Gender differentials among small scale irrigation farmers’ income: empirical evidence from cabbage farmers in KwaZulu-Natal, South Africa

Introduction: Agricultural productivity and income disparities prevail between male and female farmers in Sub-Saharan Africa. South Africa is not an exception to this ominous reality.

Methodology: Using data from smallholder irrigation farmers in KwaZulu-Natal, South Africa, this study analyzed the gender on-farm income gap by applying the Blinder-Oaxaca (OB) decomposition framework.

Results: The findings indicate that there is a significant difference in farm income between male and female farmers. Male farmers earn significantly higher on-farm income than female farmers by about R26,788/cropping season.

Discussion: The findings suggest that existing and future programs should focus on institutional factors (land tenure) that affect access to resources and services. In addition to strategies to empower women, interventions to reduce the gender-induced farm income gap should focus on improving access to resources and services such as land with tenure security, agricultural input and output markets, training, information, and credit. Increasing the resource endowment and access to services by the female-managed farms is likely to significantly close the observed gender-induced farm income gap and improve the well-being of female farmers and their households.

1. Introduction

In recent years, there has been a marked increase in the level of attention paid globally to gender, its significance for development, and the disparities (income or otherwise) that exist between men and women (Pachauri and Rao, 2013; Kiptot et al., 2014; Wondimagegnhu et al., 2019). In sub-Saharan Africa, for example, gender inequality costs about 95 USD billion annually, and if women and men were treated equally in terms of economic or other opportunities, 12–28 USD trillion could be added to the global economy (McKinsey Global Institute, 2015; UNDP, 2016; Anderson et al., 2021). Additionally, Gebre et al. (2021) showed that the degree of agricultural productivity disparities between male and female farmers vary in sub-Saharan Africa. Thus, a crucial policy agenda now is closing the gender gap in agricultural production and the food value chain. To achieve equity in resource allocation and household-level food security in low- and middle-income countries, it is crucial for policy makers and program planners to have a deeper grasp of the gender gap (Owusu et al., 2018), mainly focusing on the how and why.

The notion of gender has been described as the socio-economic and cultural traits that are associated with men and women in regard to their roles, responsibilities, rights, obligations and other privileges (Masamha et al., 2018; Adegbite and Machethe, 2020). It is embedded in cultural norms and institutions, making written or unwritten societal rules on what is right or acceptable or otherwise. Sub-Saharan Africa’s agriculture is characterized by two main attributes: the predominance of female labor (30% and 60% of all agricultural labor) and the existence of a gender gap in agricultural productivity. This is because various cultural and societal roles, obligations, restrictions, and expectations are imposed on both men and women that affect their capacity and motivations to take part in agriculture and the value chains therein (Palacios-López and López, 2015; Masamha et al., 2018; Gebre et al., 2019). Among these challenges, statutory and customary regulations continue to prevent women in many developing nations from accessing land and other resources. Because of the insecurity of land tenure, there is less access to capital and inputs, which results in inefficient land use and poorer yields (Palacios-López and López, 2015; Chigbu, 2019; Chigbu et al., 2019).

A number of empirical studies have demonstrated the crucial role that women play in African agriculture (Uduji et al., 2019; Wekesah et al., 2019; Ampaire et al., 2020; Mpanza and Mbatha, 2021). Moreover, other studies explored the choice of livelihoods on culturally determined roles, social mobility, and access to and ownership of livelihood assets, extension services, credit, technology with the focus on gender disparities (Palacios-López and López, 2015; Loison, 2019; Wondimagegnhu et al., 2019; Adegbite and Machethe, 2020; Sá et al., 2020). In this context, access to inputs, modern technologies, education, healthcare, and other resources is disproportionately in favour of men. Most women are marginalized in developing nations, where they have limited access to and power over resources like land, information, markets, education, extension services, and agricultural loans (Sharaunga et al., 2015; Lutomia et al., 2019; Paudel et al., 2020). Moreover, who makes production and marketing decisions would affect the productivity of the available livelihood assets and opportunities. Not only access to inputs, technology and information about cultural practices but also these decisions influence agricultural productivity. These suggest that gender-disaggregated analysis generates information crucial for identifying the respective factors influencing the livelihoods of households headed by men and women, aiming to articulate targeted typologies of policy actions (Wondimagegnhu et al., 2019).

Agriculture in general and smallholder farming in particular can disproportionately contribute to poverty reduction. The South African government has invested in irrigation infrastructure to address the challenges of water scarcity and drought in the sub-sector. To increase food security and the well-being of small-scale irrigation operators and their communities, investment in small-scale irrigation must be made more productive and profitable (Davis et al., 2017; Bjornlund et al., 2019). However, in South Africa, post-apartheid governments have tried to empower rural people through interventions in agriculture by reducing gender gap and empowering women in rural areas (Sharaunga et al., 2015, 2019). Women are less mobile and often stay in rural areas due to household commitments. That is why they are over-represented in smallholder farming, especially in the irrigation schemes. The empirical evidence from studies in the past confirms that, when the differences in access to inputs, resources and information are taken into account, estimates of the gender income gap become important (Tibesigwa and Visser, 2016; Flatø et al., 2017; Bjornlund et al., 2019; Cheteni et al., 2019; Gebre et al., 2021).

Other studies from other Sub-Saharan African countries looked at gender differentials in the context of agricultural productivity (e.g. Kang et al., 2020; Gebre et al., 2021), farm mechanization (e.g. Fischer et al., 2018), farm management decision-making (e.g. Sell and Minot, 2018), food security (Carranza and Niles, 2019) and climate change adaptation strategies (Owusu and Bravo-Ureta, 2022). However, these studies cannot necessarily be generalized to farm income and the South African context is different because of the country’s distinct history, traditional setting, and policy environment. Moreover, there is scant literature on gender-induced farm income differences among smallholder farmers within irrigation schemes in South Africa. Therefore, the objective of this study is to examine gender differentials in farmers’ income among small-scale irrigation farmers. This study makes policy-relevant knowledge contribution to understanding gender-induced farm income differentials, considering socio-economic, resource access, institutional, and structural differences. Given that households headed by women tend to be among the poorest segments of society, these gender inequalities in terms of access to resources and opportunities for livelihood diversification have consequences for pro-poor economic growth (Loison, 2019).

Given this motivation, the rest of the paper is structured as follows. The next section presents the theoretical and conceptual framework. This is followed by the empirical methodology in Section 3 where the study area, data collection, sampling and empirical model are described. The results and discussion are contained in Section 4. Finally, the last section concludes the paper and makes recommendations.

