Beginning in the early 1970s, the horn of Africa was racked by the ravages of hunger. Natural disasters are not new to the region, which historically could count on at least seven major droughts each country, but in the current era it has been increasing, in part due to massive deforestation and the changing pattern of weather (Edmond
Ethiopia suffers from recurrent drought and famine. In 1984-85, war and drought caused a food crisis during which around one million people died. In 1999-2000, rains failed again, affecting eight
million people. Bymid-2003, an insidious combination of sporadic seasonal rains, poverty and HIV/AIDS has conspired to leave over 12 million Ethiopians dependent on relief (Korecha, 2012). Over the past 30 years, Northern Ethiopia has been affected by a number of major drought events, most notably in 1967, 1977, 1984 and 2002. Future climate projections suggest that global warming is likely to favour conditions of the development of droughts in many regions of Ethiopia. Pre-assessment over changes in low flow characteristics indicate that Northern parts of Ethiopia are most prone to reductions in minimum flow (Kinfe, 1999).
Bryant et al. (2000), drought and climate variability studies have shifted the focus of research from the estimation of impacts to the understanding how farmers perceive drought and climate variability; because identification of those drought properties that are of most importance to farmers is vital in their decision making and thereby to propose the types of mitigation strategies. Janamora as part of North Ethiopia, drought episodes have been erupted in a matter of months, or gradually creep up on an unsuspecting society over several seasons. It goes unobserved by the public until impacts from the drought have already occurred. Given projections for increasing drought impacts, it is important to inform policy makers on the causes of drought, its impacts, various mitigation responses and possible mitigation measures perceived at local levels in order to alleviate human suffering. Based on the case of farmers in
Study area description
Data collection and analysis techniques:
The data were collected mainly from two main sources; primary and secondary. Some of the tools that were employed to collect primary data include; household Survey using standardized questions, Focus Group Discussion, In-depth interview with key informants, and field observation. Group discussion were held to substantiate and cross-checking the reliability of the output. Accordingly, 2 group discussions were held, each consisting of 8 participants. Participants were purposely selected based on three criteria: (1) settled in the area for ?20 years, (2) practice agricultural farming as a means of livelihoods for ?20 years (3) knowledge on long term trends of drought conditions of the district. These criteria also accounted the recalling ability of the farmers back to some years before. Interviews were held for key informants from environmental protection and land administration office of the district.
As it has been known, the questionnaire survey is one of the effective instruments of data collection. Semi-structured questionnaire was employed to collect the data. Both open and close ended questions were employed to gather relevant information pertaining to the study theme. The questionnaire was designed in English but the interviews were conducted in the local language, Amharic. Prior to the survey, the questionnaire was pre-tested with sub-sets of the targeted population (i.e. few farmers from three representative villages) to check the redundancy, missing information, relevancy as well as validity of the questions.
The questionnaire was then modified based on pre-test results. The individuals included in pre-test were omitted from the sample. An individual farming household is considered as a primary sampling unit. A multi-stage stratified systematic sampling technique was used to select samples from the target population. The district was divided into three different strata based on agro-ecologies. It is assumed that the different agro-ecologies will influence the farmers' perception of drought impacts, adaptation, and mitigation measures. In the first stage, the villages were selected by probability proportional to size (PPS) sampling technique, and in the second stage households were chosen from selected villages by random walk sampling technique. Sample size determination formula by Akin and Colton was used to calculate minimum sample size. Accordingly, a total of 351 households were surveyed. Considering the population proportion in all three strata, 92, 104 and 155 households were selected from
Data were analyzed using descriptive statistics. The data was entered into EpiData 3.1 software and then exported to the statistical package SPSS version 20 for the analyses.
Standardized rainfall anomaly was employed to examine the temporal characteristics and prevalence of drought over the period 1979 2013.
