Farmers’ Perception and Adaptive Capacity to
Climate Change and Variability in the Upper
Catchment of Blue Nile, Ethiopia
WORKING PAPER SERIES No. 77

ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

ATPS Working Paper No. 77

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ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

ATPS Working Paper No. 77

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Farmers’ Perception and Adaptive Capacity to
Climate Change and Variability in the Upper
Catchment of Blue Nile, Ethiopia

Bewket Amdu
Haramaya University
Ethiopia
Azemeraw Ayehu
Debre Brihan University
Ethiopia
Andent Deressa
Ambo University
Ethiopia	
  ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

ATPS Working Paper No. 77

3This paper should be cited as:
African Technology Policy Studies Network, ATPS 2013: Farmers’ Perception and Adaptive
Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia[Bewket Amdu, Azemeraw Ayehu, Andent Deressa], ATPS WORKING PAPER No. 77© 2013Published by the African Technology Policy Studies Network

ISBN: 978-9966-030-55-9

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Table of Contents
Acknowledgement						

6Abstract								

7

1.

Introduction						

8

2.

Literature Review						

9

3.

Methodology						

11

4.

Results and Discussion					

15

5.

Conclusions and Recommendations			

27

References							

29ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

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Acknowledgement
This report was produced as part of the implementation of the African Technology Policy
Studies Network (ATPS) Phase VI Strategic Plan, 2008 – 2012 funded by ATPS Donors
including theMinisterie van Buitenlandse Zaken (DGIS) the Netherlands and the Rockefeller
Foundation. The authors hereby thank the ATPS for the financial and technical support during
the implementation of the program. The Authors specially thank Prof. Kevin Chika Urama,
the ATPS Executive Director for his visionary leadership as Program Director; Prof. Atieno
Ndede-Amadi, Program Coordinator; Dr. Nicholas Ozor, Senior Research Officer, ATPS;
Mr. Ernest Nti Acheampong, ATPS Research Officer and the ATPS Team for the technicalsupport during the research process.

ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

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6Abstract
The study examined the perception of farmers towards climate change; assessed local
impacts of climate change on the agriculture sector; identified local innovations for climatechange adaptation; and assessed the barriers and determinants of climate change adaptation
options at the farm level. The study was conducted in three dominant agro-ecological zones,
Highland, Midland, and Lowland, of the upper catchment of Blue Nile based on farm-level
data collected from 384 households for 2010/2011 cropping season. According to the survey
results, the majority of the respondents, up to 85% were of the view that there has been arise in temperature. Approximately 61% of the respondents observed a decrease in rainfall
volume, whilst about 90% perceived a change in the timing of rains. The perception of most
farmers’ on temperature rise was in harmony with the trend analysis of temperature records.
However, there was a clear contradiction between the perception of majority of farmers’ on
rainfall volume and the trend analysis of the rainfall records. Most of the farmers’ who perceived
changes in climate employed local adaptation options in response to climate signals. The mostcommon adaptation options include: implementation of soil conservation practices, cropping
calendar adjustment, tree planting, change of crops, crop diversification and adjustment tocrop and livestock management. However, lack of knowledge and information, lack of inputs,
inappropriate policy, land scarcity, shortage of labor, lack of market, water scarcity, poverty
and lack of extension services were identified as barriers to adaptation. The multinomial logit
model result highlighted the frequent extension visit, resource endowment, access to climate
information, large size of productive labor and increased farming experience as positive
influences on farmers’ adaptation to climate change. On the other hand, farmers’ perceptionof possessing fertile land was a disincentive to undertake adaptation measures. The
Government could also contribute to counteract the impact of climate change on agriculture
by investing in research, soil conservation measures, technology, animal health centers, andirrigation and water harvesting development, expanding fertilizer use, market, and education,
establishing crop insurance institutes, reforming land policy, establishing local meteorology
stations, monitoring and publishing climate data, and creating job opportunities by expanding
non-agriculture sectors.

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1. Introduction
There is a growing understanding that climate variability and change poses serious challenges
to development in Ethiopia. The reason for this is that the mainstay of the Ethiopian economy
is rain-fed agriculture, which is heavily sensitive to climate change and variability. Seventy
percent of Ethiopia is arid, semi-arid, or categorized as dry sub-humid; areas that are prone
to desertification and drought (National Meteorology Agency, 2007). In addition, the Ethiopian
highlands are fragile because of over cultivation, overgrazing, erosion, and deforestation.
The country is expected to experience changing patterns of rainfall, increased temperatures
leading to elevated evaporation rates, and flooding; these will in turn lead to greater levels ofland degradation, transmission of infectious disease, and loss of surface and ground water
potential.
The poor subsistence farmers, who on average account for 98% of the total area undercrops and for more than 90% of the total agriculture output (Belshaw, 1997), are first line
victims to the impacts of the changes in climate. For instance, the farming community of
the upper Blue Nile catchment reported losses in their agricultural production due to erratic
rain, increased temperature, perils of flood and drought, and scarcity of water. According tofarmers, climate related hazards, namely, flood, drought, erratic rainfall, pest and disease,hailstorms, and land slide were not new phenomena to them, but their socio-economic andbiophysical effects had increased in intensity and coverage across decades. Even though
they are exposed to those hazards and have a low adaptive capacity, they have survived
and coped by making tactical responses to these changes. Adaptation had been practiced
in Ethiopia before the concept of “climate change” became more prominent (McKee, 2008).
However, these local adaptations have not been valued and documented so far, and hence,
recognizing and documenting the local adaptation strategies is an important entry point tostrengthen the resilience of local people to climate change. Analyzing local adaptation is,therefore, important to inform policy for future successful adaptation of the agricultural sector
to the impact of climate change.
The overall aim of this study is to provide research evidence based policy findings that would
contribute to enhancing farmers’ capacity to adapt to the impact of climate change in Ethiopia.
To this end, the specific objectives are to:• Assess the perception of farmers and development stakeholders (NGOs, local authorities,
experts in the field of agriculture and natural resource management) towards climatechange and variability; perception is a precondition for adaptation;• Assess climate hazards in the area;• Identify and document local innovation options for climate change adaptation of thedifferent agro-ecologies;• Assess the barriers and determinants of climate change adaptation options at the farmlevels.

