global livestock and poverty mapping meeting · overview of the pro-poor livestock policy...

Pro-PoorLivestockPolicyInitiative

Global Livestock andPoverty Mapping Meeting

A Living fromLivestock 6-7 February 2003 - FAO Headquarters, Rome

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TABLE OF CONTENTS

Overview of the Pro-Poor Livestock Policy Initiative Joachim Otte ..........................................1 Introduction ............................................................................................................2 The role of the central facility.....................................................................................2 The role of the regional hubs.......................................................................................4 Objectives of the meeting ..........................................................................................4

SESSION I: LIVESTOCK DISTRIBUTION AND PRODUCTION SYSTEMS

What is needed to map livestock - From data collection to extrapolation Willy Wint ...................6 Introduction ............................................................................................................7 Why? .....................................................................................................................7 What? ....................................................................................................................7 Where and when? .....................................................................................................8 How? .....................................................................................................................8 Livestock in a wider context...................................................................................... 11 What next?............................................................................................................ 12 References............................................................................................................ 14

Global pasture productivity assessment for baseline climate, historical climates and climate change Guenther Fischer ....................................................................................................... 15

Introduction .......................................................................................................... 16 Pasture adaptability................................................................................................ 16 Results for baseline-climate ...................................................................................... 17 Climate scenarios ................................................................................................... 20 CO2 fertilization assumptions..................................................................................... 21 Results with climate change...................................................................................... 21 Collaboration with IIASA-LUC..................................................................................... 23 References............................................................................................................ 24

Characterising livestock production systems and their linkages to poverty John Dixon................ 26 Why characterise?................................................................................................... 27 Livestock system characterisations ............................................................................. 27 Framework for livelihoods and poverty reduction ........................................................... 28 Choice of system .................................................................................................... 31 Farming systems and poverty: with a livelihoods perspective ............................................ 31 Ways forward ........................................................................................................ 34 References............................................................................................................ 34

Farming systems under climate change: impacts on poor people in the tropics Philip Thornton.... 35 Background ........................................................................................................... 36 What may happen in the agro-ecosystems on which the poor depend? ................................. 36 What are the options available for poor smallholders in the future? .................................... 39 What is needed in the future?.................................................................................... 41 References............................................................................................................ 42

SESSION II: POVERTY, WELFARE AND VULNERABILITY

Global population mapping Andy Nelson and Deborah Balk.................................................. 45 Introduction .......................................................................................................... 46 LandScan 2001 ....................................................................................................... 46 GPW version 2........................................................................................................ 47 GPW version 3 and the “urban-rural” database .............................................................. 47 Endnotes .............................................................................................................. 48 Data sources.......................................................................................................... 50 References and further reading.................................................................................. 50

Table of Contents

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Methods for high spatial resolution population maps for Kenya and beyond Simon Hay............... 51 Summary .............................................................................................................. 52 The problem.......................................................................................................... 52 Project overview .................................................................................................... 53 References............................................................................................................ 54

Mapping poverty and inequality Gero Carletto ................................................................. 55

Poverty, intervention and livelihoods mapping: Some examples from ILRI’s research in Kenya Patti Kristjianson and Russ Kruska ................................................................................. 58

Introduction .......................................................................................................... 59 Poverty maps and analyses using these maps................................................................. 59 Poverty intervention mapping and development of new tools ............................................ 60 Livelihood maps ..................................................................................................... 61 Community and household-level surveys....................................................................... 62 References............................................................................................................ 63

Poverty, ecosystems, and vulnerability: Spatial data issues Norbert Henninger ....................... 64 Introduction .......................................................................................................... 65 Opportunities for mapping poverty and human well-being ................................................ 66

SESSION III: LINKING LIVESTOCK AND POVERTY

Understanding how livestock can contribute to poverty reduction: What should we be mapping? Brian Perry and Tom Randolph ...................................................................................... 70

Introduction .......................................................................................................... 71 The development of poverty reduction policies.............................................................. 71 The spatial aspects of poverty reduction ...................................................................... 72 The need to understand the spatial aspects of processes.................................................. 73 The role of spatial data and analyses in ex ante modelling of livestock policy options ............. 75 References............................................................................................................ 76

Summary of group discussions Dirk Pfeiffer ..................................................................... 77 Livestock distribution and production systems ............................................................... 78 Population, welfare & vulnerability............................................................................. 80 Linking livestock to poverty....................................................................................... 81 References............................................................................................................ 82

Contact details of meeting participants........................................................................... 83

Overview of thePro-Poor Livestock Policy Initiative

Joachim OtteProject Coordinator Pro-Poor Livestock Policy Initiative

Overview of the Pro-Poor Livestock Policy Initiative

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Introduction The overall goal of the Initiative is to enhance the contribution of the livestock sector to poverty reduction through the effective use of policy and institutional instruments. The Initiative will be operationalized through a Central Livestock Policy Facility (PPLPF) at FAO HQ and a system of Regional Modules/Hubs.

The project is focusing on policy and institutional change because historically, institutional change has usually been the precursor to technological change throughout the transition from extensive livestock farming to agricultural intensification and industrialization and back to extensification of agriculture in post-industrial societies. One of the main driving forces of agricultural development has been population pressure, while cost of labour, capital inputs and availability of land have shaped the direction of development. Key institutional changes in the transition process include changes in access rights to land and water, development of markets and contractual regulations and finally rules for conservation and protection. The transition process broadly corresponds to the three scenarios outlined in the project description: reducing vulnerability, creating conditions for growth and coping with growth.

Institutions are, however, not created to be socially efficient. They are created to serve the needs of those with the power to set the rules, i.e. they are adverse to change. Thus the Initiative will have to form alliances with like-minded partners across regions and issues to generate sufficient pressure to bring about the desired institutional change.

The role of the central facility The central facility will build on FAO’s comparative advantage as an intergovernmental organization with a global mandate of guiding national and international policy towards the achievement of the Millennium Development Goals . In addition, the central facility guides, co-ordinates, links and provides technical support to the Initiative’s regional activities as well as to other partners joining the Initiative.

Standardization and harmonization of methodological approaches In order to ensure comparability of the results obtained in the various regions, the central facility will seek to develop appropriate methodological approaches for the analysis of the impact of alternative policies on the livelihoods of livestock-dependent poor. These developments will build on capacities within FAO but also draw upon the expertise and experience of reputed academic and research institutions. Core issues to be addressed are the inter-sectoral linkages, i.e. how does the livestock sector react to changes in other sectors of the economy and vice-versa, and the differential impact of selected policies on various actors within the livestock sector. The central facility will support the regional counterparts in the application of these methods and in their capacity building activities.

Information management, dissemination and development of decision support tools

The central facility will compile and collate the datasets that are required to inform and guide the development of pro-poor livestock policy, at global, regional and national levels. Policy analysis will be conducted based on relevant information such as the distributions of poor livestock keepers, livestock and livestock production systems, constraints to livestock health and production, livestock services, markets


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and market access. Due to spatial characteristics of many of these key variables geographic information systems and spatial analysis will be heavily utilised.

Decision support tools are under development that will enable analysis of the effects of existing policies on poor livestock keepers, modelling the impact of changing institutional and market environments on the outcomes of existing policies; prediction of the impact of policy changes on different groups of livestock producers and consumers; and identification of opportunities for intervention through adjustment of livestock-related policies.

These decision support activities will be supported by detailed inventories and reviews of livestock related policies, by bibliographic databases that make relevant information accessible to policy makers and analysts, by the development of Internet-based tools for the dissemination of information and of project outputs, and by the development of systems to monitor and evaluate the effects of implementing policy change.

Cross-regional assessment of key issues The central facility will carry out cross-regional or global assessments of issues recurrently identified as having a major impact on the livelihoods of poor livestock keepers in the regional debates. These assessments will provide support to policy makers and negotiators from the regions as they engage in policy dialogue at the international level, for example with international standards setting bodies.

One recurring issue for example is that of the effects of trade liberalisation on small holders in developing countries in the face of OECD member state subsidies to their farmers to the tune of USD30 billion per year. Another example of an issue regularly brought up in the national and regional policy debates surrounding the livestock sector is that of the increasing market power and political influence of Multinational Companies, often seen to be used to the detriment of small-scale producers. Both are particularly felt in the dairy sector, where small-holder farmers still predominate.

Informing policy debates The central facility endeavours to feed the materials and insights emerging from the policy analyses and cross-regional assessments into high-level global debates to ensure that the local concerns are included in the international livestock policy dialogue. It will aim to inform the stakeholders about the trade-offs and distributional implications of alternative courses of action as well as about the linkages between policy measures at various levels and thereby increase the awareness policy makers have of the impact that their decisions have on poor livestock keepers. This requires careful selection of priority arenas, topics and the development of partnerships. Examples of the type of policy arena that could be relevant for the Facility are the New Programme for Africa’s Development (NEPAD), the ongoing WTO negotiations, the EC CAP reform process and the Office International des Epizooties (OIE).

Forming coalitions for change There is a significant number of producer and civil society organizations, both at national and international level, that have a strong interest in improving rural livelihoods, and the acceptance is growing, that significant impacts can only be achieved through policy and institutional changes. The central facility will form coalitions with like-minded initiatives to push jointly for pro-poor policies, both in developing and developed countries, against the vested interests of powerful lobbies and to work towards the achievement of greater policy coherence at all levels of policy making.


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The role of the regional hubs These are an essential and integral part of the Initiative that will form the knowledge-base of the important policy issues affecting poor livestock keepers in the selected regions. It is to a large extent upon this knowledge that the central facility will base its efforts to support pro-poor livestock interventions at the international level. It is through these regional hubs that the majority of project outputs will be made operational.

Identification and analysis of issues One of the principal tasks of the regional hubs will be to analyse a range of issues at the local, national and regional scale. The development of an operational typology of livestock keepers and the identification of the main policy and institutional constraints they face will be one of the first steps undertaken.

Knowledge sharing and capacity building The regional hubs will identify and bring together those individuals and organizations who should be sharing information and knowledge. This should include donors working in complementary fields who, currently, have very little contact with each other but through collaboration could operate much more effectively.

Another important aspect of the regional hubs will be to enhance local capacity for policy impact analysis, policy formulation and policy negotiation of all interested stakeholders, but particularly of groups representing the interests of poor livestock keepers.

Negotiation and fostering the policy dialogue The regional hubs will aim to foster the formation of pro-poor alliances and partnerships, the effect of which should be to strengthen lobbying power in their favour.

Regional hubs will identify or establish negotiating fora where the policy dialogue can take place in a neutral atmosphere.

Objectives of the meeting This meeting is organized by the Pro-Poor Livestock Policy Initiative to bring together experts from various disciplines and organizations to exchange their experiences in livestock and/or poverty mapping and to:

! Define the essential spatial information required for systematic targeting of livestock dependant poor;

! Identify methodologies and models for linking ‘spatial layers’ for systematic targeting of livestock dependant poor, and to

! Develop partnerships, collaborative links and working arrangements towards the goal of improving the collective capacity of livestock and poverty mapping with the aim of better targeting interventions for poverty reduction.

SESSION I:

LIVESTOCK DISTRIBUTION AND PRODUCTION

What is needed to map livestock -from data collection to extrapolation

William WintEnvironmental Research Group Oxford Ltd

Oxford, United Kingdom

What is needed to map livestock - From data collection to extrapolation

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Introduction

“Livestock make an important contribution to most economies. Livestock produce food, provide security, enhance crop production, generate cash incomes for rural and urban populations, provide fuel and transport, and produce value added goods which can have multiplier effects and create a need for services.” (FAO 1996a)

Though these roles alone justify considerable efforts to map the distribution of livestock in as much detail as possible, there are several other incentives to devote significant resources to what has been termed ‘livestock geography’.

