Access to common resources and food security: Evidence from National Surveys in Nigeria
Access to common resources and food security: Evidence from National Surveys in Nigeria
Pedro Andrés Garzón Delvaux 0 1
Sergio Gomez y Paloma 0 1
0 European Commission, Joint Research Centre (JRC), Directorate D - Sustainable Resources, Economics of Agriculture Unit , Edificio Expo, Calle Inca Garcilaso 3, 41092 Seville , Spain
1 Sergio Gomez y Paloma
2 Pedro Andrés Garzón Delvaux
Common resources (CRs) provide a Bhidden harvest^ for rural households and can also act as a safety net in the event of poor agricultural output or seasonal food gaps, hence contributing to food security. Yet only limited empirical research has assessed the relationship between CRs and the self-assessed food security conditions recorded among rural households. This exploratory paper draws on recent data from the Nigerian General Household Survey (GHS), a nationally representative sample of households administered in 2012-2013 as part of the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA). A sustainable livelihood framework was used to contextualise CR access within the broader set of food security drivers. In Nigeria, access to common pasture and water resources is significantly associated with less reporting of food insecurity. In contrast, access to common forest tends to be associated with food insecurity, suggesting that households with access to common forest remain vulnerable (i.e. isolated from services and opportunities) despite having the advantage of the forest as a source of food. Echoing existing literature, the relative importance of these commons decreases when income of households increases. However, there are no clear signs that access to commons acts as a seasonal safety net for households during the lean season. The paper advocates streamlining CR data collection alongside agricultural data for a more integrated food security policy intervention aimed at the most vulnerable.
Common resources; Wild foods; Food security; Hidden harvest; LSMS-Isa; Nigeria
Food insecurity remains a reality for 795 million people,
despite the fact that the proportion of the global population that is
undernourished has fallen from 23% to 13% over the past
(FAO et al. 2015)
. In sub-Sahara Africa (SSA), the
proportion of the population that is undernourished decreased
from 33% to 23% over the same period, although the absolute
number of undernourished individuals increased by 25%, to
220 million. Moreover, 2 billion individuals worldwide still
suffer deficiencies of iron or other micronutrient (vitamins,
minerals, etc.), highlighting the importance not only of food
quantity but also of quality, and the need for it to be nutritious
(WHO 2012; Kloos and Renaud 2014)
The main direct response to the food and nutrition
challenges is that of agriculture and rural development (ARD)
support. The focus of intervention is raising productivity of
agriculture in SSA, where it is recorded to be lower than in the
rest of the world, with particular emphasis on staple crops
However, food insecurity persists not only because of lack
of food or the food production gap, but also because of lack of
access and entitlement to food
, conflict, lack of job
opportunities, and lack of access to social services or land
(FAO et al. 2014; Food Security Information Network
. It is also the result of unsustainable natural resource
use, on which the rural poor are directly dependent for their
(Cavendish 2000; Kamanga et al. 2009)
Sustainably responding to the food and nutrition security
challenge therefore entails going beyond improving agriculture
performance to considering how to integrate the wider
landscape and its wild resources into food security policy.
Although historically overlooked as the Bhidden harvest^ by
agricultural extension and international aid
(Scoones et al.
, wild and mainly common resources have been
demonstrated to be an important source of income in rural areas
(Vedeld et al. 2007; Kabubo-Mariara 2013; Thondhlana and
and a key asset to the very poor,
particularly in SSA
(Cavendish 2000; Kamanga et al. 2009)
Moreover, agricultural systems displaying a diversity of crop
and land uses are more resilient to extreme events, this being
an advantage in the context of climate change. Access to
forested landscapes and associated wild foods have been
identified as a safety net in the event of poor agricultural output or
seasonal food gaps
(Angelsen and Wunder 2003; Arnold
2008; Cotter and Tirado 2008)
(in Sunderland et al. 2013).
The safety net function when facing shocks is complex, being
dependent on the type of shock, and probably evolving
alongside the increased availability of alternatives to households
(Wunder et al. 2014b)
In the same vein, and although landlessness is not as acute
as in other developing regions of the world, fragmentation of
private assets makes access to common land vital to many
rural households in SSA
(Alden Wily 2008)
Forested landscapes, tree cover and tree-based agricultural
systems contribute to food security both directly (as agriculture
does, through food and fodder) and indirectly through various
(Sunderland et al. 2013)
. Some ecosystems
directly benefit agricultural productivity, although evidence is
ambiguous in this respect and depends on the woody
vegetation/crop combination and context
(Bayala et al. 2014;
Sinare and Gordon 2015)
. These sources of food provide
dietary diversity and supply key micronutrients, although their
contribution to calorie intake is limited (Powell et al. 2015).
Although tree cover is not directly linked to tenure or access
rights (i.e. land being common or otherwise), it is notable that
Ickowitz et al. (2014)
identified a significant non-linear positive
relationship between tree cover and the vegetable (and fruit)
consumption of about 93,000 children in 21 African countries
surveyed between 2003 and 2011.1 Even if wild foods fail to
meet the minimum recommended intake of fruit and
vegetables, such contributions may be critical when agricultural
production falls short of meeting global per capita needs
et al. 2014 in Rowland et al. 2016)
. A similar contribution to
diets for people close to forests is made by bush meat, which
may account for one fifth to three fifths of the national average
protein intake, sometimes meeting the minimum intake
1 The study was based on USAID standardised Demographic and Health
Surveys. It focused on children between the age of 12 and 60 months. Data
were gathered at various periods of the year, with this being controlled for in
the model. Peak consumption was reached at about 53% of tree cover. The
analysis does not provide direct causal relationships, nor does it indicate
whether access to food is via forest or agroforestry agriculture, identify the
type of trees/forest involved, or account for the effect of forest cover on
agricultural output through ecosystem services.
requirements of the most dependent households
et al. 2016)
. Most of the evidence is related to forested
landscapes, although non-forested (e.g. pasture) wild or fallow
agricultural land has shown similar effects, even in the contexts of
defaunation of large game species, as small game species are
increasingly managed and hunted in and around agricultural
(Sunderland et al. 2013)
. This is also the case for wild
plants and fruits that grow on cropland
(Scoones et al. 1992)
Finally, the support from goods and services provided by
forested and non-forested landscapes to agricultural
production systems also needs to be acknowledged
(Sunderland et al.
