Assessing the Impact of Climate Change on Agricultural Production, Trade and Food Security in the East Africa Community: The Trade Model Miriam W. O. Omolo Kenya Institute for Public Policy Research and Analysis KIPPRA Working Paper No. 28 2017 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community KIPPRA in Brief The Kenya Institute for Public Policy Research and Analysis (KIPPRA) is an autonomous institute whose primary mission is to conduct public policy research leading to policy advice. KIPPRA’s mission is to produce consistently high-quality analysis of key issues of public policy and to contribute to the achievement of national long-term development objectives by positively influencing the decision-making process. These goals are met through effective dissemination of recommendations resulting from analysis and by training policy analysts in the public sector. KIPPRA therefore produces a body of well-researched and documented information on public policy, and in the process assists in formulating long-term strategic perspectives. KIPPRA serves as a centralized source from which the Government and the private sector may obtain information and advice on public policy issues. UNECA in Brief Established by the Economic and Social Council (ECOSOC) of the United Nations (UN) in 1958 as one of the UN’s five regional commissions, ECA’s mandate is to promote the economic and social development of its member States, foster intra-regional integration, and promote international cooperation for Africa’s development. Made up of 54 member States, and playing a dual role as a regional arm of the UN and as a key component of the African institutional landscape, ECA is well positioned to make unique contributions to address the Continent’s development challenges. Published 2017 © Kenya Institute for Public Policy Research and Analysis Bishops Garden Towers, Bishops Road PO Box 56445-00200 Nairobi, Kenya tel: +254 20 2719933/4; fax: +254 20 2719951 email: admin@kippra.or.ke website: http://www.kippra.org ISBN 9966 058 74 4 The Working Paper Series disseminates results of ongoing research at the Institute. The papers in the series cover specific policy issues in detail and are meant to provide policy makers and researchers with background information that can be used in developing refined discussion and policy papers. The papers are authored and reviewed by KIPPRA researchers and disseminated to provoke debate and solicit comments. This paper has been published under the KIPPRA/UNECA Project on Regional Assessment of Agricultural Production, Climate Change, Agricultural Trade and Food Security in the East African Community. ii Foreword Climate change and climate change variability is a threat to food production patterns, thus exacerbating food and nutrition insecurity across Africa. Therefore, tackling poverty, hunger and food security is a priority for the Africa Union Agenda 2063 which underscores the right of Africans to live healthy and productive lifes. Further, the African Union has set a target to eliminate hunger and food insecurity by 2025 towards achieving the Sustainable Development Goal (SDG) 2 on ending hunger, achieving food security and improving nutrition. Unfortunately, Africa is not on track in meeting these targets mainly because the region is not producing enough food due to climate change and low adoption of technology. However, climate change has variable impacts on food production, with both production losses and gains across the region. As a result, regional trade is critical for facilitating the distribution of agricultural products to enhance food security in the region. The East Africa Community (EAC) region is particularly vulnerable to climate change. The region is already experiencing increased climate change impacts, including extreme weather conditions, persistent drought, floods, and landslides and rising sea level which threaten food security and efforts to eradicate poverty. Despite the huge potential to produce enough food, the agricultural production system in the region is mainly rainfed, which consequently leads to high food and nutrition insecurity. Finding solutions to perennial food security challenges in the EAC is crucial and urgent as climate change impacts intensify in frequency and severity. Looking beyond just agricultural production systems is thus critical in tackling this peril. Thus, there is need to apply other approaches such as the nexus approach which allows for evaluating integrative systems where, for instance, trade facilitates food security in a changing climate environment. Although agriculture production is vulnerable to climate change, food security is not necessary a result of low production but a combination of other factors such as poor food distribution caused by perverse subsidies and other trade barriers. The EAC has been able to attain a common market status, which could facilitate trade in the region and thus mitigate food shortages. Despite the various measures and programmes adopted in EAC, some parts of the region continue to face food deficits due to restrictive trade policies and barriers to trade. Opportunities exist for adopting existing policy frameworks by member countries to address food security needs. iii Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Preface The project on Regional Assessment of Climate Change, Agricultural Production, Trade in Agricultural Production and Food Security in East African Community (EAC) was carried with support from the ACPC-CLIMDEV Work Programme. The ClimDev-Africa Programme is an initiative of the African Union Commission (AUC), the United Nations Economic Commission for Africa (UNECA) and the African Development Bank (AfDB). It is mandated at the highest level by African leaders (AU Summit of Heads of State and Government). The Programme was established to create a solid foundation for Africa’s response to climate change and works closely with other African and non-African institutions and partners specialized in climate and development. Over the last few years, our understanding and certainty about how climate is changing and the possible impacts this could have has grown immensely. This notwithstanding, agricultural production systems in the EAC region are highly vulnerable to climate change, consequently affecting food and nutrition security. The region is the most developed regional economic community (REC) in Africa, and cross border trade plays a critical role in facilitating food security. In response, the United Nations Economic Commission for Africa–African Climate Policy Centre (ACPC) is increasing its efforts to improve the capacity of EAC member states for mainstreaming climate change impacts in development policies, frameworks and plans. The three-year project was launched in May 2014 covering Burundi, Kenya, Rwanda, Tanzania and Uganda. The activities carried in this study were linked to the ClimDev-Africa Programme work stream II, which focuses on solid policy analysis for decision support, and was spearheaded by the Kenya Institute for Public Policy Research Analysis (KIPPRA). The overall objective of the project was to assess whether or not agricultural production systems and trade policies in EAC can be adjusted to alleviate the impact of climate change on food security, and promote sustainable development. The project outputs include pre-project report, country scoping studies, indepth EAC studies on climate change, crop production model, economic policy and trade and finally a comprehensive regional report. iv Acknowledgements The study was conceptualized and commissioned by the African Climate Policy Centre (ACPC), United Nations Economic Commission for Africa (UNECA), under the leadership of Dr Fatima Denton, Director of the Special Initiative Division, UNECA. Dr Tom Owiyo and Dr Johnson Nkem, senior experts at ACPC, guided the conceptual framing and provided oversight during implementation. Regular technical support was provided by ACPC researchers, Dr Wifran Moufouma Okia, Mr Nassirou Ba, Dr Habtamou Adessou, and research fellows Yosef Amha and Rivaldo. The study was conducted as a part of the activities of the Climate Change and Development in Africa (ClimDev-Africa) Programme supported by the UK Department for International Development (DfID), European Union Commission, Norway, Sweden, France, Nordic Development Fund, and the United States Agency for International Development (USAID). The Executive Director of KIPPRA and the Executive Secretary of UNECA would like to acknowledge the KIPPRA technical team comprising Nancy Laibuni (Project Coordinator), Dr August Muluvi, Dr Christopher Onyango, Mr John Nyangena, Mr Simon Githuku, and Mr Nixon Murathi; and the project consultants Dr Richard Mulwa, Dr Miriam Omolo, Dr Wilfred Nyangena, Prof. Caleb Mireri, and Dr Wellington Mulinge. In addition, we appreciate the Eastern and Southern Africa Region Office of the World Metrological Organization, led by Dr Elijah Mukhala and the consultants, Mr Nicholas Maingi and Dr Joshua Ngaina for their contributions to the project. The regional Partner Institutions included Economic Policy Research Centre (EPRC)–Uganda team lead by Dr Isaac Shinyekwa, Sokoine University–Tanzania team led by Prof. Siza Tumbo, University of Burundi team led by Dr Alex Ndayiragije, and Kigali Independent University team led by Mr Paul Muzungu. The participation of the stakeholders in various stages of the preparation of the report was highly valuable in enriching the report. The Economic Commission for Africa and KIPPRA would like to express their appreciation to all the government Ministries, State Departments and Agencies in Burundi, Kenya, Rwanda, Tanzania and Uganda for their active participation and providing the data and information used in preparing the report. v Executive Summary In Africa, there is a strong link between climate change, agriculture and food security. This is because most African countries rely heavily on climate for their agricultural production. When climate change affects agricultural production, it results in a situation where a country has less food to meet the needs of her people. To meet the demands, most nations tend to trade in order to bridge the deficit. In several cases, when deficits are not met, a country becomes food insecure. Unfortunately, there is limited expertise that provides empirical linkage between agricultural production and trade and food security. This can largely be attributed to the multi-disciplinary nature of investigating these three issues together. The scarcity of grains such as maize and wheat may trigger restrictive trade policies such as quantitative restriction of exports, safeguard or anti-dumping measures and tariff peaks. These trade restrictive measures are allowed under the World Trade Organization (WTO) multilateral trading framework under conditions where trade is an imminent threat to national food security. The linkage between climate change, trade and food security is important in achieving development at both national and regional level in cases where neighbouring countries have a strong integration framework as is the case with the East Africa Community (EAC). Sustained production of commodities that ensure food security requires predictable weather patterns. However, the EAC economies’ heavy reliance on climate-sensitive natural resources and rain- fed agriculture makes these economies very vulnerable to climate change. The Intergovernmental Panel on Climate Change (IPCC) 2007 reports that it is difficult to accurately predict consequences of climate change and the risks associated with it, even though impacts such as floods, drought and decline in crop yields are vividly observed. This study intends to bridge this gap by assessing the effect of climate change on trade and food security. The study made several key findings. First, climate change will reduce agricultural output of the EAC economies under investigation: Kenya, Uganda and Tanzania, with Uganda being the most adversely affected. Second, food poverty incidence will increase for Kenya due to reduced consumption associated with climate change. For Kenya, food poverty incidence will increase by 0.2 percentage points, which translates to 1,700 households moving into food poverty. Third, Rwanda is the only EAC partner state whose agricultural output will increase due to climate change. Agricultural output will increase by approximately 71 per cent, resulting in private consumption increasing by 37 per cent. This will result in food poverty incidence reduction from 26 per cent to 12 per cent. Consequently, over 336,000 households will move out of poverty. Climate change effects on output has varied effects on consumption and food poverty incidence. This largely depends on the crops that a county consumes. Even though Uganda’s agricultural output has decreased by more than 10 per cent, food poverty incidence decreased by 0.43 percentage points, since most of the crops affected by climate change do not constitute a large proportion of Ugandan household consumption basket. Similarly, the reduction in the production of maize and rice in Tanzania does not increase food poverty incidence in Tanzanian households. vii Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Abbreviations and Acronyms ACPC African Climate Policy Centre CET Common External Tariff CGE Computable Equilibrium Model CIF Cost Insurance and Freight CPC Central Product Classification EAC East African Community FAO Food and Agriculture Organization GATT General Agreement on Trade in Tariffs GDP Gross Domestic Product HS Harmonized Standards IFPRI International Food Policy Research Institute IPCC International Panel on Climate Change ISIC International Standard Classification of All Economic Activities KEPHIS Kenya Plant Health Inspectorate Services MFN Most Favoured Nation NTB Non-Tariff Barrier SAM Social Accounting Matrix SPS Sanitary and Phytosanitary Measures SSA Sub-Saharan Africa TAM Trade Analysis Model UNECA United Nations Economic Commission for Africa UNFCCC United Nations Framework Convention on Climate Change WMO World Meteorological Organization WTO World Trade Organization viii Table of Contents Foreword ............................................................................................................................... iii Preface ...................................................................................................................................iv Acknowledgements .................................................................................................................v Executive Summary ..............................................................................................................vii Abbreviations and Acronyms ..............................................................................................viii 1. Background .....................................................................................................................1 1.1 Introduction ............................................................................................................1 1.2 Climate Change, Trade and Food Security .............................................................3 1.3 The East Africa Community (EAC) in Historical Perspective................................3 1.4 Study Objectives......................................................................................................4 1.5 Scope .......................................................................................................................5 2. Regional and National Agricultural Trade Policies .......................................................6 2.1 Overview of EAC Economies ..................................................................................6 2.2 Trade Policies and Programmes Relating to Agriculture in the EAC ....................9 2.3 Internal Tariff Elimination ...................................................................................10 2.5 Trade Related Aspects ..........................................................................................12 2.6 Export Promotion Schemes ..................................................................................13 2.7 Implications of Trade Policies on Agricultural Trade and Food Security ........... 