RFAi=RFi-RF?RF?*100
Where
Summated rating scale was used to measure farmers perception towards drought incidence trends using the guidelines suggested by Likert (1932) with necessary modifications. The perceptions were measured as evaluative perceptions of the respondents on drought incidence trends. This was done with the assumption that evaluative perception reflected their liking or disliking to a great extent and moreover it reflected their judgments on drought incidence trends and perceived effects. A Likert scale was employed to rate each item on a response scale. Respondents were asked to answer each question by rating each item on a 1-to-3 response scale, so that a higher score reflected a higher level of agreement of each item. After entering individual scores, an average or mean score and standard deviation for the whole group for each survey question was calculated. In the case of assigning higher values to stronger agreement, then higher mean scores for each question was translated into levels of agreement for each item, and thus, lower scores reflected participants disagreement with each item asked.
Results And Discussion
General Profile of Household Respondents
The mean age of farming households was found to be above 46.3 years. This implies that farming community have immense experiences so that they can easily comprehend climate trends over the area. The average year of farm experiences is 24.64 years. This indicates that under the socio-economic condition of the study area age and experience in farming are related. With longer experience in farming, a wide knowledge, skills and attitudes are gained on the operation and conduct of traditional drought coping strategies and methods of production. So, farmers with longer experience are more conservative and sceptical. It is less likely that farmers with longer farming experience will be ready to accept changes and implement new ideas and techniques of drought coping mechanisms. The overall average household size of the sampled population was 4.8, which is almost equivalent with the average size of 5 persons per household in the Amhara Regional State. Data on education indicated that 69.2%, 22.6% and 47.5% respondents had no education, completed their primary education, and secondary education respectively. Agriculture is the main income sources of 96.4% respondents. The average annual household income of each respondent is $1454.
To comprehend households perception towards drought incidents in Janamora district, households were asked to specify what they had well-known concerning long term variations in temperature and precipitation. The results of this analysis are presented below. Farmers' attitude towards drought incidents was assessed in terms of their evaluative perceptions using a scale developed for the purpose of this study.
Attitude statements |
Decision by the household |
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Agree |
Neutral |
Disagree |
|
Drought is bad |
334 | - | - |
Drought incident decreases land productivity |
233 |
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|
Prevalence of drought is very common |
284 |
5 |
45 |
Livestock are vulnerable to drought |
312 |
12 |
10 |
Outbreak of crop and animal diseases are very common |
318 |
2 |
14 |
Drought will continue affecting farmland in the future |
247 |
87 |
Source: Field Survey 2016
The value of the scale for the positive statements of evaluative perception on drought incidents were assigned 3, 2, 1 for agree, neutral, and disagree respectively, whereas the negative statements were assigned to the reverse values. Post administration reliability test for all the items considered to assess attitude on drought incidents resulted in the standardized alpha of 0.8015, which is in the acceptable range to distinguish respondents. The value above this average value was taken as a domain to classify perceptions of households whether they agree or disagree on each item. The scale values were multiplied by the respective frequency values and summed up the data series and divided to total frequency values. The value of Likert scale is found to be 2.78. Therefore respondents had common perceptions to drought incidents in terms of its prevalence, effect on livestock and crop productivity, and its future implication.
Majority of the farmers (87%) interviewed believe that temperature has become warmer (Figure-4.1). The climatic data of the district showed the weather was warm from 1979 to 2013 but became relatively cooler during 1996, and 2002. Their perception slightly differed from the recorded data probably because the cooling effect of 1996 and 2002 is not significant enough for them to notice. Households perception of temperature in the district is consistent with farmers perception in other African countries as reported by Maddison (2007) and IFPRI (2008).
About 87% of the farmers interviewed perceived long-term increment of warmer temperature. Only 2% noticed the contrary, an increment of cool temperature. About 5% have not noticed any changes in the temperature. It was very common for FGD participants to come easily to a consensus that there is an overall increase in temperatures (increase in hotness and reduction in coldness). Studies in several other developing countries indicate that most farmers perceive temperatures to have become warmer and rainfall reduced over the past decades (Deressa
The type of rainfall received in Janamora district is bi-modal which means dominantly they received
A key informant in his 80s pointed out that: ...when I was young, I used to dress a blanket and a leather the whole day during Winter and Spring seasons when I went to keep livestock but now a day our children were nothing because of hotness... ..when I was young, I used to dress a blanket and a leather the whole day during Winter and Spring seasons when I went to keep livestock but now a day our children were nothing because of hotness...
