Addis Ababa University School of Graduate Studies Environmental Science Program

ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES ENVIRONMENTAL SCIENCE PROGRAM

Assessment of climate change effects on rain fed crop production and coping mechanisms: the case of smallholder farmers of west Shoa zone, Oromia, Ethiopia

A Master’s Thesis

Submitted to the graduate program of Addis Ababa University environmental science program in partial fulfillment of the requirements

For the Degree

MASTER OF SCIENCE IN ENVIRONMENTAL SCIENCE

By- Leta Abate Acknowledgements

First of all, I would like to thank my God for his untold and all time grace that gave me courage to start and finish this thesis work. I would also like to express my deepest gratitude to my advisor Dr. Seyoum Leta for his supervision and constructive comments throughout my research work. I would like to thank West Shoa Zone Irrigation development office for giving me a chance to study at Addis Ababa University. I would also extend my inner thanks to, NMSA, CSA, West-Shoa Zone and Woreda ARDO and DPPO officials for their continuous support in providing me the necessary data and assistance during my field work. My thanks also extend to Holeta, Ambo and Bako Agricultural research centers officials and researchers. I would also like to thank my friends, Sofia Abebe, Gelana Dufera, India Abebe for their moral support and making my overall stay in Addis memorable, may God bless you all. I am also indebted to my colleagues Dagnachew Delessa, Solomon Mengistu, Chaluma Regassa, and Itaferahu G/Yes and the rest of stuffs working at West Shoa irrigation Development Office. Finally, my deepest gratitude also goes to HoA-REC/N for providing me research Fund without which my research work would have been impossible.

Table of contents Acknowledgments…..…………………………………………………………………………….i List of Tables……………………………………………………………………………………..iv List of figures……………………………………………………………………………………..v Acronyms…………………………………………………………………………………………vi Abstract……………………………………………………………………………………..…....vii

1-INTRODUCTION…………………………………………………………….…………….……1 1.1 Background and Justification ...... 1 1.2 Statement of the problem ...... 3 1.3 Objectives of the study ...... 6 1.3.1 General Objective ...... 6 1.3.2 Specific Objectives ...... 6

2. LITERATURE REVIEW………………………………………………………………..…………7 2.1 Climate change general background ...... 7 2.1.1 Concepts and definitions of climate change ...... 7 2.1.2 Components of climate and climate system tele-connections ...... 7 2.1.3 Causes of climate change ...... 9 2.1.4 Manifestations of climate change ...... 9 2.2 Overview of Ethiopia’s climate ...... 12 2.2.1 Rainfall regimes and temperature ...... 12 2.2.2 Climate related hazards in Ethiopia ...... 13 2.3 Vulnerability of Ethiopian Agriculture to climate change ...... 14 2.3.1 Impacts on food security ...... 16 2.4 Coping with climate change effects ...... 19 2.4.1 Concepts of Adaptation and resilience to climate Change ...... 19 2.4 2 Linkage between adaptation and mitigation ...... 20 2.4.3 National Adaptation Program Action of Ethiopia (NAPA) ...... 22 2.4.4 Farm level Adaptation Practices ...... 22 2.4.4.1 Tactical(short term) adaptation measures……..……………………………….…23 2.4.4.2 Strategic(Long term) adaptation measures…………….…………………………24 2.5 Analysis of farm level adaptation measures in Abay basin ...... 34 2.6 Importance of local innovations/indigenous knowledge to cope with climate change ...... 35

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3. METHODS . ……………………………………………………………………………………38 3.1 Research area description ...... 38 3.1.1 Location ...... 38 3.1.2 Population ...... 39 3.1.3 Topography ...... 39 3.1.4 Climate ...... 39 3.1.5 Vegetation ...... 39 3.1.6 Land use and Agriculture ...... 39 3.2 Research strategy and Site selection ...... 40 3.3 Data Collection ...... 41 3.3.1 Primary data ...... 41 3.3.2 Secondary data ...... 43 3.4 Data Analysis ...... 43 3.4.1 Socio-economic data analysis ...... 43 3.4.2 Trend Analysis ...... 44 3.4.3 Trend significance test ...... 44 4. RESULTS AND DISCUSSIONS…………………………………..……………………………..46 4.1 Rainfall Variability and trend ...... 46 4.2 Temperature variability and trend ...... 52 4.3 Occurrence of unusual weather events ...... 54 4.4 Impacts on crop production……………………………………………………………….55 4.5 Coping mechanisms ...... 60

4.5.1 Resources required for coping ...... 64 4.6 Institutional coping mechanisms and local knowledge ...... 65 5. CONCULUSIONS……………………………………………………………………………...68 6. RECOMMENDATIONS…………………………………………………...…………………...70 7. REFERENCES…………………………………………………………………………………..71 6. ANNEXES ... ……………………………………………………………………………………80

List of Tables Page

Table-1 Over view of traditional water harvesting technology in Ethiopia ………………..…28

Table-2 Geographical Location of Meteorological stations…………………………………..43

Table-3 Values of Rainfall trend Analysis……………………………………………………46

Table-4 Annual total Rainfall variability……………………………………………………..47

Table-5 Kiremt Rainfall and coefficient of variation…………………………………………51

Table-6 Belg Rainfall and coefficient of variation……………………………………………51

Table-7 Correlation of Belg and Kiremt Rainfall…………………………………………….52

Table-8 Values of Temperature trend Analysis………………………………………………52

Table-9 Values of Average annual temperature and Coefficient of variation………………..53

Table-10 Correlation of cereal production and Kiremt rainfall at Holeta……………………58

Table-11 Correlation of cereal production with Belg and Kiremt Rainfall at Bako…………59

Table-12 Resource required for Adaptation and level of importance………………………..64

List of Figures Page

Figure- 1 Common adaptation practices in Nile basin of Ethiopia Basin…………………….…35

Figure -2 Research area Map………………………………………………………………….…38

Figure-3 Survey design…………………………………………………………………………..42

Figure- 4 Trend and variability of annual total rainfall of Bako Station.…………………….….48

Figure- 5 Trend and variability of annual total rainfall of Ambo Station ………………………48

Figure- 6 Trend and variability of annual total rainfall of Holeta Station ………………………48

Figure- 7 Spatial Variability of rainfall in west Shoa……..…………………………………..…49

Figure- 8 Trend and variability of monthly Rainfall at Bako station……………………………50

Figure- 9 Trend and variability of monthly Rainfall at Ambo station …………………………..50

Figure- 10 Trend and variability of monthly Rainfall at Holeta station on…..……..………...…50

Figure-11 Trend and variability of Average annual temperature at Bako station ………………53

Figure-12 Trend and variability of Average annual temperature at Ambo station………………53

Figure-13 Trend and variability of Average annual temperature at Holeta station……………...54

Figure-14 Seasonal total rainfall at Holeta ana Bako……………………………………………55

Figure-15 Frequency of major weather impacts…………………………………………………56

Figure-16 Trend of Agricultural crop production of main growing season of Bako Tibe………57

Figure-17 Perception of farmers for decline in productivity…………………………………….58

Figure-18 Crop production of main growing season of West Shoa……………………………..59

Figure-19 Major coping strategies of West Shoa……..…………………………………………62

Acronyms

ARDO Agriculture and Rural Development office DPPO Disaster Prevention and Preparedness Office UNCEB United Nations Chief Executive Board IPCC Inter Governmental Panel on Climate Change UNFCC United Nations Framework Convention on Climate Change MEDaC Ministry of Economic Development and Cooperation GHGs Green House Gases CBA Community Based Adaptation PROLINNOVA Promoting Local Innovation PFE Pastoralist Forum Ethiopia UNISDR United Nations International Strategy for Disaster and Risk Reduction EU European Union IIRR Institute of International Rural Reconstruction SNNPRS South Nations Nationalities and Peoples Regional State ILRI International Livestock Research Institute NMSA National Meteorology Service Agency MoFED Ministry of Finance and Economic Development NAPA National Adaptation Plan Action LDCs Least Developed Countries OCDE Organic Chain Development in Ethiopia ICDORA International Center for Development Oriented Research in Agriculture CSA Central Statistics Authority ZARDO Zonal Agriculture and Rural Development Office PAs Peasant Associations NGOs Non Governmental Organizations DAs Development Agents

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Abstract

The rain fed crop production is inherently sensitive to climate change and variability. Climate change affects all countries but disproportionately affecting the poorest counties and the poor people with less capacity to cope with. Accordingly, this research is intended to understand the local climate trend in west shoa zone of oromia region, its impact on rain fed crop production in different agro- ecologies and how the smallholder farmers adapting their agricultural operation to the prevailing climate change and variability. The study also investigates the common weather events, the role of local indigenous knowledge and institutional coping mechanisms currently practiced in the area. The study uses two methods; firstly Monthly time series data on precipitation and temperature of three stations (Bako, Ambo and Holeta) with in the Zone were analyzed for temporal monotonic trend using Mann Kendall trend test at 95% significant level. Secondly, semi- structure questionnaire based interviews and group discussions were also used. The trend evaluation at the three stations reveals more or less stable trend for precipitation (Sample Z-values are 0.29 ,-0.27 and -0.97 for Bako, Ambo and Holeta respectively; where as that of temperature shows significant increase and Z-values are 3.54, 1.84 and 2.78 respectively. Moreover the socio-economic data analysis indicates that the weather events occurred with in the research area and their frequency are found to be different from place to place; and the major ones are prolonged drought including late on set/early offset (71.2%), flood/Excessive moisture (53.8%), heavy rain (32.6%), frost (20.5%) and strong winds (4.5%). As rain fed crop production is dependent on normal distribution and the timely onset and offset of the rainy season, the occurrence of these events hinder agricultural operations and impose different impacts. The problem is more serious in mid altitude and lowland areas. The survey indicate that total crop loss, reduced yield, reduced seeding area, delayed seeding, delayed maturity and increased crop pest are the major impacts. Smallholder farmers use different adaptation strategies such as taking the loss, crop replacement, diversifying crops, late seeding, growing of early maturing crop, zero/ minimum tillage and growing tolerant crops. The Farmers also use local knowledge such as traditional early warning and tillage practices to cope with adverse weather events. Though small holder farmers have been undertaking different adaptation measures; constraint of resources such as efficient technologies, land, labor, savings, credits and crop insurance is found to be challenging. Moreover, the available institutional coping mechanism fails to warn about rapid events like flood, unexpected heavy rain and its multi- sectoral manner to give immediate response also need consideration. Even though, the long-term change in mean values of precipitation shows stability, there is considerable seasonal and inter- annual variability. Moreover, the small variability in precipitation and temperature has significant impact on rain fed crop production compounded by other compounding biophysical and socio economic factors. Hence, this study indicates small shocks in weather event can result in significant impact, the need to strengthen adaptation options, importance of resources and appropriate and timely information on future climate change so as to alert them to take appropriate averting actions.

Key words: Climate change, climate trend, adaptation, impact, coping mechanisms

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CHAPTER ONE

1-INTRODUCTION

1.1 Background and Justification

Climate change has become a major concern and receiving serious attention at local, national, regional and global levels. While significant debate remains over the extent to which humans have induced climate change, it has generally been accepted that the effect of climate change are manifested in terms of increased weather variability, higher frequency of extreme weather events and decreased predictability (Berkes and Jolly, 2001; Smit et al., 2003; Venema ,2005). This increased weather variability as a result of climate change results in potentially sudden and irreversible disruptions to life and livelihood-sustaining natural systems, and the resulting economic, social and environmental dislocations (UN system, CEB 2008). Observable trends of ongoing climate change and current projections indicate increasingly severe negative impacts on the overwhelming majority of countries, with the most severe impacts disproportionately affecting the poorest with the weakest capacity for climate resilience. Equally, within countries, climate change is disproportionately affecting the poor and vulnerable in society. The projected impacts pose a serious threat to the achievement and sustainability of the Millennium Development Goals in developing countries and the effective enjoyment of human rights in both developed and developing countries(UNSCEB, 2008).

Despite global coverage of climate change, there is global variation in climate change manifestations based on geographic location and environmental factors. For instance, in Europe retreating glaciers, flash floods, wild fires and rising sea levels are some of the manifestations where as, in Africa, climate change is manifested as drought and floods (IPCC, 2007). Accordingly the climate change impacts global variation based on adaptive capacity, socio- economic, technological and environmental factors. In developing countries like Africa will face increasing water scarcity and stress with a subsequent potential increase of water conflicts as almost all of the 50 river basins in Africa are trans-boundary (FAO, 2008). Agricultural production relies mainly on rainfall for irrigation and will be severely compromised in many

African countries, particularly for subsistence farmers and in sub-Saharan Africa. On the other hand, in Europe agricultural sector is believed to benefit from gradual climate change due to the carbon effect and the warming climate (Tol et al., 2000; McCarthy, 2001).

Researches have been carried out by many scholars to identify the effects of climate on agriculture and different adaptation measures were proposed. Even though, there are sufficient evidences on the impact of climate change, the small holder farmers still failed to address and cope with climate related hazards. Top-down methodologies include the use of modeling and scenario analysis. This can provide useful background to decision making and is strong in terms of the biophysical aspects of impacts. However the models do not perform well in representing human interactions and local abilities to adapt. Moreover, the resolution of models used to determine climate change in developing countries is too course and often relies on data from sources in other countries. Along with the disparity in outputs from different models, this makes the use of results as a basis for adaptation action very difficult (UNFCC, 2009).Hence, as a complement to top down approach, bottom up which recognizes and builds upon local coping strategies and indigenous knowledge and technologies, and the capacity and coping range of communities, and local institutions is useful in developing specific strategies.

In Ethiopia, more than 85% of the people depend mainly on agriculture for their livelihoods, rendering them very vulnerable to climate variability and change. Accordingly, in recent times, a significant number of people in Ethiopia are being affected chronically by drought and/or flooding, leading to deaths and loss of assets and to an appeal for international support (Yohannes Gebre Michael and Mebratu Kifle, 2009). The ability of small holder farmers is of paramount importance to deal with adverse effect of climate related shocks with successful adaptation strategies. According to Yohannes Gebre Michael and Mebratu Kifle (2009), Ethiopia has been characterized by climate variability and change and, out of necessity; the local people have developed different adaptation strategies. These include early warning systems, indigenous soil and water conservation techniques, diversification of crop and livestock species, mobility, reciprocity, customary conflict resolution etc. While this is not a new concept its applicability to agricultural climate change has yet to be explored.

Smallholder farmers in west Shoa area also have been adjusting their actions to changes climate and technology as coping strategies with weather variability even though they may not describe their actions as resilience building. Building resilience into current agricultural operations may be a significant aid to producer’s abilities to adapt to weather unpredictability associated with climate change (Pearce, 2009). Hence, this research aims to know the climate trend and weather events, impacts on rain fed crop production, and coping mechanisms which can be used to up grade the perception of farmers about climate change and improve rain fed crop productions in West shoa Zone.

1.2 Statement of the problem

The success or failure of agriculture is intimately tied to the prevailing weather conditions. Small holder farmers in developing countries are the first victims whose livelihood significantly threatened by weather variability induced by climate change. Hence, smallholder farmers and institutions have to be conscious enough about climate change so that they can build resilience into the current agricultural operations.

The frequency of climate related shocks and stresses have been increasing from time to time. The increase in frequency of extreme weather events compounded by the difficulty in predicting growing conditions becomes a significant threat for the attainment of food security. Hence trend analysis of major climatic variables is vital to forecast the future weather events and its impact on production. The knowledge of expected weather events in turn can assist the smallholder farmers to implement planned adaptation practices. The impacts of climate related hazards also vary from place to place based on the adaptive capacity and resource endowment of the area; and this makes the assessment of commonalities and differences in different agro –ecologies is of paramount importance.

Research have been conducted by individuals and research institutions in this area, however most of the works are very general and with larger spatial recommendation domain. However adaptation mechanisms are specific to a locality. International conventions such as the United Nations Framework Convention on Climate Change (UNFCCC) assume that developing countries need external solutions in a top-down approach. Moreover, adaptation practices are not

3 yet thoroughly explored and little is known about the rationality of local adaptation mechanisms because of insufficient awareness and documentation. However, there is slowly growing recognition of local adaptation to the changing environment (both environmental and policy changes) in terms of efficiency, effectiveness and sustainability. Recognition of local adaptation is seen as an entry point to strengthen the resilience of local people to climate change. Similarly, the concept of community-based adaptation (CBA) is based on recognition of the competence of grassroots communities to solve their own problems (Yohannes Gebre Michel and Mebratu Kifle, 2009).

The climate change research community has identified different adaptation methods. The adaptation methods most commonly cited in literature include: use of new crop varieties and livestock species that are more suited to drier conditions, irrigation, crop diversification, mixed crop livestock farming systems, changing planting dates, diversifying from farm to non-farm activity, increased use of water and soil conservation techniques, changed use of capital and labour and shading and sheltering / tree planting (Temesgen Deressa, 2008). However the specific climatic characteristics of area, dictates the need for a specific adaptation method to climate change.

In Ethiopia study in pastoralist area conducted by PROLINNOVA–Ethiopia and Pastoralist Forum Ethiopia (PFE) find out several adaptation mechanisms to climate change. Some of the adaptations employed by the pastoralist community are traditional early-warning systems, moving with livestock, Cut-and-carry feeding system, settlement around water points, selection of livestock species, diversification, empowerment of traditional institutions and other local adaptations such as growing varieties of sorghum and maize that are widely adaptable, drought resistant and good sources of fodder and food. Regarding crop production, a research conducted in Abay basin by (Temesgen Deressa, 2008) with wide recommendation domain, found that adopting adaptation strategies such as crop diversification, use of soil conservation practices and planting multipurpose trees are found to be promising adaptation strategies.

The researches were focused on drought prone areas with less adaptive capacity; however areas which are known as productive zones are also facing weather related shocks. Regardless of the efforts made by different researchers, the magnitude of the problem is escalating from time to

4 time still fail to address the specific local problems of small holder farmers. More over Ethiopia has diversified climate types and the nature of precipitation change might be more different at different climate types due to the diversified internal interacting factors. Temesgen Deressa et al., (2008) have conducted an integrated quantitative vulnerability assessment for seven Regional States of the total eleven regions by using biophysical and social vulnerability indices of Ricardian approach. They find out that both decline in precipitation and increasing in temperature are damaging to Ethiopian Agriculture; they also acknowledge as their study was highly aggregated and further study is required at local level. Hence, small scale studies based on climate types is expected to give better results of detecting the change and pave the way to determine appropriate coping mechanisms.

West Shoa is one of the most productive zones of Oromia regional state suitable for crop production and animal husbandry. However, in recent times some of the areas with in the zone are facing weather related hazards such as land slides, flooding and rain failure. As a result the forests areas become scarcer, soil fertility has declined and over all agricultural productivity has been reduced, in extreme cases extensive areas of the lowlands are abandoned from production purposes and people are forced to remain under food aid program. Currently, according to the data obtained from DPPO 26,180 households in 11 woredas of the mid altitude and lowland areas are under food aid program due weather related shocks. These incidents were not the experience of the area in the past decades. The areas affected by these events in the last ten years are the mid altitude and lowland PAs of about 12 Woredas of the zone namely Bako, Ambo, Holeta, Meta Robi, Ada Berga, Ejeree, Jeldu, Gindeberet, Dano, Ambo, Dendi, and Ilfata.

Hence, what makes this problem worth studying is to have clear understanding of changes in weather, its impact on the rain fed crop production of smallholder farmers and also to explore coping options that fits the available resources of their locality.

1.3 Objectives of the study 1.3.1 General Objective

The overall goal of the research is to assess changes in climate trend and its impact on crop production of smallholder farmers and also to know how the farmers cope up with adverse effects of climate change.

1.3.2 Specific objectives

To find out the trends of major unusual weather events (e.g. flood, drought, erratic rainfall) and impacts experienced by the area. To assess how smallholder farmers responded to weather related adverse effects and also to identify required resources. To assess the existing experience of successful farmers in different agro ecologies and to explore options for their actions. To assess the perception of farmers about climate change and explore local innovations/indigenous knowledge that contributes to adaptation to climate change.

CHAPTER TWO

2. LITERATURE REVIEW 2.1 Climate change general background

2.1.1 Concepts and definitions of climate change

Most people define “climate change” as the alteration of the world’s climate that humans are causing, through fossil fuel burning, clearing forests and other practices that increase the concentration of greenhouse gases (GHG) in the atmosphere. However, scientists often use the term for any change in the climate, whether arising naturally or from human causes.

Climate change in IPCC usage refers to a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. It refers to any change in climate over time, whether due to natural variability or as a result of human activity. This usage differs from that in the United Nations Framework Convention on Climate Change (UNFCCC), where climate change refers to a change of climate that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and that is in addition to natural climate variability observed over comparable time periods (IPCC, 2007). Each of these two definitions is relevant and important to keep in mind.

When dealing with issues of climate change, it is important to understand the different terms used as “packages” in understanding the system. Accordingly, “climate variability” is the fluctuation in climatic parameters from the normal or baseline values, whereas “climate change” is a change in the long-term mean value of a particular climate parameter (Abebe Tadege, 2008).

2.1.2 Components of climate and climate system tele-connections

Climate involves variations in which the atmosphere is influenced by and interacts with other parts of the climate system and external forcing (Lovejoy and Hannah, 2005).The internal interactive components in the climate system include the atmosphere, the oceans, sea ice, the land and its features(including the vegetation ,albedo ,biomass and ecosystems), snow cover

,land ice, and hydrology( including rivers, lakes and surface and subsurface water). The components normally regarded as external to the system include the sun and its output; the earth’s rotation; sun -earth geometry and slowly changing orbits; and the physical components of the earth system.

