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Forests, Livelihoods and Poverty Alleviation: the Case of Uganda Forests, Livelihoods and Poverty Alleviation: the Case of Uganda

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Forests, Livelihoods and Poverty Alleviation: the Case of Uganda Forests, Livelihoods and Poverty Alleviation: the Case of Uganda Forests, livelihoods and poverty alleviation: the case of Uganda Forests, livelihoods and poverty alleviation: the case of Uganda G. Shepherd and C. Kazoora with D. Mueller Food and Agriculture Organization of the United Nations Rome, 2013 The Forestry Policy and InstitutionsWorking Papers report on issues in the work programme of Fao. These working papers do not reflect any official position of FAO. Please refer to the FAO Web site (www.fao.org/forestry) for official information. The purpose of these papers is to provide early information on ongoing activities and programmes, to facilitate dialogue and to stimulate discussion. The Forest Economics, Policy and Products Division works in the broad areas of strenghthening national institutional capacities, including research, education and extension; forest policies and governance; support to national forest programmes; forests, poverty alleviation and food security; participatory forestry and sustainable livelihoods. For further information, please contact: Fred Kafeero Forestry Officer Forest Economics, Policy and Products Division Forestry Department, FAO Viale Delle terme di Caracalla 00153 Rome, Italy Email: [email protected] Website: www.fao.org/forestry Comments and feedback are welcome. For quotation: FAO.2013. Forests, Livelihoods and Poverty alleviation: the case of Uganda, by, G. Shepherd, C. Kazoora and D. Mueller. Forestry Policy and Institutions Working Paper No. 32. Rome. Cover photo: Ankole Cattle of Uganda The designations employed and the presentation of material in this information product do not imply the expression af any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. All right reserved. Reproduction and dissemination of material in this information product for educational or other non-commercial purposes are authorized without any prior written permission from the copyright holders provided the source is fully acknowledged. Reproduction of material in this information product for resale or other commercial purpose il prohibited without written permission of the copyright holders. Applications for such permission should be addressed to the Chief, Publishing Management Service, Information Division, FAO, Viale delle Terme di Caracalla, 00153 Rome, Italy or by e-mail to [email protected] © FAO 2013 iii CONTENTS Acknowledgements . vii Executive summary . ix Introduction ix Methodology x Desk analysis x Fieldwork results xi Developing a national-level picture v Conclusion xv Acronyms . xvi 1. Introduction . .1 2. Methodology . .3 3. Desk analysis: data and methods . .5 Poverty 5 Tree cover 5 Analysis method 6 Results 6 4. Collection of field data on contexts, poverty and forest uses in the districts . 13 Tools used 13 Wealth ranking 14 Masindi District 16 Kibaale District 22 Kumi District 29 Lamwo (Kitgum) District 36 5. Field data – summary overview . 43 Contrasts among the four districts’ use of natural resources 43 Important forest products in the sample villages 44 Cash and non-cash forest income across all field sites 47 iv 6. Combining field data, Humboldt University’s categories and national-level data . 51 The rural-urban split in the four regions 53 Rural per capita income in the regions 53 Total rural income by region and forests and poverty category 54 Forest income at the national level 56 7. Conclusions . 59 Previous valuations of forest in Uganda 59 This report’s valuation of forest 60 Pathways out of poverty 60 Next steps 62 References . 63 Annex 1. Background tables: detailed calculation of rural income . 65 v Maps 1 Poverty incidence and density in Uganda 7 2 Tree cover in Uganda at various density levels 9 3. Comparison of LISA maps of tree cover (10, 20, 30 and 40 percent) and poverty rate 10 4 Selected districts and villages 11 5 Uganda’s 110 districts 51 6 Final classification by forests and poverty relationship 52 Tables 1 Selected districts and villages 11 2 Example of wealth ranking (Kachaboi village, Kumi District) 14 3 Households ranked by local wealth and poverty criteria in the eight villages 15 4 Masindi District – basic data 16 5 Kibaale District – basic data 22 6 Kumi District – basic data 29 7 Lamwo (Kitgum) District – basic data 36 8 Important forest products for cash income, aggregated (numbers of mentions) 45 9 Important forest products important for home use/consumption, aggregated (numbers of mentions) 46 10 Relative importance of categories of forest product for cash and non-cash income 47 11 Cash and non-cash forest income, by village location and household wealth category (numbers of mentions) 49 12 Cash and non-cash forest income, by village location and gender (numbers of mentions) 49 13 Proportions of urban and rural population, by region, 2006 53 14 Household consumption expenditure, 2005/2006 