13 3. Trade in Agricultural Products ..................................................................................... 15 4. Methodology ................................................................................................................ 17 4.1 Introduction .......................................................................................................... 17 4.2 Conceptual Framework ........................................................................................ 17 4.3 Empirical Models ................................................................................................. 20 4.4 Data .......................................................................................................................25 5. Results and Discussion ................................................................................................ 38 5.1 Farm Household Analysis .................................................................................... 38 ix Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community 5.2 Agricultural Production .......................................................................................41 5.3 Trade .....................................................................................................................45 5.4 Consumption and Food Poverty Impacts ............................................................ 50 6. Conclusion and Policy Recommendations ...................................................................53 References .............................................................................................................................54 Appendix ...............................................................................................................................56 x List of Tables Table 2 1: Trade policies affecting agriculture trade at the EAC .............................................................11 Table 4.1: Percentage change in maize production due to climate change (APSIM) .........................26 Table 4 2: Descriptive statistics .................................................................................................................27 Table 4 3: EAC agricultural commodity production ...............................................................................33 Table 4 4: EAC Agricultural Commodity Consumption ........................................................................36 Table 5 1: Results of yield change due to unit change in temperature and rainfall (Kenya)..............39 Table 5 2: Results of yield change due to unit change in temperature and rainfall (Tanzania) ........39 Table 5 3: Results of Yield Change due to Unit Change in Temperature and Rainfall (Uganda) .... 40 Table 5 4: Results of Yield Change due to Unit Change Temperature and Rainfall (Rwanda) ........41 Table 5 5: (%) Impact of Climate Change on Agricultural Output - (Kenya) ......................................42 Table 5 6: (%) Temperature and rainfall pattern and variability changes - (Tanzania) .....................43 Table 5 7: (%) Temperature and Rainfall Pattern and Variability Changes - (Uganda) ....................44 Table 5 8: Temperature and Rainfall Pattern and Variability (%) Changes - (Rwanda) ...................45 Table 5 9: Demand Response to Output Changes (%) - Kenya .............................................................46 Table 5 10: Demand Response to Output Changes (%) - Tanzania. .....................................................47 Table 5 11: Demand Response to Output Changes (%) - Uganda ........................................................ 48 Table 5 12: Demand Response to Output Changes (%) - Rwanda .......................................................49 Table 5 13: Intra-EAC Exports of Maize (‘000 USD) .............................................................................50 xi Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community List of Figures Figure 2 1: Real GDP indicators for East Africa Community ................................................7 Figure 2 2: Real GDP in EAC partner states (million US$) .................................................. 8 Figure 2 3: Real GDP per capita ............................................................................................ 8 Figure 2 4: Real GDP growth rate ..........................................................................................9 Figure 3 1: Total trade in the EAC (million US$) ................................................................ 15 Figure 3 2: Intra-EAC agricultural trade as a proportion of total EAC trade ..................... 16 Figure 3 3: EAC partner states total trade in selected commodities (million US$) ........... 16 Figure 4 1: Food security indicators ..................................................................................... 17 Figure 4.2: Climate change, trade and food security conceptual framework ..................... 19 Figure 4 3: Nested structure of production .........................................................................22 Figure 4 4: Mean rainfall and variation for EAC countries .................................................31 Figure 4 5: Mean temperature and variation for EAC countries .........................................32 Figure 5 1: Food Poverty Impacts (Simulation Scenarios) .................................................. 51 Appendix 1: Total EAC Trade By Partner State (000’ USD). ..............................................56 Appendix 2: Intra EAC Trade as a proportion of Total Trade .............................................56 xii 1. Background 1.1 Introduction Climate change is the overall change in a region’s weather pattern. This includes precipitation, temperature, and cloud cover. In Africa, there is a strong link between climate change, agriculture and food security. This is because most African countries heavily rely on climate for their agricultural production. When climate change affects agricultural production, it results in a situation where a country has less food to meet the needs of her people. In order to meet food demands, most nations tend to trade in order to bridge the deficit. But when this deficit is not met, a country becomes food insecure. It is important to establish the links between climate change, agricultural production and trade and food security. Unfortunately, there is limited expertise that provides empirical linkage between agricultural production, and trade and food security. This can largely be attributed to the multi-disciplinary nature of investigating these three issues together. Climate change can either affect the production of commodities such as maize, beans, wheat, vegetables, and sugarcane either negatively or positively depending on the climatic conditions that these crops require. At the same time, the supply of these crops is important in determining the food security status of a nation. As already stated, most African economies heavily rely on climate for their agricultural production. Therefore, change in climatic patterns tend to affect agricultural production, which in turn affects food security. The concept of food security has been used flexibly in research and policy arena. International organizations such as the World Bank, Food and Agricultural Organization (FAO) and World Food Summit have provided contrasting definitions. The study will use the working definition given by FAO (2003): “When all people, at all times have physical, social and economic access to sufficient, safe and nutritious food which meets their dietary needs and food preferences for an active and healthy life” Food insecurity exists when people do not have adequate physical, social or economic access to food. Trade policies, on the other hand, involve a combination of measures such as domestic support measures, export subsidies and tariffs which affect relative prices of both traded and non-traded commodities in any economy which in turn affects the allocation of resources. These changes tend to alter sectoral and aggregate output, which in turn leads to changes in income levels. 1 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community In situations where a nation experiences food shortage or there is an imminent risk of food shortage, governments tend to use a combination of policies to mitigate the risks or increase food supply. There is, however, limited empirical evidence on the impact of climate change on agricultural production and trade patterns and its impact on food security. Attempting to establish this impact requires a multifaceted approach where climate change simulations are carried out on its impact on agricultural production and how this affects agricultural trade and food security. As a result, the African Climate Policy Centre (ACPC) at the United Nations Economic Commission for Africa (UNECA) has put in place an initiative that will ensure that member states and regional economic blocs mainstream climate change impacts in their development policies, frameworks and plans. ACPC seeks to develop analytical works that seek to inform the linkages between climate change, agricultural production and trade and food security. The link between climate change and agricultural systems, trade and food security presents three components that are largely interlinked. However, from an analytical perspective, these links can be modeled as different components, which feed into each other in order to establish impact. The ACPC project has three components: (i) The Crop Model: Explores the medium and long-term spatial effects of climate change on agricultural production and food security in the EAC region. (ii) The Economic Policy Model: Explores the Implications on regional agricultural policy across political boundaries, within and across national boundaries. (iii) Trade Analysis Model (TAM): Examines the potential trade effects of climate change on food security in the EAC region. This study focuses on the trade effects of climate change on food security. It seeks to establish the evidence on the impact of climate change on agricultural production, trade and food security in order to inform policies that will result in effective climate change adaptation frameworks that ensure food security in the East Africa Community. From the Trade Analysis Model simulations, it will be possible to establish how the climate change affects agricultural trade and food security. This exercise will provide appropriate trade policy actions that can be used in mitigating food security challenges arising from climate change. 2 Background 1.2 Climate Change, Trade and Food Security Food security is a complex matter and is not solely an agricultural issue. Food security emanates from complex interactions of different sectoral players, making it a cross cutting issue. Following the FAO (2003) definition of food security, there are four dimensions to this phenomenon: availability, accessibility, utilization and stability. These dimensions are affected by different factors; for example, climate change has increasingly become a key determinant of agricultural production in most countries in Sub-Saharan Africa (SSA), the changing weather patterns tend to be detrimental to crops such as grains and pulses, which are important for food security. There are also trade policies (in most cases the absence of tariff and non- tariff barriers) that affect the availability of agricultural commodities that may stimulate economic growth through growth in exports, which in turn increase incomes. This increases household’s capacity to access more food. The presence of tariff and non-tariff barriers results in decreased access to food, which results in food insecurity. The international panel on climate change (IPCC, 2014) notes that there have been more negative impacts of climate change on crop and terrestrial production than positive impacts. Without adaptation, local temperature increases of 10c is likely to negatively impact the yields of key crops such as maize and wheat, resulting in the disruption of agricultural systems and production. The scarcity of grains such as maize and wheat may trigger restrictive trade policies such as quantitative restriction of exports, safeguard or anti-dumping measures, and tariff peaks that affect food security of a nation. These trade restrictive measures are allowed under the World Trade Organization (WTO) multilateral trading framework under conditions where trade is an imminent threat to national food security. The linkage between climate change, trade and food security is important in achieving development at both national and regional level in cases where neighbouring countries have a strong integration framework as is the case with the East Africa Community (EAC). 