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82. Literature Review
2.1 Empirical Studies
There are a few studies conducted on Ethiopian farmers’ climate change adaptation strategies.
In the following subsection relevant studies conducted in Ethiopia are briefly presented.
Mahmud et al., (2008) studied impact of climate change on food production in a typical lowincome developing country. Their analysis relies on primary data from 1,000 farms producing
cereal crops in the Nile Basin of Ethiopia. They found that climate change adaptations , namely,changing crop varieties, adopting soil and water conservation measures, water harvesting,tree planting, and changing planting and harvesting periods have significant impact on farm
productivity. Extension services, both formal (extension officer-farmer), and farmer- farmer,
as well as access to credit and information on future climate changes, affect adaptation
positively and significantly. Farm households with larger access to social capital are more
likely to adopt yield-related adaptation strategies. On the other hand, lack of information andshortages of labor, land, and money were identified as major reasons for not adapting.
There are also studies conducted on farmers’ adaptation strategies in other parts of Ethiopia.
These studies identified the use of different crops or crop varieties, tree planting, soil
conservation, changing planting dates, and irrigation as common adaptation strategies; andlevel of education, gender, age, and wealth of the head of household; access to extension
and credit; information on climate, social capital, agro-ecological settings, and temperature all
influence farmers’ choices; lack of information on adaptation methods, financial constraints,
and lack of access to land as main barriers to adapt ( Bryan et al., 2010; and Temesgen et al.,2008). According to Temesgen et al., (2008), age of the household head, wealth, information
on climate change, social capital, and agro-ecological settings have significant effects on
farmers’ perceptions of climate change.
The limitation with the above studies is that they are too aggregated to exactly indicate local
climate change and variability impacts, adaptation strategies, farmers’ perception on climatechange, barriers, and determinants of adaptation strategies at specific location. The authors
have acknowledged the need of further studies at local levels, particularly at district andvillages, one of the gaps this study is aimed at filling. However, the previous studies werevery relevant in providing good information for performing this local specific study.
2.2 Concepts of Climate Change Adaptation
This subsection deals with definitions of terms which are important to understanding climatechange adaptation issues. Accordingly, “climate change” refers to a change in the state of theclimate that can be identified by changes in the mean and/or the variability of its properties
and that persists for an extended period, typically decades or longer (IPCC, 2007a). It refers
to any change in climate over time, whether due to natural variability or as a result of humanactivity. “Climate variability” is variations in the mean state and other statistics (such as
standard deviations, the occurrence of extremes, etc.) of the climate on all temporal andspatial scales beyond that of individual weather events. Variability may result from naturalinternal processes within the climate system (internal variability) or from variations in naturalor anthropogenic external forcing (external variability) (IPCC, 2001a).Turner et al., (2003)
defined “resilience” as the degree to which an impacted system rebounds or recovers froma perturbation. Adaptive capacity is the ability or potential of a system to respond to climate
variability and change in a manner that reduces vulnerability (Brook, and Adger, 2005).
“Climate hazard” is a potential climate treat to the welfare of systems (Downing et al., 2001).
Climate change impacts necessitate responses and adjustments to the biophysical and social
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9conditions which together determine exposure to climate hazards. These responses may
occur in form of autonomous action or through public as well as private planned, individual
and institutional mechanisms.
Climate change adaptation is a very broad concept, and different authors defined adaptationdifferently. For instance, Burton (1992) defined climate change adaptation as the process
through which people reduce the adverse effect of climate on their health and well-being, andtake advantage of the opportunities that their climatic environment provides. According to
Stakhiv (1993), adaptation means any adjustment, whether passive, reactive or anticipatory,
that is proposed as a means of ameliorating the anticipated adverse consequences
associated with climate change. On the other hand, the IPCC (2001b) defines adaptation
as adjustments in ecological, social or economic systems in response to actual or expected
stimuli and their effects or impacts. This term refers to changes in processes, practices
and structures to moderate potential damages or to benefit from opportunities associatedwith climate change. Adaptation hence involves adjustments to decrease the vulnerabilityof communities, regions, and nations to climate variability and change and in promoting
sustainable development (ibid).
Depending on its timing, goal and motive of its implementation, adaptation can either bereactive or anticipatory, private or public, planned or autonomous (Klein et al.,1999).
Adaptations can also be short or long term, localized or widespread (IPCC, 2001b).

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103. Methodology
Study Area
The study area of this research is situated in the upper Blue Nile catchment of Ethiopia.
It is located approximately between coordinate 10033’06’’ to 10050’24’’ North latitude and37042’36’’ to 37058’24’’ East longitude. It covers a total estimated area of 58122.94 hectares
and with the total population of about 132069. Topographically, the area lies in the altitudes
range of 878m to 4000m.a.s.l (Figure 1).

Figure 1: Location of the study area (Source: Choke Mountain Initiative project
document, 2010)
As a result of this the area is characterized by three distinct agro-ecological zones- Dega,
Weynadega, and Kola. According to the traditional classification system, which mainly relies
on altitude and temperature for classification, Ethiopia has five climatic zones (Table 1).
The information which is obtained in Weredas’ Agricultural Offices revealed that the rainfall
amount and temperature of the area ranges from 385 -1300mm, and 10- 26oC respectively.
The area gets monomodal type of rain fall (that is Kirmt rain fall regime). The soil types wereidentified based on their colors (red, brown, black and grey); on the average percentage,
about 60 %, 39 %, 36% and 2.5% are brown, red, black and grey soil respectively. The
cultivated land covers a total area of 34161.93 ha of the area. The major annual crops
cultivated in the catchment are barley, Avena species (Ingedo), wheat, beans, peas, potato,maize, and sorghum. The common domestic animals in the area are cattle, sheep, goats,horses, mules, donkeys, and poultry.