2. Theoretical and conceptual framework

Over the past 50 years, economists’ perspectives on household behavior and gender dynamics have been affected by theoretical and empirical studies on rural households (Doss and Quisumbing, 2020). Boserup’s (1970) and Becker’s (1981) earlier studies have affected methods for comprehending rural households as well as developing agricultural development strategies and initiatives. For instance, Boserup suggested that agriculture requires more male labor in densely populated areas than it does in areas with low population density where women perform more farm work than men. Boserup contended that the more effective “conventional” farming methods, particularly plough agriculture, mostly employed male labor. When state-directed innovation accelerated technological development in rural areas of Africa, this correlation between gender and “traditional” technology forms new significance. Accordingly, while women continued to grow food crops using conventional techniques, males chose to use more sophisticated and scientifically-proven empirical methods to do so. A unitary model of the household was developed by Becker (1981), which made the assumption that either all members of the household share the same tastes, pool their resources, and reach consensus on all decisions, or that one household member takes decisions on behalf of the entire group. Accordingly, social conventions about gender roles are exogenous, with males specializing in activities related to production and women specializing in activities related to reproduction. Doss and Quisumbing (2020), on the other hand, asserted that fresh approaches to data collection at the farm level frequently contained details on the farm manager or owner. Due to this, agricultural productivity can now be analyzed at a personal rather than a household level. The investigation of how shocks differed among household members is made possible by sex-disaggregated data on assets and income. Therefore, in most cases, each farm has a single decision maker and that the decisions on each farm are made independently.

In particular, Sharaunga et al. (2015, 2019) noted that closing the gender gap contributes to the production of more food for domestic consumption and local markets and is the best way to lessen household vulnerability to poverty and food insecurity in South Africa by raising agricultural incomes and enhancing food availability. This argument has been made with a focus on women’s empowerment, which has often received little attention in the literature despite the fact that they are crucial to achieving all four pillars of food security in rural areas as food producers, wage earners, and guardians of household food and nutrition security. Moreover, there is mounting evidence that closing the gender gap contributes to better overall development outcomes in terms of health, education, eradicating poverty, lowering household vulnerability to food insecurity, and promoting economic growth (Sharaunga et al., 2015; Mugisha et al., 2019). To contribute to these rural development goals, this study argues that gender dimensions of smallholder farm incomes should be studied to empirically establish gender-induced income gap and understand the factors thereof.

3. Methodology

3.1. Study area

The data comes from a sample of smallholder farmers in four irrigation schemes (Makhathini, Ndumo B, Tugela Ferry, and Bululwane) in KwaZulu-Natal (see Figure 1). Rural residents in the province rely heavily on smallholder agriculture for their livelihoods. The area gets low rainfall, ranging from 500 to 850 mm annually. The size of the farms operated by smallholders in the schemes ranges from 0.1 to 10 ha, depending on the program and whether they farm individually or as a cooperative. Those living outside of the schemes typically own larger pieces of land. The land is typically operated on a permission to occupy basis, although there are various land leasing agreements amongst farmers as well (Wale et al., 2021). The potential of KwaZulu-Natal province is underutilized despite the fact that its diversified geography, climate, and soil nearly make it a “world-in-one.” Irrigation projects have been prioritized as crucial areas of intervention by the provincial administration to combat rural poverty, food insecurity, and unemployment. Irrigation programs are viewed as strategic investment portfolios in the province to address frequent drought and increasingly chronic water shortage. Thus, the province made significant capital investments in irrigation projects (Joubert, 2012 Chipfupa and Wale, 2018). Similar to the overall situation in the country, the majority of the irrigation projects are located in areas with the highest rates of poverty. As a result, these projects are expected to have great potential for improving rural livelihoods.

FIGURE 1

Figure 1. Location of Makhathini, Ndumo-B, Tugela Ferry and Bululwane irrigation schemes. Source: Wale and Chipfupa (2018).

3.2. Data collection

Questionnaires were designed and administered to acquire relevant information. This involved intensive engagement of the research team with the communities in the study areas at the irrigation schemes in an attempt to unpack the challenges with the intention of shedding light on outcomes such as improved farm income, employment generation, and entrepreneurial attributes of smallholder farmers. The KwaZulu-Natal Department of Agriculture and Rural Development (DARD) offices in the study sites played a vital role in mobilizing farmers and communities in the schemes.

3.3. Sampling strategy

The project team obtained a sampling frame of small-scale irrigators in the schemes from the local offices of the DARD. Stratified random sampling was used to select the participants and a total of 332 smallholder farmers growing cabbage were interviewed. Input–output data were collected at the plot level. The dataset employed in this study was collected as part of the Water Research Commission (WRC) project entitled “Entrepreneurial development for establishing small farming businesses and employment by youth in rain-fed crop farming (K5/2789//4).” Poverty is more severe in rural areas where gender is a major factor in household decision-making (Cheteni et al., 2019). That is why women are over-represented among the rural poor. The total number of farmers interviewed per area are 155, 70, 55 and 52 in Makhathin, Ndumo B, Tugela Ferry, and Bululwane, respectively. Also, out of the total 332 sampled farmers, 111 were male and 221 were female. In addition, cabbage farmers were sampled owing to the importance of the crop in the irrigation schemes. In South Africa, cabbage is one of the most cultivated and consumed vegetables. Although it is farmed all over the country, Mpumalanga and KwaZulu-Natal have the highest concentrations (Mhazo, 2018).

3.4. Empirical model

The Blinder-Oaxaca model was used to investigate the variables that contribute to the gender differentials in farm income among smallholder irrigation farmers in KZN. This model was first introduced by Blinder (1973) and supported by Oaxaca (1973), and later applied by Cotton (1988) and Neumark (2004). It has been widely used in measuring gaps of various outcomes between groups. The B-O theory allows the decomposition of the differences in an outcome variable between two groups into a part that is attributable to differences in the observed characteristics of these two groups and the part that is ascribed to differences in the estimated coefficients of these groups (Danso-Abbeam et al., 2020a; Addai et al., 2021). In this context, this study aimed to estimate the gender-induced farm income gap between male and female farmers in the study areas, i.e., groups L_M and L_F, an outcome variable Y, and a set of predictors. For example, think of males and females, farmers’ income as the outcome variable, and human capital indicators (such as education and work experience) as predictors. Following Jann (2008), the difference in the farmers’ income will be expressed as:

$\begin{array}{ll}L=E\left(L_M\right)-E\left(L_F\right)& (1)\end{array}$

where $E\left(L\right)$ denotes the expected value of the outcome variable i.e. income for male ( $L_M$ ) and female ( $L_F$ ), is accounted for by group differences in the predictors. Again, based on the linear model, further equations may be expressed as:

$\begin{array}{ll}L\mathcal{𝓁}={X_{\mathcal{𝓁}}}^{\text{'}}{\beta }_{\mathcal{𝓁}}+{\epsilon }_{\mathcal{𝓁}},E\left({\epsilon }_{\mathcal{𝓁}}\right)=0,{\epsilon }_{\mathcal{𝓁}}\left\{M,L\right\}& (2)\end{array}$

where $X$ is a vector containing the predictors and a constant, $\beta$ contains the slope parameters and the intercept, and ${\epsilon }_{\mathcal{𝓁}}$ is the error, the mean outcome difference can be expressed as the difference in the linear prediction at the group-specific means of the regressors. This can also be expressed as:

$\begin{array}{ll}L=E\left(L_M\right)-E\left(L_F\right)=E{\left(X_M\right)}^{\text{'}}{\beta }_M-E{\left(X_F\right)}^{\text{'}}{\beta }_F& (3)\end{array}$

This equation can be expressed as

$\begin{array}{ll}E\left(L_{\mathcal{𝓁}}\right)=E\left({X_{\mathcal{𝓁}}}^{\text{'}}{\beta }_{\mathcal{𝓁}}+{\epsilon }_{\mathcal{𝓁}}\right)=E\left({X_{\mathcal{𝓁}}}^{\text{'}}{\beta }_{\mathcal{𝓁}}\right)+E\left({\epsilon }_{\mathcal{𝓁}}\right)=E{\left(X_{\mathcal{𝓁}}\right)}^{\text{'}}{\beta }_{\mathcal{𝓁}}& (4)\end{array}$

with the assumption that $E\left({\beta }_{\mathcal{𝓁}}\right)={\beta }_{\mathcal{𝓁}}$ and $E\left({\mathrm{\epsilon }}_i\right)=0.$

To identify the contribution of group differences in predictors to the overall outcome difference, Eq. 4 can be rearranged as follows (see Daymont and Andrisani, 1984; Jann, 2008):

$\begin{array}{c}L={\left[E\left(X_M\right)-E\left(X_F\right)\right]}^{\text{'}}{\beta }_F+E{\left(X_F\right)}^{\text{'}}\left({\beta }_M-{\beta }_F\right)\\ +{\left[E\left(X_M\right)-E\left(X_F\right)\right]}^{\text{'}}\left({\beta }_M-{\beta }_F\right)\end{array}\text{ (5)}$

This is a “three-fold” decomposition, that is, the outcome difference is divided into three components:

$\begin{array}{ll}L=A+B+C& (6)\end{array}$

$\mathrm{i.e.}A={\left[E\left(X_M\right)-E\left(X_F\right)\right]}^{\text{'}}{\beta }_F\text{,}$

This indicates the portion of the differential that is due to group differences in the predictors such as socio-economic and institutional factors (the “endowments effect” or “the effect explained”).

$B=E{\left(X_F\right)}^{\text{'}}\left({\beta }_M-{\beta }_F\right)\text{,}$

This measures the contribution of differences in the “coefficients” or commonly referred to as “returns” or “structural effect” or “unexplained” (including differences in the intercept). In other words, this component measures the change in female’s income when male’s coefficients are applied to female’s characteristics.

$C={\left[E\left(X_M\right)-E\left(X_F\right)\right]}^{\text{'}}\left({\beta }_M-{\beta }_F\right)$

This measures the interaction term accounting for the fact that differences in endowments and coefficients exist simultaneously between the two groups.

Component A which has been derived from the decomposition (Eq. 7) is formulated from the viewpoint of female group. That is the group differences in the predictors are weighted by the coefficients of female group to determine the endowment effect (E). This implies that the E component measures the expected change in female’s mean outcome, if female had male’s predictor levels. Similarly, for the second component (B), the differences in coefficients are weighted by female’s predictor levels. That is, the second component measures the expected change in female’s mean outcome if female had male’s coefficients. Naturally, the differential can analogously be expressed from the viewpoint of the male, yielding the reverse three-fold decomposition:

$\begin{array}{c}L={\left[E\left(X_M\right)-E\left(X_F\right)\right]}^{\text{'}}{\beta }_M+E{\left(X_M\right)}^{\text{'}}\left({\beta }_M-{\beta }_F\right)\\ +{\left[E\left(X_M\right)-E\left(X_F\right)\right]}^{\text{'}}\left({\beta }_M-{\beta }_F\right)\end{array}\text{ (7)}$

In this case, the “endowments effect” amounts to the expected change in the male’s mean outcome, if the male had female predictor levels. The “coefficient effect” quantifies the expected change in male’s mean outcome if male had female’s coefficients. In addition, Neumark (1998) and Oaxaca and Ransom (1994) proposed that a pooled model should be estimated in deriving the counterfactual coefficient vector. Since this is an extension of the standard-B model, the Neumark command in Stata was also used to show the male and female advantages over gender farm income (Danso-Abbeam et al., 2020b; Bello et al., 2021a,b).

3.5. Computation of on-farm income

Since the majority of the inputs used by farmers did not vary significantly, gross income per hectare was used instead of gross margin. However, the overall costs of inputs used (seedlings, fertilizer, pesticides, herbicides, and transport expenses) were relatively small to affect the gross income per hectare of farmers as the amount of output targeted was little (Mkuna and Wale, 2022).

4. Results and discussion

4.1. Disaggregating gender characteristics among farmers

Table 1 lists the variables, measurement, and gender analysis of the demographic, farm-specific, and institutional variables used to create the models from the sampled farmers.

TABLE 1

Table 1. Gender disaggregated descriptive statistics.

Table 1 indicates that the farms managed by male had a significantly higher farm income (R26,788) than farms managed by females (R13,152). These results are similar to those in Table 2. They will further be discussed after the presentation of the Blinder-Oaxaca decomposition model results (second step). Moreover, the variable (HHS) which indicates farmers’ role in the household was significantly different between the two groups. This implies that most of the households were headed by female who also are more experienced in farming than their male counterparts. This finding echos that of Kom et al. (2022) in the Vhembe District of South Africa where they found that female farmers dominated agricultural operations in rural areas. It was also noted that females comprised most of the small-scale farmers and were mostly heads of households.

TABLE 2

Table 2. Blinder Oaxaca model results for farm income gap that can be attributed to gender.

Again, the difference in CREDIT was statistically significant between the two groups of farmers, implying that access to credit was relatively better to women than men. This result is consistent with those of Baba et al. (2015) and Berger (2021) who found that female farmers are more likely to obtain agricultural financing from formal institutions than male farmers. This is because the majority of lending programs created by banks and other development organizations like NGOs place a greater emphasis on women (Lenka and Agarwal, 2017). Furthermore, women are more prone than men to choose credit because they are seen as the most vulnerable, disadvantaged, and creditworthy groups (Chen et al., 2017).