There are a series of crucial decisions that must be made before any substantial livestock mapping effort is undertaken: Why the maps are needed, which in turn determines What is to be mapped; Where and to When must the maps refer, and how much detail is needed; and How should the maps be produced and how often should they be updated.

Why?

Justifications for livestock mapping include (in no particular order):

! the need to spatially describe the environmental impact of livestock, through greenhouse gas emissions, vegetation cover and botanical composition, overgrazing and land degradation, nutrient balance and effluent pollution, (Carter et al. 2000)

! the contribution of livestock data to calculation of (rural) poverty indicators or in identifying livelihood strategies, primarily in developing countries, and increasingly in remote areas – thereby contributing to targeted development,

! the need to determine levels of financial subsidies or tax liabilities, ! the necessity of estimating animal populations at risk from current and emerging

diseases, ! the need to identify and project the likely demand for livestock feed, the land

resources required to produce it, and the potential for conflict with the requirement for producing crops for direct human consumption,

! assessing livestock production and its requirements in relation to other possibly competing natural resource sectors (wildlife, forestry, amenities), and

! prioritising agricultural and environmental research. (Lubulwa et al. 2000)

What?

Bovines (cattle, buffaloes and yaks) have long been, and continue to be, widely perceived as the most important livestock species, presumably because they are large (and hence relatively valuable), they produce a range of valued commodities (meat, milk, hides), their ownership brings with it comparatively high status, they are a major source of draught power, and they are comparatively easy to count.

In some ways, however, the other livestock are more significant: sheep, and particularly goats, are more widely owned by the rural poor, and are suited to a wider range of environments; the monogastric species (poultry and pigs) are less tied to the land, and are well suited to intensive or peri-urban production, whilst remaining a feasible source of protein for rural farmers. Less ubiquitous species such as camels and llamas, or less numerous ones like horses, donkeys and asses, all have significant roles in rural economies.


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As a consequence, it is important to remember that monitoring or mapping livestock should incorporate as many species as possible. Further, livestock species composition is a dynamic quantity that imparts useful information in its own right.

Details of animal numbers are only a part of the information required to effectively describe livestock systems. A wide range of additional parameters are needed to provide a comprehensive information base, for example: age and sex ratios, productivity parameters, levels of trade, management practices, and breed diversity. All are amenable to mapping, provided the units used are carefully chosen.

Where and when?

In general, however, the availability of these types of information is heavily scale and development dependent: numbers, and biomass, may be available at sub-national resolutions, but production, and trade figures are available globally but usually at country level only for developing countries.

The numbers of many livestock populations, especially those in developing countries or marginal environments, also vary with the seasons, as a consequence of traditional management practices such as trans-humance and nomadic pastoralism. Mapping such populations may not prove feasible, but the potential contrast between wet and dry season distributions which result from trans-boundary movements, is a major factor to be considered when comparing population estimates from different times and places.

The scale of mapping is also usually related to the spatial resolution of the information. National data tend to be represented at global or regional scale, whilst sub-national data are rarely available to provide contiguous global coverage unless some form of extrapolation or interpolation is used to fill the gaps.

How?

In order to understand why this situation arises it is necessary to recognise the ways in which livestock data are collected. Livestock statistics are usually collected as part of more general censuses of agriculture, undertaken periodically by national governments. Agricultural censuses are organised in various ways in different countries, depending upon the resources available, the importance of agriculture and institutional traditions. Many countries restrict their efforts to obtaining data from a single agricultural census, every 5-10 years.

Census techniques vary from country to country, often in concert with economic development. In countries such as the UK and USA, for instance, agricultural census information is obtained directly from farmers who are required by law to provide information requested in periodic, postal questionnaires. It is important to recognise that many developing countries do not have adequate systems of collecting, analysing and reporting agricultural (or, indeed, human) population statistics. Available information about cropped areas and livestock resources is, therefore, often incomplete and of doubtful reliability.1 It is for this reason that alternative means of assessing land cover and livestock resources need to be considered for remote and inaccessible regions of many developing countries, especially in Africa.

1 On its FAOSTAT web site, the Food and Agriculture Organisation of the United Nations acknowledges that “many

developing countries still do not have an adequate system of statistics pertaining to the agricultural sector. Some of the available agricultural data are incomplete [and] even when data are available, their reliability may be questionable.” Source: http://apps.fao.org/notes/datasources-e.htm

http://apps.fao.org/notes/datasources-e.htm


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Neither air nor ground surveys are totally satisfactory for livestock, as ground counts tend to be road biased, whilst air counts miss an appreciable fraction of (often young) livestock hidden in settlements, cannot always differentiate reliably between for example sheep and goats, and miss those species that are too small to see from the air, especially poultry. Information on flock and herd structure is also lacking.

A solution is to use integrated air and ground survey techniques. This combines standard aerial survey assessments of livestock numbers with supplementary estimates of animals that cannot be counted directly from the air, assessed indirectly by aerial counts of rooftops in conjunction with sample ground surveys of livestock per rooftop (Bourn et al. 1994; Anon 1992).

It is rare that the livestock data collected by any of these methods have no gaps, are sufficiently extensive and are at high enough resolution to satisfy the ever increasing demand for animal maps. As a result, some form of extrapolation or interpolation is often needed.

Interpolation can be an appropriate tool for ‘improving’ point data. Logistic regression or discriminant analysis methods can also be used to ‘fill in the gaps’, but are largely restricted to using binary presence/absence or ranked training data.

Various weighting techniques have also been used to try to assign national population figures within countries. The least contentious methods ‘remove’ the animals from ‘unsuitable’ areas (e.g. glaciers, deserts, steep slopes, dense forest, cities, water bodies and protected areas) and add them to the ‘habitable’ areas. More ambitious (and thus less assured) methods have utilised the link between domestic livestock and human densities in partitioning national figures for populations as shown in Figure 1 (FAO 1996b).


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Figure 1: Example of using habitat suitability to generate a corrected map of predicted cattle density for South America

Extrapolation, based on some established statistical relationship(s) between livestock numbers and a variable or variables for which data are available for all the areas of interest, is also a possible means for filling data gaps – provided the extrapolation is not taken beyond the value limits of the training data. The Food and Agriculture Organisation of the United Nations (FAO) has devoted considerable resources towards developing this technique at a continental scale (FAO 1998; FAO 1999). An example of such a map is presented in Figure 2.


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Figure 2: Map of predicted cattle density for four continents

Such comparatively high resolution maps of animal densities have recently been used as a basis for detailed mapping of cattle production levels (for meat and milk) by assigning production parameters to cattle within each of the major production systems in each agro-ecological zone in each sub-continental region (Otte et al. 2001; FAO 1999).

There is, of course, a real danger that these predictive techniques may conceal or generate errors - it is all too easy to be seduced by the fact that a somewhat messy map of fairly reliable data has been converted into an aesthetically more pleasing one, without holes and apparently believable content. As little of the verification is likely to be accomplished using new (expensive) survey data but rather using the original polygon data, validation is also prone to problems. Thus, any variation generated within the polygon (a primary objective for the prediction in the first place) will be seen as deviation from known data (and thus error). On the other hand, as the predictions used are statistically based and designed to be interpreted en masse rather than individually, pixel by pixel comparisons are equally invidious and error prone. This suggests that a high resolution prediction can only be effectively validated when aggregated to an administrative level where summary statistics are available.

Livestock in a wider context

Livestock cannot be considered in isolation from their surroundings, nor should they only be mapped as single entities. The established link between livestock numbers, human populations and cultivation levels argues for an effort to quantify and map these associations, and, with improving Geographic Information System (GIS) capabilities, there begins to be ample potential for such combination.

These systems studies have long been the subject of qualitative definition but in recent years, as the information revolution has taken hold, a quantitative approach has become more feasible. The most widely used definitions at present identify four livestock production systems (livestock grazing, landless livestock, rainfed livestock and crops, and irrigated livestock and crops) in three agro-ecological zones described by length of growing period and temperature (FAO 1996a). Considerable effort has been made by The International Livestock Research Institute (ILRI) to produce global maps of these systems (Thornton et al. 2002). Seven broad categories of farming systems have also recently been mapped in a comprehensive global study by the


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World Bank and FAO which uses the available natural resource base in conjunction with the dominant farm activities and household livelihood patterns to define them (Dixon et al. 2001). Simple statistical classifications based on the levels of cattle, cultivation, human populations and elevation have also been attempted, and can certainly delineate areas where these parameters are similar in numerical, if not qualitative, terms as shown in Figure 3 (FAO 2000).

Figure 3: Farming systems map based on agricultural systems classification

What next?

The livestock sector is the poor relation in the agricultural development spectrum in terms of resources devoted to it, yet livestock ownership often confers high status in the developing world, and can easily grab headlines in the developed countries (mad cow disease, foot-and-mouth disease etc). However, remote sensing, statistical know-how, and the rapid proliferation of electronic information have brought us to the edge of a quantum jump in what is possible. With current techniques and emerging technologies it has become feasible to assemble more credible, standardised datasets of regional and global livestock resources and production. A number of priorities emerge. Perhaps the most pervasive challenge is to change the target resolution of livestock mapping from country to sub-national administrative units at the least, or using the statistical predictive techniques, perhaps at image resolutions in the range of a few kilometres. The information is available and requires a fairly modest investment of resources into the acquisition, collation and analysis of existing data, such as those ‘buried’ in census reports and statistical year-books. Setting up enhanced (sub-) national data reporting networks to international agencies would pay immediate dividends, provided, of course, it was reliably geo-referenced. The desired resolution enhancement techniques could soon become sufficiently reliable to provide livestock related inputs to contribute to the high profile information sectors mentioned in preceding paragraphs - poverty, food security, environmental impact and degradation, and diseases and their economic impact.

In the context of an ongoing “Livestock Revolution” it is increasingly important to locate, quantify and monitor rapidly rising populations of intensively reared poultry and pigs. A similar plea can be made for more attention to be given to small ruminants - which have less economic, climatic or dietary constraints to ownership than the high profile bovines.


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Most livestock mapping efforts to date have been directed at livestock numbers or densities, yet their major impact is through the production and economic value they represent. The tentative steps that have been taken in mapping livestock (cattle meat and milk) production, though laudable, overlook the remaining species and their produce, not to mention the need to account for and map many components of economic value in the dollar terms that attract the attention of donors and national budgetary planners (FAO 1999).

Livestock eat, and they often eat fodder grown on land that could equally well produce crops that people eat. Livestock also affect the environment they inhabit – through methane production, by returning nutrients to the soil in extensive systems, by producing concentrated effluent in intensive systems, through the effects of (over-) grazing, particularly in adverse seasons, and by displacing wildlife to name but a few.1 Efforts to quantify these effects can only be successful if estimates of livestock numbers are both reliable, and available, at the appropriate resolution.

Livestock husbandry, its interaction with other aspects of agriculture, and with other natural resource sectors, are intimately linked with the ‘socio-economics’ of animal keeping but largely elude widespread integration into quantitative cartography and analysis. The mapping and delineation of livestock (and farming) systems are an expanding area of study as geographic data become more widely available, and sharing data becomes more the norm than the exception. There is a danger, however, that definitions will proliferate to such an extent as to cause confusion rather than clarity, unless there is effective coordination of approaches and targets.

The majority of the preceding discussion has (intentionally) sidestepped the fact that livestock populations change, and that many livestock management practices involve substantial movement in pursuit of seasonal grazing or trade. Livestock distributions are thus in a state of perpetual flux. In this context, a major challenge for livestock mapping and monitoring in the future must surely be to turn the essentially static snapshots we can produce today into the dynamic descriptions and attendant projections that we will need for tomorrow.