1.3 The East Africa Community (EAC) in Historical Perspective The EAC owes its existence to the construction of the Kenya-Uganda railway. Kenya and Uganda formed a customs union in 1917, Tanzania (then called Tanganyika) joined in 1927. From 1948 to 1961 there existed the East African High Commission, the East Africa Common Services Organization (1961-1967) and the East Africa Community from 1967 with the final breakup taking place in 1977. In 1984, member states of the EAC negotiated and signed a mediation agreement on the division of the assets and liabilities of the EAC. Subsequent 3 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community meetings between the three heads of states led to the signing of the agreement that established the permanent tripartite commission for the East African Cooperation on 30 November 1996. The full EAC operations started with the launch of the secretariat in Arusha on 14 March 1996. On 7 July 2000, the treaty establishing the EAC came into force and in March 2004, the EAC summit made up of the three heads of state signed the protocol establishing the EAC Customs Union. The Republic of Rwanda and Burundi acceded to the EAC in June and July 2007, respectively, and joined the EAC Customs Union in July 2009. EAC made strides in deeper integration by ratifying the common market protocol in July 2010 and the protocol establishing the EAC monetary union in November 2013. The EAC is made up of five partner states (Kenya, Uganda, Tanzania, Rwanda and Burundi), with common languages and cultures particularly where borders are shared. This implies that there are certain foods that are common in the partner states such as grains, pulses and vegetables, among others. The productive sector therefore forms the cornerstone of livelihoods of the EAC citizenry since it employs majority of the population. Sustained production of commodities that ensure food security requires predictable weather patterns. However, the EAC economies heavy dependance on climate-sensitive natural resources, and rain-fed agriculture makes these economies very vulnerable to climate change. The Intergovernmental Panel on Climate Change (IPCC, 2007) reports that it is difficult to accurately predict consequences of climate change and the risks associated with it, even though impacts such as floods, drought, and decline in crop yields are vividly observed. This study intends to bridge this gap by assessing the effect of climate change on trade and food security. 1.4 Study Objectives The TAM model will seek to achieve the following objectives: (i) Establish the regional and national agricultural and trade policies within EAC region; (ii) Establish the agricultural commodities trade patterns at the regional and national level within the EAC; (iii) Establish the impact of climate change on agricultural commodity trade patterns and food security within the EAC using Computable General Equilibrium (CGE) and micro simulation models; and (iv) Provide policy recommendations. 4 Background 1.5 Scope The TAM component will focus on the four East African Community (EAC) partner states: Kenya, Uganda, Tanzania and Rwanda. Burundi is excluded due to data issues. 5 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community 2. Regional and National Agricultural Trade Policies 2.1 Overview of EAC Economies The East African Community (EAC) comprises five partner states, namely Kenya, Uganda, Tanzania, Rwanda and Burundi. The region has a total area (including water) of 1.82 million square kilometres and located 5030”N and 120S latitude, and 28045”E and 41050” E longitudes. Of the total surface area, excluding water bodies, Tanzania occupies 939,000 square kilometres followed by Kenya (583,000), and Uganda (241,600) while Rwanda and Burundi are the smallest countries occupying 26,300 and 27,800 kilometres square, respectively. The EAC has a population of 149.7 million people with Tanzania being the most populous country of the EAC partner states having a population of 48.8 million. Kenya and Uganda have populations of 44.2 and 35.5 million, respectively, and Rwanda 11.2 million and Burundi 10 million. In contrast, Rwanda and Burundi have much higher population densities of 445 and 379 people per square kilometre. The other partner states, Kenya, Uganda and Tanzania have densities of 76, 177 and 55 persons per square kilometre, respectively. Uganda’s population density is likely to increase faster given the high population growth rate of 3.6 per cent. Kenya’s population density is likely to remain low given the low population growth rate of 2.9 per cent per annum. The overall real GDP of the EAC has been on the rise and showing an upward trend (Figure 2.1a), driven by growth in Kenya, Uganda and Tanzania. Real GDP per capita has been on the rise for the EAC and as at 2015, it stood at US$ 813 per capita. Real GDP per capita growth is a good indicator of individual incomes and can be used to forecast future demand. However, distribution of the GDP is also important as it tells where most of the income is concentrated. Real GDP per capita growth has remained mixed in the EAC. In 2010, the growth rate went up to 3.9 per cent but late declined to 0.6 per cent in 2011 and in 2015 there was a major decline in growth rate to 0.2 per cent. The combined Gross Domestic Product (GDP at current prices) for the EAC region is US$ 122 billion of which Kenya has US$ 41.2 billion, Tanzania US$ 42.3 billion, Uganda US$ 28.3 billion, Rwanda US$ 8.2 billion and Burundi US$ 1.7 billion. In terms of GDP growth trends (Fig 2.2) Tanzania, Uganda and Rwanda growth trends are increasing as compared to Kenya and Burundi. However, the growth rates remain mixed and not consistent as shown in Figure 2.4. Tanzania, Uganda and Rwanda are showing consistent real GDP per capita growth rates compared to Kenya and Burundi whose growth trends remain eratic. 6 Regional and national agricultural trade policies Figure 2 1: Real GDP indicators for East Africa Community (a) Real GDP (billion US$) (b) Real GDP per capita (US$) (c) Real GDP growth rate (d) Real GDP per capita growth rate Source: EAC (2016) 7 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Figure 2 2: Real GDP in EAC partner states (million US$) Source: EAC (2014) Figure 2 3: Real GDP per capita Source: EAC (2014) 8 Regional and national agricultural trade policies Figure 2 4: Real GDP growth rate Source: EAC (2016) 2.2 Trade Policies and Programmes Relating to Agriculture in the EAC Trade policies in the EAC can be largely categorized as tariff and non-tariff policies. These policies are ratified at the regional level (EAC) and implemented by each partner state. The protocol that established the EAC Customs Union was signed on 30 November 2004 and came into force on 1 January 2005. The objectives of the Customs Union as set out in Article 3 are: • Further liberalize intra-regional trade in goods on the basis of mutually beneficial trade arrangements among the partner states; • Promote efficiency in production within the Community; • Enhance domestic, cross border and foreign investment in the Community; and • Promote economic development and diversification in industrialization in the Community. Article (4) defines the activities under which the protocol will apply. These include: Trade liberalization–removal of tariff and non-tariff barriers; Trade related aspects–largely simplification of customs procedures; trade remedies; national and joint institutions training programmes; and production and sharing 9 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community of customs statistics and export promotion. Article 4(2) stipulates the areas of cooperation in the Customs Union, which includes harmonization of tariffs, standards and customs procedures. 2.3 Internal Tariff Elimination Article (11) provides for the elimination of internal tariffs among the partner states. At that time, since there were only three countries, Kenya Uganda and Tanzania, it was agreed that for the purpose of transition into a Customs Union while taking cognizance of the principle of asymmetry, goods from Uganda and Tanzania into Kenya shall enter duty free, while goods from Kenya into Uganda and Tanzania would be categorized as those eligible for duty free treatment (category A) and those eligible for gradual tariff reduction (Category B). These goods (Category B) would be eliminated for a period of five years as follows: • 10 per cent first year • 8 per cent second year • 6 per cent third year • 4 per cent during the fourth year • 2 per cent during the fifth year • 0 per cent thereafter These tariff reductions were implemented from 2005 to 2010. Currently, the EAC partner states have been able to eliminate internal tariffs among themselves. 2.4 The EAC Common External Tariff Article 12(1) established a three-band common external tariff with a minimum rate of 0 per cent (raw materials), middle rate 10 per cent (intermediate goods) and 25 per cent (manufactured or processed goods). These CET tariffs are charged on third world countries importing to the EAC. There are exemptions to the CET where third world countries importing to the EAC are charged above the set CET. These products are known as the sensitive products; they are products which are of special interest to a country since they allow a country to protect their local industries at nascent stage of development, food security or because of their importance as a source of national revenue. Development of the sensitive list of products has remained controversial for the partner states since it has also been used as a non-tariff barrier. Some of the common sensitive products include maize, beans, rice, milk, tea, and coffee, among others. A conclusive sensitive list 10 Regional and national agricultural trade policies of products has not been fully attained in the EAC. Article (13) of the protocol stipulates the immediate removal of all forms of non-tariff barriers (NTBs) to importation of goods originating from partner states. Elimination of NTBs remains a challenge as new barriers come into play while old ones are removed. By end of 2012, EAC had 22 unresolved NTBs, 8 new NTBs and 69 resolved NTBs (EAC, 2014). Summaries of the key trade policies (and key features) that affect agricultural production and trade that are being implemented at the regional level are presented in Table 2.1. Table 2 1: Trade policies affecting agriculture trade at the EAC Trade Policies Features Customs Addressed in the Customs Management Act Procedures and The objective is to standardize and harmonize the customs Documentation formalities (documentation and procedure) in the member states for all commodities or products Customs Procedures Manual was adopted by EAC Council of Ministers and application commenced in 2012/13 Customs Valuation Procedure applied to assign monetary value to goods or service for the purposes of import or exports Incorporated in the EAC Customs Management Act 2004 Based on the implementation of the WTO Agreement on the implementation of Article VII of GATT 1994 on customs valuation Rules of Origin Used to determine the country of origin of a product within multilateral or regional trade framework Set up in Annex III of the Protocol on the Establishment of the EAC Customs Union Goods are defined as originating from a country if They are wholly produced or Undergo substantial transformation–import content of good is no more than 60% of c.i.f value of material used for value added Change in tariff heading Tariffs and Other MFM Applied Tariff structure duties EAC Common External Tariff (CET) Raw materials and capital goods are zero-rated Intermediate goods is 10% Finished goods 25% Sensitive products apply 35-100%, this apply to 58 tariff lines CET contains 5,274 lines at HS8-digit level. 99.8 % carry ad valorem while the rest have mixed tariffs Tariff Preferences EAC members can grant tariff preferences on reciprocal basis under bilateral agreements Tariff and Tax Under customs union protocol, members have agreed to Exemptions and harmonize their duty and tax exemptions and concessions Concessions The EAC Council on a case-by-case basis also grants country- specific waivers 11 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Internal Taxes Under EAC Common Market Protocol, members have agreed to harmonize their tax policies and laws on domestic taxes This will remove tax distortion and facilitate free movement of goods, services and capital in order to promote investment in the community Contingency Contingency measures found in Article 16-20 and 24 on the Measures Protocol Establishing the EAC Customs Union These contingencies include anti-dumping, countervailing and safeguards measures Import Provided under the Second Schedule of the EAC Customs Prohibitions, Management Act, 2004 Restrictions and EAC member states have a schedule of prohibited products Licensing Import permit is required for 31 product groups under the second schedule Standards Article 13 on Protocol Establishing the EA Customs union urges and Technical removal of non-tariff barriers (NTBs) Requirements Catalogue of East African Standards provides a comprehensive list of harmonized standards applicable to EAC Sanitary and Provides for harmonization of SPS measures Phytosanitary This agreement adheres to the WTO-SPS agreement Measures (SPS) Documentation These documentation requirements for exports Taxation and Addressed in the Customs Management Act Restrictions Competition and Article 21 of Customs Union Protocol obliges EAC member states Regulatory Issues to prohibit anti-competitive behaviours EAC Competition Act was enacted in 2006 and established the EAC Competition Authority Intellectual Addressed in Article 103 of the EAC Treaty and Article 104 of the Property Rights EAC Common Market Protocol This sets up the framework for the harmonization of EAC intellectual Property Rights Policies Agriculture The treaty establishing the EAC emphasized the importance of agriculture and food security, and made it a key cooperation area Several regional policies have been developed: Agriculture and Rural Development Policy Agriculture Rural Development Strategy EAC Food Security Action Plan Regional Protocol on Environment and Natural Resource Management (2006). Source: Authors compilation from WTO (2012) 2.5 Trade Related Aspects Trade related aspects deal with different issues that affect trade or trade facilitation. Key among them that affect agricultural trade include: 12 Regional and national agricultural trade policies • Rules of origin – this is a criterion for selecting goods that are eligible for community tariff if they originate from the partner states. Article (14) specifies the criteria set out to establish the rules of origin. • National treatment – the partner states must ensure equal treatment of like products of other partner states. • Trade remedies – these include antidumping, subsidies and countervailing and safeguards measures and how partner states should handle them in relation to third countries and among themselves. 