefficient of r = 0.85 was obtained, which, according to established standards in the literature, indicates that the questionnaire was reliable [31]. The survey instrument comprised sections aligned with specific study objectives, assessing socio-economic factors influencing crop diversification, predominant crops, cropping patterns and systems, farmers’ knowledge of crop diversification benefits, and training attendance, understanding, and knowledge utilization. Interviews were conducted in isiZulu, the respondents’ native language, to ensure clarity and cultural relevance, thereby enhancing both communication and the reliability of responses. A total of 161 questionnaires were successfully administered across the five villages, with all participants actively engaged in smallholder farming.

2.3.2. Statistical Analysis

The survey data were imported from an Excel file into SPSS (Statistical Package for the Social Sciences). Variable names were systematically reviewed to ensure clarity and consistency in coding. During data cleaning, missing values were identified and addressed using appropriate techniques. Outliers were detected with box plots and z-scores and were managed to preserve the dataset’s integrity. Categorical variables were recoded for uniformity, with numerical codes assigned where applicable. This structured approach ensured the dataset was accurate, reliable, and ready for analysis. IBM SPSS software (version 29.0) was then used to perform descriptive statistical analyses, providing frequencies, percentages, means, and standard deviations for both continuous and categorical variables to address the research objectives. The results were presented through tables, pie charts, and bar graphs for clarity and interpretability. Additionally, multiple linear regression was employed to assess the extent of crop diversification, while the multivariate probit regression model was used to examine the socio-economic factors influencing crop diversification among smallholder farmers in Bergville, South Africa.

2.4. Model Specification

2.4.1. Inferential Statistics

The study employed a multiple linear regression model due to its ability to incorporate multiple independent or explanatory variables in predicting the outcome of a continuously measured dependent variable [32]. This model was particularly effective in analyzing respondents’ socioeconomic characteristics and their influence on the extent of crop diversification, which was measured as a continuous variable. The extent of crop diversification was created as a proportion of the number of crops that were grown over the total number of crops that could feasibly be grown, which was six in this case. This made it suitable to be used as the dependent variable in the multiple linear regression model. The selection of this approach aligns with previous research [33,34,35], where multiple linear regression has been widely employed in similar contexts.

The explicit form of the model is presented as follows:

Y = β₀ + β₁X₁ + β₂X₂ + BnXn + e

(1)

where:

Y represents the extent of crop diversification.

X is a vector of hypothesized explanatory variables, including farmers’ socioeconomic characteristics (age, gender, marital status, educational attainment, household size, farming experience, farm size, market access, and sources of water for irrigation).

β is a vector of unknown parameters to be estimated, while e represents the independently and normally distributed random error term.

2.4.2. Multivariate Probit Model (MVP)

The study also utilized a multivariate probit model approach to examine the socio-economic determinants of crop diversification in the region. In contrast to other dichotomous models, the MVP model effectively accounts for unobservable factors that influence smallholder farmers’ crop diversification by permitting correlation across error terms of latent equations. The identified correlations allow for error terms that indicate positive correlation (complementarity) and negative correlation (substitutability) on crop diversification. In this study, the MVP model consists of three binary choice equations, namely vegetables, legumes, and cereals.

Hence, the study model is specified as:

P^∗_im = β_im + x_im + ε_im (m = 1, 2, 3)

(2)

P_im {1 if P^∗_im > 0 and 0 otherwise}

The above equation is formulated under the assumption that a rational i-th farm household possesses a latent variable P^∗_im, which captures unobserved factors influencing the m-th crop diversification (m = 3 crop choices). X_im consists of exogenous variables that determine crop diversification, including smallholder farmers’ socioeconomic attributes as detailed in

Table 1. Summary description of the socioeconomic variables included in the regression models.

Variables	Description	Variable Type
Age	1 = ≤25, 2 = 26–50, 3 = 51 years and above	Categorical
Gender	1 = male, 0 if otherwise
Marital status	1 = unmarried, 2 = Married, 3 =Divorced, 4 = Widow (er)	Categorical
Education	1 = no formal schooling, 2 = primary school, 3 = secondary school, 4 = tertiary school	Categorical
Household size	Number of members in the household	Continuous
Farming experience	1 = 1–10 yrs, 2 = 11–20 yrs, 3 = 21–30 yrs, 4 = 31 and above years	Categorical
Farm size	1 = ≤2 ha, 2 = 3–5 ha, 6 and above hectares	Categorical
Source of seeds	1 = own production, 2 = supermarkets, 3 = other	Categorical
Market access	1 = yes, if a farmer has market access, 0 if otherwise	Categorical
Advantages of diversifying crops	1 = perceived advantages of crop diversification, 0 if otherwise	Categorical
Sources of water for irrigation	1 = irrigation, 2 = rainfed, 3 = both	Categorical

. The coefficients βm quantify the effects of these explanatory variables on crop diversification. The error terms ε_im follow a multivariate normal distribution, each with a mean of zero and a variance–covariance matrix characterized by values of 1 along the diagonal and nonzero correlations among off-diagonal elements. The socioeconomic variables included in the models are shown in Table 1.