Rising fossil fuel burning and land use changes have emitted, and are continuing to emit, increasing quantities of greenhouse gases into the Earth’s atmosphere (UNFCCC, 2007). Greenhouse gases and aerosols affect climate by altering incoming solar radiation and out-going infrared (thermal) radiation that are part of Earth’s energy balance. Changing the atmospheric abundance or properties of these gases and particles can lead to a warming or cooling of the climate system. Since the start of the industrial era (about 1750), the overall effect of human activities on climate has been a warming influence. The human impact on climate during this era greatly exceeds that due to known changes in natural processes, such as solar changes and volcanic eruptions (IPCC, 2007).

There are also seasonal and longer time scale linkages due to the earth’s rotation which are often referred as climate system tele-connections. According to( Lovejoy and Hannah, 2005) the best known of tele-connectios is the El Nino phenomenon; a major redistribution of planetary heat and moisture occurs in the atmosphere and ocean about every three to seven years through a phenomenon centered in the equatorial pacific and its opposite phase as Lalina. El Nino is the dominant cause of droughts and floods in various parts of the world. It also plays important role in modulating carbon dioxide exchange with the atmosphere. Moreover El Nino is accompanied by change in the atmospheric pressure and winds throughout the tropics and subtropics called southern oscillation which is combined with ElNino and give rise to coupled mode of climate system called El Nino-Southern oscillation (ENSO).This in turn has impact on atmospheric convection and rainfall and thereby change atmospheric heating patterns.

The atmosphere connects all weather systems and all climates, it is sometimes useful to describe the atmosphere, oceans and Earth surface as the “global climate system” (UNISDR, 2008). Because the climate system is in a constant state of flux and has always exhibited natural fluctuations and extreme conditions, it is not possible to argue that any single extreme event is

8 attributable to climate change. Only after a sufficient period and with hundreds of extreme events recorded can scientists determine if a specific event is within normal historical variation or is due to some other cause such as climate change.

2.1.3 Causes of climate change

Climate change occurs as a result of both internal variability within the climate system and external factors. The external causes may be natural or induced by human activity. The primary cause of climate change is increase in the concentration of carbon dioxide and other greenhouse gases in the atmosphere because of human activities mainly fossil fuel burning and removal of forests (Lovejoy and Hannah,2005). At global scale, the main cause of greenhouse gas (GHG) emissions is from carbon dioxide (70%), primarily from burning of fossil fuel (petroleum) imported from industrialized countries, while the other sources for GHG are methane(CH4) and nitrous oxide(N2O) caused by deforestation and agricultural activities, particularly the use of pesticides(Yohannes Gebre Michael and Mebratu Kifle, 2009).

2.1.4 Manifestations of climate change

The knowledge of climate change manifestations is of paramount importance to understand its impact on different sectors. The increase in average temperature of the planet and change in hydrological cycle are major manifestations of climate change (Lovejoy and Hannah, 2005).The main characteristics of climate change are increases in average global temperature (global warming); changes in cloud cover and precipitation particularly over land; melting of ice caps

9 and glaciers and reduced snow cover; and increases in ocean temperatures and ocean acidity – due to seawater absorbing heat and carbon dioxide from the atmosphere (UNFCCC, 2007).Global temperature increases signify increases in the water-holding capacity of the atmosphere, and, together with enhanced evaporation, this means that the actual moisture should increase. It follows that naturally occurring droughts are likely to be exacerbated by enhanced drying. Globally there must be an increase in precipitation to balance the enhanced evaporation. Most of the moisture in extra tropical storm comes from moisture stored in the atmosphere when it began. This increased moisture provides fuel for storms and enhances rainfall and snow intensity, increasing risk of flooding. According (Love joy and Hannah, 2005), the average surface air temperature of the planet is probably the single most widely used indicator of the state of global climate while other bio-geophysical phenomena that should be measurable: for example, sea level, glacier mass balance, sea -ice thickness and/or extent, and snow cover duration are supplementary evidences.

Accordingly Climate change issues differ from place to place and not all climate change issues are equally manifested in all countries. Climate change will affect all countries, but people in the poorest countries and poor people in richer countries are more likely to suffer the most. They tend to live in high-risk areas such as unstable slopes and flood plains, and often cannot afford well-built houses. Many of them depend on climate-sensitive sectors, such as agriculture, and have little or no means to cope with climate change, for example owing to low savings, no property insurance and poor access to public services. Climate change is expected to reduce already low incomes and increase illness and death rates in many developing countries. Africa, small island states, and the Asian and African mega-deltas are likely to be particularly affected by climate change (UNISDR, 2008).

According to the IPCC (2007), Europe will need to cope with retreating glaciers and extent of permafrost, reduced precipitation in Southern Europe and the possibility of more droughts in some areas, as well as increased risk of flash floods, Wild fires and rising sea levels are likely. In Latin America water scarcity, increased salinization, desertification of agricultural land, flooding in low-lying coastal areas and productivity of some crops and livestock will decrease, with adverse consequences for food security. North America will experience altered seasonal

10 availability of water, forest fires, longer and hotter heat waves, with a greater potential for adverse health impacts. Australia and New Zealand may face more frequent extreme events such as heat waves, droughts, fires, floods, landslides and storm surges. Small island states, coastal systems and other low-lying areas are especially vulnerable to the effects of climate change, rising sea levels and extreme weather events.

Asia’s sustainable development will be challenged as climate change compounds the pressures that rapid urbanization, industrialization, and economic development have placed on natural resources. Some of the main issues will be the availability of adequate fresh water, increased flooding because of both rising sea levels and river flooding at coastal areas.

Africa is particularly vulnerable to the effects of climate change because of multiple stresses and low adaptive capacities, arising from endemic poverty, weak institutions, and complex disasters and associated conflicts. Drought will continue to be a primary concern for many African populations. The frequency of weather- and climate-related disasters has increased since the 1970s, and the Sahel and Southern Africa have become drier during the twentieth century. Water supplies and agricultural production will become even more severely diminished. By 2020, in some African countries agricultural yields could be reduced by as much as 50%. By the 2080s, the area of arid and semi-arid land in Africa will likely increase by 5-8 %( IPCC, 2007).

Generally, increasing food insecurity, water scarcity, drought, spread of diseases to new areas, damage from floods and forced migration due to desertification of previously arable land or sea- level rise are some of the likely issues in developing countries (Solomon et al., 2007).

In Ethiopia, researches conducted indicate that in the past few years, the rainfall pattern has shown a decreasing trend in amount and distribution, even though the significance of the change differ among different climate types. Moreover recurrent droughts and floods in Ethiopia are definitive sign that climate change is indeed impacting on Ethiopia. Discussion held with farmers also revealed that late start and early cessation of the main rainy season are recent changes in the rainfall regime in the past few years. More over climate change has caused change in the land use pattern, change in crop mix such as the introduction of lowland crops (including maize, haricot bean, pepper and sugarcane) to the areas where they were not traditional known in the

11 area are typical indicator of the change in the different parts of the country. Most crops produced in the main cropping season also encounter moisture deficiency at their maturity period, which brings about decline in productivity (Green Forum, 2007).

2.2 Overview of Ethiopia’s climate

2.2.1 Rainfall regimes and temperature

The main climatic features which help explain Ethiopia’s climatic resources are spatial and temporal distribution of rainfall and temperature. The highest mean annual rainfall, over 2000 mm, is in the southwestern highlands in Illubabor Zone of Oromia Region. The amount of rainfall gradually decrease about 600 mm in the north in areas bordering Eritrea, drops to less than 100 mm in the northeast in Afar, and to around 200 mm in the southeast in the Ogaden (NMSA, 1996; NMSA, 2001). The pattern of annual rainfall distribution in Ethiopia is mainly influenced by two moist wind systems, one blowing from the Atlantic Ocean, the other from the Indian Ocean. Based on this rainfall distribution pattern, the following four major rainfall regimes can be delineated: Bimodal Type-1, Monomodal Type-1, Bimodal Type-2 and Monomodal Type-2.

Central, eastern highlands and adjoining lowland areas of the country experience a bimodal rainfall (Type-1) pattern, receiving the majority of their rainfall from the Atlantic, while some derive from the Indian Ocean. The big rains from June to September come mainly from the Atlantic, while the small spring rains between February and May come from the Indian Ocean (NMSA, 1996; NMSA, 2001).

Western, southwestern and northwestern parts of the country experience a Monomodal Type-1 rainfall pattern brought about by wind systems coming from the Indian Oceans combining with those from the Atlantic to give continuous rain from March /April to October / November (NMSA, 1996; NMSA, 2001).

Lowlands of Southern and southeastern parts of the country experience a bimodal Type-2 rainfall pattern brought about by the wind system coming from the Indian Ocean from September to

November and from March to May. The most reliable rainy months are April and May (NMSA, 1996; NMSA, 2001).

Northeastern parts of the country which comprises extreme northern areas and western escarpment of the Rift valley and the adjacent Dallol depression have Monomodal Type-2 rain fall pattern. The lowlands have only one rainy season when only a little rain falls. However, the escarpment, particularly in the north, can have a third rainy season brought by moist winds from Asia which crosses the Arabian Peninsula and cool as they rise over the Ethiopian escarpment. These bring mist and rain anytime between November and February (NMSA, 1996; NMSA, 2001).

The highest mean maximum temperatures in the country, about 45o C from April to September and 40oC from October to March are recorded from the Afar Depression in northeast Ethiopia. The other hot areas are the northwestern lowlands, which experience a mean maximum temperature of 400 degree C in June, and the western and southeastern lowlands with mean maximum temperatures of 35oC to 40oC during April. The lowest mean temperatures, of 40oC or lower, are recorded at night in highland areas over 2,000 m asl between November and February. Many of these areas, particularly in valley bottoms, have 4,000 m asl and experience snow and frozen rain (NMSA, 1996).

2.2.2 Climate related hazards in Ethiopia

Climate related hazards differ from place to place. According to the national adaptation program action of Ethiopia, climate related hazards in Ethiopia include drought, floods, heavy rains, strong winds, frost and heat waves. As depicted in the drought probability map, Ethiopia is prone to drought (NMSA, 1996).It is a major hazard in Ethiopia in both highlands and lowlands and millions of people are affected by drought each year (IIRR, 2007).The country has been experiencing recurrent droughts since 1970’s. Despite long experience in dealing with drought disasters with support of external assistance, the country has not overcome the negative impacts drought has on both household and the overall development of the country.

The other climate related hazard that affects Ethiopia is flood. Flood is a hazard that can cause human deaths and reduce the asset base of households, communities and nations by destroying standing crops, livestock, infrastructure, machinery and buildings (IIRR, 2007). According IIRR areas most frequently affected by flash floods are the central and western part of Tigray; North Gonder, North and South Welo and Oromia zones of Amahara region, east,north and West Shoa zones and Hararghe areas of Oromia region; South Omo,Gurage,Sidama and Hadiya zones of SNNPR and Diredawa town in the east. The magnitude of flood hazard depend on the intensity, duration, and distribution of the rainfall in the catchments; where as the amount of damage mainly influenced by nature of precipitation/source of water, characteristics of drainage basin and speed of water flow. Due to uncertain prediction flood occurrence, the impact of flood hazards are highly significant and wide; including economic (loss of livelihood/property, crop fields, livestock disease), environment (soil loss) and social impacts (loss of life, disease, displacement and Psychological shock). In respect to food security flood in Ethiopia usually occur at the vegetative growth stage of major food crops (mostly in August) in crop fields in the low-lying areas of the water shed; and hence have a significant impact on rain fed crop production.

Heavy rain is also another climate related that have strong correlation with flood. The most important cause of flooding is excessive rainfall compounded by settlement of flood plains, deforestation, urbanization and poor management of drainage systems. Heavy rains usually resulted in flash flood when the area coverage is localized and if the precipitation covered large catchment areas the rivers may overflow cause river floods. Generally, drought, flood and heavy rain are the major climate related hazards in Ethiopia. Hence, it is important to implement interventions or adaptation strategies in all affected sectors to reduce the vulnerability of the natural and human systems.

2.3 Vulnerability of Ethiopian Agriculture to climate change

Most agricultural activities are inherently sensitive to weather variability and this may result in the agricultural industry being extremely vulnerable to climate change. Adaptation to climate change is not only essential to maintain agricultural production and for agricultural economies and communities to remain in existence; but also to reduce vulnerability for future generations (Pearce, 2009). According to IPCC (2001) Vulnerability is defined as ‘’the degree to which a 14 system is susceptible to, and unable to cope with adverse effects of climate change, including climate variability and extremes’’. Ethiopia is one of the most vulnerable countries to harsh climatic conditions. Ethiopia suffers from extreme climates, mostly manifested in the form of frequent droughts.

A recent mapping on vulnerability and poverty in Africa (ILRI, 2006), puts Ethiopia as one of the countries most vulnerable to climate change and with least coping ability. In Ethiopia, it is assumed that the temperature has been increasing annually at the rate of 0.2°C over the past five decades (Yohannes Gebremichael and Mebratu Kifle, 2009). This has already led to a decline in agricultural production, and cereal production is expected to decline still further (by 12%) under moderate global warming (Ringer, 2008). Moreover, it has led to a decline in biodiversity, shortage of food and increases in human and livestock health problems, rural-urban migration and dependency on external support. Vulnerability can be high because of high exposure (severe hurricanes), high sensitivity (small islands), or low adaptive capacity (least developed countries). Of course, vulnerability can also be reduced as a result of high adaptive capacity (Ludwig et al., 2007).

The vulnerability of the Agriculture to climate change and weather variability is greatly influenced by its adaptive capacity which influenced by factors such as economic Wealth infrastructure and technology, Institutions and Services, Information, knowledge and skills, equity and social capital. Developing countries are particularly vulnerable to climate change because of the sensitivity of their fragile environments; small changes in climate can cause large environmental changes through, for example, rapid desertification. Accordingly, Ethiopian agriculture is very vulnerable to climate change due to factors compounding the impact of climate change such as rapid population growth, land degradation, widespread poverty, dependency on rain fed agriculture, lack of awareness by policy and decision-makers about climate change and lack of appropriate policies and legislation (Wondwossen Sintayehu, 2008; Daniel Kassahun, 2008; NMA, 2007).

Ethiopia’s population grew extremely fast over the past few decades. Every year 2.5 million people are added. Currently, it is growing at a rate of 3% per year and expected to double in 22

15 years time (Green Forum, 2007).As a livelihood of Ethiopian population is directly or indirectly based on agriculture, the pressure on suitable land, Which is mostly already occupied, will continue, together with the conversion of marginal lands such as steep slopes, hillsides, semi arid regions, wetlands to unsustainable cultivation.

In Ethiopia, agricultural practices accompanied by unsustainable consumption of natural resources are responsible for massive land degradation. At the turn of the 20th century, about half the area of a country was covered with forests (Green Forum, 2007). However, this resource has been subjected to intensive degradation due to deforestation and soil degradation. There are several reasons why climate change has the most severe impact on poor people. In Ethiopia, the proportion of the poor is exceedingly high and therefore the country is expected to suffer most from climate change. For instance, the poor are more likely to occupy dangerous locations, such as semi arid area, flood plains, river banks, steep slopes and reclaimed lands. These areas already have an erratic climate with unpredictable rainfall causing both floods and droughts. More over once hazard happened to a poor community; it will take a very long time to recover and push them further into poverty.

Lack of awareness with in the general public and policy and decision makers in a country could negatively impact adaptation and mitigation efforts. Little information is available for the general public due to limited research undertaken on climate change. In addition lack of appropriate Climate change policy and legislations is a prime challenge to the development of successful adaptation measures. According to Green Forum report (2007), Ethiopia has ratified several international conventions related to climate change including the UNFCCC (Climate convention) and UN convention to combat desertification; however the ratified conventions in Ethiopia have not been translated in to laws, regulations and guidelines.

2.3.1 Impacts on food security Climate change will act as a multiplier of existing threats to food security: It will make natural disasters more frequent and intense, land and water more scarce and difficult to access, and increases in productivity even harder to achieve. The implications for people who are poor and already food insecure and malnourished are immense (UNFCCC, 2009). Particularly in the least

16 developed countries and small island developing states, it is the livelihoods and lives of the poorest and most vulnerable, including women, children and marginal communities, which are also at greatest risk to suffer from to the potential impacts of climate change. This is due to their high exposure to natural hazards, their direct dependence on climate sensitive resources such as plants, trees, animals, water and land, and their limited capacity to adapt to and cope with climate change impacts.

Climate change may affect agriculture through: changes in temperature and precipitation, Changes in soil moisture and soil fertility, changes in the length of growing season and increased probability of extreme climatic conditions((McGuigan et al., 2002). According to Pearce (2009), major climate change impacts on crop production includes crop damage/loss, lower yields, reduced seeding area, income loss, harvest difficulties, increased pest activity, and delayed seeding. However the degree of these impacts varies based on agro- ecosystem.

Global climate models (GCMs) predict that aggregate changes in world food production are likely to be small (IPCC, 2001).However there is general agreement that climate change may lead to significant reductions in agricultural productivity in developing countries. Increased co2 is expected to have a positive effect on agricultural production due to greater water use efficiency and higher rates of plant photosynthesis (Kurukulasuriya and Rosenthal, 2003). It promotes photosynthesis in plants, the process that converts CO2 and water into glucose and oxygen. By speeding up the photosynthetic rate, plants will grow, mature, and produce fruit more quickly than normal .However some scientists believe that the speed in maturity doesn’t compensate high production.

There are many uncertainties when constructing models of crop yield changes, with static models often presenting a negative outlook with decreases in crop productivity of around 20- 30%. However these assumptions disregard both the positive effects that increased carbon dioxide (CO2) can have on crop yields and the adjustment of the economic system to changes in agricultural production, given interaction in the global economy. Some people In particular the issue of CO2 fertilisation has given rise to much debate. Some argue that increasing CO2 fertilisation could make a major contribution to solving the problems created by climate change

17 for agriculture, however others feel this contribution may be overestimated. It is worth noting the differential impacts of CO2 fertilisation, with crops such as wheat, rice and soybeans [known as

C3 crops] responding positively to increased CO2, whilst other major staples, such as maize, sorghum, sugarcane and millet [C4 crops] do not benefit ((McGuigan et al., 2002). As C4 crops are those grown in the tropics, this factor alone shows the possibility of differential climate change effects and points to the fact that only agricultural production in more temperate zones, will be partially compensated by the beneficial effects of CO2 enrichment.

Climate change is already affecting all four dimensions of food security: food availability, food utilization and food system stability. The impacts are both short-term, through more extreme weather event, and long-term through changing temperatures and precipitation patterns (Glantz, 2009). According to (FAO, 2008) the climate change variables considered in the CCFS(Climate change and Food Security) framework include the CO2 fertilization effect of increased greenhouse gas concentrations in the atmosphere; increasing mean, maximum and minimum temperatures; gradual changes in precipitation: increase in the frequency, duration and intensity of dry spells and droughts; changes in the timing, duration, intensity and geographic location of rain and snowfall; increase in the frequency and intensity of storms and floods; greater seasonal weather variability and changes in start/end of growing seasons. Those entire variables have direct and indirect impact on agricultural activity and thereby affect food security significantly.

In Ethiopia small holder farmers produce 90% of the total yield of agricultural sector while 99% of the production comes from the rain fed agriculture which is highly sensitive to climate change and variability (MoFED, 2006). As the rain fed agriculture dependent on climate variables such as solar radiation, temperature and precipitation; the increased temperature and reduced rainfall trends in most parts of Ethiopia will have significant impact on the performance of Ethiopian Agriculture. The impact can be manifested in terms of yield drops and change in length of growing periods.

2.4 Coping with climate change effects

2.4.1 Concepts of Adaptation and resilience to climate Change

Resilience is a term and concept first developed by ecologists to describe the characteristics of ecosystems that maintain themselves during a disturbance produced by various forces (Pearce, 2009). This notion has since been used in the realm of social science to describe social resilience as “the ability of groups or communities to cope with external stresses and disturbances as a result of social, political and environmental change”. As a result of the dependency of social resilience on ecosystem services socio-ecological resilience is coined to describe holistically as “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity and feedback”(Walker et al., 2004).

Adaptability is closely connected to the notion of resilience. Essentially adaptability is the capacity of the actors in a system to influence resilience (Walker et al., 2004). Adaptation can be described by different scholars based on level/scope, time and management plan.

According to Burton et al. (1993), the term “adaptation measures” covers eight categories: bearing losses (doing nothing), sharing losses, modifying the threat and thus preventing effects, changing use, changing location, accessing new research based technologies, disseminating knowledge through education to change behavior, and restoration. Others have classified the different forms of adaptation as anticipatory and reactive adaptation, private and public adaptation, and autonomous and planned adaptation (IPCC, 2001).

Adaptation as applied to climate change is a very broad concept, and the concept is defined differently in the literature by different scholars. From these concepts Yohannes Gebre Michael and Mebratu Kifle (2009) extracted the following major principles of adaptation to climate:

Adaptation is a continuous and learning process. Adaptation is a response to actual or expected risks; in other words, adaptation can occur before, during or after any external stimulus or threat. Adaptation integrates prevention or mitigation in its process. Adaptation can be spontaneous and planned. Adaptation can be a practice, management practice or process. Hence, Adaptation to climate change impacts should not be approached as a separate activity, isolated from other environmental and socio-economic concerns that also impact on the development opportunities of poor people. Moreover adaptation as a response to change must be appropriate to specific hazards or threats in a given period of time; in the same way, an effective adaptation to a real or perceived change in a local climate could, overtime become inappropriate as circumstances changes (Glantz, 2009)

The IPCC’s officially used operational definition used operational definition for adaptation is as follows: Adaptation is adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploit beneficial opportunities (Glantz, 2009).

2.4 2 Linkage between adaptation and mitigation

Adaptation and mitigation measures are interlinked. According to Bruce et al. (1995), the more one succeeds in limiting climate change, the easier it will be to adapt to it. Mitigation options are options, that amongst others, strive to prevent climate change, or combat any reinforcement thereof, by reducing the net emissions of any green house gases in atmosphere, either by reducing green house gas emissions( source oriented measures) or by increasing the sinks for green house gases (sink enhancement measures).