As indicated from the figure below, about 68% of the respondents said that rainfall is highly variable for the past 20 to 30 years. Respondents observed changes in rainfall patterns over the past 20 years, Respondents were more aware of frequent drought incidents related to the rainy season than other seasons. About 8% of the respondents noticed a decrease in the amount of rainfall or a shorter rainy season and 6% of the respondents noticed an increase in the amount of rainfall or a longer rainy season.
A 66 years old farmer explained that:
The other interviewed farmer aged 72 explained that ...The set of rains that started from late March was used for tilling and planting. But nowadays, it is no longer there. Rain nowadays just comes in a jumbled manner, whenever it wants and goes at anytime. Sometimes it rains other times instead of the rains reducing and stopping, it continues and sometimes falls heavily destroying crops that were matured and supposed to be harvested. It is difficult to predict the rains character nowadays... .The set of rains that started from late March was used for tilling and planting. But nowadays, it is no longer there. Rain nowadays just comes in a jumbled manner, whenever it wants and goes at anytime. Sometimes it rains other times instead of the rains reducing and stopping, it continues and sometimes falls heavily destroying crops that were matured and supposed to be harvested. It is difficult to predict the rains character nowadays...
Items | N | Min. | Max | Mean | Std. De |
Frequency of drought for the last 15 years | 334 | 2 | 6 | 4.13 | 1.369 |
Frequency of hail for the last 15 years | 334 | 2 | 7 | 4.52 | 1.676 |
Frequency of flood for the last 15 years | 334 | 1 | 4 | 2.61 | 1.154 |
Frequency of drought related livestock epidemics for the last 15 years | 334 | 1 | 4 | 2.56 | 1.150 |
Frequency of drought related crop infestation for the last 15 years | 334 | 1 | 4 | 2.40 | 1.112 |
Frequency of drought related epidemic disease for the last 15 years | 334 | 1 | 4 | 2.46 | 1.116 |
Frequency of drought related crop harvest failure for the last 15 years | 334 |
As it can be seen from the table above, drought conditions were more rampant than floods over the past 20 years.
FGD was held to substantiate the survey study. Accordingly, FGD participants were asked whether they noticed changes in their environments. Participants reported that the temperature is higher, spread of agricultural pests and weeds on crop land; erratic rain; delayed rainfall; less clearly defined seasons (rains sometimes arrive a month late or finish early, rains quickly gave way to sun or dry periods during the rainy season. FGD participants believed that the vagaries of the climate are a sign of divine anger,
*Kruskal Wallis H-test significant at 5% significance level
Agro-ecologies strata | Land holding size | Income of HH | Education | Farming experience | |
Caused food scarcity | 0.43 | 0.78 | 0.74 | 0.64 | 0.79 |
Caused population migration | 0.66 | 0.82 | 0.56 | 0.51 | 0.64 |
Caused malnutrition | 0.92 | 0.74 | 0.02* | 0.91 | 0.58 |
Caused reduction in household income | 0.25 | 0.56 | 0.18 | 0.67 | 0.71 |
Affected on health | 0.87 | 0.36 | 0.92 | 0.37 | 0.18 |
Caused farmers suicide | 0.37 | 0.71 | 0.84 | 0.18 | 0.44 |
Affected schooling of children | 0.71 | 0.90 | 0.28 | 0.01* | 0.54 |
Limited food preferences | 0.82 | 0.72 | 0.49 | 0.77 | 0.68 |
Threatened household food security | 0.39 | 0.85 | 0.76 | 0.83 | 0.94 |
Caused unemployment | 0.48 | 0.36 | 0.16 | 0.42 | 0.28 |
Caused hopelessness | 0.53 | 0.18 | 0.52 | 0.47 | 0.88 |
Caused conflicts for water in society | 0.69 | 0.33 | 0.74 | 0.81 | 0.01* |
Reduction in spending on festivals | 0.55 | 0.19 | 0.63 | 0.56 | 0.38 |
to send their children to school, because they couldn t afford schooling expenses during drought incidence periods. A significant difference was also observed in perception of conflicts for water in the society due to drought based on farmers' education level. Less educated farmers said that drought driven water scarcity caused conflicts in the society. Similar cases were reported in drought affected rural Ethiopia.
