Smallholder Farmers’ Adaptation Strategies to Climate Change: The Case of Ankesha Guagusa District of Awi Zone, Northwestern Ethiopia

Smallholder Farmers’ Adaptation Strategies to Climate Change: The Case of Ankesha Guagusa District of Awi Zone, Northwestern Ethiopia
Smallholder Farmers’ Adaptation Strategies to Climate
Change: The Case of Ankesha Guagusa District of Awi
Zone, Northwestern Ethiopia
*Abebaw Assaye1, Mengistu Ketema2 and Adam Bekele3
1Ethiopian Institute of Agricultural Research, Fogera National Rice Research and Training Center, Bahir Dar, Ethiopia
2Haramaya University, School of Agricultural Economics and Agribusiness, Dire Dawa, Ethiopia
3Ethiopian Institute of Agricultural Research, Melkasa Agricultural Research Center, Adama, Ethiopia
Climate change is posing serious environmental, economic, and social impacts in the study area
where people’s livelihoods depend on agriculture. This study was initiated to identify the existing
adaptation strategies persuaded by smallholder farmers, and factors affecting the choices of
adaptation strategies against climate change in Ankesha Guagusa district. Primary data were
collected from a randomly selected 156 sample households in the district through interview
method and focus group discussion whereas secondary data were collected from different
organizations and published sources. Descriptive statistics, and econometric model were
employed to achieve the stated objectives. The result of multivariate probit model showed that
the likelihood of households to adopt irrigation, improved crop varieties, adjusting planting date,
use crop diversification, and soil and water conservation practices were 46.79%, 52.26%, 45.51%,
69.68% and 78.20%, respectively. The result also shows that the joint probability of using all
adaptation strategies was 11.53% and the joint probability of failure to adopt all of the adaptation
strategies was 7.7%. The model result also confirms that sex, educational level, family size,
livestock holding, land holding, off/non-farm income, farm income, extension contact, credit used,
access to climate information, distance to market, and agro-ecological zone had significant effect
on climate change adaptation strategies. Therefore, future policies shall focus on the smallholder
farmers’ technical capacity through adult education system, and on updated extension services,
improving credit facilities, irrigation facilities, farm and off-farm income earning opportunities,
and use of new crop varieties that are more suited to the local environment.
Key words: Climate Change, Adaptation strategy, Multivariate probit, Ankesha Guagusa.
INTRODUCTION
Climate is a key natural resource that influences food
production, water and energy availability. It is weather
manifested as extreme events or persistent conditions, is
experienced first as a physical phenomenon. When
extreme events such as droughts and floods occur, people
suffer injuries, incur losses, habitats are destroyed and the
built environment is damaged. Socio-economic systems
are therefore sensitive to the frequency, intensity and
persistence of these conditions, as well as potential
changes in long term trends. Some of the climate related
hazards documented as affecting Ethiopia’s agriculture
include drought, floods, heavy rains, strong winds, frost
and high temperatures (NAPA, 2007). Thus, it is important
to understand the clear effects of climate change on
agricultural productivity in the study area.
Climate change can affect both income and non-income
dimensions of poverty, either directly or indirectly. The
transmission mechanism from climate change to income
poverty is through its impact on agricultural production and
also non-agricultural practices in Ethiopia in general, in the
study area in particular are highly linked with agriculture. If
the agricultural sector performs well, it creates
opportunities for non-farm employment for the poor by
motivating producers to invest their surplus in
microenterprises in rural areas (Emerta, 2013). Climate
*Corresponding Author: Abebaw Assaye, Ethiopian
Institute of Agricultural Research, Fogera National Rice
Research and Training Center, Bahir Dar, Ethiopia.
E-mail: abebawal@gmail.com
Research Article
Vol. 6(2), pp. 821-833, September, 2020. © www.premierpublishers.org, ISSN: 2167-
0477
Journal of Agricultural Economics and Rural Development

Assaye et al. 822
change can also affect the non-income facets of poverty
including health conditions and level of educational
attainment. On top of that, climate change and variability
induced problems such as chronic food shortages, conflict,
and forced migration can limit schooling and education
attainments, which in turn affect poverty at the household
level (PACGA, 2009). All these factors in aggregate make
climate change a critical problem for mankind especially in
Ethiopia.
Ethiopia which is dependent on rain-fed agriculture
together with low level of socioeconomic development is
highly affected and vulnerable to climate change. Thus,
understanding smallholder farmers’responses to climatic
variations and climate changes are crucial in designing
appropriate adaptation strategies (Mohmud et al., 2008).
Temesgen et al. (2009) conducted a study in Nile basin of
Ethiopia and concluded that adaptation options are
location specific and policy for adaptation options should
be area specific. As site specific issues require site specific
knowledge, it is very important, therefore, to clearly
understand what is happening at community level,
because smallholder farmers are the most climate
vulnerable group. In the absence of such location specific
studies, it is difficult to fine tune interventions geared
towards achieving effective and efficient adaptation
options to cope with the adverse impact of climate change
at the local circumstances.
Farmers of Ankesha Guagusa district, like smallholder
farmers in any other part of Ethiopia, is suffering from
climate upheavals which have become common natural
disasters in the country. First, there has been more erratic
and unreliable rainfall in the rainy seasons, heavy rain,
hailstorm and reduction in crop yields and plant varieties;
the rainfall especially in the later rains towards the end of
the year has been reported as coming in more intense and
destructive downpours, bringing floods, landslides and soil
erosion. Second, there has been an increase in
temperature which disturbs the physiology of crops, the
micro-climate, and the soil system on which they grow.