1 The Livestock, Environment and Development Initiative (LEAD, www.fao.org/lead) has produced a global nutrient balance map, which attempts to address this question.


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References Anon 1992. Nigerian livestock resources. Volume I: Executive Summary and Atlas,

pp30 + 21 maps; Volume II: National Synthesis, pp xxiv + 440; Volume III: State Reports pp441; Volume IV: Urban Reports and Commercially Managed Livestock Survey Report pp345. Federal Government of Nigeria and Resource Inventory and Management Limited. Oxford: ERGO, Oxford, UK.

Bourn D., Wint W., Blench R. and Woolley E. 1994. Nigerian livestock resources survey. World Animal Review 78 (1): 49-58.

Carter J., Hall W., Brook K., McKeon G., Day K. and Paull C. 2000. Aussie GRASS: Australian grassland and rangeland assessment by spatial simulation. In G.L. Hammer, N. Nicholls and C. Mitchell (eds) Applications of seasonal climate forecasting in agricultural and natural ecosystems, the Australian experience. Kluwer Academic Publishers, Dordrecht, The Netherlands. pp 329-349.

Dixon J., Gulliver A. and Gibbon D. 2001. Global farming systems study: Challenges and priorities to 2030. FAO, Rome, Italy.

FAO 1996a. World Livestock Production Systems: Current status, issues and trends. Carlos Sere, Henning Steinfeld and Jan Groenewold. Animal Production and Health Paper 127. FAO, Rome, Italy.

FAO 1996b. Livestock Geography: A Demonstration of GIS Techniques applied to Global Livestock Systems, and Populations. Consultancy Report prepared by Environmental Research Group Oxford Ltd for the Animal Health Division, FAO, Rome, Italy.

FAO 1998. Prediction of cattle density, cultivation levels and farming systems in Kenya. Consultancy Report prepared by Environmental Research Group Oxford Ltd and the TALA Research Group, Department of Zoology, University of Oxford, for the Animal Health Division of FAO, Rome, Italy.

FAO 1999. Agro-ecological zones, farming systems and land pressure in Africa and Asia. Consultancy Report prepared by Environmental Research Group Oxford Ltd and TALA Research Group, Department of Zoology, University of Oxford, for the Animal Health Service of the Animal Production and Health Division, FAO, Rome, Italy.

FAO 2000. Livestock distribution, production and diseases: Towards a global livestock atlas. Consultancy Report prepared by Environmental Research Group Oxford Ltd and TALA Research Group, Department of Zoology, University of Oxford, for the Animal Health Service of the Animal Production and Health Division, FAO, Rome, Italy.

Lubulwa A.S.G., Menz K., White D.H., Chudleigh P., McMeniman S. and Hill D. 2000. Determining international agricultural research priorities. Australian Centre for International Agricultural Research, IAP-WP37. CSIRO Publishing, Collingwood, Victoria, Australia.

Otte, J., Chilonda, P., Slingenbergh, J. and Wint, W. (2001) The use of geographical information system in the quantitative characterisation of livestock production in sub-Saharan Africa. Poster presentation. Society for Veterinary Epidemiology and Preventive Veterinary Medicine Annual Conference, Noordwijkerhout, The Netherlands, 28-30 March, 2001

Thornton P.K., Kruska R.L., Henninger N., Kristjanson P.M., Reid R.S., Atieno F., Odero A. and Ndegwa T. 2002. Mapping poverty and livestock in developing countries. ILRI (International Livestock Research Institute), Nairobi, Kenya. 132pp.

Global pasture productivity assessment for baseline climate, historical climates and

climate change

Guenther FischerInternational Institute for Applied Systems Analysis,

Laxenburg, Austria

Global pasture productivity assessment for baseline climate, historical climates and climate change

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Introduction Recent estimates of pasture and grazing areas available globally suggest that almost a quarter of the Earth’s land surface is covered by pasture or shrub vegetation, potentially providing feed resources for ruminants. In the case of China this is more than one third (see Table 1).

Table 1: Percentage of different land cover types for the world and China

Land cover type World China

Cropland 14% 20%

Pasture and shrubs 24% 38%

Forest (and orchards etc.) 30% 21%

Others (Mainly barren or sparsely vegetated land) 32% 21%

Source: SDRN 2003 (preliminary) /IRSA

For the estimation of pasture productivity, the FAO/IIASA agro-ecological zones (AEZ) methodologies have been used. AEZ provides a standardized framework for the characterization of climate, soil and terrain conditions relevant to agricultural crops and forage production. The concepts of length of growing period (LGP) and of latitudinal thermal climates have been applied in mapping activities focusing on zoning at various scales, from sub-national to the global level. Further, AEZ matching procedures are used to identify specific limitations of prevailing climate, soil and terrain resources, under assumed levels of inputs and management conditions. This part of the AEZ methodology provides estimates of maximum potential and attainable pasture yields for basic land resource units.

The AEZ assessments were carried out for a range of climatic conditions, including baseline climate, individual historical years, and scenarios of future climates based on various global climate models. Hence, the results quantify the impact of climate change and increased CO2 concentration on pasture, both with respect to historical climate variability as well as for potential future climate change.

The present assessment of pasture productivity is based on methodologies and environmental databases described in Global Agro-ecological Assessment for Agriculture in the 21 Century (Fischer et al., 2002) and detailed information of grassland resources in China (DAHV, 1994 and FAO/UNDP/SLA, 1994). By combining the AEZ methodologies and the zonal features of grassland composition from the Chinese work a re-assessment of global pasture production potential was conducted.

Pasture adaptability The grassland species composition for China is available for 18 pasture zones with distinct environmental characteristics and pasture species composition. Based on this data, the species have been grouped into six adaptability classes, which in turn were allocated to three main thermal zone classes, i.e., zones with annual accumulated temperatures (TSt=10) of more than 5300 degree days, 3500-5300 degree days, 500 to 3500 degree days and less than 500 degree days (see Table 2).


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Table 2: Percentage occurrence of different adaptability classes by temperature class category

Occurrence of Adaptability Groups2 (%) Accumulated Temperature

Classes 1 TSt=10 C3 I C3 I

(legume) C3 II C3 II (legume) C4 I C4 II

Tropics/Subtropics > 5300 16.7 16.7 16.7 16.7 16.7 16.7

Warm temperate 3500-5300 20 20 20 20 20

Temperate 500-3500 40 40 20

Alpine/Arctic < 500 50 50

Source: FAO/UNDP/SLA (CPR/97/029) 1994

This categorization was used as a proxy model parameter in the AEZ pasture productivity assessment procedures.

The original AEZ biomass and yield calculation procedures have been revised to better deal with, in particular, arid and hyper-arid environments. The first modification entails the incorporation of monthly rainfall events, available from the Climate Research Unit’s (CRU) climate database. This enables more realistic assessments of moisture regimes in space and time. The second modification comprises the replacement of the AEZ LGP based biomass and yield estimation in arid zones by an enhanced Net Primary Productivity (NPP) calculation procedure (Zhang Xinshi and Zhou Guangsheng, 1995). This procedure is applied in the arid environments in LGP zones of less than 30 days. These two modifications have significantly improved agreement with satellite-derived data in arid pasture/shrub areas.

For the global assessment, the CRU 30� database has been interpolated to 5� grid 3 and for China station data has been interpolated resulting in a 5 km grid 4.

Results for baseline-climate Pasture suitability and productivity assessments have been carried out at the global level and for China separately. Land cover inventories have been used to target the pasture suitability assessment to all land, and land currently under grassland, woodland, desert/barren land or tundra (see Table 3). When considering all land in the analysis, results show that worldwide about 12 percent of the total land area is productive and highly productive for pasture production. In China this amounts to about 25 percent. When considering only land under grassland, woodland, desert/barren land and tundra areas, thus excluding cultivated land and closed forest, at the global level less than 6 percent is productive or highly productive for pasture.

1 TSt=10: degree days (daily temperatures > 10oC) 2 The crop adaptability groups used are according to Kassam, 1977 and FAO 1978-81. The C3 I group is

adapted to operate under cool conditions (optimum 15-20oC mean daily temperature), the C3 II group operates under warm conditions (optimum 25-30oC mean daily temperature), the C4 I group operates under warm conditions (optimum 30-35oC mean daily temperature) and the C4 II group operates under moderately warm conditions (optimum 20-30oC mean daily temperature).

3 Original 30′ data of the Climate Research Unit (CRU) at the University of East Anglia, U.K., were interpolated to 5′ using the bilinear interpolation method. For temperature, a lapse rate was included using a 5′ elevation dataset.

4 The LUC project in collaboration with W. Cramer from the Potsdam Institute for Climate Impact Research (PIK), Germany, has created a database of average monthly temperature, precipitation and cloudiness on a 5 km grid. The database has been compiled for the territory of the Former Soviet Union (FSU), Mongolia, China and Japan.


18

For China this is just over 6 percent. The land cover classification which was used as a mask is based on NOAA data 1992/93 (Olson, 1994) of the USGS Eros data center at 30 arc-sec resolution.

Table 3: Percentage distribution of productivity classes worldwide and in China

Land cover categories Productivity level World China

Highly productive 2.5% 9.9%

Productive 9.4% 16.7%

Moderately productive 10.3% 9.7%

Marginally productive 10.7% 10.5%

Very marginally productive 40.4% 18.3%

All

Not productive 26.7% 34.9%

Highly productive 2.1% 1.9%

Productive 3.6% 4.2%

Moderately productive 5.1% 3.7%

Marginally productive 7.9% 10.0%

Very marginally productive 48.0% 27.7%

Grassland, woodland, desert/bare

land and tundra

Not productive 33.1% 52.5%

Figure 1 shows the relationship between length of growing period (LGP) zone and both ruminant density and pasture productivity for Africa south of the Sahara. It suggests that ruminants are predominantly occurring in moist semi arid (LGP 120-180 days), while the potential productivity of pasture is highest in humid areas (LGP more than 270 days; FAO, 2001).


19

Figure 1: Relationship between length of growing period (LGP) classes and ruminant density and pasture productivity in sub-saharan Africa

Figure 2 shows a global land cover map and Figure 3 presents a map of the global spatial distribution of suitability for rain-fed pastures.

Figure 2: Global land cover map

0

2

4

6

8

10

12

00-2

930

-5960

-89

90-11

9

120-1

49

150-1

79

180-2

09

210-2

39

240-2

69

270-2

99

300-3

29

330-3

64 36536

5+

LGP Classes

Rum

inan

t den

sity

(h

eads

/sqk

m)

012345678910

Past

ure

Yiel

d (t/

ha)

Ruminants Pasture Yield


20

Figure 3: Global map showing suitability for rain-fed pastures

Note: Suitability index SI is defined as follows: SI=100*VS+77*S+55*MS+33*mS+16*vmS where: VS=very suitable, S=suitable, MS=moderately suitable, mS= marginally suitable, vmS=very marginally suitable (all expressed as fraction of 5’ grid-cell)

Climate scenarios The range of scenarios analyzed for the study presented in this paper addresses development paradigms as defined by the Intergovernmental Panel on Climate Change (IPCC) Working Group III for the Special Report on Emission Scenarios (SRES). The AEZ model has been applied to results of General Circulation Models (GCM), which were available for the IPCC SRES emission scenarios A1FI, A1B, A2, B1, and B2. Outputs from GCM experiments were obtained through the IPCC Data Distribution Centre (DDC) and the Climate Research Unit (CRU) at the University of East Anglia, United Kingdom. For use in AEZ, the outputs of the climate model experiments, with various spatial resolutions, have been interpolated to a grid of 0.5 x 0.5 degrees latitude/longitude and applied to reference climatology of 1961-90, which has been compiled by the CRU (New et al., 1999). Results of the following coupled atmosphere-ocean general circulation models were used in the analysis for 2020, 2050 and 2080 time horizons:

! Hadley Centre for Climate Prediction and Research (HadCM3) is a coupled atmosphere-ocean GCM developed at the Hadley Centre and described by Gordon et al. (2000) and Pope et al., (2000). Annual results of monthly weather variables of HadCM3 simulations were available from DDC for simulations using the IPCC SRES A2 and B2 emission scenarios; additionally, results for A1FI and B1 scenarios were provided by CRU.