. The contribution of forested areas and trees to the
longterm productivity of agriculture in the face of global changes
remains a key area of research, as additional evidence is
needed to find Blandscapes and land use systems that deliver
biodiversity, ecosystem services and productivity functions at the
(Sunderland et al. 2013, p4)
The direct livelihood role of common natural resources has
benefited from in-depth case studies and subsequent reviews
of these, but seldom from large dataset analysis. Exceptions
are the Poverty and Environment Network (PEN) dataset of
the Center for International Forestry Research (CIFOR)
(Wunder et al. 2014a; Rowland et al. 2016)
and the USAID
standardised Demographic and Health Surveys
et al. 2014; Johnson et al. 2013)
. The role of the hidden
harvest has been demonstrated. However, as lamented by
Wunder et al. (2014a), such a role remains mostly absent
from population-representative household surveys, limiting
our understanding of livelihoods and consequent food and
nutrition security policy implications.
The recent drive to improve agricultural statistics,
embodied in the World Bank’s Living Standards Measurement Study
— Integrated Surveys on Agriculture (LSMS-ISA)
programme in eight countries in SSA,2 offers some avenues to
explore the resources on which the hidden harvest is based, as
it records the existence of and access to common natural
resources. One of the most suitable datasets available is that of
Nigeria. First, Nigeria is a uniquely diverse and endowed
country extending over three different agro-ecological zones
(AEZs), from semiarid in the north to humid in the south. It
also provides households and agricultural surveys with large
samples, which are representatively based on a national
census. Finally, the sampling procedure used in these surveys
avoids the bias inherent in more specific case studies on wild
resources (i.e. PEN-based analyses which focus on
communities related to forests; see Wunder et al. (2014b) and Rowland
et al. 2016).
Nigeria experienced important, if unequally distributed,
GDP growth over the 2003–2013 period, averaging 6.4%
2 Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda.
Two of the countries provided information on access to common resources:
Malawi and Nigeria.
(McKinsey Global Institute 2014)
, and, as of the last available
estimates, 33% of its population live below an adjusted
purchasing power parity poverty line of US$1.4/day
. It has fared better than its regional neighbours, with a
reduction in the proportion of people living below the poverty
line2. However, the absolute number of people below this
poverty line has remained constant, and Nigeria still faces
important food security challenges, even more so given that
its growth trajectory has weakened and the country is expected
to fall into recession
. Nigerians have traditionally
exploited wild resources for food
(Harris and Mohammed
and other uses (i.e. medicines)
Opabode 2004; Soewu and Ayodele 2009; Soewu and
. The concerns raised about the sustainability
of wild resource use also indicate their importance. Initially
such concerns related to fauna
(Anadu et al. 1988)
increasingly they relate also to flora
Furthermore, the conversion of whole ecosystems, such as
wetlands, to alternative uses, leading to their loss, points to
the level of dependence of populations on such wild resources
. As identified in other contexts, it has been
documented that wild foods also serve as a safety net by
providing Bfamine foods^
(Davies et al. 2012)
. Certain types of
wild food were designated Bfamine foods^ during the great
Sahel Drought (1972–1974) in northern Nigeria
Lockett et al. (2000)
showed that, among the edible
wild plants used by northern herders, those available during
the wet season were nutritionally inferior to dry-season plants,
which played a more important role in food security. Although
not constituting a response to poverty in themselves, such wild
resources contribute to resilience, but also depend on the
maintenance of and access to their resource base (forests,
pastures) and fallow land
(Davies et al. 2012)
Accordingly, and with nationwide evidence from Nigeria,
this paper explores the following three questions: (i) Is there an
association between the recorded food security indicators of
households and access to (mostly) locally managed common
resources? (ii) Do the most vulnerable households rely more on
common resources for their food security than less vulnerable
households; and (iii) Does access to wild resource acts as a
safety net in general, and specifically during lean seasons?
The approach adopted in this paper provides a broad
picture of the connections between given food security indicators
and access to (locally) managed common resources, revisiting
with a relevant type of data (LSMS-ISA) the existing body of
in-depth case studies. Specific limitations to our approach are
discussed in the conclusions, particularly in relation to the use
of available standard population-representative household
Ultimately, this paper aims at shedding light on the
potential complementarities between conventional intervention in
favour of food and nutrition security (i.e. development of
agriculture) and other strategies linked to hidden harvest. The
association of these can strengthen the achievement of food
and nutrition security.
The data source is the Nigerian General Household
Survey (GHS), a survey of a nationally representative
sample of households conducted by the Nigerian
Bureau of Statistics
as part of LSMS-ISA.
This dataset gathers about 5000 households identified
from a larger GHS of 22,000 households. As the survey
is representatively based on a national census, its
sampling procedure avoids the general bias associated with
more specific case studies on wild resources (i.e.
PENbased analyses which focus on communities related to
forests, such as those of Wunder et al. (2014b) and
Rowland et al. (2016)
. The sample of households was
surveyed in 2012 and 2013. Both post-planting (lean
season) and post-harvest data were collected over the
period, in two waves.3 This paper focuses on data from
about 3300 households following data preparation and
the exclusion of households for which information about
access to four common resources, namely pasture, forest,
water and arable land, is lacking. Given the centrality of
this information, no specific strategy, such as imputation,
was deployed to retain more households in the sample.
The sample includes both rural (70%) and urban
households. To establish a connection between access to
common resources (CRs) and food security, a second source
was required. This was a community questionnaire,
administered in parallel to community leaders to collect
information on various socioeconomic indicators of the
enumeration areas (EAs, i.e. areas of reference for the
national census used as a base for the LSMS-ISA
sampling) in which the sample households reside. It is
important to highlight that the community questionnaire
does not collect information from communities in the
. The data cannot be used
to represent communities in Nigeria. However, the data
collected at the community level represent information
that is common to the households selected for inclusion
in the selected EAs.