Third, the crop and livestock production has been
recurrently hit by droughts, and floods. Fourth, annual river
runoff and water availability has been reported to decrease
dramatically. Food insecurity in the area is a major
challenge and all these climate shocks have exacerbated
the negative impacts on the livelihood of poorer farm
households as they have the lowest capacity to adapt to
changes in climatic conditions (AGDBAO, 2015).
Generally, this diverse climate in the study area influences
the livelihood activities of the farming community.
Generally, an assessment of adaptation strategies and
factors influencing farm-level adaptation strategies brings
more insights and recommend policies and investment
strategies that help moderate potential adverse
consequences of long-term climate change stresses.
Because smallholder farmers tend to have a low capacity
to adapt to changes in climatic conditions, policies that
help these farmers adapt to climate change and
associated climatic extremes are particularly important.
Objectives of the Study
The general objective of this study was to identify the
existing adaptation strategies persuaded by smallholder
farmers’ in response to climate change at district level.
The specific objectives of the study are:
1. To identify the existing adaptation strategies used by
farmers in response to climate change in the study
area; and
2. To identify factors that influence adaptation strategies
of smallholder farmers in response to climate change.
METHODOLOGY
Study Area
The study was conducted in Ankesha Guagusa district of
Awi Zone, which is located in the Amhara Region of
Northwestern Ethiopia. Ankesha Guagusa is one of the
eleven districts of Awi Zone located in the Southwestern
part of the zone, approximately 17 km of Kosober, capital
city of the zone. Geographically its location extends
between the coordinates of 36º 36'18" and 36 º 59'33" East
longitudes and 10º 31'46" and 10 º 41'32" North latitude.
The district in the Awi Zone, is bordered in the south by
Woberema, in the west by Guangua and Zigem, in the
north by Banja Shekudad and in the east by Guagusa
Shekudad. The altitude ranges between 1000 and 2800
m.a.s.l. (EMA, 2008). The study area receives erratic
rainfall from 1512.40 to 2438.70 mm of rainfall per annum
with mean annual value of 1920.91 mm for the years 2000-
2015. Temperature varies between the mean annual
minimum of 9.68°C and mean annual maximum of
25.46°C across the elevation gradient. The major relief
features of the district include mountains, undulating
plains, hilly and gullies and valleys. The three dominant
soil types of the district are nitosol, fluvisols (at gentler
slopes and river banks) and vertisols, locally walhi (covers
the major lower slope positions of the area) (Dessalew,
2014). The district spans three agro-climatic zones; Dega
(highlands) (10%), Woyena Dega (intermediate) (80%)
and Kolla (lowland) (10%). The total area of the district is
estimated to be 986.37 square kilometers (Tessema et al.,
2012). According to CSA (2013), the district has a total
population of 233,233 of which 49.62% are males and
50.38% are females.

J. Agric. Econs. Rural Dev. 823
Figure 1. Map and location of the study area in Amhara National Regional State
Source: Tessema (2012))
Data Sources and Collection Methods
Both primary and secondary data were used for this study.
The primary data were collected from sample households
in the district using structured questionnaire through
interview method and also a preliminary survey were
conducted through focus group discussion using checklist
to obtain general information about the study area.
Sampling Techniques: A multi-stage stratified random
sampling technique was applied to select the study
districts and sample peasant association (Kebeles) and
households. In the first stage, Ankesha Guagusa district
was selected purposely and the kebeles in the district were
stratified in to two groups, based on homogeneity of their
agro-ecological zones. Therefore, two agro-ecological
zones, dega, and woynadega having 10 and 16 kebeles
respectively were defined as strata. In the second stage
three out of sixteen kebeles from woyna dega (Midland)
and two out of ten kebeles from dega (Highland) agro-
ecological zone were selected randomly. In the third stage,
due to time and budget limitation, a total of 156 household
heads were selected randomly using simple random
sampling (SRS). This method of sample selection gave
each kebele and every household head in each kebele
equal chance of being included in the sample. Probability
proportional to size technique was applied to determine
the number of samples required at each stage of
disaggregation.

Assaye et al. 824
Data Analysis: The data obtained from interviews, focus
group discussions and the review of documents were
compiled, organized, summarized and interpreted through
concepts and opinions. In order to describe the
explanatory variables on farmers’ adaptation strategy
towards climate change, descriptive statistics such as
mean, percentage, frequency, chi-square test, and
standard deviation were used to assess adaptation
options, and constraints to adaptation strategies. It is also
used to explain the different socio-economic
characteristics of the sample respondent households. In
addition, multivariate probit model was used for identifying
the determinant factors that affect use of adaptation
strategies to climate change.
Econometric Analysis: This study employed multivariate
probit econometric model for identifying determinants of
choice of adaptation strategies to climate change. Some
recent empirical studies of technology adoption and
climate change adaptation decisions assume that farmers
consider set or bundle of possible technologies or
adaptation strategies and choose particular technology
(strategy) bundle that maximizes expected utility
(Nhemachena and Hassan, 2007; Minale et al., 2012).
Thus, the adoption decision is inherently multivariate and
attempting univariate modeling excludes useful economic
information contained in interdependence and
simultaneous adoption decisions. Farmers are more likely
to adopt a mix of adaptation strategies to deal with a
multitude of climate induced risks and constraints than
adopting a single strategy. Based on this argument, the
study adopted multivariate probit (MVP) econometric
technique to simultaneously model the influence of the set
of explanatory variables on choice of each of the different
strategies, while allowing the unobserved and/or
unmeasured factors (error terms) to be freely correlated.
The multivariate probit model is a generalization of the
probit model used to estimate several correlated binary
outcomes jointly. The correlation could be
complementarity (positive correlation) and substitutability
(negative correlation) between different strategies
(Belderbos et al., 2004; Lin et al., 2005). The dependent
variable in the empirical estimation for this study is the
choice of adaptation strategies from the set of adaptation
options (crop diversification, adjusting planting date, using
improved crop varieties, use of irrigation, and soil and
water conservation practices).
