Content uploaded by Temidayo Apata
Author content
All content in this area was uploaded by Temidayo Apata on Nov 06, 2018
Content may be subject to copyright.
Environmental Economics, Volume 2, Issue 4, 2011
74
Temidayo Gabriel
Apata (Nigeria)
Factors influencing the perception and choice of adaptation
measures to climate change among farmers in Nigeria.
Evidence from farm households in Southwest Nigeria
Abstract
There is widespread interest on the impacts of climate change on agriculture in Sub-Saharan Africa (SSA), and on the
most effective investments to assist farmers strengthen factors influencing their choice of adaptation measures. This
study uses the Heckman probit model to analyze the two-step process of adaptation measures to climate change, which
initially assesses a farmer’s perception that climate is changing and followed by an examination of the response to this
perception in the form of adaptation. Simple purposive random sampling was used to select two states out of six states.
Random sampling was used to select Ondo and Oyo States, while communities that are prone to climate change were
purposively selected. The study administered questionnaire and held Focus Group Discussions to elicit information,
where 350 valid responses were used for further analysis. The dependent variables are adaptations measures adopted by
farmers, where the independent variables are those natural, socio-economic, institutional and physical factors
influencing the choice of these measures. The analysis indicate that 53.4% of the investigated farmers have observed
increasing temperature over the past 10 years whereas 58% have observed that they noticed decreasing rainfall over the
past 10 years. Findings from the FGDs conform to secondary data gathered. This analysis show that 64.57% of farmers
have adopted one or more of the major adaptation options identified through the research survey. Education of the head
of household, livestock ownership and extension for crop and livestock production, availability of credit and
temperature are factors influencing choice of adaptation.
Keywords: adaptation, perception, climate change, Southwest Nigeria.
JEL Classification: Q12, Q54.
Introduction¤
Studies have shown that economies of most Sub-
Saharan Africa (SSA) countries are extremely de-
pendent on agricultural production (Apata et al.,
2011a; Alvaro et al., 2009; Burke et al., 2009;
ANAP, 2006). These studies revealed that about
17% of GDP was derived from agriculture in SSA
in the years of 2005-2008. Given the central role of
agriculture and the unprecedented changes in cli-
mate anticipated by various studies over the next
few decades in the region there is a need to examine
possible ways and methods farmers in these areas
cope with the vagaries of climate change. Climate
change affects agriculture and agriculture also af-
fects climate change. Agriculture affects climate
change through the emission of greenhouse gases
(GHG) from different farming practices (Edwards-
Jones et al., 2009; Byravan & Chella, 2009). Cli-
mate change in the form of higher temperature, re-
duced rainfall and increased rainfall variability re-
duces crop yield and threatens food security in low-
income and agriculture-based economies like Nige-
ria. Adverse climate change impacts are considered
to be particularly strong in countries located in trop-
ical Africa that depend on agriculture as their main
source of livelihood (Apata et al., 2011, 2011b; Ba-
digger, 2010; IAC, 2004; Dixon, Gulliver & Gib-
bon, 2001; IPCC, 2001).
¤ Temidayo Gabriel Apata, 2011.
It was evidenced that climate change will have a
strong impact on Nigeria-particularly in the areas of
agriculture; land use, energy, biodiversity, health and
water resources. Nigeria, like all the countries of Sub-
Saharan Africa, is highly vulnerable to the impacts of
Climate Change (Apata, 2011, Obioha, 2008; IPCC,
2007; NEST, 2004). It was also, noted that Nigeria
specifically ought to be concerned by climate change
because of the country’s high vulnerability due to its
long (800km) coastline that is prone to sea-level rise
and the risk of fierce storms (Apata et al., 2011b;
Apata, 2006; Adejuwon, 2004; FGN, 1997). In addi-
tion, almost 2/3 of Nigeria’s land cover is prone to
drought and desertification (ANAP, 2005). Its water
resources are under threat which will affect energy
sources (like the Kainji and Shiroro dam). Moreover,
due to rain-fed agriculture that are practiced by these
farmers, and fishing activities from which 2/3 of the
Nigerian population depend primarily on for food and
livelihood are also under serious threat. Also, the
high population pressures of 140 million people sur-
viving on the physical environment through various
activities within an area of 923,000 square kilometers
calls for attention (Oluwatayo et al., 2008; IPCC,
2007; NEST, 2004).
Data obtained from the Nigeria Meteorological Ser-
vices (NMS, 2007) and extracts from CBN (2008)
Statistical Bulletin indicated that the average mini-
mum and maximum temperatures have been in-
creasing by about 0.25°C and 0.15°C respectively
over the past decade. In addition, rainfall has been
Environmental Economics, Volume 2, Issue 4, 2011
75
characterized by a very high level of variability over
the past 30 years (CBN, 2008). Although models
predicting future precipitation from past studies
provide conflicting results reporting both increas-
ing and decreasing precipitation but there is
agreement by these studies that temperatures will
continue to increase in Nigeria over the coming
years (Oluwatayo, 2011, Ayinde et al., 2010; Alvaro
et al., 2009; Dabi et al., 2007). Moreover, studies
show that the frequency and spatial coverage of
drought have increased over the past few decades
(Ayinde et al., 2010; Obioha, 2008; Dabi et al.,
2007; Lautze et al., 2003); and this phenomenon is
expected to continue in the future. From the fore-
going it is evidenced that climate change is expected
to influence crop and livestock production, hydro-
logic balances, input supplies and other components
of agricultural systems in Nigeria. However, the
nature of these biophysical effects and the human
responses to them are complex and uncertain.