! Commonwealth Scientific and Industrial Research Organisation (CSIRO): The CSIRO Climate Change Research Program is Australia’s largest and most comprehensive program investigating the greenhouse effect and global climate change. The CSIRO coupled model involves global atmospheric, oceanic, sea-ice, and biospheric sub-models (Gordon and O’Farrell, 1997; Hirst et al., 1997). Annual results of monthly weather variables of CSIRO experiments were available from DDC for simulations using the IPCC SRES A1b, A2, B1, and B2 emission scenarios.

! Canadian Center for Climate Modelling and Analysis (CCCma): For SRES emission scenarios, results were obtained with the second version of the Canadian Global Coupled Model (CGCM2). The IPCC DDC provides annual time-series of monthly climate variables for SRES A2 and B2 scenarios.

! National Center for Atmospheric Research (NCAR): The Parallel Climate Model (DOE-PCM) is a joint effort, sponsored by the US Department of Energy (DOE), to develop a parallel climate model between Los Alamos National Laboratory (LANL), the Naval Postgraduate School, the US Army Corps of Engineers' Cold Regions Research and Engineering Lab, and the National Center for Atmospheric Research. Version 1 of the PCM couples the NCAR Community Climate Model version 3, the

Unde fine dS I > 7 5 : V ery h ighS I > 6 3 : H igh S I > 5 0 : G oodS I > 3 5 : M ediu m S I > 2 0 : M oderateS I > 1 0 : M arg ina l S I > 0 : V ery m arg in alNot suitableW ater


21

LANL Parallel Ocean Program, and a sea-ice model from the Naval Postgraduate School. Further details of the PCM control run are described in Washington et al. (2000). Results provided by the IPCC DDC are annual time-series of monthly climate variables for a grid of 68 by 128 grid-cells for IPCC SRES emission scenarios A2 and B2.

! Max-Planck-Institute for Meteorology: The ECHAM4 model was developed at the German Climate Research Centre of the Max-Planck-Institute for Meteorology in Hamburg, Germany (Oberhuber, 1993; Roeckner et al., 1992; Roeckner et al., 1996). Results provided by the IPCC DDC are annual time-series of monthly climate variables for IPCC SRES emission scenarios A2 and B2.

CO2 fertilization assumptions There is general agreement that an increase of atmospheric CO2 levels leads to increased crop productivity. In experiments, C3 plants, like wheat and soybeans, exhibit an increased productivity at doubled CO2 concentrations of about 20-30%. Response however depends on crop species as well as soil fertility conditions and other possibly limiting factors. C4 plants, such as maize and sugarcane, show a much less pronounced response than the C3 plants, on average in the order of 5-10%. In the case of natural pastures the increases are reported to be significantly lower. For this assessment we have assumed only half of the increase as compared to agricultural C3 and C4 crops. In general, higher CO2 concentrations also lead to improved water-use efficiency of both C3 and C4 plants, which is accounted of in the calculation of reference evapotranspiration.

Results with climate change In addition to the results contained in the International Institute for Applied Systems Analysis (IIASA) presentation provided on the accompanying CD-ROM, some other examples in table and map form are shown below.

Table 4 provides information on expected changes in area yield and production of pasture areas for the HadCM3 and ECHAM4 GCMs under IPCC B2 scenario for the 2080s.


22

Table 4: Changes (%) of area, production and yield of land suitable for pasture production in major regions of the world of current grassland, woodland, desert/bare land and tundra areas combined, for the HadCM3 and ECHAM4 GCM’s under IPCC B2 scenario for the 2080s

Grassland, Woodland, Desert/Bare land and Tundra combined

Land suitable for pasture Changes in suitable area, production and yield for pasture

Reference climate 1961-90

HadCM3-B2 2080

ECHAM4-B2 2080

Totals

Area Prod. Yld. Area Prod. Yld. Area Prod. Yld.

Region

1000 ha 1000 ha 1000 t t/ha % % % % % % North America 1095080 626501 498570 0.8 8.5 29.8 19.6 8.1 32.2 22.3

Eastern Europe 4496 4357 9954 2.3 0.1 -6.4 -6.5 0.0 7.8 7.8

Northern Europe 32786 25672 21122 0.8 0.1 56.6 56.4 -0.3 31.1 31.4

Southern Europe 23625 19999 27108 1.4 0.1 -17.8 -17.9 0.1 -6.0 -6.1

Western Europe 8549 7016 15512 2.2 0.4 -6.3 -6.7 0.0 3.7 3.7

Russian Fed. 664795 449960 362026 0.8 2.9 42.1 38.1 3.0 39.2 35.1

Centr. Amer & Carrib. 122358 110960 84255 0.8 0.4 -10.2 -10.6 -0.4 -4.8 -4.4

South America 646514 509304 1118122 2.2 0.8 -17.4 -18.0 0.6 7.4 6.7

Oceania & Polyn. 698065 658752 389901 0.6 -0.2 -14.8 -14.7 -0.1 10.4 10.6

Eastern Africa 649526 517804 697313 1.3 0.6 -4.8 -5.3 2.4 15.3 12.6

Middle Africa 385397 319863 600277 1.9 0.9 -11.1 -11.8 2.9 1.7 -1.1

Northern Africa 533022 150578 42790 0.3 -6.2 -24.8 -19.8 7.4 -3.1 -9.8

Southern Africa 193395 163064 100420 0.6 -2.3 -38.4 -36.9 -1.4 -0.7 0.6

Western Africa 554133 323697 196299 0.6 -3.0 -2.6 0.5 2.2 7.3 5.0

Western Asia 364572 230244 29551 0.1 -4.0 -6.6 -2.7 0.1 1.0 0.8

Southeast Asia 31049 27070 104397 3.9 -0.8 -1.5 -0.7 -0.5 3.4 3.9

South Asia 339741 204721 84016 0.4 -0.2 3.9 4.0 -0.5 52.8 53.5

East Asia & Japan 715667 395494 392669 1.0 2.6 23.3 20.2 2.6 27.8 24.6

Central Asia 283819 166244 54158 0.3 0.2 13.6 13.4 0.3 21.9 21.6

Developed 2522569 1789473 1300900 0.7 3.7 19.4 15.2 3.5 27.2 22.8

Developing 4824020 3121827 3527560 1.1 -0.4 -7.4 -7.1 1.6 10.6 8.8

World 7346589 4911300 4828460 1.0 1.1 -0.2 -1.3 2.3 15.1 12.4

Figure 4 presents an example of predicted pasture suitability distribution in the 2080s according to ECHAM4 GCM’s predictions under IPCC A2 scenario and Figure 5 shows the possible spatial distribution of pasture suitability change relative to the current climate conditions.


23

Figure 4: Global distribution of pasture suitability based on ECHAM-A2 GCM predictions for the 2080s

Figure 5: Predicted change of pasture suitability (%) for the 2080s based on ECHAM-A2 GCM relative to reference climate conditions (1961-90)

Collaboration with IIASA-LUC The list of tasks IIASA-LUC proposes to collaborate on could include the following:

1. Global, regional and national historical trends and variability in pasture productivity vis-à-vis distribution of large and small ruminants;

2. Global, regional and national historical trends and projections (based on FAO’s AT2015/30 cropping changes) of livestock feed balances (as IIASA-LUS is doing already for the EC-funded China project “Chinagro”);

3. Mapping of areas of current livestock distribution where adaptation to climate change will be necessary (2020s, (2030s), 2050s, 2080s);

4. Climate change and feed supply: impacts on pasture productivity; impacts on crop production (primary products, crop residues, by-products).

UndefinedSI > 75 : Very highSI > 63 : High SI > 50 : GoodSI > 35 : Medium SI > 20 : ModerateSI > 10 : Marginal SI > 0 : Very marginalNot suitableWater


24

References DAHV (Department of Animal Husbandry and Veterinary) 1994. Data on Grassland

Resources of China. China Agricultural Science and Technology Press, Beijing, 1994.

FAO 1978-81. Report on the Agro-Ecological Zones Project. World Soil Resources Report 48, FAO, Rome.

FAO/UNDP/SLA 1994. Assessment of Livestock Production Potential. Project CPR/87/029, Beijing, China.

FAO 2001. Livestock Geography: New Perspectives on Global Resources. CD-ROM prepared with assistance of the Environmental Research Group Oxford. FAO, Rome.

Fischer G., van Velthuizen H., Shah M., and Nachtergaele F.O. 2002. Global Agro-ecological Assessment for Agriculture in the 21 Century: Methodology and Results. Report RR-02-002, International Institute for Applied Systems Analysis, Laxenburg, Austria.

Gordon C., Cooper C.A. Snr, Banks H., Gregory J.M., Johns T.C., Mitchell J.F.B. and Wood R.A. 2000. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 16: 147-168.

Gordon H.B. and O’Farrell S.P. 1997. Transient climate change in the CSIRO coupled model with dynamic sea ice, Monthly Weather Review 125 (5): 875-907.

Hirst A.C., Gordon H.B. and O’Farrell S.P. 1997. Response of a coupled ocean-atmosphere model including oceanic eddy-induced advection to anthropogenic CO2 increase. Geophysical Research Letters 23 (23): 3361-3364.

Kassam A.H. 1977. Net Biomass Production and Yield of Crops. FAO, Rome.

New M.G., Hulme M. and Jones P.D. 1999. Representing 20th century space-time climate variability. I: Development of a 1961-1990 mean monthly terrestrial climatology. Journal of Climate 12: 829-856

Oberhuber J.M. 1993. Simulation of the Atlantic circulation with a coupled sea-ice mixed layer-isopycnal general circulation model. Part I: Model description. Journal of Physical Oceanography 13: 808-829.

Olson J.S. 1994. Global Ecosystem Framework-Definitions. USGS EROS Data Center Internal Report, Sioux Falls, SD, USA.

Pope V.D., Gallani M.L., Rowntree P.R. and Stratton R.A. 2000. The impact of new physical parametrizations in the Hadley Centre climate model – HadAM3. Climate Dynamics 16: 123-146.

Roeckner E., Arpe K., Bengtsson L., Brinkop S., Dümenil L., Esch M., Kirk E., Lunkeit F., Ponater M., Rockel B., Suasen R., Schlese U., Schubert S. and Windelband M. 1992. Simulation of the Present-Day Climate with the ECHAM 4 Model: Impact of Model Physics and Resolution. Max-Planck Institute for Meteorology. Report No. 93, Hamburg, Germany.

Roeckner E., Arpe K., Bengtsson L., Christoph M., Claussen M., Dümenil L., Esch M., Giorgetta M., Schlese U. and Schluzweida U. 1996. The Atmospheric General Circulation Model ECHAM-4: Model Description and Simulation of Present-Day Climate. Max-Planck Institute for Meteorology. Report No. 218, Hamburg, Germany.


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Washington W.M., Weatherly J.W., Mechl G.A., Semtner A.J., Jr., Bettge T.W., Craig A.P., Strand W.G., Jr., Arblaster J.M., Wayland V.B., James R., Zhang Y. 2000. Parallel climate model (PCM) control and transient simulations. Climate Dynamics 16 (10/11): 755-774.

Zhang Xinshi and Zhou Guangsheng 1995. A New NPP Model. In Ye Duzheng, Lin Hai et al (eds) China Contribution to Global Change Studies. China Global Change Report No. 2., Science Press, Beijing China.