2.6 Export Promotion Schemes The export promotion schemes under the EAC are meant to accelerate development, promote and facilitate export oriented investments, production of export competitive goods, developing an enabling environment for export promotion schemes and attracting foreign direct investment. There are several schemes in place to promote the set-out objectives; these include: duty draw backs, tax remission, manufacturing under bond and export processing zones. 2.7 Implications of Trade Policies on Agricultural Trade and Food Security The most common policies that have been used in the EAC are export/import bans/lift. In May 2011, the Government of Tanzania issued export restrictions on grains in order to safeguard the economy from depleting the existing stock that was causing inflation in the country. Several parts of the country lacked food supply, and therefore this move was to divert cereals into the domestic market so that excess supply would lead to reduced prices. Compete (2011) note that this ban did not effectively curtail cross border trade due to parallel markets through Democratic Republic of Congo, Malawi, Zambia and Kenya borders. This approach by Tanzania can be termed as a self-sufficiency approach to food security since the country was attempting to be able to meet consumption needs (particularly of staple foods) through buying rather than importing (IFPRI, 2010). In 2009, Kenya experienced food shortage following the 2008 drought; the government initiated a lift on maize import ban in order to meet the local demands through importation and subsidizing maize milling prices. This approach to food security where food is imported from the world market when prices are cheaper that growing it at home is termed as self- reliance (Deb et al., 2009). It ensures that countries produce where they have comparative advantage. It tends to promote importation and cross border trade in 13 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community general. Consequently, in 2008 and 2009, Kenya was the largest maize importer from the EAC region. Trade policies that promote cross border trade in order to ensure food security as was the case with Kenya have resulted in increased trade as compared to restrictive trade policies geared towards food security as was the case in Tanzania. However, this restriction still resulted in higher prices of staple foods and worsened inflation. Sanitary and Phytosanitary (SPS) measures are normally put in place to protect plants and animals from the risks of entry or spread of pests, diseases, disease- carrying organisms or disease causing-organisms or protect human or animal life from risks of additives, contaminants, toxins or disease-causing organisms in foods, beverages or feedstuffs or any other damages. These standards tend to restrict trade since they are not easy to administer when there are no clear standards or standards are seasonal. SPS become trade restrictive when they are administered in a non-transparent manner or when used by country authorities to prohibit imports of certain commodities without providing the scientific evidence that are required when doing so. Standards and technical requirements are non-tariff barriers that are also used to ensure that commodities that enter a country meet a certain criterion for standards. Like SPS, they can also be abused by countries and become a hindrance to trade. Currently, Kenya through KEPHIS (Kenya Plant Health and Inspectorate Services) has imposed charges on plant import permit at Malaba border posts for tea from Uganda and Burundi that are destined for auction at the Mombasa port. While this is a legal requirement (national level) for tea destined for Mombasa auction, this raises the cost of doing business for the exporters from Uganda and Burundi. Consequently, lower profit margins result in lower incomes for tea farming households, which increases the risk to food insecurity since these households are selling their tea at much lower prices. At the same time, the Ministry of Agriculture in Kenya does not recognize the SPS certificates issued by Ugandan authorities for tea destined for Mombasa. This equally raises the cost of doing business since more certification is required; the ultimate outcome is reduction in prices received by farmers. The EAC Customs Union has a common external tariff for commodities with three tiers (0 (raw), 15 (intermediate) and 25 (manufactured) per cent). The implementation of this CET has seen the increase in intra-EAC trade (Figure 3.1). Total intra EAC trade has gradually been on the rise since 2005 in terms of value. Increased trade in cereals, which are important for food security, brings in the element of self-sufficiency since partner states buy commodities where they have comparative advantage. 14 3. Trade in Agricultural Products Total trade (exports and imports) has been on the rise within the EAC. In 2001, total intra-EAC trade was valued at US$ 944,000 and this value increased to 1.9 million in 2004. After the coming into force of the Customs Union, intra- EAC trade increased to US$ 2.3 million. After 2006, intra-EAC trade has been gradually rising and in 2015 it was valued at US$ 5,636 million. Intra-EAC trade in agricultural commodities has been rising; in 2001, agricultural commodities trade was US$ 523 million, rising to to US$ 948 million in 2006 and currently stands at US$ 558 million. Total EAC agricultural trade as a proportion of total EAC trade has remained mixed, ranging between 13 per cent in 2006 to 10 per cent in 2015 (Figure 3.2). One of the main factors that affected agricultural trade was drought that was experienced in 2008. Appendix figures 1 and 2 further provide disaggregated data for tables 3.1 and 3.2 each of the partner states. Maize, millet and rice are the most traded commodities in the EAC as shown in Figure 3.3. The value of total trade (export + imports) has been rising for maize, but with a series of fluctuations particularly in 2013. Wheat, on the other hand, is the lowest ranked commodity traded within the EAC when one reviews cereals under 2-digit HS heading 10. Appendix tables 1-4 provide exports and imports (respectively) that each EAC partner state trades within the EAC. Further to this, detailed analysis of trends for agricultural commodity trade in the EAC is presented in the EAC Scoping Report, which constitutes a component of this project “Assessing the Impact of Climate Change on Agricultural Production, Trade and Food Security in the East Africa Community (EAC).” Figure 3 1: Total trade in the EAC (million US$) Source: ITC Database 15 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Figure 3 2: Intra-EAC agricultural trade as a proportion of total EAC trade Source: ITC Database Figure 3 3: EAC partner states total trade in selected commodities (million US$) Source: ITC Database 16 4. Methodology 4.1 Introduction This section presents the theoretical framework that is used to analyze the impact of climate change on agricultural trade and food security. Here, we review food security indicators and how they link to climate change and national policies. The methodological framework provides a step-by-step process of how to move from climate change to agricultural production, trade and lastly how food security is affected. This involves the use of farm household analysis to obtain productivity impacts of climate on key crops identified by partner states as important for food security, simulating the impacts of the productivity on consumption using a CGE model, and lastly simulating the impacts of consumption on food security using a micro-simulation model. 4.2 Conceptual Framework Food insecurity exists when people do not have adequate physical, social or economic access to food. Trade policies, on the other hand, involve a combination of measures such as domestic support measures, export subsidies and tariffs which affect relative prices of both traded and non-traded commodities in any economy, which in turn affects the allocation of resources. These changes tend to Figure 4 1: Food security indicators Source: FAO (2012) 17 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community alter sectoral and aggregate output leading to changes in income levels. Positive changes in income levels will result in food security while negative changes in income will result in food insecurity. Following the FAO (2003) definition of food security, there are four dimensions to this phenomenon: availability, accessibility, utilization and stability. FAO (2012) launched a set of indicators that can be used to establish the status of food security in a country. The indicators are largely grouped into two distinct dimensions: First, those that describe the determinants of food security. These indicators describe the structural conditions of a country that worsened food security. These conditions can be improved by appropriate public policies. These include food production indices, physical access to foods through road or rail networks, food price indices and access to water and sanitation services. The second dimension is the outcome which tends to capture the consequences of food insecurity irrespective of the policies in place – these include undernourishment, share of food expenditure of the poor, per centage of children under 5 with stunted or wasted growth, among others. The final set of indicators capture conditions that capture future food insecurity vulnerabilities – these include food price volatility, food production and supply variability and food dependency ratios, among others. 4.2.1 Linking Climate Change, Agricultural Trade and Food Security Climate change has direct impact on agricultural trade policies as can be seen in situations where there is a food crisis due to change in weather patterns. Trade measures such as safeguard mechanisms tend to be used to protect a country against the imminent threat of food insecurity. The United Nations Framework Convention on Climate Change (UNFCCC) and the World Trade Organization (WTO) governs climate change and agricultural trade. The 1997 Kyoto protocol seeks to improve human welfare. Similarly, the WTO also seeks to improve human welfare through international trade. Food security is a common expected outcome for both climate change mitigation initiatives under UNFCC and the WTO agricultural trade negotiations. The international panel on climate change (IPCC, 2014) notes that there have been more negative impacts of climate change on crop and terrestrial production than positive impacts. Without adaptation, local temperature increases of 10c is likely to negatively impact the yields of key crops such as maize and wheat, resulting in the disruption of agricultural systems and production. The scarcity of grains such as maize and wheat may trigger restrictive trade policies such as quantitative 18 Methodology Figure 4.2: Climate change, trade and food security conceptual framework Source: Adopted from Scavione (2010) restriction of exports, safeguard or anti-dumping measures and tariff peaks that ensure food security of a nation. There are also trade policies (the absence/presence of tariff and non-tariff barriers) that affect the availability of agricultural commodities. These policies may stimulate economic growth through growth in exports which in turn increase incomes. Increases in incomes of households has the direct impact of increasing household expenditure on food hence a household has capacity to access more food. The presence of tariff and non-tariff barriers result in decreased access to food which results in food insecurity. Trade restrictive measures are allowed under the World Trade Organization (WTO) multilateral trading framework under special conditions where trade becomes an imminent threat to national food security. The trade policy tools that have largely been used in the EAC are the export/import bans (restrictions) /lifts. Export ban has a direct implication on food self-sufficiency since it encourages local production to meet consumption needs. Lifting of import/export bans encourages trade and importation of cheaper commodities to meet local demand; this encourages production where there is comparative advantage. It brings out the self-reliance approach to food security. The linkage between climate change, trade and food security is important in achieving development at both national and regional level in cases where neighbouring countries have a strong integration framework as is the case with the East Africa Community (EAC). 19 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community 4.3 Empirical Models 4.3.1 Farm Household Analysis Most farm households in developing countries rely heavily on weather conditions for their agricultural production as a major input; therefore, climate determinants such as temperature, and humidity are key inputs to agricultural production. Variability in climate therefore affects both animals and crop production which in turn affects productivity. The relationship between households and agricultural productivity can be stated as a production function (1). qi = f(xi,βi)+εij (1) Where q is a vector of outputs and x is the vector of inputs which include climate change variables, βj is a vector of coefficients to be estimated while εij is the error term. Equation (1) is therefore an increasing function with respect to output and a decreasing function with respect to inputs. Molua and Lambi (2007) used a structural approach that links the crop model which measures crop yield changes under different climatic conditions to economic model of the agricultural sector which estimates the changes in acreage and supply and its impact on market clearing prices and consumer and producer welfare. In their model, agricultural production capability is assumed to be restricted to exogenous climate and other socio-economic variables; therefore, the farmers maximize their returns subject to critical outputs and environmental factors. They regress farm value on climate and other socio-economic variables with a non-linear function. Matovu (2013) used a similar model to that of Molua and Lambi (2007) in establishing climate change impacts. They regressed yield in tonnes per hectare on both climate and socio-economic variables which include rainfall, temperature, rainfall and temperature variation and physical inputs such as family labour. In their case, they used a linear functional model. Temesgen et al. (2009) used a different approach in establishing the climate impacts on households. In their model, the welfare of a household is measured by farm income and is affected by observable household characteristics (household size, location, educational attainment of the household head, etc) and climatic shocks (droughts, floods and hailstorms). The main assumption in their model was that when a household experiences climatic shocks such as drought, flood and/or hailstorms, there is an increased probability of this household falling below a given consumption/income level, or forced it to stay under such a level if already below it. Matovu (2013) in establishing the climate change effects on yield, used equation (2). 