As far as carbon dioxide is concerned source oriented measures are: energy conservation and efficiency improvement, fossil fuel switching, renewable energy and nuclear energy; where as sink enhancement measures are those related to capture and disposal of CO2 and enhancement of forest sinks. There is growing understanding of the possibilities to choose and implement climate

20 response options in several sectors to realize synergies and avoid conflicts with other dimensions of sustainable development.

According to IPCC report (2007), reducing both loss of natural habitat and deforestation can have significant biodiversity, soil and water conservation benefits, and can be implemented in a socially and economically sustainable manner. Forestation and bio-energy plantations can restore degraded land, manage water runoff, retain soil carbon and benefit rural economies, but could compete with food production and may be negative for biodiversity, if not properly designed. Moreover there is synergies and trade off exist among adaptation and mitigation measures. Examples of synergies include properly designed biomass production, formation of protected areas, land management, energy use in buildings, and forestry, but synergies are rather limited in other sectors. Potential trade-offs include increased GHG emissions due to increased consumption of energy related to adaptive responses (IPCC, 2007).

There is high confidence that neither adaptation nor mitigation alone can avoid all climate change impacts. Adaptation is necessary both in the short term and longer term to address impacts resulting from the warming that would occur even for the lowest stabilization scenarios assessed. There are barriers, limits and costs that are not fully understood. Adaptation and mitigation can complement each other and together can significantly reduce the risks of climate change.

According to Bruce et al. (1995), adaptation is primarily a compliment, not an alternative to green house gas abatement .Hence unmitigated climate change would, in the long term, be likely to exceed the capacity of natural, managed and human systems to adapt. Reliance on adaptation alone could eventually lead to a magnitude of climate change to which effective adaptation is not possible, or will only be available at very high social, environmental and economic costs.

In present discussions of climate change, the general belief is that little can be done to prevent the coming impacts, so societies have no choice but to prepare for those impacts by developing adaptation measures. However, the societies have to look beyond reacting to climate change to the down stream impacts of their proposed adaptive policies and practices, as they too will generate their own impacts (Glantz, 2009).

2.4.3 National Adaptation Program Action of Ethiopia (NAPA)

Adaptation is recognized as critical response to the impact of climate change, because current agreements to limit emissions, even if implemented, will not stabilize atmospheric concentrations of greenhouse gas emissions and climate change. Hence adaptation should be considered as an integral part of the response to the climate variability and change (Green Forum proceeding No. 2 2007). Ethiopia also recognized climate change and its impact; has already attempted to set up coping mechanisms for the likely consequences.

National adaptation programs of action (NAPAs) provide a process for Least Developed Countries (LDCs) to identify priority activities that respond to their urgent and immediate needs to adapt to climate change – those for which further delay would increase vulnerability and/or costs at a later stage (Abebe Tadege, 2008). The NAPA for Ethiopia intended to address common climate related hazards such as drought, floods, heavy rain, strong wind, frost, and lightening. The main human vulnerability and livelihood impacts include land degradation, soil erosion, and deforestation, loss of biodiversity, desertification, recurrent drought, flood, and water and air pollution. The national adaptation plan of Ethiopia comprises thirty seven priority adaptation projects. More over the country’s Environmental policy document has given due attention to climate change. One of the 10 sartorial policies adopted by the council of ministers in April 1997 in the document focuses on “Atmospheric pollution and climate change “which states that the government wishes to promote a climate monitoring program and to have a firm and visible commitment to the principle of containing climate change.

2.4.4 Farm level adaptation practices

Adaptations to climate change and weather variability are extremely varied and may occur at varying scales. According to Pearce (2009), adaptation measures may occur at organizational level through technological advances such as crop development, machinery improvements and weather forecasting. Adaptation also occurs at the farm level through tactical and strategic adaptations. Tactical adaptation would include practices such as changing planting times, input uses and harvesting to accommodate weather variability. Strategic farm-level adaptation would take the form of alteration of soil management practices, selection of crop varieties, purchasing crop insurance, or diversifying their farming operation. Smit and skinner (2002), group

22 agricultural adaptation options into four non-mutually exclusive categories. The first technological development which is the most frequently advocated strategies for adaptation to climate change. The second category identified by Smith and skinner (2002) is government program and insurance. These programs include income stabilization and disaster reliefs that have the potential to stabilize farming incomes during times of weather variability associated with climate change. Farm production practices are third category which involves changes in the actual operation of the farm. This category ultimately describes farm level decisions with respect to farm production such as land use, irrigation and operational timings (Smit and Skinner, 2002).The final category is farm financial management highly influenced by government agricultural support. It is a farm level response using farm income strategies to reduce the risk of climate related income loss. Few researchers have addressed adaptation in analyzing the decision making process at farm level (Smit and Skinner, 2002).

2.4.4.1. Tactical (Short term) adaptation measures

Tactical adaptation would include systematic practices such as changing planting time (early planting or late harvest), input uses and harvesting to accommodate weather variability (Pearce, 2009).

2.4.4.1.1 Modification of crop calendars and input uses

Farmers make adjustments of time of sowing based on the prevailing trend of precipitation. Early sowing of crops that can mature with in a short period of time such as chickpea, potatoes, haricot bean and katumani maize is practiced in most part of Ethiopia (Abate, 2009). According to studies conducted on Ethiopian highlands by Astatke et al. (2003), the early crop harvest associated with the minimum tillage system was highly beneficial for small-holder farmers-since the early harvest coincided with the cyclical period of severe household food deficits and high grain prices in local markets. In “Humbo” woreda, welayita Zone, farmers opt to plant early maturing varieties of potato and Haricot bean which requires less moisture as compared to cereals (IIRR, 2007).

The application of animal manure to the soil which reduces inorganic fertilizer use can improve soil water structure and water holding capacity. This in turn contributes to on farm moisture conservation (Nelson, 2009). According to Wild (1993), the general principle of organic farming

23 is to create a system which relies on biological processes for the production of crops and livestock and their protection from pests and disease. To meet the requirements of for nitrogen organic farmers use leguminous crops, the nitrogen left in the soil supplying part of the requirements of the succeeding non- leguminous crop. Ground rock phosphate and basic slag are used in stead of water soluble phosphate fertilizers, and crushed rock (some shale are suitable) is suitable for conventional potassium fertilizer (Wild, 1993).

According to OCDE (Organic Chain Development in Ethiopia) report 2007, Ethiopia is a country of mostly smallholder farmers and its agricultural systems are a mix of animal and crop production. It is one of the 12 major Vavilov centres of crop genetic diversity in the world. Because of these strengths, it has suffered relatively insignificant crop genetic erosion. This makes it easy to intensify agricultural production in Ethiopia without resorting to industrial agriculture. The large crop genetic diversity will also enable Ethiopia to adapt to climate change more easily than most other countries in the world. Strengthening its existing organic agriculture with needed scientific inputs will, therefore, give Ethiopia a globally competitive edge in agriculture. Hence it can be concluded by stating his belief that organic agriculture can ensure that the children and grandchildren of Ethiopia and their following generations into the future are healthily fed in a hospitable biosphere.

2.4.4.1.2 Harvesting to accommodate weather variability

Farmers have experience of harvesting their crops whenever they have expectation that extreme weather event is going to happen in the near future. People in the rural area exchange information in various ways and also usually people travelling from one place to the other to exchange information about the potential risk of extreme weather events. People also congregate in the village to listen to a radio for information and to exchange their own news (IIRR, 2007)

2.4.4.2. Strategic (Long-term) adaptation Measures

Strategic adaptation measures at farm level adaptations would take a form of alteration of soil and water management practices, selection of resistant crop verities that can withstand adverse weather shocks and diversifying the farm operation (Bradshaw et al., 2004).

2.4.4.2.1 On- farm water management

On farm water management is one of the strategic adaptation measures that involves efficient utilization and proper water management for farm operations. It includes modification of irrigation techniques, adoption of water efficient technologies, and improved water harvesting practices.

Modification of irrigation techniques

The objectives of irrigation are to add the amount of water needed when the plant requires it (Miller and Donahue, 1997). A water shortage reduces vegetative growth, forces premature “seed” production or both. Some factors to be considered when planning the amount and frequency of irrigation are:- • Depth and distribution of plant roots • The amount of water retained with in the rooting depth • The minimum water potentials to be maintained in the root zone • The rate of water use by the plant(Consumptive use) • Adequacy of irrigation water available to add when it is needed How ever, determination of appropriate amount and frequency is very decisive for efficient utilization of the irrigation water. Irrigation timing by small holder farmers is, the art of irrigation learned by experience, can be effective in determining watering timing but extra water beyond minimum need is usually applied. Irrigation water can be applied by ranging from haphazard flooding to enormous center. Pivot mobile sprinklers and, more recently, trickle(drip) irrigation has made it possible to irrigate almost all arable soils even those of rolling hills deep and steep slopes. However, making irrigation doesn’t necessarily make it, economical, practical and or even desirable (Miller and Donahue, 1997).

Flood or furrow irrigation is the oldest method of irrigation. Prehistoric people used this same method of irrigation to water their fields. It remains, to this day, the most common method of irrigation. It is relatively inexpensive and easy. Essentially, it is pouring water directly on the fields. This method is also extremely inefficient. About half of the water that is used never actually gets to the crops. There are three new developments that modern farmers are using to make this method more efficient. Farmers are leveling fields because furrow irrigation uses

25 gravity. Any hill, even a tiny one, gets missed by the water. By leveling fields, you ensure that the water you are using actually gets to all of the crops. Farmers are also using a method of surge flooding. This is releasing the water at pre-arranged intervals. This reduces the amount of runoff and therefore waste from the fields. The last is to capture and reuse the runoff from the fields. Farmers capture the runoff from the fields in ponds and then pump it back to the release point to use for the next irrigation.

Modification of irrigation techniques are employed in special circumstances. Recently innovations in furrow irrigation automated water delivery and sprinkler system improvements have been subjects of investigation. According to (Miller and Donahue, 1997) special techniques may not decrease consumptive water use for a given yield, but they may improve efficiency by reducing the leaching of nutrients, water losses by drainage, and so on. In Ethiopia, nowadays the use of efficient technologies such as drip irrigation, furrow and basin irrigation is being practiced by smallholder farmers rather than flooding type.

Adoption efficient irrigation technologies

The introduction of irrigation techniques which are affordable and efficient is also area of focus for effective utilization of irrigation water. In Ethiopia the use of drip irrigation and low-cost spray/sprinkler is becoming a good practice by intervention of government and Non governmental organizations (Glimmer of Hope foundation, 2009).

Drip irrigation is where water is sent through plastic pipes with holes along the sides of them. These pipes can be laid along the rows of crops or even buried along their root lines. This method significantly reduces the amount of water wasted by evaporation. On average, ¼ of the water used is saved compared to traditional furrow irrigation; Better yet, is spray irrigation. This is the most modern form of irrigation. It requires machinery to function, so it is more expensive. Spray irrigation uses a center pivot system that is similar to watering your lawn with a hose. However, seems to be the perfect balance between efficiency and affordability. It is believed that with the proper funding, it will be affordable to help all sustenance farmers in Ethiopia increase their yields. There are also some programs that center specifically on irrigation. One of these is the A Glimmer of Hope foundation. In areas where the foundation has given irrigation tools, farmers have doubled, tripled and quadrupled their annual earnings. 26

Water harvesting

In Ethiopia there is evidence that ancient churches, monasteries and castles used to collect rain water from rooftops and the history of rain water harvesting by the Axumite kingdom dates back as early as 560BC( Habtamu Geleta,1999). During this period rainwater was harvested and stored in ponds for agriculture and water supply purposes. This is confirmed from literature and visual observations on the remains of ponds that were once used for irrigation during that period. Further more, roof water harvesting setup still exists in the remains of one of the oldest places in Axum. Even to this day water harvesting is often practiced in different parts of Ethiopia using small bunds or dikes made of dirt, stone or living vegetation along slope contours. Case studies presented in Oslo (Sweden) in 2008 by the working groups from Ethiopia, Nicaragua, Nepal & Sri Lanka indicated that the water shed approach being undertaken in Tigray (Northern Ethiopia) is a good experience water conservation and water harvesting techniques. Rainwater harvesting: It is a method for inducing, collecting, storing and conserving local surface runoff for agriculture in arid and semi-arid regions (Yilma Sileshi and Yusuf Kedir, 2005).

There are several traditional rainwater harvesting technologies in Ethiopia, which have been used by communities in area of water shortage since long ago. For many traditional communities in rural areas where natural sources of water are lacking, collection of rain water from pits on rock out crops and excavated ponds are common practices (Habtamu Geleta, 1999).

In semi arid lowland areas where rain is not adequate for crop growth, farmers use runoff irrigation as a source of a few lifesaving irrigation supplies. Run off irrigation is widely practiced in Cherher plains around Mahoni and Waja near Alamata in Tigray,the Gato valley in north Omo, parts of eastern and western Hararghe, and many other places (Negigi, 2005) According Yilma Sileshi and Yusuf Kedir (2005) Water harvesting techniques includes Rainwater harvesting, Floodwater harvesting, Dry weather flow river diversion and Groundwater harvesting.Table-1 below shows some of water harvesting technologies practiced in different parts of Ethiopia.

Table- 1 Overview of water harvesting techniques in Ethiopia

N WH techniques Water source Uses Category Users Regions Oromiya/ 1 Above Ground tankers Roof Drinking Modern School, Family South Oromiya/ 2 Under ground tanker Roof Drinking Modern School/Hotels South Domestic/ Indigenous, Oromiya/ 3 Ponds Surface Runoff livestock Modern Community South Domestic/ Family, 4 Haffirs(Embankments) Runoff livestock Indigenous individual Somali, Domestic/ 5 Cistern Runoff livestock Indigenous Community Yabelo Domestic/ 6 Birka(tank) Runoff livestock Indigenous Family Somali, Crop 7 Earthen Dam Runoff production Modern Community Somali,

8 Micro dams Runoff/Stream Irrigation Modern Community Tigray Domestic/ 9 Bore Holes Ground water livestock Modern Community Somali Oromiya/ 10 Above Ground tankers roof Drinking Modern School, Family South Oromiya/ 11 Under ground tanker roof Drinking Modern School/Hotels South Somali, Domestic/ Indigenous, Oromiya, 12 Ponds Surface Runoff livestock Modern Community South Domestic/ Family, 13 Haffirs(Embankments) Runoff livestock Indigenous individual Somali,

Source: (Yilma Sileshi and Yusuf Kedir 2005)

However, for effective implementation of water harvesting techniques at farm level there are also challenges that one has to consider. According to Yilma Sileshi & Yusuf Kedir (2005), the challenges can be grouped in to two; technical and environmental challenges. Technical challenges involve leakage problems, catchments’ runoff guiding problems, and the application of tanks tried in one area for different soil, topographic & climatic conditions. Environmental problems are related to safety aspect as it could be breeding site for mosquitoes in arid areas. More over economic sustainability related to the problem of siltation is also need due attention.

Hence for sustainable agriculture of the country water harvesting technologies have significant contribution from adaptation point of view It can be constructed and managed by the community with minor technical know how however the right choice for water harvesting technique for specific locality is required.

2.4.4.2.2 Selection of drought resistant crops

Farmers have indigenous technical knowledge in selection of crops that resist drought and other adverse weather shocks from the locally available varieties. Planting crops that are drought tolerant and/or resistant to temperature stresses; taking full advantage of the available water and making efficient use of it. In addition, as apart of institutional level adaptation strategy, agricultural research center & universities are putting strong effort to develop crop varieties and technologies used as adaptation option to climate change. Some of the crop varieties that have been used by the farmers are sorghum, maize, and Haricot bean.

In SNNPRS Enset(false banana) is a drought resistance crop considered as a crop of bad times using it as last resort (IIRR, 2007). Hence, this is also one of the coping mechanisms where drought anticipated. Ensete, known locally as false banana, is an important food source in Ethiopia's southern and southwestern highlands. It is cultivated principally by the Gurage, Sidama, and several other ethnic groups in the region. Resembling the banana but bearing an inedible fruit, the plant produces large quantities of starch in its underground rhizome and an above-ground stem that can reach a height of several meters. Ensete flour constitutes the staple food of the local people. Taro, yams, and sweet potatoes are commonly grown in the same region as the ensete.

Sorghum, millet, and corn are cultivated mostly in warmer areas at lower altitudes along the country's western, southwestern, and eastern peripheries. Sorghum and millet, which are drought resistant, grow well at low elevations where rainfall is less reliable. Afar pastoralists are growing varieties of sorghum and maize that are widely adaptable, drought resistant and good source of fodder and food. Most of the varieties are local land races while some have adopted from the lowland farmers, extension workers and market centers (Yohannes Gebre Michael and Mebratu Kifle, 2009).

2.4.4.2.3 Soil management practices/ Conservation tillage practices/

Conservation tillage is one of the components of sustainable agriculture, which is environmentally friendly. US soil conservation service, defines conservation tillage as any tillage system that leaves at least 30% of surface covered by plant residues for control erosion by water; for controlling erosion by wind, it means leaving about 1000-2000 kg/ ha of flattened small grain straw equivalent(Miller and Donahue, 1997).

In addition to soil moisture conservation, conservation tillage has also significant contribution to soil carbon sequestration (Nelson, 2009). It involves adding maximum amount of carbon to the soil. The technical potential of this processes higher in degraded soils that have been managed with extractive farming practices. Hence, this practice can increase the soil carbon pool. However no single technology is appropriate for all soils, climates or cropping or farming system, the identification of site specific technologies that create a positive soil carbon budget is of great importance. So, soil carbon sequestration is then one of the clearest examples of a mitigation measure that also protects against changes in climate and enhances adaptation and sustainability of crop production (Nelson, 2009). Conservation tillage comprises reduced tillage and Zero tillage.

Minimum/ Reduced tillage

It is any combination of tillage operations that do less tillage than all the operations used in “conventional tillage”. Reducing the number of operation maybe accomplished by eliminating plowing or one pass with a disk, or it may be almost no till operation with only a single disking or pass with wide sweeps (Miller and Donahue, 1997). In Ethiopia minimum tillage is becoming one of the promising soil conservation practices for on farm ecosystem stability. According to Menale Kassie et al. (2008), the research results conducted on 435 farm households showed that there is indeed strong evidence that adoption of reduced tillage have impact on agricultural productivity on low rainfall areas compared to high rain fall areas. By contrast, we do not find significant productivity differences between conserved and non- conserved plots as well as between reduced tillage and non-reduced tillage plots in our high rainfall area, which is the Amhara region. It is believed that productivity difference is emanated due to greater benefits of moisture conservation in low rainfall areas, whereas moisture conservation in high rainfall areas

30 may contribute to problems such as water-logging, increased weed growth and enhanced pest infestation. However, even though the use of reduced tillage for soil and moisture conservation in high rainfall areas may not increase short term productivity, this does not mean that no conservation techniques are required. In fact, placing appropriate conservation measures could help protect soils during extreme rainfall.

The reason for going to reduced tillage system is primarily to reduce erosion of the soil, save time in operations and save on labor costs. Reduced tillage operations should reduce costs of pesticides and nitrogen fertilizer. According to Miller and Donahue (1997), the total reductions in tillage costs with reduced tillage are about 5-10% lower than with conventional moldboard or disk- plow tillage. Other benefits of reduced tillage include that more area can be planted, soil dries less than with conventional tillage, stepper land can be formed because of better erosion control, storage of water in root zone and reduction of soil compaction. More over in lowland areas reduced tillage not only increase yields but could also provide other benefits: farmers may also be able, increase environmental benefits, reduce crop failure risk due to moisture stress, and decrease adverse effects caused by weather variability. However the use of less persistent herbicide is mandatory.

Zero tillage

A farm management scheme using no tillage, except to insert seed and sometimes fertilizer, in a slit the entire field is not plowed or disked. As indicated in IPMS environmental assessment & screening report, although zero tillage crop production is not a new idea, the current interest in zero tillage was initiated with the development of herbicides. Herbicides have allowed weeds and other vegetation to be controlled before the crop emerges (non-selective herbicides) and in the growing crop (selective herbicides).

Zero tillage as a cropping practice was first adopted in corn in the USA, since excellent weed control could be obtained with atrazine (herbicide), and planters could be modified to allow good seed placement in untilled soil. In Ethiopia zero tillage a practice in many parts of the country where soil moisture is a limiting factor. According to Yohannes Gebre Michael and Mebratu Kifle (2009), with perceived change in climate, the Dassanach (Pastoralists in SNNPRS) are giving more attention to traditional zero tillage (using planting sticks). More in many parts of 31

Ethiopia zero tillage is becoming a common practice to cope up with climate change induced moisture stresses.

Generally, reduced tillage or zero tillage will reduce soil sediments that move into the air and water. Thus reduced tillage seems desirable. However, reduced tillage also increases the amount of pesticide applied. But incase of Ethiopia, where labor is available hand weeding and the use of less environment damaging herbicides in the area can be an alternative to get maximum return from implementation of conservation tillage practices.

2.4.4.2.4 Diversification of farm operation (Agro forestry/crop/Livestock)

Agro forestry system increases above and below ground carbon storage while also increasing water storage below ground even in the face of extreme climate events (Nelson, 2009).The use of different crop variety is the most commonly used adaptation strategy where by a variety of crops are grown on the same plot or on different plots, thus reducing the risk of complete crop failure since different crops are affected differently by climate events.

More perennial crops (e.g. Forages) are grown, thus improving drought tolerance by enhancing soil quality and moisture retention. Where possible, some farmers are re- introducing native grasses for pasturing. These grasses are drought resistant when rotational grazing is practiced on them. According to Pearce (2009), a diversity of crop types and varieties are grown in rotation and in different areas of farm properties. This spreads the risk of losing an entire years’ production since conditions can vary across fairly small areas and different crops vary in how they respond to those conditions. When possible, some producers also stagger their seeding and therefore, harvesting dates by choosing a variety of crops that require a range of growing conditions so that crops are at different stages and therefore more or less vulnerable if and when climate/ weather conditions start having negative impact. Moreover many farmers are including more livestock in their operations to make use of increased forage production and to add value on the farm.

