Perception and adaptation models of climate change by the rural people of lake Tana Sub-Basin, Ethiopia
© Addisu et al. 2016
Received: 11 December 2015
Accepted: 3 February 2016
Published: 16 February 2016
Agriculture is the most susceptible sector to climate change related hazards. Unusual temperature and rainfall occurrence in terms of amount and distribution usually lead to poor harvest and/or complete crop failure and shortage of pasture and animal feed in Ethiopia. Such extreme conditions ultimately result in drought with a resultant depletion of assets, societal vulnerability, mass migration and loss of life. This research work has been conducted to fill such knowledge gaps of the target population in Lake Tana Sub-Basin. The objectives of the research were to assess the perception of the rural people about climate change and adaptation models. To attain this research objective, both primary and secondary data from different sources were collected. The collected data statistical analyses were done by STATA version 11 computer program.
Results of Heckman probit and multinomial logistic regression models revealed that age, educational level, wealth status, agricultural extension services, and distance to the nearest health center are found to be significant for determining climate change adaptation. The farmers ‘perceptions to climate change found to be statistically significant related to those factors such as: marital status, farm size, climate change information access and the level of income generations. The majority of the respondents argued that the strategies and programs of climate change adaptations need further enforcement to implement it fully up to the level of expectations.
It is therefore recommended that the legislative bodies and development planners should design strategies and plans by taking into account impacts of declining summer rainfall and increasing temperature on rural livelihoods. Moreover, adverse impacts of climate change and adaptation strategies should be a crosscutting issue.
KeywordsClimate change perception Adaptation Heckman probit model Multinomial logistic regression model
The intensity, frequency and the effects of drought in Ethiopia and the number of people in need of food aid have increased since the mid-1970s. Reports indicate global climate change to be the cause of such dramatic increase in the intensity and frequency of drought. El Nińo-Southern Oscillation (ENSO) episodes are reported to be the main cause of drought in Ethiopia because of its effect on the rain producing weather systems over Ethiopia (Country report 2010).
The high vulnerability of people in Africa to climate variability and or change is attributed largely to their low adaptive capacity, which results from deteriorating, extensive poverty, ecological resources, unequal land distribution and high dependency on the natural resource base. Improving adaptive capacity is important in order to reduce vulnerability to climate change (Elasha et al. 2006). The third assessment report by IPCC (2001) foresees a temperature rise in the range of 2–6 °C by 2100. Temperature increases in the Millennium Ecosystem Assessment scenarios are in the lower range of 1.5–2.0 °C above pre-industrial revolution temperatures in 2050, and 2.0–3.5 °C higher in 2100. Such temperature increases might lead to reductions in crop yield. The combined effect of temperature rises and carbon dioxide enhancement varies among crops (Robinson et al. 2012).
Despite the low adaptive capacity of Africa in general and Ethiopia in particular, people have developed traditional adaptation strategies to face the great climate inter-annual variability and extreme events. They have been trying, testing, and adopting different types of coping strategies (Elasha et al. 2006). An unusually persistent drought may increase people’s vulnerability in the short term; but, it may encourage adaptation in the medium to long term. This reinforces the observation that local people have perceived, interacted with, and made use of their environment with its meager natural resources and changing climatic conditions. This practical coping mechanism is particularly true for the drought prone areas in Ethiopia and in the African Sahel region, which is susceptible to frequent climatic hazards (Elasha et al. 2006).
Diversification of herds and incomes,
Growing of drought and heat resistant and early maturing crop varieties,
Use of small-scale irrigation, water harvesting and storage,
Improved water exploitation methods,
Labor migration, response farming,
Increased agro-forestry practices,
Changes in farm location,
Reduction in herd and farm sizes, and food storage,
Crop and animal diversification,
Selling of assets,
Communal holding of grazing lands, and
Indigenous early warning and forecasting systems.
Ethiopian poverty reduction strategy document also clearly stated the impacts of climate variability recognized that agriculture is very vulnerable to the variation. It has set Industrialization Led Agricultural Development as a key to poverty reduction. However, the subject of climate change has not been treated in any of the agricultural sectors directly or indirectly. The document clearly states that Economic development through agriculture to be fundamental to the use of available water, land and improved inputs. Capacity building through training of farmers and the development of human capacity through training at the middle level is among the key areas of focus (Elasha et al. 2006).
This study was conducted in LTSB which is designated as one of the development corridors in the country and huge investments are being incurred to promote large scale farmer managed irrigation and also to generate hydroelectric power. It is found in the Amhara National Regional State (ANRS) and is situated within the upper reaches of the Blue Nile River. It is located within latitudinal and longitudinal ranges between 10°58N–12°47N and 36°45E–38°14E, respectively. It covers a total area of 1,589,654.98 ha.
As climate change affects the socioeconomic condition of a given area, a socioeconomic survey was conducted at the household level of the Lake Tana Sub-Basin. The sample households from the upper and the lower sub-basin were selected. Then structured questionnaires which consist of both open and close-ended questions were prepared, pre-tested and administered. To substantiate the result, interviews and focus group discussions (FDG) were prepared and conducted. Moreover, secondary data such as crop, livestock, population, etc. were collected from the districts agricultural bureaus.
