Embed Size (px)
Citation preview
DETERMINANTS OF CASSAVA FARMERS’ STRATEGIES FOR ADAPTING TO CLIMATE CHANGE IN EKITI STATE, NIGERIA.
By *A.I. Fatuase and A.I. Ajibefun
Department of Agricultural Economics and Extension, Federal University of Technology, Akure, Ondo State, Nigeria.
Reference: 0234
ABSTRACT
Climate change is expected to have serious environmental, economic and social impacts on Nigeria, particularly on rural farmers whose livelihoods depend largely on rainfall. This study therefore investigated the factors responsible for the choices of adaptation employed by cassava farmers in the study area. The study examined the adaptation choices of respondents from the two agro-ecological zones in Ekiti State. Data were collected and analyzed from a total of 40 respondents from each agro-ecological zone. Descriptive statistics and multinomial logit regression analysis were used to analyse the collected data. The study examines how cassava farmer perceptions correspond with climate data recorded at meteorological stations of Ekiti State. The statistical analysis of the climate data reveals that temperature and rainfall are increasing. The perceptions of farmer on temperature were in line with recorded climate data but contrary with that of rainfall which were perceived to be decreasing by the farmers. The respondents identified inadequate funds and climate information as the major constraints to adaptation. The study therefore concluded that educational level, farming experience, access to extension services, household size, access to climate information and household farm income are major factors statistically affecting choice of climate adaptation using multinomial logit regression.
Government policies and investment strategies must support the factors highlighted above in order to rescue the poor cassava farmers from the danger of climate change.
Key words: climate change, perception, adaptation, cassava farmers, multinomial logit
INTRODUCTION
Climate change is a phenomenon induced by global warming. It is a typical issue
affecting every facet of the world’s economy. Developing countries such as Nigeria are
vulnerable to the catastrophic effects of climate change. Developing countries’ vulnerability
is explained in line with the acute poverty, governance crisis, and the problem of
underdevelopment and so on, which characterized these economies (Kelly et al., 2000).
Climate change and agriculture have tremendous effects on each other. Agriculture affects
climate change through the emission of Greenhouse Gases (GHG) from different farming
practices while climate change in form of higher temperature, reduced rainfall and increased
rainfall variability reduces crop yield and threatens food security in low-income and
agriculture-based economies. Therefore climate change is expected to have serious impacts
on the environment, economy and social life of people, most especially on the rural farmers
whose livelihoods depend largely on rainfall. It was even reported by Mahendra (2010) that
climate change will result in ecological degradation and further threaten the fragility of
drylands, with serious consequences for crop and livestock production and food security.
Agriculture is the most assured engine of growth and development and a reliable key
to industrialization in Nigeria. This sector has been fallen short of expectations due to many
factors in which climate-related disasters like drought and floods are the major ones. Out of
the major staple foods produce in Nigeria, cassava (Manihot spp) plays an important role in
household food security. Cassava has several advantages compared with other carbohydrate
sources, especially other root crops. It has a high productivity under marginal climatic and
soil fertility conditions, which result in a low cost raw material (Ospina and Wheatley, 1991).
Nigeria is the largest producer of cassava in the world (Ogbe et al., 2003). Cassava is grown
throughout the tropic and could be regarded as the most important root crop in terms of area
cultivated and total production. It is a very important staple food consumed in different forms
by millions of Nigerians. The importance of cassava in bridging the food gap in Nigeria
cannot be overemphasized. It is reputed for being a hardy crop, producing economic yields
under conditions of drought, low soil fertility, locust attack, poor husbandry and other
adverse production conditions.
Climate change is liable to truncate the world food chain. At present food supply and
demand is not the same. There is a shortage in the amount of food supplied. Cassava is one of
such important food that is unfortunately in short supply. The role of cassava in food systems
will depend on what happens to real income. Cassava is a crisis crop. In times of war, drought
or low national incomes, cassava consumption increases relative to alternative food staples
such as yam, maize, rice, and wheat (Mendelsohn et al., 2006).
Cassava in certain forms is a low income consumers' staple. Although an individual
may not increase the quantity of cassava consumed in a year, as national income declines,
annual average cassava consumption per person increases because more people begin to
substitute cassava for more expensive alternative food staples (USDA, 2001).
