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S. Afr. J. Agric. Ext. Mmbengwa, Rambau, Rakuambo and Qin
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KEY FACTORS FOR THE IMPROVEMENT OF SMALLHOLDER FARMERS’
PARTICIPATION IN AGRO-PROCESSING INDUSTRIES OF GAUTENG
PROVINCE OF REPUBLIC OF SOUTH AFRICA: LESSONS FOR THE EXTENSION
ADVISORY SERVICES
Mmbengwa, V.M.
1
, Rambau, K.
2
, Rakuambo, J .N.
3
and Qin, X.
4
Corresponding author: V.M. Mmbengwa Email: VMmbengwa@namc.co.za
ABSTRACT
This study aims at identifying factors that could be used as parameters to improve the
smallholder farmers’ participation in the agro-processing industries of Gauteng province in
order to enhance job creation and self-employment. The study used both qualitative and
quantitative research approaches. The focus sessions were used to exploit the respondents’
views regarding their participation or lack thereof. On the other hand, the quantitative
approaches were used to quantify the effect of the factors under consideration. A sample of (n
= 78) smallholder farmers were purposively selected across ten (n=10) local municipalities.
The data were analysed using a logistic regression model where non-participation and
participation were coded 0 and 1, respectively. The effect of profit, access to advice, age of the
farmers, and information flow to the participation of the smallholder farmers was tested. The
study found that five identified parameters {information supply (beta = 0.315, p = 0.002),
bonds (beta = 0.332, p = 0.000), mutual trust (beta = 0.410, p = 0.000), age (beta = 0.242, p
= 0.004) and access to study group (beta = -0.416, p = 0.000)} have significant probabilities
to improve the participation of smallholder farmers in the agro-processing sub-sector. The
results imply that extension advisors and policymakers can use these parameters to improve
the participation and representativeness of the smallholder farmers in the agro-processing
industries.
Keywords: agro-processing, smallholder, participation, sector, factors
INTRODUCTION
Agro-processing is referred to as the activities that change the form of agriculture, forestry, and
fisheries products into various forms to facilitate more comfortable handling and often increase
shelf life and market access (DAFF, 2016a). Other authors refer to agro-processing as a set of
techno-economic activities carried out for conservation and handling of agricultural produce
and to make it usable as food, feed, fiber, fuel, or industrial raw material (Gebrehiwet and
Mathibeli, 2013). All these definitions make it clear that agro-processing does not have a single
1
Manager, Smallholder Market Access Units, National Agricultural Marketing Council (NAMC), 536 Francis
Baard, Pretoria, 0002, Republic South Africa, Email: VMmbengwa@namc.co.za, ORCID: 0000-0002-7618-9925
2
Research officer, Smallholder Market Access at the National Agricultural Marketing Council (NAMC), P/Bag
x 935, Block A, 4th Floor, Arcadia, 0002. Tel. 0123411115; E mail: KRambau@namc.co.za
3
Production scientist, Research at Gauteng Department of Agriculture & Rural Development, 56 Eloff , Umnotho
House, JOHANNESBURG 2000, tel: 011 240 3130, E mail: JULIET.RAKUAMBO@gauteng.gov.za
4
PhD Student, University of Pretoria and CEO of ACASA, Association of China-Africa Small-holder
Agriculture: GSTM, University of Pretoria , Provate Bag X20, Hatfield , 0028 . Tel. 076 829 2606; E mail:
xiaoshun.qin2020@gmail.com/xiaoshun.qin@acasa.org.za, ORCID: 0000-0002-5938-4753
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universal definition in the agricultural sector. Mazungunye (2019); Owoo, and
LambonQuayefio (2018), and Black, et al., (2020) have reported that agro-processing has the
potential to provide a bridge from primary agricultural products to industrialization while
increasing the demand for the agricultural products and opportunities for rural employment.
In countries like Ethiopia, Tanzania, Kenya, South Africa, and Ghana, up to hundreds of
thousands of workers are employed by fresh fruit and vegetables (FFV) export companies
(Feyaerts et al., 2020). According to these authors, contract farming with the agro industries
and smallholder farmers had a significant impact on the rural employment and poverty
alleviation. Despite limited access to land and the related agro‐ecological conditions including
lack of access to water, as well as socio‐political and spatial dimensions, smallholder tree‐crop
farming’s in agro-processing industries key to rural economic development (Olofsson, 2020).