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11Table 1: Traditional climatic zones and their physical characteristics
Zone

Altitude(meters)

Rainfall(mm/year)

Average temperature(Co)

Wurch(upper highland)

3200 plus

900- 2,200>11.5

Dega(highlands)

2,300- 3,200

900- 1,200

17.1/16.0- 11.5

Weynadega(midlands)

1,500- 2,300

800- 1, 200

20.0- 17.5/16.0

Kola(lowlands)

500- 1,500

200- 800

27.5- 20.0

Berha(desert)

Under 500

Under 200

>27.5

Source: Ministry of Agriculture (MOA, 2000)

Data Collection and Method
A household survey was conducted among 384 household heads during the 2010/2011
production year in the upper catchment of Blue Nile. Three sample districts, namely, Sinan,Goziamn and Dejen were purposely selected to represent Dega, Weynadega, and Kolla
agro-ecological zones respectively. The purpose of analysis in relation to agro-ecologicaldifferentiation is to investigate how farmers living in different agro-ecologies perceive, andadapt climate change and how different agro-ecologies are affected by climate change andvariability. In the survey, farmers were asked to evaluate the temperature and precipitation
trends of the area over the last three decades. The primary qualitative data on farmers’
perception of temperature and rainfall changes was triangulated with trend analysis of 38
years (1970- 2008) climate records obtained from Debre Markos meteorological station.
Descriptive statistics (count, percentage, and mean) and Multinomial Lobit Model (MNL) wereused to analyze the data collected. To assess farmers’ perception, adaptation options, local
climate impacts and the barriers they faced to adapt, descriptive statistics based on summary
counts of the questionnaire structure were used. MNL model was used to identify the major
factors determining adoption of adaptation options to climate change and variability. MNL
model is preferred because it is most widely used in adoption decision studies involving
multiple choices, and computationally easier and more convenient.
Multinomial Logit Model (MNL) SpecificationLet
be a random variable representing the adaptation measure chosen by any
farming household. We assume that each farmer faces a set of discrete, mutually
exclusive choices of adaptation measures. These measures are assumed to depend
on a number of climate attributes, socioeconomic characteristics and other factors X.
The MNL model for adaptation choice specifies the following relationship between theprobability of choosing option Ai and the set of explanatory variables X as (Greene, 2003):............................................................ (1)Whereis a vector of coefficients on each of the independent variables X. Equation (1) can
be normalized to remove indeterminacy in the model by assuming that and the probabilities
can be estimated as:..........................................(2)Estimating equation (2) yields the J log-odds ratios

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...........................................................(3)The dependent variable is therefore the log of one alternative relative to the base alternative.
The MNL coefficients are difficult to interpret, and associating with the jth outcome is tempting
and misleading. To interpret the effects of explanatory variables on the probabilities, marginal
effects are usually derived as (Greene, 2003):........................................................... (4)The marginal effects measure the expected change in probability of a particular choice being
made with respect to a unit change in an explanatory variable (Greene, 2003). The signs of
the marginal effects and respective coefficients may be different, as the former depend on
the sign and magnitude of all other coefficients.
In the adoption of various adaptations measure by farm household the MNL model is used toestimate the effects of explanatory variables on the dependent variables.
Based on the above theoretical justification, we specify the multinomial logit model for farmhouseholds’ adaptation option strategies (AOS) like Portfolio diversification, water-additions
and management, enhancing livelihoods productivity, economizing land, change use of
inputs, diversifying income, and changing agricultural calendar that safe guard the farmhousehold from climate change hazard and increase their agricultural productivity as follows:....................................................................................................... (5)Whereis reported adaptation option strategies used by the surveyed farm households,
is regression parameters, is vector of the explanatory variable and is the error term. Thismodel is estimated by using the standard econometric software, STATA.
Definition of Variables
The major variables expected to have influence on the adoption of adaptation measures are
explained below:
The dependent variable of the model : In the study the following 25 common adaptation
strategies, which are all explained by similar explanatory variables, were identified: change
crop variety, plant a different crop, diversify crops, shift planting dates, move to a different
site, change quantity of land under cultivation, build a water-harvesting scheme, implement
soil conservation techniques, buy insurance, put trees for shading, irrigate more or using
irrigation, change from crop to livestock, adjust to livestock management, reduce number oflivestock, change from livestock to crops, adjust to crop management, migrate to urban area,find off-farm job, diversify from farming to non farming activities, lease your land, change useof capital and labor, use of weather derivatives, pray more or increase your ritual offerings to
traditional rainmakers, change use of chemicals or fertilizers, and decreased use of irrigation/
ground water/watering.
The explanatory/ independent variables: The independent variables that are hypothesized
to affect the farmers’ adoption decision of an adaptation measure are combined effects ofvarious factors, such as: household characteristics, socioeconomic characteristics and
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13institutional characteristics in which farmers operate. Based on the review of adoption andadaptation literatures, and past research findings, 17 potential explanatory variables wereconsidered in this study and examined for their effect on a farmer’s adoption decision of an
adaptation option to climate change and variability (see Appendix: Table 11).
Variable Adjustments for MNL Analysis
Econometric analysis with cross-sectional data is usually associated with problems of
multicollinearity, and heteroscedasticity. Thus, prior to running the logit model the presenceor absence of multicolliniarity was checked using 1Variance Inflation Factor (VIF) for testing
association among the continuous explanatory variables and Contingency Coefficients(CC) for the dummy variables. Based on the test results, all the screened variables except
credit service usage, perception, and gender included in the model for analysis. To avoid
heteroscedasticity the logit analysis was run with robust.
To avoid multicollinearity effects in the dependent variables and to make the analysis simple
and suitable, the model was restructured by grouping closely related choices together in thesame category. Using different crop variety, changing crops, and crop and animal diversification
were grouped in the same category, labeled “portfolio diversification.” These three choices areclosely related because they are considered for the same purpose of risk-spreading. Similarly,
digging water wells, using irrigation, and reducing the frequency use of irrigation were groupedin the same category, labeled “water-additions and management.” They are related to the useof water for the purpose of increasing productivity and withstanding rainwater shortages.
Adjustment to crop production, adjustment to livestock production, changing from livestock
to crop, changing from crop to livestock, reducing the number of livestock and planting
trees were grouped in the same category, labeled “enhancing livelihoods productivity”. The
management aspects including reducing number of livestock and planting trees for shade,and the swapping of livelihoods are done to have better productive livelihoods. Change ofthe quantity of land under cultivation, implementing soil conservation, leasing land, moving todifferent farming site were merged to the “economizing land” category, as they all are using
land as a base to enhance their income in terms of crop production or capital. Changing
use of capital and labor, changing use of chemicals or fertilizers were grouped to the same
category, labeled “change use of inputs.” They are grouped on the same category because
capital, labor and agro-chemicals are important inputs for the agriculture sector. Migrating
to urban areas, finding off-farm jobs, and diversifying from farming to non farming activities
were pooled together as “diversifying income.” These are related in that they are the means
to earning additional incomes other than their own farm income. Change of planting date,and use of weather derivatives were grouped on the same category, labeled “changing
agricultural calendar’’. Changing planting dates could be one of the practical applicationsof weather derivatives, which may also include other applications like changing harvesting
dates, managing pest and disease prevalence, managing floods and droughts, and others.
Ritual offering and insurance were removed from analysis. This is because there is no asingle respondent who offered rituals to traditional rainmakers, and there is also no crop
insurance institute around the study areas. Accordingly, the choice set in the restructured
multinomial logit model included the following adaptation options: 1) Portfolio diversification,
2 water-additions and management, 3) enhancing livelihoods productivity, 4) economizing
land, 5) change use of inputs, 6) diversifying income and 7) changing agricultural calendar.