Accordingly, the household data were merged with a
selected set of characteristics from the relevant area derived
from the community questionnaire. This was based on
3 The original datasets can be request here: http://econ.worldbank.org/
information on the existence and status of CRs for water,
grazing land, arable land and forest/bush, gathered in surveys
of leaders of representative communities from the EAs to
which households belonged.
2.2 Empirical model and variables
This exploratory analysis focuses on the drivers expected to
provide a given household with food security, or to push it
into insecurity. The analysis translates into a sustainable
livelihood approach that Bcomprises the capabilities, assets
(including both material and social resources) and activities
required for a means of living. A livelihood is sustainable
when it can cope with and recover from stress and shocks
and maintain or enhance its capabilities and assets both
now and in the future, while not undermining the natural
(Chambers and Conway 1991; DFID 2001)
A more concrete use of this approach as an aid policy tool
was initially fostered through the Sustainable Livelihoods
Approach (SLA), supported by the United Kingdom (UK)
Department for International Development (DFID)
Haan 2012; Morse and McNamara 2013)
. The SLA views
livelihoods as systems and focuses on the following four
elements: (i) the assets of people; (ii) the strategies
developed; (iii) the context of the livelihood; and (iv) specific
factors contributing to the vulnerability or resilience of
livelihood to shocks
. The approach has been
extensively used to analyse poverty in general
2012; Morse and McNamara 2013)
, including household
food poverty in Nigeria. Oni and Fashogbon (2013)
analysed factors of food insecurity with a sustainable
livelihood approach, using 2004 and 2009 data from the
Nigerian Living Standard Survey
, which is akin
to the LSMS-ISA, and accounting for very broad
agroecological variation. However, they did not integrate natural
assets or contextual variables, such as distance to key
markets or services.
The integration of assets offers a suitable framework to
analyse the potential contribution to food security made by
access to locally managed natural CRs, away from narrower
concepts of income and consumption.
To adapt the SLA to food and nutrition security as a
subset of livelihood, key characteristics and assets of
households and their environment, including
geographical proximity to commercial opportunities (i.e. distance
to markets), were gathered from the LSMS-ISA database.
A multinomial logit (mlogit) analysis was used to
examine the effects of the characteristics of households and
their environment on the likelihood of reporting food
insecurity during the post-planting and post-harvesting
seasons, during both seasons, or never, controlling for
the clustering of standard errors (i.e. by EA) and related
post-estimation commands in Stata 14. The probability of
households reporting food insecurity according to the
four possibilities can be written as follows:
Pi ¼ Prob y ¼ ijX ¼ 1 þ ∑k¼1expðX βk Þ
where i = 1, …, 4 represents one of the types of food
(in)security situation reported by a household.
∑1Pi ¼ 1
X is a vector of explanatory variables (i.e. characteristics
of households, their assets and environment, and access
to CRs), β is a vector of parameters associated with the
explanatory variables and k is the baseline (i.e. never
reporting food insecurity).
The probability of reporting one type of food insecurity (in
either the post-harvest or the post-planting seasons, or in both
seasons) is considered in comparison with the probability of
reporting the base outcome. This probability can be expressed as:
Pi ¼ Prob y ¼ m þ 1jX ¼ 1 þ ∑km¼1expðX βk Þ
The basic interpretation of the multinomial logit results
looks at the variables that influence the probability of
reporting food insecurity with reference to never reporting it.
To generalise the interpretation of the results, marginal effects
of changes in the explanatory variables were estimated using
the margins command.
The model was first developed by including all potential
listed variables of interest from the general framework, as well
as references for Nigeria
(Oni and Fashogbon 2013; Oni 2014)
and the role of wild resources as income source and safety nets
(Angelsen et al. 2014; Wunder et al. 2014b)
. The final model
structure was determined through a stepwise process of
backward elimination of non-significant (P > 0.05) variables and
tests on model specification (Bayesian information criterion
(BIC) and Akaike information criterion (AIC) fit tests).
The variables used to perform the analysis are presented in
detail in section 3.1 Descriptive statistics are discussed below.
Our dependent variables are a selection of indicators of
The food security indicators used here are derived from
self-assessment measures of food security (SAFS). This
approach is flexible and reduces the cost of information (e.g.
implying more recurrent data collection over time).
However, critiques highlight the subjective nature of SAFS,
and the fact that they are potentially subject to framing effects
(Headey and Ecker 2013)
and more generally to strategic
responses. They show a weak but consistent correlation with
(Headey and Ecker 2013)
. Food security
involves not only availability but also access to and utilisation
of food within a stable context
(Sen 1981; World Food
, and no single indicator can capture its reality
(Pinstrup-Andersen 2009; Leroy et al. 2015)
Moreover, various approaches demonstrate the different
dimensions of food security
(Headey and Ecker 2013;
Maxwell et al. 2014; Leroy et al. 2015)
The first indicator selected for this analysis captured
whether or not the household was unable to feed itself at some point
in the year previous to the survey (i.e. in the past 12 months,
have you been faced with a situation when you did not have
enough food to feed the household?). A second set of
indicators collected related to food insecurity events during the
7day period prior to each survey. We retained two variables of
food insecurity: (i) whether or not households reported that
they had ever been completely without food (for at least 1 day)
and (ii) whether or not members of the household ever had to
go to sleep hungry.
Various indicators of food (in)security were collected by
the survey and are tested here. In a recent comparative
assessment of various indicators in Ethiopia,
Maxwell et al.
showed that SAFS were more likely than alternative
measures to classify households as food insecure. Moreover,
these indicators tend to depict more persistent food insecurity,
even when others may hint at a move towards food security.
More detailed dietary components of the database were not
considered in this exploratory analysis.
Such measures are approximations and prone to
measurement errors (i.e. recalling) in the context of household surveys
of this type. That said, and accounting for the more general
issues with this type of subjective indicator in economic
analyses, indicators recording concrete events such as Bgoing to
sleep hungry^ reduce the associated bias
(Headey and Ecker
but limit measurements to a very extreme type of food
insecurity, and in this sense are not sufficient. At the study
level, the strategy of gathering both post-planting and
postharvest season data partly controls for the bias introduced by
the subjective nature of these food and nutrition security
The independent variables gather characteristics of
households and their assets, including user rights (i.e. access to
CRs). Being a right, access to CRs is a livelihood asset for a
household. Households from different communities are
compared, and one characteristic of a given household is that of
having or not having that right. As such, access to CRs is a
household characteristic. This approach implies that, although
we can assess that a household with access to certain CRs (i.e.
pasture) is less likely to systematically record food insecurity,
we cannot infer that a community as a whole is more food
secure, as this should be based on different type of data.