Some farmers are enhancing established shelterbelts and/or add new ones. This can reduce negative impacts from drought by maintaining water tables, increasing biomass in soil and

32 ensuring surface moisture kept on the land. Shelter belts also provide protection from heat and wind for livestock.

Crop diversity must be conserved and well-managed in order to achieve a sustainable planet, but also to provide a positive development path for some of the poorest people on the planet (Long et al., 2000). Over the last 20 or 30 years, plant breeders have been trying to produce higher yielding varieties of crops. As a result, for many crops we now rely heavily on a few `modern’ varieties. Each of these modern varieties is very uniform and often contains less genetic diversity than farmers’ varieties.

Why does crop diversity matter? Uniform modern varieties do not resist diseases and other extreme weather events in the same way that landraces do of crop failures due to genetic uniformity. Modern varieties need good land and a lot of fertilizer in order to yield well: they are not so much use for poorer farmers on less fertile land. Other reasons for maintaining crop diversity are in order to provide different dishes to eat, to ensure a harvest at different times of year, and also simply as a safe-guard for the future (Long et al., 2000).

Many authors identify the strong links between crop diversity and social, economic and cultural factors and point out that on-farm conservation consists of a range of interlinked elements which together support diversity as part of a dynamic system. Bellon (1996), for example, is a good explanation of the multidisciplinary links involved and suggests that agro ecological heterogeneity, socio-economic factors and the availability of family labor have a significant impact on the levels of crop intra specific diversity maintained. Hodgkin et al. (1993) recognize that, crop diversity conservation choices made by farmers may not always coincide with those that conservationists would favor to maintain the long term adaptiveness of landraces.

Jarvis and Hodgkin (1999) summaries five aspects (see below for more detail), of farmer decision-making that affect crop diversity: what agro morphological characteristics to select for; what farming practices to use; where to plant; size of population to plant; and seed sources, but conclude that, in the most part, the link between the effect of farmer management decisions and the amount of genetic variation within the crop population has not been studied in detail.

Hodgkin (1996) classifies factors that influence farmers to maintain diversity into: Economic (e.g. less risk of crop failure but low yields), Ecological (e.g. use of micro niches), Political (e.g. national agricultural policies, donor policies), Social (e.g. recognition of traditional knowledge) and Cultural (e.g. value systems). Even though on farm diversification is influenced by different biophysical and socioeconomic factors, for countries like Ethiopia with better genetic diversity crop diversification still remains promising adaptation measures against climate change. In Ethiopia on farm diversification is a common practice especially in high lands where farm land is limited as a result of dense population. According to IIRR (2007), in “Humbo” woreda, Wolayita Zone, when drought is anticipated farmers use crop diversification by planting different type of crops (coffee, cassava, Enset, beans, potato, etc) on their farms and in their backyards so that relative moisture retention will be maintained.

According to ICDORA in Sheka Zone Opportunity for diversification focusing on improved coffee, taro, cassava (Manihot esculenta), enset, banana, sugarcane, mango, avocado, papaya, groundnut, linseed, soybeans, horse bean, ginger, turmeric and long peppers exists. The level of cultivation (area and management) of these crops at present is very low. Diversification of crop production to encompass the crops mentioned above has the advantage of reducing the risk of crop failure, promoting the efficient utilisation of available farm resources including land, labour, capital, soil moisture, nutrients and light. Besides it provides cheaper and alternative nutrition and increased income to the farm family. Crop diversification would result in better protection of soils especially on sloppy lands because of the soil protecting nature of many tree and root crops.

2.5 Analysis of Farm level adaptations measures in Abay Basin

Farmers take different actions to cope up with the negative impact of climate change. The adaptation options vary with income level, awareness and access to different technologies. According to Temesgen Deressa (2008), the survey conducted in the Nile basin of Ethiopia indicates that the use of different crop verities is commonly used method where as use of irrigation is least practiced among the major adaptation methods identified (Figure.1). More use of different crop varieties as adaptation could be associated with least expense and ease of access by the farmers; and limited use of irrigation could be attributed to need for more capital and the

34 low potential for irrigation. Moreover as indicated in the figure, 42% of the surveyed farmers reported not to have taken any adaptation method due to many reasons.

Figure-1 Adaptation practices in the Nile basin of Ethiopia (source: Temesgen Deressa, 2008)

Implementation of different farm level adaptation requires different resources and access to information; thus have many constraints. As indicated by Temesgen Deressa (2008) the analysis of barriers to adaptation of climate change in the Nile basin of Ethiopia indicates that there are five major constraints to adaptation. These are lack of information, Lack of money, shortage of labor, shortage of land and poor potential for irrigation. Most of these constraints are associated with poverty. For instance, lack of information to adaptation options could be attributed to the fact that the research on climate change and adaptation options have not been strengthen in the country, and thus information is lacking in this area. Lack of many hinders farmers from getting the necessary resources and technologies which assist to adapt to climate change.

2.6 Importance of local innovations/indigenous knowledge to cope with climate change

Farmer’s knowledge is the starting point in seeking solutions for resilience of agricultural operations. Small holder farmers hold a wealth of knowledge about their land and its ability to produce foods. The importance of local knowledge and indigenous technologies must be recognized because of its role in reducing risks and uncertainties. It is however important to promote opportunities to share information and knowledge at household, local, and regional levels, so that the farmers can learn from each other and work together.

According to Pandley (2006), there is very little documentation on local adaptation to extreme climate variability, adaptive systems to (extreme) climate variability. She recommends that this knowledge gap be filled: “… in order to explore options for adaptations to abrupt climate change, several issues need to be explored with rural people. These include learning the spontaneous as well as planned physical adaptation strategies employed by villagers in the event of abrupt climate change. The relevance of such studies could be to create original knowledge needed to design policy and specific actions for societal adaptation to abrupt climate change.” She also argues that “institutional and societal” adaptations are as important as (or more important than) biophysical ones.

In Ethiopia, studies took place in Gashamo District (Somali Region), Awash Fentale District (Afar Region), and in Daasanach District (South Omo Zone of Southern Nations, Nationalities and Peoples Region, referred to as Southern Region). These three areas in Ethiopia face very different problems – drought, land-use conflict and flooding, respectively – and partners wanted to explore this divers (Yohannes Gebre Michael and Mebratu Kifle, 2009).

As pointed out in the Ethiopia study report by Yohannes Gebre Michael and Mebratu Kifle (2009), in the arid and semi-arid areas, drought is part of a normal cycle and pastoralists have developed some strategies to cope with it, such as mobility, livestock species diversity, reciprocity in use of resources, territorial fluidity and social safety nets.

This knowledge has to be assessed thoroughly not only in pastoralist areas but also from crop production point of view as on farm adaptation practice, since small holder farmers have also local indigenous knowledge to cope with climate related shocks.

According to International Food Policy Research Institute in Nile basin of Ethiopia, In response to perceived climate changes, farming households implemented several adaptation measures, including: altering crop varieties to adjust to changes in rainfall and temperature, adopting soil and water conservation measures such as stone bunds, soil bunds and waterways, changing

36 planting and harvesting periods; for example planting later or earlier depending on long-term rainfall change harvesting rainwater for watering crops and planting trees for shading.

Local innovation in adaptation to climate change needs to be assessed together with other environmental, socio-economic and policy changes. This helps avoid the trap of romanticizing local practices as if they were evidence of deliberate local adaptation to climate change. Moreover, it implies that the initial scanning of local innovation is only partially addressing the complex and diverse issues of adaptation to climate change (Wongtschowski et al., 2009).

There seems to be a clear need to continue to investigate the way local practices and innovation respond to climate-change related challenges, if only to better inform policymakers and other stakeholders of the potential role local capacities can play in local adaptation, and to trigger a process of recognition and reflection. The focus here is not on specific innovations, but rather on documenting local innovation as a process. Though, at local level, farmers might be able to benefit from knowing what other – adapting their innovations and practices to their own situations – the documentation of innovations (understood as specific techniques, ideas and technologies) is not an end it itself. It remains, nevertheless, important as a symbol of the local capacity to create and react to local problems.

The Government, development agencies and policymakers should design policies that enable farmers to cope with the adverse effects of climate change. For example: provide farmers with appropriate and timely information on predicted changes in climate to empower them to take appropriate steps to adjust their farming practices, facilitate access to credit markets and ensure that farmer-to-farmer extension services include farmers in poor communities.

CHAPTER THREE

3. METHODS 3.1 Research area description

3.1.1 Location

West Shoa is one of the 18 administrative zones of oromia regional state located between 8o 17’ and 9o 56’ N and 37o17’ and 38o 45’E. Its North south and East west extensions are 174 km and 183 km, respectively. Its boundaries on north by Amahara Regional state and North shewa, on the East Addis Ababa region, On the west East wollega and on the South west by south west Shoa and Jimma Zone. The total area of the zone is 15086.15 km2 and subdivided to 18 woreda and two independent urban administrative zones namely Ambo and Holeta. Ambo the biggest urban center in the zone is the capital, 114 km to the west of Addis Ababa. Figure- 2 below shows map of the research area.

Fig. 2 Research area Map

3.1.2 Population

According to CSA (2003) the total population estimated 2,080,698 out of which 1,021,484 (49.1%) are male and 1,059,214 (50.9%) are female. The zone is predominantly settled by Oromo ethnic groups, out of total population 1,845,162.9 (88.68%) are living in rural areas while the rest 235,535 (11.32%) are urban dwellers. Active and productive labor force is estimated to be at 52% while economically dependent population constitute about 48 % (CSA, 2003).The population density varies from 84p/km2 to 218p/km2.

3.1.3 Topography

Topographically, West Shoa is characterized by diversified land forms consisting of mountain peaks, plateaus, plains, valleys, spurs and lowlands. Elevation varies from 900m to 3500masl

3.1.4 Climate

The climate of the zone is more influenced by altitude and latitude than others. Based on the simplified agro climatic classification of Ethiopia, it lies with in the three agro climatic zones, Highland 27%,mid altitude 56% and 17 % lowlands (ZARDO).The zone has bimodal rainfall pattern, Summer is the main rainy season with its peak in July( June to August) and the short rainy season from February to April. Rainfall varies from 813.2 mm to1669.1mm. The maximum temperature ranges from 24oC to 29oC while the minimum temperature ranges from 11oC to 13oC.

3.1.5 Vegetation

Six percent of west shoa’s area is covered with protected and dense natural forests. These include Menagesha suba forest, Jibat forest, Chilimo forest and Gura forest.

3.1.6 Land use and Agriculture

Mixed agriculture is the occupation of over 90 % of the population. The area is suitable for the production of major cereal crops such as Tef, Wheat, Barley, Maize, sorghum, and also oil seeds such as Noug, Flax and linseeds. The area is also rich in its livestock population and hence animal husbandry is practiced as supplementary to rain fed crop production. As to the land use, according to CSA (2003) out of total parcels estimated to 787,522 ha, 596325 ha (75%) covered

39 by temporary crops (cereals, pulses, oil crops and others),16054 ha permanent crops (Such as Coffee and fruit trees),104,863ha grazing land,46428ha fallow land,5687ha wood land and 18165 ha others such as swampy and rocky areas. The average land holding per household is estimated to1.51 ha (CSA, 2003).

As to the soil conditions the chromic and pellic vertisols, chromic and orthic Luvisols, Rendzinas, Halphic and luvic phaezems and distic Nitosols are the major soil types. There are three agricultural research institutions namely Ambo plant protection Research, Holeta agriculral Research center and Bako agricultural research centers, currently undertaking different agricultural research activities.

3.2 Research strategy and Site selection

The research was aimed to see the trend of major climatic variables in order to investigate the extent to which change in physical factors and socioeconomic impact faced agree with one another as a result of climate change and variability. As case study research, an exploratory questions, ‘’what’’ and ‘’how’’ and also both qualitative and quantitative approach, is used to undertake this research. According to (Abate Senbeta, 2009), methodological triangulation; obtaining data from different sources, such as observations, documentations and interviews, helps to harnesses diverse ideas about the same issue and assist in cross-checking the results, and consequently helps to increase the validity, reliability of the findings and eases data analysis .

Impact assessment alone is subtle and may not be sufficient enough to show consequences of climate impact on different member of community (Abate Senbeta, 2009); hence vulnerable PAs selected from different Agro-ecologies based on past exposure to weather variability. Moreover, to assess vulnerability of rural livelihood strategy in context of shocks and other stressors used as indicators such as (Landholding, water availability, biological resources, social interconnectedness, labor availability, saving and credit availability) and asset access modifications by social relations, institutions and organizations Ellis (2000). This in turn also used to identify the resources required to cope up with the prevailing extreme weather events.

The site selected based on past exposure to different climatic shocks and due to the increase in frequency unusual weather events in the area especially in mid altitude and lowland PA with Zonal ARDO and DPPO experts. The occurrences of unusual weather events were common in mid altitude and lowland PAs of almost all Woredas with in the Zone. Beside this the number of people under food aid in the affected areas and those areas facing frequent weather events is also used as selection criteria.

3.3 Data Collection

Both primary and secondary data were used to undertake this research. The data were obtained from primary sources (field observation, household, government officials, Development agents) and secondary data (government documents, meteorological data, and crop production data).

3.3.1 Primary data

The research was concentrated to west Shoa zone, which comprises 18 woredas (Ambo, Bako, Toke-kutaye, Ejere, Ginchi, Holeta, Ada Berga, Meta Robi, Jeldu, Gindeberet, Ilfata, Cheliya, Mida-kegn, Ilu-Gelan, Tikur-inchini, Jibat, Nono and Dano). According to traditional classification by zonal agriculture and rural development office (ZARDO) based on crop diversity , altitude, rainfall and temperature, as highland (Holeta, Jeldu ,Cheliya, Mida-kegn and Tikur Inchini), as mid altitude (Ambo, Toke-kutaye, Ejere, Ginchi, Ada Berga, Meta Robi, Gindeberet, Ilfata, Jibat, Nono) and the rest Ilu Gelan, Dano and Bako are classified as lowlands. Since the area is vast to undertake survey in all the woredas and also as more comprehensive study, representative sampling was employed to undertake the research.

Three representative woredas namely Holeta, Ambo, and Bako were selected based on availability of meteorological stations, agro-ecology and accessibility in order to examine differences and commonalities in respect to climate related hazards as well as coping mechanisms employed. Two Peasant Associations were selected from each woreda, Talacho and Dufa from Holeta, Senkelle and Kisose from Ambo, Amarti-Gibe and Dembi-Gobu from Bako based on their previous exposure to precipitation variability, involvement under food aid program and weather related hazards based on Agricultural Census data. The sample farmers were selected by random

41 sampling from each village and from those who have lived in the area for more than ten years. Figure-3 below shows the survey design.

West Shoa Zone

Holeta Ambo Bako

PA 1 PA 2 PA 1 PA 2 PA 1 PA 2

Figure-3 Survey Design

The first approach used was to conduct household interviews using semi-structured questionnaire in selected PAs and Primary data on impact, adaptation measures and coping mechanism employed were collected from April to May 2010. The interview was conducted on 60 lowlanders, 60 households from mid-altitude, and 32 from highlands based on proportion weather related incidents in the area; and a total of 152 key informants from three representative Woredas of the study Area.

The summary of questionnaires on major findings was presented to the farmers through focal group discussions so that they can make comments, suggestions and approvals.

The second approach was review of E-books, Journals and Published local researches on response to extreme weather events and existing local innovation/ indigenous knowledge to adapt to weather shocks in study areas. Moreover unpublished reports of some NGOs working at the grassroots level and respective government offices also reviewed to share their findings. In addition, a literature review was conducted on some global and regional issues related to climate change and local adaptation to relate and compare with the local context.

Interviews and discussions also held with relevant government and NGO staff working at village, district, and zonal level (District and Zonal ARDO and DPPO).

Field observation was conducted in the Bako, Ada Berga, Ambo, Tikur Inchini and Meta Robi, regarding the existing situations of the research area.

3.3.2 Secondary Data

Published and unpublished literatures were collected from different government offices. Data on crop production were collected from District and Zonal Agriculture and rural development office where as data on climate shocks and victim people were obtained from District and Zonal DPPO. Precipitation and temperature values were collected from NMSA (three sample meteorological stations namely, Holeta, Ambo, and Bako) which are found at different locations and altitudes with in the research area. Contemporary climate change was studied by records of values which have been obtained by standard equipments. Monthly total precipitation and Temperature values of 41 years, 41 years, and 15 years were used at Holeta, Bako, and Ambo respectively for the trend analysis. Only 15 years values were used at Ambo due to the poor organization and quality of the data. Geographical location of Meteorological stations Table-2 Geographical location of Meteorological Stations No. Station Name Location Altitude(masl) Latitude Longitude 1 Holeta 09o00’N 38o30’E 2400 2 Ambo 08o58’N 37o51’E 1977 3 Bako 09o06’N 37o09’ E 1650

Before the analysis, some pre-analysis activities such as filling of the missing years from Archives of the stations, adjustment of incomplete data and adjustment of outliers were employed for quality data.

3.4 Data analysis

3.4.1 Socio-economic data analysis

Quantitative and qualitative data obtained from Questionnaires and interviews were analyzed by using SPSS data editor and descriptive statistical methods such as mean, and standard deviation. In addition frequencies, percentiles, histograms were used to summarize and present the result.

The annual rain fed crop production data on major cereals collected from Bako, Ambo, Zonal ARDO and CSA were analyzed using quantitative statistical method, excel. The extreme Values (precipitation and temperature) and/or gradual climatic change trend were analyzed by comparing with the climatic data and the primary data and secondary data collected from pertinent government offices and literatures.

Correlation Analysis also employed to identify the relationships between rainfall variability and crop production as well as relationship among seasonal rainfall amount.

3.4.2 Trend analysis

Precipitation and Temperature data shows a long-term change of data or some pattern changes in the given temporal scale series. XLSTAT software was employed to analyze the trend analysis and to consider seasonal component of precipitation at the same time. Hence, to describe a trend of a time series Mann-kendall trend test was used to see weather there is a trend or not. Mann- kendall statistics (S) is one of non-parametric statistical test used for detecting trends of climatic variables. It is the most widely used method since it is less sensitive to outliers (extraordinary high values with in time series data) and it is the most robust as well as suitable for detecting trends in precipitation.

3.4.3 Trend significance test

Different soft wares such as SPSS and Excel can be used for trend analysis and significant test. However, SPSS and Excel soft wares are very sensitive to outliers (extraordinary high values). Hence, Mann- Kendall trend test was used to detect the trend and normalized Z-score for significant test. A score of +1 is awarded if the value in a time series is larger, or a score of -1 is awarded if it is smaller. The total score for the time-series data is the Mann-Kendall statistic, which is then compared to a critical value, to test whether the trend in rainfall is increasing, decreasing or if no trend in rainfall can be determined.

Let X1,X2,X3………….. Xn represents n data points (Monthly) Where Xj represents the data point at time J. Then the Mann-Kendall statistics (S) is given by

Where Sign ) =1 if Xj- Xk>0

=0 if Xj- Xk=0

=-1 if Xj-Xk<0 Where Xj and Xk are the sequential precipitation values in months J and K(J>k) respectively A positive value is an indicator of increasing (upward) trend A negative value is an indicator of decreasing (downward) trend

Trends considered at the three sites, were tested for significance. A normalized test statistics (Z- score) was used to check the statistical significance of the increasing or decreasing trend of mean precipitation and temperature values. The trends of temperature data from three stations are determined and their statistical significance are tested using Mann-Kendall trend significant test with the level of significance 0.05 (Zα/2 = ±1.96) Z if S>0

Z if S=0 Z if S<0

Where Var (S) = [n (n-1)] (2n+5)

n is the number of data points q is the number of tied groups (tied groups is a set of sample data having the same value) th tp is the number of data values in P group

Hypothesis testing Ho=µ= µo (there no significant trend/stable trend in the data)

HA= µ≠ µo (there is significant trend/ unstable trend in the data)

If –Z 1-α/2≤Z≤ Z 1-α/2 accepts the hypothesis or else Reject Ho

CHAPTER FOUR

4-RESULTS AND DISCUSSIONS

The result of the research in this paper presented by categorizing in to four parts based on the objectives set at the beginning. The first part includes the trend of climatic variables; rainfall and temperature. The second part comprises occurrence of usual weather events experienced by the area. The third part is about impact of weather events on rain fed crop production and the perception of farmers. The final part is the existing coping mechanisms, the resource required by small holder farmers and local indigenous technical knowledge of the farmers that contribute to cope with climate change.

4.1 Rainfall Variability and trend

According to the Mann Kendall monotonic trend analysis the results indicate there is increasing trend at Bako whereas decreasing trend at Holeta and Ambo stations; however the trend analysis indicates only weather the trend is increasing or decreasing over certain period of time. Hence significance test for the trend is of paramount importance in order to predict the occurrence of extreme weather events and their likely impacts. Accordingly the trend significance tests show there is no statistically significant trend. Hence the trend evaluation indicates there is stable trend at the three sites (Table-3). According to Mankendall significance test at level of significance 0.05, the test hypothesis is

H0 = µ = µ0 (there is no significant trend/stable trend in the data

HA= µ ≠ µ0 (there is significant trend/unstable trend in the data.