1. Decision rule: statistically strong correlation if VIF > 10(Gujarati, 2003) ,and CC >0.75 (Paulos ,2002). The tools of analysis were STATA and SPSS.

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144. Results and Discussion
4.1 Farmers’ Perception of Long-term Changes in Temperature and Precipitation
4.1.1 Perception of Temperature Changes
84.4% of the interviewed household heads perceived an “increase” in temperature volume,
2.3% of respondents perceived a “decrease” in temperature volume, 8.1% of respondents
perceived “no change” in temperature volume, 3.6% respondents did not have any idea
about the trend of the temperature volume , and 1.6% of the respondents refused to give
their opinion on the issue. On the other hand, 85.2% of the respondents felt an increase inheat intensity; 1% of the respondents perceived a decrease in heat intensity; 9.9 % of the
respondents claimed no change in heat intensity; 2.3% of the respondents did not give enoughattention about the heat intensity trend; and 1.6% of the respondents failed to respond. As
indicated in Figure 2 below, the trend analysis of both annual mean minimum and maximum
temperatures follow increasing trends. This was in accordance with the perception of most
farmers towards temperature.

Figure 2: Annual mean minimum and maximum temperature trends of upper Blue Nile
catchment.
There was no statistically significant variation in perception of temperature across the agroecological zones (Appendix: Table 12). This implies that the change in temperature occurredin all agro-ecologies and it was felt more or less equal by every farming community. The
analysis of variance for perception of temperature change shows significant variation among
the different educational levels (Table 2).

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Table 2: Farmers perception on temperature across educational levels
Perception on temperature (%)

2

X

Heat intensity per day
Educational
level

Increased

No change

Decreased

I do notknow

No answer

Illiterate

78.9

18

0

1.5

1.5

Read and
write

89.2

5.9

0

3

2

Complete first
cycle

83.3

7.1

7.1

2.4

0

Completesecond cycle

90.9

0

9.1

0

0

Illiterate

75.8

15.6

2.3

3.9

2.3

Read and
Write

90.1

3.9

0.5

3.9

1.5

Complete first
cycle

81

7.1

9.5

2.4

0

0

0

41.4*(df=12)

Number of hot days per year

Complete
90.9
09.1
second cycle* Significant at 1% level; Source: Own survey, 201132.6*(df=12)

Highest proportion of respondents in all educational levels perceived the increase in heat
intensity and volume. However, there was not a single respondent who had not noticed
or felt no change in temperature from those who completed second cycle education. The
implication is that educated peoples are keener in noting changes in temperature more thanless or uneducated people; educated people become very conscious about their environmentand sense the changes in climate better. Same tests indicated a significant difference
between the views of experienced and inexperienced farmers existed in temperature change
perception (Table 3). Most farmers with short farming experience (< 10 years) observed nochange in heat intensity where as farmers with more experience (> 10 years) perceived anincrease in heat intensity. On the other hand, highest number of respondents in all experiencegroups perceived an increase in temperature volume on time scale of three decades andmore. However, farmers with more experience who perceived the increase in temperaturevolume are more than twice of the respondents of short experience. Besides, the percentageof respondents with short farming experience who felt no change in temperature volume
are more than six times to that of farmers with medium and long experience. Moreover, aconsiderable number of short experience farmers did not give enough attention about thetemperature trend of the area they are living (Table 3). This implies that experienced farmers
tend to perceive an increase in temperature more than less experienced farmers.

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Table 3: Perception of changes in temperature by farmer experience
Perception on temperature (%)

X

Heat intensity per day
Farmingexperience

Increased

No change

Decreased

I do notknow

No answer

Short(<10
yrs)

42.9

48.6

2.9

5.7

0

Medium(1030yrs)

86.9

8

2.2

0.7

2.2

Long(>30yrs)

91

5.6

0

3.4

0

94.6*(df=16)

Number of hot days per year
Short(<10yrs)

40

37.1

5.7

17.2

0

Medium(1030yrs)

86.9

6.6

2.2

1.5

2.9

Long(>30 yrs) 89.3
5.1
2.2
* Significant at 1% level; Source: Own survey, 20113.4

083.4*(df=16)

All other stakeholders interviewed believe that temperature has increased in the area overthe last three decades.
4.1.2 Recognition in Changes of Precipitation
Most of the interviewed farmers perceived precipitation changes, quantity of rainfall and/or
distribution, in the study area over the last 30 years. Substantial percentage of respondents(88.3%) perceived the change in the amount of rainfall. Out of 88.3% respondent who
perceived the change in rainfall amount, 60.7% of the respondents felt a decrease in theamount of rainfall, and the remaining 27.6 % respondents oppositely felt an increase in theamount of rainfall; on the contrary, 7.8% of the respondents noticed no change in the amountof rainfall; 2.6% of the respondents did not give enough attention about the trend of therainfall volume; and 1% of the respondents refused to respond. The result also indicated thatthe majority of the respondents (89.6 %) noticed a change in the timing of rains, specifically,
73.9 % observed shorter rainy seasons, and 15.7 % observed extended rainy seasons; 5.7%of the respondents observed no change in the rainy season; 2.9% of the respondents had
no opinion about the rainfall pattern; and 1.6% of the respondents did not give a response.
According to the recorded data on rainfall from 1970 to 2008, the annual and the seasonalrainfall volume follow increasing trends over the period (Figure 3). There was a clear contrast
between the majority of the farmers’ perception towards amount of rainfall and rainfall trend
over the past 30 years. The divergence in farmers’ perception and the actual rainfall data
could be seen from two different perspectives.