The resources potentially contributing to food security are
broadly identified as commonly managed, with rules of access
and use independently determined by the community itself (to
be distinguished from a national park or other resource
managed by the state at national or regional level). These include
forest land, pasture land, arable land and water resources. At
this stage it is important to highlight that, although distinctions
are drawn between these base resources, wild foods and
services span a continuum
(Wiersum 2004; Cronkleton et al.
from forest and pasture to fallow land and active
cropland. Another concrete sign of such continuity is the
transplantation by farmers of wild species from their field boundaries to
their own land (Harris and Mohammed 2003). Access to
common arable land is of limited value as an explanatory variable
given that most arable land under cultivation is under common
rights anyway. Arable land is allocated to only a few
landowners (3.2% of the sample), reducing the discriminatory
value of this variable and suggesting that it has limited
Data on CRs indicate whether or not communities have
access to and locally manage a given CR within the EA. It is
important to highlight that merging the existing datasets
results in inefficiencies. A perfect match is not possible given
the required preservation of household confidentiality.
Households cannot be specifically located except within a
radius of 0–5 km (for 1% of the population this range can
reach 10 km). As structures of local management are not
limited to a single village and can be under the jurisdiction of
several villages under a council of elders, as described by
, the match is assumed to be consistent with
an analysis of this type.
Access to some or all of these resources by households is
expected to reduce the likelihood of reporting food insecurity,
as all potentially contribute to the agricultural harvest (land,
water and pasture) but also to the Bhidden harvest^ (pasture,
forest, water and fallow land). That said, the literature shows
that such a relationship is not linear, and depends on
household characteristics and context, as well as on the nature of
contingencies and shocks
(Cavendish 2000; Angelsen et al.
2014; Wunder et al. 2014b)
Variation in the characteristics of households is controlled
for by including variables for age, gender and education level
of the head of the household, as well as their size (i.e. total
number of individuals in the household, whether adults,
children or the elderly) and dependents ratio. Regarding gender,
environmental resources are expected to be more important
for women in general, and in particular for female-headed
households, which tend to be marginalised in terms of
agricultural land tenure but also with respect to off-farm
(Pouliot and Treue 2013)
. Reviews of the gendered
extraction of forest products point to a dominance of women’s
involvement, particularly in the case of flora in Africa
(Sunderland et al. 2014)
. However, this is not always the case
(Pouliot and Treue 2013)
Household total income is captured through the proxy of
aggregate consumption, assuming that not much is left for
savings. Dummy variables indicating whether or not the
household head is primarily involved in agriculture, and
whether or not the head is also active in an off-farm activity,
were tested. The relation between agriculture as a primary
activity per se and food security is ambiguous, but
involvement in secondary off-farm activities is expected to be
positively associated with food security. For reference, the
household food expenditure ratio is reported, including both
purchased food and the value equivalent of self-consumption.
Higher income (i.e. consumption) is associated with a reduced
likelihood of food insecurity, as it is linked to lower food
The household property variable captures whether or not a
household owns its dwelling and whether or not it owns part
or all of its farming plots. Independently of tenure, the variable
farm size per capita (the total area of all recorded farm plots
divided by the number of individuals in the household) is used
to integrate the basic endowment of the household, accounting
for its size. More liquid assets are embodied by livestock and
smaller animals. Better endowed households are expected to
be less vulnerable to food insecurity. Liquid assets are
expected to be used to smooth consumption when needed.
Various (negative) shocks to households were recorded
during the interviews, ranging from loss of the main income
earner (death, change in the composition of the household) to
events with implications for whole communities. Such
occurrences are likely to negatively influence food security. To
capture the effect of such shocks and their interaction with access
to CRs, they were divided into idiosyncratic shocks (affecting
individual households or a small proportion of households
only, such as labour supply) and covariate shocks (potentially
affecting all households, such as drought, input and output
price fluctuations, etc.). The expected direction of these
relationships follows that of Wunder et al. (2014b), who, in their
comprehensive review, found that alternative strategies were
more important than increasing environmental harvesting.
The same review reported that only 10% of households used
wild resources as their main shock response, with most of
those resources harvested from the forest. In addition,
environmental harvesting is expected to increase more
significantly when covariate shocks occur, as such harvesting requires
additional labour, seldom available to single households faced
with idiosyncratic shocks. In turn, households have a wider
range of options for dealing with idiosyncratic shocks (e.g.
seeking outside help) than they do when faced with covariate
shocks. Responses to shocks vary, with increased
environmental extraction tending to be favoured by asset-poor
households, while extraction of more valuable resources may be
limited to better-off members of the community because it
requires access to capital
(Wunder et al. 2014b)
. The related
concept of gap-filling resource extraction on a seasonal basis
is not well supported by the evidence gathered by Wunder
et al. (2014b), who found that income from extraction from
the wild is positively related to crop and general income.
Although Angelsen et al. (2014) clearly show that wild
products are a key addition to income, the seasonal safety
net role is less clear and should probably be seen as an option
of last resort
(Wunder et al. 2014b)
Market access is captured by proxy as distance to the
nearest market in kilometres, with the expectation that the
closer the market, the less likely a household is to face food
insecurity. However, this is a limited way of conceptualising
market access, and the various ways of translating it into a
measurable variable have shown empirical ambiguity of the
effects on welfare
(Chamberlin and Jayne 2013)
limitation of a single variable for market access is linked to the fact
that access is defined not only by distance but also by its
variation through time (e.g. seasonality), infrastructural and
non-infrastructural components of access, impact of new
information and communication technologies (i.e. mobile
phones), liberalisation of trade, and characteristics of
(Chamberlin and Jayne 2013)
. To explore some of
these effects, interaction variables with the agro-ecological
zones (distance to market X AEZ dummy variable) were also
introduced to identify regionally differentiated effects of
distance to market on food insecurity.