Characterising livestock production systemsand their linkages to poverty

John DixonAgricultural Management, Marketing and Finance Service

FAO, Rome, Italy

Characterising livestock production systems and their linkages to poverty

27

Why characterise? Increasing demand for food and other agricultural products and population pressure on agricultural resources places a heavy burden on crop and livestock producing households to increase productivity. This situation is aggravated by the persistence of extensive poverty, estimated at an estimated 1.2 billion people worldwide living in absolute poverty. More than two-thirds of the poor live in rural areas, and a great proportion are livestock producers. The international development goals set by the World Food Summit in 1996 called for achieving food security for all by reducing hunger, with an immediate goal to half the population of 800 million under-nourished by the year 20151. This goal of the international community was re-affirmed in the first Millennium Development Goal. Available projections of the anticipated under-nourishment reduction rate (of 8 million people per annum) indicate persistent high levels of hunger in 2030, with sub-Sahara Africa still having the highest percent of the under-nourished (FAO 2002).

Any short field trip in a developing country underlines the diversity of agricultural settings in any one country, let alone across continents. These situations need to be understood and analysed systematically, and the results shared with scientists, policy makers and development practioners in other locations. The effective analysis of livestock development policy options across these diverse settings requires relevant analytical frameworks that are appropriate yet easily understood by policy makers. An ideal framework would allow the tremendous diversity of agricultural settings to be simplified and codified, without eliminating important differences that need to be taken into account by policy makers. Generally such an ideal framework would be hierarchical (Fresco and Westphal 1988; Conway 1997) in order to fulfil the different requirements of policy and decision makers at different levels.

Livestock system characterisations There has been a long tradition of livestock systems classification. During the 1970s Ruthenberg developed the now-famous farming systems classification (Ruthenberg 1980). Livestock played an important role in his concept of the farm system, and crop-livestock inter-linkages were underscored. He also analysed the dynamic evolution of systems, which is an important element for policy makers to take into account. The progressive refinement of systems definitions led to livestock classification systems in the mid-1980s that highlighted the economic importance of cropping and its influence on livestock development options, e.g., in Mali. Building on this tradition, in the mid-1990s Sere and Steinfeld (1996) proposed a global classification of livestock systems. This classification utilised the global agro-ecological zones (AEZ) and incorporated aspects related to the integration of crops. More recently the relationship of livestock production in the various zones to the environment has been analysed, as seen in the LEAD website 2.

The more recent livestock systems classifications focused on the animal-land relationship, resulting in a broad framework comprising grassland systems, mixed crop-livestock systems and landless systems. Livestock feed in the grasslands systems is almost entirely from natural pasture and browse; and livestock represents a dominant source of household livelihoods. These systems lend themselves to further subdivision according to the AEZ, i.e., the number of plant growing days in the year. The mixed crop-livestock systems are classified by irrigation, altitude and AEZ. Crop by-products can be an important source of livestock feed; integration with cropping affects herd composition and management; and livestock are less dominant as a source of household livelihoods. The third category of livestock systems, so-called

1 The commitment referred to the total of under-nourished worldwide. 2 http://www.fao.org/ag/againfo/projects/en/lead.html

http://www.fao.org/ag/againfo/projects/en/lead.html


28

“landless” or “industrial” systems, is growing rapidly in size. Examples are confined broilers and layers, confined pig systems and cattle feedlots, in which feed is brought to the confined animals and waste is sometimes carried away. Often these are specialist animal production systems that are often found, in developing countries, near urban areas.

The livestock systems outlined above have much practical usefulness for analysing livestock health and production parameters and communicating with livestock specialists. However, there are some obvious shortcomings in analysing household livelihoods and poverty reduction (particularly in mixed systems), and therefore in analysis of pro-poor policy options for livestock producers – because of the inter-dependencies of crop and livestock management on the one level, and of crop, livestock and off-farm livelihoods on another level. For these reasons, livestock production system zones also have shortcomings for targeting and policy impact assessment.

Framework for livelihoods and poverty reduction Livelihoods concepts and approaches are now well known: Ellis (2000) provides an excellent overview, and also summarises a variety of applications. In the context of poverty reduction, one of the pertinent features of the livelihoods approach is its emphasis on vulnerability. The following sections consider the global drivers of system evolution and poverty reduction, the sources of poverty reduction, and an appropriate systems concept for pro-poor policies for livestock producers.

Global drivers of poverty reduction As mentioned above, agricultural settings are continuously evolving. As shown in Figure 1, there are five principal drivers of change in farming systems and poverty reduction: natural resources and climate; science and technology; trade liberalisation and market development; policies, institutions and public goods; and information and human capital (Dixon et al. 2001). The availability of markets and the prices on offer influence farmers' decisions on enterprise pattern, on purchases of inputs and on the timing of produce sales, and thereby household livelihoods and income. Globalisation and trade liberalisation has had a profound effect on household livelihoods. The availability of economic and social infrastructure in rural areas determines the transport costs and the availability of services to the household - notably human and animal health. Similarly, information and educational services affect household strategies and decisions; in this respect, rural areas are changing extremely rapidly. Technologies, which determine the nature of production and processing, and natural resources, are largely endogenous (internal) factors and therefore lie mainly within the boundary of the farming system. In general terms, the biophysical factors tend to define the set of possible farming/livestock systems, whilst the socio-economic factors determine the actual farming system that can be observed at a given time.


29

Figure 1: Global drivers of poverty reduction

Sources of poverty reduction In broad terms, there are five main farm household strategies to improve livelihoods (Dixon et al. 2001). These are depicted in Figure 2, namely: intensification of existing production patterns; diversification of production and processing; expanded farm or herd size; increased off-farm income, both agricultural and non-agricultural; and complete exit from the agricultural sector within a particular farming system. These strategic options are not mutually exclusive, even at the individual household level; any particular household will often pursue a mixed set of strategies.

Pop’n, Nat Resources& Climate Change

Technology

Markets & globalisation

Policies &institutions

Information &knowledge

+ / -Livestock

linked poverty


30

Figure 2: Farm-household strategies to escape poverty

The first two of the five strategies - intensification and diversification - form important components of the FAO Special Programme for Food Security. Intensification is defined as increased physical or financial productivity of existing patterns of production; including food and cash crops, livestock and other productive activities. Although intensification is frequently associated with increased yields as a result of greater use of external inputs, e.g., increases in milk yield from better feed, it may also arise from improved varieties and breeds, utilisation of unused resources, improved labour productivity, and better farm management - for example improved pasture/gazing management or better disease control.

Diversification is defined as an adjustment to the farm enterprise pattern, e.g., addition of a goat fattening enterprise, in order to increase household livelihoods or to reduce vulnerability. The exploitation of new market opportunities or existing market niches may be an important aspect; and also on-farm processing, e.g., from milk to cheese. Third, some households escape poverty by expanding herd size, e.g., acquiring another milk buffalo. Off-farm income represents an important source of livelihood for many poor farmers. Seasonal migration has been one traditional household strategy for escaping poverty and remittances are often invested in land or livestock purchases. In locations where there is a vigorous off-farm economy, many poor households augment their incomes with part-time or full-time off-farm employment. Where opportunities for improved livelihoods are perceived, pastoralists may abandon their way of life entirely and move into other farming systems, or into off-farm occupations in rural or urban locations – the latter is, de facto, exit from agriculture. Generally speaking, in irrigated mixed livestock-crop farming systems, intensification is extremely important in terms of potential for reducing poverty, whereas exit from agriculture has relatively little attraction as a poverty reduction pathway. Conversely, in the pastoral farming system the greatest potential lies in households leaving the system altogether - the so-called exit strategy. In this particular farming system, the poverty reduction potential of intensification, diversification and increasing farm size, is considered to be low.

Intensification(existing..)

Diversification (new, incl value added)

Herd size (asset growth)

Off-farm income(& remittances)

Exit from agriculture

Escape from

poverty


31

Choice of system Given the above discussion of drivers and sources of poverty reduction, what is the appropriate system to analyse for pro-poor livestock policies? From one perspective, a “pure” livestock system could be considered as a narrowly focused farming system. As a crude generalisation, the degree to which crop and other livelihoods have been incorporated into livestock “systems” increased progressively from the 1970s to the present. During the 1980s the system for analysis was often the livestock-crop system, otherwise known as “farming systems sensu strictu”. At the turn of the Century, the broader concept of “farming systems sensu lata” (embracing livestock, crops, off-farm income and other activities of the household) incorporating livelihoods has been adopted in many livestock related studies.

The importance of the broader conception is demonstrated in Figure 3, which illustrates feasible orders of magnitude of the principal sources of livelihoods in different hypothetical livestock-oriented farming systems (see above). Because these components or sub-systems are interacting, separate treatment can produce misleading results. From the perspective of poverty reduction, in either of the above cases a sole focus on the livestock component would give little total impact and often little leverage on household poverty outcomes. Therefore, it is argued here that the relevant unit of analysis for pro-poor livestock policies is not the livestock sub-system but rather the whole farm household system.

Figure 3: Relative importance of various livelihood sub-systems (hypothetical)

Farming systems and poverty: with a livelihoods perspective The following section outlines a relevant farming systems knowledge base that could be used for the identification and appraisal of pro-poor livestock policies. The knowledge base was created during the FAO/World Bank Global Farming Systems Study, which contributed to the updating of the World Bank Rural Development Strategy. The Study team blended information from global Geographic Information Systems (GIS), field studies, decentralised administrative data and expert knowledge. The teams used the FAO Agro-Ecological Zone (AEZ) maps as a base and added other GIS layers as relevant, including irrigation, environmental constraints, cultivated extent, livestock (in some regions) and human population. Taking into account the

L20

i50

L50

C50

i30

Mixed FarmingSystem

GrasslandsFarmingSystem


32

broad trends documented in FAO (2000), the teams identified the specific trends, emerging constraints and strategic development priorities for each farming system.

As a result, eight generic farming system categories were defined across the developing regions of the world, namely, irrigated smallholder farming systems (in large irrigation schemes); wetland rice-based farming systems; rainfed farming systems in humid areas; rainfed farming systems in steep and highland areas; rainfed farming systems in dry or cold areas; dualistic farming systems with both large-scale commercial and smallholder farms; coastal artisanal fishing mixed farming systems; and urban-based farming systems (see Table 1). Large-scale farms are of importance only in the dualistic farming systems.

Table 1: Comparison of farming systems categories 3

Category characteristic

Small- Holder

irrigated schemes

Wetland rice

based

Rainfed humid

Rainfed highland

Rainfed dry/cold

Dualistic (large/ small)

Coastal artisanal fishing

Urban based

No. of Farming Systems 3 3 11 10 19 16 4 6

Total Land (m ha) 219 330 2013 842 3478 3116 70 n.a.

Cultivated Area (m ha) 15 155 160 150 231 414 11 n.a.

Cultivated/Total (%) 7 47 8 18 7 13 16 n.a.

Irrigated Area (m ha) 15 90 17 30 41 36 2 n.a.

Irrigated/Cultivated (%) 99 58 11 20 18 9 19 n.a.

Agric. Population (m) 30 860 400 520 490 190 60 40

Agric. Pers/Cult (p/ha) 2.1 5.5 2.5 3.5 2.1 0.4 5.5 n.a.

Market Surplus high medium medium low low medium high high

Within these eight categories a total of 72 broad farming systems were identified and mapped (varying from 11 to 16 per region). In each region there are more than a dozen thematic layers which have been overlaid on the farming systems maps (including AEZ, rainfall, environmental constraints, altitude, cultivated extent, livestock population, human population), resulting in more than 100 regional maps which are available through the FAO website4. A basic set of parameters is available for each of the eight categories of farming system, as well as for each of the 72 identified broad farming systems.