2.5. Ethical Consideration

Ethical clearance for this study was obtained from the University of KwaZulu–Natal Ethics Committee, facilitated by the School of Agricultural, Earth, and Environmental Sciences, under reference number HSSREC/00008179/2025. During the administration of the questionnaires, the researcher sought informed consent from the participants, ensuring confidentiality throughout the process. The questionnaires were distributed at times and locations that were convenient for the participants. Before completing the questionnaires, participants were fully informed about the scope of the study, and their anonymity was preserved by not disclosing any personal identities. The study was conducted with a strong emphasis on participant welfare, ensuring that no harm was inflicted during the process. Furthermore, all participants were acknowledged and thanked for their valuable time and contribution to the completion of the questionnaires.

3. Results

3.1. Socio-Economic Attributes of the Smallholder Farmers

The results in

Table 2. Distribution of the socio-economic attributes of smallholder farmers.

Socio-Economic Variables	Frequency (%)
Age (Years)
≤25	9 (5.6)
26–50	82 (50.3)
51 and above	70 (44.1)
Gender
Female	129 (80.1)
Male	32 (19,9)
Marital Status
Unmarried	85 (52.8)
Married	59 (36.6)
Divorced	1 (0.6)
Widow (er)	16(9.9)
Education
No formal schooling	33 (20.5)
Primary school	40 (24.8)
Secondary school	79 (49.1)
Tertiary school	9 (5.6)
Household Size
1–5 people	52 (32.2)
6–10 people	81 (50.3)
11 and above	28 (17.3)
* Source of income
Temporal employment	25 (15.5)
Welfare grant	113 (70.2)
Remittances	34 (21,1)
Crop sales—irrigated	45 (28)
Crop sales—rainfed	21(13)
Livestock sales	26 (16.1)
Other	17 (10.6)
Source of food
Own production	50 (31.1)
Purchased	110 (68.3)
Food aid	1 (0.6)

* Multiple choice response.

revealed the socio-economic attributes of the smallholder farmers in the surveyed region. The results indicated that the majority of the respondents (50.3%) were between 26 and 50 years old and 44.1% were between the ages of 51 and above, followed by 9% of smallholder farmers who were less than 25 years of age. About 80.1% of the smallholder farmers were females, while males accounted for only 19.19%. The marital status reveals that a significant proportion (52.8%) of the farmers were unmarried, while 36.6% were married, and only a few (9.9%) were widowed and 0.6% were divorced. The findings from Table 2 further reveal that 49.1% of the farmers had secondary education, 24.8% had primary education, and 20.5% had no formal education, with only a few (5.6%) of the respondents having tertiary education. A little above average (50.3%) had a household size of between 6 and 10 people, 32.2% had between 1 and 5 people, and only 17.3% had 11-plus people living together under the same roof. Also, the results revealed that 70.2% of the smallholder farmers’ source of income was from welfare grants, 28% and 13% of income was from irrigated and rainfed crop sales, respectively, 16.1% was from livestock sales, and 21.1% of the source of income was from remittance, followed by 15.5% from temporary employment and only a few (10.6%) from other sources (sewing, handicrafts, etc.). In addition, the majority (68.3%) of farmers’ sources of food were from the supermarket, while 31.1% of the farmers produced their own food, and only a few (0.6%) relied on food schemes or aid programs.

Furthermore, the results in

Table 3. Distribution of the socio-economic attributes of smallholder farmers contd.

Socio-Economic Variables	Frequency (%)
Years of farming experience
1–10 yrs	137 (85.1)
11–20 yrs	14 (8.7)
21–30 yrs	6 (3.7)
31 and above	4 (2.5)
Farm size (hectares)
≤2	150 (93.2)
3–5 ha	10 (6.2)
6 and above	1 (0.6)
Sources of water for irrigation
Irrigation	14 (8.7)
Rainfall	41 (25,5)
Both	106 (65.8)
Sources of seeds
Own production	35 (21.7)
Supermarkets	115 (71.4)
Other	11 (6.8)
How easy to get seeds?
Very easy	80 (49.7)
Easy	65 (40.4)
Not easy	16 (9.9)
Market access
Yes	32 (19.9)
No	129 (80.1)

indicated that a significant proportion (85.1%) of the smallholder farmers had between 1 and 10 years of experience in farming and 8.7% had between 11 and 20 years, while 3.7% ranged between 21 and 30 years and only 2.5% had 31 and above years of farming experience. About 93.2% owned less than two hectares of land, 6.2% owned between three and five hectares, and a few (0.6%) owned six-plus hectares. The results further revealed that the majority (65.8%) of the smallholder farmers relied on both rainfall and irrigation as the primary source of water for farming. Conversely, a minority (8.7%) of the farmers relied exclusively on irrigation as the primary source of water, while 25.5% relied on rainfall. About 71.4% of the farmers relied on supermarkets for the source of seeds, while 21.7% produced their own seeds, utilizing seeds from previous harvests for the next planting season. Only a few farmers (6.8%) relied on alternative seed sources, such as Farmers Support Groups and the Department of Agriculture. The results also indicated that 49.7% of farmers had very easy access to seeds, while 40.4% had easy access, and 9.9% found it difficult to obtain seeds. In addition, the majority (80.1%) of the farmers had no access to the market, while a minority (19.9%) had access to the market.

3.2. Farming Practices Utilized by Smallholder Farmers in the Study Area

The results in Figure 3 present an analysis of smallholder farmers’ farming practices or cropping systems in the study area. The study found that a significant proportion (48.4%) of farmers practice intercropping, followed by 33.5% who practice monoculture (sole cropping), while 16.8% adopted crop rotation. Only a minority (1.2%) engage in relay cropping (Figure 3).