20 Methodology Yij=α0+α1Rij+α2Tij+α3Rvij+α4Tvij+β1Lij+β2Mij+β3Kij+εij (2) Where yij is yield in tons per hectare (t/ha) for crop j for farmer i; R is mean rainfall in millimeters; T is mean temperature in degrees Celsius obtained from the World Metrological Organization (WMO); Rv is rainfall variation; and Rt is temperature. Both Rt and Rv were measured as the standard deviation from the mean, following evidence from literature by Skoufias et al (2011) and Matovu (2013). This data was also obtained from WMO. The letters L, M and K represent the physical inputs of family labour–measured in person days, manure in kilogrammes and capital –which is an aggregation of the cost of purchased inputs (for example land rent, seed, fertilizer, and pesticides) used in production of a particular crop. In case the crop is produced under the intercropping system, the inputs are determined according to the proportion on area under the crop. The choice of dependent variables and functional forms depend on the question under study. In this study, the objective was to establish the effect of climate variables temperature, rainfall, temperature and rainfall variation on yield. In order to obtain changes that could be simulated in a CGE model, equation (2) is transformed into a log-linear model (equation 3) in order to obtain the per centage changes in yield associated with the climate variables. The percentage changes in the yield were used to shock the efficiency variable in the production function of the CGE model; this is explained in the subsequent section. Log(Yieldij )=α0+α1 Rij+α2 Tij+α3Rvij+α4Tvij+β1Lij+β2Mij+β3 Kij+εij (3) 4.3.2 The Computable General Equilibrium (CGE) Model CGE models provide an analytical approach that views the economy as a system of interdependent sectors of the economy. In this framework, economic shock emanating from one sector creates ripples in other sectors. Secondly, it is possible to undertake quantitative analysis by solving the general equilibrium numerically, hence one can undertake economy-wide analysis at global or even regional level. The CGE model can handle a broad spectrum of issues such as taxation, trade, pollution, welfare etc, and it is equally possible to establish forward and backward linkages between sectors. This model is therefore appropriate for establishing the climate change, agricultural production and trade impacts on food security. This is because the model will be able to simulate the functioning of the economy under certain climatic conditions which affect productivity and total production. These effects are transmitted through price and quantity adjustments in the various markets. Secondly, given that a CGE is based on a social accounting matrix, it will 21 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community be possible to establish the effects of climate change on different sectors of the economy. Linking the model to household survey enables an indepth assessment of household welfare effects due to climate change; it is from here that the policy implications are drawn. It should be noted that this is a static equilibrium model which seeks to establish the effects of climate change on production, trade and household welfare at a given point in time. The time here is based on data sources as explained in section 4.4.3. The CGE model will follow the works of Löfgren et al. (2001). There are four blocks of equations: prices, production and trade, institutions and the systems constraints block. Calibration of the model will be based on working by Lofgren et al. (2002). In the CGE model, there are parameters, variables and equations that are defined. In this paper, the key equations that are affected by climate change are presented and discussed. It is important to show the structure of production and how it is affected by changes in yield. Production takes a nested structure as presented in Figure 4.3. Total output is a CES function of total value added and aggregate intermediate inputs. QXa= α a a a a a a × [δa × QVAa^(ρa ) + (1 - δa ) × QINTa^(ρa )]^[-1/(ρ a a )] (4) At the second level, QVA is a CES function of capital and labour. QVAa = A[δK -ρ + (1 − δ) L-ρ]^(-1⁄ρ) (5) A is the efficiency parameter, (A>0; 0< <1; -1< ) the variables K and L are capital and labour, respectively; is the distribution parameter is the substitution Figure 4 3: Nested structure of production 22 Methodology parameter which determines the value of the (constant) elasticity of substitution. The CES is homogeneous of degree one, displays constant returns to scale with a negative slope. A Leontief function also combines intermediate inputs in fixed proportions in order to produce aggregate intermediate input. The changes generated from the crop yield associated climate change (derived from equation 2.5) resulted in equation (4) changing to (4.5), in this case, A* has incorporated the changes in yield associated with climate change. This change affects QA and QINT on the supply side. In the case of maize, because the effect of climate change on production has been estimated by WMO, the changes in production will be simulated on QX as presented in equation (3) QVA * a = A* [δK -ρ + (1 − δ) L-ρ]^(-1⁄ρ) (4.5) On the demand side, the total output (QX) is allocated between domestic sales and exports so that suppliers maximize their sales revenue subject to imperfect substitution between exports and domestic sales based on the constant elasticity of transformation (CET). QX = at [γQEρt + (1 − γ) QDSρt]^(1/ρt ) (5) At the same time, a CES production function is used to produce a composite commodity made of domestic sales and imported goods. QQ = ac [δQM-ρc + (1 − δ) QDSρc]^(-1/ρc) (6) The parameters for CET, CES and the Armington functions are exogenously determined. The trade flows in the economy (QM and QE) are of interest since we are keen to establish how changes in aggregate output is affected by changes in yields due to climate change, and in turn, how it affects trade flows i.e. imports and exports. With changes in QX a due to climate change, then QM This composite commodity (QQ) is consumed between households, government, investment and intermediate use. For food security analysis, household consumption in this model becomes of interest. The household is assumed to maximize a ‘Stone-Geary’ utility function, subject to a consumption expenditure constraint. This model does not assume unitary elasticity of substitution and minimal and discretionary components of consumption (Löfgren et al., 2002). Ci = Cmin i + αi/pi [Y − Σ Pj Cmin j] (7) Ci is consumption for good i and Cmin i is the minimum consumption for each 23 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community commodity i. Y is income and Y − Σ Pj Cmin j represents the supernumerary or residual income. αi/pi is the marginal share of good i in the household consumption budget. Equilibrium must be achieved in the labour, product, government budget, foreign trade and in savings and investments. The macro closure consists of the external balance, government balance and the savings investment balance. Investment is taken as exogenous in order to align to the reality of governments or policy makers wanting to achieve a certain investment objective. Given that the price for investment is endogenously determined in the model while foreign and public savings are exogenous, savings will equal the fixed investment quantities when the base year of non-government institutions savings rate adjust to achieve equilibrium. This is therefore an investment driven closure. Simulation Scenarios The simulation scenarios undertaken were derived from equation (2), where the Yieldij was used to shock the model in equation (4), which then becomes (4.5). Several other equations were affected in this process these are (5) (6) and (7) depending on the magnitude of yield for the following three scenarios: 1. The base scenario replicated the original data and was a situation of no climate change (i.e. temperature and rainfall changes) associated in productivity i.e. equation (4) are not affected. 2. Scenario 1: where there are productivity changes associated with changes in rainfall and temperature mean. 3. Scenario 2: where there are productivity changes associated with rainfall and temperature variations. 4. Scenario 3: where there are productivity changes associated with changes in rainfall and temperature mean and variation. 4.3.3 The Micro-Simulation Model: Food Security Foster et al. (1984) using the common measure of overall poverty commonly known as the FGT index, which is used to quantify three elements of poverty, namely: level, depth and severity, which are also, respectively, known as incidence, inequality and intensity of poverty. The FGT index is defined as: Pα (y|z) = 1/n Σ q i=1 [g α i/z] (8) y = (y1 ...... yn) – the individual income 24 Methodology q = q (y; z) – number of poor households with income no greater than z n = n (y) – total number of households α can take the value 0, 1 or 2. Z > 0 – is the predetermined poverty line gi = z - yi – income shortfall in ith household When α = 0, then po = q/n this is the headcount ratio and is the proportion of the population below the specified poverty line. αo Measures the level or incidence of poverty. When α = 1, this gives the income short fall for a household to move out of poverty, when α = 2, this measures the poverty severity. In measuring food security impacts, some adjustment was made to the definition of poverty line (z). While most studies define a poverty line using total consumption expenditure of both food and non- food commodities, this study defined the poverty line using food expenditure only, hence transforming the poverty line to a food poverty line. With this definition, it was possible to establish those households that are food poor and hence food insecure due to non- availability of food. The definition of following variables in equation (8) changed: y = (y1 ...... yn) – the individual food expenditure Z > 0 is the predetermined food poverty line gi = z - yi – food expenditure shortfall in ith household In this section, household consumption changes associated with equation (7) affected gi in equation 8. With α = 0, then poverty headcount would also change. The changes in household consumption would enable the establishment of how changes in household consumption associated with climate change affected the headcount ratio, it was therefore possible to establish how many people/ households fell below/rose above the food poverty line z. 4.4 Data The two main data sources for the analysis are the household survey data and the social accounting matrix (SAM). One major distinction between these two data sources is the classification used for the commodities. In the SAMs for the four EAC countries under study, the International Standard Classification of All Economic Activities (ISIC Rev.4) while the household survey datasets use Central Product Classification (CPC) system. In order to conduct the analysis, the data was harmonized to ISIC Rev.4 system of classification. Thirdly, the household survey data sets and SAMs are for different years: Kenya (2005/06), Uganda 25 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community (2009/10), Tanzania (2008/09) and Rwanda (2010/11). The SAMs for the four countries are as follows: Kenya (2003), Uganda (2007), Rwanda (2006) and Tanzania (2009). These datasets can still be used for analysis even though the years of data production do not match since the structure of economies tend not to change much within a span of five years. In the case of impact of climate change on maize production, data obtained from the WMO for simulating these changes. Table 4.1 simulating the impact of climate change on maize production. Table 4.1: Percentage change in maize production due to climate change (APSIM) Burundi Kenya Rwanda Uganda Tanzania RCP 4.5 -0.150 -2.240 -0.100 -1.900 -0.200 RCP 8.5 -0.150 -1.820 -0.880 -1.200 -0.500 Source: WMO Simulations 4.4.1 Household Survey Farm household data was collected from household surveys of the various countries under study. Household survey data was collected by the various governments of the partner states with the main objective of obtaining a wide spectrum of socio- economic indicators required to measure, monitor and analyse the progress made in improving living standards in a single integrated household survey. This data contains information on demographics, housing, education, health, agriculture and livestock, enterprises, expenditure and consumption, household social amenities and community perspectives. From the household data, the dependent variable (yield) measured by tonnes per hectare (t/ha) for the following crops: beans, rice, millet, sorghum and wheat, that ensure food security in the four EAC countries was obtained. The choice of crops is based in commodities that constitute food crops that are important for food security in the EAC . The dependent variables included mean rainfall (mm), mean temperature (OC), rainfall and temperature variation measured using standard deviation (OC), physical inputs (labour –wages/ person days, manure (kg) and capital was measured using cost of purchased inputs such as land rent, seed, fertilizer, and pesticides, used in production of a particular crop production. Table 4.2 presents the descriptive Statistics for the four EAC countries. 