The importance of agro-processing industrialization in the South African economy in relation
to smallholder farmers cannot be underestimated (DAFF, 2012). Its’ reported contribution to
the GDP of approximately R49-billion in 2013 makes it a significant player in the macro-
economic sphere of South Africa (BrandSA, 2014). Thus, this industry is amongst the
industries identified by the Industrial Policy Action Plan (IPAP), the New Growth Path and the
National Development Plan (NDP) for its potential to spur growth and create jobs because of
its perceived strong backward linkage with the primary agricultural sector (DAFF, 2016).
According to the reports from the Department of Agriculture, Forestry, and Fisheries (DAFF),
the agro-processing industry is economically resilient (DAFF, 2016a). However, during the
third quarter of 2016, it has shed 2 624 jobs in the food, furniture, footwear, textiles, and leather
products (Dimant et al., 2016, DAFF, 2016a). Although job losses had occurred, some agro-
processing industries managed to create jobs in the beverages and tobacco, wearing apparel,
wood and wood products, paper and paper products, and rubber divisions in the same industry
(DAFF, 2016a). While this industry could be vulnerable to the economic downturn, it
undoubtedly plays a significant role in job creation and sustainability of the economy. This
industry is one of the industries that have the potential to alleviate poverty in South Africa.
Gauteng province is deemed to be the economic hub with negligible primary agricultural
production (GDARD, 2015; Dimant et al., 2016). The province is a host of numerous industries
and the agro-processing industry being one of them. This hosting makes the province integral
in its quest to promote transformation in the participation of agro-processing industries. The
existence of smallholder farming in Gauteng is severely constrained by the shortage of land
and high land purchase prices (Dimant et al., 2016). With growing immigration in Gauteng
Province, the need for smallholding farming for food security and job creation is inevitable.
This study seeks to determine parameters that could be used to improve the smallholder
farmers’ participation in the agro-processing industries in Gauteng Province in order to
enhance job creation and self-employment.
1.
PROBLEM STATEMENT
According to Gauteng agro-processing strategies, South Africa’s agro-processing activities are
concentrated in the Gauteng Province (GDARD, 2015). It had the most substantial agro-
processing value addition (28.5%) and significant job creation (23% or 124 000 jobs) in 2013
(GDARD, 2015). The challenges are that this industry is not transformed, and the agro-
processing industry participants still do not reflect the demographic representation of the
province and the country at large. Gauteng agro-processing strategies characterize this industry
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as dominated by a few large players that own large proportions of the market share (GDARD,
2015). There seem to be a few new role-players, which are the smallholder farming sector, and
black entrepreneurs who are participating in this industry (Dimant et al., 2016). These new role
players face challenges of poor access to agro-processing markets, technologies, skills,
inadequate infrastructure, and limited growth incentives. These further hinder the entry and
growth of smaller processors into the market. Consequently, they fail to progress to the agro-
processing industry. Thus, participation in this industry appears to be skewed toward the
previously advantaged individual entrepreneurs who often see the new entrepreneurs as a
threat.
2.
CONTEXTUALIZATION OF THE STUDY
South African Government has found it challenging to transform the agro industries to include
smallholder-farming entrepreneurs. Thus, Khoza et al. (2019) reported that smallholder
farmers in South Africa had not been linked successfully to sustainable agro-processing value
chains. Currently, commercial agriculture is the leading player in the agro-processing industries
in South Africa, whereas smallholder farmers play a limited role despite receiving support from
the Government (Khoza et al., 2019 and Mmbengwa et al., 2011). The lack of linkages to these
chains had various productive and economic implications for the whole smallholder farming
fraternity, including the extension advisory services whose critical roles are to advise the
farmers to access opportunities in their respective commodity sub-sectors.
Baloch and Thapa (2019) reported that the roles of the extension services are associated with
the training, visiting farmers, transferring Technology (ToT), Farmer Field Schools,
conducting agricultural research, and extension services in promoting sustainable agriculture
development. According to Cai et al. (2020), agricultural extension services are an essential
way of spreading new technologies. These authors reported the significant growth of Chinese
agricultural technologies because of the role that the extension services played. This evidence
explains why China's agricultural extension is dominated by the Public Agricultural Extension
System (PAES), which provides agricultural extension services with widely distributed
Government institutions and a multitude of extension agents’ networks (Hu et al. 2010, 2009;
Hu et al. 2012).