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Increases in temperature | 85 | 22 | 64 | 15 | 51 | 4 | 12 | 33 | 28 | 27 |
Decreases in temperature | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Variations in temperature | 15 | 12 | 18 | 4 | 33 | 1 | 11 | 23 | 16 | 18 |
Increases in precipitation | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Decreases in precipitation | 82 | 18 | 53 | 11 | 79 | 3 | 67 | 15 | 64 | 19 |
Changes in timing of precipitation | 68 | 10 | 49 | 9 | 65 | 3 | 57 | 11 | 26 | 42 |
Increases in frequencies of drought | 81 | 22 | 47 | 10 | 77 | 4 | 23 | 59 | 43 | 38 |
Changes in precipitation patterns | 44 | 8 | 30 | 6 | 40 | 4 | 26 | 18 | 14 | 30 |
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Farmers perceptions of climate change have been classified according to their years of experience and their level of education. The responses of farmers having less than 15 years, between 15 and 30 years, and 30 or more years of experience had been identified. Pertaining to the level of education, three categories had been distinguished; i.e no formal education, primary education and above secondary education. Using the Kruskal-Wallis test, this study assessed if the perceptions of households on drought according their farming experience and level of education differed significantly. The results show that a slightly higher proportion of farmers with more than 30 years of experience claimed that temperature is increasing and rainfall is decreasing, and noted change in the frequency of droughts and floods. Farmers with more than 30 years of experience are also less likely to claim no change in temperature and no change in rainfall. However, the Kruskal-Wallis test indicated that the views between experienced and inexperienced farmers are statistically not significant. The results also indicated that there is statistically no difference between the views of the educated and less-educated farmers.
*Kruskal WallisH-test significant at 5% significance level.
Perceive change in Temperature |
Perceive change in precipitation | |
Education | -0.0049 | -0.0371*** |
Farming Experience | 0.0136* | 0.0048 |
Access to Extension services | 0.3361** | 0.2271 |
Access to Irrigation | -0.5917** | -0.7279** |
Log likelihood: -124.0148 | ||
Number of Observations: 148 | ||
Athrho: 0.7047*** | ||
Rho: 0.6784 |
*** Significant at 1% level; ** significant at5% level; * significant at 10% level
Given the fact that the study area is highly dependent on the agricultural sector for income and food security, irregular precipitation would adversely affect the lives of the majority of the population. Farmers perceptions of climatic variability are in line with climatic data records. Indeed, farmers in Janamora district of North West Ethiopia are able to recognize that temperatures have increased and there has been a fluctuation in the volume of rainfall. Farmers with access to extension services are likely to perceive changes in the climate because extension services provide information about climate and weather. Having access to water for irrigation increases the resilience of farmers to climate variability; therefore, they do not need to pay as much attention to changes in the
patterns of rainfall and temperature. With more experience, farmers are more likely to perceive change in temperature. Although farmers are well aware of climatic changes, few seem to take steps to adjust their farming activities. Only approximately 30 percent of farmers have adjusted their farming practices to account for the impacts of climate change.
The main mitigation measures of farmers in Janamora district are switching crops, changing crop varieties, changing planting dates, increasing irrigation, building water-harvesting schemes, changing the amount of land under cultivation, and buying livestock feed supplements. Government policies should therefore ensure that farmers have access to affordable credit to increase their ability and flexibility to change production strategies in response to the forecasted climate conditions. Because access to water for irrigation increases the resilience of farmers to climate variability, irrigation investment needs should be reconsidered to allow farmers increased water control to counteract adverse impacts from climate variability and change. Further studies are required to be carried out to establish other drought characteristics such as extreme rainfall, rain days, and other climate change parameters for this district to verify whether the significant trend has occurred and also to establish a correlation between temperature and extreme rainfall.
The author declare that no competing interests exist.