Cassava is affected by various diseases and insect pests. These pests and diseases
include the African cassava mosaic disease, bacterial blight and mealy bug. Green spider mite
and larger grain borer also attack dry chips of cassava in storage. White ants sometime vest
deleterious effect on cassava production by destroying the stems that were planted before
they sprout. Climate change and its associated temperature increase will increase the
incidence of pest and diseases. Temperature increase has been said to encourage the
evolvement of new diseases that will be able to thrive in specific temperature and humidity.
This will pose new risks for food security, food safety and human health (FAO, 1999).
CLIMATE CHANGE, PERCEPTION AND ADAPTATION Adaptations are adjustments or interventions, which take place in order to manage the losses
or take advantage of the opportunities presented by changing climate (IPCC, 2001).
Nhemachena and Hassan (2007) reports adaptation as the process of improving society’s
ability to cope with changes in climate conditions across time scales, from short term (e.g.
seasonal to annual) to the long term (e.g. decades to centuries). The IPCC (2001) defines
adaptive capacity as the ability of a system to adjust to climate change (including climate
variability and extremes), to moderate potential damages, to take advantage of opportunities,
or to cope with the consequences. Also, adaptation measures help farmers guard against
losses due to increasing temperatures and decreasing precipitation (Hassan and Nhemachena,
2008). The literature on adaptations also makes it clear that perception is a necessary
prerequisite for adaptation. The preliminary evidence from a number of African countries
reveals that large numbers of agriculturalists already perceive that the climate has become
hotter and the rains less predictable and shorter in duration (Maddison, 2007). For the
purposes of analysis and decision-making, the present state of scientific knowledge of the
environment is usually taken to be ‘objective reality’ (Kamau, 2010). In this sense, the
environmental perception of an individual or group may be brought more closely into line
with scientific, ‘objective’ understanding by education and information (Perri, 2005).
Environmental perception provides a systematic and scientific understanding of the view
from the inside-out; in order to complement the more traditional and external scientific
approach. Therefore, to increase management efficiency of natural resources, the perceptions
of the people directly involved need to be taken along with those of experts (Kamau, 2010).
Adaptation to climate change is a response to climate change that seeks to reduce the
vulnerability of natural and human systems to climate change effects. Even if emissions are
stabilized relatively soon, climate change and its effect will last many years, and adaptation
will be necessary. Climate change is especially important in developing countries since those
countries are predicted to bear the brunt of the effects of climate change. That is, the capacity
and potential for human to adapt (adaptive capacity) is evenly distributed across different
regions and populations, and developing countries generally have less capacity to adapt
(Schneider et al., 2007). The economic costs of adaptation to climate change are likely to cost
billions annually for the next several decades, though the amount needed is unknown.
Since an efficient international mitigation policy cannot halt global warming in the
next decades if not century, there is a growing consensus to embrace adaptation strategies as
a way of coping with the changing climate. Adaptation is especially urgent for low latitude
developing country farmers who are expected to bear the brunt of the climate change impacts
(Mendelsohn et al., 2006).
Failure to address the issue of climate change may lead to a situation where Nigeria
and other West Africa countries incur agricultural losses of up to 4% of GDP due to climate
change (Mendelsohn, et al, 2005). Parts of the country that experienced soil erosion and
operate rain-fed agriculture could have decline in agricultural yield of up to 50% within
2000-2020 due to increasing impact of climate change (IPCC, 2007). Considering the above,
it is pertinent to examine cassava farmers’ perception about climate change, identify major
constraints to adopt adaptation measures and determine factors influencing choice of
adaptation measures among the cassava producing households in Ekiti State, Nigeria.