Hence Z α/2 = ±1.96

If -1.96≤Z≤1.96 accept the hypothesis or else Reject H0 Table-3 Values of Precipitation Trend analysis (Source: NMSA) S.N Sample Station N “S” Value Sample Trend Evaluation Z-value (α=0.05) 1 Bako 492 1077 0.29 Stable 2 Ambo 276 -423 -0.27 Stable 3 Holeta 492 -3553 -0.97 Stable

Even though there is no significant trend at the three stations the values show there is variability with in the research area. The result of Annual total rainfall and monthly rainfall analysis at the three selected stations namely Holeta, Ambo and Bako shows year to year variations. Rainfall data analyzed for Holeta and Bako is from the year 1969 to 2009 (41 years) while that of Ambo is from 1987 to 2009 (23 years).The average annual rainfall analysis in Table-4 below shows the total annual rainfall shows variability at the three sample meteorological sites and the variability is relatively high at Bako. As one goes from higher altitude to the mid altitude and low altitude areas the variability of the Rainfall also increase accordingly which also consistent with primary data obtained by survey. Table- 4 Annual total Rainfall Variability (Source: NMSA) N Minimum Maximum Mean Std. Variance CV Stations Deviation Bako 41 830.2 1658.8 1248.34 184.40 34005.63 0.148 Holeta 41 728.8 1261 1033.79 121.71 14814 0.118 Ambo 23 748.2 1230 995 151 22802.14 0.152

Accordingly the results indicate that trends are not uniform with in the research area. The trend analysis of total annual rainfall shows that rainfall trend shows decreasing trend for Holeta and Ambo station while that of Bako shows increasing trend with variability (fluctuations from the base line values). The temporal variation of the total annual rainfall is displayed in the figures below.

Figure-4 Trends and variability of annual total rainfall at Bako station (Source: NMSA)

Figure-5 Trends and variability of annual total rainfall at Ambo Station (Source: NMSA)

Figure-6 Trend and variability of average annual total Rainfall at Holeta Station (Source: NMSA)

There is also spatial variability in total annual amount with in the zone. Woredas found at the boundaries of south western part of the country have relatively high total annual rainfall. The altitude difference is 2400 masl at Holeta, 1977masl and 1650 masl for Ambo and Bako respectively. Figure- below shows spatial variability of rainfall with in the research area.

Figure-7 Spatial Variability of rainfall of west Shoa Zone (Source: NMSA)

Measurements taken from Ethiopian meteorological stations show that the annual volume of rainfall over the past fifty-five years has remained more or less constant when looking at the average for the whole country across the period 1951-2006 (UNFCC, 2001). However, if one looks at how rainfall is distributed across the country, there is a marked difference: there is a tendency for less rain to fall in the northern part of the country where there is already massive environmental degradation. On the contrary the central highlands characterized by increasing trend; However, decreasing trend at Holeta and Ambo where as increasing trend is observed at Bako. Hence, the frequent occurrence of flush flood, heavy rain, could be associated with its variability in distribution and the degradation of the land which lacks its capacity to store rain water as a result of excessive exploitation.

Seasonality effects also analyzed to know whether the seasonal component has a trend or not. Seasonality analysis indicates what has happened with in the year; like fluctuations in the peak rainfall time. The peak rainfall time does not occur at the same date each year the pattern of the variation is altering from year to year which also affect crop production. Accordingly, monthly values for 491 months at Holeta and Bako and 276 months at Ambo. Hence, monthly average

49 rainfall values were used to analyze the seasonality of the rainfall and the result indicates that the pattern of the peak time shows variation where as the12 month seasonal component has no trend except for Holeta station.

Figure-8 Trend and variability of Monthly Rainfall at Bako Station (Source: NMSA)

Figure-9 Trend and variability of Monthly Rainfall at Ambo Station (Source: NMSA)

Figure- 10 Trend and variability of Monthly Rainfall at Holeta Station (Source: NMSA)

The impact of rainfall on crop production can be related to its total seasonal amount or its intra- seasonal distribution. In the extreme case of droughts, with very low total seasonal amounts, crop production suffers the most. But more subtle intra-seasonal variations in rainfall distribution

50 during crop growing periods, without a change in total seasonal amount, can also cause substantial reductions in yields. This means that the number of rainy days during the growing period is as important, if not more, as that of the seasonal total (Woldemlak Bewket, 2006). According to the rainfall trend analysis the change in annual total is insignificant while the impact on crop production and variation in total production is still increasing which indicates that the annual total alone is inadequate to determine impact on crop production. Hence, for better insight of impacts on crop production analysis of seasonal variation is very decisive since variation in all seasons of the year are not equally important from crop production point of view. The following tables show seasonal total rainfall and coefficient of variation of kiremt and Belg season. Table-5 Kiremt (June- September) Rainfall and coefficient of variation N Minimum Maximum Mean Std. Variance CV Stations Deviation Bako 41 472.8 1190 854.7 159.60 25473 0.187 Holeta 41 561.8 903.2 743.0 84.26 7101.4 0.113 Ambo 23 367.8 837.8 640.2 127.8 16333.3 0.20

Table-6 Belg (March-May) rainfall and coefficient of variation N Minimum Maximum Mean Std. Variance CV Stations Deviation Bako 41 116.5 429.6 259.1049 74.49 5550.2 0.287 Holeta 41 46.22 435.4 199.581 71.27 5079.8 0.357 Ambo 23 0 449.4 219.8 104.06 10827.9 0.47

The seasonal rainfall shows variability as indicated by their coefficient of variation in the tables above. The Belg rainfall (March to May) is more variable than the kiremt (June to September).This is also consistent with the conclusion made by Engida(1999) which reveals that Belg rainfall is more variable than kiremt rainfall. As Belg rainfall is more favorable for crops and its variability also affect production by hindering agricultural activities (such as land preparation and sowing time) and crop physiology such as germination and growth of seedlings. The Belg the rainfall is favorable as there is no aeration problem, no cloud cover and the crops are also photo synthetically active. Moreover the seasonal variability of rainfall also associated with late on set and early offset of the rainy season.

The correlation analysis of kiremt and Belg season also shows inverse relationship at Holeta and Bako station while it is positively correlated at Ambo. The positive correlation at Ambo may be associated with short length period of rainfall data. The survey also supports that when there is good distribution in Belg rainfall; the kiremt rainfall become less in amount and more variable and vise versa. However, the relationships of seasonal rainfall is found to be statistical insignificant however small variation in rainfall has significant impact on production due high dependency on rain-fed agriculture and other compounding factors.

Table-7 correlation of Belg and Kiremt rainfall at three stations

Stations Pearson's Correlation of Belg and Kiremt Rainfall Bako(N=41) Holeta(N=41) Ambo(N=23) Correlation Coefficient -0.138 -0.021 0.174

4.2 Temperature variability and trend

Unlike that of rainfall Mann Kendall trend analysis for average monthly temperature shows statistically significant increase except Ambo stations. According to the survey the respondents of Ambo area indicate that the temperature has been increasing in the area in the past two decades; however the statistical analysis shows non significant. Hence the stable trend at Ambo may be due to the short length the data as Mann Kendall trend analysis become stronger depending on the length of the data. The seasonal Mankendall test for temperature also shows that there is significant trend (Annex-2). Table- below shows values for temperature trend analysis. Table-8 Values of Average temperature Trend Analysis (Source: NMSA)

S.N Sample Station N “S” Sample Trend Evaluation Value Z-value (α=0.05) 1 Bako 492 12932 3.54 Unstable (increasing) 2 Ambo 180 1493 1.84 Stable

3 Holeta 492 10158 2.78 Unstable (Increasing)

The results of average annual temperature trend analysis indicate that trends show there is variability at the three sampled stations (Bako, Ambo and Holeta).Table –9 below shows descriptive statistics of average annual temperature.

Table-9 Values of Average annual Temperature and coefficient of variation (Source: NMSA)

Stations Temperature N Minimum Maximum Mean Standard Variance CV deviation Bako Max 41 26.8 29.7 27.9 0.63 0.405 0.02 Min 41 12.27 14.67 13.60 0.56 0.319 0.04 Ambo Max 15 25.2 27.3 26.08 0.54 0.293 0.02 Min 15 10.58 12.35 11.56 0.52 0.272 0.05 Holeta Max 41 20.8 25 22.3 0.69 0.479 0.03

Min 41 -2.6 12.2 6.15 2.89 8.35 0.47

The temporal variation average annual Temperature trend is displayed in the figures below.

Figure-11 Trends and variability of Average annual Temperature at Bako Station (Source: NMSA)

Figure-12 Trends and variability of Average annual Temperature at Ambo Station (Source: NMSA)

Figure-13 Trends and variability of Average annual Temperature at Holeta Station (Source: NMSA)

According to UNFCC (2001) there has been a general trend of atmospheric warming in Ethiopia. Average annual minimum and maximum temperature have been increasing by 0.25 and 0.1 o C per decade respectively. The average temperature increase per decade for Holeta and Bako is o.25 and 0.2oC respectively. According to the survey, in Bako Tibe and Ambo Woredas, the respondents also indicate there is frequent soil moisture stress due to the increased temperature and farmers compelled to grow maize varieties of short growing period. Moreover in areas around Holeta which is relatively higher altitude, the farmers have started to grow maize which was formerly limited to low altitude areas. This indicates that the soil moisture stress is not only due to meteorological drought but due to increased evapo-transpiration due to the overlap of time of maximum temperature period (March to May) and Belg rain variability during the growing periods. This result would have been sounder if Aridity index of the areas is calculated but organized data on evapo-transpiration was lacking.

4.3 Occurrence unusual weather events

The trend analysis depicts that precipitation and temperature values have shown significant variation and extreme values have been increasing in the last 20 years. Each year mid-altitude and lowland areas are facing weather related shocks that affect the rain fed crop production of the area. This also witnessed by survey conducted on 152 households. More over the data obtained from the zonal DPPO also support the respondent’s perception. Annex-1 indicates that the numbers of woredas affected by weather shocks, the number of

54 people impacted and supported by the government thorough food aid have been increasing. Though the degree differs the occurrence of weather events consistent with that Ethiopia already suffers from extremes of climate, manifested in the form of frequent drought (1965, 1974, 1983, 1984, 1987, 1990, 1991, 1999, 2000, and 2002) and recent flooding (1997 and 2006) (Di falco et al., 2008).The survey also indicates that drought occurred in the year 1983,1992 and 2001 while the heavy rain fall occurred in 1998 resulted in flooding both at Bako and Holeta. Figure- 14 below shows the seasonal total amount of kiremt (June to September) and Belg (March to May) rainfall at Holeta and Bako and its consistency with the above events.

Figure-14 Seasonal total RF at Holeta and Bako stations

According to the survey the major frequently occurred climatic shocks in the area are prolonged drought (including late on set/early offset), heavy rain, flood/excess moisture, and frost. Frequency of the weather events indentified by the respondents as having a significant impact on crop production is found to be 71.2 % for prolonged drought (including late on set/early offset), 53.8 % for flood/Excessive moisture, 32.6% heavy rain, 20.5% frost and 4.5% strong winds.

4.4 Impacts on crop production The occurrences of these events seriously affect crop production activities. As rain fed crop production is dependent on the onset of the rainy season, late onset of the rain hinder agricultural

55 operations such as land preparation and sowing time. The onset of rain for the main growing season (Meher) is March but delay of the onset might go till May and mid June in extreme cases. This becomes very frequent phenomena especially in mid altitude and lowland areas of west Shoa zone. The early offset of the rain when most the crops are at flowering stage also directly affect fruit setting of the crops. The impact of the events found to be variable from place to place based on the type of event, adaptive capacity and resource endowment of the area, though it is more serious in mid altitude and lowland areas of the zone. Hence Intervention of stakeholder government and Non governmental organizations will be quite important in promoting irrigation and locally accepted water harvesting technologies to supplement the drought periods. According to the survey, the occurrence of frost is also become more frequent in Holeta area and other areas of similar agro-ecologies of the Zone. In general, the impacts of weather events vary from reduced seeding to the total loss of the crop. But lack of organized data on crop production at woreda level hinders to present the impact in terms of the actual yield. Figure-15 below shows the frequency the significant weather impacts identified in West Shoa Zone.

Figure-15 Frequency of significant weather impacts in West Shoa Zone (n=152)

In Bako Tibe district in the prolonged drought occurred in the year 2001/2002 resulted significant loss of production. According to the data obtained from Bako Tibe Woreda ARDO, 33,970 hectares of land was cultivated and 358,912 quintals of production obtained during the

56 main growing season. The loss in 2001/2002 estimated to 362,034 quintals compared to the previous year. Moreover, the on set of the rain for the main growing season started late in mid May and resulted in heavy rain fall which seriously affect PAs found along Gibe basin (ZARDO). This data also consistent with the research conducted by (Di falco et al., 2008) that indicates period of frequent drought and flooding. Figure-16 Below shows trend of Agricultural production of main growing season of Bako Tibe Woreda.

Figure-16 Trend of Agricultural crop production of main growing season of Bako Tibe (Source: Bako Tibe Woreda ARDO)

As observed from the above figure the fluctuations (the production decline in 1997, 2002 and 2004) of the yield at Bako Tibe Woreda is mainly attributed to weather events such as drought, late onset, erratic rain and early off set of the rain. The total production data for major rain fed crops (cereals, pulses, and oil crops) for the whole zone obtained from CSA also show declining trend. Accordingly the total seasonal rainfall (Belg and Kiremt) also show declining trend after the year 2006. Figure-17 below shows the trend of the main Agricultural production of west Shoa zone.

Figure-17 Crop production of main growing season of West Shoa Zone (Source: CSA)

Rainfall Variability and crop production correlation The correlation analysis of seasonal rainfall distribution and annual production of Bako and Holeta woredas also indicated in the Table-10. Even though the correlation values are statistically insignificant the correlation analysis between seasonal rainfalls and production of Tef, barley and wheat production at Holeta show considerable positive correlations with the kiremt rainfall. This associated with the crop calendar that those cereals are grown during kiremt season and hence kiremt rain is influential. Sorghum production shows positive correlation with the Belg rains at Bako while maize production shows significant negative correlation with Belg and weak negative correlation with kiremt rainfall. The negative correlation of maize production may be associated with non linear correlation between the seasonal rainfall and production or due to poor organization of production data at woreda level. Table-10 Correlation of crop production and Kiremt rainfall at Holeta

Pearson’s Correlation of cereal production and kiremt rainfall at Holeta (N=10) Season Wheat Production Barley Production Tef Production Kiremt Rainfall 0.403 0.453 0.32

Table-11 Correlation of cereal Production with Belg and Kiremt rainfall at Bako

Pearson’s Correlation of Maize and sorghum production with seasonal totals (N=17) Season Maize Production Sorghum Production

Belg Rainfall -0.509* 0.058

Kiremt Rainfall -0.098 0.199

*Significant at 0.05 level (two tailed)

According to the survey the famers also perceive that weather variability is one of the major reasons for yield decline. Out of 152 respondents 90, 64, 35, and 6 farmers respond weather variability, fertility decline, water erosion and lack of input found to be major reasons for yield reduction respectively. Figure-18 below indicates the perception of farmers for yield decline.

Figure-18 Perception of farmers for decline in productivity (N=152)

In lowland areas in addition to yield decline, erratic rainfall also create conducive environment for increased prevalence of crop pests. In Meta Robi District reduced seeding area, increased pest, and loss of production has been frequently occurred from the year 2001-2009 as a result of late on set and erratic rain. Moreover heavy rainfall resulted in flood and Land slide. For instance, according to the data obtained from the Zonal DPPO in the year 2003, 16220 hectares

59 of land remain uncultivated, seedlings of Tef and sorghum on 176 hectares of land infested by pest (grasshoppers) and resulted in production loss of 271,681.72 quintals. The occurrence of such weather events is not new for the area, what worth consideration is that the frequency of occurrence has been increasing from time to time.

4.5 Coping Mechanisms

As the variability of weather condition increase from time to time the smallholder farmers have been adjusting their farm operation so that they can reduce the harm and exploit beneficial opportunities. The coping mechanisms depend on the perception of the farmers about climate change, income level and availability of resources. The survey indicates 17.7 % do not undertake any action to cope up with weather related hazards. This can be attributed to lack awareness and resources. As to the awareness 67 % of the farmers know or discuss about climate change and the resulting weather variability but of the farmers most not yet develop planned adaptation action while the rest 43 % have no know how about.

The responses by small holder farmers in different agro ecologies vary from no action to changing crop diversity and farming practices. These major responses include taking the loss, diversifying crops, late seeding, growing short season crops, Zero tillage and growing tolerant crops. Rainfall patterns increase the probability of adoption of yield-related adaptation strategies. And this adoption, in turn, is positively correlated with productivity (Di Falco et al., 2008). Irrigation and multiple field location had low response as it is based on the resource endowment of the area (availability of surface water, Land) and capital requirement of constructing river diversion structure. Besides this lack of input for irrigable agriculture is also a challenge for the farmers to grow crops during the dry period. This is associated with the supply input by cooperative offices which limited to Meher season. Zero tillage is also become a common practice in Bako Tibe area in the last 5 years. Primarily zero tillage is practiced among the small holder farmers due to lack of oxen not as adaptation measure, but now days with the occurrence of soil moisture stress the farmers are using as coping mechanism. The draw back with this measure is its herbicide requirement for weed control.

Soil and water conservation activities have relatively very low response, because of labor and capital intensiveness. About 72% of the respondents have erosion problem on their farm; 60 however only 31.2% of the farmers undertake soil conservation activities such as terracing, and stone bunds. More over there is over use land and depletion of vegetation resources which result in land degradation that exacerbating the impact of climate variability. During the field observation the woredas that have been facing climate related shocks frequently have serious problem of land degradation. Besides this, according to the respondents in lowland and Mid- altitude areas limited fertility management also found to be a factor that increases the impact of small weather shocks.

As far as tree planting is concerned 41% of the respondent plant trees on their farm while the rest do not plant trees due to land scarcity, lack of seedling and intensive care required as compared to planting Eucalyptus. According to field observation and respondent response, the majority of the farmers opt to plant Eucalyptus than planting multipurpose tree as a cash source, which is more common in Holeta and Ambo woredas. Even though a land use policy guideline is available among the local institution, still the implementation is a challenge to deprive the farmers from planting on fertile lands. Generally, the limited activity in soil and water conservation program is found to be one factor exacerbating the climate related shocks in the area.

According to the survey conducted in west shoa late seeding is the major commonly used adaptation measure by the majority of the smallholder farmer (32.4%) since there is no cost incurred to undertake the measure (Figure-19).Moreover, the rain fed agricultural activity is purely dependent on the onset of the rain season and therefore, the producers compelled to stay till the onset to take an action. About 17.7 % of the smallholder farmers surveyed accepts the loss induced by weather events due to different socioeconomic and bio-physical reasons. The figure below indicates some of the common coping mechanisms employed by the farmers.

Figure-19 Major coping strategies of West Shoa Zone (N=152)

As indicated in Figure-19, accepting loss is one the common coping strategy in the area. The response associated with the inability of the farmers to respond to unexpected weather events. In this case the farmers are not well prepared and lack enough time to take actions that mitigate the loss. Hence, accepting the loss is a measure taken when the farmers have no choice. Crop replacement (replanting) is also one of the coping strategy exercised by the farmers after the failure of the main crop by weather shocks. The replacement can be by the same crop if the time of sowing is not too late or by different crop that can be used as emergency crop; hence it is found to be costly to small holder farmers as it result in additional expense for purchase of seeds and other inputs.

The other coping mechanism employed in the area is diversification of the crops on the farm. According to the respondents this measure has got two major advantages; it reduces the risk since the crops have different susceptibility to weather hazards and also diversify the income source of the households. In Bako Tibe woreda the farmers are currently growing maize, haricot bean, pepper and sugarcane rather than relying only on mono cropping of maize. Moreover farmers are also integrating growing of fruit trees like Mango (Mangifera indica), and coffee to their normal farm activities. This practice helped the farmers to cope with weather events that otherwise result in total loss of their farm income.

Growing of short season crops such as potatoes, short season maize varieties and local varieties of Tef were found to be widely used option by smallholder farmers of the area. For instance, in the last 15 years farmers in Bako Tibe Woreda the farmers used to plant “BH660” maize variety that requires longer growing season and relatively better moisture availability. But now days the famers prefer to plant short growing season maize varieties like 540 and 541. These varieties are early maturing and require less moisture compared to BH660 variety and found to be promising to adapt to moisture stress resulted from early offset of the rain. To sustain such adaptation measures public institutions should implement variety improvement programs to improve the productivity of such as drought tolerant varieties. In highland and Mid-altitude areas farmers opt to plant potatoes both for consumption and seed production purpose. Potato seed production is becoming a good source of income, has been contributing a lot to raise the household income level that can be used as coping mechanism during the bad times. More over Small grains, such as sorghum and millet, are more likely to meet these needs than maize hybrids, because they are more drought-tolerant, their seeds can be stored for much longer, and they can be relied upon to germinate after several seasons of storage. They also require fewer pesticides and fertilizers for cultivation.

The other coping mechanism used in the area is the use of tolerant crops. As crops have different susceptibility, some crops are found to be useful in different agro ecologies. The famers opt for crops such as sorghum, chickpea, enset and haricot bean that have relatively better drought tolerance and water use efficiency. Crops like chickpea, and haricot bean planted by the lowland and mid altitude farmers as emergency crop after the failure of the main crop due to unexpected cessation of rain. These crops require minimum moisture to grow and low input demand, as a result it is considered as important short term strategy.

In highland areas growing “Enset”(False banana) has been identified as a good coping mechanisms due to its less sensitivity to frost and ability to revive after occurrence of weather shock. Field observations also revealed that “enset” is becoming very popular and widely disseminated among highlanders. Another crop preferred in the highland areas is “Trticale” (wheat variety).In the former time the farmers are not interested in sowing “Triticale”, because it

63 is labor intensive to remove the seed from the spikelet. However as the frequency of heavy rain increases it attracts the farmers’ interest due to its resistance to heavy rain impact.

4.5.1 Resources important to coping strategies

Farmers have been coping with climate change by implementing tactical and strategic adaptations measures. Strategic (long term) adaptation measures are pre planned and require adjustment of agricultural operations based on resource endowment of the area. For instance, soil and water management activities require labor and technologies that goes beyond the capacity of the small holder farmers. Accordingly every coping mechanisms employed by small holder farmers require resources to cope with the expected and actual weather events. Table-12 below illustrates different resources identified as a constraint by the smallholder farmers and their level importance.