Several studies have indicated farmers do perceive
that climate is changing and have developed coping
strategies to adapt or reduce the negative impacts of
climate change on their farming operations (Deresaa
& Rashid, 2010; Mertz et al., 2009; David et al.,
2007). Some attempts have been made to analyze
factors influencing the choice of adaptation meas-
ures to climate change and how farmers adapt to
climate change in Africa (Apata, 2011; Hassan and
Nhemachena, 2008; Deressa and Hassan, 2009;
Admassie and Adenew, 2007; Deressa et al., 2009).
The studies of Deressa and Hassan (2009) and Apa-
ta et al. (2011b) employed the Ricardian approach to
estimate the monetary impact of climate change on
agriculture. Even though the applied Ricardian ap-
proach includes adaptation, it does not explicitly
address factors influencing the choice of adaptation
and what adaptation methods they employ. Studies
that have examined factors influencing the choice of
adaptation measures to climate change and adapta-
tion strategies in Africa, although informative, did
not address the extent to which different socio-
economic and environmental factors affect percep-
tions of climate change and adaptation (Akter &
Bennet, 2009; Niggol & Mendelsohn, 2008 and
Agrawala & Frankhauler, 2008). Others that have
analyzed the factors affecting the choice of adapta-
tion methods failed to explicitly explain how far-
mers perceive climate change and adapt to it (like
Deressa et al. (2009) for Ethiopia and Apata et al.
(2009) for Nigeria). This is the research gap that this
study will like to address.
Furthermore, past studies have argued that adaptation
to climate change is a two-step process, which
initially requires the perception that climate is
changing and then respond to changes through
adaptation (Wang et al., 2009; Aggrawal, 2009).
Fussel (2007) argues that emphasis should focus on
adaptation because human activities have already
influenced vagaries in climate fluctuation. This study
has benefitted from the work of Maddison (2006) and
Deressa et al. (2009) that addressed this two-step
process of adaptation at the regional level for Africa.
Their methodology helped to develop the model
adapted for this study. Consequently, the objective of
this study is to identify factors influencing the choice
of adaptation measures to climate change and
quantify the extent to which these identified factors
influence perceptions and adaptation to climate
change in Southwest Nigeria.
1. Conceptual framework
The conceptual framework of this study is that
agricultural technology adoption, climate change
adaptation methods and other related models
involve decisions on whether to adopt or not.
Previous studies have observed that agricultural
technology adoption models are based on farmers’
utility or profit maximizing behaviors (Norris and
Batie, 1987; Pryanishnikov and Katarina, 2003).
Probit and logit models are the most commonly used
models in agricultural technology adoption research
(Hausman & Wise, 1978; Wu and Babcock, 1998).
Binary probit or logit models are employed when the
number of choices available is two (whether to adopt
or not). Extensions of these models, most often
referred to as multivariate models are employed when
the number of choices available is more than two.
The most commonly cited multivariate choice models
in unordered choices are multinomial logit (MNL)
and multinomial probit (MNP) models. Multivariate
choice models have advantages over their
counterparts of binomial logit and probit models in
two aspects (Wu and Babcock, 1998). First, they
allow exploring both factors conditioning specific
choices or combination of choices and second, they
take care of self-selection and interactions between
alternatives.
These models have also been employed in climate
change studies because of conceptual similarities
with agricultural technology adoption studies. For
example, Nhemachena and Hassan (2007) employed
the multivariate probit model to analyze factors
influencing the choice of climate change adaptation
options in Southern Africa. Kurukulasuriya and
Mendelsohn (2006) employed the multinomial logit
model to see if crop choice by farmers is climate
sensitive. Similarly Seo and Mendelsohn (2006)
used the multinomial logit model to analyze how
livestock species choice is climate sensitive.
Additionally Deressa et al. (2009) adopted the
Environmental Economics, Volume 2, Issue 4, 2011
76
multinomial logit model to analyze factors that
affect the choice of adaptation methods in the Nile
basin of Ethiopia. These studies revealed that the
decision processes of farmers to adopt a new
technology require more than one step. In other
words models with the two-step regressions are
hence, employed to correct for the selection bias
generated during the decision-making processes. For
instance, William and Stan (2003) employed the
Heckman two-step procedure to analyze the factors
affecting the awareness and adoption of new
agricultural technologies in the United States. In the
William and Stan (2003) study, the first stage is the
analysis of factors affecting the awareness of new
agricultural technologies and the second stage is the
adoption of the new technologies. Similarly, Yirga
(2007) and Kaliba et al. (2000) employed Heckman’s
selection model to analyze the two-step processes of
agricultural technology adoption and the intensity of
agricultural input use. Deressa et al. (2009) employed
the Heckman probit model to analyze the two-step
process of adaptation to climate change, which
initially assesses a farmer’s perception that climate is
changing, followed by an examination of the
response to this perception in the form of adaptation.
This study therefore use the conceptual constructs of
the reviewed of past studies above to analyze the
perception and factors influencing the choice of
adaptation measures to climate change among
farmers in Southwest Nigeria using Heckman’s two-
step regressions procedure.