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17Figure 3: Rainfall trend of upper Blue Nile catchment (1970-2008)
On one hand, there could be an increasing demand for water and food in the study areas due
to recorded population growth (CSA, 2008). The increasing population could have put a lot ofpressure on existing water bodies for crop production, livestock production, and household
consumption. Moreover, the demand for stable crops to feed the increased population might
fail because of low agricultural productivity which could not be necessarily resulted fromclimate change and variability. Farmers, however, might perceive the decrease in water
availability due to these factors, and the deficit in crop production to feed the increased
population as decrease in rainfall amount, despite it remained stable or increased throughout
the years. On the other hand, the recorded monthly meteorological rainfall data might hide theactual rainy days within a month or the amount of rain in a single shower. From experiencemost farmers know specific days or weeks with in a critical crop growth period when a crop
demand for water is highest; if it does not fall enough in those actual time of the critical crop
growth period, farmers might perceive it as decrease in rainfall amount.
Unlike temperature, perception in precipitation shows significant variation across thedifferent agro-ecological zones (Table 4 and Table 5). The lowland community is the one
with the highest proportion of respondents who felt a decrease in rainfall amount and theleast to perceive an increase in amount. This is probably because in the lowland zone wateris already very scarce, and a little change in the amount of rainfall could be felt highly, forexisting livelihoods are already on climatically stressed conditions.

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Table 4: Perception of rainfall amount change by agro-ecology

2

Perception on rainfall amount per day (%)

X

Agro-ecology

Increased

No change

Decreased

I do notknow

No answer

Highland

30

0.8

67.5

1.7

0

Midland

30.7

14.1

48.5

4.9

1.8

0

2

Lowland
19.8
5.9
72.3
* Significant at 1% level; Source: Own survey, 201136.28*(df=10)

Table 5: Perception of rainfall pattern change by agro-ecology

2

Perception on rainfall pattern (%)
Agro-ecology

Not changed

ChangedNo Answer

Highland

0

100

0

0

Midland

11

82.8

6.2

2.2

Lowland4.9
91.1
4* Significant at 1% level; Source: Own survey, 2011

I do notknow

X

24.3*(df=6)

No answer

0

94.6*(df=16)

The statistical analysis of variance for perception of precipitation change shows significant
variation among the different educational levels (Table 6). Except those who completed
second cycle education all others perceived the change in more or less similar manner.
Table 6: Farmers perception on precipitation by educational levels
Perception on Precipitation (%)

2

X

Rainfall amount per day
Educational
level

Increased

No change

Decreased

I do notknow

NoanswerIlliterate

31.3

6.3

58.6

1.6

2.3

Read and
write

28

7.4

63.1

0.5

1

Complete first
cycle

11.9

4.8

66.7

16.7

0

Completesecond cycle

36.4

45.5

18.1

0

0

Illiterate

19.5

5.5

70.3

1.6

3.1

Read and
Write

16.7

3.9

76.8

1

1

Complete first
cycle

2.4

4.8

73.8

16.7

2.4

68.5*(df=12)

Number of rainy days per year

Complete
0
45.5
54.5
0
0
75.0*(df=12)
second cycle*,**,*** Significant at 1%, 5%, and 10% respectively; NS= not significant at 10% level Source: Own survey, 2011

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The same tests indicated a significant difference between the views of experienced andinexperienced farmers existed in rainfall change perceptions. The number of persons who
felt a decrease and no change in rainfall volume and pattern seems to decrease as farming
experience increased. On contrast, the number of people who felt an increase in rainfall
volume and pattern seemed to increase as farming experience increased (Table 7).
Table 7: Perception of changes in precipitation by farmer experience (% of respondent)
2

Perception on Precipitation (%)

X

Rainfall amount per day
Farmingexperience

Increased

No change

Decreased

I do notknow

No answer

Short(<10
yrs)

11.4

11.4

74.3

0

2.9

Medium(1030yrs)

23.4

9.5

63.5

2.2

1.5

Long(>30yrs)

36

5.6

54.5

3.9

0

Short(<10yrs)

8.6

8.6

77.1

2.9

0

Medium(1030yrs)

13.9

6.6

75.9

2.2

1.4

Long(>30 yrs)

20.2

5

69.7

3.9

0.5

65.8*(df=20)

Number of rainy days per year

50.1*(df=20)* Significant at 1% level; Source: Own survey, 2011

There was also statistically significant variation of perception in precipitation by gender (Table
8). Highest proportion of respondents in both sexes believed a decrease in rainfall volume
and a shrink in rainy season. However, substantial proportion of female respondents i.e.,
31.6% confirmed no change in rainfall volume or pattern.
Table 8: Perception of changes in precipitation by gender
X2

Perception on Precipitation (%)
Rainfall amount per day
GenderIncreased

No change

Decreased

I do notknow

No answer

Female

18.4

31.6

50

0

0

Male

28.6

5.2

61.8

2.9

1.2

Female

13.2

31.6

55.3

0

0

Male

15.9

2.9

75.7

3.2

1.7

34.2*(df=5)

Number of rainy days per year
53.2*(df=5)

70% of the interviewees from other stakeholders felt an increase in rainfall amount, 25% of
the respondents believed a decrease in rainfall amount, and the rest 5% believed no change
in amount. On the other hand, 93% of the respondent observed a change in the patternof rainfall; out of the 93% who observed the change in rainfall, 82 % of them observedlate coming and early cessation, and 11% of the respondents noticed late coming and latecessation; and on the contrary, 4 % of the respondents felt no change on the rainfall pattern.

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4.2 Local Impacts of Climate Change and Variability
In the household survey, farmers were provided with a list of production problems and asked
to prioritize in order of importance. As seen in table 9, farmers’ prioritized erratic rainfall, soil
erosion, decline of soil fertility, land shortage and drought as major production constraints.
Except land shortage all others are climate related factors.
Table 9: Farmers’ perceived production constraints and their priority ranking
Highland (n=120)

Midland (n=163)

Lowland (n=101)

Constraint

1st

2nd

3rd

4th

Mean
Rank

1st

2nd

3rd

4th

MeanRank

1st

2nd

3rd

4th

Mean

ChisquareLand shortage

25

10

1

6

162

35

14

8

33

201

43

9

8

3

215

18.6*

Labor shortage

2

5

2

0

187

4

6

5

7

199

1

1

5

1

188

3.5NS

Oxen shortage

1

9

6

6

203

2

7

8

4

192

2

3

2

0

181

6.3**

Decliningsoil fertility

8

20

33

31

206

7

28

30

38

181

21

20

15

15

195

5.6***

Drought

21

8

21

29

213

14

16

54

4

190

2

13

10

21

173

9.6*

Pest anddiseases

8

4

5

7

202

6

2

7

2

184

2

6

4

5

196

5.2***

Soil erosion

15

37

28

16

191

30

58

18

21

179

4

6

9

33

217

12*

Erratic
rainfall

35

20

16

15

154

49

13

21

24

218

20

31

25

10

198

36.3*

Planting
mat.