Access to credit, both formal and informal, is accounted
for. However, it is difficult to distinguish households that
choose not to use credit from those that wish to do so but
are unable. A third credit-related variable is also used,
capturing rejections of credit request, which should prove a better
indicator. Governmental social aid is also included, and is
expected to reduce the likelihood of insecurity. However, its
targeting could prove ambiguous as it is supposed to be
directed at the most vulnerable.
Basic environmental indicators were needed to control for
the level of abundance of given resources. Precipitation and
temperature indicators as well as other more comprehensive
indicators were available, such as AEZ classes and key
(Arino et al. 2012)
. For simplicity,
dummy variables identifying households living in the
semiarid (Sahelian) and subhumid zonea of Nigeria were used,
derived from the available AEZ classification.
3.1 Descriptive statistics
The proportion of households that experienced difficulties in
feeding themselves, based on their recall of the last 7 days or
the last 12 months, was 11.5% and 33.5%, respectively. As
expected, households are more likely to run into difficulties
during the post-planting season (identified as the Blean^
period) than after harvest. However, about 40% of the
foodinsecure sample remained in difficulties even during the
post-harvest period (Table 1).
Descriptive statistics of food insecurity indicators (dependent variables), according to season
BNever^ food insecure (in both waves of the survey)
BAlways^ food insecure (in both waves of the survey)
Merged datasets from Households and Community questionnaires (both seasons, 2012 2013). General Household Survey-Panel
(GHSPanel) conducted in 2012/13 by the Nigeria National Bureau of Statistics (NBS) in collaboration with the World Bank Living Standard Measurement
Study - Integrated Surveys on Agriculture (LSMS-ISA)
Household without food, last
Household empty of food, last 7 days
Household members went to
Access to common resources is widespread, yet the majority
of households are recorded as having no such access. The
available data suggest that 40% of households surveyed have access
to one or more common resources under local management, of
which one third have access to only one type of resource.
However, fewer than 10% have access to all four types of
resources (pasture, forest, water and land) as commons (Table 2).
Household composition varies: the average size is 5.9
individuals. Some 84% of households have a male head, and the
average age of household heads is 52-years. The average
education level of household heads is close to 6 years, but only
20% completed primary education, and 46% dropped out
Annual household expenditure per capita (as a proxy of
income per capita) ranges from NGN7,150 to NGN4,639,410
(EUR32 to EUR 211,109),4 with an average of NGN112,156
(approximately EUR515) and standard deviation of
NGN98,762. In turn, the average food expenditure ratio, that is
the proportion of income spent on food, reached 73.8%. This
confirms the validity of using total consumption as a proxy for
income in this context. The majority (55.5%) of household heads
had agriculture as their main activity, and about 20% had a
complementary off-farm job.
Although most of the households own their dwelling, they
are rarely land owners. In the countryside, a household holds
(at least with user rights, as private arable land property is the
exception rather than the norm) 0.7 ha on average, which
equates to 0.13 ha of available land per capita.
The survey registered a series of shocks affecting
households. Idiosyncratic shocks affected 30% and 20% were
affected by covariate shocks, along with the rest of their
Formal access to credit is granted to only 5% of the sample.
In contrast, access to informal services reaches at least 10%
among the heads of households surveyed. However, 13% of
the individuals reported being refused credit when requested.
Governmental social aid directly reaches only 2.8% of the
surveyed heads of households.
The households surveyed live an average of 66.45 km from
a market centre and the most common climate zone is a warm
subhumid zone akin to the Guinean savannah (55% of
households), followed by warm semiarid or Sahelian savannah
(30% of households). Most of the remaining households are
located in the southern and more humid part of the country,
comprising savannah and humid forest (Table 3).
The distribution of households indicates that about 50% of
households live in landscapes dominated by agricultural areas
(Arino et al. 2012)
. The remainder live in
areas that are dominated by other types of landscape,
including grasslands and forests, which do not preclude agriculture
or livestock, but are not dominated by them (details are
available in Table 6 in the appendix).
3.2 Estimations and interpretation
3.2.1 Livelihood model: Interpretation
The inclusive approach, which tests a number of
potential drivers of food insecurity in order to contextualise
the potential contribution of access to common resources,
has a downside. Several variables used include missing
values, so that the total number of households analysed
is reduced from 3302 to 3203 (Table 4) for the final
analysis. Marginal effects were estimated on the 3203
sample size. Following estimation with all variables, fit
tests (BIC and AIC) were performed to identify more
parsimonious specifications (models with all variables
are available in Table 8 in the appendix). The final
models are presented in Table 4. With multinomial logit
specification, coefficients need to be interpreted relative
to a base outcome. Here, the base outcome is Bnever
reporting food insecurity^. Therefore, a significant
coefficient with a negative sign indicates that such a variable
reduces the likelihood of reporting Bunable to feed
household^ in a given season (in either the
Code: forest, land,
0 0 0 0
0 0 0 1
0 0 1 0
0 0 1 1
0 1 0 0
0 1 0 1
0 1 1 0
0 1 1 1
1 0 0 0
1 0 0 1
1 0 1 0
1 1 0 0
1 1 0 1
1 1 1 0
1 1 1 1
Merged datasets from Households and Community questionnaires (EA level) and Households from
each EA (both seasons, 2012–2013).
Merged datasets from Households and Community
questionnaires (both seasons, 2012 2013). General Household Survey-Panel (GHS-Panel) conducted in 2012/
13 by the Nigeria National Bureau of Statistics (NBS) in collaboration with the World Bank Living Standard
Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)
planting or the post-harvest seasons, or in both seasons)
relative to never doing so (e.g. total expenditure).
3.2.2 Model results: Common resources
Having access to common resources is significantly
related to the selected food security indicators, particularly
when these are systematically reported during both
seasons. Households’ likelihood of reporting food
insecurity is reduced, and this is significant for access to both
pasture and water CRs. At the same time, being
associated with access to common forests exacerbates the
likelihood of reporting food insecurity throughout all
Access rights to pasture land significantly reduce the
likelihood of reporting events of extreme (all season)
food insecurity, compared with not reporting any event.