Sample kebeles of the study area
The total number of administrative kebeles in each sub-basin was counted. The sub-basin was subdivided based on the climate hazard exposure namely flood for the lower sub-basin (LSB) and drought for the upper sub-basin (USB). This was done based on the local knowledge and ground truth exercise with local administration and the community leader during the reconnaissance survey. Areas that have less than 2 % slope and below 1825 m.a.l were delineated as flood plain areas. This was finally recalculated by employing flood zoning analysis technique, which uses the weighted sum overlay of slope, elevation, drainage density, land use/land cover and soil types.
The proposed and actual sample size of households
Number of HHs in each sub-basin
Adjusted sample size
Proposed sample size
Actual sample size
Analytical framework Heckman probit and multinomial logistic regression model
Using the MNL model for this research analysis gives rise to a sample selectivity problem because only those who perceive climate change will adapt the adverse impacts of climate change. Certainly, adaptation to climate change begins with two processes as: perceiving change, and then decide on a particular adaptation choice. Therefore, Both the Heckman sample selectivity probit model and MNL model have been used to study the determinants of climate change (Glwadys 2009).
use of climate change resilient variety
change planting date,
soil and water conservation,
fertilizer use and others
Those who respond no adaptation methods were considered in the model analysis. Generally, the household characteristics considered to have differential impacts on adaptation decisions. Age, educational level, household size, marital status, women’s participation in social affairs and wealth status were taken in the analysis as common household characteristics in both USB and LSB. According to Teklewold et al. (2006), older farmers, in the range of productive age group, positively influence climate change adaptation and perception of their farm experiences. It could also be that older farmers have more experience in farming and are better able to assess the characteristics of modern technology than younger farmers, and hence a higher probability of adopting the practice.
Higher level of education is often hypothesized to increase the probability of adopting new technologies (Daberkow and McBride 2003). Indeed, education is expected to increase one’s ability to receive, decode, and understand information relevant to make innovative decisions. Sex of the household head is also hypothesized to influence the decision to adapt changes based on the changing climate situations. Based on the discussions held in both USB and LSB, the rural women in LTSB have taken more responsibility to manage the source of energy and house management more than men and hypothesized as more vulnerable to climate change. Household size as a proxy to labor availability may influence positively as its availability reduces the labor constraints (Teklewold et al. 2006). However, according to Tizale (2007), there is a possibility that households with many family members may be negatively respond unless it is forced to divert part of the labor force to off-farm activities. Wealth is believed to reflect past achievements of households and their ability to bear risks. Thus, households with higher income and greater assets are in better position to adapt climate change (Shiferaw and Holden 1998).
Of the many sources of information available to farmers, agricultural extension is the most important for analyzing the adaptation strategy implementation. It is hypothesized that access to extension services is positively related to climate change adaptation, in the specific case of climate change adaptation, access to climate information may increase the likelihood of uptake of adaptation techniques. The occupation of the farmer in LTSB is an indication of the total amount of time available for farming activities. Hence, off-farm employment might be positive for larger family size and negative for smaller family size based on the availability of labor. Similarly, farm size can contribute to adapting an argument that has justified numerous efforts to reduce tenure insecurity (Tizale 2007).
Variable hypothesized to affect adaptation decision by farmers in the LTSB
Age of the head of the farm household
1 = <35 and 0 =>35
Cannot be signed
Educational level of the HHs
1 = illiterate, 0 = literate
Sex of the head of the farm HH
1 = female, 0 = male
Number of family members of a HH
Marital status of head of the HH
1 = married, 0 = unmarried
An index was constructed using household ownership
1 = rich and medium, 0 = poor and better off
Participation of women in social affairs
1 = yes, 0 = no
Land holding size of the HH
Land shared in
Land holding size shared in by the HH
Land shared out
Land holding size shared out by theHH
If household gets information about climate
1 = yes, 0 = no
If household has access to extension services
1 = yes, 0 = no
Income from off-farm activities during the survey year
1 = yes, 0 = no
Agro-ecology of the household head
1 = USB, 0 = LSB
Distance market and health center
Distance from the HH to the nearest market place in KM
Means of transport
Means of transport from the HH to the nearest market place
1 = on foot, 0 = vehicles
Income from non-farm/off-farm activities
Income from crop sale
Income from selling surplus products
Results and discusions
Correlation matrix of the independent variables Multicollinearity
This sub-topic presents the results of the Heckman probit adaptation model. The model determines the likelihood of perceiving any change in the climate as well as the likelihood of farmers’ adapting to these changes. The dependent variable in the selection equation is binary indicating whether or not a farmer perceives climate change; the dependent variable in the outcome equation is also binary indicating whether or not a farmer responded to the perceived changes by adapting farming practices. The likelihood function for the Heckman probit model was significant (Wald χ2 = 46.99 with P < 0.0000), showing a strong explanatory power.