Table 1 compares the generic of “mega” farming systems, in respect of areas of total land, cultivated land and irrigated land, agricultural population and market surplus. The six irrigated and rice based wetland systems 5 contain an agricultural population of nearly 900 million people with some 170 m ha of cultivated land, of which nearly two-thirds is irrigated. There are three major categories of smallholder rainfed farming system (in humid, highland or dry/cold areas), which together contain an agricultural population of more than 1,400 million people with around 540 million ha of cultivated land. Dualistic systems comprising farms of mixed size contain a further 200 million farm people with a cultivated area of 11 million ha. Finally, two further

3 Source: Dixon et al. 2001, based on FAO data and expert knowledge. Note: Cultivated area refers to both annual and

perennial crops. Livestock populations were estimated for all farming systems in several of the regions but could not be aggregated world-wide for lack of data in Latin America.

4 http://ww.fao.org/farmingsystems/ 5 One irrigated farming system in Eastern Europe and Central Asia has relatively large farms and, for the purpose of the

present discussion, is included in the category of dualistic systems.

http://ww.fao.org/farmingsystems/


33

minor categories of smallholder system – four coastal artisanal fishing mixed and six urban based systems – contain a combined total of about 100 million people.

For each of the 72 farming systems expert panels have estimated the relative importance of the five source of poverty reduction. An example appears in Figure 4, where the relative importance is indicated for the maize mixed farming system in East Africa.

Figure 4: Relative importance of farm household poverty escape strategies for maize mixed system in Africa

Farm Size20%

Off-Farm Income

20%

Exit10%

Diversification30%

Intensification20%

It is worthwhile to consider the relationship between the above farming systems and the livestock production systems outlined in the LEAD website. A preliminary assessment suggests that there is a high degree of correspondence for many of the systems. Table 2 lists some approximate equivalences between the two classifications. Following the above discussion, it is not surprising that a fair degree of correspondence exists between the classifications. The relationships are not necessarily 1:1; for example the landless ruminant systems exist in the vanity of cities but also exist elsewhere. It would be relatively easy to make a full listing of equivalences at this aggregate level.

Table 2: Approximate equivalences between the farming systems and livestock production system classifications (illustrative)

Mega farming system (FAO/World Bank) Livestock production system (LEAD)

Rainfed humid (sub) humid (sub) tropics mixed rainfed

Rainfed dry/cold (semi) arid (sub) tropics grassland-based

Irrigated wetland rice Irrigated mixed farming systems

Urban Landless ruminant systems


34

Ways forward In conclusion, there is an opportunity for merging the livestock production and farming systems classifications. Many livestock production professionals have been traditionally system-oriented; and increasingly over the past three decades the more experienced professionals view the livestock production as one component of the farm system. The logical culmination of this trend would be to adopt the farm system as the unit of analysis for pro-poor policy identification and assessment.

The consolidation of basic spatial data on livestock populations, crop areas and poverty should be supported. Whilst reasonable estimates are available for cultivated areas, livestock population spatial databases need to be extended. The most critical gap at present is spatial data on poverty; fortunately FAO in conjunction with other partners is making good progress in this area. Second, the systematic identification of equivalences between the farming systems and livestock production systems classifications would be extremely useful; this would permit, inter alia, the integration of the two knowledge bases. Perhaps the most important recommendation of all would be to adopt a multiple livelihoods orientation to the pro-poor policy analysis within, as noted above, a whole farm system context. In this connection, household system profiles or models for major farming systems (which, as noted above, largely correspond to the livestock production systems) should be described and the relative importance of livelihood-enhancing household strategies estimated, along with system projections to 2030 for each of the main systems.

Finally, policy makers should be encouraged to share analyses of policy options and experiences with policy impacts on a system basis. This would be facilitated by systematic documentation of findings and feasible policy interventions on a systems basis.

References Conway G. 1997. The doubly green revolution: Food for All in the 21st Century.

Penguin, London, U.K.

Dixon J. and Gulliver A. 2001. Farming systems and poverty: Improving farmers’ livelihoods in a changing world. FAO, Rome, Italy & World Bank, Washington, D.C. U.S.A.

Ellis F. 2000. Rural livelihoods and diversity in developing countries. Oxford University Press, Oxford, U.K.

FAO 2002. World agriculture towards 2015/2030. An FAO perspective. FAO and Earthscan, Rome and London.

Fresco L. and Westphal E. 1988. Hierarchical classification of farming systems. Experimental Agriculture 24: 399-419.

Ruthenberg H. 1980. Farming systems in the tropics. 3rd edn, Clarendon Press, Oxford, UK.

Sere C. and Steinfeld H. 1996. World livestock production systems: Current status, issues and trends. Animal Production and Health Paper 127, FAO, Rome.

Farming systems under climate change:Impacts on poor people in the tropics

Phil K. ThorntonSystems Analysis and Impact Assessment,

International Livestock Research Institute, Nairobi

Farming systems under climate change: Impacts on poor people in the tropics

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Background Various expected characteristics of climate change are becoming clear (IPCC, 2001a; 2001b):

! Global average temperatures and sea levels are projected to rise under all scenarios looked at. It is expected that temperatures will rise by between 1.4 and 5.8º C in the next 100 years.

! The balance of evidence suggests that much of the global warming observed over the last 50 years is attributable to human activities.

! Rainfall and temperature changes may have substantial impacts on (among other areas) agriculture and human health in Africa.

! Africa is the continent most vulnerable to the impacts of projected changes because widespread poverty limits adaptation capabilities.

While there is still enormous uncertainty surrounding the wider debates on these issues, a definite move towards scientific consensus is occurring. It is now accepted that the impacts of global warming will be highly variable and region- or even location-specific. The ultimate impacts of climate change on poverty may be enormous; adoption lags of technologies can be very long (15 years at least); and even though policy lags may be much shorter, adaptation and mitigation strategies have to be assessed now, based on the best science we can muster, even if the uncertainty is still large.

Various studies have been carried out at the macro scale, to assess likely impacts on food production systems at a global level of changing climate throughout the present century (see, for example, Fischer et al., 2002). To study specific impacts on the poor, however, much higher-resolution studies are needed at the systems level. The sections below summarise the approach being taken by the International Livestock Research Institute (ILRI) and partners in the arena of climate change to provide information on options that may be of benefit to the highly vulnerable sections of the human population. These higher-resolution systems studies are urgently needed if the poverty alleviation mandate of international agricultural research and development is to be adequately served.

What may happen in the agro-ecosystems on which the poor depend?

To study the impacts of climate change on livestock systems, and on agro-ecosystems in general, two spatial themes developed by ILRI and partners have proved particularly useful. One of these is a highly preliminary cut at human population density in Africa in the years until 2050, first published in Reid et al. (2000). It combines present-day data by Deichmann (1996) and UN best estimates of country population to 2050, to make within-country predictions using Deichmann’s model. While this approach is acceptable as a first step, since it distributes human population increases throughout each country in a plausible way, it does not take into account the situations where pixels go from “no people” to “some people” by 2050, or the reverse. Urbanisation is also somewhat inadequately dealt with. Despite these shortcomings, the resulting map theme has been used as a basis for various analyses of change, briefly described below. The value of human population as a proxy for many variables at a coarse scale can hardly be over-estimated. We have produced similar maps for Asia and Latin America.

The spatial themes representing climate change for Africa until 2055 are the other key dataset. These were developed using a computerised modelling tool developed at the


37

International Center for Tropical Agriculture (CIAT) over many years, with ILRI input, called MarkSim. This is a software package to generates site-specific daily weather data for Latin America, Asia and Africa needed to run computerized crop models (Jones and Thornton, 2000; Jones et al., 2002). The programme is based on a stochastic weather generator that uses a third-order Markov process to model daily weather data. The model has been fitted to data from more than 9200 stations using long sequences of daily data from throughout the world. The climate normals for these stations were assembled into 664 groups using a clustering algorithm. For each of these groups, rainfall model parameters are predicted from monthly means of rainfall, air temperature and diurnal temperature range and the elevation and latitude of the station. The programme identifies the cluster relevant to any required location using interpolated climate surfaces at a resolution of 10 minutes of arc (18 km squared) for Latin America and Africa and at 5 minutes of arc for Asia, and evaluates the model parameters for that point. Version 1 of MarkSim was released at the end of 2002.

From a climate change perspective, one of the greatest assets of this tool is that it generates characteristic daily weather patterns from surfaces of climate normals. If the climate normals change, as in the case of climate change, then running MarkSim from the new values will again generate characteristic daily data, and in this way, relatively high-resolution “changed” weather data can be obtained and used to run a wide variety of analyses and models. In fact, it would be theoretically possible to re-parameterise the MarkSim clusters using changed climate normals, and this would allow predictions about changes in the probabilities of extreme weather events.

We have developed climate change surfaces only for Africa and Latin America so far, and only based on outputs produced by the general circulation model (GCM) HadCM2. The various GCMs used in climate change studies have outputs which differ at the detailed level, although there seems to be agreement that East Africa will generally experience some sort of wetting, with overall drying of the climate in southern Africa. The development of regional GCMs and tools such as MarkSim generating localised outputs offer great potential for high-resolution systems studies in the future.

These datasets have been used in various ways. Spatial themes of length-of-growing period (LGP) were generated for current climate and for 2055, combining the outputs from HadCM2 converted into localised predictions by MarkSim with a simple water balance model. Continental analyses indicate that, when coupled with expected increases in human population, the location and extent of pastoral and mixed systems in West and East Africa may shift considerably (see Figure 1). This poses opportunities and threats to poor livestock keepers that have to be addressed.


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Figure 1: Expected changes in production systems, 2000 to 2050 (system definitions after Thornton et al. 2002)

We have also used these datasets to look at what may happen to tsetse fly habitat as a result of population increase and bush clearance to 2050 (Reid et al., 2000), and to assess, in very broad terms, the likely impacts of climate change on the distribution of tsetse and trypanosomosis (McDermott et al., 2001). This analysis indicates that the areas currently under trypanosomosis risk will contract continent-wide, but the trend will not be uniform. The greatest decrease in the impacts of animal trypanosomosis will occur in the semi-arid and sub-humid zones of West Africa, where the climate will be drier, human population will increase, and disease control will have greater impacts. The risk of animal trypanosomosis will also decline in many but not all areas of Ethiopia and eastern and southern Africa. The disease situation in the humid zone of central and western Africa will change less. Sleeping sickness, particularly the T. gambiense type, will continue to be a major problem, if concerted control efforts are not implemented.

Most recently, we used these geographical themes to conduct an analysis with a point crop simulation model, CERES-Maize, to investigate what might happen to maize in dry-land agriculture in Latin America and Africa until 2055 (Jones and Thornton, 2003). Although the results indicate an overall reduction of only 10% in maize production until 2055, equivalent to losses of $2 billion per year, the aggregate results hide enormous variability, but areas can be identified where maize yields may change substantially. So what do these impacts on maize really mean at the level of the agricultural systems? What are the implications for pests, diseases and food security? The pastoral lands of southern Kenya may become much wetter - these are areas with some of the greatest large-mammal biodiversity on the planet. What are the implications for the increased disease challenge that will surely occur in such a situation? There are many similar questions that need answering, but we are finally starting to have the tools available which will enable us to look at such issues with reasonable credibility. The situation is complicated by the fact that climate change is

2000 2050


39

occurring in parallel with many other drivers of change, particularly in Africa, leading to the intensification of agricultural systems in many places (Staal et al., 2001), coupled with the predicted increase in demand for livestock products over the next few decades (Delgado et al., 1999). Understanding all these processes, and how the drivers of change interact, is crucial if research is to assist in achieving the appropriate development targets in the coming decades.