Mabe and Oladele (2020) reported that in Africa, most farming communities rely on the public
Agricultural Extension services for technical farming advice and information to induce
agricultural development activities. (Hu et al. 2004; Huang, Yang and Rozelle 2010). Given
this context, it could be induced that South African extension services have significant tasks in
changing the agro industries' profile.
3.
RESEARCHMETHOD
3.1 Participants and procedure
The participants were smallholder farmers from Gauteng Province in the Republic of South
Africa. The Gauteng Department of Agriculture Rural Development (GDARD) granted
permission to solicit their participation in the cross-sectional survey. Before the permission was
granted, the research team had to present the research proposal to GDARD, followed by all
regional GDARD offices, extension workers, and to the smallholder farmers themselves. In
this study, seventy-eight (n=78) smallholder farmers participated in the survey. Men comprised
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53.85% (n= 42) of the sample whilst women respondents constituted 46.15% (n = 36). The
mean age of the smallholder farmers who participated was found to be 53 years old (SD=
14.514). The youngest participant was 23 years old, while the oldest was 83 years old. The
majority of the participants come from Emfuleni local municipality {26 (33.3%)}, followed by
Ekurhuleni {21 (26.92%)} and Mid-Vaal {17 (21.79%)}.
3.2 Materials and Methodology
During data collection, the study used qualitative and quantitative methodologies. For
qualitative data collection, focus group sessions and official meetings were used. The purpose
of using the qualitative approach in this study was to enrich the discussion. Furthermore, it was
to discover new narratives on the phenomenon under consideration with the view to unearthing
the practical experiences of the participants. This exercise was essential to reveal the actual
reality. On the quantitative data collection, the study used a close-ended questionnaire. The
purpose of this quantitative research design was to enable researchers to conduct both
descriptive and explanatory (inferential) analyses. Before the collection of quantitative data
collection, the draft questionnaire was piloted and also evaluated by industry experts to ensure
its reliability and validity.
3.3 Measurement of variables
In this study, participation (dependent variable) was measured as a binary response variable
where zero (0) stands for nonparticipation, and one (1) stands for participation in agro-
processing. Nonparticipation in agro-processing is used as a baseline. The explanatory
variables which were regarded as the parameters to enhance participation of smallholder
farming in this study were measured on the five-point Likert scale (1 = strongly agree to 5 =
strongly disagree).
Table 1: Factors hypothesized to influence participation of smallholder farmers in agro-
processing in Gauteng province
Variable
name
Description of Variables
Measurements
Expected
sign
Dependent
Variable
Participation
Are you participating in the agro-
processing industry
(1=Participating, 0 = Not
participating)
+
Explanatory Variables
Information
Supply
I do get information from the agro-
processing industry sector?
(1=Strongly agree – 5 =
Strongly disagree)
-
Bonds
Participation in agro processing
increase the chances of getting
bonds.
(1=Strongly agree – 5 =
Strongly disagree)
+
Mutual trust
Mutual trust improves
participation in agro-processing.
(1=Strongly agree – 5 =
Strongly disagree)
+
Age
Aging improve participation in
agro-processing.
Number
-
Access to study
group
Access to study groups improve
participation in agro-processing.
(1=Strongly agree – 5 =
Strongly disagree)
-
Source, Survey, 2017
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3.4 Data analysis
The study used descriptive and correlational analysis to determine the means, standard
deviation, and the correlation coefficient of the responses variables. On the inferential analysis,
the study used a stepwise multiple logistic regression model where non-participation and
participation were coded 0 and 1, respectively. The effect of profit, access to advice, age, and
information flow to the participation of the smallholder farmers was tested. A backward
elimination strategy was used to remove access to the study group from model B. This is
elimination was informed by the assumption that all smallholder farmers have an equal
probability of accessing the study group provided they are registered with Gauteng Department
of Agriculture Rural Development.