26 Methodology Table 4 2: Descriptive statistics Country Description Obs. Mean Std. Dev. Min Max KENYA Wheat Crop Area (ha) 38 2 3 0 16 Labour (KES) 38 11 49 0 300 Capital (KES) 38 14,910 27,719 0 156,000 Yield (t/ha) 38 1,622 1,676 0 8,340 Rice Crop Area (ha) 30 1 0 0 2 Labour (KES) 30 38 72 0 300 Capital (KES) 30 1,095 1,481 0 4,700 Yield (t/ha) 30 3,580 4,700 0 20,850 Millet Crop Area (ha) 294 0 0 0 3 Labour (KES) 296 5 18 0 180 Capital (KES) 296 570 2,753 0 39,600 Yield (t/ha) 294 674 978 0 7,413 Beans Crop Area (ha) 4,035 0 1 0 40 Labour (KES) 4,095 7 83 0 4,200 Capital (KES) 4,095 1,316 4,063 0 108,220 Yield (t/ha) 4,035 496 1,927 0 80,062 TANZANIA Cassava Crop Area (ha) 206 0 0 0 2 Labour (TZS) 308 4 9 0 60 Capital (TZS) 308 761 3,982 0 49,000 Yield (t/ha) 205 2,054 2,362 0 13,591 Rice Crop Area (ha) 501 0 0 0 3 Labour (TZS) 539 10 29 0 396 Capital (TZS) 539 3,569 9,380 0 70,000 Yield (t/ha) 497 1 2 0 15 27 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Country Description Obs. Mean Std. Dev. Min Max Sorghum Crop Area (ha) 272 1 1 0 8 Labour (TZS) 297 4 14 0 142 Capital (TZS) 297 4,532 29,555 0 480,000 Yield (t/ha) 269 1 4 0 62 Millet Crop Area (ha) 108 1 1 0 2 Labour (TZS) 114 7 19 0 134 Capital (TZS) 114 1,869 8,347 0 70,000 Yield (t/ha) 107 1 1 0 3 Beans Crop Area (ha) 557 0 0 0 4 Labour (TZS) 609 7 17 0 210 Capital (TZS) 609 5,046 9,899 0 103,200 Yield (ha) 550 0 0 0 4 UGANDA Matoke Yield (t/ha) 1,198 6,745 27,793 0 741,316 Capital (UGS) 1,243 4,084 64,831 0 2,241,000 Labour (UGS) 1,243 1 8 0 231 Family Labour (mdays) 1,243 37 48 0 579 Crop Area (ha) 1,198 0 1 0 16 Rice Yield (t/ha) 74 5,693 34,462 0 296,526 Capital (UGS) 75 20,399 38,001 0 240,000 Labour (UGS) 75 13 34 0 210 Family Labour (mdays) 75 57 66 0 320 Crop Area (ha) 74 0 0 0 2 Millet Yield (t/ha) 373 979 3,926 0 49,421 Capital (UGS) 384 1,468 3,868 0 50,000 Labour (UGS) 384 4 15 0 228 Family Labour (mdays) 384 38 34 0 198 Crop Area (ha) 374 0 0 0 3 28 Methodology Country Description Obs. Mean Std. Dev. Min Max Sorghum Yield (t/ha) 593 626 1,757 0 29,653 Capital (UGS) 611 2,005 6,352 0 125,002 Labour (UGS) 611 2 11 0 140 Family Labour (mdays) 611 29 27 0 240 Crop Area (t/ha) 593 0 2 0 40 Beans Yield (t/ha) 1,845 1,479 7,446 0 148,263 Capital (UGS) 1,924 10,069 34,742 0 1,250,000 Labour (UGS) 1,924 2 10 0 240 Family Labour (mdays) 1,924 45 67 0 880 Crop Area (ha) 1,848 0 1 0 40 RWANDA Beans Yield (t/ha) 15,132 0 0 0 6 Capital (RWF) 15,132 4,489 10,106 0 166,133 Labour (RWF) 15,132 3,398 9,422 0 150,000 Crop Area (ha) 15,132 71 148 0 6,600 Sweet Potato Yield (t/ha) 2,518 19 33 0 566 Capital (RWF) 2,518 2,995 4,723 0 104,800 Labour (RWF) 2,518 3,111 9,116 0 150,000 Crop Area (ha) 2,518 74 159 2 6,600 Source: Household Surveys Kenya, Uganda, Tanzania, and Rwanda 4.4.2 Rainfall and Temperature Rainfall and temperature data was collected at the station/district level, for each country, from the WMO, under this section; the data is summarized and presented at the national level. Figures 4.4 and 4.5 present the average rainfall and temperature (mean and variability) respectively for the periods under analysis, for each of the EAC countries. The box plots provide the interquartile range (i.e. the width of the box Q3 minus Q1) of the distribution. The lower whiskers provide the smallest non- outlier while the whiskers after Q3 goes to the largest outliers. Uganda has more temperature and rainfall outliers as compared to the other three counterparts (Kenya, Tanzania and Rwanda). Uganda has the highest 29 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community annual mean rainfall of approximately 1,300 mm; with a range (spread without outliers) of 850 mm. Figure 4.5 shows the average annual temperature for each of the four EAC countries. Rwanda has the lowest mean annual temperature of approximately 18.7°C but with the highest mean variability of 0.32°C. Tanzania has a mean temperature of 23.0°C with a mean temperature variability of 0.17°C. Matovu (2014) note that high low rainfall coupled with high variation in both rainfall and temperature has grave implications on the yield of farmers who solely depend on rainfall for crop production. Alexandrov and Hoogenboom (2000) also note that crop yields are affected by variations in climatic factors such as air temperature and precipitation, and the frequency and severity of extreme events like droughts, floods, hurricanes, windstorms and hail. 4.4.3 The Social Accounting Matrix (SAM) The SAM provides a summary of the national accounts and traces out the flow of incomes from production activities, to factor payments, to incomes of institutions and back to demand of commodities Most of these SAMs have at least 30 activity-commodity mapping which can be broadly categorized into agriculture, manufactures and services. The activity/commodities are further classified using ISIC (International Standard Industrial Classification). The disaggregated model enables one to understand the differences in sectors and can enable one to distinguish the impact of more vulnerable sectors. Agricultural Commodity Production Agricultural commodity production derived from the social accounting matrices for Kenya, Uganda, Tanzania and Rwanda (Table 4.3). The top ten agricultural commodities with the highest gross output in Kenya are maize tea, dairy, vegetables, oils, beef, cut flowers, fruits, roots and tubers and poultry. These commodities make up 82 per cent of all agricultural commodities produced. The agricultural commodities produced make up 20 per cent of gross output, with maize and tea contributing more than ten per cent respectively to total agricultural output. In the case of Tanzania, agricultural output constitutes approximately 23 per cent of gross output, with maize and mining being the top two agricultural commodities, while rice ranks third. The other agricultural commodities produced are forestry, vegetables, cattle, pulses, fisheries, cassava and poultry. The top ten agricultural commodities produced in Tanzania make up 74 per cent of total agricultural output. Given that agricultural commodities are priced in different currencies for the four EAC partner states, comparisons can only be made using proportions/ ratios. In both countries, agricultural commodity production is around 20 per cent of gross output; infact Tanzania has a higher gross output ratio of 23 per cent. 30 Methodology 31 Figure 4 4: Mean rainfall and variation for EAC countries Kenya Tanzania Uganda Rwanda Rainfall Rainfall variation Source: Author’s calculations from Kenya, Uganda, Tanzania and Rwanda World Meteorological Organization (WMO) weather data Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community 32 Figure 4 5: Mean temperature and variation for EAC countries Kenya Tanzania Uganda Rwanda Temperature Temperature variation Source: Author’s calculations from Kenya, Uganda, Tanzania and Rwanda World Meteorological Organization (WMO) weather data Methodology The proportion of the top ten agricultural commodities produced as a proportion total agricultural commodity production is higher for Tanzania (84 per cent) as compared to Kenya (71 per cent). In Uganda, total agricultural commodity output constitutes 18 per cent of gross output. The top ten agricultural commodities produced are forestry, pulses and oilseeds, fishing, roots, Matoke, livestock, cassava, coffee, other cereals and maize. These commodities constitute 91 per cent of agricultural commodity outputs. In the case of Rwanda, agricultural commodity output constitutes 33 per cent of gross output. The top ten commodities produced include banana/plantain, Irish potatoes, sweet potatoes, pulses, cassava, forestry, sorghum, maize, mining and fruits. These commodities make up 82 per cent of total agricultural commodity output. In terms of agricultural commodity production diversification, Kenya can be considered more diversified in terms of production since it has a lower percentage proportion of the top ten produced commodities. Table 4 3: EAC agricultural commodity production Commodity Total (%) Agric. Rank (%) of Gross Output Output Kenya (Million KES) Maize 56,109 14.9 1 3.0 Tea 51,419 13.6 2 2.7 Dairy 35,019 9.3 3 1.9 Vegetables 32,256 8.6 4 1.7 Oils 30,710 8.1 5 1.6 Beef 24,398 6.5 6 1.3 Cut flower 21,668 5.7 7 1.1 Fruit 21,651 5.7 8 1.1 Roots & Tuber 18,804 5.0 9 1.0 Poultry 18,223 4.8 10 1.0 Total Agric. 377,199 20.0 Gross Output 1,886,249 UGANDA (Million UGS) Forestry 1,151 18.8 1 3.4 Pulses& oilseeds 808 13.2 2 2.4 Fishing 675 11.0 3 2.0 Roots 568 9.3 4 1.7 Matoke 522 8.5 5 1.6 Livestock 518 8.5 6 1.5 Cassava 495 8.1 7 1.5 33 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Commodity Total (%) Agric. Rank (%) of Gross Output Output Coffee 277 4.5 8 0.8 Other cereals 274 4.5 9 0.8 Maize 270 4.4 10 0.8 Total Agric. 6,130 18.3 Gross Output 33,559 Tanzania (Billion TZs) Maize 1,253,464 13.3 1 3.1 Mining 1,151,001 12.3 2 2.9 Rice 802,655 8.5 3 2.0 Forestry 741,873 7.9 4 1.8 Vegetables 638,521 6.8 5 1.6 Cattle 585,309 6.2 6 1.5 Pulses 491,685 5.2 7 1.2 Fisheries 464,754 4.9 8 1.2 Cassava 460,075 4.9 9 1.1 Poultry 381,517 4.1 10 0.9 Total Agric. 9,391,114 23.3 Gross Output 40,250,130 Rwanda (Million RWF) Bananas/plant 123,114 17.8 1 5.9 Irish potatoes 111,285 16.0 2 5.3 S. potatoes 76,560 11.0 3 3.6 Pulses 75,459 10.9 4 3.6 Cassava 53,861 7.8 5 2.6 Forestry 41,594 6.0 6 2.0 Sorghum 26,767 3.9 7 1.3 Maize 21,178 3.1 8 1.0 Mining 17,967 2.6 9 0.9 Fruits 16,637 2.4 10 0.8 Total Agric. 693,566 33.0 Gross Output 2,100,768 Source: Kenya SAM 2003, Tanzania SAM 2007, Rwanda SAM 2006, Uganda SAM 2007 34 Methodology Household Agricultural Commodity Consumption Household commodity consumption for the four EAC partner states is presented in table 4.4. For Kenya, the top ten commodities consumed by households are maize, beef, vegetables, oils and pulses, roots, forestry, rice, fruits dairy and fish. These commodities constitute 17 per cent of total commodities consumed. The rest of the 83 per cent are shared between exports, intermediate consumption and consumption by other non- household institutions. These top ten commodities consumed by the Kenyan households make up 93 per cent of total commodities consumed by households in Kenya. The top ten commodities consumed in Tanzania are maize, mining, rice, forestry, vegetables, cattle, pulses, fisheries, cassava and poultry. Tanzania’s total household consumption of agricultural commodities make up 23 per cent of total consumption while the top ten commodities make up 74 per cent of total agricultural commodities by households. For Uganda, total household agricultural commodity consumption makes up 25 per cent of total consumption, the top ten commodities include: forestry, pulses and oil seed, matoke, cassava, roots, fishing, other cereals, livestock, horticulture and maize. These commodities make up 98 per cent of total agricultural commodities consumed by households. Lastly for Rwanda, agricultural commodities make up 44 per cent of total commodities consumed by households. The top ten commodities consumed by households are Irish potatoes, sweet potatoes, pulses, bananas/plantains, cassava, forestry, maize, vegetables, sorghum and other roots. These agricultural commodities make up 39 per cent of all agricultural commodities consumed by households. Kenya and Uganda have a less diversified agricultural commodity consumption basket since the top ten commodities consumed by their households are more than 90 per cent (93 and 98 per cent respectively), as compared to Tanzania with 74 per cent. Rwanda has a more diversified agricultural commodity consumption basket since the top ten commodities constitute 39 per cent of total agricultural commodities consumed by the households. Overall, even though the EAC has several food crops that constitute the food security basket, countries were found to have different consumption patterns for these commodities, hence the consumption of a crop like maize or sweet potatoes varied from country to country. 35 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Table 4 4: EAC Agricultural Commodity Consumption Commodity Total (%) of Agric. Rank (%) of Total Commodity Consumption Kenya (Million KES) Maize 21,114 16.8 1 2.8 Beef 20,134 16.0 2 2.7 Vegetables 17,023 13.5 3 2.3 Oils &Pulses 12,886 10.2 4 1.7 Roots 9,564 7.6 5 1.3 Forestry 8,921 7.1 6 1.2 Rice 8,670 6.9 7 1.1 Fruits 6,756 5.4 8 0.9 Dairy 6,482 5.1 9 0.9 Fish 5,278 4.2 10 0.7 Agric. Cons. 125,962 16.7 Total Cons. 1,886,249 UGANDA (Million UGS) Forestry 1,256 26.8 1 6.7 Pulses , oilseed 671 14.3 2 3.6 Matoke 651 13.9 3 3.5 Cassava 560 11.9 4 3.0 Roots 559 11.9 5 3.0 Fishing 418 8.9 6 2.2 Other cereals 187 4.0 7 1.0 Livestock 118 2.5 8 0.6 Horticulture 98 2.1 9 0.5 Maize 65 1.4 10 0.3 Agric. Cons. 4,694 25.0 Total Cons. 18,743 Tanzania (Billion TZS) Maize 579,672 15.8 1 3.9 Other cereals 396,330 10.8 2 2.7 Vegetables 386,153 10.5 3 2.6 Mining 381,448 10.4 4 2.6 Forestry 340,337 9.2 5 2.3 Poultry 288,353 7.8 6 2.0 Cassava 204,267 5.6 7 1.4 Rice 190,442 5.2 8 1.3 36 Methodology Commodity Total (%) of Agric. Rank (%) of Total Commodity Consumption Pulses 147,992 4.0 9 1.0 Cattle 113,389 3.1 10 0.8 Agric. Cons. 3,679,410 25.0 Total Cons. 40,250,130 23.3 Rwanda (Million RWF) Irish potatoes 114,267 19.9 1 8.7 S. potatoes 89,334 15.6 2 6.8 Pulses 76,942 13.4 3 5.9 Bananas/plant 56,543 9.9 4 4.3 Cassava 48,985 8.5 5 3.7 Forestry 38,556 6.7 6 2.9 Maize 25,725 4.5 7 2.0 Vegetables 19,885 3.5 8 1.5 Sorghum 18,221 3.2 9 1.4 Other roots 16,746 2.9 10 1.3 Agric. Cons. 573,219 43.7 Total Cons. 1,312,273 Source: Kenya SAM 2003, Tanzania SAM 2007, Rwanda SAM 2006, Uganda SAM 2007 37 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community 5. Results and Discussion 5.1 Farm Household Analysis The objective of the farm household analysis is to obtain the coefficients of the temperature variables that will be used to simulate the productivity data in the CGE model. While temperature, rainfall, capital and labour data is presented in the country results, only results for rainfall, temperature and their respective variation will be the focus for this section. Ray et al (2014) found that over 60 per cent of yield variations in crops are explained by temperature variation, making temperature variation a very significant component for establishing the productivity effects in yield. The yield results show that climate variation (rainfall and temperature) have adverse effects on most of the crops, this is consistent with results by Rimi et al. (2009) who found that temperature variations decreased rice production significantly. For all the regressions undertaken for each country for the selected crops, the models were all significant and one per cent even though the respective models had low percentages for the R2 statistics. It was expected that increase in crop area would increase crop yields, however for the crops examined, there was a decrease, this can be due to the quality of land and its suitability for the crop in question. The relationship between crop yields and crop areas would require further investigation, however, this does not fall within the scope of this research. Kenya The dependent variable in the analysis was log of yield regressed on weather variables, capital and labour. For Kenya (table 5.1), a unit (mm) increase in rainfall reduced the yield of millet, sorghum and beans. A unit increase in rainfall variation increased yields for millet (0.071), sorghum (0.232), while for beans the yield would decrease by 0.038. Rainfall variation was found to adversely affect sorghum. A unit increase in temperature reduced millet yields, but increased yields of beans by 0.032. Temperature variation had adverse effects on most of these crops, millet yields would reduce 7 times with increase in temperature variation, and while beans yields would reduce almost 8 times. Wheat would be adversely affected by temperature variation since the yields would reduce 42 times. 