Table-12 Resources required for adaptation and their level of importance S. Type of resource Frequency (%) Ranking Remark N required 1 Efficient technologies 58.3 1st (Irrigation, Water harvesting, Package of intensive agriculture)

2 Land 46.2 2nd 3 Labor 43.2 3rd Farm labor 4 Saving 19.7 4th 5 Credit System 11.4 5th 6 Insurance 6.8 6th Incase of total crop loss

The result from the survey shows that lack efficient technologies, land and labor are the major constraints identified by the farmers. The results are also consistent with the findings of Temesgen Deressa (2008). According to the respondents efficient technologies are required to conserve and use the available water resources. Irrigation technologies such as drip irrigation and water pumps found to be quite useful but the majority of the small holder farmers can not afford. Drought resistant varieties of crops are highly required by the farmers; however the supply of these varieties does not match with the demand. Shortage of farm land is also identified as a constraint to implement adaptation measures like multiple field location and tree planting

64 activities. No land distribution takes place in the area in the last 20 years, and the available plot had been shared among families. Currently, with the increased house hold family size the land reached the level that no further classification can be made. As a result most of the farmers use plot of other relatively better farmers on share crop bases; hence they don’t develop sense of ownership to undertake soil improvement and water conservation activities. Out of the respondents that have erosion problem on their farm 40.8% of the respondents do not undertake any soil and water conservation activities on the farm). In this case increasing production by expanding the farm land will not be a promising option. Hence, intensive farming should be encouraged to increase productivity per unit area by combining efficient use of available organic and inorganic fertilizers. In addition intervention by government to reduce the number of landless through well planned resettlement programs and promotion of the Agro-industry sector.

Labor is also one of the constraints identified at the household level to under take labor demanding strategic (long-term) adaptation practices such as soil conservation and fertility management activities. Savings and credit systems are also quite important to resist the weather shocks on their farm operations. The study conducted by (Di Falco et al., 2007) also indicates extension services (both external and farmer to farmer) and access to credit affect adaptation positively and significantly. According to the survey the number of farmers that have access to saving and Credit services is very insignificant. Especially, small holder farmers in lowland and mid altitude areas do not have access to credit service due to lack of information and the concentration of service providers only to accessible areas. Crop and livestock sale is one of the major sources for small holder farmers to get other resources contribute to crop production. In the absence those resources farmers are obliged to seek for external support to tackle weather variability. Hence, availability of saving and credit where vulnerability relatively high (lowland and mid-altitude areas) is found to be one the decisive factor to strengthen the capacity of the farmers to cope with weather shocks and reduce the subsequent impact.

4.6 Institutional coping mechanisms and local knowledge

Government and non government institution have been implementing various coping mechanisms in order to cope up with adverse weather events. ARDO is one of the government institutions that provide different extension services that contribute to build up the capacity of the farmers to adjust their farming operation in accordance with the dynamic weather condition. 65

ARDO have been working on extension of water harvesting technologies; even though the acceptance of the technology is not as expected due to poor extension approach, lack of materials, technical feasibility and safety aspects. Promotion of efficient irrigation technologies such as drip irrigation, treadle pumps and water pumps are currently employed; however capital intensiveness of the technologies hinder to address the problems smallholder farmers. ARDO distributed BBM (Broad Bed Maker) used to drain the farms when there is excessive moisture; however the technology was not as effective as expected due to its heaviness for the oxen and its cost.

Conventional early warning system by DPPO also monitor and report on monthly and seasonal basis on unusual weather event using indicators such as crop status, human and animal health status, vegetation, rainfall and market situation. The time of reporting is every 7 days to the regional offices and every 14 days to the federal. The weakness of the early warning system is that, it fails to warn about rapid events like flood and its multi-sectoral manner to give immediate response. But currently the office has a plan to solve the problem at the spot in a decentralized manner so that the target community can get solution at their locality. As a mandate DPPO collect and prepare of early warning reports, baseline data before the disaster and also arrangement of food aid for the community who faced food shortage in collaboration with regional offices and NGOs after the disaster. NGOs in the area provide different crop seeds for victim farmers after the failure the main crop so that they can replant their crop land; however the agro ecology of the area may not be equally suitable for emergency crop. For instance in lowland and mid altitude areas maize and sorghum fields are converted to Tef and pulses after the failure of Belg rainfall but the farmland marginally suitable for Tef and Pulses.

DPPO also coordinates emergency aid distribution programs and other development activities performed by victim community. Though there are coping mechanisms at household, farm and institutional level still the available mechanisms are not sufficient to address the challenge due to combination of socio-economic factors. Hence, capacity building for stakeholder government offices will be vital so that they can warn about rapid events before the disaster.

Local knowledge and innovations are also of paramount importance to adapt to the prevailing weather shocks. Practices such as traditional early warning system by which the elders of the

66 community anticipate the upcoming weather condition has to be encouraged since it is vital to safeguard the produce from unexpected weather event. Farmers in Ambo and Ginchi area are also using traditional method of drainage system in case of excessive moisture and water logging conditions created by heavy rain. The farmers used “Shaga” a flat wooden frame tied to both sides of the plough with the same function as BBM. More over they use traditional tillage method known as “Shurube” (a traditional tillage where they make very closer ridges) on water logged or flood prone farms.

In lowland and mid altitude areas when extreme weather event happened replacement of crops has been used by the farmers. For instance, maize growers of lowland areas immediately shift to Tef incase of late on set of Belg rain as reactive adaptation strategy. In high land areas the farmers cover seedlings of potatoes with soil to reduce the impact of frost, which has to be scientifically proved and documented.

5. CONCULUSIONS

Agriculture is inherently very sensitive to climate change and weather variability and hence requires continuous change and adaptation to different bio-physical and socio-economic events. According to this research the smallholder farmers of west shoa zone have perceived that the climate has been changing and the challenge to rain fed crop production has increased from time to time. The long term temporal trend analysis of climate variables; rain fall and temperature shows considerable variability. The long term change in mean values of the rainfall remains more or less constant while that of temperature shows increasing trend. Though the long term changes in values of climatic variables are not statistically significant the variability from the baseline is found to be considerable and occurrence of extreme weather events become more frequent. Moreover, as rain fed crop is highly dependent on rainfall small shock in weather has significant impact on production of small holder farmers since it is compounded by other exacerbating factors such as land degradation and household income.

As a result of increased climate variability the small holder farmers have been facing different weather events and still adversely affected. Some of the weather events identified include prolonged drought, late onset/ early offset of rainy season, heavy rain, flood and frost. These events are not new to the area; what worth consideration is the frequency of occurrence and magnitude has been increasing and resulted in impacts that vary from reduced seeding area to total crop loss based on agro-ecology, household income, adaptive capacity and resource endowment of the area. However; the small holder farmers are also adjusting their farm operation by implementing different short term and long term coping strategies. These coping strategies/adaptation measures identified are taking the loss, crop replacement, diversifying crops, late seeding, growing of short season crop, zero/ minimum tillage and growing of tolerant crops.

Those yield related adaptation practices alone can not cope up with the prevailing climate change; and has to be supplemented by institutional level adaptation measures through development of drought resistant crop varieties, development of efficient technologies, and

68 weather forecasting. The decision to employ adaptation is assumed to be function of formal and informal support, climatic factors and agro ecological setting of the household. However implementations of adaptation practices require resources such as efficient technologies, land, labor, savings, credits and crop insurance. In addition addressing gaps in implementation of land use policies, reducing the number of landless through well planned resettlement programs and encouraging soil and water conservation activities should remain as area of attention to mitigate the future impact climate change.

6. RECOMMENDATIONS

Further study should be conducted to identify, scientifically proof and document the available coping mechanisms and indigenous knowledge/innovation.

Institutional Coping strategies should consider Local knowledge and innovations.

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6. ANNEXES

Annex-1 Occurrence weather events and victims under food aid

S.N Year Events No. of Woredas Victim Total no of affected Households beneficiaries 1 2000 Flood, landslide, late onset of Rain, 1 527 2634 Pest due to weather change 2 2001 Late on set of rain, Land slide, 2 9610 48052 flood, Heavy Rain 3 2002 Flood, Heavy rain, unexpected Rain 1 29 145 4 2003 Flood, Heavy Rain, landslide, Late 5 2630 13149 onset, early offset, Pest due to weather change, land degradation 5 2004 Flood, Heavy Rain, landslide, Late 5 137088 685440 onset and death of livestock due to prolonged drought, striga infestation, land degradation 6 2005 Flood, Heavy Rain, landslide, Late 5 22869 114346 onset, Pest due to weather change 7 2006 No extreme cases 0 0 0 8 2007 Flood, Heavy Rain, landslide, Late 3 6092 30458 onset, early offset, Land degradation 9 2008 Flood, Heavy Rain, landslide, Late 6 8756 43778 onset, early offset 10 2009 Flood, Heavy Rain, landslide, Late 10 19752 98759 onset, early offset 11 2010 Flood, Heavy Rain, landslide, Late 12 26180 130900 onset, early offset Source: West Shoa Zone FS- DPPO

Annex -2 XLSTAT 2010.- Mann-Kendall trend tests

Significance level (%): 5 Bako Station Seasonal Mann-Kendall Test / Period = 12 / Serial independence / Two-tailed test (Temprature):

Kendall's tau 0.226 S' 2097.000 p-value (Two-tailed) < 0.0001 alpha 0.05 The p-value is computed using an exact method.

Test interpretation: H0: There is no trend in the series Ha: There is a trend in the series As the computed p-value is lower than the significance level alpha=0.05, one should reject the null hypothesis H0, and accept the alternative hypothesis Ha. The risk to reject the null hypothesis H0 while it is true is lower than 0.01%. Continuity correction has been applied.

Ties have been detected in the data and the appropriate corrections have been applied.

Holeta Station Seasonal Mann-Kendall Test / Period = 12 / Serial independence / Two-tailed test (Temprature):

Kendall's tau 0.138 S' 1353.000 p-value (Two-tailed) < 0.0001 alpha 0.05 The p-value is computed using an exact method.

Continuity correction has been applied.

Ties have been detected in the data and the appropriate corrections have been applied.

Ambo Station Seasonal Mann-Kendall Test / Period = 12 / Serial independence / Two-tailed test (18.1):

Kendall's tau 0.208 S' 226.000 p-value (Two-tailed) 0.000 alpha 0.05 The p-value is computed using an exact method.

Test interpretation: H0: There is no trend in the series Ha: There is a trend in the series As the computed p-value is lower than the significance level alpha=0.05, one should reject the null hypothesis H0,And accept the alternative hypothesis Ha. The risk to reject the null hypothesis H0 while it is true is lower than 0.04%.

Continuity correction has been applied.

Ties have been detected in the data and the appropriate corrections have been applied.

Annex-3 Values of Monthly Precipitation and Temperature data of Sampled Stations

Monthly Max. Temp. in Element oC Region: SHOA Station: Holetta I.A.R Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1969 22.2 21.3 22.0 23.6 23.4 21.2 19.1 18.7 20.1 22.3 22.7 24.2 1970 22.7 23.7 22.5 23.9 24.6 22.6 19.4 18.5 19.9 21.6 21.6 22.1 1971 22.3 24.4 23.7 23.2 22.5 19.7 17.8 17.2 18.2 20.0 20.6 20.6 1972 22.7 21.8 23.7 21.4 23.5 22.1 19.1 19.7 20.3 22.1 22.7 22.6 1973 24.1 25.8 26.9 26.8 24.1 22.7 19.7 18.8 19.8 21.1 21.8 21.6 1974 23.1 19.8 22.7 24.7 23.5 21.9 19.8 19.7 20.3 21.8 22.0 22.8 1975 23.0 24.3 25.5 24.1 24.8 21.4 19.2 18.0 19.5 21.3 21.6 22.1 1976 22.6 24.5 24.1 23.6 23.0 21.5 19.2 18.7 20.6 20.7 20.9 21.0 1977 22.5 23 24 24.1 22.9 21.6 18.9 18.9 20 20.9 21.1 21.9 1978 22.9 23.4 23.4 24.2 24.2 21.9 18.9 19.2 19.5 20.6 21.7 22.3 1979 21.5 23.3 23.6 23.5 23.7 22.5 19.7 19.7 20 21.8 22.5 23.3 1980 22.9 25.3 24.5 24.3 24.4 22.6 19.5 19.4 20.4 21.5 22.6 23.2 1981 24 24.2 22.5 22.4 24.6 24.5 19.6 19.3 19.6 21.4 22.2 22.6 1982 22.5 22.7 23.9 22.9 23.3 23.2 19.7 18.6 20.1 20.7 21.3 21.8 1983 22.7 23.6 24.2 23.1 22.7 22.4 21.1 19 20.1 21.3 22.2 22.4 1984 23.1 24.6 25.3 26.3 23.6 21.2 25.2 19.9 20.6 22.5 23.1 22.7 1985 23.6 24.1 24.6 22.5 23.3 23.4 19 19 20.1 21.4 22.5 22.8 1986 23.7 23.9 23.4 22.2 23.6 21.8 20.1 19.5 20.4 21.8 22.8 23.7 1987 22.9 23.6 22.5 22.6 22.6 22 21 20 21.5 22.6 23.3 23.5 1988 22.9 23.7 26 24.4 25.5 22.1 18.7 19.4 19.9 28.2 22 22.3 1989 22.4 22.2 23.2 21.6 24.1 22.6 19.4 19.1 20 21.3 22.5 22.1 1990 23 21.9 22.8 23.1 24.6 22.2 19.8 19.5 20.1 21.9 22.8 23.1 1991 24.2 23.9 23.3 24.4 25.9 23.8 19.5 19.8 20.9 22.2 20.9 22.7 1992 22.4 22.6 24.8 24.6 24.3 22.5 19.4 18.9 19.7 21 21.7 22.5 1993 23 22 25.1 22.4 22.9 21.6 19.6 19.4 19.3 21.5 22.4 23 1994 24.1 25.1 24.4 24.1 25.1 21.4 19 19.3 20.6 22.3 22.5 23.1 1995 24 24.4 24.5 22.6 24 23.6 19.4 19.7 20.6 22.6 23.4 22.8 1996 21.8 24.9 24.2 23.2 23.1 20.6 19.9 19.5 20.4 22.3 22.5 22.6 1997 22.9 24.7 25.6 23.8 25.3 23.4 20.1 20.2 22.1 22.5 22.5 23.6 1998 23.8 24.4 24.8 25.7 24.3 23.2 25.9 19.7 20.6 21.5 22.4 22.4 1999 23.2 25.1 23.9 25.6 24.6 23.0 19.0 19.5 20.5 20.6 21.9 22.3 2000 23.3 24.5 25.7 23.8 29.8 21.1 20.2 19.0 20.1 21.5 22.2 22.8 2001 23.0 24.8 22.4 24.3 30.5 21.2 19.9 19.5 20.6 22.8 23.0 23.4 2002 23.1 24.9 24.0 25.3 25.7 22.9 21.0 20.3 21.2 23.3 23.9 23.3 2003 23.4 25.3 23.5 23.3 23.4 21.6 18.1 18.7 19.8 22.0 22.4 22.2 2004 23.5 23.9 24.5 22.2 24.2 21.2 19.4 19.1 19.8 20.9 22.5 23.0 2005 22.9 25.3 24.5 23.8 22.0 21.1 19.5 19.7 20.1 21.4 22.0 22.9 2006 23.5 24.4 23.2 22.6 22.8 21.3 19.5 18.2 19.4 21.6 21.4 22.6 2007 23.5 23.5 24.0 23.0 24.0 21.2 19.0 19.1 19.6 21.0 19.6 22.3 2008 28.0 25.0 26.8 25.4 24.7 25.0 24.5 22.5 24.0 24.0 26.0 24.5 2009 23.4 24.6 26.4 17.2 26.6 25.5 21.4 20.9 22.0 23.0 24.4 23.1

Element: Monthly Minimum Temperature in oC Region: Shoa Station: HOLLETA Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1969 7.7 10.0 10.4 9.7 10.4 9.6 10.2 10.2 9.4 3.4 2.6 0.1 1970 4.4 5.9 7.9 7.6 7.5 7.1 8.2 8.9 7.8 4.8 -1.8 -2.6 1971 2.1 1.7 5.4 5.6 5.7 5.6 6.1 7.6 6.3 6.9 1.8 0.7 1972 2.1 1.7 5.3 5.6 5.6 5.6 6.1 7.6 6.3 6.8 1.9 0.8 1973 1.5 1.2 2.3 5.7 5.8 5.8 8.4 9.4 8.2 4.6 1.2 -1.5 1974 1.4 4.3 8.7 6.3 7.5 6.9 8.8 9.6 7.6 3.8 -1.8 -1.1 1975 0.1 4.7 6.6 8.6 8.1 6.9 8.8 10.0 8.0 4.1 0.0 0.0 1976 1.7 4.2 6.5 6.7 7.9 5.1 7.5 8.1 7.6 4.5 2.4 1.2 1977 1.8 5 6.1 x 8.7 8 9.3 9.6 8.2 8.1 4.9 2.2 1978 1.7 6.4 7 7.7 6.9 8.2 9.9 9.6 8.8 6 1.5 3.6 1979 7.2 6.5 7.5 7.6 8.5 7.9 10.1 9.5 8.4 5.4 1.5 2.8 1980 4.2 6.7 7.9 8.6 8 8.4 9.5 9.4 8.3 5.2 3.1 0.6 1981 3.3 4.2 9.2 9.4 7.5 6.9 9.7 10 8.8 4.3 1.5 1.3 1982 5.7 8.3 6.3 9.2 8.9 6.9 8.9 9.5 8.1 5.5 5.4 4.4 1983 3 7.4 9 9.7 10.3 8.4 9.7 10.7 8.8 6.3 4 4 1984 4.1 3.8 8.6 9.4 9.7 9.2 8.7 8.3 7.4 2.2 3 1.8 1985 2.5 3.3 7 9 7.7 6.8 8.3 8.9 7.3 4.5 1.4 1.1 1986 0.2 6.3 6.2 9.1 8.2 8.5 8.6 8.1 7.0 3.4 1.3 2.1 1987 2.4 6 10.1 8.2 8.8 7.8 8.8 9.7 7.8 5.7 2.2 3 1988 4.7 8.5 5.6 8.8 6.9 8.3 10.4 9.9 8.7 5 0.2 0.3 1989 2.1 5.1 6.5 8.8 6.4 7.3 9.2 9.1 8 4.4 1.9 5.9 1990 2.8 9.9 7.2 8.5 7.7 7.3 9.3 9.2 8.5 4.3 3.5 0.7 1991 4.6 7.2 8.5 7.9 7.6 8.4 10.1 9.6 7.9 3.4 1.5 2.6 1992 5.7 8.6 8.2 8.2 8 7 9 10.6 7.5 5.9 3.1 2.6 1993 5.6 7.4 4.4 9.6 8.3 8.3 9.1 8.6 7.7 5.8 1.8 1 1994 1.6 3.9 8.3 7.9 7.3 8.1 9.9 9.1 6.6 2.5 2.6 0.5 1995 1.3 5.8 7 9.8 7.6 6.5 8.6 9.1 6.3 3.4 1.8 3.3 1996 5.4 3.6 7.7 7.4 7.3 8.2 8.7 8.3 7.1 3.2 2.5 1.7 1997 5.6 1.1 7.3 8.1 6.6 8.4 8.4 8.4 7.2 6.4 5.6 2.5 1998 6.4 7.8 8.8 8.9 9.1 7.8 12.2 9.3 7.9 6.6 0.7 -1.5 1999 1.8 1.0 6.1 5.7 6.6 6.7 8.4 8.1 6.4 5.8 0.1 0.4 2000 0.3 0.6 3.5 7.1 6.9 6.5 7.7 7.6 6.6 4.7 2.3 1.0 2001 2.8 2.4 7.4 6.4 7.0 6.8 8.0 8.3 5.2 3.5 1.2 3.2 2002 4.7 4.7 8.1 8.3 9.0 8.0 9.1 8.3 6.8 4.2 2.4 6.5 2003 5.0 5.8 7.0 9.4 8.4 7.9 9.3 9.0 7.8 3.8 2.2 2.2 2004 5.3 4.5 6.7 9.6 7.0 8.1 8.7 8.7 7.7 4.3 2.5 3.7 2005 3.4 4.5 7.3 8.0 9.4 7.0 8.7 8.8 8.3 4.3 1.9 -0.3 2006 3.6 6.5 6.9 9.2 7.4 7.8 9.5 9.5 7.8 6.2 4.3 5.0 2007 4.6 6.4 6.2 8.8 8.5 8.3 10.0 9.4 8.3 4.3 1.9 -0.3 2008 4.0 4.2 3.5 8.2 8.9 9.3 9.5 9.7 8.2 6.2 4.2 2.0 2009 4.8 5.2 7.5 8.6 55.0 8.6 9.8 10.4 8.8 6.6 2.9 7.2