2. Methodology
2.1. Description of the area of study. Southwest
Nigeria is one of the six major political zones in
Nigeria. This zone has six states in it. They are La-
gos, Oyo, Ogun, Ondo, Osun and Ekiti States. This
area is known for its arable food crop production
(NPC, 2006). The local farmers are experiencing
climate change even though they have not considered
its deeper implications. This is evidenced in the late
arrival of rain, the drying-up of stream and small
rivers that usually flows year-round, the seasonal
shifting of the “Mango rains” and of the fruiting pe-
riod in the Southern part of Oyo State (Ogbomosho),
and the gradual disappearance of flood-recession
cropping in riverine areas of Ondo state are among
the effects of climate disturbances in some communi-
ties of Southwestern Nigeria (BNRCC, 2008).
2.2. Data source and sampling procedure. The
study used cross-sectional household survey data of
400 mixed crop and livestock farmers collected
during the 2008-2009 production year in southwest
of Nigeria. The study administered questionnaire
and held Focus Group Discussions to elicit informa-
tion. Both structured questionnaire and interviews
were held with indigent and local government officials
and all other stakeholders on climate change know-
ledge and adaptation. The study decomposes various
measures of climate change adaptation. In addition, the
study also uses Focus Group Discussions (FGDs) to
find out the level of understanding of climate change
from the farmers, also communities perception of the
vagaries in weather conditions as well coping strate-
gies adopted to survive’.
Simple purposive random sampling was used to
select respondents used for this study. Random
sampling was used to select Ondo and Oyo States
out of six states in the Southwest zone, while com-
munities that are prone to climate change were pur-
posively selected (BNRCC, 2008; and Apata, 2006).
The communities selected are, Ayetoro and Mahin-
tedo in Ondo State and Fiditi and Ogbomosho in
Oyo State respectively. One hundred farmers were
randomly selected from each community identified
above, making a total of 400 farming households
interviewed, but only 360 data were useful for fur-
ther analysis. Temperature and rainfall data were
obtained from monthly/annually meteorological
weather related data that were collected from the
Nigerian Meteorological station/Unit and Central
Bank of Nigeria (CBN) annual reports. In addition
to collecting data on different socio-economic and
environmental attributes, the survey also included
information on farmers’ perceptions of climate
change and adaptation methods. Farmers were specif-
ically asked to respond to questions on patterns of
change in temperature and rainfall over the past 20
years. The study uses Heckman probit regression
model to examine the characteristics that best explain
variation in the measures of attitudes of the indigent
perception and adaptation level to climate change and
also factors that influences such decisions.
3. Empirical model and variables
3.1. Empirical model. Adaptation to climate change is
a two-stage process involving perception and
adaptation stages. The first stage is whether the
respondent perceive that there is climate change or not,
and the second stage is whether the respondent adapt
to climate change depending on the first stage that
he/she has perceived climate change. Because the
second stage of adaptation is a sub-sample of the
first stage, it is likely that our second stage sub-
sample is non-random and different from those who
did not perceive climate change creating sample
selection bias. This study, therefore, used the well
known maximum likelihood Heckman’s two-step
procedure (Heckman, 1976) to correct for this
selectivity bias. Heckman’s sample selection model
assumes that there exists an underlying relationship
which consists of. The latent equation is given by:
Environmental Economics, Volume 2, Issue 4, 2011
77
yj* = jȕ + μ1j. (1)
Such that we observe only the binary outcome given
by the probit model as:
yj probit = (yj* > 0). (2)
The dependent variable is observed only if the ob-
servation j is presented in the selection equation:
yj select = (Zjį + μ2j > 0),
μ1 ~ N (0, 1),
μ2 ~ N (0, 1) corr (μ1, μ2) = ȡ, (3)
where, x is a k-vector of explanatory variables which
include different factors hypothesized to affect
adaptation and z is an m vector of explanatory
variables which include different factors hypothesized
to affect perception; u1 and u2 are error terms. The first
stage of the Heckman’s sample selection model is the
perceptions of changes in climate and this is the
selection model (equation (3)). The second stage,
which is the outcome model (equation (1)), is
whether the farmer adapts to climate change,
depending on the first stage that she/he perceives a
change in climate.
Literature revealed that the use of standard probit
model techniques on equation (1) may produce
biased results. To address this biased results
Heckman probit model are mostly used. Thus, the
Heckman probit provides consistent, asymptotically
efficient estimates for all parameters in such models
(Van de Ven and Van Praag, 1981). Hence, the
Heckman probit selection model was used to
analyze the perception and adaptation to climate
change by farming households in the South-western
part of Nigeria.
3.2. Dependent and explanatory variables for the
selection and outcome equations. The dependent
variable for the outcome equation is whether a
farmer has adapted or not to climate change. The
key concern of this issue is to discuss the factors
influencing the choice of adaptation measures if the
farmers have adapted. This means that the
dependent variables are the adaptations measures
adopted by farmers such as intercropping, mulching,
zero tillage, ridges, etc. The independent variables are
those natural, socio-economic, institutional and
physical factors influencing the choice of these
measures. The explanatory variables are chosen based
on previous studies, climate change adaptation
literature and data availability. These variables
include: education of the head of the household,
household size, gender of the head of the household,
non-farm income, livestock ownership, extension
for crop and livestock production, access to credit,
farm size, distance to input and output markets,
temperature rainfall and precipitation. A detailed
description of the theoretical relationships between
these variables and adaptation to climate change is
included in Apata et al. (2009), Deressa et al. (2009),
Hassan and Nhemachena (2007), Nhemachena (2009)
and Apata et al. (2011b). Tables 1 and 2 provide a
description of the model variables for the Heckman
probit selection model.