38 Results and Discussion Table 5 1: Results of yield change due to unit change in temperature and rainfall (Kenya) Rice Millet Sorghum Beans Wheat Rainfall (mm) -0.039 -0.012** -0.031* -0.031* 0.009 Temperature (0C) 0.016 -0.120** 0.032 0.032* 0.115 Rainfall Variation (mm) 0.435 0.071*** 0.232* -0.038* -0.033 Temperature Variation (0C) 0.000 -7.675*** 2.432 -7.455* -42.423** Capital (KES) 0.001 0.000* 0.000 0.000** 0.000 Labour (man days) 0.004 0.007*** 0.000 0.000 0.002 Crop Area (hectares) -1.668 -0.988* -0.355* -0.249* -0.103 Constant 1.635 11.341* 3.926* 8.824* 15.883* R-adjusted 0.364 0.169 0.087 0.088 0.176 No. of observations 23 244 582 3,104 23 *1 %, **5% and ***10% significance level Source: Author’s calculations from Kenya Household Survey Data 2005/06 Tanzania A unit increase in rainfall had a positive effect on the yields. Variation in rainfall reduced the yields of maize, millet and cassava. A unit increase in temperature was found to reduce the yields of rice, beans, sorghum, millet and cassava. A unit increase in temperature variation increased the yields of millet almost five times, while that of beans increased 3 times, while this change was found to reduce the yields of sorghum almost 4 times. Like Kenya the question of land quality and impact on crop yields should be an area for further research. Table 5 2: Results of yield change due to unit change in temperature and rainfall (Tanzania) Rice Sorghum Millet Cassava Beans Rainfall (mm) -0.001* 0.000 -0.001 -0.005* 0.000 Temperature (0C) -0.262* -0.087*** -0.401* -0.414* -0.017 Rainfall Variation (mm) -0.002 -0.001 -0.025** -0.010* -0.001 Temperature Variation (0C) 0.401 -3.572*** 4.867** 3.506 3.435* Capital (TZS) 0.000 0.000 0.000 0.000 0.000* Labour (man days) 0.004* 0.006 0.001 0.018 0.010* Crop Area (hectares) -0.640* -0.173** -0.145 -1.043* -1.092* Constant 6.458* 1.898** 7.289** 18.150* -1.740* R-adjusted 0.187 0.057 0.088 0.317 0.317 No. of observations 495 268 107 204 550 *1 %, **5% and ***10% significance level Source: Author’s Calculations from Tanzania Household Survey Data 39 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Uganda In Uganda, change in rainfall was found to reduce the yields of sorghum, though by a small amount (0.001 times). Rainfall variation also increased yields of beans only. Change in temperature reduced the yields of sorghum and millet but increased the yield of beans. Temperature variation was found to adversely affect rice yields by 1.3 decrease in output per hectare, beans yields were also decreased (0.378 times). Table 5 3: Results of Yield Change due to Unit Change in Temperature and Rainfall (Uganda) Rice Sorghum Millet Matoke Beans Rainfall (mm) 0.005*** 0.001** 0.001 0.000 0.000 Temperature (0C) -0.259 -0.004*** -0.259* -0.113 0.066** Rainfall Variation (mm) 0.002 0.002 0.000 0.001 0.001*** Temperature Variation (0C) -1.299*** -0.116 -0.239 -0.364 -0.378* Capital (UGS) 0.000 0.000* 0.000 0.000 0.000** Hired Labour (UGS) 0.012** 0.001 0.017** 0.004 0.002 Family Labour (Man Days) 0.001 -0.001 0.010* -0.001 0.001** Crop Area (hectares) -1.310* -0.147* -1.590* -0.410 -0.214* Constant 4.988 4.923* 11.092* 10.267 4.432* R-adjusted 0.086 0.077 0.229 0.061 0.055 No. of observations 61 374 281 1,048 1,528 *1 %, **5% and ***10% significance level Source: Author’s Calculations from Uganda Household Survey Data Rwanda In Rwanda, a unit increase in rainfall decreased the yields of beans and sweet potatoes. Variation of rainfall on the other hand increased the yields of the same crops, with a magnitude of less than one. A unit increase in temperature also increased the yields of beans while decreasing the yields of sweet potato. Temperature variation was found to have adverse but positive effects for beans, whose yields increased 13 times. 40 Results and Discussion Table 5 4: Results of Yield Change due to Unit Change Temperature and Rainfall (Rwanda) Beans S. Potato Rainfall (mm) -0.023* -0.028* Temperature (0C) 0.143* -0.350* Rainfall Variation (mm) 0.158* 0.173* Temperature Variation (0C) 13.210* 2.493 Capital (RWF) 0.000 0.000 Labour (man days) 0.000* 0.000 Crop Area (hectares) -0.004* -0.003* Constant -14.382* 8.119 R-adjusted 0.261 0.172 No. of observations 15,132 2,518 *1 %, **5% and ***10% significance level Source: Author’s Calculations from Rwanda Household Survey Data 5.2 Agricultural Production The productivity changes associated with the rainfall and temperature changes obtained from the econometric regressions were applied in the CGE model by altering the total factor productivity. Given that there are spillover effects from agriculture, both manufacturing and services sector were evaluated at a macro level. The effects of productivity in the four EAC economies were modeled using three scenarios. In the first case, where rainfall and temperature change; second scenario, increase in temperature and rainfall variability and the last scenario where both rainfall and temperature change and variability also changes. The percentage changes were applied to the productivity for each of the selected crops. The first scenario examined the changes in rainfall and temperature patterns, the second scenario examined the rainfall and temperature variability while the last scenario examined rainfall and temperature changes and changes in variability. The baseline scenario presents the ‘business as usual’ scenario, in this scenario; there are no productivity changes associated with temperature and rainfall patterns. The base scenario is then compared to the simulated scenarios on order to establish how the economy will be affected when rainfall and temperature patterns change. 41 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Kenya A change in rainfall and temperature mean (scenario 1) was found to increase agricultural production by 0.004 per cent. Of this overall reduction in agricultural production, maize production increased by 0.001 per cent. The production of other grains (beans, millet and sorghum) was adversely affected by rainfall and temperature changes and resulted in approximately 21 per cent reduction in production. The changes in manufacture and services sector output due to climatic changes in the agricultural sector is an indicator of sectorial linkage. Manufacture increased by 0.0003 per cent while services output increased by 0.001 per cent. The service sector recorded a higher increase in output due to output changes from the agricultural sector. This is because increase in agricultural output would affect services such as hotels, trade, restaurants, which use agricultural output as intermediate products. Changes in the variation of temperature and rainfall (Scenario 2), was found to decrease agricultural output by 0.13 per cent. This trend was also found in scenario 3 with slightly varying magnitudes. Table 5 5: (%) Impact of Climate Change on Agricultural Output - (Kenya) Base (Million Temp. & Temp. & Temp. & KES) Rainfall mean Rainfall Rainfall mean Scenario 1 Variation and Variation Scenario 2 Scenario 3 Output Agriculture 290,637 0.004 -0.13 -0.12 Of which Maize 44,453 0.001 -0.04 -0.04 Wheat 510 0.001 -86.15 -86.15 Rice 2,812 0.89 0.00 0.89 Other Grains 88 -20.84 -33.65 -33.65 Manufacture 395,587 0.0003 0.02 0.02 Services 1,107,541 0.001 0.001 0.002 Source: Author’s Simulations from Social Accounting Matrix (Kenya 2003) Tanzania Changes in temperature and rainfall mean (scenario 1) were found to reduce agricultural output by approximately 3 per cent. The most affected commodities were found to be millet, whose production reduced by 37 per cent. In scenarios 2, the outputs of crops that were adversely affected by temperature and rainfall variation were pulses (beans), which increased by 77 per cent, while production 42 Results and Discussion millet decreased by 12 per cent and sorghum by 15 per cent. This trend was also found in scenario 3 with slightly varying magnitudes. Furthermore, the reduction in services output instigated by climate changes is low in all the scenarios at less than one per cent. Compared to the manufacturing sector, the output in this sector is largely affected by changes temperature and rainfall mean, and a combination of changes in temperature and rainfall variation and patterns. This shows that the services sector has weaker backward linkage to agriculture sector as compared to the manufacturing sector. Table 5 6: (%) Temperature and rainfall pattern and variability changes (Tanzania) Base (Billion Temp. & Temp. & Temp. & TZS) Rainfall Rainfall Rainfall patterns Variation Pattern and Scenario 1 Scenario 2 Variation Scenario 3 Output Agriculture 7,523,774 -3.40 2.64 -2.57 Of which Maize 944,392 -3.27 -1.66 -2.93 Sorghum 150,833 -15.29 -15.06 -8.13 Millet 53,335 -36.93 -11.62 48.30 Rice 730,679 -16.43 -0.88 -16.26 Cassava 230,354 -23.89 -6.04 -19.54 Pulses 354,434 -1.22 76.84 -1.20 Manufacture 4,817,098 -3.53 -1.89 -2.55 Services 25,196,473 -0.11 -0.01 -0.04 Source: Author’s Simulations from Social Accounting Matrix (Tanzania 2009) Uganda The results of simulations for Uganda are presented on table 5.7, it should be noted that only crops of interest are presented in this table. Changes in temperature and rainfall mean (Scenario 1) were found to reduce agricultural output by approximately 20 per cent. The most affected commodities associated with this change are maize (4 per cent) and other cereals (16 per cent), which include millet and sorghum. There is however an increase in beans output by 6.5 per cent. In scenario 2, temperature and rainfall variation had an adverse effect on rice production, which reduced by 100 per cent resulting in increase for imports by more than 400 per cent. In scenario 3, the magnitude of change in output is particularly high for beans, whose output decreased by 46 per cent 43 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Table 5 7: (%) Temperature and Rainfall Pattern and Variability Changes - (Uganda) Base (Million Temp. & Temp. & Temp. & UGS) Rainfall Rainfall Rainfall mean Variation meanand Scenario 1 Scenario 2 Variation Scenario 3 Output Agriculture 6,129,555 -20.46 -24.46 -12.51 Of which Maize 269,738 -4.04 -4.09 -11.33 Rice 67,980 -2.63 -100.00 -100.00 Other Cereals 273,825 -16.53 -1.95 -19.69 Matoke 522,083 -6.45 -20.06 -27.94 Beans 676,342 6.46 -37.39 -45.77 Manufacture 5,765,502 -42.18 -33.21 1.59 Services 21,664,094 -86.52 -74.15 -0.43 Source: Author’s Simulations from Social Accounting Matrix (Uganda 2007) Rwanda Changes in rainfall and temperature patterns (scenario 1) were found to reduce agricultural output by 3 per cent. Change in rainfall and temperature would adversely affect sweet potatoes production, which would reduce by 35 per cent. Maize production would reduce by less than 1 per cent. Beans production would however increase by around 11 per cent. In scenario 2, rainfall and temperature variation increased beans and sweet potato production, but reduced maize production by almost 2 per cent. Changes associated with scenario 3, were found to be similar to scenario 2 with varying magnitudes. 44 Results and Discussion Table 5 8: Temperature and Rainfall Pattern and Variability (%) Changes - (Rwanda) Base (Million Temp. & Temp. & Temp. & RWF) Rainfall Rainfall Rainfall patterns Variation Pattern and Scenario 1 Scenario 2 Variation Scenario 3 Output Agriculture 639,420 -2.54 72.00 71.42 Of which Maize 21,178 0.08 -1.55 -1.50 Sweet Potato 76,560 -35.25 151.63 140.62 Pulses (Beans) 75,459 11.42 525.86 531.16 Manufacture 657,936 0.40 -6.03 -5.83 Services 550,786 -0.40 3.45 3.36 Source: Author’s Simulations from Social Accounting Matrix (Rwanda 2006) 5.3 Trade Kenya Adjustment of output due to climate change focused on scenario 3, which was the more realistic scenario that looked at the combined effect of changes in mean rainfall and temperature and variation. Adjustment of output due to climate change resulted in the alteration of allocation of outputs on demand: how much is exported, sold domestically and whether importation would take place in order to meet consumption needs. In Kenya, the reduction in output associated with climate change resulted in demand for output being altered as follows: There was adjustment between exports and domestic sales so that overall exports increased by 0.06 per cent, while domestic sales decreased by 2.43 per cent, in order to meet the consumption demand, imports increased by 0.04 per cent. A review of specific crops showed that even though domestic sales decreased by 0.02 per cent, sales of maize increased by approximately 42 per cent, the increase in maize domestic sales is largely triggered by supply price reduction of maize, which resulted in increased demand for maize. Domestic wheat sales reduced by 84 per cent, while beans sales, which was included under other grains reduced by approximately 1 per cent. The increased domestic sales of maize resulted in reduced maize imports by 0.3 per cent, while wheat imports increased by 3.6 per cent. 45 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community Table 5 9: Demand Response to Output Changes (%)- Kenya Base Temp. & Temp. & Temp. & (Million Rainfall mean Rainfall Rainfall mean KES) Scenario 1 Variation and Variation Scenario 2 Scenario 3 Exports 281,116 0.004 0.07 0.06 Of which Maize 286.07 0.01 0.24 0.24 Wheat 74.28 0.002 -97.76 -97.76 Other Grains 37 -51.66 -92.65 -92.65 Domestic Sales 1,530,330 -0.33 2.44 2.43 Of which Maize 44,167 -5.18 41.82 41.55 Wheat 436 -0.39 -84.03 -84.04 Rice 2,812 0.90 -0.31 0.56 Other Grains 51 -0.66 -1.05 -1.05 Imports -416,892 0.00 0.04 0.04 Of which Maize 920 -0.01 -0.30 -0.30 Wheat 11,803 0.00 3.62 3.62 Rice 4,605 -0.18 -0.07 -0.25 Private 848,484 0.00 -0.05 -0.04 Consumption Source: Author’s Simulations from Social Accounting Matrix (Kenya 2003) Tanzania This output is allocated between exports and domestic sales, the overall reduction in aggregate output reduced imports, exports and domestic sales. In Tanzania, the decrease in output (scenario 3) led to reduction in both exports and domestic sales by 0.42 and 0.99 per cent respectively, imports further decreased by 0.27 per cent. A review of crops of interest showed that maize, rice, wheat beans and sorghum reduced, while domestic sales of millet went up by approximately 23 per cent. This resulted in millet imports reducing by approximately 83 per cent. Even though Tanzania recorded major changes in demand, especially for exports, imports and domestic sales, this not translate to major reduction in private consumption, which reduced by 2 per cent. Even though crops like maize and rice output was found to reduce, this did not translate to private consumption since these products constitute less than 25 per cent of total agricultural commodities consumed by households. 