Crop Cultivation Patterns Among Smallholder Farmers in the Study Area

Figure 4 presents an analysis of crop cultivation patterns among smallholder farmers in the study area. The results indicate that vegetable cultivation is the most predominant, practiced by 87% of farmers, followed by cereal cultivation at 56%. Additionally, 43% of respondents engage in legume cultivation (Figure 4).

3.3. Smallholder Farmers’ Socio-Economic Determinants of Crop Diversification

The results presented in

Table 4. Socio-economic determinants of crop diversification extent among smallholder farmers.

Characteristics	Coeff.	Std.Err	T-Value	p > t	VIF	Tolerance
Age	0.060	0.104	0.58	0.562	2.02	0.494553
Gender	−0.161	0.284	−0.57	0.570	1.04	0.963210
Marital status	0.189	0.148	1.28	0.203	1.46	0.683164
Education	0.437	0.177	2.46	0.015 **	1.94	0.515743
Household size	0.079	0.032	2.49	0.014 **	1.25	0.799583
Farming experience	0.076	0.095	0.79	0.428	1.09	0.920088
Farm size	0.269	0.291	0.93	0.356	1.06	0.947418
Source of seeds	0.366	0.231	1.59	0.114	1.13	0.882091
Market access	1.335	0.297	4.50	0.000 ***	1.13	0.881867
Advantages of diversifying crops	1.616	0.279	5.79	0.000 ***	1.09	0.915646
Sources of water for irrigation	0.226	0.178	1.27	0.206	1.07	0.935039
Constant	−0.883
F	8.95
Prob > F	0.000
R-squared	0.398
Adj R-squared	0.354
Mean VIF					1.30

Note: Statistical significance *** p < 0.01, ** p < 0.05.

revealed the estimates of socio-economic attributes influencing the crop diversification extent by smallholder farmers using a multiple linear regression model. The results of the multiple linear regression indicated a relationship (R² = 0.398) between the independent variables and the extent of crop diversification. The model predicted about 40% of the variation in farmers’ crop diversification, with an F-test value of 8.95 and statistical significance at p < 0.01. This demonstrates that the model is a good fit and confirms that the selected independent variables play a significant role in determining diversification decisions among smallholder farmers. The Variance Inflation Factor (VIF) was used to test for multicollinearity among the variables in the model, and it was discovered that multicollinearity was not a problem as the VIF value was 1.30 with a high tolerance value across the variables. The results further revealed that education level (t = 2.46) and household size (t = 2.49) were significant at p < 0.05, while market access (t = 4.50) and advantages of diversifying crops (t = 5.79) were highly significant at p < 0.01. This implies that these four variables significantly influence the crop diversification extent in the region.

3.4. Socio-Economic Factors Influencing Crop Diversification Among Smallholder Farmers

The results in

Table 5. Socio-economic factors influencing crop diversification among smallholder farmers.

Characteristics	Vegetables		Legumes		Cereals
	Coeff.	Std.Err	Coeff.	Std.Err	Coeff.	Std.Err
Age	−0.416 ***	0.155	0.107	0.085	0.140	0.083
Gender	−0.362	0.413	−0.117	0.270	0.311	0.259
Marital status	0.754 ***	0.307	0.026	0.138	0.299 **	0.141
Household size	−0.043	0.052	0.031	0.028	−0.020	0.030
Farming experience	0.204	0.183	0.219 **	0.102	0.190 **	0.093
Farm size	−0.856 ***	0.345	0.670 *	0.422	0.275	0.230
Source of seeds	0.273	0.324	−0.030	0.216	0.134	0.199
Market access	4.926	151.764	0.414	0.277	−0.160	0.264
Advantages of diversifying crops	1.039 ***	0.370	0.748 ***	0.283	−0.009	0.254
Sources of water for irrigation	0.871 ***	0.287	0.002	0.172	−0.188	0.170
Constant	−0.308	1.110627	−2.454	0.873	−1.408	0.760
N	161
Wald chi2(30)	62.65
Log-likelihood	−208.631
Prob > chi2	0.000

*, **, and *** signify statistical significance at p < 0.1, p < 0.05, and p < 0.01 levels, respectively.

show the socio-economic factors influencing crop diversification among smallholder farmers in Bergville using a multivariate probit regression model. The Wald test (chi2 (30) = 62.65, Prob > chi2 = (0.000)) is highly significant at p < 0.01, suggesting that the error terms across the crop diversification equations are correlated. The significance of this lies in the fact that applying an MVP regression model was suitable for identifying the smallholder farmers’ socio-economic attributes influencing crop diversification. These significant socio-economic attributes include age, marital status (MS), farming experience (FE), farm size (FS), advantages of crop diversification (ACD), and sources of water for irrigation (SWR). The significant variables that were positively related to diversification of vegetables were ACD (p < 0.01) and SWR (p < 0.01), while age (p < 0.01), MS (p < 0.01), and FS (p < 0.01) were negatively related to vegetable diversification among the smallholder farmers in the study area. Furthermore, variables such as FE (p < 0.05), FS (p < 0.1), and ACD (p < 0.01) were positively related to legume crop diversification, while only MS (p < 0.05) and FE (p < 0.05) were positively related to cereal crop diversification.

3.5. Smallholder Farmers’ Knowledge Level on Crop Diversification Benefits

The results in

Table 6. Knowledge level of smallholder farmers on crop diversification benefits.