Table 1. Description of model variables for the selection equation of the Heckman probit selection model
Dependent variable
Description Farmers that have perceived
change (%) Farmers that haven’t perceived
change (%)
Perception of climate change (takes the value of one if adapted and zero otherwise) 89 11
Independent variables
Description Mean Standard deviation
Size of household (continuous) 5.8 1.7
Gender (takes the value of 1 if male and 0 otherwise) 0.72 0.32
Education of household head in years (continuous) 2.3 1.8
Farm income in Nigerian currency (continuous) 13,500 7500
Non-farm income in Nigerian currency (continuous) 19,720.00 8,650.00
Ratio of number of consumers to number of labors in the farm household 2.5 1.8
A
ccess to credit (takes the value of 1 if access and 0 otherwise) 0.43 0.61
Farming experience in years (continuous) 7.3 4.6
A
ge of household head in years (continuous) 51 21
A
ccess to information on climate (takes the value of 1 if access and 0 otherwise) 0.65 0.38
Farmer-to-farmer extension (takes the value of 1 if access and 0 otherwise) 0.82 0.25
Knowledge on local agro-ecology (takes the value of 1 if knowledgeable and 0 otherwise) 0.62 0.37
A
ccess to adaptation measures (takes the value of 1 if access and 0 otherwise) 0.45 0.52
Environmental Economics, Volume 2, Issue 4, 2011
78
The dependent variable for the selection equation is
whether a farmer has perceived climate change or
not. The explanatory variables for the selection
equation include different socio-demographic and
environmental factors based on a literature review
of factors affecting the awareness of farmers to
climate change or risk perceptions as discussed in the
previous section. It is hypothesized that the age and
education of the head of the household, information on
climate, farmer-to-farmer extension, and ratio of
number of consumers to number of labors in a farm
household, farm and non-farm incomes, and agro-
ecological settings are variables influencing the
awareness of farmers to climate change.
Table 2. Description of model variables for the outcome of the Heckman probit selection model
Dependent variable
Descriptio n Farmers that have adapted
to change (%) Farmers that haven’t adapted
to change (%)
A
daptation to climate change (takes the value of one if adapted and zero otherwise) 62 38
Independent variables
Description Mean Standard deviation
Size of household (continuous) 5.8 1.7
Gender (takes the value of 1 if male and 0 otherwise) 0.72 0.32
Education of household head in years (continuous) 2.7 2.5
Farm size in acres (continuous) 5.0 3.2
Non-farm income in Nigerian naira (continuous) 23,000.00 12,500.00
Ratio of number of consumers to number of labors in the farm household 2.5 1.8
A
ccess to credit (takes the value of 1 if access and 0 otherwise) 0.43 0.61
Farming experience in years (continuous) 7.3 4.6
A
ge of household head in years (continuous) 49 17
Livestock ownership (takes the value of 1 if owned and 0 otherwise) 0.35 0.61
A
ccess to extension work (takes the value of 1 if access and 0 otherwise) 0.65 0.38
Distance to output market in kilometers (continuous) 3.3 2.7
Temperature in degree centigrade (continuous: annual average during the survey period) 17.5 6.6
A
nnual rainfall (continuous: annual average during the survey period) 85.00 49.8
The age of the head of the household represents
experience in farming. Studies indicated that
experienced farmers have a higher probability of
perceiving climate change as they are exposed to past
and present climatic conditions over the longer
perspective of their life span (Maddison, 2006; Ishaya
and Abaje, 2008, Deressa et al., 2009). Thus, we
hypothesize that older and more experienced farmers
have a higher likelihood of perceiving climate
change. Education of the head of household, as
discussed with the case of factors affecting adaptation
in the outcome equation, is also hypothesized to
positively affect awareness of climate change. Access
to information on climate change through extension
agents or other sources creates awareness and
favorable condition for adoption of farming practices
that are suitable under climate change (Maddison,
2006). Thus, it is hypothesized that farmers’ contact
with extension agents or any other sources, which
might provide information on climate change,
increase awareness of climate change. Higher
income positively affects public perception of
climate change (Semenza et al., 2008). By the same
attestation, it is hypothesized that higher farm and
non-farm incomes positively influence farmers’
perception of climate change. In technology adoption
studies, social capital plays a significant role (Isham,
2002) in information exchange, and hence, it is
hypothesized that social capital is associated with the
perception of climate change.
4. Results and discussion
Table 3 presents the results of farmers’ perception
of long-term temperature, rainfall and precipitation
changes during Focus Group Discussions (FGDs). The
analysis indicated that 53.4% of the investigated
farmers observed increasing temperature over the past
10 years whereas 58% have observed that they noticed
decreasing rainfall over the past 10 years. This result
was from information gathered from the FGDs. FGDs
were used to evaluate data gathered from secondary
sources. For instance, FGDs revealed that majority of
farmers perceived increasing temperatures over the
past 10 years, this is in line with the information
retrieved from Nigeria Meteorological Services.
Likewise, farmers’ perceptions of decreasing rainfall
could be accredited to noticeable changes in their
environment like drying of rivers (that usually flows
all year round), delayed rainfall, drought. These
observations by the people correspond with reports
from weather stations that revealed high level of
variability of rainfall distribution over the past 50
years (CBN, 2008 ).
Environmental Economics, Volume 2, Issue 4, 2011
79
Those farmers who asserted to have observed
changes in climate over the past 10 years were
afterwards asked how they have responded to the
situation. The results of this analysis were presented
in Table 4. Table 4 presented the farmers who
claimed to have observed climate change and level
of response. This analysis show that 226 farmers
have adopted one or more of the major adaptation
options identified through the research survey, such
as planting trees, mixed farming, mixed cropping,
soil conservation, use of different crop varieties,
changing planting dates and irrigation (Table 4).