What are the options available for poor smallholders in the future?

The tools that are being assembled are allowing us to start to assess the possible impacts of global change, and to suggest ways in which the deleterious effects might be dealt with, and any new opportunities exploited, at the household level. A general framework for assessing such options is provided by the Intergovernmental Panel on Climate Change (IPCC) in relation to both adaptation and mitigation options (see Figure 2). Some of the mitigation options available to livestock owners in pastoral lands are reviewed by Reid et al. (2003). The potential of carbon credit schemes, for example, is dependent on a long list of institutional needs, and for the foreseeable future these may have to be looked at on the basis of community-based approaches.

Figure 2: Climate change: an integrated framework (yellow arrows, cause and effect; blue, societal response to Climate Change impacts; from IPCC, 2001b)

There are many adaptation options that may be appropriate in particular places in particular systems, and modelling will clearly play a key role in helping to assess what is appropriate where. Given the importance of mixed systems in the tropics (mixed crop–livestock systems provide over 50% of the world’s meat and over 90% of its milk, and include at least 70% of the world’s poor livestock keepers), modelling frameworks that can take account of the key crop-livestock interactions are required. Some of these are outlined in Thornton and Herrero (2001), and progress in assembling a global crop-livestock systems database is described in Herrero and Thornton (2003), using a

Climate change

Temperature riseSea level rise

Precipitation changeDroughts and floods

Impacts on humanand natural systems

Food and water resourcesEcosystems and biodiversity

Human settlementsHuman health

Emissions andconcentrations

Greenhouse gasesAerosols

Socio-economicdevelopment paths

Economic growthTechnologyPopulation

Governance

Adaptation

Miti

gatio

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systems characterisation tool called IMPACT (Integrated Modelling Platform for Animal Crop sysTems, see Figure 3). This tool has been designed to store system-level information on farm resources (land, labour, capital), management practices, use of labour, and household objectives and attitudes, so that systems can be compared across different places and system models run, allowing users to assess the impacts of changes in the system on household resource use, income, food security, and other indicators of household well-being.

Figure 3: User interface of the IMPACT tool, and the information tree showing the data resources used by the system

One of the adaptation options that has been cited as possibly providing some relief to farmers faced with climate change and changes in the probability of extreme events is weather forecasting (Hansen, 2002). There are substantial institutional constraints to be overcome before extensive use can be made of these in the tropics and sub-tropics. It is also likely that the value of such forecasts is somewhat situation-dependent. Thornton et al. (2003a) indicate that for commercial livestock keepers in the Northwest Province of South Africa, use of three-month weather forecasts based on the Southern Oscillation Index can be profitable, but at the expense of greatly increased year-to-year variation in household income. This will suit the risk profiles of some ranchers, but not all.

Another major adaptation option is likely to be diversification. Again, appropriate systems models will be of considerable value in identifying what strategy might be worth trying in the future, or where and how particular pockets of poor livestock keepers should be targeted and with what. Thornton et al. (2003b) describe some highly preliminary and tentative steps along the road of identifying target groups of pastoralists in areas of high large mammal biodiversity situated some distance (but not too far) from major roads and densely populated areas; there may be potential for


41

such people to benefit from household income derived from wildlife conservation and tourism, for example.

What is needed in the future?

Further work is required in terms of identifying adaptations and mitigation possibilities for poor smallholders in the tropics and sub-tropics. We still need studies to answer the question, where are the hotspots of change, globally, driven by population increases, climate change and systems intensification? Once these are identified, we need to identify the impacts at the (high-resolution) systems level on incomes, food security, and sustainable livelihoods, and from there to identify the options available to poor smallholders in these areas. A related key activity is to engage decision makers on the basis of what is already happening now, rather than what may happen in 50 years’ time. Changes in the probabilities of extreme events are already happening, and changes in rainfall coefficients of variation over 5 to 10 years, for example, offer a very real way of getting the attention from policy makers that climate change vulnerability really requires.

A key activity in all this is database collation, maintenance, and dissemination. Many of the datasets referred to above are the result of international collaboration, and the refinement of these is dependent on forging and maintaining appropriate alliances of interested individuals and institutions.

By themselves, even high-resolution targeting and holistic integrated assessment will not necessarily be sufficient guarantees of poverty alleviating impacts for smallholders in the future. More is required to understand better the roles and flow of information in relevant socio-cultural systems - in pastoral communities themselves, in terms of the role of external information in decision-making processes and its flow up, down and across the social hierarchy; and in the role of information in decision making and its flow in the socio-cultural system of policy makers, from local-level community leaders to national political leaders who make and implement policy for government.

The impacts of change that will be felt at the household level depend on many factors related to the production system and the constituent enterprises, as well as the socio-economic and politico-cultural milieu within which rural and urban smallholders live. Being able to assess impacts at high resolution is critical, and linking the results of such studies to poverty maps and natural resource vulnerability assessments should lead to substantial improvements in targeting policies and technology options that really can help the poor. However, there is not a great deal of time, given the rapidity of the changes that are occurring in African agricultural and pastoral systems, and the problems that are emerging have to be dealt with sooner rather than later.


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References

Deichmann U. 1996. Africa Population Database, third version. National Center for Geographic Information and Analysis (NCGIA), University of California, Santa Barbara as a cooperative activity between NCGIA, Consultative Group on International Agricultural Research (CGIAR), United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), and World Resources Institute (WRI), (available on: http://grid2.cr.usgs.gov/globalpop/africa)

Delgado C., Rosegrant M., Steinfeld H., Ehui S., and Courbois C. 1999. Livestock to 2020: the next food revolution. Food, Agriculture and the Environment Discussion Paper 28. IFPRI/FAO/ILRI, Washington, DC, USA.

Fischer G., Shah M. and van Velthuizen H. 2002. Climate Change and Agricultural Vulnerability. Special Report, IIASA, Laxenburg, Austria, 152 pp.

Hansen J.W. 2002. Realising the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agricultural Systems 74: 309-330.

Herrero M. and Thornton P.K. 2003. An integrated platform for studying crop-livestock systems in the tropics. Agricultural Systems (in preparation).

IPCC 2001a. Climate Change 2001: The Scientific Basis. WG I contribution to the IPCC Third Assessment Report. http://www.ipcc.ch

IPCC 2001b. The Regional Impacts of Climate Change: An Assessment of Vulnerability. http://www.ipcc.ch

Jones P.G. and Thornton P.K. 2000. MarkSim: software to generate daily weather data for Latin America and Africa. Agronomy Journal 92 (3): 445-453.

Jones P.G. and Thornton P.K. 2002. Spatial modeling of risk in natural resource management. Conservation Ecology 5 (2): 27.

Jones P.G. and Thornton P.K. 2003. The potential impacts of climate change in tropical agriculture: the case of maize in Africa and Latin America in 2055. Global Environmental Change 13, 51-59.

McDermott J.J., Kristjanson P.M., Kruska R.L., Reid R.S., Robinson T.P., Coleman P.G., Jones P.G. and Thornton P.K. 2001. Effects of climate, human population and socio-economic changes on tsetse-transmitted trypanosomosis to 2050. In S.J. Black and J.R. Seed (eds), World Class Parasites – Vol. 1. The African Trypanosomes. Kluwer Academic Press, Boston, USA. pp 25-38.

Reid R.S., Kruska R.L., Deichmann U., Thornton P.K. and Leak S.G.A. 2000. Will human population growth and land-use change control tsetse during our lifetimes? Agriculture, Ecosystems and Environment 77: 227-236.

Reid R.S., Thornton P.K., McCrab G.J., Kruska R.L., Atieno F. and Jones P. 2003. Is it possible to mitigate greenhouse gas emissions in pastoral ecosystems of the tropics? Environment, Development and Sustainability (in press).

Staal S.J., Ehui S. and Tanner J.C. 2001. Livestock-environment interactions under intensifying production. in D.R. Lee and C.B. Barrett (eds) Tradeoffs or Synergies? Agricultural Intensification, Economic Development and the Environment. CAB International, Wallingford, UK. pp 345-364.

Thornton P.K. and Herrero M. 2001. Integrated crop-livestock simulation models for scenario analysis and impact assessment. Agricultural Systems 70: 581-602.

http://grid2.cr.usgs.gov/globalpop/africa

http://www.ipcc.ch

http://www.ipcc.ch


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Thornton P.K., Fawcett R.H., Galvin K.A., Boone R.B., Hudson J.W. and Vogel C.H. 2003a. Evaluating management options that use climate forecasts: modelling livestock production systems in the semi-arid zone of South Africa. Climatic Change (in preparation).

Thornton P.K., Reid R.S. and Kruska R.L. 2003b. Adapting to global change in Africa: studying the implications for rangelands. Invited paper for the 7th International Rangelands Congress, Durban, South Africa, 26 July-1 August 2003.

SESSION II:

POVERTY, WELFARE AND VULNERABILITY

Global population mapping

Andy Nelson and Deborah BalkCentre for Computational Geography,

University of Leeds, Leeds, United Kingdom


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Introduction Digital mapping of the global population has come a long way from the simple national level maps based on UN population projections or similar data sources (Tobler et al. 1995). A quick web search for large-scale population data will reveal several sources of freely available and detailed spatial population data from 1990 to 2001 with resolutions as high as one square kilometre (see Table 1).

Table 1: Summary of global and continental datasets available via the Internet

Datasets Interpolation Data available for Resolution

GPW v2 No adjustments 1990 & 1995 5km

UNEP/GRID Population Database

Accessibility Potential Interpolation

1960-1990 (Africa) 1995 (Asia) 1960-2000 (L. America)

5km

LandScan “Smart” Interpolation 1998, 2000 & 2001 1km

GPW v3 GPW-Urban-Rural1

No adjustments Rural / Urban split

1990, 1995, & 2000 1990, 1995 & 2000

5km 1km

There are two globally consistent datasets available at the moment; the LandScan Global Population Database (LandScan 2001), produced by Oak Ridge National Laboratory (ORNL), and the Gridded Population of the World (GPW v2) produced by a consortium headed by the Center for International Earth Science Information Network (CIESIN).

This paper briefly reviews these two datasets and a new GPW database due for release in 2003.

LandScan 2001

This is the latest of three gridded population datasets produced by ORNL. It is described as “…a worldwide population database compiled on a 1km x 1km latitude/longitude grid, where sub-national census counts are apportioned to each grid cell based on likelihood coefficients, which are derived from proximity to roads, slope, land cover, night-time lights and other data sets, as part of the Global Population Project for estimating ambient populations at risk…” (ORNL, 2001). The database is designed for estimating ambient populations at risk from natural and man-made disasters, hence the focus on compiling any relevant ancillary data in order to produce a best estimate of the spatial distribution of the population.

This high level of modelling produces a visually impressive and detailed map (see Figure 1a). However, it is important to note that the input population data is not of particularly high quality (around 80,000 census units worldwide with over 60,000 of those in the USA) and the quality of the ancillary data is invariably poorer in developing countries and rural areas in general. There is also a danger of circularity in using LandScan 2001 in any spatial models that contain variables similar to those used to generate the likelihood coefficients. The black-box nature of the LandScan methodology may also deter the use of this dataset in further modelling. With those caveats in place, LandScan 2001 is still the most recent and detailed (in terms of resolution if not quality) spatial population dataset available.

1 GPW v3 and GPW-Urban-Rural are expected to be released on a continent-by-continent basis throughout 2003.


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GPW version 2

Developed in 1999-2000, GPW v2 provides “…estimates of the population of the world in 1990 and 1995 on a 5km x 5km grid, where national figures have been reconciled to be consistent with United Nations population estimates…”, (CIESIN et al. 2000). GPW v2 has been widely used as both a descriptive and analytic database in areas as diverse as agriculture, health, urban studies and climate change.