However, the REMITTANCE variable was also statistically significant and different between the two groups which implies that access to remittance was relatively higher to male household heads than their female counterparts. The Variable CABB_QTY, which indicates the quantity of cabbage produced, was statistically significant and different between the two groups which implies that access to credit was relatively higher to female than male farmers. The findings suggest that male-managed farms had higher level of output than female managed farms which is why it translated into higher farm income to male household heads than their female counterparts.

In sum, the descriptive findings confirm gender differences among farmers. This imbalance is caused by the existence of socio-cultural constraints (Fatoki and Chindoga, 2011), by and large unwritten but entrenched. According to Jones and George (2008), socio-cultural constraints are pressures emanating from the social structures and traditions of a community or a country. Such constraints mainly faced by females include difficulties in securing credit (Gorji and Rahimian, 2011) and owning productive resources such as land or water rights in their communities. Moreover, most females are constrained by their reproductive roles of being wives and mothers, restricted to their homestead duties, and hence cannot engage in other economic activities other than farming. Consequently, males are more likely to be entrepreneurial compared to their female counterparts. This is not because males are more entrepreneurial in their inherent attributes but their freedom and mobility connects them to information and opportunities.

4.2. Determinants of gender income gap among smallholder irrigation farmers

Table 3 presents the determinants of farm income of the overall sample and disaggregated by gender.

TABLE 3

Table 3. Model results decomposing farm income by gender and the determinants.

EDUC was found to be statistically significant and negatively influencing the income of male managed farms. This evidence suggests that an educated male farmer is earning less farm income than a counterpart. Most of the educated farmers are part-time farmers. This finding is in line with other studies that found that educated female farmers have a higher propensity to adopt improved farming technologies and practices compared to educated male farmers. For example, education enhances adoption of improved varieties by female farmers more than it does for their male counterparts (Anang et al., 2020). Also, other studies found that, in households where women are more educated, there is a higher degree of consensus in farm decision-making, leading to an increase in farm productivity and eventually farm income (Anderson et al., 2017; Bjornlund et al., 2019). However, Wilkening (2019) found contrary findings with this study, and he argued that educated farm women are more likely to be inclined to work outside of the farm.

IRRI_EXP was also statistically significant and positively influenced the farm income for female than male farmers. This implies that female farmers are benefiting more from irrigation experience than their male counterparts. Irrigation experience helps farmers with efficient water management practices which, in turn, increases crop outputs and contribution to household incomes (Belay and Bewket, 2013).

OCCUP was statistically significant and negatively influenced the farm income of overall sample and female farmers. This implies that if the overall sample of respondents as well as female farmers are full time farmers, the farm income will decrease. This could be due to the fact that full time farmers might not have sufficient funds for financing the agricultural operations such as purchases of inputs and hiring of labor. Eventually, full time farmers might not get more income as compared to part-time farmers who can outsource funds from other income generating activities such as business and employment. In addition, CREDIT was statistically significant and negatively influenced the farm income of pooled sample. The reasons could be that farmers in general receive informal credit which is mainly used for consumption and addressing immediate cash-flow issues.

CABB_QTY was statistically significant and positively influenced the overall farm income as more so female farmers than their male counterparts. This suggests economies of scale in cabbage production and marketing. According to Mkuna and Wale (2022), increasing cabbage output also helped farmers escape poverty by significantly raising their incomes and lowering poverty. In addition, TRAINING_MGT was also found to be statistically significant and positively influencing the overall income and the incomes of female-managed farms. The findings suggest that female farmers who received training on farm management are earning more income than their male counterparts. Thus, investing on training female farmers has better welfare impact. Therefore, ongoing farm management training on sound agronomic practices and commercial orientation will promote more market participation by female farmers, which will lead to an increase in farm income (Kabir et al., 2019; Martey et al., 2021).

4.3. Blinder Oaxaca model results and discussions

Table 2 shows the findings from the Blinder Oaxaca decomposition method that was used to calculate the percentage of gender differences that could be attributed to: (i) variations in the average characteristics of farm income (endowment effects), (ii) variations in their unexplained part of farm income (coefficients/structural effects), and (iii) interactions of endowments and coefficients.

In Table 2, the mean value of farm income reflects R27,148.97 for men and R13,515.93 for women, leading to a significant positive difference (R13,633). This suggests an uneven situation between men and women regarding farm output and in this case male managed farms earned higher farm incomes than their female counterparts. Similar findings were reported by Anang et al. (2020) who found that in most rural communities, men typically have more ownership and control over the resources and assets used for agricultural production. In addition to farming, women often do petty trading and take other responsibilities at home (e.g. caring for children and the elderly, managing family issues and serving as the first point of call when any shock strikes the family or other families in the community). This limits the amount of time they can spend on the farm and lowers their level of agricultural income.

Furthermore, the findings show that the main source of the gap after decomposing it was the resources/endowments which accounted for about 92% of the farm income gap. The endowment effect is negatively related to the gender income gap, i.e., increasing the endowment/resources held by the female-managed farms is likely to significantly reduce the observed gender farm income gap and improve the well-being of female-headed households. These results are consistent with Mukasa and Salami (2015) who found that female-managed farms have clear endowment disadvantages in farm size, use and intensity of non-labor inputs/services. Dolan (2004) also found that male-managed farms obtained significantly higher incomes compared to female-managed farms. This was attributed to cultural norms and inequality of access to productive resources, mainly land and capital. Simtowe (2010) further found that the male-managed farms obtained significantly higher incomes compared to female-managed farms. As a result, women were pushed into low-wage labour. On the other hand, the coefficient and interaction terms are not significant sources of the income gap though they collectively account for almost 8% of the gender income gap.

4.4. Further decomposition of farm income gap

Since the results in Table 2 show that the income gap between men and women in the study area was statistically influenced by endowment factors while coefficient (structural) and interaction factors were not significant, further decomposition (Table 4) of the determinant factors focused on the endowment part of the farm income gap. A positive and significant coefficient means that a given variable increases the gender endowment gap or it makes men disproportionately benefit in terms of resource the endowment difference between male and female farmers. On the contrary, a negative and significant coefficient means that the variable reduces the gap.

TABLE 4

Table 4. B-O decomposition results for farm income endowment factors.

AGE was a statistically significant factor and increases the gender-based resource access/endowment gap. In addition, since there are more older male farmers than female farmers, they have better resource endowment. To the extent that resource endowment translates to better farm income, older men will earn higher income than women most of whom are relatively younger. Other studies also found that older farmers with longer farming experience invest more of their accumulated wealth of experience and resources than the younger ones (Adimassu et al., 2012; Oduniyi and Olawuyi, 2020). Sharaunga et al. (2016) also found that older farmer’s households tend to earn more from smallholder farming. Such households are more likely to have accumulated assets, have adult children who can contribute labour to the household’s own production.

