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ANALYSIS OF FOOD SECURITY STATUS AMONG AGRICULTURAL HOUSEHOLDS IN THE NKOMAZI LOCAL MUNICIPALITY, SOUTH AFRICA

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The study analysed the food security status of agricultural households in Nkomazi Local Municipality, South Africa. Descriptive statistics, the food security index and multivariate analysis were used to realise the objectives of the study. The majority of respondents were females. Furthermore , respondents aged between 61 and 70 years and those who had only completed primary school education were also in the majority. Just under half of the respondents had a farming experience of more than 21 years and had large households (6-10 household members). Although most agricultural households in the study area were food secure, overall food insecurity among the respondents was very high. The marital status, education level and annual farm income of the respondents were positively and significantly associated with food security. Farming is practised mainly by older people with low levels of education. The level of food insecurity among agricultural households was approximately twice the South African national household food insecurity index. The findings of this study provide a basis for the formulation of a policy framework to help tackle the high food insecurity observed in the study area.
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Journal of Agribusiness and Rural Development
www.jard.edu.pl
pISSN 1899-5241
eISSN 1899-5772
3(61) 2021, 323–336
Tulisiwe P. Mbombo-Dweba, Department of Agriculture and Animal Health, University of South Africa, South Africa, e-mail:
mbombtp@unisa.ac.za, https://orcid.org/0000-0001-8395-3322
http://dx.doi.org/10.17306/J.JARD.2021.01412
ANALYSIS OF FOOD SECURITY STATUS
AMONGAGRICULTURAL HOUSEHOLDS
INTHENKOMAZI LOCALMUNICIPALITY, SOUTH AFRICA
Themba Andries Sambo1, James Wabwire Oguttu1,
Tulisiwe Pilisiwe Mbombo-Dweba1
1University of South Africa, South Africa
Abstract. The study analysed the food security status of ag-
ricultural households in Nkomazi Local Municipality, South
Africa. Descriptive statistics, the food security index and
multivariate analysis were used to realise the objectives of
the study. The majority of respondents were females. Further-
more, respondents aged between 61 and 70 years and those
who had only completed primary school education were also
in the majority. Just under half of the respondents had a farm-
ing experience of more than 21 years and had large households
(6-10 household members). Although most agricultural house-
holds in the study area were food secure, overall food insecu-
rity among the respondents was very high. The marital status,
education level and annual farm income of the respondents
were positively and signicantly associated with food security.
Farming is practised mainly by older people with low levels
of education. The level of food insecurity among agricultural
households was approximately twice the South African nation-
al household food insecurity index. The ndings of this study
provide a basis for the formulation of a policy framework to
help tackle the high food insecurity observed in the study area.
Keywords: agricultural households, household food security,
Phezukomkhono Mlimi Programme
INTRODUCTION
Among the countries of the Southern African Develop-
ing Community (SADC) region, South Africa has a con-
siderably high gross domestic product (WEF, 2017). It is
a net exporter of cereals (FAO, 2020) and, concurrently,
is the largest importer of agricultural products (Viljoen,
2017). While South Africa is considered food secure at
the national level (EIU, 2019), there are households and
individuals in South Africa who experience high levels
of food insecurity (Masuku et al., 2017). For example, in
2016, approximately 19.9% of households at the nation-
al level in South Africa and 22.2% in the Mpumalanga
province ran out of money to buy food (SSA, 2016a).
In addition, access to food in South Africa was moderate-
ly insucient in about 15% of households, while in 5.2%
of households access to food was severely inadequate.
However, in the available literature, there have been
contradicory reports on food insecurity statistics. For
example, according to the SSA (2019a), food insecurity
was at 28.4% in the Mpumalanga province and 34.3%
in the North West province. Yet, in a study by Alem-
uet al. (2015), the food insecurity statistics in these two
provinces were at 76% and 76%, respectively. While it
can be argued that this dierence can be attributed to
the time dierence, it is noteworthy that Statistics South
Africa used the household food insecurity access scale
(HFIAS), while Alem et al. (2014) used the income and
expenditure survey and Wooldridge’s (WCLM) estima-
tor to determine food security. In fact, in a food secu-
rity study conducted by Ijatuyi et al. (2018), using the
food security index, the authors observed that 56.58%
of agricultural households were food secure in the North
Accepted for print: 9.09.2021
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
324 www.jard.edu.pl
West province. Therefore, the authors of this study are
of the view that these discrepancies result from dierent
methodologies and seasonality.
Households with severely inadequate access to food
and suering hunger in South Africa are estimated to be
at 13.4 and 1.6 million, respectively (SSA, 2019b). With
reference to the demographics, Africans and female-
headed households tended to be more severely aected
by food insecurity. In addition, high levels of food in-
security are mostly observed in households with more
than eight family members (SSA, 2019b). Literature at-
tributes this to the fact that larger numbers of members
in a household put more pressure on food consumption
in the household (Dula & Berhanu, 2019; Jeyarajah,
2018; SSA, 2019b). Food insecurity is also predominant
among elderly people (IOA, 2017; Steiner et al., 2018)
and within households whose members have low educa-
tion levels (Mutisya et al., 2016; Steiner et al., 2018).