The household decision of whether or not to undertake
adaptation strategies against climate change is considered
under the general framework of utility or profit
maximization (Temesgen et al., 2008). The economic
agents such as households used adaptation strategies
options only when the perceived utility or net benefit from
using a particular adaptation strategy was significantly
greater than in the base category (Aemero et al., 2012;
Zivanomoyo and Mukarati, 2013). In this context, the utility
of the economic agents is not observable, but the actions
of the economic agents could be observed through the
choices they made. The model is appropriate for this study
since the farmers also more likely adopt a mix of
adaptation strategies to deal with a multitude of climate
induced risks and constraints than adopting a single
strategy.
The multivariate probit econometric approach for this study
is characterized by a set of 𝑛 binary dependent
variables 𝑦ℎ𝑝𝑗 such that:
𝑦∗
ℎ𝑝𝑗
= 𝑥′ℎ𝑝𝑗 𝛽𝑗 + 𝑈ℎ𝑝𝑗, 𝑗 = 1, 2, … . 𝑚. (1)
𝑦ℎ𝑝𝑗 = 1, if 𝑦∗
ℎ𝑝𝑗
> 0 or (if the farmer adopt) (2)
0, otherwise
Where 𝑗 = 1,2, … 𝑚 denote the climate change adaptation
strategies available; 𝒙′ℎ𝑝𝑗 is a vector of explanatory
variables, 𝜷 𝑗 denotes the vector of parameter to be
estimated, and 𝒖ℎ𝑝𝑗 are random error terms distributed as
multivariate normal distribution with zero means and
unitary variance. It is assumed that a rational ℎ 𝑡ℎ
farmer
has a latent variable, 𝑦∗
ℎ𝑝𝑗
which captures the unobserved
preferences or demand association with the 𝑗 𝑡ℎ
choice of
adaptation strategy. This latent variable is assumed to be
a linear combination of observed households and other
characteristics that affect the adoption of adaptation
strategies, as well as unobserved characteristics captured
by the stochastic error term.
Given the latent nature of the variable, 𝑦∗
ℎ𝑝𝑗
, the
estimation is based on the observed variable 𝑦ℎ𝑝𝑗 which
indicates whether or not a household adopt a particular
climate change adaptation strategy. Since adoption of
several adaptation strategy is possible, the error terms in
equation (1) are assumed to jointly follow a multivariate
normal distribution, with zero conditional mean and
variance normalized to unity. The off-diagonal elements in
the covariance matrix represent the unobservable
correlation between the stochastic component of the
𝑗 𝑡ℎ
and 𝑚 𝑡ℎ
type of adaptation strategies. This assumption
means that equation (2) gives a MVP model that jointly
represents decisions to adopt a particular adaptation
strategy. This specification with non-zero off-diagonal
elements allows for correlation across the error terms of
several latent equations which represent unobserved
characteristics that affect the choice of alternative
adaptation strategies.
Variables included in the model
The dependent variables in the empirical estimation are
adaptation strategies that are chosen by the sample
households. The choice of adaptation strategies are based
on the actions the sample households take to counteract
the negative impact of climate climate change. The
independent variables of the study are those which are
expected to have association with use of adaptation
strategies are presented in Table 1.

Table 1: Summary of the explanatory variables that affects
adaptation strategy
Variables Types of
variables
Expected
sign
Dependent variables
Adaptation strategies Nominal
Independent Variables
Sex Dummy +
Age Continuous +
Education level Continuous +
Family size (Number) Continuous -
Market distance (Km) Continuous -
Livestock holding (TLU) Continuous +
Off/non-farm income (Birr) Continuous +/-
Farm income (Birr) Continuous +
Extension contact (Number) Continuous +
Access to credit Dummy +
Access to climate information Dummy +
Land holding (Hectares) Continuous +
Agro ecology Dummy +/-
RESULTS AND DISCUSSION
Demographic Characteristics of the Households
For this study, essential data were collected from a total of
156 sampled households. Different studies have revealed
that sex of the household head plays an important role on
smallholder farmers’ response to climate change
adaptation strategy. Out of the total sample households
surveyed, 70.5% of the respondents were male headed
while 29.5% accounts female headed households (Table
3). The survey results also show that 33.3% of the
household heads were from dega agro ecology and the
other 66.7% households from woyna dega agro ecology.
Educational level of sampled household heads was
believed to be an important feature that determines the
readiness of the household head to accept new ideas and
innovations regarding climate change adaptation
strategies and efficient use of resources. The empirical
result shows that the educational status of the smallholder
farmers ranges from 0 to 10 grade with mean of 1.90 and
standard deviation of 3.1 (Table 3). The survey results also
show that 32.48 % of the household heads were literate,
and 67.52% were illiterate. From this, it can be inferred that
there is high level of illiteracy in the study area.
It was put in the literature, age is considered as a proxy to
the farming experience of the household, which is likely to
have a significant influence on choice of adaptation
strategies to climate change. The youngest household
head that was interviewed was aged 23 years whilst the
oldest was aged 78 years with a mean age of 46.41 years
(Table 3).
The survey data indicated that the family size of the
sampled households varies from 1 to 12 with an average
household size of 5.67 and a standard deviation of 2.09,
which is higher than the national average family size of 5.32
(CSA, 2011).
Table 1: Demographic characteristics of sampled
households
Variable Mean/proportion Std.
Dev.