The variable RELATION_HH was found to be statistically significant and reduces the gender-based resource access/endowment gap. This further suggests that being a head of household reduces the farm income gap in terms of gender. Specifically, most of the female farmers, on average, were the head of the household which indicates that women farmers who are head of household earn higher than their counterparts. This could be because being head of household enables women to have access to household resources, making it possible to allocate and invest on their farm.

On the other hand, REMITTANCE and CREDIT were both statistically significant and increases the gender-based resource access/endowment gap. This indicates that both access to remittance and credit increase the farm income gap in terms of gender. Specifically, male received more remittance than women (see Table 1) which suggests that they are employing these funds to enhance the profitability of their farms and earn higher farm income. On the other hand, women receive more credit than men which also suggests that women who receive credit earn higher farm income than men in the South African context.

In addition, EXP_FARM was also found statistically significant and reduces the gender-based resource access/endowment gap. This indicates that farming experience reduces farm income gap. In this study, female farmers who, on average, were found to be more experienced in farming than their male counterparts, earn higher income. This might be attributed to the fact that more experienced female farmers usually diversify their income sources to earn off-farm income, invest on the farm and support their livelihoods. Ma et al. (2019) have also found that years of farming experience may also affect revenue earned off the farm. As farming experience grows, so does the possibility that household heads will make money off the farm.

DIST_IRR was also found statistically significant and increases the gender-based resource access/endowment gap. This suggests that distance taken by female farmers to the irrigation farms increases the farm income gap with their male counterparts. This is because the time taken from their homes to the farms consumes most of their energy but also hinders them to be productive. In some cases, they are also expected to be back home early for house chores.

4.5. Neumark decomposition results

Table 5 presents a detailed decomposition using the methodology proposed by Neumark for a given set of socioeconomic, institutional, and input factors. It presents the income differential between men and women.

TABLE 5

Table 5. Decomposition results of the income differential using the Neumark method.

The decomposition results show gender-induced income differences and the advantage of the favoured group which is men (that is, a group advantaged in terms of access to assets). Also demonstrated is the disadvantage of women (that is, a group disadvantaged in terms of access to assets), contributing to the farm gap (Nchanji et al., 2020). The results indicated that differences in farm income between male and female farmers contributed about 87.23% to the overall farm income. The income advantage of men over women contributed 8.46% to the farm income gap while the disadvantage of women over men contributed 4.31%. These findings point to the fact that farm income gaps between men and women are to a large degree a result of gender differences that could, in turn, be attributed to socio-economic, cultural, historical and institutional factors.

5. Conclusions and recommendations

Low agricultural productivity, which eventually translates into low levels of farm income, has been linked to the high rates of poverty among rural farming communities in Sub-Saharan Africa (SSA) in general in South Africa in particular. Recent research into this topic has shown that a substantial component of this issue is caused by the gender gap in terms of resources and services which, in turn, results in productivity and income gaps. This study estimates the gender gap in farm income among smallholder irrigation farmers in KwaZulu-Natal, South Africa. It unpacks this gap into portions accounted for by differences in farm-manager characteristics and in returns to these characteristics. Evidence from this study shows that there is a significant difference in the determinants of farm income between male and female farmers. Furthermore, there is an uneven gender-based farm income gap, i.e., male farmers earn significantly higher farm income than female farmers. Further analysis employing the Blinder Oaxaca decomposition procedure indicates that differences in farm incomes between male and female farmers can be explained by differences in resource endowments and access to services. In addition, different socio-economic and institutional factors influence the endowment portion of the farm income gap between male and female farmers.

These findings suggest that existing and future programs should focus on resource accumulation for farmers (e.g., increasing productive capacity and building technical knowledge through training programs) since the quantity of cabbage output seems to influence the farm income for both male and female farmers. Specifically, investing on training female farmers has better welfare impact. Also, since the gender-based farm income gap was found to be largely influenced by farmers’ endowment factors and not because of being male or female (unexplained factors), any intervention to reduce the gap should focus on improving women farmers’ access to resources, technology and services. This is reinforced by the evidence that both access to credit and remittance were significant factors influencing farm endowment income. Access to finance can aid in financing the investments required to increase competitiveness and distribute the advantages of competition among communities.

There is a need for mindset shift and cultural transformation on value systems that have long existed and are still held by communities and traditional leaders in rural areas. These changes should be able to redress the gender and age-based stereotypes and improve equitable access to land, inputs, technology and other services, regardless of their age, gender, and marital status. Women have to be empowered in terms of access to services and resources such as land with tenure security, agricultural input and output markets, training, information, and credit. It is possible to achieve this by expanding and deepening the range of private sector financial services available to fund small-scale farming, which has a significant potential to boost smallholder competitiveness because credit was found to be statistically significant factor in influencing farm income. Further analysis of the significant endowment effects from the Blinder Oaxaca framework shows that increasing the endowment/resources held by the female-managed farms is likely to significantly close the observed gender-based farm income gap and improve the well-being of female-headed households.

In addition, it would be of interest for future studies to focus on the spatial analysis of gender differentials across different localities to find out if variations in on-farm income between male and female plot farm owners is due to locality or other aspects beyond what this study has covered.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

EM analyzed data and wrote the original draft of the manuscript while EW also supervised, corrected and involved in the management of the entire project. All authors contributed to the article and approved the submitted version.

Funding

This paper emanates from a project funded by the Water Research Commission (WRC), South Africa.

Acknowledgments

The authors would like to thank the commission for funding the project (Grant No. K5/2789//4). Special thanks go to the farmers who spent time with the research team and enumerators who worked hard during the data collection.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Addai, K. N., Lu, W., and Temoso, O. (2021). Are female rice farmers less productive than male farmers? Micro-evidence from Ghana. Eur. J. Dev. Res. 33, 1997–2039. doi: 10.1057/s41287-020-00342-4

CrossRef Full Text | Google Scholar

Adegbite, O. O., and Machethe, C. L. (2020). Bridging the financial inclusion gender gap in smallholder agriculture in Nigeria: an untapped potential for sustainable development. World Dev. 127:104755. doi: 10.1016/j.worlddev.2019.104755

CrossRef Full Text | Google Scholar

Adimassu, Z., Kessler, A., and Hengsdijk, H. (2012). Exploring determinants of farmers' investments in land management in the central Rift Valley of Ethiopia. Appl. Geogr. 35, 191–198. doi: 10.1016/j.apgeog.2012.07.004