The food insecurity gures in South Africa are ex-
pected to increase due to the outbreak of the COVID-19
pandemic. This is because the COVID-19 pandemic has
put pressure on and disrupted the South African food
system. This has consequently aected the availability
and access to food among households (Troskie, 2020).
In addition, the COVID19-associated lockdown restric-
tions resulted in a signicant contraction in the South
African economy (SSA, 2020b). This contraction has
directly impacted food supply and demand, and indi-
rectly the food supply by reducing the purchasing pow-
er, production capacity and distribution of food (De-
vereux et al., 2020; HLPE, 2020; Pu and Zhong, 2020).
It is the poor and vulnerable households (HLPE, 2020),
characterised by low levels of education and low salary
incomes (Arndt et al., 2020), whose food security status
is mostly aected in the event of outbreaks such as the
COVID-19 pandemic (SSA, 2020b).
Agriculture plays a key role in improving food se-
curity (Jain and Bathla, 2016) by contributing to food
availability (Wegren and Elvestad, 2018), access, sta-
bility and dietary diversity (HLPE, 2016). Therefore,
household food production is regarded as one of sustain-
able strategies for ghting food insecurity, especially by
under-resourced households. It is not only a source of
food but also contributes to the generation of income
and employment (Khanna and Solanki, 2014; Vasylieva,
2018; World Bank, 2018). In a study conducted in central
Malawi by Mango et al. (2018), agricultural production
signicantly increased access to food. In South Africa,
particularly in Cape Town, urban agriculture is reported
to have signicantly contributed to improved access
to food (Philander and Karriem, 2016) and income for
households that participated in agricultural projects
(Swanepoel et al., 2017). This was also conrmed by
Khumalo and Sibanda (2019), in a study conducted in
Tongaat, KwaZulu-Natal, where the majority (66%) of
households involved in agricultural activities were food
secure. Moreover, these households had a higher dietary
diversity score, compared to the households that did not
engage in agriculture.
In view of the above-mentioned benets of being
engaged in agriculture, the Phezukomkhono Mlimi
(PKM), a food security programme, formerly known
as the Masibuyele Emasimini programme, was initiated
in 2005 by the Mpumalanga Provincial government to
help to improve the accessibility and availability of food
among the residents of the study area. The overall objec-
tive of the programme is to ght poverty and household
food insecurity in rural areas by assisting peasant farm-
ers and poor households in the cultivation of under-uti-
lised pieces of land, to produce sucient food and thus
achieve household food security (DALA, 2007). The
PKM programme is intended to provide the beneciar-
ies with production inputs, that is, seeds, fertilisers and
chemicals; mechanisation support for tilling the land;
support with basic infrastructure for farming, such as
fencing, boreholes and irrigation pipes; and agricultural
advisors for extension and advisory assistance.
However, there is no evidence of studies that have
investigated how the PKM programme contributes to
household food security in the Nkomazi Local Munici-
pality. Studies that have been conducted in other are-
as show that the programme has been unsuccessful in
meeting the intended objectives and the needs of small-
scale farmers (Grobler, 2016; Nyathi, 2014). According
to these studies, production inputs are delivered late in
the season (Shabangu, 2015), the programme fails to
meet the set targets, with a considerable number of trac-
tors broken and malfunctioning (Grobler, 2016). Addi-
tionally, it is reported that tractors are inadequate for the
mechanisation service required (Shabangu, 2015). In
addition to the fact that these past studies were conduct-
ed ve or more years ago, and in other areas (Masoka,
2014; Shabangu, 2015), their ndings could not be gen-
eralised, due to the methodology used (Kothari, 2004;
Kumar, 2011). For example, in the study by Shabangu
(2015), non-standardised food security measurement
325
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
www.jard.edu.pl
tools were employed, while in the study by Grobler
(2016), the contribution of the programme to food secu-
rity was not assessed.
This paper aims to assess the status of food secu-
rity among households beneting from the PKM pro-
gramme and to identify factors that are associated with
food security among the agricultural households bene-
tting from the PKM programme in the Nkomazi Local
Municipality, South Africa.
METHODOLOGY
Study area
The study was conducted in the Nkomazi Local Munici-
pality (NKLM). The NKLM is located in the eastern part
of the Ehlanzeni District Municipality (EDM) of Mpu-
malanga, South Africa. The municipality borders with
Mozambique (in the east) and the Kingdom of Eswatini
(in the south). It has an estimated population of 410,900
people (SSA, 2016b). Its climate is subtropical, with
a rainfall of 755 mm and an annual temperature of 28°C,
on average (Adeola et al., 2016). The NKLM is mainly
rural, with agriculture as one of the main economic ac-
tivities (NKLM, 2016). The main agriculture activities
in the study area include vegetable, sugar cane, banana,
citrus and sub-tropical fruit farming under irrigation as
well maize and cotton under dry land conditions (van
Niekerk, 2015). The NKLM was selected because it has
a high number of households involved in agricultural ac-
tivities (SSA, 2011) and a high poverty rate (MPT, 2015).