Element: Monthly Rainfall in mm Region: Shoa Station: HOLETA Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1969 51.9 63.4 63.6 78.8 76.5 144.8 239.7 240 105 0.2 37 0 1970 43.2 67.2 74.4 57.8 27.7 58 262.4 407.8 102.4 9.4 0 0.6 1971 20.2 0 77.6 59.4 93.3 137.9 312.29 311.2 110.7 4.7 7 4 1972 0 58.2 49.2 150.6 50.7 109.4 230.3 232.4 127.5 1.1 20.3 8 1973 27.6 1.5 2.8 34.8 84.8 97.6 262.4 296.1 184.5 39 0.9 5.9 1974 0 20.6 135.4 16.7 116.6 93.2 224.7 217.8 139.7 6.6 0 0 1975 0 5.8 30.5 79.6 73.9 73.4 252.7 280.6 190.9 2.8 0 0 1976 0 29.4 46.6 87 0 122.4 169.2 331.5 83.1 4 70.1 0 1977 0 25 39 75 61.4 113.1 221.7 255.8 104.1 176 30.2 0 1978 0 24.6 52.2 52 48.3 180.9 305.9 240.1 150.6 54.4 0 3.3 1979 34.2 56.4 56.2 96.6 94.3 141.3 241.6 243.1 105.5 8.8 0.6 15.2 1980 45.3 21.2 81 88 62.1 167.3 241 232.3 110.9 25.8 1.2 0 1981 0 17.7 117.7 111.8 34.9 56.1 366.5 258.1 188.8 2.6 0.5 15.4 1982 41.6 54.7 84.7 120.1 48.9 84.9 250.9 283.7 124.6 38.2 46.7 30.3 1983 19.9 49.9 90.2 126.1 219.1 87.5 241.2 284.1 92.6 23.8 3.9 22.7 1984 0 0 41.8 8.5 146.6 178.5 260.3 221.8 106.1 0 0.6 13.1 1985 15.2 0 47.7 49.1 48.5 45.6 257.6 281.9 104.6 21.1 4.1 0 1986 0 51.2 88 139.1 88.5 160.4 243.9 279.4 142 11.9 0 0 1987 2.4 77.3 112.1 95.7 136.5 87.1 182 261.8 107.6 19 1.4 27.4 1988 10.3 80.7 7.8 89.6 21.1 108 291.6 263.7 239.9 31.9 0 0.2 1989 7.1 86.9 78 69.8 8.3 74.9 240.7 279.3 117.5 3 0.3 21.6 1990 0.5 163.5 34.9 95.4 55.5 131.8 242.4 338.2 165.5 8 0 0.9 1991 21.1 74.8 118 21.3 37.2 89.9 232.1 229 172.5 2.6 0.3 5.8 1992 57.4 33.5 58.8 95 34.6 115.4 190.9 312.6 135.1 36.3 0.6 7.8 1993 18.2 83.8 3.8 127 59.3 103.1 210 271.6 214.3 26.5 0 0 1994 0 2.3 86.7 85.9 25.7 107.3 216.4 203.3 149.7 0 34.8 0 1995 11.2 84.6 41.9 123.8 81.3 91.6 196.9 264.5 82.1 15.5 0 34 1996 18.1 8.5 94.1 58.4 55.4 183.8 213.9 236.4 120.7 5.3 1.4 0 1997 15.3 0 21.1 95.4 13.5 95 233.2 193.1 40.5 46.5 23.6 0 1998 54.6 42.3 25.7 65.7 80.4 141.5 341.6 238.1 168.3 67.4 0.8 0 1999 77.3 4.6 34 16.6 54.6 98.9 272.8 307.7 88.9 65.4 0 0 2000 0 0 12.5 123.8 50.8 89.8 187.1 260.6 120.5 9.5 38.9 0 2001 7.9 10.6 130.7 48.6 101.2 176.5 301.6 161.2 103.2 24.2 0 0 2002 72.6 25.7 56.9 38.1 49.4 123.2 273.1 194 77.4 0 0 0 2003 17.5 11.3 33.3 84.2 13.6 117.1 194 237.2 107.4 10 0 0 2004 12.7 0.8 42.5 155.1 27 121.4 204 226.6 119.7 3.6 0.7 0 2005 22 4.5 61.7 49.1 94.4 81.8 253.9 187.5 130.7 31.5 3.8 0 2006 0 9 68.4 99.1 98.4 113.7 288.6 250 116.1 13.1 0 0.6 2007 0 14 61.2 36.4 73.5 253.3 159.31 249.53 90 18.8 0 0 2008 0 18.5 1 31.3 88.8 99 287.1 220 195.7 54 64 3.5 2009 20.4 8.6 4.7 30.9 10.62 64.8 208.8 242.7 75.9 15.6 6.4 39.4

Element : Monthly Maximum Temperature Region: SHOA Station: Ambo J.C.A Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1995 26.2 26.5 27.9 26.1 26.2 25.9 21.7 21.1 23.6 25.5 25.9 26.1 1996 25.4 28.7 27.2 27 26.6 23.5 22.7 22.1 24.5 26.1 26 26.5 1997 26.3 30.5 29.1 27 28.4 24.2 23.7 25.2 26.2 26.4 26.7 27.8 1998 27.7 28.9 28.6 30 30.4 24.6 21.5 22.6 24 25.7 26.4 26.4 1999 28.6 29.6 28.3 28.7 28 24.9 21.4 21.9 23.6 24.1 25.3 25.8 2000 26.9 28.3 29.7 27.1 26.8 24 22.5 22 23.5 24.8 25.3 25.8 2001 26.7 28.5 26.6 28.1 26.6 23 22.3 22.3 24.2 26.2 26.2 26 2002 26.1 28.2 27.7 28.5 29.3 25.2 23.9 22.5 24.3 26.4 27 26.8 2003 27.4 28.7 28.3 27.4 28.9 24.8 22.3 22.3 23.3 25.7 26.3 26.1 2004 27.9 28 29.2 27.4 28.2 24.9 22.4 22.8 23.6 25.2 26.7 26.9 2005 27 30.1 29.2 28.4 27 25.3 22.4 23.3 24.3 24.9 26.3 26.7 2006 27 29.2 27.8 28.5 28.5 24.3 26.5 24.5 26.2 25.8 26.4 22.8 2007 27.4 27.6 28.9 27.9 26.9 23.9 22 22 23.4 25.7 26.3 26.5 2008 28 28.7 30.1 29 26.6 24.3 22.5 22.3 24.2 26 24.9 26.5 2009 26.4 28.2 28.9 28 29.7 28.3 25.6 25.6 27 26.3 27 26.9

Element Monthly Minimum : Temperature Lon. Lat. Alt. Region: Shoa Station: Ambo J.C.A Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1995 10 10.2 12.5 13.5 12.6 11.4 11.5 12.1 10.7 9.7 9.6 10.8 1996 11.2 11.2 12.9 12.1 12 12.2 11.9 11.4 11.2 9.5 10 9.5 1997 9.7 11.5 12.7 11.8 12 11.3 11.6 11.1 10.1 10.6 11 10.3 1998 12.5 13.3 14 13.9 13.6 11.9 11.6 12.9 10.7 10.2 8.5 8.7 1999 9.6 10.9 12.4 12 10.8 11 12 11.3 10.3 10.8 7.7 9.8 2000 10.9 12 14.2 13.1 11.6 11.1 11.7 11.8 11.4 10.9 10.3 9.7 2001 10.3 11.9 12.9 13 12.3 12 12 12.7 9.8 10.3 9.9 10.8 2002 11.5 12.5 11.8 13 12.9 12.1 12 12.1 11.5 10.6 11.5 12.5 2003 11.3 12.7 13.4 13 11.2 11.8 12.8 12.9 11.2 9.4 11.2 10.4 2004 12.7 12.4 13.9 13.5 11.9 12.2 12 11.9 11.1 10.6 10.4 11.9 2005 11 12.6 12.9 14.1 12.6 11.3 11.8 11.6 11.5 9.4 9 9 2006 11.9 12.5 12.3 11.5 9 12.5 10 9 10.1 8.7 9.6 9.9 2007 12.6 11.9 13 13.5 13.5 12.8 12.6 12.5 12.3 10.1 11.4 10.3 2008 12.6 13.1 13.8 14 12.7 12.6 12.2 11.8 11.3 11.2 11.3 11.6 2009 12.2 11.1 13.4 13.3 11.2 12.3 12 12.5 11 11.7 11.5 11

Element: Monthly Rainfall in mm Region: Shoa Station: Ambo J.C.A Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1987 1.5 38.1 144.2 119.8 185.4 129.1 123.1 229.1 62.9 13.6 1.9 49.2 1988 80.0 232.6 8.5 31.5 35.6 116.7 213.5 169.0 171.6 0.6 0.0 0.0 1989 20.7 5.0 37.3 61.5 52.9 164.1 219.5 171.7 134.5 51.8 0.0 42.7 1990 20.0 109.9 78.2 19.5 89.2 185.1 315.9 197.1 94.8 3.3 0.0 0.0 1991 1.6 58.7 52.4 89.8 111.6 179.6 268.0 203.6 95.9 12.7 0.0 0.0 1992 21.6 94.3 62.9 160.0 134.0 174.0 220.0 210.0 97.0 22.0 3.0 0.0 1993 20.0 35.6 10.5 181.9 121.2 178.3 268.5 215.4 121.3 25.1 0.0 0.0 1994 13.3 23.5 22.1 169.8 105.4 133.0 218.7 135.5 88.6 21.2 5.1 27.1 1995 6.6 11.4 33.6 157.7 89.5 87.6 168.8 55.6 55.8 17.2 10.2 54.2 1996 48.4 3 138.4 89.1 113.5 240.3 315.9 174.3 86.5 0 16.7 4.7 1997 28.7 0 31 83.1 59.7 132 92.1 183.4 59.2 87.5 22.1 2.2 1998 74.3 10.4 40.6 30.7 70 120 96 158.2 112.3 96.5 0 0 1999 8.4 1 39.5 20.1 99.2 108.4 195.9 132.9 95.9 119.9 1.3 0 2000 0 0 9.3 51.5 93.7 121.2 185.5 191.6 131.2 83.7 20.7 14.8 2001 0 10.2 52.5 70.4 148.3 148.5 180.8 243.1 109.9 41.8 5.4 11 2002 78.7 16.9 55.6 56.3 39.5 178 171.9 111.7 40.3 3 0 17.2 2003 41.7 100 54.8 106 9.4 209 133.9 142.2 71.2 9.3 1 10.4 2004 39.2 16.6 30.2 108.7 26.8 137.1 203.6 215 138.9 19 0 8.9 2005 25.2 0 86.8 47.5 58.1 166.3 158 187.1 98.4 19 10.2 0 2006 0 4.7 150.7 72.3 157.7 109.9 196.8 298.6 76.5 17.9 18.8 0 2007 49.3 54.3 40.2 38.9 131.6 275.2 232.6 218.3 111.7 11 0 0 2008 0 0 1 18.7 157.3 161.8 308 260.2 84 64.8 101.8 2.7 2009 2.7 0 0 0 0 105.3 235.1 107.4 92.3 79.9 0 34.4

Element: Monthly Maximum Temperature Region: SHOA Station: Bako Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1969 28.1 29.1 30.1 30.3 28.9 25.6 25.7 27.3 28.1 28.5 30.0 30.3 1970 29.1 31.8 30.4 31.9 30.3 25.5 24.1 23.9 25.1 26.4 27.2 28.2 1971 28.4 30.7 30.6 31.1 27.2 25.1 23.9 23.2 24.7 26.7 27.7 28.1 1972 31.6 30.0 31.4 29.3 28.9 26.5 23.3 24.7 26.0 33.2 29.0 30.1 1973 29.7 31.5 33.2 32.2 27.0 25.8 23.9 23.6 24.8 27.1 28.5 28.2 1974 29.6 31.2 29.2 32.1 27.4 25.3 23.0 24.0 24.3 27.3 28.1 28.8 1975 29.3 29.4 31.3 30.6 28.8 23.0 23.0 22.8 24.2 26.1 27.6 28.1 1976 28.8 30.3 30.0 30.2 26.7 25.4 23.7 25.6 27.8 26.8 26.8 28.5 1977 29.1 29.4 30.0 31.7 28.4 25.1 23.1 24.4 24.9 26.0 27.1 27.6 1978 28.9 29.8 30.6 30.1 27.1 26.2 23.0 24.1 24.9 26.9 27.6 27.8 1979 27.0 29.4 31.1 31.4 28.3 25.5 23.9 24.5 25.5 27.4 28.4 28.7 1980 29.5 29.4 30.3 28.6 26.8 26.5 22.9 23.7 25.1 26.4 27.3 28.6 1981 30.2 31.4 29.4 31.5 28.7 26.4 23.1 24.4 25.7 27.7 28.9 29.5 1982 30.3 30.6 31.8 30.6 30.0 29.0 25.3 23.5 25.2 26.4 27.5 27.5 1983 29.7 30.7 30.8 30.9 29.2 27.0 25.1 23.3 24.3 26.3 27.5 28.5 1984 29.4 31.4 32.0 32.6 28.5 24.6 23.7 24.5 25.2 28.4 28.6 29.3 1985 30.7 30.5 31.8 29.5 25.7 25.6 23.4 23.1 25.3 27.3 28.7 29.1 1986 29.9 30.1 31.0 29.3 30.7 23.9 22.4 23.1 22.2 25.7 29.4 29.8 1987 29.3 28.9 29.9 30.1 28.4 26.3 25.8 24.8 27.3 27.5 28.6 29.4 1988 30.1 29.6 32.6 33.1 30.3 26.7 23.5 23.9 24.3 26.7 27.8 29.0 1989 28.8 30.2 31 28.4 28.5 26 24.2 24.6 24.6 27.8 29.2 28.4 1990 29.8 29.2 30.1 30.7 29.6 26.5 24.9 25.2 25.4 28.1 29.4 30.4 1991 30.8 31.2 30.4 30 29.8 26.9 25.1 26.4 24.8 28.3 29.2 28.8 1992 29.3 30.1 31.2 30.7 29.1 27 24.6 23.6 25.9 26.7 27.5 28.9 1993 29.4 29.6 31.9 28.1 27.8 26.5 24.5 24.8 25.3 27.8 29.1 28.9 1994 30.7 31.6 31.9 31.6 27.5 25.5 23.9 23.8 25.6 29 29.5 30.7 1995 31.4 31.8 32 31.1 28.8 27.6 24.2 25 26.6 29.2 29.8 29.6 1996 29.3 31.8 31 29.8 28.5 23.9 24.3 24.7 26.5 28.7 29.9 29.4 1997 30.1 31.2 31.4 28.9 27.5 24.4 23.3 23.8 24.3 25.5 27.1 27.7 1998 28.7 30.7 30.5 32.7 28.2 27.3 23.3 23.5 24.6 26 28.3 29.9 1999 30 32.1 30.9 31 25.6 24.1 20.1 21.7 23.8 27.4 27.1 27.5 2000 30.1 30.2 30.9 31.8 26.4 22.9 23.9 21.6 22.3 25 28.5 29.1 2001 30.3 29.9 29.2 30.6 28.4 26.3 23.8 24.3 25.7 27.6 29.6 30.6 2002 30.2 32 30.9 31.4 31.1 26.5 25.2 24.3 25.7 28.9 30.7 30.7 2003 31.4 32.6 31.1 31.1 32.9 26.2 23.3 23.3 24 26.9 30 30.5 2004 31.4 32.1 32.2 29.9 30.5 25.5 24.4 24.8 25.7 27.4 29.7 30.8 2005 31 32.2 32.7 32.6 30.3 29 25.2 26.5 26.6 28.2 29.5 32 2006 30.8 32.2 28.9 28.5 28.3 25.9 24.9 24.4 25.4 28.9 29.3 29.2 2007 30.3 31.1 32.6 30.9 28.1 25.2 23.8 24 25 28.1 29.7 30.1 2008 31.3 32.1 33.1 30.7 27 26.2 22.7 24.9 27.9 28 29.8 29.6 2009 30.2 31.3 31.3 30.9 29.8 26.9 26.1 26.4 25.7 27.5 29 29.6

Element: Monthly Minimum Temprature Region: SHOA Station: Bako Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1969 11.7 13.6 13.3 14.4 14.0 14.0 14.5 14.0 13.3 10.9 10.5 18.5 1970 11.3 12.8 14.8 14.1 15.5 14.9 14.3 14.9 13.7 13.6 9.7 8.4 1971 10.6 9.5 12.9 14.0 15.9 15.4 15.0 14.3 14.4 13.8 11.2 7.8 1972 9.4 10.2 12.8 16.5 14.3 13.9 14.5 14.3 13.4 18.2 11.0 8.8 1973 10.4 11.9 14.2 15.5 15.5 15.1 14.9 14.2 14.1 11.5 9.9 5.6 1974 9.3 11.9 13.4 14.0 15.4 14.3 14.3 15.1 14.3 11.7 8.0 8.5 1975 10.1 12.5 14.6 15.6 15.4 15.5 14.4 15.7 14.6 12.9 9.4 9.9 1976 10.1 12.5 14.0 13.5 14.7 14.0 14.3 13.9 13.4 11.3 11.8 10.0 1977 12.4 13.5 15.0 14.3 15.5 15.0 15.1 14.2 14.2 14.4 10.9 10.9 1978 9.6 12.1 14.2 15.8 14.5 14.5 15.1 15.1 14.3 12.7 9.8 11.2 1979 12.6 12.4 13.7 15.3 15.8 15.2 14.9 14.8 14.1 12.4 11.1 11.5 1980 11.5 13.8 15.1 16.5 15.9 15.4 15.1 14.9 14.5 12.4 11.5 10.8 1981 11.2 11.6 15.4 15.1 15.4 15.1 14.9 14.7 14.4 9.4 10.9 9.0 1982 11.9 12.8 13.4 13.8 15.1 14.4 14.8 15.2 13.9 12.9 12.6 11.3 1983 11.1 12.2 16.0 15.9 16.5 15.2 15.3 15.5 14.9 13.3 11.6 8.9 1984 9.7 9.9 15.2 16.5 16.1 15.0 14.5 14.4 13.6 10.0 12.2 11.1 1985 11.0 13.8 15.8 15.2 15.2 14.8 14.3 14.6 14.3 11.9 11.5 11.3 1986 10.6 12.3 13.4 14.9 16.3 15.6 14.9 15.2 14.0 12.0 11.0 10.9 1987 11.7 13.8 15.6 16.1 16.2 15.5 15.5 15.4 14.7 14.0 12.2 12.4 1988 12.6 14.8 14.7 14.9 16.4 15.4 15.3 14.9 15.2 13.8 10.4 9.4 1989 10.6 12.9 14.3 14.5 15.2 14.9 14.6 14.1 14.6 12.6 10.2 12.7 1990 12.8 14.4 14.8 15.1 15.8 15.3 15.2 14.8 14.5 12.2 12 10.1 1991 13 13.3 16 16.1 15 15.4 15.9 14.7 15.4 12.1 11.4 11.5 1992 13.7 15.4 16.2 16.4 16 15.4 14.8 15.4 14.8 14.1 11.9 11.6 1993 12.6 13.8 14.6 16.3 15.4 15.8 14.7 14.8 14.6 13.5 11.1 10.3 1994 11.2 12.6 15.3 11 11.6 15.1 14.7 14.8 13.7 11 12.5 10.3 1995 10.9 13.2 13.1 16.2 16 15.4 15.5 15.7 14.6 13 13.4 13.7 1996 13.2 13.5 15.9 16.4 15.8 15.4 16.3 16 15.2 12.9 9.6 10.3 1997 12.2 11.2 14.7 14.7 15.4 15.1 15.1 15.3 14.3 14.2 14.2 13.5 1998 13.2 13.4 14.7 17 13.7 15.6 15.8 15.7 15.4 15.5 8.4 9.5 1999 11 11.1 14.1 17.8 16.9 15.8 14.9 15 15 15.3 11.9 11.1 2000 10.8 11 13.9 12.9 15.5 15 15 14.3 14.3 14.7 13 9 2001 12.2 13.8 15 13.9 15.6 15.2 15.7 15.2 14.8 14.2 11 11.7 2002 12.4 12.4 13.8 13.6 13 14.2 14.8 15 15 14.1 12.9 13.8 2003 13.4 15.4 15.3 16.4 16.8 14.8 15.6 15.4 15 13.9 12.7 11.3 2004 11.8 11.4 11.7 14.5 13.6 14.2 14.2 15.4 14.4 12.7 11.8 13.1 2005 12.2 14.1 14.5 15 14.7 14.1 14.8 15 14.6 14.2 11.5 7 2006 11.2 13.6 14.5 15.3 15 14.7 15.1 13.4 15.3 15.7 14.5 12 2007 12.4 13.2 13.6 15 15.2 14.8 15 14.4 14.4 12.9 12.8 10.4 2008 13.1 13.1 15.5 15.9 14.5 14.2 15.6 13.6 14.6 13.4 9.7 10.1 2009 11.7 13 13.6 14.1 13.6 12.1 11.9 11.5 11.4 11.3 10.5 12.5