CrossRef Full Text | Google Scholar

Ampaire, E. L., Acosta, M., Huyer, S., Kigonya, R., Muchunguzi, P., Muna, R., et al. (2020). Gender in climate change, agriculture, and natural resource policies: insights from East Africa. Clim. Chang. 158, 43–60. doi: 10.1007/s10584-019-02447-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Anang, B. T., Bäckman, S., and Sipiläinen, T. (2020). Adoption and income effects of agricultural extension in northern Ghana. Sci. Afr. 7:e00219. doi: 10.1016/j.sciaf.2019.e00219

CrossRef Full Text | Google Scholar

Anderson, C. L., Reynolds, T. W., Biscaye, P., Patwardhan, V., and Schmidt, C. (2021). Economic benefits of empowering women in agriculture: assumptions and evidence. J. Dev. Stud. 57, 193–208. doi: 10.1080/00220388.2020.1769071

CrossRef Full Text | Google Scholar

Anderson, C. L., Reynolds, T. W., and Gugerty, M. K. (2017). Husband and wife perspectives on farm household decision-making authority and evidence on intra-household accord in rural Tanzania. World Dev. 90, 169–183. doi: 10.1016/j.worlddev.2016.09.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Baba, H., Abdallah, A. H., and Hudu, Z. (2015). Factors influencing agricultural credit demand in northern Ghana. Afr. J. Agric. Res. 10, 645–652. doi: 10.5897/AJAR2014.9330

CrossRef Full Text | Google Scholar

Becker, G. S. (1981). A treatise on the family. Cambridge, MA: Harvard University Press.

Google Scholar

Belay, M., and Bewket, W. (2013). Traditional irrigation and water management practices in highland Ethiopia: case study in Dangila woreda. Irrig. Drain. 62, 435–448. doi: 10.1002/ird.1748

CrossRef Full Text | Google Scholar

Bello, L. O., Baiyegunhi, L. J., and Danso-Abbeam, G. (2021a). Productivity impact of improved rice varieties’ adoption: case of smallholder rice farmers in Nigeria. Econ. Innov. New Technol. 30, 750–766. doi: 10.1080/10438599.2020.1776488

CrossRef Full Text | Google Scholar

Bello, L. O., Baiyegunhi, L. J., Danso-Abbeam, G., and Ogundeji, A. A. (2021b). Gender decomposition in smallholder agricultural performance in rural Nigeria. Sci. Afr. 13:e00875. doi: 10.1016/j.sciaf.2021.e00875

CrossRef Full Text | Google Scholar

Berger, M. (2021). “Key issues on women's access to and use of credit in the micro- and small-scale enterprise sector” in Women in micro and small-scale enterprise development. ed. L. Dignard (London: Routledge), 189–215.

Google Scholar

Bjornlund, H., Zuo, A., Wheeler, S. A., Parry, K., Pittock, J., Mdemu, M., et al. (2019). The dynamics of the relationship between household decision-making and farm household income in small-scale irrigation schemes in Southern Africa. Agric. Water Manag. 213, 135–145. doi: 10.1016/j.agwat.2018.10.002

CrossRef Full Text | Google Scholar

Blinder, A. S. (1973). Wage discrimination: reduced form and structural estimates. J. Hum. Resour. 8, 436–455. doi: 10.2307/144855

CrossRef Full Text | Google Scholar

Boserup, E. (1970). Women's role in economic development. London, UK: George Allen & Unwin.

Google Scholar

Carranza, M., and Niles, M. T. (2019). Smallholder farmers spend credit primarily on food: gender differences and food security implications in a changing climate. Front. Sustain. Food Syst. 3:56. doi: 10.3389/fsufs.2019.00056

CrossRef Full Text | Google Scholar

Chen, D., Li, X., and Lai, F. (2017). Gender discrimination in online peer-to-peer credit lending: evidence from a lending platform in China. Electron. Commer. Res. 17, 553–583. doi: 10.1007/s10660-016-9247-2

CrossRef Full Text | Google Scholar

Cheteni, P., Khamfula, Y., and Mah, G. (2019). Gender and poverty in South African rural areas. Cogent Soc. Sci. 5:1586080. doi: 10.1080/23311886.2019.1586080

PubMed Abstract | CrossRef Full Text | Google Scholar

Chigbu, U. E. (2019). Anatomy of women’s landlessness in the patrilineal customary land tenure systems of sub-Saharan Africa and a policy pathway. Land Use Policy 86, 126–135. doi: 10.1016/j.landusepol.2019.04.041

CrossRef Full Text | Google Scholar

Chigbu, U. E., Paradza, G., and Dachaga, W. (2019). Differentiations in women’s land tenure experiences: implications for women’s land access and tenure security in sub-Saharan Africa. Land 8:22. doi: 10.3390/land8020022

CrossRef Full Text | Google Scholar

Chipfupa, U. , and Wale, E. (2018). Farmer typology formulation accounting for psychological capital: implications for on-farm entrepreneurial development. Dev. Pract. 28, 600–614.

Google Scholar

Cotton, J. (1988). On the decomposition of wage differentials. Rev. Econ. Stat. 70, 236–243. doi: 10.2307/1928307

CrossRef Full Text | Google Scholar

Danso-Abbeam, G., Baiyegunhi, L. J., and Ojo, T. O. (2020a). Gender differentials in technical efficiency of Ghanaian cocoa farms. Heliyon 6:e04012. doi: 10.1016/j.heliyon.2020.e04012

PubMed Abstract | CrossRef Full Text | Google Scholar

Danso-Abbeam, G., Dagunga, G., and Ehiakpor, D. S. (2020b). Rural non-farm income diversification: implications on smallholder farmers' welfare and agricultural technology adoption in Ghana. Heliyon 6:e05393. doi: 10.1016/j.heliyon.2020.e05393

PubMed Abstract | CrossRef Full Text | Google Scholar

Davis, B., Di Giuseppe, S., and Zezza, A. (2017). Are African households (not) leaving agriculture? Patterns of households’ income sources in rural sub-Saharan Africa. Food Policy 67, 153–174. doi: 10.1016/j.foodpol.2016.09.018

PubMed Abstract | CrossRef Full Text | Google Scholar

Daymont, T. N., and Andrisani, P. J. (1984). Job preferences, college major, and the gender gap in earnings. J. Hum. Resour. 19, 408–428. doi: 10.2307/145880

PubMed Abstract | CrossRef Full Text | Google Scholar

Dolan, C. S. (2004). “I sell my labour now”: gender and livelihood diversification in Uganda. Can. J. Dev. Stud. 25, 643–661. doi: 10.1080/02255189.2004.9669007

PubMed Abstract | CrossRef Full Text | Google Scholar

Doss, C. R., and Quisumbing, A. R. (2020). Understanding rural household behavior: beyond Boserup and Becker. Agric. Econ. 51, 47–58. doi: 10.1111/agec.12540