Minimum Maximum
Sex (male) 0.705 0.46
Education
level
1.90 3.1 0 10
Age of the
household
(years)
46.41 0.97 23 78
Family size
(Number)
5.67 2.09 1 12
Source: Own computed result, (2015)
Socioeconomic Characteristics of the Households
Farmers in the study area are engaged in mixed farming
activities, including staple food crops production (such as
teff, maize, finger millet and barley) and rearing domestic
animals such as cows, oxen, goats, sheep, horse and
donkey. Land is the everknown limiting factor for
agricultural production in Ethiopia, in general and in the
study area in particular. As it is indicated in Table 4, the
land holding of sampled households ranges from 0 to 3
hectares with an average size of 0.99 hectares with a
standard deviation of 0.55. About 80.3%, 65.1%, 52%, 39.5
and 29.6% of the sample households have grown teff,
maize, finger millet, barely and pepper as a major crop,
respectively as food and cash crops. On average 0.39 ha,
0.29 ha, 0.13 ha, 0.13 ha and 0.06 ha of land was covered
by teff, maize, finger millet, barely and pepper, respectively.
The study showed that the major sources of income in the
study area are on-farm activities mainly from sale of crops,
livestock and livestock products (milk and butter). Farm
income from sale of crops and forest products of the
surveyed households ranged from 120 to 120,000 birr with
an average of 11,104 birr per annum, with standard
deviation of 12, 062. The average farm income of the
households in the year 2015 from sale of livestock and
livestock products was 2605 birr, with standard deviation of
3379, and with a minimum of 0 and maximum of 30000
(Table 4).
Off/non-farm incomes were also other sources of income
for some of the sample households. Petty trade,
handicrafts and off/non-farm salary employment, daily
labor, renting their assets and remittance are some of the
off/non-farm income sources in the study area.
Engagement in these types of activities may help
households to avoid sale of major household assets,
renting out agricultural land, and borrowing for coping
purposes. Surveyed farmers’ income from off/non-farm
activities ranged from 0 to 31,470 birr with an average of
1711 birr per annum (Table 4).

Assaye et al. 826
Table 4: Socioeconomic characteristics of the household
Variable Mean Std. Dev. Minimum Maximum
Land holding
(Hectare)
0.99 0.55 0 3
Livestock
holding (TLU)
4.32 3.89 0 26.71
Farm income
(Birr)
11104 12,062 120 120000
Off/non-farm
income (Birr)
1711 1729 0 31470
Livestock
income (Birr)
2605 3379 0 30000
Source: Own survey result, (2015)
Livestock holding size is one of the indicators of wealth
status and an important component of farming system of
the households in the study area. Cattle provide draught
(traction) power for crop cultivation, their dungs serve as
manure or organic fertilizer and household fuel. They also
provide meat and milk for consumption and other products
like hides and skins. Small ruminants are used to meet
immediate cash need of the households and also for meat
production and home consumption especially during
holidays. Poultry is kept for egg and meat production both
for cash and home consumption.
Cattle are the dominant type of livestock that farmers
possess. About 84.62 % of the households have at least
one head of cattle, and the average holding of cattle for the
sample households was 3.96 heads. Poultry was the
second dominant type of livestock that the sample farmers
possess. The average holding of poultry for the sample
households were 2.44. The third dominant livestock type
are small ruminants, particularly shoats. About 43.4% of
the sampled households have at least one head of small
ruminant. The average small ruminant holding was 1.97
with a maximum holding of 12 heads, whereas 42.8% and
12.5% of the farmers' own horses and donkeys,
respectively. On average, 37.1 % of the households have
a pair or more of oxen, 28.5% have only a single ox, and
the remaining 34.4% of the farmers do not own oxen at all.
Owners of a single ox in the area plough their land through
a system called wonfel, through sharing their ox. Those
who do not have oxen at all also have a system of
ploughing their land. This system is sharing a pair of oxen
from other farmers in exchange of their labor, the system
is called yebere gulbet. The exchange ratio here is two
labor days for a single oxen-pair day.
To assess the livestock holding of each household in terms
of tropical livestock unit (TLU), the TLU per household was
calculated. According to the survey result, the mean
livestock holding of the sampled households in terms of
tropical livestock unit (TLU) was 4.32, the maximum and
minimum being 26.71 and 0 TLU, respectively (Table 4).
This shows that there is a high population of livestock as
well as a wide variability in terms of livestock ownership
among the smallholders.
Institutional Characteristics of the Household
The patterns of crop production, livestock rearing and
choice of climate change adaptation strategy of the
smallholder farmers can be mainly determined by the
nature and development of markets for inputs and outputs
as well as extension services. The main markets for the
smallholder community in the district are Agew Gimja
Bet, Azena, Hudit and Ayehu. Accordingly, the average
market distance the respondents traveled to reach the
nearest market center at the time of survey was about
4.34 kilometers with the minimum and maximum distance
of 1 and 12 kilometers, respectively (Table 5).
It is necessary to provide basic information related to
agriculture and enhance the knowledge and skills of
farmers. The survey result also shows that the frequency
of extension contact with the farmers’ ranges from 0 to 30
times with an average contact of 13 times per year with a
standard deviation of 10.73 (Table 5). The survey results
show that only 39. 9% of the respondents accounted that
they were trained on issues related to climate change
adaptation strategies.
The availability of credit for resource poor farmers is quite
important to finance agricultural technologies and
management options that could enable them to increase
farm investment. Access to credit by/for smallholder
farmers is one way of improving economic capacity and
ultimately adaptive capacity. In this study, out of the total
sample households surveyed, 67.95% reported that they
received credit, while 32.05% accounted the inverse for
the most recent two production years. The received credit
was used to buy improved seed and fertilizer, necessary
goods, services, and to diversify livestock production and
income sources. The major sources of credit for the
farmers in the study area were Amhara micro finance
(45%), primary farmers’ cooperatives (19.3%), local
money lenders (2.7%) and other friends and relatives
(2.1%) (Table 5).