3.5 Analytical framework
Stepwise multiple logistic regression models were used to assess the participation of
smallholder farmers in agro-processing in Gauteng Province. The general form of the logit
model is:
(1)
Where is the standard logistic distribution function which takes values between 0 and 1
(Verbeek, 2012). Equation (1) can be re-written in terms of odds ratios, as shown in equation
(2):
(2)
Where is the probability of observing the outcome (participation and
) is the
odds ratio which is equivalent to exponential coefficients. The odds ratio can be interpreted as
the number of times by which the odds of the outcome will be higher than the odds of
the outcome (non- participation) if the jth predictor increases by one unit. However, to
see the effect of an explanatory variable on the probability of being a participant (), the
marginal effects are estimated.
The empirical formulation of the model A & B (participation) used in the analysis was:
(3)
Where:
Y is a dummy dependent variable, =1 if event happens, =0 if event does not happen,
a is the coefficient on the constant term,
β is the coefficient(s) on the independent variable(s),
IS is the information supply
MT is the mutual trust
ASis the access to study group
Ei is the error term.
(4)
Participation was the dependent variable taking the value of 1 if individual i was a participant
and 0 if a non-participant. The explanatory variables are as described in Table 1. Equations
(3&4) and Odds Ratios were estimated using STATA 12.
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4.
RESULTS ANDDISCUSSION
4.1 Descriptive and correlational analyses
Table 2 shows the results of the mean, standard deviation, and relationship between response
variables that were analysed using Pearson's product-moment correction coefficient (Pearson's
r) after a normality test was performed.
Table 2: Means, standard deviation and inter-correlations of explanatory variables
Explanatory variables
Mean
SD
(1)
(2)
(3)
(4)
(5)
Information supply (1)
3.641
1.377
1.000
Investment (bonds) (2)
3.936
1.073
0.327**
(0.004)
1.000
Mutual Trust (3)
0.744
0.439
0.383***
(0.001)
0.323
(0.004)
1.000
Age (4)
52.667
14.514
0.143
(0.210)
0.095
(0.406)
0.151
(0.186)
1.000
Access to study groups (5)
3.564
1.410
0.587***
(0.000)
0.341**
(0.002)
0.341**
(0.002)
0.254**
(0.025)
1.000
Notes: N = 78, *= P<0.05, ** = P<0.01, *** = P<0.001
This test was done to test the linearity of the response variable. From the results, it was clear
that moderate positive statistically significant correlations between bonds and information
supply {r (78) = 0.327, p = 0.004}, and between mutual trust {r (78) = 0.383, p = 0.001} and
positive highly statistical significant correlation between access to study group and information
supply {r (78) = 0.587, p = 0.000} were observed. It appears that access to study group has a
positive statistical significant correlation with bonds {r (78) = 0.341, p = 0.002}, mutual trust
{r (78) = 0.341, p = 0.002}, and age {r (78) = 0.254, p = 0.025}. Based on the evidence above,
there is a positive inter-relationship between some explanatory variables. This relation implies
that there are some parameters which could be increased when increasing the others.
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4.2 Inferential Analysis
4.2.1 Causal effects of the response variables
3.7
2.7
3.7
Fig. 1 The path way diagram for the tools that cause of the smallholder farmers participation
in the agro-processing industries
Table 3 and Figure 1 present the results of the causal effects for agro-processing industries in
Gauteng Province.
Table 3: Causal effect of tools that can improve participation in the agro-processing industries
of Gauteng Province
Standardized
Coeff (β)
Beta
P> І z І
95% Conf.
Interval
Information supply
0.091***
(0.031)
0.315***
(0.102)
0.002
0.116- 0.514
Bonds
0.123***
(0.037)
0.333***
(0.095)
0.000
0.146 -0.520
Mutual Trust
0.370***
(0.084)
0.410***
(0.085)
0.000
0.245- 0.576
Age
0.006***
(0.002)
0.242***
(0.083)
0.004
0.079-0.405
Access to study
group
-0.117***
(0.033)
-0.416***
(0.111)
0.000
-0.634-0.198
Constant
-0.214
(0.172)
-0.538
(0.416)
0.192
-1.358-0.273
Var
(e.participation)
0.083
(0.083)
0.538
(0.073)
Legend: *= P<0.05, ** = P<0.01, *** = P<0.001
According to these results, it was found that the following parameters: information supply (beta
= 0.315, p = 0.002), bonds (beta = 0.332, p = 0.000), mutual trust (beta = 0.410, p = 0.000) and
Bonds
Mutual trust
Age
Access to study
group
Informed
-.54
Participation
E
1
2.