PubMed Abstract | CrossRef Full Text | Google Scholar

Fatoki, O., and Chindoga, L. (2011). An investigation into the obstacles to youth entrepreneurship in South Africa. Int. Bus. Res. 4, 161–169. doi: 10.5539/ibr.v4n2p161

CrossRef Full Text | Google Scholar

Fischer, G., Wittich, S., Malima, G., Sikumba, G., Lukuyu, B., Ngunga, D., et al. (2018). Gender and mechanization: exploring the sustainability of mechanized forage chopping in Tanzania. J. Rural. Stud. 64, 112–122. doi: 10.1016/j.jrurstud.2018.09.012

CrossRef Full Text | Google Scholar

Flatø, M., Muttarak, R., and Pelser, A. (2017). Women, weather, and woes: the triangular dynamics of female-headed households, economic vulnerability, and climate variability in South Africa. World Dev. 90, 41–62. doi: 10.1016/j.worlddev.2016.08.015

CrossRef Full Text | Google Scholar

Gebre, G. G., Isoda, H., Amekawa, Y., and Nomura, H. (2019). Gender differences in the adoption of agricultural technology: the case of improved maize varieties in southern Ethiopia. Women's Stud. Int. Forum 76:102264. doi: 10.1016/j.wsif.2019.102264

PubMed Abstract | CrossRef Full Text | Google Scholar

Gebre, G. G., Isoda, H., Rahut, D. B., Amekawa, Y., and Nomura, H. (2021). Gender differences in agricultural productivity: evidence from maize farm households in southern Ethiopia. Geo J. 86, 843–864. doi: 10.1007/s10708-019-10098-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Gorji, M. B., and Rahimian, P. (2011). The study of barriers to entrepreneurship in men and women. Aust. J. Bus. Manag. Res. 1, 31–36. doi: 10.52283/NSWRCA.AJBMR.20110109A05

CrossRef Full Text | Google Scholar

Jann, B. (2008). The Blinder–Oaxaca decomposition for linear regression models. Stata J. 8, 453–479. doi: 10.1177/1536867X0800800401

CrossRef Full Text | Google Scholar

Jones, G.R., and George, J.M. (2008). Essentials of contemporary management. McGraw-Hill Irwin, New York, United States.

Google Scholar

Joubert, R. (2012). Huge projects promised for KZN. Farmers’ Weekly, Pretoria, South Africa.

Google Scholar

Kabir, M. S., Radović Marković, M., and Radulović, D. (2019). The determinants of income of rural women in Bangladesh. Sustainability 11:5842. doi: 10.3390/su11205842

PubMed Abstract | CrossRef Full Text | Google Scholar

Kang, M., Schwab, B., and Yu, J. (2020). Gender differences in the relationship between land ownership and managerial rights: implications for intrahousehold farm labor allocation. World Dev. 125:104669. doi: 10.1016/j.worlddev.2019.104669

CrossRef Full Text | Google Scholar

Kiptot, E., Franzel, S., and Degrande, A. (2014). Gender, agroforestry and food security in Africa. Curr. Opin. Environ. Sustain. 6, 104–109. doi: 10.1016/j.cosust.2013.10.019

PubMed Abstract | CrossRef Full Text | Google Scholar

Kom, Z., Nethengwe, N. S., Mpandeli, N. S., and Chikoore, H. (2022). Determinants of small-scale farmers’ choice and adaptive strategies in response to climatic shocks in Vhembe District, South Africa. Geo J. 87, 677–700. doi: 10.1007/s10708-020-10272-7

CrossRef Full Text | Google Scholar

Lenka, U., and Agarwal, S. (2017). Role of women entrepreneurs and NGOs in promoting entrepreneurship: case studies from Uttarakhand, India. J. Asia Bus. Stud. 11, 451–465. doi: 10.1108/JABS-07-2015-0088

CrossRef Full Text | Google Scholar

Loison, S. A. (2019). Household livelihood diversification and gender: panel evidence from rural Kenya. J. Rural. Stud. 69, 156–172. doi: 10.1016/j.jrurstud.2019.03.001

CrossRef Full Text | Google Scholar

Lutomia, C. K., Obare, G. A., Kariuki, I. M., and Muricho, G. S. (2019). Determinants of gender differences in household food security perceptions in the Western and eastern regions of Kenya. Cogent Food Agric. 5:1694755. doi: 10.1080/23311932.2019.1694755

CrossRef Full Text | Google Scholar

Ma, W., Zhou, X., and Renwick, A. (2019). Impact of off-farm income on household energy expenditures in China: implications for rural energy transition. Energy Policy 127, 248–258. doi: 10.1016/j.enpol.2018.12.016

CrossRef Full Text | Google Scholar

Martey, E., Etwire, P. M., and Mockshell, J. (2021). Climate-smart cowpea adoption and welfare effects of comprehensive agricultural training programs. Technol. Soc. 64:101468. doi: 10.1016/j.techsoc.2020.101468

PubMed Abstract | CrossRef Full Text | Google Scholar

Masamha, B., Thebe, V., and Uzokwe, V. N. (2018). Mapping cassava food value chains in Tanzania's smallholder farming sector: the implications of intra-household gender dynamics. J. Rural. Stud. 58, 82–92. doi: 10.1016/j.jrurstud.2017.12.011

CrossRef Full Text | Google Scholar

McKinsey Global Institute . (2015). The power of parity: how advancing Women’s equality can add $12 trillion to global growth. London: McKinsey Global Institute.

Google Scholar

Mhazo, M. L. (2018). The effectiveness of Solanum panduriforme (Mey) based extracts on the cabbage aphid, Brevicoryne brassicae (Linnacus) on brassicas (South Africa: Doctoral dissertation, University of Venda).

Google Scholar

Mkuna, E., and Wale, E. (2022). Explaining farmers’ income via market orientation and participation: evidence from KwaZulu-Natal (South Africa). Sustainability 14:14197. doi: 10.3390/su142114197

CrossRef Full Text | Google Scholar

Mpanza, N. T., and Mbatha, M. W. (2021). The role of women in improving access to food at the household level in Mandeni local municipality, KwaZulu-Natal, South Africa. Afr. J. Gender Soc. Dev. 10:135. doi: 10.31920/2634-3622/2021/v10n2a8

CrossRef Full Text | Google Scholar

Mugisha, J., Sebatta, C., Mausch, K., Ahikiriza, E., Kalule Okello, D., and Njuguna, E. M. (2019). Bridging the gap: decomposing sources of gender yield gaps in Uganda groundnut production. Gend. Technol. Dev. 23, 19–35. doi: 10.1080/09718524.2019.1621597

PubMed Abstract | CrossRef Full Text | Google Scholar

Mukasa, A. N., and Salami, A. O. (2015). Gender productivity differentials among smallholder farmers in Africa: a cross-country comparison. Abidjan: African Development Bank.