1

3

3

6

209

16

19

12

25

205

1

7

18

8

154

27.4*

No answer

4

4

4

4

-

0

0

0

5

-

5

5

5

5

-

-

*, **, *** Significant at 1%, 5%, 10% level respectively, NS-Not Significant at 10% level; Source: Own survey, 2011

Flood frequency and its associated impacts were worst in the highland before 2002, and thiswas mainly attributed to the sloppy nature of the farmlands. There was statistically significant
difference in the flood impacts across agro-ecology. But after 2002 all agro-ecologies
affected by flood more or less equally and there is no statistically significant difference inflood occurrence. This could be attributed by the compounding effect of the ever degrading
vegetation cover and unstable rainfall pattern in all agro-ecological zones.
Drought was most common in the lowland than in the highland or midland a decade ago(before 2002). With highest level of impact on the lowland, there was a significant impact
difference across the agro-ecologies. The number of respondents who were once affectedby drought in the production period between 1990 and 2001 was 6.7%, 17.8 %, and 98%
in the highland, midland, and lowland zones respectively. However, the number of farmers
who claimed to be affected by drought at least once in the 2002- 2011 production period was
100% in the highland, 96.9% in the midland, and 97% in the lowland. The survey result also
indicated that in the 2002-2011 production period drought brought a very serious damage (>50% possession loss) in the highland and midland than in the lowland, and the difference inthe level of damage was statistically significant This lowest level of drought damage recordin the lowland despite its’ increased severity could be resulted from the relatively longest
experience of the lowlanders with drought which helped them to manage it well.
Erratic rainfall was not a very common event to the farmers before 1990. Rainfall pattern
was relatively stable, and most farmers responded “no damage on livelihoods” due to erratic
rainfall between 1978 and 2001 production seasons. However, the rainfall was very unstablebetween 2002 and 2011 production seasons, and caused heavy crop losses and increased
flood frequency. Besides, its damage was not skewed to any agro-ecology and there is no
ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

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21significant impact difference across the agro-ecologies. This indicates that rainfall variability
is not a localized problem; it affects the farming communities of the different agro-ecologicalzones in more or less balanced manner.
Pest and diseases, and ice fall were other production constraints that brought the highest
damage in production periods that lie between 2002 and 2011. Highest level of pest anddisease damage was observed in the lowland and the level of damage show statisticallysignificant difference across the agro-ecologies. Ice fall incidence was highest in the highland
and there was also significant difference in occurrence across agro-ecologies. Frost and land
slid were not serious problems in the study area.
4.3 Farmers’ adaptation to Climate Change and Variability
4.3.1 Coping Mechanisms
To escape climatically bad years the farming community has been using a wide range of
inbuilt coping mechanisms. All climate hazards end up either in eroding the asset of the
farmers, or the natural base on which their livelihood depends. Hence, coping strategies
adopted are not specific to the climate hazard type. Farmers in the upper catchment ofBlue Nile have used a wide range of strategies to cope with the natural hazards. Reducedsocialization for saving(37.5%), inter household transfers and loans(29.7%), reducing
household consumption(22.5%), store grains(16.4%), sale forest products(8.5%), rent tools/
animals(7.3%), Wage laborer (5.8%), government assistance(4.5%), sale of household
assets(4.3%), petty trading(4.3%), appeal for food aid(4.3%), land Mortgage(3.2%), migrate
in search of employment(2.6%), community service (food for Work) (2.2%), Handicraft(1.6%),
credit from merchants or money lenders(1.5%), making of local drinks(0.7%), andBegging(0.16%) were the main coping mechanisms experienced by the farming community.
Recurrent impacts of climate change on crops and livestock, and its considerations- farm
sector and yield vulnerabilities -forced the rural community to focus on adaptive strategies
that reduce impact and vulnerability. The following sub- sections focus on the various
adjustments that farmers in the survey area made in their farming activities in response to
climate change and variability.
4.3.2 Changes in Agricultural Calendar
In the household survey, farmers were asked to evaluate the current cropping calendar against
the cropping calendar that was 10 or 20 years ago. A considerable number of respondents
shifted the sowing and harvesting dates of their crops as an adjustment to the changing
climate especially rainfall variability. For instance, in the 2010/2011 production season 287
household heads out of 384 reported to sow crops lately; from the 287 household heads
250 of them confirmed to harvest the lately sown crops earlier This production period waspreferred, for it was the most recent in terms of chronology, and had the least missed data.
This result implies that crops were harvested before completing their life cycle or reaching
maturity period due to rain fall pattern change. This in turn result low grain filling period, andthereby low yield. Farmers shorten the growing length of the crops knowing the side effectbecause they preferred low yield to 100% production loss. The benefit of this strategy is highly
minimized unless drought resistance or early maturing varieties are used. Group discussion
and key informant interviews report indicated that the present agricultural calendar of mostcrops is shorter than it was before 1990 (Figure 4).
The result implicitly showed a tendency towards shorter growing season, indicating that mostcrops are planted today during mid- to end of June, and are often harvested some weeks
earlier as well. For instance, the growing period of wheat and barley has shortened by one
month now compared to the period before 1990s. Presently, potatoes are planted 2-3 weeks
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22later and harvested 1 week earlier than they were before the 1990s.The reduction in thegrowing period of crops is more likely resulted from the long dry spell, warming, and erratic
rainfall.
Agricultural calendar before 1990