Study population
The study population included agricultural households
in the NKLM that were beneciaries of the PKM pro-
gramme in the 2018/19 production season. All the 543
agricultural households supported by the PKM pro-
gramme in the study area during the 2018/19 production
season were targeted to participate in the study.
Data collection
Face-to-face interviews, using a pretested structured
questionnaire were conducted with agricultural house-
holds by trained enumerators. The questionnaire con-
sisted of three sections which captured information on
socio-economic characteristics, food security status and
factors connected to the food security of the respond-
ents. Each interview took 30 to 60 minutes. The data
was collected from 1 February to 24 March 2020. Out
of the 543 agricultural households supported by the Phe-
zukomkhono Mlimi Programme in the study area during
the 2018/19 production season, only 355 (65% response
rate) assented to be part of the study and signed the con-
sent form and completed the questionnaire.
Data analysis
The Statistical Package for the Social Science pro-
gramme (SPSS version 25) was utilised to analyse the
data. Descriptive statistics, the food security index (FSI)
and multivariate analysis were used to realise the ob-
jectives of the study. Households were classied into
two groups: food secure and food insecure households,
using the FSI as described by Omotayo and Ganiyu
(2017). The equation for the food security index (Fi) is
specied as:
Fi =
Per capita food expenditure
for each household (1)
2/3 Mean per capita food expenditure
of all households
A household with monthly per capita food expendi-
ture exceeding or equivalent to two-thirds of the mean
per capita food expenditure was regarded as food se-
cure. Conversely, if a household had a per capita food
expenditure that was less than two-thirds of the mean
per capita monthly food expenditure, it was regarded as
food insecure (Omonona and Agoi, 2007).
The FSI was used to classify households in the study
sample as either food secure (coded = 1) or food insecure
(coded = 0). This led to the formulation of a binary out-
come variable (food security status). A probit regression
model was employed to identify factors associated with
food security status among agricultural households. The
equation for the probit regression model is specied as:
Y* = W0 + W1 X1 + W2X2 + W3X3 + …. +W14X14 + ε (2)
where:
Yi – household food security status (food secure
households = 1, food insecure households =
0). From the FSI measured above, households
with scores equal to or higher than 1 will be
classied as food secure (1); while those with
scores of less than 1 will be classied as food
insecure (0).
W0 – the intercept
W1W14 – parameters to be estimated
X – sets of independent variables
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
326 www.jard.edu.pl
ε – an independent distributed error term.
In the probit regression analysis, the independent
variables are as follows:
X1 – age of household head (in years)
X2 – gender (dummy; male = 1, female = 0)
X3 – Marital status (dummy; married = 1, otherwise
= 0)
X4 – mariage (dummy; polygamous marriage = 1,
otherwise = 0)
X5 – size of the household (number of people in the
household)
X6 – dependency ratio (number, continuous)
X7 – level of education (years of formal education)
X8 access to extension services (dummy; yes = 1,
otherwise = 0)
X9received mechanisation assistance (dummy;
yes = 1, otherwise = 0)
X10 – received support with production inputs (dum-
my; yes = 1, otherwise = 0)
X11 – received infrastructure support (dummy; yes
= 1, otherwise = 0)
X12 – annual farm income (income in rands)
X13 received training (dummy; yes = 1, otherwise
= 0)
X14 – engagement in non-farm activities (dummy;
yes = 1, otherwise = 0)
RESULTS AND DISCUSSION
Socio-economic characteristics
ofrespondents
Socio-economic details of the respondents are presented
in Table 1. Most (27.9%; n = 99) of the respondents in
this study were between 61 and 70 years of age. These
results concur with the results obtained by Ijatuyi et al.
(2018), who noted a high proportion of ageing farmers
in a study that was conducted in the North West prov-
ince, South Africa. The signicantly low numbers of the
younger generation involved in farming are worrying,
as it could have a negative implication on the future of
agriculture in the area. The low numbers of youth partic-
ipating in agriculture could be put down to the diculty
in accessing credit (Rakgwale and Oguttu, 2020) and
negative perceptions of the youth on farming (Swarts
and Aliber, 2013). Omotayo (2018) is of the view that
programmes to attract the youth into the agricultural
sector are needed so that the younger generation can
take over from aged farmers.
With regard to gender (Table 1), 40.6% (n = 144)
of the respondents were males, while 59.4% (n = 211)
were females. The results of the study support the nd-
ings reported by Khumalo and Sibanda (2019), who
also observed that there were more females (54.8%) in
a study that assessed the impact of urban and peri-urban
agriculture on household food security status in Ton-
gaat, eThekwini Municipality, South Africa. The high
number of females in this study was an expected situ-
ation because females are usually the main custodians
of food production, procurement and processing at the
household level (Botreau and Cohen, 2019). However,
this nding contradicts the ndings by Olayiwola et al.