7
2.
5
.54
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age (beta = 0.242, p = 0.004) significantly caused participation of smallholder farmers in the
agro-processing to occur. Interestingly, the results showed that access to the study group (beta
= -0.416, p = 0.000) has a negative causal effect on the participation of these farmers. These
effects imply that access to the study group hinders the smallholders from participating in agro-
processing industries.
4.2.2 Effects of key parameters on agro-processing industry participation
The results in table 4 show that in all of the models under consideration, the predictors'
variables are jointly significant.
Table 4 Factors affecting smallholder farmers’ participation in agro-processing in Gauteng
province
VARIABLES
MODEL A
MODEL B
INDEPENDENT
VARIABLES
ODDS RATIO (CI-
95%)
Z
ODDS RATIO (CI-
95%)
Z
Information supply
2.490***
(1.189- 5.214)
2.42
1.797*
(0.971- 3.323)
1.87
Investment (Bonds)
3.893***
(1.391- 0.888)
2.59
3.023**
(1.145 - 7.981)
2.23
Mutual Trust
14.101***
(2.202- 0.290)
2.79
8.979***
(1.930 - 41.767)
2.80
Age
1.082***
(1.007- 1.162)
2.16
1.074**
(1.002 - 1.151)
2.03
Access to study group
0.397**
(0.155- 1.014)
-1.93
Constant
0.000***
(3.43- .060)
-2.87
0.000***
(2.76 - .058)
-2.87
Number of observations
78
Number of observatio
78
Model A (LR chi2(4)
33.73
Model B (LR chi2(4)
33.73
Prob > chi2
0.000
Prob > chi2
0.000
Pseudo R-Squad
0.503
Pseudo R-Squad
0.441
LRtest: LR chi2(1
4.76
Prob > chi2 =
0.0292
Legend: *= P<0.05, ** = P<0.01, *** = P<0.001
In other words, in both model A and B {Model A (LR chi2 (4) = 33.73, Prob > chi2 = 0.000)
and Model B (LR chi2 (4) = 33.73, Prob > chi2 = 0.000)}, the null hypothesis of no joint
significance of the predictor variables is not supported. These findings imply that all the
predictor variables jointly influence or explain the dependent variables. Therefore, there is a
correlation between participation and the predictor variables in question. In the sample, we
failed to accept the null hypothesis that information supply, bonds (Investment), mutual trust,
age, and access to study groups do not influence or predict the participation of smallholder
farmers in the agro-processing industries.
This result seems to imply that all the predictor mentioned above variables are significant to
influence whether the smallholder farmers participate (or not participate) in the agro-
processing. In model A, it was found that access to the study group was the only predictor
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variable that harms the participation of smallholder farming in agro-processing industries in
Gauteng Province. On the other hand, in model B, all the predictor variables under
considerations were found to have a positive effect on the smallholder farmers' participation in
agro-processing. Concisely, model (A and B) have 0.503 and 0.441pseudo R-squad. This
result implies that the expression of the variability in model B of smallholder farmers'
participation is reduced by 0.062 because of the exclusion of the access to study group variables
is realized. This result could be observed by the reduction in the odds ratios of the parameters
that determine the participation.
a) Effect of mutual trust in the smallholder farmers' participation
Mutual trust is essential in facilitating the co-operative behavior of entrepreneurs (Thindisa,
2014). The current study sought to determine whether the mutual trust has an influence on the
participation of smallholder farmers' in the agro-processing industries. In model A, the results
showed that mutual trust was 14.101 times more likely to significantly influence participation
of smallholder farmers in agro-processing when other confounding variables are held constant.
On the other hand, the results in model B showed the significant reduction in the odds ratio of
5.122 times (that is from 14.101 to 8.979 odds ratio). This may imply that although mutual
trust is significantly critical in determining the participation of smallholder farmers in agro-
processing, its impact heavily depend on the inclusion of the access to study group predictor
variable. The exclusion of access to study group variable seems to have a negative effect on
the mutual trust variable and thus reduced the effect on the participation of smallholder farming
in the agro-processing industries.
b) Effect of age on the smallholder farmers' participation
Current research findings have shown that age plays an integral part in productivity of the
smallholder farming enterprises (Oladele 2011 and Maponya et al., 2014). In this type of
farming, age is associated with the level of farming experience (Maponya et al., 2014). It is
assumed that the older the farmer is, the more experienced they are. These authors, also
reported the results that indicated a positive relationship between age and market participation.