RESEARCH METHODOLOGY
Study Area
The study was carried out in Ekiti State, Nigeria. Its geographical coordinates are longitudes
40 51 and 50 45 East of the Greenwich meridian and latitudes 70 51 and 80 51 North of the
equator. It shares boundary in the south with Ondo State, North with Kwara State and East
with Kogi State. The State is largely agrarian. Agriculture is the mainstay of the State’s
economy. It employs 75% of the State working population. Approximate population
according to 2006 census was 2,384,212 with approximate land mass of 10,898.68 sq.km
(NPC). The state has two agro-ecological zones. Tropical forest exists in the South, while
guinea savanna predominates in the Northern peripheries. The state is one of the largest
producers of arable crops in Nigeria in which cassava production is prominent in both agro-
ecological zones. The State enjoys lowland tropical rain forest climate type with distinct rainy
season (April – October) and dry season (November – March). The temperature ranges
between 210 and 280C with high humidity. The South westerly wind and the Northeast winds
blow in the rainy and dry (Harmattan) seasons respectively.
Methodology
Both primary and secondary data were used for this study. Primary data were collected
through direct personal interview and structured questionnaire to obtain pertinent information
on socioeconomic characteristics of the cassava producers, perception of the cassava
producers about climate change and their coping strategies to the changing climate.
Secondary data were collected on climate attributes from the Intergovernmental Panel on
Climate Change (IPCC) and journals. A multi-stage sampling technique was used for the
random selection of respondents. It started by purposively selecting one Local Government
Area (LGA) from each agro ecological zones for the study which are Ise-orun and Oye LGAs
in the tropical forest zone and guinea savanna zone respectively based on their contribution to
the overall production of cassava in the State. Four (4) communities were randomly selected
from each LGA while ten (10) respondents were randomly selected in each community.
Therefore, a total of eighty (80) cassava producers were randomly selected for the study. The
data collected were analyzed using descriptive statistics and multinomial logit regression
model (MNL)
Multinomial Logit Regression Analysis
The decision of whether or not to use any adaptation option could fall under the general
framework of utility and profit maximization. Consider a rational farmer who seeks to
maximize the present value of expected benefits of production over a specified time horizon,
and must choose among a set of J adaptation options. The farmer i decides to use j adaptation
option if the perceived benefit from option j is greater than the utility from other options (say,
k) depicted as
( ) ( ) jkXUXU kikikjijij ≠+ʹ′>+ʹ′ ,εβεβ ,
Where Uij and Uik are the perceived utility by farmer i of adaptation options j and k,
respectively; Xi is a vector of explanatory variables that influence the choice of the adaptation
option; βj and βk are parameters to be estimated; and εj and εk are error terms.
The probability that farmer i will choose adaptation option j among the set of
adaptation options could accordingly be defined as follows:
( ) ( )XUUPXYP ikij //1 >== ……………………….(1)
( ),/0 XXXP kikjij >−ʹ′−+ʹ′= εβεβ
( )( )XXP kjikj /0>−+ʹ′−ʹ′= εεββ
)()/0( ***ii XFXXP βεβ =>+= ,
Where ε* is a random disturbance term, β* is a vector of unknown parameters that can be
interpreted as the net influence of the vector of the two set of explanatory variables
influencing adaptation, and F (β*Xi) is the cumulative distribution of ε* evaluated at β*Xi
(Deressa et al.,2008)
The standard form of the logit model is
Log [P/ (1-P)] = α0 +∑αi Xi + ε …………………………(2)
Where P = probability that the dependent variable Y=1;
(1-P) is the probability that Y= 0
αs are parameter estimates for the independent variable, X
And ε is the unexplained random component.
Y= f(X)
X is a vector of socioeconomic characteristics which are Farming experience (years),
Household size (numbers), Income from cassava production (Naira), Farm size (hectares),
Access to climate change information (dummy: yes=1 and 0 otherwise), Access to loan
(dummy: yes=1 and 0 otherwise), Marital status (dummy: married=1 and 0 otherwise), Level
of education of household head (dummy: educated=1 and 0 otherwise).
The parameter estimates of the MNL model provide only the direction of the effect of
the independent variables on the dependent (response) variable, but estimates do not
represent either the actual magnitude of change nor probabilities.
Differentiating equation (2) with respect to the explanatory variables provides marginal
effects of the explanatory variables given as:
k
j
XP∂
∂ = Pj (βjk ─ ∑−
=
1
1
J
J
Pj βjk ) ……………………….. (3)
The marginal effects or marginal probabilities are functions of the probability itself
and measure the expected change in probability of a particular choice being made with
respect to a unit change in an independent variable from the mean (Green, 2000; Koch, 2007;
Deressa et al., 2008).