Tested Knowledge on Crop Diversification Benefits	Frequency (%)	Frequency (%)
	Yes	No
a. Did you know that crop diversification alleviates food insecurity by improving yields?	82 (50.9)	79 (49.1)
b. Did you know that crop diversification ensures production stability through providing insurance (you can still rely on the other crop if one fails)?	91 (56.5)	70 (43.5)
c. Did you know that crop diversification increases farmers’ economic returns (income)?	95 (59.0)	66 (41.0)
d. Did you know that crop diversification reduces the risks associated with agricultural production (reduces pests and suppresses weeds and disease pressure)?	61 (37.9)	100 (62.1)
e. Did you know that crop diversification improves crop water-use efficiency?	84 (52.2)	77 (47.8)
f. Did you know that crop diversification improves soil fertility?	89 (55.3)	72 (44.7)
g. Did you know that crop diversification increases the resilience of farming systems?	65 (40.4)	96 (59.6)

revealed that the majority (50.9%) of smallholder farmers had knowledge of the potential of crop diversification to alleviate food insecurity through improving yields, while only 49.1% had limited knowledge. About 56.5% and 59% knew that crop diversification ensures production stability by providing insurance and increases farmers’ economic returns (income), while 43.5% and 41.0% had no knowledge, respectively. A minority (37.9%) of farmers had knowledge of crop diversification’s ability to reduce the risks associated with agricultural production (reducing pests, suppressing weeds, and disease pressure), which resulted in 62.1% having limited knowledge. In addition, only less than half (40.4%) of the farmers had knowledge of crop diversification’s potential to increase the resilience of farming systems, which led to more than half (59.6%) having no knowledge. Furthermore, the results indicated that 52.2% knew that crop diversification improves crop water-use efficiency and soil fertility (55.3%), while 47.8% and 44.7% had limited knowledge, respectively.

3.6. Smallholder Farmers’ Training Attendance, Understanding, and Knowledge Utilization

The findings in

Table 7. Training attendance, understanding, and knowledge utilization among smallholder farmers in the study area.

Statement	Frequency (%) Yes	Frequency (%) No
a. Attended all training sessions that are held by the Farmers Support Group/Department of Agriculture’s extension officers.	80 (49.7)	81 (50.3)
b. Fully understand the information provided in the training sessions.	88 (54.7)	73 (45.3)
c. Put into practice all the advice given in the training.	93 (57.8)	68 (42.2)

show that smallholder farmers receive training from both the Farmers Support Group and the Department of Agriculture extension officials. About 49.7% attended all training sessions that were held by both organizations, while 50.3% only attended a few training sessions. In addition, more than half of the study population understood the information provided in the training sessions, which resulted in 45.3% lacking understanding. The results further revealed that a significant proportion (57.8%) of the farmers put into practice all the advice provided in the trainings, while 42.2% failed to practice or adopt all the advice provided in the trainings.

4. Discussion

4.1. Socio-Economic Attributes of the Smallholder Farmers in the Study Area

4.1.1. Gender and Age Dynamics in Smallholder Cropping Systems

The present study revealed significant gender disparities in smallholder cropping systems, with women comprising 80.1% of smallholder farmers engaged in crop production. The distribution of male (19.9%) and female (80.1%) farmers in this study reflects the sampled participants. However, several studies have confirmed that many smallholder farmers in Bergville are women [27,36]. This finding indicates that agricultural activities in the study region are predominantly conducted by female farmers, aligning with previous research that identified women as the primary workforce in crop production, often surpassing male participation [37,38]. This could be attributed to women’s central role in maintaining food security in the Global South [39]. However, despite their substantial involvement in agriculture, women frequently face resource constraints, particularly regarding land ownership, due to prevailing socio-cultural norms. These limitations may hinder their ability to adopt diversified cropping systems at a higher rate. In addition to gender dynamics, the study also examined age-related trends in agricultural participation. The findings indicated that only 9% of smallholder farmers were below the age of 25, whereas 44.1% were aged 51 years or older. This suggests minimal youth engagement in agriculture, as younger individuals tend to perceive farming as an occupation for older generations, a notion previously reported by Kom et al. [37] and Geza et al. [40]. Furthermore, older farmers often demonstrate a preference for traditional agricultural practices over the adoption of new crop species or diversification strategies. To ensure the sustainability of smallholder farming systems, it is crucial to foster youth participation in agriculture, particularly in crop diversification initiatives. Targeted training programs and agricultural development policies should be designed to address structural barriers limiting youth involvement in the sector. Geza et al. [40] emphasized that existing agricultural development programs inadequately tackle the systemic challenges underlying youth participation in economic activities. Therefore, the implementation of context-specific, integrated agricultural interventions is necessary to encourage meaningful youth engagement and facilitate their active role in shaping future food systems.

4.1.2. Household Size, Marital Status, and Their Implications for Agricultural Practices

The findings of this study indicate that the average household size among smallholder farmers ranges between six and ten individuals, suggesting relatively large family units. Due to substantial household sizes, crop diversification becomes a crucial strategy for enhancing agricultural productivity and ensuring an adequate food supply. As highlighted by Sisha [41], household size is a key determinant of food security among rural smallholder farmers, influencing both production capacity and resource allocation. Furthermore, the study revealed that a majority (52.8%) of the farmers were unmarried. This finding is consistent with research conducted in Southern Ethiopia by Gebre et al. [42], which established a strong association between marital status and household decision-making processes. Previous studies [43,44,45] have also demonstrated that households led by married individuals are more likely to adopt diverse crop varieties. This could be attributed to the greater access that married farmers have to agricultural networks, including extension agents and agro-input suppliers, compared to their unmarried counterparts, who primarily depend on peer-to-peer knowledge exchange as their main source of agricultural information. These findings underscore the importance of targeted agricultural policies that address household dynamics and social structures, promoting crop diversification strategies that enhance food security and sustainable farming practices among smallholder farmers.