Other authors (Pote and Obi, 2007) confirmed the existence of a positive association between
age and market participation. The objective of this study was to find out whether age affects
the smallholder farmers' participation in the agro-processing industries. The outcome confirms
that age is statistically significant to influence the smallholder farmers' participation in the agro-
processing industries. In model A, it was found that age was 1.082 times more likely to
influence participation of smallholder farmers in the agro-processing when the availability of
information, investment (bonds), mutual trust, and access to study groups were held constant.
On the contrary, the effect of age on the participation of smallholder farmers in agro-processing
industries was slightly reduced (1.074) after the access to the study group variable was removed
in the model. Although the effect of age was reduced, it remains statistically significant to
explain the participation of smallholder farming in the agro-processing industries.
c) Effect of access to a study group on the smallholder farmers' participation
A study group for smallholder farmers was introduced as a practice that seeks to promote
sustainable agriculture (Zeweld et al., 2017). According to these authors, this strategy was
introduced after realising some deficiency in the understanding of socio-psychological
behaviour of smallholder farmers which had some negative impact on their adoption of
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technological innovations. Tallam et al. (2016) indicated that farmer groups (study group) are
basic socio-economic safety nets for rural communities in Sub-Saharan Africa. According to
these authors, these groups provide mutual support to farmers through collective action to
enhance improvement of livelihoods. The question is: can access to study group affect the
smallholder farmers' participation in the agro-processing industries of Gauteng Province? This
study found that access to study groups does affect the participation of smallholder farming in
agro-processing. However, its effects appear to be negative. This effect implies that as the
smallholder farmers increase their involvement in the study group, they reduce their
participation in agro-processing industries by 0.397 folds adjusting for the effect of other
variables in the model. This effect appears to indicate that extension officers should use the
study group with caution if they intend to ensure that farmers are involved in the agro-
processing industries.
5. CONCLUSION/RECOMMENDATIONS
The study aimed to identify factors that could be used as parameters to improve the smallholder
farmers' participation in the agro-processing industries in Gauteng Province. The objective of
identifying these parameters was to ensure that smallholder farmers can participate in the agro-
processing industries and thereby reaping the economic benefits such as job creation and self-
employment. In a nutshell, the study successfully identified five parameters (i.e., the
availability of information, investment, mutual trust, age of the farmers, and access to study
group) that were significant in influencing the participation of the smallholder farmers in the
agro-processing industries. Of the five parameters, only access to study group was negatively
correlated with the participation of smallholder farming in agro-processing in model A.
Consequently, access to the study group was excluded in the model B.
The study concluded that in order to reverse the skewed participation of the previously
disadvantaged smallholder farmers in the ago-processing industry, the factors identified could
be used to select participants in these industries. The selection of the participant should also be
coupled with the promotion of market access, technology adoption, skills development, and
adequate infrastructure, and some incentives to participate in the existing agro-processing
industries. Considering these industries are owned by a few role players who may not easily
allow the transformation to take place because of uncertainty and risk aversion. It may be a
good idea for the province to develop a dedicated smallholder farmer agro-processing industry.
The development of such an industry may require new policies that may specify criteria for
participation and food safety standards to adhere to.
Should the participation of smallholder farmers in the agro-processing value chain improve,
some of these farmers will be able to derive the profit and thereby grow to be commercially
viable? The growth of these farmers may be different, as outlined by differential advantage
theory (Clark, 1940 and Wickham, 2004). The theory advocate that buyers and sellers associate
on a more permanent basis in order to serve specific needs for specific buyers group. Secondly,
it also indicates that industries are limited in their ability to increase prices. Moreover, lastly,
industries may seek to improve products to make them more attractive to consumers. This
theory appears to indicate that the involvement of smallholder farmers in the agro-processing
value chain may give them competitiveness such that they could innovate the right products
that may be attractive to the buyers.
Furthermore, these farmers may have a permanent association with the market. This strategy
could reduce their inability to access sustainable and niche markets in the long term. The
S. Afr. J. Agric. Ext. Mmbengwa, Rambau, Rakuambo and Qin
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implication of this access to the market in this way may boost their economic contribution and
thereby improve their capacity to employ more people in their agricultural sector.