RESULTS AND DISCUSSION
The results from Table 1 reveal that over 60 percent of the respondents in the study area are
at most 50 years indicating that majority of them are youth and therefore are still in their
productive working age with a mean of 47.21 and mode of 40 years. The results also show
that 90 percent of the cassava producers have at least 10 years of farming experience in which
the highest fall between 11 and 20 years of experience. 37.5 percent of the respondents have
no formal education but in all, more than 60 percent were literate with at least primary
education. Male genders dominated the study with a difference of 45 percent from female
counterpart. About 90 percent of the respondents (71) are married while just 6.3 percent are
single depicting that most of the respondents are matured and responsible. 95 percent of the
respondents have at least 6 household sizes while just 5 percent of them have at most 5
household sizes which mean that their household size is fairly large. However, household
income of less than equal to N100,000 (56.2%) predominate among the respondents. 37.5
percent of the respondents have annual income of N100,001 – N500,000; 5 percent earned
between N500,001 – N1,000,000 while 1.3 percent earned more than N1,000,000 annually.
Majority (58.8%) of the households plant both improved and local varieties of cassava, 23.7
percent plant local variety only while 17.5 percent plant improved variety only. The study
also discovered that 66.2 percent had no access to climate information showing that much is
still need to be done in enlightening the households on climate issues.
Table 1: Distribution of the households by their socio-economic characteristics
Household characteristics Frequency Percentage of respondents
Age (years)
≤ 30
31 – 40
41 – 50
51 – 60
61 - 70
≥ 71
8
20
23
15
11
3
10.0
25.0
28.8
18.7
13.7
3.8
Farming Experience(years)
≤ 10
11 – 20
21 – 30
31 – 40
41 - 50
≥ 51
8
26
20
14
8
4
10.0
32.5
25.0
17.5
10.0
5.0
Educational level
No formal education
Primary education
Secondary education
30
25
22
37.5
31.2
27.5
Tertiary education 3 3.8
Gender
Male
Female
58
22
72.5
27.5
Marital status
Single
Married
Divorced
Widowed
5
71
1
3
6.3
88.8
3.8
1.3
Household size
≤ 5
6 – 10
11 – 15
≥ 16
4
63
12
1
5.0
78.7
15.0
1.3
Income level (N)
≤ 100,000
100,001 – 500,000
500,001 – 1,000,000
≥ 1,000,001
45
30
4
1
56.2
37.5
5.0
1.3
Source: Field survey, 2011.
Comparism between Perceptions of Changes in Climate by cassava producing farmers
and Meteorological Stations’ Recorded Data (Mean Temperature and Mean Rainfall).
To assess farmers’ perceptions of climate change and variability, we first look at how climate
data recorded at meteorological stations evolved i.e. trends and variability and how farmers
perceived these changes. Tests were undertaken for linear trend in annual means of
temperature and rainfall in Ekiti State. Descriptive statistics based on summary counts of the
questionnaire structure are used to provide insights into cassava producers’ perceptions of
climate change and variability. Several studies have undergone the same type of analysis such
as Vedwan and Rhoades (2001), Hageback et al. (2005), Maddison (2006) all cited in
Gbetibouo (2009).
Perception about Temperature Changes
Over 95 percent of the cassava farmers interviewed perceived long-term changes in
temperature. Most of them (93.7% or 75 farmers) perceived temperature to be increasing. It
was only 2.5 percent of the respondents that noticed a decrease in temperature while 2.5
percent also noticed that temperature has stayed the same over the years. Only one
respondent (1.3%) does not know whether temperature is changing or not as shown in Table
2 below.
Table 2: Perceptions of cassava farmers on changes in temperature over the years.
Temperature Frequency Percent
Increase 75 93.7
Decrease 2 2.5
No change 2 2.5
Do not know 1 1.3
Total 80 100.0
Source: Field survey, 2011.