Element: Monthly Rainfall in mm Region: SHOA Station: Bako Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1969 106.1 21.0 100.6 92.2 70.6 383.8 345.0 206.6 147.6 74.5 5.3 0.0 1970 13.9 3.0 77.7 19.1 158.3 140.2 274.0 256.5 118.0 70.5 0.0 3.8 1971 3.0 0.0 15.2 22.2 235.5 226.8 203.4 182.5 90.2 97.5 27.5 0.0 1972 18.8 34.8 58.3 133.5 90.7 236.3 266.5 218.6 114.6 15.5 18.8 177.0 1973 2.0 0.0 4.0 97.0 207.4 147.9 219.3 414.9 146.9 138.9 13.8 21.4 1974 0.0 5.4 42.1 9.2 203.4 187.2 201.9 236.3 222.1 0.0 0.0 0.0 1975 1.4 55.7 29.1 27.7 123.4 185.1 268.3 350.6 188.4 84.8 2.6 13.3 1976 5.2 22.0 87.0 38.5 285.0 178.6 145.7 130.0 110.2 44.2 18.1 2.4 1977 6.4 17.7 79.3 3.7 180.4 259.5 247.2 191.1 138.8 119.1 15.7 15.4 1978 0.0 13.3 91.0 88.2 161.4 219.7 400.0 251.0 257.0 16.9 39.5 21.1 1979 33.4 62.1 29.1 11.7 207.1 238.6 264.0 238.1 83.1 43.2 0.5 6.1 1980 1.6 18.1 44.5 145.8 119.7 167.7 257.9 228.1 74.5 20.0 5.8 0.0 1981 0.0 0.3 35.9 32.9 78.8 168.1 271.4 171.4 185.4 21.0 18.4 0.0 1982 43.3 3.9 33.8 50.4 78.0 190.4 143.1 59.6 79.7 123.1 24.5 0.4 1983 5.5 29.2 84.7 55.6 133.9 108.3 227.4 235.5 146.9 137.8 54.1 0.0 1984 5.6 0.0 19.0 44.9 163.5 224.7 263.3 165.5 117.9 9.1 30.0 5.7 1985 2.3 0.0 74.1 105.4 130.7 197.9 209.7 261.0 101.2 78.4 7.0 2.7 1986 6.1 28.1 45.9 90.8 27.5 305.1 202.6 174.6 154.3 24.0 1.3 15.9 1987 4.3 6.7 129.2 63.2 172.7 260.3 175.6 120.0 94.1 76.9 3.8 25.5 1988 21.9 56.6 3.2 16.5 100.1 180.9 341.3 194.5 230.4 87.2 6.4 0.2 1989 1.5 2.9 102.3 60.4 107.9 145.3 267.8 274.9 165.9 46.5 6.3 70.9 1990 3.3 26.6 78.7 35.5 112.8 326.8 287.4 219.3 112.2 7.4 7.7 0 1991 44.4 28.6 100.1 40.2 120.5 163.3 66.7 158.5 288.3 17 5.7 23 1992 8.4 10.5 67.2 86.1 119.8 337.2 234.1 270.8 152.2 102.6 42.1 6.9 1993 2.8 24.7 41 195.6 193 219.5 317.1 418.3 154.9 83.3 8.4 0.2 1994 16.9 7.5 50.4 95.2 204.2 143.7 250.1 316.4 203 25.5 17 2 1995 0 12.5 38.2 72.6 144.5 196.4 260.5 205 83.4 27.7 21.6 46.2 1996 38.6 14.5 100.7 91.2 181.2 84.3 209.1 154.3 129.3 45 47.1 6 1997 40.9 0 16.3 117.3 168.9 173.3 303.3 169.3 77 252.4 66.8 3.2 1998 15.3 6.3 105.5 27 126.9 309.6 154.5 316.1 221.9 248 30 0 1999 29.9 0 3 40.9 172.7 264.6 218.3 257.1 144.6 122.7 8.2 5.1 2000 0 0 0 79.3 135.1 378.2 236.9 289.6 162 103.4 48.4 12.6 2001 0 42.8 87.2 51.8 161.3 219.3 328.9 264.3 96.7 92.7 1.5 7.7 2002 23.5 15.1 88.8 73 68.3 236 239.2 205.9 42.1 0 6.8 42.2 2003 4 34.3 51.7 59.1 5.7 265.1 420.6 434.4 69.9 21.5 1.2 27.6 2004 9.4 5 23.6 66.1 114.1 268.6 225.5 257.8 85.2 43.5 48.2 14.3 2005 10.4 0 43 99.5 79 221.2 268.8 230.8 242.2 26.2 37.1 0 2006 0 6.1 32.7 12.8 124.7 288.9 255.7 335.3 145.2 109.7 8 46 2007 10.2 55.5 36.3 47 179.5 297.4 254.7 216.6 138.8 51.4 0 0 2008 6.5 0 0.6 87.2 280.6 396.7 289.1 146.3 167.2 73.9 78.4 1.1 2009 0 10.3 57.6 90.4 9.2 192.4 278.3 203.9 101.1 90.9 0 2.7

Annex-3 Crop production data of West shoa Zone and selected Woreda

West Shoa Zone crop production Data S.N Year Area(ha) Production(Qt) 1 2003/2004 396989 5719041 2 2004/2005 581766 8812502 3 2005/2006 596669 10207820 4 2006/2007 543602 9559473 5 2007/2008 553135 7681135 6 2008/2009 582007 8166233 Source: (CSA,2009)

Area Cultivated and Production( Bako Tibe Woreda 1992/1993 1993/1994 1994/1995 1995/1996 1996/1997 1997/1998 1998/1999 1999/2000 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 Year 35382 35028 32715 32397 32290 33549 33387 33587 33970 34100 30426 31093 34330 36406 Area(Ha) 33880.5 38202.5 213455 235145 303497 456184 465544 688259 672066 720946 358912 609350 513218 677962 619286 580218 610236 Production(Qt) 302070.3 6.0 6.7 9.3 8.9 Productivity/ha 14.1 14.4 20.5 20.1 21.5 10.6 17.9 16.9 21.8 18.0 15.2 16.8 Source: Bako Woreda ARDO

Major Crops S.N Year Wheat Barley Tef Production Production Production Area(ha) Area(ha) Area(ha) (Quintals) (Quintals) (Quintals) 1 1993 8023 222237 8183 211531 8009 114448 2 1994 8048 223332 8197 212712 8017 114643 3 1995 8061 224499 8200 213200 8033 115675 4 1996 8093 226199 8211 213486 8079 117145 5 1997 8105 117640 8212 218796 8105 117640 6 1998 12010 337412 11086 279460 11480 155899 7 1999 13157 529262 11026 330450 11249 218900 8 2000 13659 549208 11588 375457 10124 182232 9 2001 13702 539828 12039 342265 10104 199580 10 2002 13829 522168 11027 316563 11137 210073 Source: Holeta Woreda ARDO

Annex-4 Survey Questionnaires

Household survey questionnaire

Questionnaire No: ______Survey Area: Region:______Zone: ______Woreda: ______PA: ______Village: ______Date of interview: ______Name of interviewer: ______Name of head of Household: ______Age: ______Sex: ______Respondent’s Name (if different from the head): ______Age: ______Sex: ______

PART I. Climate change Hazards and Adaptations Section A: Extreme weather events and Coping Mechanisms

We would like to know weather related problems you have faced in your area and how you cope up with these events? 1. Is there any weather related hazards in your area in the last 20 years ? Yes ------1, go to Ques. #2; No ------2, go to Ques. #14

2. What is the 3. What 4 When 5. What 6. Did you 7. When 8. How did 9. Did you see problem? the did you do you take any did you you learn any change or Drought=1 impacts observe think Adaptation start these improvement Heavy rains=2 did you the are the measures? taking methods? after the Flood=3 observe? problem? causes? Yes------1, these (Code d) measures? Frost=4 (Code a) (year) (Code Code c measures? Yes ------1 Strong winds=5 b) No------2, (year) No ------2 Heat Waves=6 to # 10 Other(Specify)=7

Code a: Total Crop loss = 1; Reduced yield=2; Reduced seeding area=3; Delayed seeding=4; Delayed maturity=5; Increased pest/ disease=6; others (specify)=7 Code b: Deforestation=1; Land degradation=2; Absence of protection natural resources=3; others (specify)=4 I don’t know=5 Code c: Taking the loss (No Action)=1; Diversifying crops=2;Late seeding =3; Seeding short season crops=4; Zero/Minimum tillage=5; Irrigation=6 Multiple field location=7; Selection of tolerant crops =8 ; Others(Specify) Code d: From parents (inherited) =1; From neighbors=2; From extension agents (training)=3; From NGOs=4; From school=5; Others (specify)=6

10. Why didn’t you take measures? ______.

Lack of information =1; Lack of finance=2; Lack of labor=3; Lack of efficient technology=4 Other reasons (specify)=5 11. Have you noticed the frequency of these events? Yes ---1, No ---2; If yes how often? Every year=1; Every 5 years=2; every 10 years=3; if other (specify)=4 12. Do you think the problem is a result of deforestation in your village? Yes ------1, No ------2. 13. What do you think will happen to your farm in the next 10 years if you don’t take any adaptation measures?______. I will lose total production from my land by erosion=1; Productivity will decrease as a result of weather variability =2; Nothing more will happen=3; I don’t know=4; others (specify)=5 14. Do you discuss Climate change induced problems with your neighbors, local authorities, extension agents or other community members? Yes ------1, No ------2. 15. Have you ever participated in any climate change adaptation work initiated by the above mentioned agents? Yes ------1, No ------2.

Section B: Resource requirement for Adaptations

1. Is there any resource required at farm level to cope up with climate change? Yes ------1, Ques. #2, No ------2.

2. What are the resources required? 3. Did you get these 4. From where did you get these Land=1 resources?Yes__1 resources? Saving=2 No__2 Code-a Credit systems=3 If yes_____1 go to Ques #4 Insurance=4 Efficient technologies=5 Labor=6 Other ( Specify)=7

Code a: From Government=1; NGO’s=2; Local community based organizations (Idir,Mahiber, Etc)=3; From neighbors=4 From parents=5 (specify)=6 No where=7

Section C: Soil Erosion/Sedimentation and Protection

1. Is there any soil erosion or sedimentation problem in your farm? Yes ------1, go to Ques.#2; No ------2, go to Ques.#12

2. On 3. What is the 4. What 5. When 6. 7. Did you 8. When 9. How 10. Did you which problem? symptoms did you What take any did you did you see any plot? Erosion=1 or observe do protection start learn change or Plot Sedimentation=2 indicators the think measures? taking these improvement no. did you problem? are the Yes------these methods? after the observe? (year) causes? 1, Code c measures? (Code d) measures? (Code a) (Code No------(year) Yes ------1 b) 2, to # 11 No ------2

Code a: Sheet erosion = 1; Sediments in ditches and furrows=2; Rills in the farm=3; Surface pans=4; Gullies in the farm=5; Pedestals=6; others (specify)=7 Code b: Improper tillage=1; Slope/terrain=2; Deforestation=3; High rainfall=4; Absence of protection measures=5; others (specify)=6; I don’t know=7 Code c: Terraces=1; Ditches/trenches=2; Contour planting=3; Stone bunds=4; Check dams=5; soil bunds=6 Others (specify)=7; Nothing=8 Code d: From parents (inherited)=1; From neighbors=2; From extension agents(training)=3; From NGOs=4; From school=5; Others (specify)

11. Why didn’t you take measures? ______. It is costly=1; High labor demanding=2; I don’t know how=3; Other reasons (specify)=4 12. What do you think will happen to your farm in the next 5-10 years if you don’t take protection measures?______. I will loss portion of my land by erosion=1; Productivity will decrease as a result of top soil erosion=2; Nothing more will happen=3; I don’t know=4; others (specify)=5

Section D: Soil fertility decline and Management

1. Is there any soil fertility problem in your farm? Yes ------1, Ques. #2, No ------2.

2. On 3. When did you 4. What 5. What management 6. How did you 7. Did you see any which realize the indicators did practices have you learn these improvement? plot? problem? you observe? applied? methods? Yes ------1 Plot (years) (Code a) (Code b) (Code c) No ------2 No.

Code a: Yield decline=1; Soil structure and color change=2; increased input demand=3; Others (specify)=4 Code b: Fallowing=1; Crop rotation=2; Intercropping=3; Manure=4; Fertilizer=5; Mulching=6; Legume trees=7; Others (specify)=8

Code c: From parents (inherited) =1; From neighbors=2; From extension agents (training) =3; From NGOs=4; From school=5; Others (specify)=6

PART II. FORESTRY

Section A: Trees on Farms 1. We would like to ask you questions about trees that are grown in your farm. Do you have trees (natural or planted) in your farm? Yes -----1, #2 .No --2 #8.

2. What are the 3. Are they 4. How many 5. What 6. What is the 7. What do you types of planted (1) or trees of each age? arrangement of trees think are the major species? natural (2)? species are there? in the farm? (Code a) uses of the species?

Code a: Scattered=1, In lots= 2, In lines= 3

8. Do you normally plant trees on your farm? Yes ------1, No ------2. 9. If yes, where do you get the seedlings? From government nurseries=1 From NGOs=2 Own nursery=3 10. Do you have the right to use trees in your farm? ______. Yes ------1, No ------2. 11. Can you tell us the advantages of having trees in your farm? ______. Soil fertility improvement=1; Fuel wood= 2; Fodder=3; Shade=4; Soil erosion protection=5; Others (specify)=6 12. What are the sources of fuel for the household?______. Wood=1; cow dung=2; charcoal=3; Crop residues=4; Kerosene=5. 13. Where do you get wood and charcoal? ______. From own farm=1; From nearby forest=2; Community forest (land)=3; From Market=4; others (specify)= 5

Section B: Forests

1. Is there a forest currently in this village? Yes ------1, No ------2. 2. If No, was there a forest 5 years ago? Yes-----1, No-----2; 10 years ago? Yes----1, No-----2; 20 years ago? Yes---- -1, No-----2; 30 years ago? Yes----1 3. If yes, what type and how much? Natural ______ha, Plantation ______ha. (Local units 4. Who owns the forest? ______. Government =1; Community=2; Individuals=3 5. What changes have you observed in the forest cover since the last 5 years?------.10 ears------? 20 years------? 30 years------? Natural forest has disappeared=1 plantation forest has increased=3 Natural Forest has increased= 5 Natural forest has decreased=2 plantation forest has decreased=4 6. Is there anything that you used to get and but now lost due to the change in the forest cover? Yes ------1, No ------2. 7. If yes, can you tell us what they are? ______. 8. Has the change negatively affected your land, adjacent land and the uplands in general? Yes ------1, No ------2 9. If yes, can you mention some of the negative changes? Stream flow decreased or dried=1; More gullies and rills created= 3 yield has declined= Run-off increased=2 variable weather =4 others (specify)=6

PART III. AGRICULTURE

Section A: Land holding, land use and Tenure

1. We would like to ask you questions about all the land your household is using. Please include all the land owned by you and all land that is cultivated by you (even though it belongs to others). What is the size of your farm? Please list plot by plot.

2. Plot size Plot No. Enumerator’s identificationnote (crop type) Area Unit* 3. What is the currentof use the plot? (Code a) 4. Ownership (Code b) 5.did Whenyou get the (year) land 6. When itwas cultivated for the first time (year) 7. did How you get the land? (Code c) 8. is Howthe fertilityof soil? the (Code d) 9. What is the slope plot?of the (Code e) 10.What of type crops grownare on plots? the (Sate all) 1 2 3 4 5 6 7 8 9 10 *Local units = Timad =1, Gasha = 2, Kert = 3, Gemed = 4, hectare = 5, Massa = 6

Code a: Cultivated crop land = 1, Grazing land = 2, Woodlot (forest) = 3, Backyard garden = 4, Unusable = 5, Fallow = 6, others (specify) = 7 Code b: Own = 1, Share cropped in = 2, Share cropped out = 3, obtained as loan=4 Code c: Inherited from parents=1 Allocated from the family=2 during redistribution=3, Local administration= 4, Purchased= 5, Leased= 6, clearing forest=7 Code d: Lem=1, Lem-tef=2, tef=3 Code e: Meda=1, Degetama=2, Gedelama=3

11. Has land re/distribution ever been done in your village? Yes ------1, If yes How many times? ______. No ------2, go to Ques.#20. 12. Are there conflicts concerning land among villagers after the redistribution, especially between gainers and losers? Yes------1, No------2. 13. When did the last re/distribution take place? ______(year) 14. Did you lose or gain any land during last distribution? Yes ------1,go to Ques.#15; No ------2, go to Ques.#20

15. What is the size 16. What 17. What kind of property 18. Did you receive or pay 19. Was the of land that was lost or was the land or land management and any compensation for the compensation gained? used for? conservation works done on last or gained land? If yes, enough or (Code a) the land? (Code b) what kind? (Code c) satisfactory? Yes------1, No------2. Plot No Unit* Lost Gained

*Local units = Timad =1, Gasha = 2, Kert = 3, Gemed = 4, hectare = 5, Massa = 6

Code a: Cultivated crop land = 1, Grazing land = 2, Woodlot (forest) = 3, Backyard garden = 4, Unusable = 5, Fallow = 6, others (specify)= 7 Code b: Trees naturally grown=1, Trees planted by the household=2, Stone terraces=3, soil bunds= 4, Grass strips=5, gullies/rill stabilized=6 others (specify)=7 Code c: Money=1, Substitute land=2, others (specify)=3

20. Have you done or are you currently doing any soil improvement or any soil and water conservation works on your land? Yes ------1, No ------2. 21. If not, what are your main reasons?______. I don’t have any problem on my land= 1; Such type of works are very expensive=2; The land may be taken sometime in the future=3; I don’t have the knowledge=4; Other reasons (specify)=5 22. What type of rights do you have on your land?______. Use for any purpose=1; Use for specified purpose=2; Right to sell=3; Right to transfer=4; Right to lease out=5 23. Is the land you have now sufficient for the household? Yes ------1, No ------2. 24. When a member of the household gets married, where does she/he gets her/his own land? From the household land=1; from local administration (kebele)= 2; by clearing the nearby forest=3; others (specify)= 4 25. How long do you think all the land you have will remain yours? ______. Forever=1; Until next redistribution=2; Until I pass it to my children=3; I don’t know= 4 26. Has any member of the household migrated to other areas due to land shortage? Yes ------1, No ------2. 27. If yes, who, where and when?

Section B: Crop production

1. We would like to ask you questions on the type of crops you cultivate, the yield you obtained and kinds of inputs you used. Please include all the land that is owned and cultivated by you (even though it belongs to others).

2. Which type of 3. On which plots and 4. What type and 5. What types of 6. What was the crops did you how much area was amount of commercial other inputs did amount of yield you cultivate in the covered? fertilizer did you you use? obtained? last season? (put a apply? (Code a) mark) Plot Area Unit* Type Amount Amount Unit** No. (Urea or DAP) Tef Wheat Barely Maize Sorghum Haricot bean Sorghum Faba bean Chick pea Others (specify)

*Local units = Timad =1, Gasha = 2, Kert = 3, Gemed = 4, hectare = 5, Massa = 6 Estimate of 1 ______(local unit) = ______kg. ** Local units: Kuna, Silicha, Ensira, Akmada, Dawla, Tasa, others Estimate of 1 ______(local unit) = ______kg. Code a: Improved seed=1, Pesticides and herbicides=2, others (specify)=3 7. When did you start using - Fertilizer ------(year); Improved seeds------(years); Pesticides? ------(year).

8. Who supplies the inputs? ______Government (extension)=1; NGOs=2; Private dealers= 3; others (specify)=4 9. If you can recall, we would like to know the types of crops you cultivated, type of inputs you used and the amount of yield you obtained in the last 10 years.

Three years ago (1999E.C) Six years ago (1996E.C) Nine years ago (1993E.C)

Typeof crops Type of seed Improved=1 Local =2 Amountof fertilizer (Urea+DAP) kg Yield obtained Typeof crops Type of seed Improved=1 Local =2 Amountof fertilizer (Urea+DAP) Yield obtained Typeof crops Type of seed Improved=1 Local =2 Amountof fertilizer (Urea+DAP) kg Yield obtained (unit* ) kg (unit* ) (unit* )

* Local units: Kuna, Silicha, Ensira, Akmada, Dawla, Tasa, others Estimate of 1 ______(local unit) = ______kg. 10. Do you think that production per unit area has been declining since the last 10 years? Yes ------1, No ------2. 11. If yes, what do you think are the major reasons? Fertility decline=1; Water erosion= 2; Lack of inputs=3; Weather variability =4 Other reasons (specify)=5 PART IV. HOUSEHOLD PROFILE

Section A: Demography

We would like to ask you some questions about the family. N.B. Fill in the order of Head, spouse, children etc… ID 1. Name 2. 3. Age 4. Sex 5. Education 6. Marital 7. Member’s main Cod (permanent Relationship (Years) Male= 1 Illiterate=0 status activity e HH member) to the head Female=2 Literate= (Code b) For ages >8 (Code a) Grades:1,2,31 (Code c) … 1 2 3 Code a: Spouse/Husband =1; Daughter/Son =2; Father/Mother = 3; Sister/Brother = 4; Niece/Nephew= 5; Grand Child= 6;Grand parents=7 Others (specify)= 8 Code b: Single=1; Married=2; Divorced=3 Code c: Farm work= 1; Domestic work= 2 (in the house); Off-farm work=3 (skilled & unskilled

8. How long have you lived here in this village? For______years or since______regime.

1 If the person is still studying, put a mark‘s’ along with his grade. 98

Section B: Credit

1. Did you take any loan in cash or in kind in the last 2 years? Yes------1, No------2, Ques.#7

2. Source of 3. 4. Reasons for 5. Amount borrowed 6. Is loan paid back? loan which taking the Yes ------1 Friends or year did loan? In In kind Amount Unit* No ------2 neighbors=1 you Cash Bank=2 borrow?

* Local units: Kuna, Silicha, Ensira, Akmada, Dawla, Tasa Estimate of 1 ______(local unit) = ______kg.

7. Does anyone of the household member has a bank account? Yes------1, Id codes ______. No------2. 8. Did you give any kind of loan to others in the past two years? Yes ------1, No------2, Ques.#13

9. 10. Reasons 11. Amount lent 12. Is loan paid back? Yes ------Which for giving the --1 In Cash In kind Amount Unit year loan? No ------2 did For interest you gain=1 lend? For cooperation=2

13. Is any member of the household a member of ‘ekub’? Yes ------1, go to 14; No ------2, Next section.

14. 15. Total 16. How many 17. How much was paid out to you in the last five Household contribution in times do you months? member the last five contribute per ID code moths? month? How much?

Interview questions with Government offices A. DPPO 1. What are the impacts of climate change on crop production of west Shoa Zone? 2. Which areas are more vulnerable to the impact? 3. What is your role in prevention of socio-economic disaster caused by climate change and variability before and after the disaster? how? 4. What are your major challenges in alleviation of the problem and what should be done? 5. Is there any institutional level Coping mechanisms?

B.ARDO Officials/Development Agents/ Experts 1. What is the agro-ecology of your zone/district/peasant association? 2. Have you noticed any form of climate change in your zone or district? If your answer is yes, please can you explain? 3. If the answer to Q2 is yes, please would you like to explain the extent of climate change and variability? Impact on crop production your zone/district/peasant association? 4. What are the local coping mechanisms used to reduce the impacts at farm level? 5. What are the institutional level coping strategies to reduce future impacts? 6. What are the main challenges and how do you think they can be improved? 7. What are resources required for institutional level and farm level coping mechanism?

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