The study recommends that for these farmers to improve their participation in agro-processing
industries, the parameters should be part of the agro-processing strategy, policy, and models.
The capacity building initiatives should, amongst others, include these parameters. Therefore,
Gauteng Province should prioritize these parameters in its effort to enhance the transformation
of the agro-processing industries. Future research could investigate the impact of the identified
parameters along with the commodity classifications and also in various regions of Gauteng
Province. It may also be interesting to have studies that could look at agro-processing
participation of smallholder farmers in both formal and informal markets with the view to test
the parameters along with the different market segments
ACKNOWLEDGEMENT
We wish to acknowledge the Gauteng Department of Agriculture and Rural Development
(GDARD) and the National Agricultural Marketing Council (NAMC) for their support.
REFERENCES
BALOCH, M.A. AND THAPA, G.B. 2019. Review of the agricultural extension modes and
services with the focus to Balochistan, Pakistan. Journal of the Saudi Society of
Agricultural Sciences, 18(2), pp.188-194.
BLACK, A., EDWARDS, L., GORVEN, R. AND MAPULANGA, W. 2020. Agro-processing,
value chains, and regional integration in Southern Africa. WIDER Working Paper
2020/36.United Nations University. UNU-WIDER 2020.
BRANDSA, 2014. Huge potential’ in SA’s agro-processing industry.
https://www.brandsouthafrica.com/investments-immigration/business/economy/agro-
processing-110314 (Accessed on the 15th of February 2017). Pp.1.
CAI, J., JIA, Y., HU, R. AND ZHANG, C. 2020. Four decades of China'sChina's agricultural
extension reform and its impact on agents'agents' time allocation. Australian Journal of
Agricultural and Resource Economics 66, pp. 104-125.
CLARK, C. 1940. The conditions of economic progress. London: Macmillan & Co. Ltd.
DAFF. 2012. Economic profile of the agro-processing Industry in South Africa: 1970-2010.
Department of Agriculture, Forestry and Fisheries. Arcadia, Pretoria. Republic of South
Africa (RSA). Pp. 1-15
DAFF. 2016a. Agro-processing Quarterly Brief: No. 3, 2016. Department of Agriculture,
Forestry and Fisheries. Arcadia, Pretoria. Republic of South Africa (RSA).pp. 1-14
DAFF. 2016. National Policy Framework on the support and development of small and
medium Agro-Processing Enterprises in the Republic of South Africa .2014/30.
Department of Agriculture, Forestry and Fisheries. Arcadia, Pretoria. Republic of South
Africa (RSA). Pp. 1-12
DIMANT T, LEBONE K, MACKAY G, MOLOI L, NDEBELE T, PHUNG R. AND VAN
HEERDEN G. 2016. South Africa survey. Institute of race relations, Johannesburg,
Republic of South Africa. Pp. 1-5.
FEYAERTS, H., VAN DEN BROECK, G. AND MAERTENS, M. 2020. Global and local
food value chains in Africa: A review. Agricultural Economics, 51(1), pp.143-157.
GDARD. 2015. Gauteng agro-processing strategy. Final Draft (October 2015). Johannesburg,
RSA.
S. Afr. J. Agric. Ext. Mmbengwa, Rambau, Rakuambo and Qin
Vol. 48 No.2, 2020: 153 - 165
http://dx.doi.org/10.17159/2413-3221/2020/v48n2a545 (License: CC BY 4.0)
164
GEBREHIWET, Y. AND MATHIBELI, K. 2013. Trade profile of South Africa’s agro-
processing products to BRIC countries. Department of Agriculture, Forestry and
Fisheries. Arcadia, Pretoria. Republic of South Africa (RSA).
HU, D., REARDON, T., ROZELLE, S., TIMMER, P. AND WANG, H. 2004. The emergence
of supermarkets with Chinese characteristics: challenges and opportunities for China's
agricultural development. Development policy review, 22(5), pp.557-586.
HU, R., CAI, Y., CHEN, K.Z., AND HUANG, J. 2012. Effects of inclusive public agricultural
extension service: Results from a policy reform experiment in western China. China
Economic Review, 23(4), pp.962-974.
HU, R., CAI, Y., CHEN, K.Z., CUI, Y. AND HUANG, J. 2010. Effects of Inclusive Public
Agricultural Extension Service. Discussion paper 01037. International Food Policy
Research Institute.