This analysis revealed that mixed cropping (57.4%)
is the most adaptation options used, follow by
planting early or late (44.6%) due to variability in
climate (Table 4). The survey analysis also showed
that 35.4% of the farmers noticed climate change
but failed to adapt to it. Respondents listed series of
difficulty to adaptation, among which are lack of
information on adaptation methods, no access to
effective adaptation methods, lack of money or
access to credit facilities, shortage of labor, shortage
of land and poor capability for irrigation.
Table 3. Farmers’ perception of long-term tempera-
ture, rainfall and precipitation changes (N = 350)
Climatic variables Farmer’s response (%)
Temperature increase 187 (53.4)
Temperature decrease 83 (23.7)
No change in temperature 42 (12.0)
Rainfall increase 30 (0.9)
Rainfall decrease 203 (58.0)
No change in rainfall 15 (0.4)
Precipitation increase 148 (42.3)
Precipitation decrease 113 (32.3)
No change in precipitation 35 (10.0)
Table 4. Adaptation options adapted by respondents
from the areas of study
S/N
A
daptation options Farmer’s response (%)
Yes to adaptation 226 (57.6)
1 Planting of trees 48 (13.7)
2 Mixed farming 104 (29.7)
3 Mixed cropping 201 (57.4)
4 Soil conservation 73 (20.9)
5 Intercropping 45 (12.9)
6 Mulching 80 (22.9)
7 Zero tillage 103 (29.4)
8 Making ridges 135 (38.6)
9 Irrigation 15 (04.3)
10 Early or late planting operations 156 (44.6)
11 No adaptation 124 (35.4)
4.1. Results and discussions of the Heckman
probit regression model. In the running of the
Heckman probit model, the model was first run and
tested for its appropriateness over the standard probit
model. The outcome of this operation revealed the
presence of a sample selection problem (that is
dependence of the error terms on the outcome and
selection models) hence, justifying the use of the
Heckman probit model with rho significantly
different from zero (Wald = 10.84, with P = 0.001).
Moreover, the likelihood function of the Heckman
probit model was significant (Wald = 72.17, with P
< 0.0001) showing a strong explanatory power of
the model. Moreover, results show that most of the
explanatory variables and their marginal values are
statistically significant at 10% or less and the signs
on most variables are as expected, except for a few
as explained below (Table 5). The calculated
marginal effects measure the expected changes in
the probability of both perception of climate change
and adaptation with respect to a unit change in an
independent variable.
The results from the selection model indicated that
age of the head of the household, farm income,
information on climate change, farmer-to-farmer
extension, ratio of number of consumers to number
of labors in the farm household and agro-ecological
settings are factors affecting the perception of
climate change. However, findings revealed that
most of the explanatory variables affected the
probability of adaptation as expected, except farm
size. Variables that positively and significantly
influence adaptation to climate change include
education of the head of the household, household
size, gender of the head of the household being
male, livestock ownership, extension for crop and
livestock production, availability of credit, and
temperature. On the other hand, farm size and
annual average precipitation are negatively related.
The implications of this result is that higher
likelihood of perceiving climate change with
increasing age of the head of the household is
associated with experience which lets farmers
observe changes over time and compare such changes
with current climatic conditions. Information on
climate change through extension or other public
sources, farmer-to-farmer extension and ratio of
number of consumers to number of labors in the
farm household increase the likelihood of climate
change perception as they play an important role in
the availability and flow of information.
Moreover, increasing household size increases the
likelihood of adaptation. This finding is in line with the
argument, which assumes that a large family size is
normally associated with a higher labor endowment,
which would enable a household to accomplish
various agricultural tasks, especially during peak
seasons (Croppenstedt et al., 2003). The fact that
adaptation to climate change increases with increasing
temperature agrees with the expectation that increasing
temperature is damaging to African agriculture and
Environmental Economics, Volume 2, Issue 4, 2011
80
farmers respond to this through the adoptionof
different adaptation methods (Kurukulasuriya and
Mendelsohn, 2006; Deressa et al., 2009 and Apata et
al., 2011).
Independent variables that have demonstrated negative
relationship to adaptation such as farm size could be
attributed to the fact that adaptation is plot-specific as
documented by Deressa et al. (2009). In other words
it is not the size of the farm, but the specific
characteristics of the farm that dictate the need for a
specific adaptation method to climate change. Thus
future research, which accounts for farm
characteristics, could reveal more information about
factors dictating adaptation to climate change at the
farm versus the plot level. Moreover, the probable
reason for the negative relationship between average
annual precipitation and adaptation could be due to
the fact that increasing precipitation does relax the
constraints imposed by increasing temperature on
crop growth. In addition, factors identified as
affecting the perception of an adaptation to climate
change in the study areas are directly related to the
development of institutions and infrastructure.