The effect captured by the model increases as food
security reporting goes from only one season to all seasons
with respect to never reporting, pointing to the
importance of access to common pasture for the more
vulnerable. The marginal effects of access to pasture are more
important to the poorest households (Table 5). For
example, the probability of reporting food insecurity in both
seasons (7-day recall) is reduced by 5.8% if there is
access to pasture for the poorest, decreasing to 3.14%
at average income and 1.73% for the wealthiest. Access
to pasture is also significant in reducing the probabilities
of households reporting food insecurity during one or the
other season (7-day recall). A first connection between
access to pasture land and food security is that of
sustaining livestock, known for its storage value properties.
The model, however, controls for the value of animals
owned by households, as these could be sold to smooth
consumption (significant in reducing the likelihood of
households having to record food consumption
adaptation). However, the direct food security value of pasture
land could also lie elsewhere, as a key contributor to the
Bhidden harvest^. Average non-forest environmental
income reaches only 5% in the PEN world review
(Angelsen et al. 2014)
, but this non-forest source is
considered to be critical (Cavendish 2000) and tends to have
a substantial subsistence focus, more closely related to
questions of food security than total income. Moreover,
non-forest sources as a proportion of all income can
reach 30–35%, as demonstrated for Ghana and Burkina
Faso in rural West Africa
(Pouliot and Treue 2013)
Many Bfamine foods^
(Davies et al. 2012; Mortimore
are found in pasture and on the edges of farms.
*Data for each season data (lean and post-harvest) is presented in Appendices
**combinations of access to resources are presented in Table 2
***simplified to 15 years or more
****Agro-ecological zones and characteristic landscapes summary statistics are provided for reference in Appendices
NBS (2013) Household surveys, (both seasons, 2012–2013).
Merged datasets from Households and Community questionnaires
(both seasons, 2012 2013). General Household Survey-Panel (GHS-Panel) conducted in 2012/13 by the Nigeria National Bureau of Statistics (NBS)
in collaboration with the World Bank Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)
Finally, the existing continuity between various resource
bases, from forest to pasture and agricultural land, is
expected to be at play in this case
Cronkleton et al. 2013)
Access t o commonly managed water r esources
emerges as a positive asset regarding food security.
Although the pathway of the connection is not direct, it
most probably operates through agriculture, but not
3 6 )8 1 )5 58 )0 47 8* )5 0* 3 )0 04 )1 7 )0 03 )1 7 09 )6 10 )8 **
tircyu tsou sehou aenL .1800 .540− .340− .(51− .210− .(50− .000− .(02− .0000 .400− .740− .(82− .9900 .762− .7810 .840− .110− .(51− .000− .(62− .410− .(01− .000− .(81− .1100 .481− .100− .(51− .200− .(03− .6440 .790− .3611 .762− .8420
e o o
s i h
2* )1 .29 1
.893− .1130 .708− .811− .(32− 0323 .7459 511− .8150
itrfrsaeebooubvnom ilr-eaauqdhSC ililseekodughodoP r-eaeuqduoSR
N W oL sP
exclusively so, as wetlands can provide many goods and
(see, for example, the section about Nigerian
wetlands in Schuyt 2005)
. At the margins, access to
water is also more important for low-income households in
reducing the probability of facing food insecurity
Conversely, access to forests increases the likelihood of
reporting the most critical cases of food insecurity (both
12-month and 7-day recall). Marginal effects also suggest
that this trend is exacerbated as household income
decreases (Table 5). Such results do not contradict the
literature and do not prevent extraction from the forest of
valuable resources, even making an important contribution to
(with an average of 22% in the PEN sample
in Angelsen et al. 2014)
. However, a plausible hypothesis
is that households associated with this asset and access to
common forest could be still more vulnerable without
access to common forest. The relationship which emerges
from the analysis of other cases is that households closer
to this type of asset are worse off than others because they
may have lower access to social services and infrastructure
(Wunder et al. 2014a)
. Another dimension is that
the types of food resource extracted from forest tend not to
be associated with staple foods, with the exception of some
roots, limiting their impact from a caloric intake point of
(Rowland et al. 2016)
. However, other studies have
shown that households with access to forest as a CR may
be better off than other households sharing similar
characteristics of isolation, but without access to forest as a CR
(Clements et al. 2014)
, highlighting the need for further
investigation of this dimension. At the margin (Table 5),
the effect of living in areas with access to forest is
exacerbated by low incomes. The probability of always reporting
not having food at home increases from 6.79% at the 90th
percentile to 16.07% at the 10th percentile of total income.
Reporting food insecurity during the lean season only is
not affected by having access to common forests, pointing
to a weak seasonal effect.
Access to common arable land is explored only at initial
stages (Table 8 in the appendix), as it is non-significant and
not included in the final model.
The degree to which households’ reporting of food
security is sensitive to their access to commons is most
acute when reporting in both seasons. This result is
similar to what Wunder et al. (2014b) found for forests
when looking at their potential gap-filling function,
suggesting that wild resources are more likely to be an
option of last resort than a mechanism used regularly
to deal with seasonal fluctuation. In an independent
survey of coping mechanisms and food insecurity in
Akerele et al. (2013)
did not record wild
resource harvesting to be among the most common
Unable to feed household (12 months)
Faced with no food at home (7 days)
3.2.3 Model results: Contextual factors
We expected that food insecurity would be reported
more frequently by households whose heads were older
and had lower levels of education. However, ageing was
found to be significant in increasing the likelihood and
frequency of reporting food insecurity only for the
12month recall indicator. When contextualising food
security indicators to gender, female-headed households are
more likely to report frequent food insecurity than those
with male heads. Marginal effects indicate that access to
pasture land reduces the probability that female-headed
households will report food insecurity (7-day recall),
both during the lean season only and always, by
4.04% (for comparison, access to pasture land reduces
the probability of food insecurity among the poorest
10% of households by 3.83%). This points to a limited
safety net effect for female-headed households, in
addition to a more structural effect.