4.1.3. Education Levels Among Smallholder Farmers

The findings presented in Table 2 further reveal that only 5.6% of smallholder farmers had attained tertiary education, while a significant proportion had no formal education. This raises concerns regarding the achievement of Sustainable Development Goal 2 (Zero Hunger), as education plays a critical role in enhancing farmers’ understanding of modern agricultural practices and their capacity to adopt innovative strategies, including crop diversification. As highlighted by Adjimoti and Kwadzo [46], higher levels of education are positively associated with increased awareness and implementation of improved farming techniques. Similar findings were reported by Samim et al. [47], who found that farmers’ decisions to increase the adoption of climate-smart agricultural practices were influenced by their education, household family laborers, agricultural, and other factors. Limited educational attainment among smallholder farmers may, therefore, hinder the widespread adoption of diversified cropping systems, potentially affecting food security and agricultural sustainability in the region. These findings underscore the need for targeted educational interventions and extension services to enhance farmers’ knowledge and facilitate the adoption of sustainable agricultural practices.

4.1.4. Socioeconomic Constraints and Crop Diversification Among Smallholder Farmers

The findings of this study indicate that 70.2% of smallholder farmers rely on welfare grants as their primary source of income, highlighting the dominance of elderly farmers in the crop production sector who receive government assistance aimed at supporting vulnerable populations. These findings emphasize the need for integrating crop diversification into smallholder farming systems from the outset of agricultural development strategies. As a result, farmers could incrementally enhance their cropping systems, optimize resource utilization, and invest in high-value crops on small plots. The study further reveals that financial constraints and limited access to critical infrastructure, particularly irrigation facilities, pose significant challenges to smallholder farmers [17,48]. Consequently, only 28% of farmers’ income is derived from irrigated crop sales, while merely 8.7% of the surveyed farmers rely exclusively on irrigation as their primary water source, with the majority depending on rainfall. These findings align with Bjornlund et al. [49], who reported that Sub-Saharan Africa has the lowest irrigation development among all developing regions, with only 4% of arable land under irrigation, compared to 47% in Asia and 18% globally [50]. Due to these constraints, enhancing the productivity of existing irrigated land through crop diversification remains a viable strategy for meeting future food demands. Additionally, the study reveals that most farmers source their food from supermarkets, while only 31.1% produce their own food. This may be attributed to the limited adoption of crop diversification and the exclusion of drought-tolerant crops, which exacerbates food insecurity, a persistent challenge in Sub-Saharan Africa. However, the inclusion of a diverse range of traditional crop varieties within production systems can significantly improve resilience, enabling farming populations to better adapt to fluctuating environmental and economic conditions [45,51]. These findings highlight the importance of promoting diversified and sustainable agricultural practices to enhance food security and economic stability among smallholder farmers.

4.1.5. Farming Experience, Land Ownership, and Their Implications for Crop Diversification

The findings presented in Table 3 indicate that a significant proportion of smallholder farmers have between 1 and 10 years of farming experience, while only 2.5% have been engaged in agriculture for more than 31 years. This suggests that elderly farmers, who have accumulated extensive agricultural knowledge, are more likely to engage in diversified farming. Similarly, younger farmers, being more dynamic and adaptable, may also be inclined to diversify their cropping systems to enhance productivity. These observations align with the findings of Kemboi et al. [23], who reported that farming experience can have both positive and negative associations with crop diversification, depending on various socio-economic and environmental factors. The study revealed that 93.2% of farmers owned less than two hectares of land. This finding underscores the limited access of smallholder farmers, particularly women, to agricultural resources such as land, capital, and technology when compared to their male counterparts. These results are consistent with the findings of Zakaria et al. [52], who highlighted gender disparities in access to productive agricultural assets. The restricted availability of land may be attributed to multiple factors, including population growth, agricultural development pressures, land tenure policies, and broader socio-economic and climatic conditions [17]. Addressing these constraints through policy interventions and targeted support mechanisms is essential for enhancing smallholder farmers’ capacity to adopt sustainable and diversified farming practices.

4.1.6. Impact of Land Size, Seed Availability, and Market Access on Crop Diversification

The findings of this study suggest that the decline in land size may negatively impact crop diversification, as farmers with larger landholdings are generally more inclined and capable of cultivating a diverse range of crops [23,53]. Limited land availability restricts opportunities for crop rotation, intercropping, and the introduction of new crop varieties, thereby hindering the potential benefits of diversified farming systems. Additionally, the study revealed that only 21.7% of smallholder farmers produce their own seeds, indicating that limited seed availability may be a significant barrier to crop diversification in the region. Access to quality seeds is essential for promoting diversified agricultural systems, and the lack thereof may contribute to low adoption rates of improved and alternative crop varieties. Furthermore, the study found that 80.1% of farmers lack access to formal markets. Market access plays a crucial role in determining smallholder farmers’ ability to diversify their crops, as it influences both input availability and the profitability of diverse cropping systems. Hlatshwayo et al. [54] highlighted that limited market access is closely linked to education levels and various structural barriers that hinder smallholder farmers from engaging in commercial agriculture. In South Africa, many smallholder farmers reside in remote areas with poorly maintained roads, inadequate market infrastructure, limited transportation and storage facilities, and insufficient skills and information. These challenges contribute to high transaction costs, further discouraging market participation and affecting crop diversification efforts. Enhancing smallholder farmers’ market access through infrastructure development, improved transportation networks, and capacity-building initiatives can play a pivotal role in promoting crop diversification. Strengthening market linkages and reducing barriers to entry can help farmers actively participate in diversified farming systems, ultimately contributing to food security and poverty alleviation in rural communities.