Access to information about seasonal forecast of the
weather condition and climate change is necessary to
understand the coming weather condition and to take
measures. Even though, there were no formal sources
that deliver weather information, 64.11% of the sample
households had access to forecasted climate information
from mass media, development agents and other farmers
while the rest 35.89 % household heads reported the
opposite (Table 5).

Table 5: Institutional characteristics of the household
Variable Mean/proportion Std.
Dev.
Minimum Maximum
Extension
contact
(Number)
12.59 10.73 0 30
Distance
from
market
(Km)
4.34 2.97 0 12
Access to
training
(yes)
0.3975 0.49
Access
climate
information
(yes)
0.6411 0.48
Access to
credit (yes)
0.6795
Climate Related Hazards in the District
The main climate-related hazard affecting smallholder
farmer in the district were crop pest, diseases, heavy
hailstorm, erratic nature of rainfall, and late cessation of
rainfall. In response to ranking the main climatic hazards
affecting their livelihoods, 22.31% of the respondent’s
ranked crop pest and disease as the main hazard for their
vulnerable livelihood. While heavy hailstorm, erratic
nature of rainfall, late cessation of rainfall, flood, livestock
disease, land degradation, frost and human disease were
also among the top ranked hazards by 18.03%, 17.36%,
13.46 %, 7.7% 7.02, 6.5%, 4.67% and 2.95% of the
respondents, respectively.
The Most Affected Social Groups
Even though the exposures to climate change have been
the same for smallholders in the area, its impact on
livelihood of different wealth categories and social
groups is, however, not the same. From the FGD, most
of them agreed that the poor and the landless were the
most vulnerable social groups in their locality. Accordingly,
64.74%, 25.64%, and 9.62% of the respondents agreed
that the most affected social group were children, elders,
women respectively. The participants of the FGDs also
reported that children, elders, and women, were the main
social groups that are affected by climate related hazards
as they have poor adaptive capacity due to various
biological, economic and social factors. The vulnerability
of women and children to the existing climate related
hazards can be related to factors like lack of ownership
over productive assets, low social status, overburdened
in raising and caring for children. On the other hand, one
of the main elderly related factors identified in the study was
migration of elders during flood and drought seasons to
nearby cities to escape the effects and to search for a better
future.
Climate Change Adaptation Strategies
Different local adaptation strategies are undertaken by the
smallholder farmers in the study area to alleviate the
current climate change related hazards. Based on the
household survey data collected from 156 households,
significant proportion of farmers have observed change in
climate over the past 15 years and adopted different
adaptation strategy to reduce the impact of climate
change. To respond to climate change and reduce its
negative effects, use of irrigation, using improved varieties
of crops, adjusting planting date, crop diversification, and
soil and water conservation practices are used by farmers
in the study area as major adaptation strategies to climate
change. These strategies, however, are mostly used in
combination with other strategies to safeguard against
losses that could result from changes in temperature and
precipitation. In addition to the strategies mentioned
above, the participants of FGDs also identified the various
adaptation strategies they sometimes used. These
include, participating in non/off farm activities,
afforestation, livestock diversification, change from crop to
livestock and reduce number of livestock.
Table 6. Summary of adaptation strategies used by
farmers
Adaptation strategy Number of users Percent
Using improved crop varieties 81 51.92
Adjusting planting date 71 45.51
Crop diversification 108 69.23
Use of irrigation 73 46.80
Soil and water conservation 121 77.56
Source: own survey data (2015)
Note that a farmer can use more than one adaptation
strategy at a time.
Constraints to Adaptation Strategies of Climate
Change
The farmers in the district faced with various constraints
that can make the adaptation mechanisms ineffective at
the farm level. The sampled households reported that
they had various interrelated constraints that can make
their life very difficult in the presence of climate change
and climate related hazards. Farmers also sort out their
major challenge for their failures to adapt which includes
lack of technical knowledge about adaptation strategies,
lack of irrigation water, lack of money to finance their
adaptation strategies, lack of weather information, lack of
improved crop varieties that adapts the environment,
shortage of labor, and shortage of land. Households
surveyed had encountered more than one constraint for
a given adaptation option that they favor. Accordingly,
from the total sampled households, 56.1% faced lack of
money to finance, 52.7% lack of access to irrigation
water, 41.10% lack of technical knowledge, 20.9% lack of
forecasted weather information, 25.7% lack of improved
crop varieties, 64.2% shortage of land and 14.2%
shortage of labor (Figure 2).

Assaye et al. 828
Figure 2. Major constraints of farmers that hinder
adaptation strategies
Determinants of Farmers’ Choice of Adaptation
Strategies
Multivariate probit model was used to identify the
determinant factors that influence the choice of adaptation
strategies of 156 sampled smallholder farmers in response
to climate change. The model was selected based on the
justification illustrated earlier in the methodology part.
The MVP model is significant because the null hypothesis
that probability of adoption of the five adaptation strategies
are independent was rejected at 1% significance level. The
Wald χ2 test value of 355.86 which is statically significant
at 1% significance level indicated that separate estimation
of choice of these adaptation strategies is biased and the
decisions to choose the five strategies were
interdependent.
Furthermore, the Likelihood ratio test is also significant,
implying that the null hypothesis of all the Rhoij values are
jointly equal to zero, was rejected. This showed the
goodness-of-fit of the model. The Chi squaretest result
verifies that separate estimation of adoption of these
adaptation strategies is biased and the decisions to use
those five adaptation strategies are interdependent
household decisions. The results of correlation coefficients
of the error terms also indicate that there is
complementarity (positive correlation) between different
adaptation options being used by farmers. The results
support the assumption of interdependence between the
different adaptation options which may be due to
complementarity in the different adaptation options and
other factors that affect uptake of all the adaptation
options. The maximum likelihood method of estimation
results suggested that there was positive and significant
interdependence between household decisions to adapt
use of crop diversification and irrigation; soil conservation
and using improved crop varieties and crop diversification
and soil and water conservation practices.