Larger households (in terms of number of individuals) tend
to report more food insecurity, but this effect is systematically
significant only for the 12-month recall indicator. The effect of
the ratio of dependants within each household was also tested
but was not found to be significant. The same can be said of
the interaction variable linking size and dependency ratio of
households, but for one, significant, counterintuitive finding
during the post-harvest period. Controlling for income is
expected to be a defining factor in reducing the effect of these
Total household expenditure (as a proxy of total income),
as expected, is a significant variable in reducing the likelihood
of frequently (i.e. in both seasons) reporting food insecurity
compared with never doing so. At the margins (Table 5), as
income decreases, the influence of access to CRs also
increases. Poorer households are less likely to report systematic
food insecurity if they have access to common pasture and
water. Wealthier households are marginally influenced by
When looking at the conventional household assets
assessed, ownership of arable land and the area of arable
land per capita were not found to be significant in the
models. This is line with evidence that land size per se is
not necessarily correlated with a likelihood of being
poor, as highlighted for Nigeria (2010/2011 season) by
Oseni et al. (2014)
. There are two main reasons that
could explain this. One is that farm size is not
necessarily associated with wealth, as most land is allocated
through family or community. The second is linked to
differences in land productivity, for example between
extensive farming systems and intensive ones. However,
owning a home is associated with a lower likelihood of
frequently reporting food insecurity events. Finally, the
value of all livestock significantly reduces the likelihood
of frequently reporting Bno food at home^. Some
elements of the expected consumption-smoothing role of
this asset do indeed help but selling livestock does not
seem to be triggered by seasonal variation, as the
likelihood of food insecurity during specific seasons is
not significantly reduced by the value of all livestock.
Idiosyncratic shocks make food insecurity reporting
significantly more likely during both seasons. However, they do not
significantly increase the likelihood of reporting food
insecurity during the lean season only. Covariate shocks are
significant only when households report food insecurity based on
12-month recall. Marginal effects estimates indicate that
access to common resources does not significantly change when
there is shock, suggesting a limited use of this asset as a
response mechanism to shocks.
BDistance to market^ as a proxy for market access is
neither significantly nor coherently related to self-reporting of
food insecurity in this Nigerian sample when controlling for
regional effects (with the exception of the 12-month recall
indicator in the post-harvest season). In this specific case,
the exception indicates that as distance increases between
households and market, the probability of food insecurity
decreases. This echoes the fact that distance to market is not
significantly associated to gross crop income either, but for a
minority of farms in Nigeria, also with the expected sign
(Scandizzo and Savastano 2017)
. Moreover, urban and closer
to market populations seem to be more at risk from food
insecurity, as suggested by the variable capturing whether
the household is classified as urban or rural. However, and
this is an important caveat, interpreting these results is
complicated by the fact that urban-rural classification in Nigeria is
outdated, as many areas classified as rural could be now
considered as urban
(World Bank 2014)
. When controlling for
regional specificities, simplified here by three AEZs
(semiarid, subhumid and humid, following a north-south divide),
being distant from a market has a more negative impact on food
security for a household located in the humid zone than for a
household in the subhumid zone. In contrast, there is no clear
effect when comparing impact of distance from market for the
semiarid zone (where there are significant results with
Despite having lower average income, households located
in the semiarid (Sahelian, i.e. northern) region are significantly
less likely than others to face food insecurity situations,
corroborating existing analysis of the whole LSMS-ISA sample
. A possible explanation is related to
the average value of livestock and farm animals in this region,
which is higher than that of households located in other AEZs,
although this effect was only weakly captured by the models.
4 Conclusions and discussion
To the best of our knowledge, the influence of access to
commons on livelihoods — and by extension on food
security — has generally been approached through cases
studies using a limited number of households, with rare
exceptions such as the cross-continental reviews by
Wunder et al. (2014b) and
Angelsen et al. (2014)
However, census-based surveys do not routinely include
information on CRs. Such resources, and in particular
forest and non-forest (fallow and arable land, pasture,
etc.) resources, make important contributions to rural
and peri-urban income and livelihoods. This inquiry on
Nigeria focused on arable land, pasture, forest and water
under common management. Accounting for but
bypassing the question of environmental income by
identifying the possible direct connections with food security
indicators, the paper explored the following questions: (i)
Is there is a connection between the recorded food
security indicators of households and access to locally
managed CRs in Nigeria? (ii) Is access to common resources
relatively more important for the food security of the
most vulnerable households? and (iii) Does access to
wild resources acts as a safety net in general, and during
lean seasons in particular?
The answer to the first question is positive, but not
for all resources. Access to pasture reduced the
likelihood of reporting events of extreme food insecurity, and
even, to a limited extent, reduced the likelihood of
reporting food insecurity during one of the seasons.
Access to commonly managed water sources is also
positively linked with food security. This confirms the
importance of non-forest resources in West Africa
(Pouliot and Treue 2013)
. However, access to common
arable land is non-significant and is not associated with
a consistent change in either direction. It is possible
that, given that common arable land in rural Nigeria is
ubiquitous, the variable Baccess to common arable land^
does not allow for discriminating between households,
as it does not include elements about quality or
Access to forest is more closely related to food insecurity.
However, the fact that access to forest is associated with
foodinsecure events does not mean that forest is an unimportant
supplier of food for households, but rather that it assumes
more importance for households in a more vulnerable
situation. Furthermore, access to forest is known to improve the
diversity of the diet, albeit such a diet is deficient in starchy
With regard to the second question, total income (using as a
proxy total expenditure) and dwelling ownership were
identified as the significant livelihood variables influencing whether
or not a given household reported food insecurity. The value
of livestock captures some positive effect but is rarely
The probable greater reliance on access to CRs among more
vulnerable households was also confirmed by looking at
sensitivity when income rises. The marginal effects of access to these
commons lowers the probability of facing food insecurity,
particularly for the most critical cases, and is more important for
households with lower incomes (Table 5). In the same vein, part
of the evidence also points to the fact that female-headed
households can benefit more positively from access to pasture lands.
Finally, and relating to the third question, access to pasture
acts as a safety net by reducing the probability of reporting food
insecurity in both seasons, with similar effects regarding water
and associated wetlands. However, an apparently greater
sensitivity to access to commons during the lean season could not be
confirmed with the data available under this format for Nigeria.