Table 5. Results of the Heckman probit selection model
Selection model
A
daptation model
Explanatory variables Regression Marginal values Regression Marginal values
Coefficient
P
-value Coefficient
P
-value Coefficient
P
-value Coefficient
P
-value
Size of household (HH) 0.053 0.029 0.017 0.038 0.025 0.291 0.005 0.275
Gender 0.473 0.012 0.154 0.010 0.026 0.011 0.003 0.002
Education of household head 0.064 0.019 0.021 0.018 0.057 0.023 0.034 0.013
Farm size -0.139 0.013 -0.052 0.018
Non-farm income 0.000258 0.125 0.000315 0.103 0.000128 0.139 0.00047 0.014
Ratio of number of consumer
to number of labor in the
farm (HH) 0.012 0.041 0.002 0.0028 0.075 0.072 0.005 0.014
A
ccess to credit 1.015 0.072 0.005 0.014 1.032 0.002 0.039 0.052
Farming experience 0.019 0.0215 0.041 0.193 0.025 0.013 0.0038 0.005
A
ge of the household head 0.115 0.002 0.041 0.193 0.025 0.013 0.038 0.052
Livestock ownership 1.013 0.005 0.304 0.001
A
ccess to extension 1.015 0.013 0.083 0.022
A
ccess to information on
climate 1.039 0.008 0.438 0.033
Farmer-to-farmer extension 0.372 0.014 0.015 0.009
Knowledge on local agro-
ecology 1.082 0.000 0.038 0.004
A
ccess to adaptation
measures 1.011 0.000 0.349 0.001
Distance to output market -0.053 0.315 -0.016 0.013
Temperature 0.078 0.000 0.055 0.022
A
nnual rainfall -0.018 0.000 -0.003 0.000
Constant -3.064 0.0000 -0.216 0.004
Total observation 350
Censored 164
Uncensored 186
Wald Chi square (zero slopes) 73.28
P =0.0001
Wald Chi square (independent
equations) 12.63
P = 0,001
Conclusions
Due to low outputs from farms, farmers seem to be
abandoning mono-cropping for mixed and mixed
crop-livestock systems while considering risky,
mono-cropping practicing under dry land. Farming
experience and access to education were found to
promote adaptation. This implies that education to
improve awareness of potential benefits of adapta-
tion is an important policy measure.
Focus Group Discussions revealed lack of effective
access to information on climate change. Thus, there
is need for effective and reliable access to informa-
tion on changing climate to dissuade farmers mind
from spiritual angle. In addition, empowerment
(credit or grant facilities) is crucial in enhancing
farmers’ awareness. This is vital for adaptation deci-
sion making and planning. Combining access to
extension and credit ensures that farmers have the
Environmental Economics, Volume 2, Issue 4, 2011
81
information for decision making and the means to
take up relevant adaptation measures.
Consequently future policy should focus on awareness
creation on climate change through different sources,
such as mass media and extension. Also, facilitating
the availability of credit especially for adaptation
technologies could improve level of adaptation
measures. Moreover, encouraging informal social
networks and importing adaptive technologies from
other countries with similar socio-economic and
environmental settings could enhance the adaptive
capacity of Nigerian farmers.
Acknowledgements
This work is an extract from the Contributed Paper
prepared for presentation at the International Asso-
ciation of Agricultural Economists’ 2009 Confe-
rence, Beijing, China, August 16-22, 2009. The
author would like to thank Professor M.A.Y. Rahji,
Dr. S.A. Sanusi and Sola Agboola for reviewing this
report and giving constructive comments. Thanks
also go to all field officers of this research
through participating in pre-test and adjusting the
questionnaire accordingly and also for final enu-
meration.
References
1. Adejuwon, S.A. (2004). Impact of climate variability and climate change on crop yield in Nigeria, Contributed
paper to Stakeholders workshop on Assessment of Impact & Adaptation to Climate Change, pp. 2-8.
2. Admassie, A. & Adenew, B. (2007). Stakeholders’ Perceptions of Climate Change and Adaptation Strategies in
Ethiopia, EEA Research Report, Addis Ababa: Ethiopia.
3. Aggarwal, P.K. (2009). “Vulnerability of Indian Agriculture to Climate Change: Current State of Knowledge”,
Paper presented at the National Workshop – Review of Implementation of Work Programme Towards Indian Net-
work of Climate Change Assessment, 14 October, Ministry of Environment and Forests, New Delhi,
http://moef.nic.in/downloads/others/Vulnerability_PK%20Aggarwal.pdf (last accessed on 14 June 2010).
4. Agrawala, S. and S. Fankhauser (eds) (2008). Economic Aspects of Adaptation to Climate Change: Costs, Benefits
and Policy Instruments, Paris: OECD.
5. Akter, S. & Bennet, J. (2009). Household perceptions of climate change and preferences for mitigation actions: the
case of the Carbon Pollution Reduction Scheme in Australia. Paper presented on the 53rd annual conference of
Australian Agricultural and Resource Economics Society, 11-13 February, Cairns, Australia.
6. Alvaro, C., Tingju, Z., Katrin, R.., Richard, S.J. & Claudia, R. (2009). Economy-wide impact of climate change on
Agriculture in Sub-Saharan Africa, International food policy research Institute (IFPRI) discussion paper, No
00873, p. 1.
7. Assessment of Nigeria Agricultural Policy (2005). Agriculture in Nigeria: Identifying Opportunities for Increased
Commercialization and Investment, IITA press, IITA Ibadan.
8. Apata, T.G., Folayan, A., Apata, O.M. and Akinlua, J. (2011a). The Economic Role of Nigeria’s Subsistence
Agriculture in the Transition Process: Implications for Rural Development. 85th Annual Conference of the Agricul-
tural Economics Society (U.K.), Warwick University, U.K., April, pp. 18-20.
9. Apata, T.G. (2011). Effects of Global Climate Change on Nigerian Agriculture: An Empirical analysis, Central
Bank of Nigeria (CBN), Journal of Applied Statistics, Vol 2 (1), pp. 31-50.