(2017), who discovered that the majority (79.3%) of the
respondents in the study conducted in the Oluyole Lo-
cal Government area of Oyo State, Nigeria, were males.
Apart from dierences in geographical areas, the dis-
crepancies observed between these two studies could be
attributed to the existence of the vulnerable household
producer subcategory of subsistence farmers under the
PKM programme. This subcategory caters for women,
persons with disabilities, child-headed households and
farmworkers who have an interest in improving their
food security levels through food crop production
(DARDLEA, 2019).
The present study also discovered that most (49.9%;
n = 177) of the respondents were married. The results
reported here are also consistent with ndings by Sani
and Kemaw (2019), who observed that most farmers in
their study were married. Marital status is postulated
to inuence the extent of involvement in farming and
non-farm activities (Gordon and Craig, 2001). Available
evidence shows that household food security status in-
creases when the head of the household is married (Ag-
boola et al., 2017; Mustapha et al., 2018).
With regard to household size, households that had
six to ten persons were in the majority (52.4%; n = 186).
This nding contradicts the nding by Olayiwola et al.
(2017), who found that just less than half (48.7%) of
households had a family size of one to ve persons.
This contradiction might be due low levels of income
and education of the respondents in this study. Accord-
ing to Debebe (2014), households with lower levels
of income and education are less probable to access
family planning services. As a result, females with low
levels of education use less protection against unwanted
pregnancy and have many children, compared to fe-
males with higher levels of education. Household size
327
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
www.jard.edu.pl
and food security tend to be negatively correlated (SSA,
2019b; Tiwasing et al., 2018), which means that as the
number of members of a household increases, the food
security status of that household declines (Sambo et al.,
2017; Yousaf et al., 2018). A national study conducted in
South Africa by SSA (2019b), revealed that inadequate
food access was more prevalent among households that
have more than eight members.
Most respondents (43.7%; n = 155) had primary
school education and this was followed by 42% (n =
149) who had no formal education. Meanwhile, 9.9% (n
= 35) had secondary education and 4.5% (n = 16) had
attained tertiary education level. The results of the study
indicate that, generally, the education level among farm-
ers in the NKLM was low and that low education level
was biased towards the aged respondents. This concurs
with the ndings of Alam et al. (2020), who reported that
44.6% of respondents had no formal education, in their
study conducted in the coastal area of Noakhali, Bang-
ladesh. The low education levels of the farmers in this
Table 1. Socio-economic prole of participants (n = 355)
Variable Frequency Percentage
1 2 3
Age
22–30 10 2.8
31–40 15 4.2
41–50 43 12.1
51–60 88 24.8
61–70 99 27.9
71–79 71 20.0
> 80 29 8.2
Gender
Male 144 40.6
Female 211 59.4
Marital status
Single 44 12.4
Married 177 49.9
Divorced 20 5.6
Widowed 114 32.1
Household size
1–5 members 123 34.6
6–10 members 186 52.4
11–15 members 40 11.3
16–20 members 06 1.7
Education level
No formal education 149 42.0
Less than Grade 12 education 155 43.7
Grade 12/matric certicate 35 9.9
Tertiary education 16 4.5
Farming experience
1–5 years 56 15.8
6–10 years 62 17.5
11–15 years 28 7.9
16–20 years 39 11.0
> 21 years 170 47.9
Table 1 – cont.
1 2 3
Farm size
< 3 hectare 214 60.3
3–5 hectares 99 27.9
5–10 hectares 30 8.5
> 10 hectares 12 3.5
Annual farm income
< R40 000 342 96.2
R40001–R80000 10 2.8
R80001–R120000 01 0.3
> R120000 02 0.7
Engaged in non-farm activities
Yes 131 36.9
No 224 63.1
Received mechanisation assistance
Yes 249 70.1
No 106 29.9
Total 355 100
Source: eld survey, 2020.
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
328 www.jard.edu.pl
study could be attributed to the inequalities of the past
apartheid government which prevented black people
from getting formal education in South Africa (Antwi
and Nxumalo, 2014; De Cock et al., 2013). The results
are worrisome, as the literature indicates that high edu-
cation levels are highly positively correlated with house-
hold food security status (Omonona and Agoi, 2007; Ya-
haya and Danmaigoro, 2020). Education has been shown
to empower farmers, as it helps them to acquire skills
and knowledge needed to improve their productivity and
food security status (Antwi and Nxumalo, 2014).
Nearly half (47.9%; n = 170) of the farmers had
a farming experience of more than 21 years. This was
followed by 15.8% (n = 56) of the farmers that had
a farming experience of less than 5 years. The propor-
tion of farmers with farming experience between 6 and
10 years accounted for 17.5% (n = 62), while those with
11-15 years of farming made up 7.9% (n = 28) of the
study population. Farmers with between 16 and 20 years
of farming experience accounted for 11.0% (n = 39). The
ndings of this study concur with the results of Sambo
et al. (2017), who found that the majority (40.1%) of
farmers had between 16-20 years of farming experience.