Figure 4: Comparison between the agricultural calendar before 1990 and the present
4.3.3 Farm Level Adaptation Strategies
A diversity of adaptation options was employed by the farming community to counteract the
impacts of temperature and rainfall pattern changes. The strategies used by the different
farming communities to minimize the impacts of the perceived changes of climate were moreor less similar, but the extent of implementation varied.
Farmers’ Response to Perceived Change to Temperature
Out of 86.7% of respondents who perceived the change in temperature, 84.4% employed atleast one adaptation measure to counteract the impact of the change in temperature. About
39% respondent implemented soil conservation measures, 36 % respondent planted trees forshade, 28% respondent changed crop, 27.1% respondent adjusted crop management, 22.1%
repondent changed planting dates, 21.1 % respondent adjusted to livestock management,
14.1% respondent reduced number of livestock, 10.7 % respondent sow different crop
verities, 8.1% respondent engaged in off-farm activities, 8.1% respondent diversified fromfarming to non farming, 6.5% respondent build water harvesting schemes, 4.2% respondentchanged use of chemicals or fertilizers, 3.1% respondent changed use of capital and labor,
2.6% respondent Irrigated more or used irrigation, 1.8% respondent changed from crop to
livestock, 0.5% respondent diversified their crop and animal, 0.3% respondent moved to adifferent site, and 0.3 % respondent changed quantity of land under cultivation. 86.5% of
the interviewed household heads believed that under the existing constrained environment
the adaptation measures they employed were best and suitable for the current and futurechanges of temperature.
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Farmers’ Response to Perceived Change to Precipitation
Change in precipitation pattern caused far more serious problems in crop production thantemperature change in the study area. This could be evidenced by two of the tree dominant
climate hazards in the study area, namely, flood and erratic rainfall, are resulted from change
in precipitation, not in temperature. Even the remaining high level impact climate hazard in
the study area, drought, is partly resulted from the change in precipitation. Hence, larger
number of respondents (97.4%) took adaptation measures in response to changing rainfall
patterns than apprehended temperature change. Among those who made adjustment toperceived precipitation change, 82.3% implemented soil conservation practices, 55.2%
changed planting dates, 40.6% planted trees for shade, 34.4% switched to other crop,
33.3% planted different crop verities, 25.3% adjusted crop management, 10.9% extended
Irrigation coverage, 8.6% adjusted livestock management, 8.1% changed use of capitaland labor, 8.1% changed use of chemicals or fertilizers 6% changed quantity of land undercultivation, 4.7% moved to a different site, 3.6% engaged in off-farm activities, 2.1% usedcrop & animal diversification, 2.1% diversified from farming to non farming, 1.8 % build water
harvesting schemes, 0.5% decreased use of water, and 0.3% reduced number of livestock.
81.5% of the respondents believed that under the current constrained environment theadaptation measures employed were best and suitable for the current and future changesof precipitation. This indicates that the farming community will continue to use the existing
adaptation strategies despite increasing effects of climate change in the future unless local
specific adaptation interventions are done.
4.4 Barriers to Adaptation in the Upper Blue Nile Basin
The adaptation section of this paper explicitly indicated that the farming community had tried
to counteract the impact of climate change and variability by employing local adaptation
strategies. However, farmers’ perceived adaptation measures were not the same with theadaptation measures they actually employed, for lack of access to information, knowledge,
productive resources, institutional arrangements, infrastructure, and other factors which aredescribed below.
4.4.1 Farmers Perceived Barriers to Adaptation
Results on barriers to taking up adaptation options indicated that lack of knowledge (99.2%),
lack of information (99.2%), not feasible and/or lack of belief(99%), lack of agricultural
technologies and inputs (97.7%), presence or absence of agricultural policy (97.2%) land
scarcity (91.7%), shortage of labor(85.5%), market problem (78.5%), water scarcity (76.9%),poverty or lack of credit or saving services(59.8%), lack of extension service (30.8%), andlack of health service (7.8%) to be major constraints of adaptation for most farmers.
4.5 Determinants of Farmers’ Adaptation
Farmers’ adaptation behavior, especially in low-income countries, is influenced by a complex
set of socio-economic, demographic, technical, institutional and biophysical factors (Federet.al., 1985). Hence, modeling farmers’ response to agricultural adaptations has become
important in identifying major determinants of adoption of the various adaptation measures.
Statistically influential determinants are factors on which efforts should be exerted to enhancefarm-level agricultural adaptations to climate change and variability in the study area.
Out of the thirteen explanatory variables hypothesized to affect farmers’ adaptation
education level, size of productive labor, wealth, farm size, farming experience, perceptionof soil fertility status, frequency of extension visits, access to media information, and agroecology were flagged as being statistically significant at 5% level and above (Table 10). The
results of the multinomial logit model highlighted that, frequent extension visit encourages
the adoption of enhancing livelihood productivity strategy; resource endowment promotes
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portifolio diversification, water addition and management, economizing land, and enhancing
livelihood productivity strategies; access to climate information increases the use of portifolio
diversification and economizing land strategies; large size of productive labor encourages
the use of economizing land and diversifying income; and farming experience positively
influences farmers to use water addition and management, economizing land, changing
agricultural calendar. However, perception of having a fertile soil discourages the useof enhancing livelihood productivity, and increased market distance decreases the useof different agricultural inputs; on the other hand, having large farm size encourages the
adoption of water addition and management to produce more than once and discourages
enhancing livelihood productivity, and diversifying income ( Table 10). Findings from Bryan
et al., (2010), Mahmud et al., (2008) and Temesgen et al., (2008) identified wealth, accessto extension, credit, and climate information as factors influencing farmers’ adaptation in
Ethiopia, which are in agreement with this study.

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ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

ATPS Working Paper No. 77

26-1.925736*

Midland0.9979419

3.857046*-0.006451-0.3957069-2.148138*

0.2450501-0.1482988-0.0895975

0.0056881

0.0575824*

0.0439862

0.0011932

0.3648458

0.0268566*

0.6556129**0.8099018*

0.3718583

Economizing land

0.962623*-1.336209*-1.624273*

1.25544*-0.0230277-0.0033672

0.0270215*

0.0430084*-0.0220486*-0.2608765*

0.0095987***

1.027497*-0.2005943**

0.2816848

Enhancing livelihood productivity

2.492288*

0.7764744

0.0076348

0.1294997-0.2295205*-0.0035179

0.0276453*-0.0477418

0.0032055-0.0458638-0.0129019-0.1164325

0.1720983

0.1805782

Changing useof inputs

2.50576*-0.6918312-0.0402502**

0.5263476-0.0773399-0.4111038

0.0178055**-0.2925768

0.0084933-0.4964536**-0.0007229

0.1145385

0.6570776*-0.0457751

Diversifying
income-2.261947*-1.801694*

0.1697459-0.2263743

0.0993896***0.0203514***-0.0080937

0.1707737-0.0572679***0.1298603***0.0144243*-0.3000091-0.0851975

0.0848938

Changing agriculture calendar*Significantat1%**significantat5%***significantat10%;Illiterate,resourcepoor,non-fertilesoil,andnovisitbyextensionagentaredroppedbasecategories;Source:Owncomputation,2011.