The marginal success probability for each adaptation
strategies are presented below. The likelihood of
households to adopt irrigation, improved varieties of crops,
adjust planting date, use crop diversification and soil and
water conservation practices were 46%, 52%, 45%, 69%
and 78%, respectively. The result also shows that the joint
probability of using all adaptation strategies was 11.53%
and the joint probability of failure to adopt all of the
adaptation strategies was 7.70%. Regarding the
determinants of climate change adaptation strategies, the
results suggest that different household, socioeconomic,
and farm characteristics are significant in determining the
households’ decisions to choose adaptation strategy.
Sex of household head: Sex of the household head is an
important variable affecting adaptation decision at the farm
level. The negative coefficients for sex variable shows that
female headed households are more likely to take up crop
diversification and using improved crop varieties as
adaptation strategy. Women perform many activities for the
wellbeing of their familymembers, which simultaneouslycan be
regarded as well-designed adaptation practices. They intensify
their efforts in homestead production through diversifying their
crops and using improved crop varieties especially
vegetables as adaptation options to cope up with food
deficit situations and climate change impacts. The result of
this study suggests that, as compared to male headed
households, being female headed household increases
the probability of using crop diversification and improved
crop varieties as climate change adaptation strategy. This
result is consistent with findings of Nhemachena and
Hassan (2007), Baten and Khan (2010), Bewket (2013)
and Seid (2014). However, this result contradicts with the
finding of Temesgen et al. (2010), Minale (2012) and
Belaineh et al. (2013) that argued that female headed
households have limited access to information, land, and
other resources due to traditional and social barriers, while
male headed households have high adaptive capacity than
its counterpart.
Education level of the household head: The study
established that the probability of more educated farmers
were more likely to use improved crop varieties as
adaptation strategies to climate change than that of less
educated farmers. This suggests that being educated
would improve access to information, capable to interpret
the information, easily understand and analyze the
situation better than less educated farmers. The study
was hypothesized that farmers with higher levels of
education should more likely adapt better the climate
change. The result of this study indicated that educational
status increase the awareness of farmer about the
consequence of climate change on agricultural
productivity and benefit of improved crop varieties to
reduce the impact of climate change. This finding was
similar to Temesgen et al. (2009), Aemro et al. (2012) and
Gutu et al. (2012). They noted that higher levels of
education is likely to enhance information access to the
farmer for improved technology up take and higher farm
productivity.

Table 7: Multivariate probit simulation results for smallholder farmers’ adaptation strategy to climate change
Explanatory
variables
Use of improved
crop varieties
Use of irrigation Crop
diversification
Adjusting
planting date
Soil conservation
practices
Coef.(Robust S.E) Coef.(Robust S.E) Coef.(Robust S.E) Coef.(Robust S.E) Coef.(Robust S.E)
Sex -0.625*(0.350) 0.383(0.329) -0.778**(0.306) -0.388(0.282) 0.081(0.350)
Age -0.004(0.014) -0.010(.0121) -0.004(0.012) -0.017(0.011) -0.001(0.014)
Education level 0.102**(0.052) 0.012(0.041) 0.029(0.042) -0.032(0.037) 0.052(0.054)
Family size 0.164**(0.083) -0.066(0.069) -0.029(0.071) -0.049(0.063) 0.320***(0.099)
Livestock holding 0.049(0.049) 0.141***(0.049) 0.074(0.046) 0.067(0.038) 0.122**(0.061)
Land holding 0.036(0.361) 0.546**(0.271) 0.649**(0.286) 0.586**(0.256) 0.154(0.353)
Off/non-farm
income
-0.001(0.002) 0.0004(0.0003) -0.0002(0.0002) 0.0005(0.003) 0.003(.004)
Farm income 0.214*(0.123) -0.139(0.104) -0.134(0.103) -0.136(0.097) -0.048(0.116)
Extension contact 0.018(0.016) 0.0417***(0.0151) -0.008(0.014) 0.007(0.013) 0.067***(0.023)
Access to credit -0.119(0.336) 0.533*(0.289) 0.327(0.261) 0.670***(0.251) 0.223(0.315)
Access to climate
information
0.242(0.301) 0.309(0.282) 0.447*(0.260) 0.168(0.247) 0.207(0.311)
Distance to mrkt -0.016(0.047) 0.136***(0.043) -0.011(0.042) 0.029(0.036) 0.012(0.044)
Agro ecology -1.849***(0.338) 1.261***(0.297) 0.095(0.288) 0.128(0.253) 0.769**(0.374)
Constant -2.106(1.431) -1.680(1.217) 0.950(1.123) 0.646(1.074) -2.422(1.474)
Rho2 0.034 (0.198)
Rho3 0.172 (0.198) 0.5995***(.1488)
Rho4 -0.109 (0.177) -0.140 (0.152) 0.126 (0.151)
Rho5 0.628***(0.169) 0.179 (0.200) 0.640***(0.136) 0.197 (0.187)
Predicted
probability
0.5226 0.4679 0.6968 0.4551 0.7821
Joint probability(success) 11.53%
Joint probability(failure) 7.7%
Number of observations 156
Number of simulations 100
Log likelihood -332.313
Wald χ2(degree of freedom) 355.86***(65)
Likelihood ratio test of H0: Rhoij =0; χ2(10) = 38.11 0.0000
***, ** and * significant at 1%, 5% and 10% probability level, respectively. Coef. = coefficient and S.E = standard error
Family size of household (FSIZE): As expected, family
size of the households has significant impact on improved
crop varieties and soil and water conservation practices as
adaptation strategy to climate change. The model result
shows that family size has positive and significant impact
on the likelihood of using soil and water conservation
practices and improved crop varieties as adaptation
strategy to reduce the negative impact of climate change.