The high number of farmers having many years of ex-
perience in this study is good news for the food secu-
rity level in the study area. Available evidence suggests
that households headed by individuals that have been
in farming for many years are likely to be food secure
(Mohammed et al., 2014).
The ndings also revealed that a high proportion
(60.3%; n = 214) of households in this study had less
than three hectares (ha) of land, and only 3.5% (n = 12)
of households had more than 10 hectares (Table 1). The
results are in agreement with those of the study con-
ducted among urban farmers in Kaduna State, Nigeria,
by Saleh and Mustafa (2018), who also found that most
farmers cultivate a land area smaller than three hectares.
However, according to Khumalo and Sibanda (2019),
small plots are associated with low yields that nega-
tively aect household food security. Jeminiwa et al.
(2018), are of a similar view and were able to conclude
that the level of productivity is inuenced by farm size.
The majority of households in this study (96.2%;
n = 342) had an annual farm income that was below
R40,000.00. Only 0.7% (n = 2) of the households had
an annual farm income higher than R120,000.00, fol-
lowed by 0.3% (n = 1), who had an income of R80,001-
R120,000.00 (Table 1). The results reported here suggest
that the households in the study area generally had a low
income, with an average of R6,490.99 per annum. The
low income among households in the study area could
be attributed to the smaller sizes of plots under cultiva-
tion, as explained above. The area of agricultural land
under production is positively associated with farm in-
come (Ryś-Jurek, 2019). However, the ndings reported
here do not concur with the ndings of the study carried
out in the North West province, South Africa, by Ijatuyi
et al. (2018), who reported that 44.9% of the house-
holds had an annual income from the farm ranging from
R40,000.01 to R80,000.00 per annum. The low-income
levels observed in this study are worrisome, because
household income signicantly contributes to food se-
curity status (Cheteni et al., 2020; Sambo et al., 2017).
The majority (63.1%; n = 224) of respondents in this
study stated that they were not involved in non-farm ac-
tivities. The results are inconsistent with those reported
by Bila et al. (2015), in a study conducted in Hawul
Local Government Area, Borno State, Nigeria, which
found that the majority (95.6%) of farming households
were involved in non-farm activities. The inconsisten-
cies observed between the present study and that by
Bila et al. (2015) can be attributed to the dierence in
the age of the two study populations and the low educa-
tional levels of respondents in the current study. Almost
all (98.5%) of the households in the study by Bila et
al. (2015) were below 45 years of age. Therefore, they
are likely to partake in o-farm activities to earn extra
income, because they belong to the active labour force.
On the contrary, slightly more than half (55.6%) of the
households in this study were above 61 years of age and
mostly dependent on the old age grant for extra income.
Involvement in non-farm activities oers households
extra income that enables them to access basic essen-
tials such as clothing, schooling and healthcare services
in addition to food (Adem et al., 2018). Moreover, o-
farm income is positively correlated with food security
(Apanovich and Mazur, 2018).
Most (70.1%, n = 249) of the households received
support from the PKM programme in the form of mech-
anisation service. Masoka (2014) had earlier observed
a similar phenomenon in a study conducted in the Nkan-
gala District of the Mpumalanga province, South Africa.
The study by Masoka (2014) observed that 68% of the
beneciaries of the PKM programme received assis-
tance in the form of mechanisation. Bastian et al. (2019)
argue that the mechanisation programme is eective in
329
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
www.jard.edu.pl
developing smallholder farmers and boosts production
and household food security status. This is because, as
Hemming et al. (2018) suggest, agricultural subsidy
schemes provide agricultural inputs and services to
farmers at lower rates, and further contribute to rising
productivity and economic growth, as well as reducing
food insecurity and poverty.
Food security status of agricultural
households
The FSI, which is computed as per capita food expendi-
ture for a given household, divided by two-third (2/3)
mean per capita food expenditure of all households, was
used to determine the food security status of agricultural
households. A household with a food security index (F1)
higher than or equal to one (≥ 1) was considered food
secure. Conversely, a household with food security (F1)
lower than one (< 1) was considered food insecure.
The monthly mean per capita food expenditure (MP-
CHHFE) (Table 2) for all the households was R1 581.07,
while the two-third mean per capita food expenditure for
all the households was R 1,054.05. Slightly more than
half (52.4%; n = 186) of the investigated agricultural
households had a food security index of ≥ 1, while just
under half (47.6%; n = 169) of households had a food
security index of < 1. The results are similar to those re-
ported by Olayiwola et al. (2017), in a study conducted
in the Oluyole Local Government Area of Oyo State,
Nigeria, where 58.7% of rural households were food se-
cure. However, the number of food insecure households
in this study was slightly lower than what was reported
by Ijatuyi et al. (2018) in what is known as the ‘Plati-
num Province’ of South Africa. Although this result is
appreciated, the number of food insecure households in
the current study is still high, as it is double that of the
national average of 20.2%.