There is a safety net effect but a seasonal effect is only weakly
captured by the data. This is in line with Wunder et al. (2014b),
who argue that this safety net function is probably evolving
alongside the widening of alternatives to households through
development, and depends on the magnitude of the shock.
Although CRs as a whole are significantly related to food
security indicators, various data limitations apply. To its credit,
the sample is not biased towards more environmentally
dependent households or towards households particularly close to the
resources analysed, but benefits from the sampling procedure of
the LSMS. However, the nature of the data regarding access to
resources is limited because environmental resources are not
systematically integrated in its protocol. Also, and given the
confidentiality constraints of the survey, a partial mismatch
between the data from the enumeration area and a given household
has potentially misclassified access to resources for some of the
households. As developed in the analysis of results, access to
common land as a contributor to food security is ambiguous
given some definition problems, which limit its study.
The other issue with the data, which needs to be accounted
for, is that they are based on recall over 12 months or 7 days and
self-assessment of food security variables, and thus of limited
value in reflecting the actual food security of a given household.
However, the analysis addressed this problem by controlling for
the various waves of surveys on food security challenges. In
addition, as discussed by
Headey and Ecker (2013)
, not all
indicators are the same, with some being more reliable and more
consistent than others. In our case, the time of recall of the
indicators creates a continuum of reliability, from lower
reliability of the 12-month recall to higher reliability of the 7-day recall.
Using a sustainable livelihood approach to contextualise
the potential represented by commons could be adequately
performed despite the constraints of the existing database.
Initial responses to this weakness of the LSMS-ISA are
currently being explored, such as the extension of the LSMS-ISA
with forest modules
(Bong et al. 2016)
. Their integration is
required to capture with more confidence and precision the
influence of the Bhidden harvest^ on the dimension of quality
and quantity of household food security. Independently of
these developments, further analysis of the relationship
between access to CRs and nutrition security could be
undertaken with the data available, calculating a composite score based
on dietary diversity, food frequency and the relative nutritional
importance of different food groups, such as a food
consumption score card
This brief analysis has two direct implications. The first is that
food security intervention can be enhanced by integrating the
question of access to CRs, alongside more conventional
agricultural yield-enhancing approaches, when targeting the most
vulnerable (i.e. those who tend to always report food insecurity
events). This is especially the case for the most vulnerable
who cannot successfully mobilise agricultural support because
of their limited resources, and could be described as Bsubsistence
farmers without profit potential^
(Fan et al. 2013)
. A relevant
framework in this direction is that of Bintegrated landscape^
approaches, which encompass multiple goals (e.g. improved
agricultural productivity, climate change resilience, improved
CR management) at various scales (farm, community and
(Gray et al. 2016)
. Although more advanced in other
developing regions, such approaches are considered an
emerging trend in SSA in a recent review by
Milder et al. (2014)
Secondly, this exploratory paper highlights the limitations of
current standard population representative household surveys
with regard to the role played by the hidden harvest (i.e.
environmental income and source of subsistence for food security).
If food security is to be addressed in a more effective way, the
addition of complementary questionnaire modules to existing
agriculture surveys is warranted. A more detailed record of
access to the hidden harvest, aligned with both farm and
nonfarm activities, allows for a more coherent grasp of population
needs, and particularly those of the more vulnerable.
Acknowledgements We would like to thank the two anonymous
reviewers for their insights as well as early comments received on a poster
version of the paper presented at the Tropentag conference held in Berlin
in September 2015. In addition, the suggestions of Laura Riesgo, Angel
Perni, Liesbeth Colen, Aymeric Ricome and Simone Pieralli were greatly
appreciated during the preparation of the paper. Thanks to Hans Jensen
and the team at PrePress Projects for revising the English.
Funding This work was partially funded by the project BTechnical and
scientific support to agriculture and food and nutrition security sectors
(TS4FNS)^ within the administrative arrangement between
DirectorateGeneral International Cooperation and Development (DEVCO) and
Directorate-General Joint Research Centre (JRC) of the European
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
Disclaimer The views expressed are purely those of the authors and may
not in any circumstances be regarded as stating an official position of the
Distribution of households by landscape category (GLOBCOVER
(Arino et al. 2012)
N. Majority landcover class
1 Rainfed Croplands
Mosaic cropland (50–70%)/vegetation (grassland/shrubland/forest) (20–50%)
Mosaic vegetation (grassland/shrubland/forest) (50–70%)/cropland (20–50%)
4 Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5 m)
Open (15–40%) broadleaved deciduous forest/woodland (>5 m)
Mosaic forest or shrubland (50–70%)/grassland (20–50%)
Mosaic grassland (50–70%)/forest or shrubland (20–50%)
8 Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (<5 m)
9 Closed to open (>15%) herbaceous vegetation (grassland, savannas or lichens/mosses)
10 Sparse (<15%) vegetation
11 Closed to open (>15%) grassland or woody vegetation on regularly flooded orwaterlogged soil – fresh, brackish or saline 10
12 Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water 28
13 Artificial surfaces and associated area
14 Bare areas
Household survey, post-harvest wave data 2012–2013.
Merged datasets from Households and Community questionnaires
(both seasons, 2012 2013). General Household Survey-Panel (GHS-Panel) conducted in 2012/13 by the Nigeria National Bureau of Statistics
(NBS) in collaboration with the World Bank Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)
Distribution of households by agro-ecological zones
Household survey, post-harvest wave data 2012–2013.
Merged datasets from Households and Community questionnaires
(both seasons, 2012 2013). General Household Survey-Panel
(GHSPanel) conducted in 2012/13 by the Nigeria National Bureau of
Statistics (NBS) in collaboration with the World Bank Living Standard
Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)
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s * 7
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llseaA .01507 .192− .00025− .()803− .3909− .()118− .2230 .151− .6351* .177− .210 .620− .5880* .187− .7740* .674− .50709 .270− .541− .(292− .3051* .323− .641− .(34− 9313 .8357 8236− .0130
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tsou sehu aenL .10130− .)(651− .20410− .)(223− .1430 .561− .6150 .80− .3*38*0 .954− .9020 .700− .9220 .371− .80970− .)(510− .1160− .)(607− .7420 .390− .514**1 .662− .64*71− .)(722−
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