The possible reason could be that large family size is
normally associated with a higher labor endowment, which
would enable a household to accomplish various
agricultural tasks which are labor intensive like soil and
water conservation practices. In addition, a large family
might be forced to divert part of its labor force into non-
farm activities to generate more income and adopt a
number of coping strategies like using improved crop
varieties. Results obtained in the present study are in
agreement with arguments of Hassan and Nhemachena
(2008) and Seid, (2014) which states that households with
larger family size are expected to enable farmers to
implement various adaptation measures, while the result
contradicts the finding of Aemro et al. (2012) Bewket et al.
(2013) and Belaineh et al. (2013).
Land holding (LANDHD): Land holding of the households
has positive and significant impact on use of irrigation, crop
diversification and adjusting planting date as adaptation
strategies. Large land holdings allow farmers to diversify
their crop and livestock options and help spread the risks
of loss associated with changes in climate (Hassan and
Nhemachena, 2008). Since land holding is associated with
greater wealth, the study was hypothesized land holding
has positive relation with adaptation to the climate change.
The possible reason could be if the farmers have more
land holding they can benefit from the economic scale of it
as compared with those who have small land holding. This
result is consistent with the findings of Temesgen et al.
(2008) and Seid (2014).

Assaye et al. 830
Livestock ownership (TLU): The ownership of livestock
of the households has positive and significant impact on
use of irrigation and soil and water conservation practices
as adaptation strategy. The possible reason could be
livestock plays a very important role by providing traction
(especially oxen) and manure required for soil fertility
maintenance. This is also explained by the fact that herd
size is a proxy for wealth status of farmers. Those farmers
with large herd size have better chance to earn more
money to invest on tools required for soil conservation
practices and irrigation activities. This result is in
agreement with the previous works of Temesgen et al.
( 2008), Aemro et al. (2012) and Seid, (2014).
Farm income (ONFARM): The farm income of the
households has a positive and significant impact on use of
improved crop varieties. This could be apparent because
use of improved crop varieties requires financial resources
to purchase improved seeds and hence increased income
will encourage the investment capacity on this adaptation
strategy. The result confirms the hypothesis which states
that farm income of the households has a positive and
significant influence on the adaptation of climate change.
The implication of the result was that availability of farm
income improves farmers’ financial position, which in turn,
enables them to purchase farm inputs such as, improved
seeds and fertilizer. The result of this study is consistent
with the findings of Temesgen et al. (2008); Aemro et al.
(2012) and Gebre et al. (2015), they reported that adoption
of new crop varieties requires more financial resources
than adoption of other adaptation strategies.
Extension contact: As it was hypothesized, the result
indicates that the frequency of extension visit to the
households has significant positive impact on use of
irrigation and soil and water conservation practices, which
could in turn helps to reduce the negative impact of climate
change. Aymone (2009), Temesgen et al. (2009), Minale
(2012), and Belaineh et al. (2013) also found similar
results. This could be due to the fact that extension
services create access to information on agronomic
practices, climate change and adaptation strategies. To
put in another way, farmers with more access to
information and technical assistance on agricultural
activities have more awareness about the consequence of
climate change. Therefore, this study suggests that the
availability of better climate and agricultural information
helps farmers make comparative decisions among
alternative adaptation options and enable them to adapt
better with changes in climate.
Credit used (CREDITR): The result indicates that credit
used have a positive and significant impact on likelihood of
using irrigation and adjusting planting date as adaptation
strategies to climate change on agricultural production.
According to Nhemachena and Hassan, (2007) access to
affordable credit increases financial resources of farmers
and their ability to meet transaction costs associated with
the various adaptation options they might want to take.
With more financial and other resources at their disposal
farmers are able to change their farm management
practices in response to changing climatic and other
factors. This result also implies the important role of
increased institutional support in promoting the use of
adaptation options to reduce the negative impact of
climate change. The result of this study is consistent with
the finding of Temesgen et al. (2009), Aemro et al. (2012),
Minale (2012), and Gebre et al. (2015) those who worked
on climate change adaptation strategy, reported that
access to credit has a positive and significant impact on
the likelihood of using soil conservation, changing planting
dates, and using irrigation.
Access to climate information (CLIMINFO): Information
on climate change represents access to the reliable
information required to make the decision to adapt to
climate change. Reliable information on temperature and
rainfall has a significant and positive impact on the
likelihood of using different adaptation options. As
expected, access to climate information has significant
impact on the use of crop diversification as adaptation
strategy to cope up the negative effects of climate change
in the study area. Farmers who are aware of changes in
climatic conditions have higher chances of taking adaptive
measures in response to observed variability’s. The survey
result revealed that, getting information on seasonal
forecasts and climate change increase the probability of
using crop diversification. This result is in line with
Maddison (2006), Aemro et al. (2012), and Gebre et al.
(2015) they argued that access to temperature and rain fall
information from different sources has significant impact
on the likelihood of using different adaptation measures
like improved crop variety and crop diversification.