53 15 Main income sources for the rural population, by region (Annex 1, Tables 3, 4, 5 and 6) 55 16 Main income sources for the rural population, by forests and poverty category (Annex 1, Tables 7, 8, 9 and 10) 55 17 Total annual value of forest products to rural people 56 18 Hypothetical total rural income once stability has been established in all four regions 57 vi Figures 1 Tree cover percentages along varying threshold values 8 2 Relative wealth and poverty levels, by local criteria, in the eight villages 15 3 The two villages selected in Masindi District 18 4 Comparisons of wealthier and poorer villagers in Kyangamwoyo and Kilanyi 19 5 Cash income from forests in Kyangamwoyo 20 6 Non-cash income from forests in Kyangamwoyo 20 7 Cash income from forests in Kilanyi 21 8 Non-cash income from forests in Kilanyi 21 9 The two villages selected in Kibaale District 24 10 Comparisons of wealthier and poorer villagers in Paachwa and Kiryanga 26 11 Cash income from forests in Paachwa 26 12 Non-cash income from forests in Paachwa 27 13 Cash income from forests in Kiriyanga 27 14 Non-cash income from forests in KIriyanga 28 15 The two villages selected in Kumi District 32 16 Comparisons of wealthier and poorer villagers in Ongino Trading Centre and Kachaboi 33 17 Cash income from forests in Ongino Trading Centre 34 18 Non-cash income from forests in Ongino Trading Centre 34 19 Cash income from forests in Kachaboi 35 20 Non-cash income from forests in Kachaboi 35 21 The two villages selected in Lamwo District 39 22 Comparisons of wealthier and poorer villagers in Palabek Ogili Trading Centre and Padwat 40 23 Cash income from forests in Palabek Ogili Trading Centre 41 24 Non-cash income from forests in Palabek Ogili Trading Centre 41 25 Cash income from forests in Padwat 42 26 Non-cash income from forests in Padwat 42 27 Livelihood sources in all four districts 44 28 Relative importance of categories of forest product for cash and non-cash income 47 vii ACKNOWLEDGEMENTS At FAO, grateful acknowledgement is made to Fred Kafeero, Mette Loyche-Wilkie, Eva Mueller, Ken MacDicken, Ewald Rametsteiner, Sophie Grouwels and Adam Gerrand. The Sustainable Development Centre in Kampala would like to thank the following people for their contributions to the study: Zainab Birungi, Frank Kizza, Agnes Twebaze, Cornelia Asiimwe, Prisca Kisembo, Robert Esimu, Sarah Akello, Lilian Wanican, Mercy Alungat, Emmanuel Okalang, John Robert Elatum and Abok Openytho. The authors would also like to acknowledge the essential analytical work and assistance of Cornelius Senf, who worked with Daniel Mueller at Humboldt University in Berlin. ix EXECUTIVE SUMMARY INTRODUCTION The value of forests to the livelihoods of poor people has been under discussion for about 15 years. There is growing interest in the roles that forests play in supporting the poor, reducing their vulnerability to economic and environmental shocks, and reducing poverty itself. However, these debates have gone on largely among forestry researchers, while policy-makers in key non-natural resource ministries – such as those of finance, planning or local government and others responsible for poverty reduction – are often unaware of forests’ contribution towards poverty reduction. At the same time, forestry ministries are only beginning to face the challenge of demonstrating the ways in which forests contribute to poverty reduction. In most countries, very few data are available to illustrate how forests contribute to the livelihoods of poor households. Poverty reports tend to underestimate the contribution of forests – and off-farm natural resources in general – to livelihoods, while forestry reporting is typically in terms of the physical resource and its status and extent. Such reporting sheds no light on the contributions made by forests to the lives of the poor. It is therefore very significant that FAO is seeking to stimulate the collection of data about local people’s reliance on forests in the context of their national forest monitoring systems , and many developing countries have asked FAO for assistance in these efforts. Including these aspects in the Global Forest Resources Assessment (FRA) will further encourage governments to start collecting them, which will result in more official recognition of forests’ value at the national level and fuller assessment of that value internationally. Data gathering on forests and poverty will in due course need to be built into or at least strongly linked to other government data collection processes such as living standards surveys, household budget surveys and censuses. One process would be to: i. undertake a few country studies to investigate the ways in which forests support local livelihoods, and the overall importance of these forest contributions at the national level; ii. work with national bureaux of statistics and the World Bank to develop forest-oriented modules for incorporation in data collection instruments; iii. institutionalize this data collection in the long term, also facilitating the collection of data for FRA in the future.
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