Google Scholar

Nchanji, E. B., Collins, O. A., Katungi, E., Nduguru, A., Kabungo, C., Njuguna, E. M., et al. (2020). What does gender yield gap tell us about smallholder farming in developing countries? Sustainability 13:77. doi: 10.3390/su13010077

CrossRef Full Text | Google Scholar

Neumark, D. (1998). Employers’ Discriminatory behavior and the estimation of wage discrimination. J. Hum. Resour. 23, 289–295.

Google Scholar

Neumark, D. (2004). “Employers’ discriminatory behavior and the estimation of wage discrimination” in Sex differences in labor markets. ed. D. Neumark (London: Routledge), 163–177.

Google Scholar

Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. Int. Econ. Rev. 14, 693–709. doi: 10.2307/2525981

CrossRef Full Text | Google Scholar

Oaxaca, R. L. , and Ransom, M. R. (1994). On discrimination and the decomposition of wage differentials. J. Econom. 61, 5–21.

Google Scholar

Oduniyi, O. S., and Olawuyi, S. O. (2020). Exploring the link between livelihood capital assets as index of climate change adaptive capacity and socio-economic issues: a case of small-scale maize farmers in South Africa. J. Hum. Ecol. 71, 198–211. doi: 10.31901/24566608.2020/71.1-3.3258

CrossRef Full Text | Google Scholar

Owusu, E. S., and Bravo-Ureta, B. E. (2022). Gender and productivity differentials in smallholder groundnut farming in Malawi: accounting for technology differences. J. Dev. Stud. 58, 989–1013. doi: 10.1080/00220388.2021.2008364

CrossRef Full Text | Google Scholar

Owusu, V., Donkor, E., and Owusu-Sekyere, E. (2018). Accounting for the gender technology gap amongst smallholder rice farmers in northern Ghana. J. Agric. Econ. 69, 439–457. doi: 10.1111/1477-9552.12236

CrossRef Full Text | Google Scholar

Pachauri, S., and Rao, N. D. (2013). Gender impacts and determinants of energy poverty: are we asking the right questions? Curr. Opin. Environ. Sustain. 5, 205–215. doi: 10.1016/j.cosust.2013.04.006

CrossRef Full Text | Google Scholar

Palacios-López, A., and López, R. (2015). The gender gap in agricultural productivity: the role of market imperfections. J. Dev. Stud. 51, 1175–1192. doi: 10.1080/00220388.2015.1028539

CrossRef Full Text | Google Scholar

Paudel, G. P., Gartaula, H., and Craufurd, P. (2020). Gender differentiated small-scale farm mechanization in Nepal hills: an application of exogenous switching treatment regression. Technol. Soc. 61:101250. doi: 10.1016/j.techsoc.2020.101250

PubMed Abstract | CrossRef Full Text | Google Scholar

Sá, C., Cowley, S., Martinez, M., Kachynska, N., and Sabzalieva, E. (2020). Gender gaps in research productivity and recognition among elite scientists in the US, Canada, and South Africa. PLoS One 15:e0240903. doi: 10.1371/journal.pone.0240903

PubMed Abstract | CrossRef Full Text | Google Scholar

Sell, M., and Minot, N. (2018). What factors explain women's empowerment? Decision-making among small-scale farmers in Uganda. Women's Stud. Int. Forum 71, 46–55. doi: 10.1016/j.wsif.2018.09.005

CrossRef Full Text | Google Scholar

Sharaunga, S., Mudhara, M., and Bogale, A. (2015). The impact of ‘women's empowerment in agriculture’on household vulnerability to food insecurity in the KwaZulu-Natal province. Forum Dev. Stud. 42, 195–223. doi: 10.1080/08039410.2014.997792

CrossRef Full Text | Google Scholar

Sharaunga, S., Mudhara, M., and Bogale, A. (2016). Effects of ‘women empowerment’on household food security in rural KwaZulu‐Natal province. Dev. Policy Rev. 34, 223–252.

Google Scholar

Sharaunga, S., Mudhara, M., and Bogale, A. (2019). Conceptualisation and measurement of women's empowerment revisited. J. Hum. Dev. Capab. 20, 1–25. doi: 10.1080/19452829.2018.1546280

CrossRef Full Text | Google Scholar

Simtowe, F. P. (2010). Livelihoods diversification and gender in Malawi. Afr. J. Agric. Res. 5, 204–216.

Google Scholar

Tibesigwa, B., and Visser, M. (2016). Assessing gender inequality in food security among small-holder farm households in urban and rural South Africa. World Dev. 88, 33–49. doi: 10.1016/j.worlddev.2016.07.008

CrossRef Full Text | Google Scholar

Uduji, J. I., Okolo-Obasi, E. N., and Asongu, S. A. (2019). Corporate social responsibility and the role of rural women in sustainable agricultural development in sub-Saharan Africa: evidence from the Niger Delta in Nigeria. Sustain. Dev. 27, 692–703. doi: 10.1002/sd.1933

CrossRef Full Text | Google Scholar

UNDP (2016). Africa human development report 2016 accelerating gender equality and women’s empowerment in Africa (no. 267638). New York, USA: United Nations Development Programme Regional Bureau for Africa (UNDP).

Google Scholar

Wale, E., and Chipfupa, U. (2018). Appropriate entrepreneurial development paths for homestead food gardening and smallholder irrigation crop farming in KwaZulu-Natal province. Research report to the Water Research Commission, Water Research Commission.

Google Scholar

Wale, E., and Chipfupa, U., and Hadebe, N. (2021). Towards identifying enablers and inhibitors to on-farm entrepreneurship: evidence from smallholders in KwaZulu-Natal, South Africa: Heliyon, p. 7.

Google Scholar

Wekesah, F. M., Mutua, E. N., and Izugbara, C. O. (2019). Gender and conservation agriculture in sub-Saharan Africa: a systematic review. Int. J. Agric. Sustain. 17, 78–91. doi: 10.1080/14735903.2019.1567245

CrossRef Full Text | Google Scholar

Wilkening, E. A. (2019). “Farm families and family farming” in The family in rural society. eds. R. T. Coward and W. M. Smith (London: Routledge), 27–37.

Google Scholar

Wondimagegnhu, B. A., Huluka, A. T., and Nischalke, S. M. (2019). Determinants of farm livelihoods of smallholder farmers in Yayu biosphere reserve, SW Ethiopia: a gender disaggregated analysis. Cogent Econ. Finance 7:1645583. doi: 10.1080/23322039.2019.1645583

CrossRef Full Text | Google Scholar