4.2. Farming Practices Utilized by Smallholder Farmers in the Region

The findings of this study indicate that less than half of smallholder farmers practice intercropping, primarily integrating maize (Zea mays) with pumpkins (Cucurbita spp.) or sugar beans (Phaseolus vulgaris), while 33.5% continue to engage in monoculture, predominantly maize. Only 16.8% of farmers have adopted crop rotation (Figure 3). These results highlight the limited adoption of crop diversification in the region, despite its well-documented benefits, including increased crop yield [55,56], reduced vulnerability to climatic shocks, improved soil fertility [57], enhanced pest and disease control, and reduced reliance on agrochemical inputs [55]. The observed low adoption rates emphasize the need for targeted interventions to promote crop diversification, particularly in smallholder farming systems in sub-Saharan Africa. Strategies should focus on improving access to training, resources, and knowledge dissemination to enhance the uptake of diversified cropping systems. These interventions must be tailored to accommodate local agronomic conditions, socio-economic factors, and prevailing challenges, as suggested by Gitari et al. [58]. The predominance of cereal monocropping suggests that many farmers remain reliant on traditional agricultural practices, with limited transition toward diversified cropping systems. This finding aligns with the conclusions of Nyamayevu et al. [59], who attributed the persistence of monoculture to factors such as limited access to high-quality seeds, labour shortages in low-input farming systems, and land constraints. Additionally, the diffusion of diversified cropping systems may be influenced by farmer-to-farmer knowledge exchange, commonly referred to as peer-to-peer extension. The lack of widespread engagement in such knowledge-sharing mechanisms has likely contributed to a significant gap in awareness, thereby impeding the broader adoption of crop diversification practices among smallholder farmers.

Crop Cultivation Patterns Among Smallholder Farmers in the Study Area

Crop production in the study area is primarily focused on vegetable cultivation, followed by cereals, while a smaller proportion of farmers engage in legume farming. Vegetable cultivation encompasses a wide range of crops, including spinach (Spinacia oleracea), beetroot (Beta vulgaris), onion (Allium cepa), carrot (Daucus carota), green pepper (Capsicum annuum), sweet potato (Ipomoea batatas), potato (Solanum tuberosum), chili pepper (Capsicum spp.), eggplant (Solanum melongena), and cabbage (Brassica oleracea). The predominant legumes grown include sugar bean (Phaseolus vulgaris), kidney bean (Phaseolus vulgaris), and cowpea (Vigna unguiculata). The primary cereals cultivated in the region are maize (Zea mays) and wheat (Triticum aestivum). Similar findings were reported by Ojiewo et al. [60] and Mkhize et al. [61], who highlighted that vegetable production and consumption serve as a potent mechanism for disadvantaged smallholder farmers to obtain essential nutrients in their diets while generating much-needed income through trade. This underscores the need to improve market access for other crops, such as legumes, to enhance their diversification, which plays a crucial role in diversifying farm incomes and improving dietary nutrition.

4.3. Socio-Economic Determinants of Crop Diversification Extent Among Smallholder Farmers

The study findings on the socio-economic determinants influencing the extent of crop diversification using a multiple linear regression model indicate that the education level of smallholder farmers was statistically significant at p < 0.05, with a coefficient of 0.437. This positive correlation suggests that as farmers’ education levels increase, so does the extent of crop diversification. Educated farmers demonstrated a greater propensity to diversify their cropping systems compared to their illiterate counterparts. These findings are consistent with those of Mengistu et al. [62], who reported that an increase in the literacy level of household heads enhances the likelihood of household food security by 7.9%. This implies that educated farmers may expand their crop diversification efforts to achieve higher yields and stability, thereby alleviating food insecurity. Additionally, the results indicate that household size was statistically significant at p < 0.05, with a coefficient of 0.079. This finding suggests that household size influences the extent of crop diversification in the study region. Larger farm households typically possess a greater labour force, facilitating the timely planting of crops and mitigating delays that could be exacerbated by climate variability, as highlighted by Khan et al. [63].

A highly significant (p < 0.01) positive relationship was observed between farmers’ perceptions of the advantages of crop diversification (coefficient = 1.616) and the extent of diversification. This finding suggests that as farmers become increasingly aware of the benefits of crop diversification, they are more inclined to implement diversification strategies to a greater extent. Agricultural extension services were identified as a primary source of information for smallholder farmers. As a result, frequent interactions with extension agents are likely to enhance the adoption of improved agricultural innovations, including diversification into higher-value crops [64]. The study also revealed that market access was highly significant at p < 0.01, with a positive coefficient of 4.926. This finding indicates that market access plays a crucial role in influencing the extent of crop diversification. As market accessibility improves, farmers are increasingly motivated to diversify their crop production within the region. These results highlight the importance of enhancing education, extension services, and market access as key strategies for promoting sustainable crop diversification extent among smallholder farmers.