Given the low involvement of the respondents in
non-farm activities and the small farm areas for the
farmers, it was not surprising that just under half of the
respondents were food insecure. In addition, according
to the literature, the study area has a high poverty level
(MPT, 2015), which could also explain the high food
insecurity in the study area. This is because poverty and
food insecurity are positively correlated (Sati and Van-
gchhia, 2017).
Households’ food expenditure approach measures
the food accessibility dimension of food security (i.e.
economic access to food), which is inuenced by house-
holds’ purchasing power (aordability) and spending on
food. Findings reported here show that 52.4% (n = 186)
of the households in the study area were food secure and
could aord the price of food relative to their income.
Thus, just over half of the households in the study area
had economic access to food (i.e. could aord food) at
the household level, by buying from the market.
Factors associated with food security among
the households
The results of the probit regression of the factors associ-
ated with food security among agricultural households
in the study area are presented in Table 3. Among 14
variables tted into the probit model, only the marital
status, level of education and annual farm income were
found to be signicantly associated with food security
of agricultural households in the study area.
The marital status variable was statistically signi-
cant (p < 0.05) and positively associated (coecient =
0.385) with the food security status of households in this
model. This is in line with the a priori expectation of this
study. This result is corroborated by ndings by Agboola
et al. (2017), as well as Mustapha et al. (2018), who con-
cluded that household food security status improved if
the head of the household was married. According to
Aboaba et al. (2020), if the head of a household is mar-
ried, they are mature and take the responsibility for pro-
viding for their families.
Table 2. Food security status of the respondents based on food security index (n = 355)
Food security status F % MPCHHFE Two-Third MPCHHFE
Food secure 186 52.4
Food insecure 169 47.6
Total 355 100 R 1 581.07 R 1 054.05
Source: eld survey, 2020.
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
330 www.jard.edu.pl
The coecient of the level of education level was
likewise positive (0.052) and signicantly (p < 0.05) as-
sociated with food security among agricultural house-
holds in the study area. This is consistent with previous
studies (Ibok et al., 2014; Masahudu, 2019; Mohammed
et al., 2014) that have reported that households of edu-
cated farmers have a high probability of being food se-
cure. These ndings suggest that the higher level of edu-
cation attained by the household head, the more likely
the household is to be food secure. Secondly, according
to Antwi and Nxumalo (2014), education is social capi-
tal and increases the responsiveness of farmers to up-to-
date agricultural practices, which results in higher yields
and farm incomes, thus ensuring food security. Thirdly,
SSA (2020) is of the view that education is an essential
and powerful tool for economic and social development,
and has a signicant eect of reducing poverty and food
insecurity.
Annual farm income revealed a positive (coecient
= 0.020) and signicant (p < 0.05) association with food
security. This indicates that a rise in income from sell-
ing agricultural produce boosts the households’ purchas-
ing power and so the possibility of households becoming
food secure also increases. This is corroborated by the
results of Ibok et al. (2014) and Ijatuyi et al. (2018), who
reported that annual farm income was positively associat-
ed with food security. This is also supported by other au-
thors, who have reported that low income is a signicant
risk associated with food insecurity (Alam et al., 2020).
Although the age of household head receiving mech-
anisation assistance and production input support, as
well as infrastructure support had a positive coecient,
they were not signicantly associated with food security
(p > 0.05). In line with a study by Aragie and Genanu
(2017), these ndings suggest that although production
inputs such as seeds and fertilisers contribute positively
to household food security, their contribution is insig-
nicant (p > 0.05).
Variables such as gender of household head, depend-
ency ratio, access to extension services, training received
Table 3. Probit regression results of the factors associated with food security among agricultural households (n = 355)
Food security Coecient Std error Z P > z
Age 0.007 0.0071 0.986 0.303
Gender –0.056 0.1609 –0.348 0.726
Marital status 0.385 0.1652 2.331 0.020*
Marriage Type 0.216 0.2591 0.834 0.405
Level of education attained 0.052 0.00188 27.660 0.006*
Household size 0.030 0.0224 1.339 0.183
Dependency ratio –0.030 0.0750 –0.400 0.626
Annual farm income 1.78 7.70 0.231 0.020*
Mechanisation assistance 0.064 0.1609 0.398 0.690
Production inputs support 0.039 0.2929 0.133 0.894
Access to extension services –0.210 0.1641 –1.280 0.201
Infrastructure support 0.117 0.2345 0.499 0.618
Training received –0.116 0.1636 –0.709 0.479
Engaged in non-farm activities –0.050 0.1493 –0.335 0.740
Constant –1.023 –1.536 0.124 0.6660
Prob > chi20.000
* 5% signicant level.
Source: eld survey, 2020.
331
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
www.jard.edu.pl
and engagement in non-farm activities were found to be
negatively associated with the food security status of the
respondents, albeit not signicant (p > 0.05). What is
more, Aragie and Genanu (2017) observed a signicant
negative association between household size and food
security. Although the study is unable to explain why the
association in this study failed to reach signicance, it is
known that an increase in the size of the household, es-
pecially by members that are unable to work, puts more
pressure on food consumption in the household (Dula
and Berhanu, 2019; Jeyarajah, 2018). Furthermore, it
has been reported that an increase in dependency ratio
by one member in a household, is likely to decrease
household food security status by almost 50% (Aboaba
et al., 2020).