Distance to market (MKTDIS): Contrary to expectation,
distance to the nearest market is positively associated with
the use of irrigation as adaptation strategies. It was
assumed that proximity to market is an important
determinant of use of irrigation as adaptation strategy,
presumably because the market serves as a means of
exchanging information with other farmers (Maddison,
2006). When farmers are far from the market, the
transaction cost for acquiring input and output will be high
and this will, in turn, reduce the relative advantage of
adopting new technologies (Temesgen, 2010). However,
remoteness of the market area weakened the smallholder
farmer not to use improved crop varieties and farm inputs,
availability and accessibility of rivers and irrigation water
increases the probability of using irrigation as adaptation
strategy in the study area. In addition, framers who live far
from the market have more land and livestock holding than
those near to the market, which will in turn influence use
of irrigation as adaptation strategy.
Agro ecological setting: Different farmers living in
different agro ecological settings employ different
adaptation methods (Aemro et al., 2012; Belaineh et al.,
2013; Gebre et al., 2015). For instance, as compared with

farming in weynadega, farming in the dega zone
significantly increases the probability of using irrigation
and soil and water conservation practices as adaptation
strategies. However, farming in dega significantly
increases the probability of not using improved crop
varieties. This study was also hypothesized that different
households living in different agro ecological settings uses
different adaptation methods. This is due to the fact that
climatic conditions, soil, and other factors vary across
different agro ecologies, influencing smallholder farmers’
decisions to adapt. This result is in line with Temesgen et
al. (2009) who argued that farming in dega significantly
decreases the probability of planting trees as compared
with farming in weyna dega.
SUMMARY AND CONCLUSIONS
The main climate-related hazards affecting smallholder
farmers in the district were crop pest, diseases, heavy
hailstorm, erratic nature of rainfall and late cessation of
rainfall. The impact of climate change on livelihood of
different wealth categories and social groups was not the
same. Women, children, elders, the poor and the landless
were the main social groups that were affected by climate
related hazards as they have poor adaptive capacity.
Adaptation strategies used by farmers in the study area
include changing/adjusting planting date, using soil and
water conservation techniques, using improved crop
varieties (changing crop varieties), diversifying crop
(mixed cropping, intercropping and dividing farm lands in
to varying crops) and using irrigation.
The results from the MVP analysis indicate that sex,
educational level, age, farm income, livestock ownership,
land holding, number of extension contact, credit used,
access to climate information, distance to market and
agro-ecological setup of the area have significant impact
on adaptation to climate change. The likelihood of
households to adopt soil conservation practices, crop
diversification, improved varieties of crops, irrigation, and
adjusting planting date were 78%, 69%, 52%, 46%, and
45% respectively. The result also shows that the joint
probability of using all adaptation strategies was 11.53%
and the joint probability of failure to adopt all of the
adaptation strategies was 7.7%.
Multivariate probit model results also confirm that livestock
ownership, land holding, number of extension contact,
credit used, distance to market and agro-ecology of the
area have a significant impact on the use of irrigation as
climate change adaptation strategy. The result also shows
that sex, education level, family size, farm income and
agro-ecological zone of the area significantly affect the use
of improved crop varieties to adapt to climate change. In
addition, sex, land holding and access to climate
information significantly affect use of crop diversification as
adaptation strategies. Moreover, land holding and credit
used significantly affects farmers’ using adjusting planting
date to adapt to climate change impacts. Finally, family
size, livestock ownership and number of extension contact
significantly affect use of soil conservation practices to
adapt to climate change in the study area.
Thus, the results of the study is believed to give
information to policy makers and extension workers on
how to improve farm level adaptation strategies and
identify the determinants for adaptation strategies. This
could contribute to reduce the adverse effects of climate
change and generally help agricultural as well as
economic development. These findings call for the need
for appropriate policy formulation and implementation
which will enable farmers to reduce the impact of climate
change as this is expected to have multiplier effects
ranging from farm productivity growth to economic growth
and poverty reduction at macro level.
RECOMMENDATIONS
Based on the findings of the study, the following
recommendations are suggested to be considered:
 In terms of policy implications, efforts should be done
to improve education level of the household through
adult education system established by the government
in each kebele.
 Improving irrigation facilities by providing irrigation
inputs through affordable credit scheme should be
given emphasis at reduced interest rate.
 Improving smallholder farmers’ farm income through
providing yield increasing technology packages and
through affordable credit schemes in the rural areas
would enhance the capacity of the farmers to adopt
climate change adaptation strategies. This could be
underlined as policy options to reduce the negative
impacts of climate change.
 On the other hand, reliable weather forecast is also of
crucial importance to take the best suitable adaptation
strategies for the recent climate scenario. As well as
improved coordination, communication, and
information-sharing among different government
agencies and NGOs from national to the local levels,
especially regarding weather, climate and food security
information are important. The information delivery
system from the existing district level metrology station
should be established.
 Extension services has to be updated in line with the
current existing climate condition needs. Government
offices and NGOs have to work on the development
agent’s by continually updating the extension workers’
knowledge so as to improve the productivity and
production level of both crop and livestock sub sectors
through the transfer of improved technologies,
knowledge and practices which are environmentally,
socially and economically viable.

Assaye et al. 832
 In addition, future policy options need to fine-tune
climate change adaptation strategies based on sex and
agro ecological settings.
 Generally, future policies shall focus on awareness
creation about climate change through different
sources such as mass media, extension services,
facilitating the availability of credit especially to
adaptation technologies, improving irrigation facilities,
researches on use of new crop varieties that are more
suited to the local environment, improving farmers farm
income earning opportunities and improving their
literacy status of the household.
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Accepted 19 June 2020
Citation: Assaye A, Ketema M, Bekele A (2020).
Smallholder Farmers’ Adaptation Strategies to Climate
Change: The Case of Ankesha Guagusa District of Awi
Zone, Northwestern Ethiopia. Journal of Agricultural
Economics and Rural Development, 6(2): 821-833.
Copyright: © 2020: Assaye et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

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