4.4. Socio-Economic Factors Influencing Crop Diversification Among Smallholder Farmers

The results of this study indicate that socio-economic factors, including age, marital status (MS), farming experience (FE), farm size (FS), perceived advantages of crop diversification (ACD), and sources of water for irrigation (SWR), significantly influence crop diversification among smallholder farmers. Using a multivariate probit regression model, MS, ACD, and SWR were found to be highly significant at p < 0.01, demonstrating positive correlations of 0.754, 1.039, and 0.871, respectively, with vegetable crop diversification. These findings suggest that an increase in water sources for irrigation and perceived benefits of crop diversification enhances the adoption of vegetable diversification. Additionally, marital status strongly influenced farmers’ decisions regarding vegetable diversification in the study area. The increased knowledge and adoption of vegetable diversification in the region may be attributed to training programs and the provision of vegetable seedlings and seeds by the Farmers Support Group. During interviews, farmers reported cultivating up to six or more vegetable varieties in a small piece of land. Commonly produced vegetables included spinach, cabbage, carrots, beetroot, onions, green peppers, chilies, potatoes, tomatoes, and eggplant. Furthermore, findings revealed that farmers with access to reliable water sources were more inclined to produce a greater variety of vegetables compared to those reliant solely on rainfall. This supports the hypothesis that crop diversity declines with decreasing water availability. The results align with the findings of Harrison et al. [65], who posited that water is a key limiting factor in arid and semi-arid ecosystems, shaping plant diversity and primary productivity. Moreover, the influence of marital status on vegetable diversification may be attributed to the necessity for food security among unmarried farmers, who constituted a dominant demographic in the study area. In contrast, a study by Lemma and Sharma [66] reported that married women are more committed to ensuring a stable food supply and prioritize household well-being compared to their unmarried counterparts. The observed differences may be due to the focus of Lemma and Sharma [66] on urban agricultural settings, whereas the present study is based on rural agricultural communities.

The findings of this study further indicate that both age and farm size (FS) exhibit a statistically significant negative correlation with vegetable diversification, as evidenced by their correlation coefficients of −0.416 and −0.856, respectively (p < 0.01). This negative correlation implies that an increase in one variable corresponds with a decrease in the other. The results suggest that age plays a critical role in determining the extent of crop diversification, aligning with the conclusions of Inoni et al. [64]. Older farmers may be less inclined to diversify their crops due to factors such as risk aversion, adherence to traditional farming practices, physical constraints (e.g., declining strength), and a reduced propensity to adopt innovative agricultural techniques. Furthermore, the observed inverse relationship between FS and vegetable diversification may be attributed to larger farms prioritizing monoculture over diversified vegetable production. This could be due to their access to agricultural inputs, which facilitate the practice of monoculture farming. These findings are consistent with the results of Mortensen and Smith [67], who argued that farmers can effectively cultivate crops in monocultures or simplified crop rotations due to the availability of synthetic fertilizers and pesticides. As a result, continuous monoculture systems would not be viable without such chemical inputs. Moreover, the study findings indicate that farmers’ experience (FE) (p < 0.05), farm size (FS) (p < 0.1), and awareness of crop diversification (ACD) (p < 0.01) exhibit statistically significant positive correlations with legume crop diversification, as reflected in their respective correlation coefficients of 0.219, 0.670, and 0.748. These results suggest that as these factors increase, the level of legume crop diversification also increases, implying that more experienced farmers demonstrate a slightly higher propensity to diversify their legume crops. Interactive interviews with farmers further revealed that sugar bean (common bean) is the most extensively cultivated legume in the region, followed by kidney bean and cowpea, to a lesser extent. Additionally, larger farm sizes were associated with greater legume crop diversification, aligning with the findings of Inoni et al. [64], who noted that increased farm size enhances farmers’ willingness and capacity to engage in crop diversification. The probability of adopting diversified cropping systems increases as land availability expands, highlighting agricultural land as a crucial production factor in rural livelihoods. Moreover, farmers who perceive greater benefits from legume crop diversification are more likely to engage in diverse cropping practices. However, field observations indicate a persistent lack of awareness and unfavourable attitudes toward legume crop diversification among farmers. These findings underscore the necessity of enhancing farmers’ knowledge and perceptions regarding diversified legume cultivation, as recommended by Lema et al. [68] and Marie et al. [69], to facilitate greater adaptation to climate variability. Furthermore, the results indicate that only marital status (MS) (p < 0.05) and FE (p < 0.05) show statistically significant positive correlations with cereal crop diversification, particularly maize, with correlation coefficients of 0.299 and 0.190, respectively. This finding suggests that marital status may influence diversification decisions, as married farmers often face increased household responsibilities and financial demands, incentivizing them to adopt diversification as a risk management strategy. Moreover, married individuals may have better access to labour, as family members contribute to farming activities. Experienced farmers, on the other hand, are more likely to possess a comprehensive understanding of market dynamics, climate variability, and soil suitability, thereby enabling them to implement diverse cropping strategies to enhance productivity and mitigate agricultural risks.