According to Aragie and Genanu (2017), house-
holds partaking in non-farm activities, in addition to
farming activities, have a higher probability to be food
secure than those that do not partake in non-farm ac-
tivities. This is because households that are involved
in non-farm activities have an opportunity to earn ad-
ditional income from non-farm activities and are thus
able to boost their purchasing power, which, in turn,
improves the food security status of a household. There-
fore, negative coecients for the engagement in non-
farm activities observed in this study that did not reach
signicance (p > 0.05) were not expected. This could
be due to the low proportion of respondents involved in
non-farming activities.
Although the coecients for the gender of house-
hold head and access to extension services were nega-
tive, thus suggesting a negative association with food
security, they failed to reach signicance (p > 0.05).
This is contrary to what the authors had anticipated.
According to Botreau and Cohen (2019), due to gender
inequalities, men have more access to livelihood assets
than women. Eneyew and Bekele (2012) are of the view
that households headed by females are more vulnerable
to food insecurity, due to restricted access to resources.
According to Mustapha et al. (2018), access to extension
Table 4. Probit regression results of the factors associated with food security among agricultural households (n = 355)
Food security Coecient Std error Z P > z
Age 0.007 0.0071 0.986 0.303
Gender –0.056 0.1609 –0.348 0.726
Marital status 0.385 0.1652 2.331 0.020*
Marriage Type 0.216 0.2591 0.834 0.405
Level of education attained 0.052 0.00188 27.660 0.006*
Household size 0.030 0.0224 1.339 0.183
Dependency ratio –0.030 0.0750 –0.400 0.626
Annual farm income 1.78 7.70 0.231 0.020*
Mechanisation assistance 0.064 0.1609 0.398 0.690
Production inputs support 0.039 0.2929 0.133 0.894
Access to extension services –0.210 0.1641 –1.280 0.201
Infrastructure support 0.117 0.2345 0.499 0.618
Training received –0.116 0.1636 –0.709 0.479
Engaged in non-farm activities –0.050 0.1493 –0.335 0.740
Constant –1.023 –1.536 0.124 0.6660
Prob > chi20.000
* 5% signicant level.
Source: eld survey, 2020.
Sambo, T. A., Oguttu, J. W., Mbombo-Dweba, T. P. (2021). Analysis of food security status among agricultural households in the
Nkomazi Local Municipality, South Africa. J. Agribus. Rural Dev., 3(61), 323–336. http://dx.doi.org/10.17306/J.JARD.2021.01412
332 www.jard.edu.pl
services has a positive contribution to household food
security. Fisher and Lewin (2013) further suggested that
a single visit by an agricultural extension advisor during
each production season would lower food insecurity by
at least 5.2%.
CONCLUSION
ANDRECOMMENDATIONS
To the best of our knowledge, the food security status
of households benetting from the PKM programme
and associated factors have not been studied at NKLM.
Therefore, this study adds to the body of literature and
sheds light on the food security status of PKM bene-
ciaries and associated factors. Generally, farmers in the
study area were elderly people, mostly female, with low
educational levels, had limited access to arable land and
had low levels of farm income. Despite participation in
the programme, the level of food insecurity among ag-
ricultural households in the study area was very high;
double the national and provincial household food in-
security levels. However, considering that the food
security levels in the study area are low compared to
other areas, these ndings support the use of agricul-
ture as one of aordable sustainable strategies to reduce
food insecurity. The authors are of the view that farm-
ers should use other non-farm activities to help boost
the food security status of their households. Given that
a large proportion of the farming community in this
study was over 60 years of age, it is recommended that
programmes be implemented to make agriculture more
appealing to the youth, to safeguard the future of agri-
culture in the study area. The ndings of the study pro-
vide a basis for the formulation of a policy framework
to help tackle the high food insecurity observed in the
study area. Based on the ndings of this study, the fol-
lowing policy measures, aimed at improving the food
security status of households in the study area, should
be considered: i) the government, together with farmers,
should focus on increasing the farm size for each par-
ticipating household—a rural land reform programme
can play an important role in increasing the farm size
of participating households; ii) taking into consideration
the age of the farmers in this study, alternative means,
such as adult-based education, should be investigated
and encouraged, so that farmers can acquire skills and
information to help them to improve their productivity
and food security status.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the University
of South Africa for funding the project.
SOURCE OF FINANCING
The project was funded by the University of South Af-
rica’s postgraduate research funding.