The climate data recorded at meteorological stations on annual mean temperature of Ekiti
between 1975 and 2007 was statistically analyzed to depict its trend over the years. The data
shows an increasing trend as indicated in Figure 1. In 32 years, the temperature has risen
around 0.5 degree Celsius with an average of 26.00C in the study area. Therefore, it appears
that farmers’ perceptions are in accordance with the statistical record in the study area.
Actual
Fits
Actual
Fits
2005199519851975
27
26
25
(oC
)Te
mpe
ratu
re
Years
Y = 25.6814 + 1.73E-02*X
MSD:MAD:MAPE:
0.111020.277031.06482
Key
Linear Trend Model
Figure 1: Trend Analysis for Annual Mean Temperature data for the study area: 1975 –
2007.
Source: Computed by author
Perceptions about Change in Rainfall
About 98 percent of the cassava farmers observed changes in rainfall patterns over the years.
70.0 percent (56 respondents) noticed a decrease in the amount of rainfall (a shorter rainy
season) over the years. In contrary, 27.4 percent observed increase while 1.3 percent said that
amount of rainfall has stayed the same over the past 20 years as all indicated in Table 3
below.
Table 3: Perceptions of cassava farmers on changes in rainfall pattern over the years.
Rainfall Frequency Percent
Increase 22 27.4
Decrease 57 71.3
No change 1 1.3
Do not know - -
Total 80 100.0
Source: Field survey, 2011.
The statistical record of rainfall data from Ekiti between 1975 and 2007 shows an increase
trend over the years (Figure 2). In 32 years, amount of rainfall has been increasing by 0.312
cm per year (average of 120.6mm/year). The result from meteorological station analysis on
rainfall was contrary to the view of farmers on perceptions on rainfall in the study area. A
large proportion of cassava farmers noticed a decrease in rainfall which is contrary to the
outcome of meteorological trend analysis on rainfall data and this could be explained by the
fact that during the last few years most especially last and present years, there was a
substantial decrease in the amount of rainfall. Thus, farmers’ perceptions of a reduction in
rainfall over the years could be explained by the fact that most of the farmers place more
weight on recent information than its efficient as also noticed by Maddison, 2006 and
Gbetibouo, 2009.
ActualFits ActualFits
2005199519851975
150
140
130
120
110
100
90
(mm
/yea
r)R
ainf
all
Years
Y = 115.254 + 0.311631*X
MSD:MAD:MAPE:
216.648 11.334 9.693
Key
Linear Trend Model
Figure 2: Trend Analysis for Rainfall Data in the Study Area: 1975 – 2007
Source: Computed by author
The issue of rainfall patterns analyzed above was in contrary with several studies but that of
temperature has been in accordance with several studies carried out on perceptions of and
adaptation to climate change most especially in Sub–Saharan Africa. Maddison (2006),
Ishaya and Abaja (2008), Mertz et al. (2008), Deressa et al. (2009), Gbetibouo (2009),
Benedicta et al. (2010) among others have observed increased and decreased in temperature
and amount of rainfall patterns respectively.
Identifying Major Barriers to Adaptation Measures in the Study Area.
The analysis of local knowledge gaps on adaptation to climate change in the study area
indicates major constraints to fully adopt most of the adaptation measures identified by the
cassava farmers. Out of 80 respondents interviewed, 97.5 percent (78 respondents) said that
they experienced one difficulty or the other in adapting to climate change. These constraints
are being identified by few farmers as an impediment to adopt adaptation measures while
many of the farmers identified them as a result of their inadequacy or shortage to adopt
adaptation measures. The major barriers are inadequate funds (89.6%), inadequate
information (64.4%), shortage of labour (41.5%), shortage of land (34.1%), inadequate
technology know how (29.6%) and others (23%) as indicated in Figure 3 below.
*Multiple response
Figure 3: Shows Major Barriers to Adaptation to Climate Change in the Study Area
Source: Field Survey, 2011.