Results of the Heckman probit model of adaptations behavior
P > |z|
95 % Conf. interval
Distance to w
Distance to HC
As it was hypothesized in the above section, educational level, wealth status, off-farm employment, agricultural extension services and the distance to the nearest health center of the household head is found to be statistically significant at 10 % level of significance. It implies that as the educational level of the household head increases, the level of understanding about climate change adaptation increases. In LTSB, a significant number of household heads are illiterate and highly vulnerable to climate change hazards. Similarly, households who are rich in wealth relatively cop up climate change more than the poor. They stated in the focus group discussion also that during the loss of crop and livestock might lead to food problems for the coming season more by the poor than the rich households. Agricultural extension services played a significant role for climate change adaptation. Access to extension services also increases the likelihood of adaptation to climate change impacts. This suggests that extension services help farmers to take climate change and weather patterns into account and help advise them on how to tackle climatic variability and change. The likelihoods of agricultural extension services and off-farm employment is also found to be significant in adapting climate change in LTSB more by those who engaged in than the non-users.
The likelihood of climate change perception of households for the variables of agro-ecology, marital status, farm size, climate change information access and income from crop sale found to be statistically significant at the 1 % level of significance. Farmers’ perception of having large farm size increases the probability of taking up adaptation in response to changes in the climate. This implies that farmers who have large farm size have high probability to diversify crops for better adaptation options.
The likelihoods of getting resistant variety increase due to the larger farm size. The results also show important regional variation in the case of agro-ecology. Farmers in the LSB region are more likely to adapt compared with farmers in the LSB. Indeed, in both cases, the population is largely rural and the main rural economic activity is agriculture. The accessibility of small scale irrigation and moisture enabled the LSB households for better resistance to climate change than the USB.
Use of climate change resilient variety (both crop and livestock)
Change planting date
The MNL adaptation model with these restructuring choices was run and tested for the IIA assumption using the Hausman specification test statistics. Thus, the application of the MNL specification to the data set for modeling climate change adaptation behavior of farmers has been justified. Appendices XIII and XIV present the estimated coefficients and the marginal effects, respectively. The likelihood ratio statistics as indicated by χ2 = 102.07 were found to be highly significant at 1 % level of significance, suggesting strong explanatory power of the model. It is important to note that the estimated coefficients should be compared with the base category of land resource management any of the adaptation choices.
Marginal effects after multinomial logistic regression adaptation model
Dummy (1 = value, 0 otherwise)
CCI resilient varity
Change planting data
Off farm E
Transport to the market
Distance to water
Distance to health center
Income from off-farm
Income from crop sale
In the climate change resilient category, the household’s response of whether they have used highly resistant livestock and crop varieties to adapt climate change during data collection was considered. On the other hand, means of transport to the nearest market is found to be statistically significant at with the base category under the crop diversification options at the 1 % level of significance.
The coefficient on agro-ecology is statistically significant and positively correlated with the probability of choosing irrigation as an adaptation measure. Indeed, the nature of weather and climate in both USB and LSB households is more likely to adapt because of the high vulnerability to the frequent occurrence of climate change hazards. Farmers argued in the FGD also that adaptation strategies have a number of obstacles to fully implement since most of the rural households are depending on subsistence agricultural practice in a fragile environment.
The Heckman probit and multinomial logistic regression models were applied to examine the determinants of adaptation to climate change impacts. Results revealed that age, educational level, wealth, status, agricultural extension services, and distance to the nearest health center are found to be statistically significant factors for climate change adaptation in LTSB. The farmers’ perception to climate change are also found statistically significant for the factors of agro-ecology type, marital status, farm size, climate change information access and the level of income generations. Moreover, agro-ecology and educational level of the household heads also significant by using climate change impact resilient varieties; On the other hand, means of transport to the nearest market was significant at with the base category under the crop diversification. Most of the adaptation plans, policies, and strategies have tried to be implemented, but most of the respondent’s evaluations indicated that inefficient, which means did not meet the demand up to the anticipated level.
Designing and implementing a well-organized climate awareness mechanism based on dynamic empirical information at different spatial scales;
Designing and implementing interventions that address capacity, technology and information needs of households experiencing different climatic hazards;
Promotion of “saving as a culture” among rural households and strengthening service provision capacity of rural financial institutions;
Research based and farmer friendly technology intervention in irrigation, water harvesting, and moisture management practices for drought mitigation;
Massive restoration of degraded lands and more importantly through indigenous flora and in view of community friendly economic and ecological benefit mechanisms;
Rural agro-processing, value chain development, and risk financing schemes;
Organization, capacity building, and access to inputs for landless and unemployed segments of rural households;
SA has made substantial contributions in conception design, acquisition of data, and interpretation of results and leading the overall activities of the research; He has given also the final approval of the version to be published. BG and GF have been involved in data collection, entry, coding, and analysis. YA contributed in writing, drafting the manuscript, revising it critically for important intellectual content. All authors read and approved the final manuscript.
This study would never be completed without the contribution of many people to whom we would like to express our gratitude. The administrative kebeles development agents, district agricultural officials, local guiders, committee leaders and respondent households in each of the sampling kebeles were indispensable for the successful completion of the field work. We would like also to acknowledge people who contributed their knowledge and time in data collection and entry processes.
The authors declare that they have no competing interests.
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