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Fertiliser and seed subsidies are associated with increased use of these inputs, higher agricultural yields and increased income among farm households, but evidence of their effects on poverty is limited. There is much evidence that subsidy schemes are prone to inefficiency, bias and corruption. Models show that introducing or increasing subsidies generally results in positive effects for consumers and wider economic growth. However, the models indicate that the way subsidies are funded, world input prices and beneficiary targeting all have important influences on predicted outcomes. The authors were not able to find any studies examining subsidies for machinery. Plain language summary Agricultural input subsidies raise input use, yields and farm income. The review in brief Agricultural input subsidies raise input use, yields and farm income, but the evidence base is small and comes from a limited number of schemes and countries. What is this review about? Greater use of improved seeds and inorganic fertilisers, and increased mechanisation, could boost agricultural productivity in some low‐ or lower‐middle‐income countries, but there is disagreement about whether subsidising these inputs is an effective way to stimulate their use. This review examines the evidence for impacts of input subsidies on agricultural productivity, beneficiary incomes and welfare, consumer welfare and wider economic growth. What is the aim of this review? This Campbell systematic review examines the effects of input subsidies on agricultural productivity, beneficiary incomes and welfare, consumer welfare and wider economic growth. The review summarizes evidence from 15 experimental and quasi‐experimental studies and 16 studies that use computable models, the majority concerning sub‐Saharan Africa. What are the main findings of this review? What studies are included? This review examines studies that evaluate the impact of agricultural input subsidies, including subsidies for agricultural machinery, seeds or fertilisers, on farmers, farm households, wage labourers or food consumers in low‐ or lower‐ middle‐income countries. It includes 15 experimental and quasi‐experimental studies and 16 simulation modelling studies. The majority relate to sub‐Saharan Africa (15 to Malawi) and to subsidised fertilizers and seeds. What are the main results of this review? Fertiliser and seed subsidies are associated with increased use of these inputs, higher agricultural yields and increased income among farm households, but evidence of their effects on poverty is limited. There is much evidence that subsidy schemes are prone to inefficiency, bias and corruption. Models show that introducing or increasing subsidies generally results in positive effects for consumers and wider economic growth. However, the models indicate that the way subsidies are funded, world input prices and beneficiary targeting all have important influences on predicted outcomes. The authors were not able to find any studies examining subsidies for machinery. What do the findings of this review mean? Input subsidies can increase input use, and raise agricultural productivity with wider benefits. However, the design of subsidy schemes is crucial to their effectiveness, if they are to reach the desired farmers and stimulate input use. The effectiveness of subsidies in comparison to other interventions requires further study. A relatively small number of appropriate studies were found, and well‐documented research in countries beyond sub‐Saharan Africa is needed to ensure the wider relevance of these results. Mixed‐methods, theory‐based impact evaluations would help explore the impacts of different levels of subsidies for different beneficiaries. Simulation models studies could make more use of rigorous evidence from experimental and quasi‐experimental studies and examine more helpful subsidy comparisons. More clarity is needed in the reporting of methodological approaches, statistical information and the type and size of input subsidy implemented or modelled. How up‐to‐date is this review? The review authors searched for studies up to 2013. This Campbell systematic review was published in 2018. Executive summary/Abstract Background In recent decades, agricultural productivity in low‐ and lower‐middle‐income countries, particularly in Africa, has fallen increasingly behind that of upper middle‐income countries. Adequate use of agricultural inputs such as improved seeds and inorganic fertilisers has been identified as one way of enhancing agricultural productivity. However, these inputs can be financially unaffordable or unattractive to many poor farmers in developing countries. 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What are the effects of agricultural input subsidies on agricultural productivity and beneficiary incomes and welfare? • 2. What are the effects of agricultural input subsidies on consumer welfare and wider economic growth? Search methods We carried out a systematic search for includable studies in a wide range of sources and using a variety of search methods. We searched academic and online databases, carried out forwards and backwards citation tracking of included studies, and consulted experts. There were no restrictions on publication year, type or language, though searches were undertaken in English. The main searches were completed in November 2013. However, we incorporated additional papers after this date where they became available before our analysis was completed. Selection criteria To be included, studies had to examine the effects of agricultural input subsidies, including products, machinery, seeds or fertilisers, on farmers, farm households, wage labourers or food consumers in low‐ or lower‐ middle‐income countries. Eligible comparisons included no active agricultural input subsidy intervention, wait‐list, alternate input subsidy intervention, or other interventions providing access to inputs. We included experimental or quasi‐experimental studies to address our primary research question regarding primary outcomes of adoption, productivity and farm income. We included econometric modelling studies to address our secondary research question on consumer welfare and wider economic growth outcomes. Studies were assessed by a single reviewer at both title and abstract level and full‐text level. A second reviewer then checked screening decisions taken at full‐text level. 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Models simulating subsidy effects show the introduction or increase in subsidies generally results in positive effects for consumers and wider economic growth. However, the review also indicates the importance of programme implementation and wider contextual factors. A narrative synthesis of data from experimental and quasi‐experimental studies finds implementation problems, with inputs not always made available or used as planned. Modelling studies indicate that the positive effects of subsidies are sensitive to changes in contextual factors endogenous and exogenous to the subsidy itself. There are also a number of implications for research. The review finds a relatively small evidence base of both experimental and quasi‐experimental studies, and econometric modelling studies. The evidence base focuses on a limited number of countries and evidence from a wider set of contexts where subsidies are used would be welcome. 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