10. Apata, T.G., Agboola, T.O., Kehinde, A.S.L., and Sanusi, R.A. (2011b). Economic Impacts of Climate Change on
Nigerian Agriculture and Adaptation Strategies Response among Farming Households in Nigeria, Journal of Agri-
cultural Science and Technology, Vol. 5, No.2 (Serial No. 33), pp. 203-214.
11. Apata, T.G., K.D. Samuel, A.O. Adeola (2009). Analysis of climate change perception and adaptation among
arable food crop farmers in south western Nigeria, in: Contributed Paper prepared for presentation at the Interna-
tional Association of Agricultural Economists’ 2009 Conference, Beijing, China, August, pp. 16-22.
12. Apata, T.G. (2006). Income and Livelihood Diversification among Farming Households in Crude-oil Polluted Areas of
Ondo State, Nigeria. PhD. Thesis, Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria.
13. Ayinde, O.E., Ajewole, O.O., Ogunlade, I., and Adewumi, M.O. (2010). Empirical analysis of agricultural production
and climate change: A case study of Nigeria, Journal of Sustainable Development in Africa, 12 (6), pp. 275-283.
14. Badiger, S. (2010). “Integrated Modeling Framework for Assessing Impacts of Changes in Land-use and Climate
on Hydrologic Regimes and Water Distribution”, presentation made at SANDEE, MSE and MSSRF Workshop on
Economics of Climate Change Adaptation, during 12-13 February, Madras School of Economics, Chennai.
15. Building Nigeria’s Response to Climate Change (BNRCC), in 2008 Annual Workshop of Nigerian Environmental
Study Team (NEST): The Recent Global and Local Action on Climate Change, Hotel Millennium, Abuja, Nigeria,
October 8-9, 2008.
16. Burke M.B., Lobell D.B. and Guarino L. (2009). Shifts in African crop climates by 2050, and the implications for
crop improvement and genetic resources conservation, Global Environmental Change, 19, pp. 317-325.
17. Byravan, S. and Chella Rajan, S. (2009). “Warming-up to Immigrants: An Option for US Climate Policy”, Eco-
nomic and Political Weekly, 44, pp. 19-23.
18. Central Bank of Nigeria (CBN). Statistical Bulletin, 2008.
Environmental Economics, Volume 2, Issue 4, 2011
82
19. Dabi, D.D., A.O. Nyong, A.A. Adepetu, V. Ihemegbulem and J. Agaye (2007). “Determinants of adaptive strate-
gies and opportunities of rural households in northern Nigeria”. In N. Leary, J. Adejuwon, V. Barros, I. Burton and
R. Lasco (eds)”, Adaptation to Climate Change, Earthscan, London, U.K.
20. David, S., Thomas G., Twyman C., Osbahr H. & Hewitson B. (2007). Adaptation to climate change and variability:
farmer responses to intra-seasonal precipitation trends in South Africa, Climatic Change, 83, pp. 301-322.
21. Deressa, T.T. and Rashid M. (2010). Economic Impact of Climate Change on Crop production in Ethiopia: Evi-
dence from Cross-section Measures, Journal of African economies, Vol. 18, ʋ 4, pp. 529-554.
22. Deressa, T.T. & R.M. Hassan (2009). Economic Impact of Climate Change on Crop Production in Ethiopia:
Evidence from Cross-Section Measures, Journal of African Economies, 18, pp. 529-554.
23. Deressa, T.T., Hassan, R.M., Ringler, C., Tekie, A. & Mahmud, Y. (2009). Determinants of farmers’ choice of adapta-
tion methods to climate change in the Nile Basin of Ethiopia, Global Environmental Change, 19, pp. 248-255.
24. Dixon, J., Gulliver, A. & Gibbon, D. (2001). Farming systems and poverty: improving farmers’ livelihoods in a
changing world, Rome and Washington, D.C.: FAO and World Bank.
25. Edwards-Jones, G., Plassmann, K. & Harris, I.M. (2009). Carbon foot printing of lamb and beef production systems:
insights from an empirical analysis of farms in Wales, UK, Journal of Agricultural Sciences, 147, pp. 707-719.
26. Federal Government of Nigeria (1997). Drought Management in Nigeria; What can people do to minimize its
impact? Abuja: Federal Ministry of Environment.
27. Füssel, H. (2007). Vulnerability: A generally applicable conceptual framework for climate change research.
Global Environmental Change, 17(2), pp. 155-167.
28. Hassan, R. & Nhemachena, C. (2008). Determinants of climate adaptation strategies of African farmers:
Multinomial choice analysis, African Journal of Agricultural and Resource Economics, 2(1), pp. 83-104.
29. Hausman, J. & Wise, D. (1978). A conditional probit model for qualitative choice: Discrete decisions recognizing
interdependence and heterogeneous preferences, Econometrica, 46, pp. 403-426.
30. Heckman, J.J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent
variables and a simple estimator for such models, Annals of Economic and Social Measurement, 5, pp. 475-492.
31. IAC (Inter Academy Council) Report (2004). Realizing the promise and potential of African agriculture, Royal
Netherlands Academy of Arts and Sciences, NL-1000 GC Amsterdam, The Netherlands.
32. Intergovernmental Panel on Climate Change (2007). Climate change: The scientific basis, http://www.ipcc.ch/,
cited, June 7, 2008.
33. Intergovernmental Panel on Climate Change (2007). Climate Change 2007: Impacts, Adaptation, and Vulnerabili-
ty: M.L. Parry, O.F. Canziani, J.P. Palutikof, van der Linden, J. Paul, C.E. Hanson (Eds.), Contribution of Work-
ing Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Uni-
versity Press, Cambridge, United Kingdom, pp. 1000.