Most of these constraints are associated with poverty and negligent of agricultural sector by
the government. For instance, inadequate funds limit farmers from getting the necessary
resources most especially inputs and technologies needed to facilitate adaptation to climate
change. Inadequate information on appropriate adaptation options could be attributed to the
dearth of research on climate change and adaptation options as well as inefficient
meteorological stations in the country. The information on climate change is not reaching
farmers who are the major stakeholder in this scene because the means of disseminating the
climate information is inaccessible and unaffordable to the farmers. Among other critical
constraints to adaptation identified by the farmers are bad road networks, market problems
and so on. Most of their farms are in bad condition, unmotorable and inaccessible. Farmers in
this study area are not poor neither are they rich but cannot afford to invest on the
construction of roads and purchase of new technologies to adapt to climate change.
Analyzing Factors Influencing Farmers’ Choice of Adaptation Measures using MNL
Model.
The results of MNL model showed how factors of socio-economic characteristics influence
farmers’ choice of adaptation measures in the study area. Therefore, the choice set in the
MNL model included the following adaptation options:
1. Change Planting Date
2. Planting Different Crops
3. Planting Different Varieties
4. Other adaptations
5. No adaptation (mono-cropping i.e. one variety of cassava only)
The estimation of the MNL model for this study was undertaken by normalizing one
category, which is normally referred to as the “base category”. In this analysis, the first
category (no adaptation) is the base category. The likelihood ratio statistics from MNL model
indicated that χ2 statistics (83.51) are highly significant (P < 0.0005), suggesting the model
has a strong explanatory power. As indicated earlier, the parameter estimates of the MNL
model provide only the direction of the effect of the independent variables on the dependent
(response) variable: estimates do not represent actual magnitude of change or probabilities.
Thus, the marginal effects from the MNL, which measure the expected change in probability
of a particular choice being made with respect to a unit change in an independent variable,
are reported and discussed. Therefore, Table 4 presents the marginal effects along with the
levels of statistical significance.
The results show that marital status and farm size had no significant effect on adaptation in
this study which is in contrary to many studies in literature. Farming experience and access to
extension services were not statistically significant but had positive coefficients, meaning that
they influence adaptation positively.
However, education of the household was statistically significant at 5% level of probability
meaning that it increases the probability of adapting to climate change. As observed in Table
4, a unit increase in number of years of schooling would result in 2.2% increase in the
probability of adapting to climate change. Household income is positive and statistically
significant in influencing adaptation to climate change. For instance, a unit increases in the
household income result in 0.00015% increase in the likelihood of influencing adaptation to
climate change. Access to climate information was positive and statistically significant at 1%
level of probability. It means that access to climate information increases the probability of
influencing adaptation to climate change. A unit increases in access to climate information
result in 1.05% increase in the chance of adapting to climate change. Again, household size
was statistically significant at 5% level of probability in influencing adaptation to climate
change but has a negative coefficient. This signifies that a unit increases in the number of
household size result in 3.4% less the likelihood of engaging in adaptation measures to
climate change.
Table 4: Result of marginal effects of explanatory variables from MNL adaptation
model.
Explanatory Variables Estimates Standard Error P-value
Marital Status 0.0764222 0.0719958 0.288
Educational Level 0.0216956** 0.0087202 0.013
Household Size -0.0343859* 0.0176665 0.052
Farming Experience -0.0040662 0.0025387 0.109
Household Income 1.46e-06** 6.00e-07 0.015
Access to Extension
Services
0.0453644 0.0429613 0.291
Farm Size -0.0520785 0.0375454 0.165
Access to Climate
Information
0.0104509*** 0.0607678 0.003
Note: ***significant at 1%, **significant at 5%, *significant at 10%.
SUMMARY AND RECOMMENDATION
Findings from this study indicate that climate is changing and cassava farmers were
also making decisive efforts to cushion its impact in order to avert both foreseen and
unforeseen dangers it might bring. The perceptions of farmers about climate change and
variability revealed that over 93% of the respondent perceived temperature to be increasing.
This observation was in line with meteorological trend analysis from recorded data on the
temperature. In the same vine, 70% of the respondent noticed a decrease in the amount of
rainfall over the years. The rainfall observation was contrary to the trend analysis on recorded
rainfall data from meteorological stations which tend to be increasing. This difference could
be as a result of erratic rainfall in the last two seasons because farmers put more weight on
recent information. The study also discovered inadequate funds and information on climate
change as the most common major constraints and limitations faced by the cassava farmers to
be adequately taken adaptation measures into practice in the study area.