HU, R., ZHIJIAN, Y.A.N.G., KELLY, P. AND HUANG, J. 2009. Agricultural extension
system reform and agent time allocation in China. China Economic Review, 20(2),
pp.303-315.
HUANG, J., YANG, J. AND ROZELLE, S. 2010. China's agriculture: drivers of change and
implications for China and the rest of world. Agricultural Economics, 41, pp.47-55.
KHOZA, T.M., SENYOLO, G.M., MMBENGWA, V.M., SOUNDY, P. AND SINNETT, D.
2019. Socio-economic factors influencing smallholder farmers'farmers' decision to
participate in the agro-processing industry in Gauteng province, South Africa. Cogent
Social Sciences, 5(1), p.1664193
MABE, L.K., AND OLADELE, O.I. 2020. Application of Information Communication
Technologies for Agricultural Development through Extension Services: A Review.
In Environmental and Agricultural Informatics: Concepts, Methodologies, Tools, and
Applications (pp. 182-232). IGI Global.
MAPONYA, P., MODISE, D., VAN DEN HEEVER, E., MAHLANGU, S., BALOYI, N.,
MALULEKE, R. AND MPHAHLELE, M. 2014. The Establishment of Vegetable and
Fruit Markets and Nurseries: A Case Study in the Greater Sekhukhune District,
Limpopo Province, South Africa. Journal of Agricultural Science, 6(9), 144.
MAZUNGUNYE, P. 2019. ''Industrialisation for economic transformation: Economy-wide
impacts of agro-processing development in Tanzania.'' SA-TIED Working Paper
2019/42. Helsinki: UNU-WIDER.
MMBENGWA, V.M., RAMUKUMBA, T., GROENEWALD, J.A., VAN SCHALKWYK,
H.D., GUNDIDZA, M.B. AND MAIWASHE, A.N. 2011. Evaluation of essential
capacities required for the performance of farming small, micro and medium enterprise
(SMEs) in South Africa. African Journal of Agricultural Research, 6(6), pp.1500-1507.
OLADELE, O. I. 2011. Contribution of Indigenous Vegetables and Fruits to Poverty
Alleviation in Oyo State, Nigeria. Journal of Human Ecology, 34(1), 1-6.
OLOFSSON, M. 2020. Socio‐economic differentiation from a class‐analytic perspective: The
case of smallholder tree‐crop farmers in Limpopo, South Africa. Journal of Agrarian
Change, 20(1), pp.37-59.
OWOO, N.S., AND M.P. LAMBON-QUAYEFIO. 2018. ''Chapter 10: The Agro-Processing
Industry and its Potential for Structural Transformation of the Ghanaian Economy'',
192–212. In R.S. Newfarmer, J. Page, & F. Tarp (eds), Industries without smokestacks:
Industrialisation in Africa reconsidered. Oxford: Oxford University Press.
POTE, P.T. AND OBI, A. 2007. Technical constraints to smallholder agriculture and their
implication for market access: Case study of Nkonkobe municipality, Eastern Cape,
South Africa. Department of Agricultural Economics and Extension, University of Fort
S. Afr. J. Agric. Ext. Mmbengwa, Rambau, Rakuambo and Qin
Vol. 48 No.2, 2020: 153 - 165
http://dx.doi.org/10.17159/2413-3221/2020/v48n2a545 (License: CC BY 4.0)
165
Hare, Alice, RSA.
TALLMAN, M.A., CARLSON, M.P., BRESSOUD, D.M. AND PEARSON, M. 2016. A
characterization of calculus I final exams in US colleges and universities. International
Journal of Research in Undergraduate Mathematics Education, 2(1), pp.105-133.
THINDISA, L.M.V. 2014. Participation by smallholder farming entrepreneurs in agro-
processing activities in South Africa (Masters Dissertation), University of Witwatersrand,
Johannesburg, RSA.
ZEWELD, W., VAN HUYLENBROECK, G., TESFAY, G. AND SPEELMAN, S. 2017.
Smallholder farmers' behavioural intentions towards sustainable agricultural
practices. Journal of Environmental Management, 187, 71-81.
WICKHAM, P.A. 2004. Strategic entrepreneurship. Third Edition, Harlow, England.