4.5. Smallholder Farmers’ Knowledge Level on Crop Diversification Benefits

The findings of this study indicate that a significant proportion of smallholder farmers (49.1%) have limited knowledge regarding the potential benefits of crop diversification in mitigating food insecurity through enhanced agricultural yields. About 56.5% of the respondents recognized that crop diversification contributes to production stability by providing insurance against crop failure and increasing farmers’ economic returns. In addition, only 37.9% of farmers were aware of crop diversification’s role in reducing agricultural production risks, such as pest infestations, weed suppression, and disease pressure. This lack of awareness highlights a significant gap in knowledge dissemination and extension services, which may have contributed to the low adoption rates of diversified cropping systems in the region. Moreover, only 40.4% of farmers understood the potential of crop diversification to enhance the resilience of farming systems, while the majority lacked knowledge of its role in improving water-use efficiency and soil fertility. These findings suggest that many smallholder farmers could not fully comprehend the agronomic and economic advantages associated with diversification. Limited awareness could be attributed to restricted access to agricultural extension services, a lack of formal agricultural education, or a strong reliance on traditional monocropping practices. The implications of these findings underscore the need for targeted interventions aimed at enhancing farmers’ knowledge of diversification strategies. Strengthening agricultural extension programs, implementing farmer training workshops, and promoting participatory learning approaches could facilitate knowledge transfer and encourage broader adoption of crop diversification. These findings align with previous research [66,69], which emphasizes the critical role of education and extension services in influencing farmers’ decisions to adopt diversified cropping systems. Addressing this knowledge gap among smallholder farmers is essential for fostering a more resilient and sustainable agricultural sector, particularly in the context of climate change and fluctuating market conditions.

4.6. Smallholder Farmers’ Training Attendance, Understanding, and Knowledge Utilization

The study results indicate that smallholder farmers receive agricultural training from both the Farmers Support Group and the Department of Agriculture. Approximately 49.7% of farmers reported attending all training sessions conducted by these organizations, whereas 50.3% participated in only a limited number of sessions. This suggests a relatively balanced distribution in training attendance, with a significant proportion of farmers not fully engaging in the available capacity-building initiatives. The study findings further revealed that more than half of the study population demonstrated an adequate understanding of the information provided during the training sessions. However, 45.3% of the farmers reported difficulties in comprehending the training content, which could hinder the effective application of the knowledge imparted. This gap in understanding could be attributed to factors such as the complexity of training materials, language barriers, or variations in educational backgrounds among farmers. In addition, the study found that 57.8% of farmers implemented the recommendations provided during training sessions, while 42.2% either partially adopted or failed to implement the advised agricultural practices. This variation in adoption rates could be influenced by constraints such as limited access to necessary resources, financial constraints, or resistance to change due to entrenched traditional farming practices. Therefore, these findings underscore the need for enhanced training methodologies that consider farmers’ diverse educational backgrounds and learning capacities [70]. Strengthening the effectiveness of training programs through interactive and practical learning approaches, follow-up support, and tailored extension services could improve comprehension and encourage higher adoption rates of recommended agricultural practices as reported by Mbesa et al. [71]. Addressing these challenges is crucial for maximizing the impact of training initiatives on smallholder farmers’ productivity and overall agricultural sustainability.

5. Conclusions, Recommendations, and Future Directions

This study assessed the socioeconomic factors influencing crop diversification among smallholder farmers in South Africa, using the KwaZulu–Natal Province as a case study. Farmers in the study area practice crop diversification. However, the findings indicate that socioeconomic factors play a crucial role in shaping diversification decisions within smallholder farming systems. In line with previous research, this study confirms that limited access to land, water availability, market access, and education level constrain farmers’ ability to diversify their crop choices. Marital status, household size, farming experience, perceived advantages of crop diversification, and limited technical knowledge further influence crop diversification. Additionally, older farmers were less likely to diversify due to risk aversion, adherence to traditional farming practices, physical limitations, and reluctance to adopt innovative agricultural techniques. To address these challenges, the study recommends promoting youth engagement in agriculture, particularly in crop diversification initiatives, by fostering their active role in shaping future food systems. This can be achieved through targeted training programs and policies that address structural barriers limiting youth participation. Integrating crop diversification awareness into local policies and development programs could contribute to more sustainable farming systems, enhancing food security.

Despite the promising results of this study, it is essential to acknowledge that, like other research studies, this article has limitations. The study specifically focused on smallholder crop farmers in Bergville. This may introduce some bias, as the sample comprised crop farmers affiliated with the Farmers Support Group in the region. However, this study attempted to mitigate bias by using a multistage sampling strategy to ensure representation of diverse demographic groups, particularly resource-constrained farmers actively engaged in various farming practices. Moreover, the study employed techniques to enhance data accuracy and reliability, including the use of multiple question formats to capture comprehensive information and minimize response errors. Despite these limitations, the study provides valuable insights for policymakers and contributes to a more profound understanding of how socioeconomic factors influence crop diversification among resource-constrained farmers in rural communities of the Global South. Based on the outcomes of this study, future studies should examine the long-term impact of crop diversification on farm productivity, income stability, and resilience to climate change. Additionally, research should explore the role of digital technologies, climate-smart agriculture, and financial incentives in influencing crop diversification decisions. A gender-focused approach is also crucial to better understand how social and economic disparities affect diversification outcomes. Furthermore, longitudinal studies assessing the effectiveness of policy interventions and farmer training programs could provide deeper insights into strategies for enhancing diversification practices among smallholder farmers.