46 Results and Discussion Table 5 10: Demand Response to Output Changes (%) - Tanzania. Base Temp. & Temp. & Temp. & (Billion Rainfall mean Rainfall mean Rainfall mean TZS) Scenario 1 Scenario 2 and Variation Scenario 3 Exports 6,179,379 -0.96 1.14 -0.42 Of which Maize 6,745 -4.91 -3.14 -4.69 Sorghum 730 -25.27 -100.00 -7.33 Millet 550 -73.12 57.46 1113.11 Rice 199 -88.57 3.29 -88.73 Pulses 92,966 -2.77 187.87 -3.06 Domestic Sales 31,604,887 -1.27 0.06 -0.99 Of which Maize 937,647 -3.26 -1.65 -2.92 Sorghum 150,103 -15.25 -14.76 -8.14 Millet 52,785 -36.62 -12.47 23.14 Rice 730,480 -16.42 -0.88 -16.24 Cassava 230,354 -23.89 -6.04 -19.54 Pulses 261,468 -0.68 28.83 -0.55 Imports -9,543,170 -0.62 0.74 -0.27 Of which Maize 28,041 -1.49 -0.38 -1.12 Sorghum 32 -4.30 344.63 -8.73 Millet 2,701 42.59 -51.25 -83.16 Pulses 4,780 1.64 -40.82 2.04 Private 17,427,110 -2.50 2.02 -2.05 Consumption Source: Author’s Simulations from Social Accounting Matrix (Tanzania 2009) Uganda Out of the six food security crops under study, Uganda was found to export only maize and beans, hence the decrease in maize output would reduce maize exports by 10 per cent, while exports for bean would increase by 21 per cent (Scenario 1). As can be seen, the decrease in outputs for maize, rice and other cereals would result in an increase in their import. In scenario 3, reduction in output resulted in the reduction of exports by 2.3 per cent, domestic sales reduced by 2.28 per 47 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community cent, while imports reduced by 1.15 per cent. Maize and beans exports for Uganda reduced by 24 and 94 per cent respectively. The reduction in output resulted in the further reduction of domestic sales for maize, beans, rice and other cereals. Consequently, Uganda would record a very large increase in the imports of rice as compared to maize and other cereals. The adjustment of output demand between exports; domestic sales and imports would result in private consumption reducing by 4.26 per cent. Table 5 11: Demand Response to Output Changes (%)- Uganda Base (Million Temp. & Temp. & Temp. & UGS) Rainfall mean Rainfall Rainfall mean Scenario 1 Variation and Variation Scenario 2 Scenario 3 Exports 3,632,979 0.03 -1.13 -2.30 Of which Maize 53,814 -10.04 -7.96 -25.09 Beans 117,722 20.50 -85.66 -93.81 Domestic Sales 30,224,587 -0.36 -1.48 -2.28 Of which Maize 215,924 -2.56 -3.13 -8.01 Beans 558,621 3.44 -28.94 -37.72 Rice 67,980 -2.63 -100.00 -100.00 Other Cereals 273,825 -16.53 -1.95 -19.69 Imports -7,259,720 0.01 -0.56 -1.15 Of which Maize 59,553 4.78 5.02 15.78 Rice 25,072 3.28 413.83 409.42 Other Cereals 150,891 17.91 1.06 21.27 Private 18,742,540 -0.59 -2.84 -4.26 Consumption Source: Author’s Simulations from Social Accounting Matrix (Uganda 2007) Rwanda In scenario 1, even though there is substantive reduction in production for the sweet potatoes, there was increase in exports by 0.44 per cent. The crops under study, do not form part of the exports, this is a major drawback of CGE, if from 48 Results and Discussion the initial data presented, a crop was not exported, even with an increase in production that necessitates export due to surplus, the crop will not be exported, as can be seen with beans. The imports of maize and rice also increased due to reduction in maize production as well as sweet potatoes. In scenario 3, the reduction in output associated with climate change altered demand for output. The 71.42 per cent increase in output resulted in an increase in a reduction in exports by 6.2 per cent; however, the food crops of interest (maize, beans and sweet potatoes) did not form part of the exports. Domestic sales further increased by approximately 74 per cent in adjustment to increase in output. The increase in domestic sales was largely driven by the increase in domestic sales of sweet potatoes and beans (pulses). The increase in domestic sales resulted in a reduction of overall imports by 1.74 per cent, particularly maize and rice, which both reduced by 5 per cent. The increase in domestic sales increased private consumption by approximately 37 per cent. Table 5 12: Demand Response to Output Changes (%)- Rwanda Base (Million Temp. & Temp. & Temp. & RWF) Rainfall mean Rainfall Rainfall mean Scenario 1 Variation and Variation Scenario 2 Scenario 3 Exports 75,671 0.44 -6.45 -6.20 Domestic Sales 2,036,000 -1.04 25.66 25.43 Maize 21,178 0.08 -1.55 -1.50 Rice 13,137 0.05 -0.92 -0.89 Imports -269,596 0.13 -1.81 -1.74 Of which Maize 4,745 0.06 -5.02 -4.96 Rice 7,513 0.23 -5.12 -5.01 Private 1,312,273 -1.52 37.23 36.90 Consumption Source: Author’s simulations from Social Accounting Matrix (Rwanda 2006) Intra- EAC Trade in Maize Further to the analysis presented in tables 5.5 to 5.12, intra EAC trade on maize was simulated for the three scenarios following the changes in maize exports. Several assumptions were made, first, the percentage change in maize exports 49 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community applied to all maize exports irrespective of destination, and second, exports were assumed to mirror the imports of the country of destination. Overall exports of maize within the EAC would reduce due to climate change. Even though Rwanda did not export maize from the 2006 SAM, there were minimal maize exports from the trade map data in 2015, exports for Rwanda also decreased. Table 5 13: Intra-EAC Exports of Maize (‘000 USD) From… To… Base Temp. & Temp. & Temp. & Scenario Rainfall Rainfall Rainfall mean Variation mean and Scenario 1 Scenario 2 Variation Scenario 3 Uganda Kenya 44,835.00 40,333.57 41,266.13 33,585.90 Tanzania 880.00 791.65 809.95 659.21 Rwanda 10,632.00 9,564.55 9,785.69 7,964.43 Kenya Uganda 757.00 756.92 755.18 755.18 Tanzania 827.00 826.92 825.02 825.02 Rwanda 2,530.00 2,529.75 2,523.93 2,523.93 Tanzania Uganda 1,520.00 1,456.31 1,472.27 1,448.71 Kenya 94,090.00 90,147.63 91,135.57 89,677.18 Rwanda 220.00 210.78 213.09 209.68 Rwanda Uganda 12.00 12.05 11.23 11.26 Kenya 2.00 2.01 1.87 1.88 Tanzania 185.00 185.81 173.07 173.53 Source: Author’s Simulations from ITC Database and Simulation results 5.4 Consumption and Food Poverty Impacts Food security is largely affected by how much food is available for consumption. The increase or decrease in private consumption, was further disaggregated in order to establish how each household category was affected in terms of change in consumption. Figure 5.1 presents the results of food poverty incidence due to climate change. Comparison was made for the base scenario and scenario 3, given that scenario 1 and 2 were intermediate steps for achieving scenario 3 The increase or decrease in private consumption, was further disaggregated by household quintiles in order to establish how each household category was affected in terms of change in consumption. Figure 3.1 presents the results of food poverty incidence due to climate change. In Kenya, the base scenario food poverty incidence was at 39.01 in the climate change scenario, with a 0.04 per cent 50 Results and Discussion decrease in private consumption, food poverty incidence increased to 39.03 per cent. The reduction in private consumption has resulted increase in food poverty. This can largely be attributed to the country’s consumption basket, which is largely made up of maize approximately 58 per cent of households consume maize, so any negative change in consumption will result in less of the food being available to the households, this makes a household food insecure. Even though the percentage changes in food poverty incidences were found to be generally low, when this is translated to number of households, the number would show a different picture. At the Base scenario, there were 3,316,314 poor households, with climate change, an additional 1,700 households become food poor. In Tanzania, food poverty incidence was at 3.51 per cent in the base scenario, however, when private consumption decreased to 2.05 per cent in climate change scenario, food poverty incidence remained at 3.51 per cent. This is because the most commonly consumed products by Tanzanian households: cereals, vegetables, forestry and poultry were not affected by climate change, the effect associated by reduction in the production of maize, was not felt in the households even though 15 per cent of households consumed maize, due to substitution effects. At the base scenario, there were 326,430 poor households, with climate change, the number of poor households remain the same. In Uganda, food poverty incidence was at 9.71 per cent, however, when private consumption decreased by 4.3 per cent in the climate change scenario 1, this resulted in food poverty incidence of 9.28 per cent. Conventionally, it is expected Figure 5 1: Food Poverty Impacts (Simulation Scenarios) Source: Author’s calculations from Various Household Survey Data 51 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community when private consumption decreases, food security incidence goes up. However, in the case of Uganda, decrease in private consumption resulted in the decrease in food poverty incidence, for several reasons, first, the decrease in private consumption largely affected non- poor households, secondly, commodities like sweet potatoes and forestry, which is largely, consumed by households recorded increases in production this improved the welfare of most households even though private consumption decreased in general. Lastly, information could have been lost between the social accounting matrix and the household survey data, the households are classified by rural, urban and farm and capital, while the households survey data classifies these households by quintiles. Matching these households could have resulted in some lost information. With climate change, the numbers of poor households reduce by 26,600 from the base number of 602,020 households. Rwanda, unlike most of the other EAC countries, has very positive results on food poverty incidence due to climate change. In the climate change scenario, food poverty incidence reduced to 11.94 per cent following a 37 per cent increase in private consumption. One major challenge encountered with the Rwanda data is that the Social accounting matrix had all households lumped together as one, however, in the household survey data, the household were divided into quintiles, this meant that all households were assumed to bear the same impact of changes in private consumption, however, in reality different households, classified by quintiles or location rarely have unitary impacts associated with changes in private consumption. With an original number of 626,351 households being food poor, more than half the households, 336,818 households move out of food poverty. 52 6. Conclusion and Policy Recommendations The objective of the study was to establish the effect of climate change on agricultural production (supply), and how the four EAC countries meet their needs through trade (imports and exports) and the overall impact on food security. The study used several models to answer these questions. First the study used a log- linear regression model to model the effects of climate change on crop yield, the productivity changes from the model were then used to shock the CGE model in order to determine how trade and private consumption change due to changes in productivity triggered by climate change. The consumption changes derived from the CGE model were then simulated in a micro model in order to establish the changes incidence of food security in the selected EAC countries. The study found that climate change would overall reduce private consumption in three EAC countries (Kenya, Uganda, and Tanzania), which would in turn result in increasing food poverty incidence. There were cases particularly in Uganda where food poverty incidence did not increase even though private consumption decreased. This was largely attributed to the particular commodities where consumption decreased and the household’s quintiles, which consumed these commodities. Even though the study focused on six food security crops: (maize, rice, beans, wheat, sorghum and millet), for each of the four EAC countries that were considered, the main food crop consumed by the households varied, for example in Kenya and Tanzania, maize was consumed by more than 15 per cent of the households, making it get ranked the main food crop consumed by these households. In Uganda and Rwanda, the top food crops consumed were pulses (beans) and Irish potatoes respectively. In providing policy recommendations, the changes in rainfall and temperature affect crops that are important for food security in Kenya, Uganda and Tanzania as compared to rainfall and temperature variability, these changes affect the planning activities by farmers, hence adequate planning by farmers to circumvent changes in rainfall and temperature patterns is paramount. The respective governments also improve their meteorological facilities so that weather patterns can be predicted and reported more accurately. This will also enable farmers’ plan well. There should be more investments in irrigation schemes that help in mitigating rainfall variations, by ensuring steady water supply for the crops. In order to address the challenge of temperature change, the respective governments could invest in seeds that can withstand varied temperatures. 53 Assessing the impact of climate change on agricultural production, trade and food security in the East Africa Community References Challinor A.J., T.R. Wheeler, P.Q. Craufurd, J.M. Slingo “Simulation of the impact of high temperature stress on annual crop yields” Agricultural and Forest Meteorology, Volume 135, Issue 1, Pages 180-189. 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L. and Lambi C.M (2007). “The Economic Impact of Climate Change on Agriculture in Cameroon” World Bank Policy Research Working Paper No. 4364. Ray D. K., Gerber, J. S., Macdonald G.K., West P.C. (2015) “Climate Variation Explains a Third of Global Crop Yield Variability” Nature Communications, www.nature.com/naturecommunications. Rimi, R.H.; Rahman, S.H.; Karmakar, S.; Hussain, S.G. Trend Analysis of Climate Change and Investigation on its Probable Impacts on Rice Production at Sathkhira, Bangladesh. Pak. J. Meteorol. 2009, 6, 37–50. Scavione, A. (2010), “Trade and Climate Change Implications for Food Security in Mainland Southeast Asia” http://www.iisd.org/tkn/pdf/trade_climate_ security_southeast_asia_brief.pdf Sen A. (1979) “Issues in the Measurement of Poverty”. The Scandinavian Journal of Economics, 81(2): 285-307. Skoufias E., Vinha K, Conroy H.V. (2011) “The Impacts of Climate Variability on Welfare in Rural Mexico” World Bank Policy Research Working Paper 5555. Temesgen T. 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