Again, the multinomial logit model was applied to examine the determinants of choice of
adaptation to climate change and variability among cassava producer farmers in Ekiti State.
The results of the findings revealed that educational level, household income, access to
climate information and household size are the factors enhancing the adaptive capacity to
climate change among cassava farmers in the study area.
Government policies and investment strategies must support the factors highlighted above in
order to rescue the poor farmers from the danger of climate change. The policy must also
encompass that farmers should have access to affordable credit as well as subsidized
agricultural inputs in order to increase their ability and flexibility to change production
strategies in response to the forecasted climatic conditions.
References
Benedicta, Y.F.; Paul, L.G.V. and Ahmad, M.M. (2010). Farmers’ Perception and adaptation
to Climate Change: A Case Study of Sekyedumase District in Ghana. World Food
System- A Contribution from Europe. Center for Development Research
(ZEF),University of Bonn, Walter-Flex-Str. 3,53113, Bonn, Germany.
Deressa, T.T.; Hassan, R.M.; Ringler, C.; Alemu, T. and Yusuf, M. (2009). Determinants of
farmers’ choice of adaptation methods to climate change in Nile Basin of Ethiopia.
Global Environmental Change 19 (2009), 248-255.
Food and Agricultural Organization (FAO) of the United Nations, 1999. The state of food
security in the world. Rome: FAO
Gbetibouo, G.A. (2009). Understanding Farmers’ Perceptions and Adaptations to Climate
Change and Variability: The Case of the Limpopo Basin, South Africa. IFPRI
Discussion Paper No. 00849. Cited 3 May, 2009.
Green, W.H. (2000). Econometric analysis, 4th ed. Prentice Hall, Upper Saddle River, New
Jersey: Prentice-Hall.
(IPCC) Intergovernmental Panel on Climate Change (2001). ClimateChange 2001: Impacts,
adaptation, and vulnerability. Intergovernmental Panel on Climate Change.
Cambridge, UK: Cambridge University Press.
(IPCC) Intergovernmental Panel on Climate Change (2007). Impacts, Adaptation and
Vulnerability. Contribution of Working Group II to the Fourth Assessment Report
of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge
University Press.
Ishaya, S. and 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), 138-143.
Kamau, M.W. (2010). Perceptions of climate change, adaptation and the policy process
within the Forestry Commission of Great Britain – a comparative study of England,
Scotland and Wales.
Koch, S. (2007). Lecture notes presented to students in EKT 816 at the University of Pretoria
in South Africa during the fall semester of 2006. University of Pretoria.
Maddison, D. (2006). The perception of and adaptation to climate change in Africa. CEEPA.
Discussion Paper No. 10. Centre for Environmental Economics and Policy in
Africa. Pretoria, South Africa: University of Pretoria.
Maddison, D. (2007). The Perception of and Adaptation to Climate Change in Africa. Policy
Research Working Paper 4308: The World Bank, Development Research Group,
Sustainable Rural and Urban Development Team, August, 2007.
Mahendra, M. (2010). Innovations in Climate Risk Management: Protecting and Building
Rural Livelihoods in a Variable and Changing Climate’ SAT eJournal, Vol. 4,
Issue 1.
Mendelsohn, R., A Dinar, and L. Williams, 2006. “The Distributional Impact of Climate
Change on Rich and Poor Countries” Environment and Development Economics
11: 1-20.
Mertz, O.; Mbow, C.; Reenberg, A. and Diouf, A. (2009). Farmers’ perceptions of climate
change and agricultural adaptation strategies in rural Sahel. Environmental
Management 43(5), 804-16.
Nhemachena, C. and 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.
(NPC) National Population Commissions (2006). Nigeria: Report on the Survey of
Demographic and Health Survey.
Ogbe, F.O, and Olojede, A.O (2003) Economic performance of improved cassava variety
(NR8082) and local best (NWAIBIBI) Conference paper.
Perri (2005). “What’s in a frame? Social organisation, risk perception and the sociology of
knowledge.” Journal of Risk Research 8(2): 91-118.