0.1492-0.6447678

Highland

Pseudo0.0869807

Tenure Security-0.419601

0.00002760.3716-0.6818418***

Non/off-farmactivities

0.2201145***0.0050855

Prob >
Log pseudo likelihood0.1109852**

Market Distance

1176.700.0067909

Credit Usage-0.7110336

Wald

0.0237762**

Access to climate
information-0.0458624-0.0006027

353

0.0233558***

0.1995393*

0.0540775*

1.281404*-0.0238653

0.1504134

Water addition &Management

No. of observation

0.0094905-0.0023619

Farming Experience

Extension Visit

0.4815438**

Wealth Status

Soil Fertility

0.0697929

Productive Labor

0.0629484

0.3565169**

Educational
Level

Farm Size

Portfolio diversification

Variables

Table 10: Results of marginal effects of the multinomial logit model for rainfall variability

5. Conclusion and Recommendations
Farmers living in the Upper Catchment of Blue Nile perceive changes in temperature andrainfall. They are able to recognize that temperature, and rainfall volume have increased,
and rainfall pattern has become unpredictable. They feel a major shift in agro-ecological conditions i.e., the area is becoming hotter and drier. However, the way farmers perceived thechanges in climate significantly varies across agro-ecologies, farming experience, gender,and educational level. The majority of farmers’ perception towards temperature change is inline with the temperature records, but a clear contradiction was observed between rainfall
volume records and most farmers’ perception towards rainfall volume.
The community identified erratic rainfall, flood, and drought as dominant natural calamities inthe study area. According to farmers these climate hazards are not new phenomena, but thefrequency of occurrence of these events, and their associated impacts are increasing in due
course of time. Erratic rainfall, soil fertility decline, soil erosion, and drought were prioritized
as major production constraints.
			
Evidence for farmers’ perceived changes in climate is reflected in the adjustment of agricultural calendar and adoption of different adaptation strategies. Survey results confirmedthat farmers have shortened the cropping calendar, and the majority of the respondents
have adjusted their farming practices to counteract the impacts of changes in temperatureand rainfall patterns. The common adaptation strategies of farmers were: implementing soil
conservation practices, changing planting dates, plant trees for shade, switch to other crop,
plant different crop verities, adjust crop management, extend Irrigation coverage, adjust
livestock management, change use of capital and labor, change use of chemicals or fertilizers, change quantity of land under cultivation, move to a different site, engage in off-farmactivities, use crop and animal diversification, diversify from farming to non farming, build
water harvesting schemes, decrease use of water, and reduce number of livestock. These
options are reactive ones, born out of necessity by the farmers themselves. Farmers in thearea can be considered as good adopters against climate change impacts. However, theiractual adaptations were not the same as their perceived adaptations due to various barriers.
The study identified lack of knowledge, inappropriate agricultural policy, shortage of labor,water scarcity, land scarcity, poverty or lack of credit or saving services, market problems,
lack of information, forage and feed scarcity, lack of agricultural technologies and inputs, lackof health service, and others such as lack of institution, ineffectiveness of an option, strong
family ties, lack of job opportunities, etc., as major barriers of adaptation. The results of themultinomial logit model highlighted that frequent extension visit encourages the adoption of‘enhancing livelihood productivity’ strategy; resource endowment promotes ‘portifolio diversification’, ‘water addition and management’, ‘economizing land’, and ‘enhancing livelihood
productivity’ strategies; access to climate information increases the use of ‘portifolio diversification’ and ‘economizing land’ strategies; large size of productive labor encourages theuse of ‘economizing land’ and ‘ diversifying income’; and farming experience positively influences farmers to use ‘water addition and management’, ‘economizing land’, and ‘changing
agricultural calendar’.
However, perception of having a fertile soil discourages the use of ‘enhancing livelihood productivity’, and increased market distance decreases the use of ‘different agricultural inputs’;
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on the other hand, having large farm size encourages the adoption of ‘water addition andmanagement’ and discourages ‘enhancing livelihood productivity’, and ‘diversifying income.
The implications of these findings are that adaptation measures significantly increase forhouseholds with more educational level, frequency of extension visits, farming experience,resources (assets), and climate information. Hence, designing policies with the aim of improving these factors will improve farm-level adaptations.
The Government could contribute to counteract climate change impacts on agriculture by
investing in research (drought and disease resistance varieties, early maturing varieties),
soil conservation measures , technology (farming machineries like improved plough design
which could penetrate the hard soil pan to enhance water percolation and minimize flood
impacts; Introduce mud bricks house), health centers, irrigation and water harvesting development, expanding fertilizer use, markets, and education (farmers training centers, formal
education), establishing crop insurance institutes, reforming land policies, establishing local
meteorology stations, monitoring and publishing climate data, developing climate forecasts,
and formulating planned and anticipatory adaptation strategies, and creating job opportunities by expanding none-agricultural sectors. For instance, early warning system prepares
farmers to use agricultural technologies, soil conservation structures, and complementary or
alternative livelihoods in the face of climate change. Institutions such as local meteorological station which provides climate information, crop insurance which insures crop losses due
to climate change, market which provides access to agricultural technologies and Teaching
and Vocational Educational Training (TVET) Center which provides skills and knowledge tofarmers to engage in other livelihoods are also important in expanding the coping ranges of
farmers to climate change. Current land use policy of Ethiopia which promotes land fragmentation and hence, discourages soil conservation measures need to be reviewed in the meritof the farmers.

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ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

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ISBN: 978-9966-030-55-9
ATPS (2013): Farmers’ Perception and Adaptive Capacity to Climate Change and Variability in the Upper Catchment of Blue Nile, Ethiopia

ATPS Working Paper No. 77

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