34. Isham, J. (2002). The Effect of Social on Fertiliser Adoption: Evidence from Rural Tanzania, Journal of African
Economies, 11, pp. 39-60.
35. Ishaya, S. & Abaje, I.B. (2008). Indigenous people’s perception of climate change and adaptation strategies in Jema’s
local government area of Kaduna State, Nigeria, Journal of Geography and Regional Planning, 1 (18), pp. 138-143.
36. Kaliba, A., Verkuijl, R.M. & Mwangi, W. (2000). Factors affecting adoption of improved seeds and use of
inorganic fertilizers for maize production in the intermediate and lowland zones of Tanzania, Journal of
Agricultural and Applied Economics, 32 (1), pp. 35-48.
37. Kurukulasuriya, P. & Mendelsohn, R. (2006). Crop selection: adapting to climate change in Africa. CEEPA
Discussion Paper No. 26, Center for Environmental Economics and Policy in Africa, Pretoria, South Africa:
University of Pretoria.
38. Lautze, S., Aklilu, Y., Raven-Roberts. A., Young H., Kebede G. & Learning J. (2003). Risk and Vulnerability in
Ethiopia: Learning from the Past, Responding to the Present, preparing for the future. Report for the U.S. Agency
for International Development, Addis Ababa, Ethiopia.
39. Maddison, D. (2006). The perception of and adaptation to climate change in Africa, CEEPA, Discussion Paper
No. 10., Center for Environmental Economics and Policy in Africa, Pretoria, South Africa: University of Pretoria.
40. Mertz, O., Mbow, C., Reenberg, A. & Diouf, A. (2009). Farmers’ perceptions of climate change and agricultural
adaptation strategies in rural Sahel, Environmental Management 43 (5), pp. 804-816.
41. Nhemachena, C. & Hassan, R. (2007). Micro-Level Analysis of Farmers’ Adaptation to Climate Change in
Southern Africa, IFPRI Discussion Paper No. 00714, International Food Policy Research Institute,
Washington DC.
42. Nigerian Environmental Study Team (NEST), Regional Climate Modelling and Climate Scenarios Development in
Support of Vulnerability and Adaptation Studies: Outcome of Regional Climate Modelling Efforts over Nigeria,
NEST, Ibadan, Nigeria, 2004.
43. Niggol S.S., and Mendelsohn, R. (2008). Measuring impacts and adaptations to climate change: a structural Ricar-
dian model of African livestock management, Agricultural Economics, 38, pp. 151-165.
44. Norris, E. & Batie, S. (1987). Virginia farmers’ soil conservation decisions: an application of Tobit analysis,
Southern Journal of Agricultural Economics, 19 (1), pp. 89-97.
45. Obioha, E. (2008). Climate Change, Population Drift & Violent Conflict over land resources in North Eastern
Nigeria, Journal of Human Ecology, 23 (4), pp. 311-324.
Environmental Economics, Volume 2, Issue 4, 2011
83
46. Oluwatayo, I.B. (2011). Climate change and adaptation strategies of small-scale arable crop Farmers in rural Nige-
ria, International Journal of Agriculture and Food Science, 1, pp. 12-27.
47. Oluwatayo, I.B., A.B. Sekumade and J.O. Amao (2008). Vulnerability of Agriculture to Climate Change in Rural
Nigeria: Implications on Food Crop Production in Nigeria, Paper Presented at the 20th African Economic Research
Consortium (AERC) Anniversary Conference on Climate Change and Natural Resource Management in Africa
held between September 15-17, 2008 at the Hotel Intercontinental, Nairobi, Kenya, 28 p.
48. Pryanishnikov, I. & Z. Katarina (2003). Multinomial logit models for Australian labour market, Australian Journal
of Statistics, 4, pp. 267-282.
49. Semenza, J.C., Hall, D.E., Wilson, D. J., Bontempo, B.D., Sailor, D.J. & George, L.A. (2008). Public perception of
Climate Change Voluntary Mitigation and Barriers to Behaviour Change, American Journal of Preventive
Medicine, 35 (5), pp. 449-487.
50. Seo, N. & Mendelsohn, R. (2006). Climate change adaptation in Africa: a microeconomic analysis of livestock
choice, CEEPA Discussion Paper No. 19, Center for Environmental Economics and Policy in Africa, Pretoria,
South Africa: University of Pretoria.
51. Van de Ven, W.P. & Van Praag, B.M. (1981). The Demand for deductibles in Private Health Insurance: A probit
model with sample selection, Journal of Econometrics, 17, pp. 229-252.
52. Wang, J. Mendelsohn, R., Dinarc, R., Huangd, J., Rozellee, S., and Zhangd, L. (2009). The impact of climate
change on China’s agriculture, Agricultural Economics, 40, pp. 323-337.
53. William, D.M. & Stan, G.D. (2003). Information and the Adoption of Precision Farming Technologies, Journal of
Agribusiness, 21 (1), pp. S21-S38.
54. Wu, J., and Babcock, B.A. (1998). The choice of tillage, rotation, and soil testing practices: Economic and envi-
ronmental implications, American Journal of Agricultural Economics, 80, pp. 494-511.
55. Yirga, C.T. (2007). The dynamics of soil degradation and incentives for optimal management in Central High-
lands of Ethiopia. Ph.D. Thesis, Department of Agricultural Economics, Extension and Rural Development, Uni-
versity of Pretoria, South Africa.