The simple population distribution algorithm reflects the ideology that better population maps can be most effectively and transparently produced though collecting better input data, rather then data modelling (see Figure 1b). The resolution is somewhat lower than LandScan, yet the input data is almost twice as detailed with over 120,000 census units. Given that roughly 60,000 of the units in both GPW and LandScan are US census tracks, the difference in the input resolution for the rest of the world is effectively three times as great for GPW. If a plain, assumption-free population map is required for further modelling or other purposes, then GPW v2 is the obvious choice.

GPW version 3 and the “urban-rural” database

Almost half of the world’s current population is estimated to live in urban areas, and that proportion is expected to increase to over 60 percent in another 25 years (United Nations, 2001). Despite the development of these spatially disaggregated global population databases, the extent and shape of urban areas is not sufficiently delineated. As a result, the accuracy of the estimated population distribution in both urban and rural areas is poor, producing potentially inaccurate conclusions in research and policy (Balk 2003).

CIESIN, in collaboration with the Consultative Group on International Agricultural Research (CGIAR) and the World Bank have addressed this issue by developing a global geo-referenced population database including urban extents that merges conventional census data with satellite and other geographic data (see Figure 1c). As this database has not been formally named, herein we shall call it the “urban-rural” database. The urban-rural database is apart from updates to GPW - i.e. GPW version 3 based on year 2000 population and improved boundary data (where available), which CIESIN is also undertaking. The outputs of the urban-rural database are:

! A database of human settlements with 5,000 people or more (points) ! An urban mask, derived from various sources and models (polygons) ! A complete urban-rural population density surface for 1995 and 2000 at 1km x 1km

resolution (grid)

These build upon the data collected for GPW v2 but also include new census and administrative boundary information. Some 150 countries have been updated amounting to over 220,000 census units. Clearly, collecting and reconciling this information is a massive task, and is still underway, but the improvement in spatial accuracy provided by delineating urban areas offers a huge range of new applications. For example, Balk (2003) states the demand for better urban and rural population data in agricultural research as follows:

“…there are increasing demands for greater specificity in defining the impacts of agricultural change and development, particularly with regard to the likely impacts of policy, technology, and institutional changes on poverty (Wood et al. 1999). However, most means of assessing the economic consequences of change in the agricultural sector involve the identification of impacts on producers—predominantly located in rural areas—and consumers, increasingly represented by urban populations in many countries. Improved knowledge of the spatial distribution of urban and rural


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population is important for assessing the scale and distribution of the impacts of policy, technology, and institutional change on both rural and urban households. It is also necessary for analysis on the relationship of rural populations with production patterns and with land quality - in particular the distribution of rural population in the so-called ‘marginal’ or ‘less-favoured lands…”

Endnotes

Perhaps the biggest task facing population modellers is temporal population prediction. Whilst country level predictions have proven quite robust, the application of these projections (even if the projections are available at sub-national level) to high-resolution population data with any degree of accuracy is extremely difficult.

There have been few efforts to assess the accuracy of these global datasets, often because the best available population data are used as an input; hence, there is nothing to compare them with. LandScan have provided three assessments, all in developed countries, whilst Hyman et al. (2000) demonstrated that there was no clear “best method” when comparing various population models (estimated population) to very high resolution data (observed population) in two developing countries. The best available dataset may depend more on the intended use than the approach. That is, each approach has benefits and shortcomings.

Nevertheless, census data are becoming available in digital form at increasing resolutions. Thus, improving estimates of the global population is a never-ending task. Ancillary data also improve year on year; with new land cover imagery, urban extent information, transportation network vectors and perhaps most crucially terrain maps (in the form of the highly anticipated Shuttle Radar Topography Mission 90m resolution digital elevation data) being made available.

We now have access to globally consistent population datasets that were unheard of, even 5 years ago. As these efforts continue, we will see more detailed and more accurate population maps for applications in areas such as poverty reduction strategies, and sustainable development policies.


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Figure 1: A 3-D perspective view of three population density data sets for Kenya. LandScan 2001 (a), GPW v2 (b) and 1995 GPW v3-beta 2000 (c).

Figure 1a

Figure 1b

50,000 25,000 10,000 5,000 1,000 500 100 50 10 0

People / k

Figure 1c


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Data sources

Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IFPRI); and World Resources Institute (WRI) 2000. Gridded Population of the World (GPW), Version 2. CIESIN, Columbia University, NY. Available at http://sedac.ciesin.columbia.edu/plue/gpw

Oak Ridge National Laboratory (ORNL) 2001. LandScan Global Population Database. Oak Ridge, TN. Available at http://www.ornl.gov/gist

National Center for Geographic Information and Analysis (NCGIA); United Nations Environment Program (UNEP); World Resources Institute (WRI); International Center for Tropical Agriculture (CIAT) 2000. Asia, Africa and Latin America and Caribbean Population Database. UNEP/GRID Sioux Falls, SD. Available at http://grid2.cr.usgs.gov/datasets/datalist.php3

References and further reading

Balk D. 2003. The spatial distribution of global population in urban and rural areas. Center for International Earth Science Information Network. CIESIN, Columbia University, NY, USA.

Deichmann U. 1996. A review of spatial population database design and modelling. Paper prepared for the UNEP/CGIAR Initiative on the Use of GIS in Agricultural Research, National Center for Geographic Information and Analysis, Santa Barbara, California, USA.

Dobson J.E., Bright E.A., Coleman P.R., Durfee R.C. and Worley B.A. 2000. LandScan: A global population database for estimating populations at risk. Photogrammetric Engineering & Remote Sensing 66 (7): 849-857.

Geertman S.C.M. and Ritsema van Eck J.R. 1995. GIS and models of accessibility potential: an application in planning. International Journal of Geographic Information Systems 9 (1): 67-80.

Hyman G. and Nelson A. 2000. Population Maps of Latin America. Presentation at the Workshop on Gridding Population Data, Columbia University, 2-3 May 2000. CIAT, Colombia.

Tobler W., Deichmann U., Gottsegen J. and Maloy K. 1995. The global demography project. Technical Report 95-6, NCGIA, Santa Barbara, California, USA.

United Nations 2000. World Urbanization Prospects: The 1999 Revision. United Nations Department of Economic and Social Affairs, UN: New York.

Wood S., Sebastian K., Nachtergaele F., Nielson D., and Dai A. 1999. Spatial Aspects of the Design of Targeting of Agricultural Development Strategies. Environmental and Production Technology Discussion Paper No. 44, International Food Policy Research Institute, Washington, DC.

http://sedac.ciesin.columbia.edu/plue/gpw

http://www.ornl.gov/gist

http://grid2.cr.usgs.gov/datasets/datalist.php3

Methods for high spatial resolution population maps for Kenya and beyond

Simon HayResearch Fellow, TALA Research Group

Department of Zoology University of OxfordOxford, OX1 3PS (Home institution) and

KEMRI - Wellcome Trust Collaborative ProgrammeP.O. Box 43640 00100 Nairobi GPO, Kenya

(Overseas affiliation)


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Summary This paper presents a brief overview of a talk given at FAO headquarters in Rome on 06 February 2003 at the Global Livestock and Poverty Mapping Meeting during Session II: Population, poverty, welfare and vulnerability. Since the project has recently started it simply serves to provide an aide-memoir for participants and is not intended as a detailed methodological document.

The problem Historical and contemporary malaria mapping have focussed on the extrinsic climatic determinants of disease distribution; using predominantly temperature, rainfall and humidity regimes in combination with elevation for risk mapping (Hay et al. 2000; Rogers et al. 2002; Figure 1). The inclusion of other spatial determinants of malaria risk has been largely ignored, most notably the effects of population settlement and urbanisation (the trend toward an increasing proportion of the population living in settlements above 2000 persons). This is primarily because these parameters (urbanisation, as well as land-use and water body distribution) have been harder to define at the coarse spatial resolution of the climate and elevation data used to model risk; most often at the regional and continental levels.

Figure 1: Annual EIR across Africa (Hay et al. 2000; Rogers et al. 2002)

Predominant among these influences are the environmental changes concomitant with urbanisation. Global human ecology is increasingly defined by urban living and in Africa 38% of the 784 million inhabitants were urban dwellers in 2000. This is estimated to increase to 55% by 2030 as virtually all of the continent’s population

= 260 - 703 = 90 - 259 = 31 - 89 = 5 - 30 = 0 - 4 = No prediction

Annual EIR


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doubling during this time will be concentrated in urban areas (U.N. 2002). The profound development and epidemiological impacts of these changes is increasingly being realised. Foremost among these considerations is the “epidemiological transition” or the movement of human populations from environments supporting predominantly communicable disease burdens to conditions lending themselves to non-communicable disease systems. There has been little work on the current impact of urbanisation on malaria risk mapping and burden estimates and no quantitative consideration of how the “epidemiological transition” will impact on the future malaria burden despite its importance in the monitoring of interventions.

In addition, there is a growing body of evidence demonstrating the effects of urban settlement reducing the risks of malaria parasite exposure among human populations across Africa. In a recent meta-analysis of 159 sites across Africa where annual entomological inoculations rates (EIR) had been recorded, we were able to show that people in rural areas received on average 146 Plasmodium falciparum infected bites per annum compared to only 14 for people resident in urban areas (Hay et al. 2000).

Efforts to apply coarse spatial resolution, climate-derived malaria risk and population surfaces to better define the public health burden of malaria in Africa, therefore, may have considerably over-estimated disease outcomes by excluding the effects of human settlement on the continent. The understanding of malaria risk, disease burden, its influence on poverty and the long-term evaluation of interventions must begin to reflect the influences of urbanisation. This project intends to provide a platform to examine these demographic changes in relation to climate-based models of malaria risk at high spatial resolution. Kenya will be used as a demonstration country owing to its varied malaria ecology and human settlement patterns which reflect the diversity across much of Africa. Kenya also provides a unique opportunity to develop population models given the accessibility of high-resolution national census data and further data generated through our previous Wellcome Trust supported work on malaria infection risk and climate modelling (Snow et al. 1999). Expanding these analyses across Africa will be supported at an intermediate spatial resolution.

Project overview In December 2002 a four year project funded by the Wellcome Trust, entitled “Mapping human population in relation to malaria risk” began. The project is sponsored by Professor David Rogers in the UK and Professor Robert Snow in Kenya. The project proposes to extend the application of geographic information system (GIS) and remote sensing (RS) technology to the quantification of human population distribution (HPD) to allow more accurate malaria risk mapping and disease burden estimation. This will be achieved by combining some of the latest satellite sensor data with information from recent censuses. The high spatial resolution of these efforts will be more appropriate to the scale of human population and disease processes and an order of magnitude finer than previous attempts. High spatial resolution (100 x 100m) burden of disease (BOD) estimates will be achieved for Kenya and at an intermediate level (500 x 500m) for East Africa and the wider continent.

These accurate, spatially explicit BOD estimates for malaria are the prerequisite for planning and monitoring of all intervention and control operations. The specific aims will be to i) provide a methodology to accurately map HPD using satellite sensor data and public-domain census information; ii) relate these HPD patterns to malaria infection risk; and iii) re-model malaria risk and disease burden on the basis of HPD and project the impact of human population trends (particularly urbanization) on the epidemiology and public health risks of malaria in Africa. The process of generating these products will also allow fundamental questions about the environmental determinants of HPD and how these relate to the BOD to be addressed. Furthermore, high spatial resolution HPD and malaria risk maps will help facilitate commodity needs


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estimation for Kenya, health service and intervention equity issues and some basic analyses on the efficacy of delivery mechanisms for control services.

References Hay S.I., Omumbo J.A., Craig M.H. and Snow R.W. 2000. Earth observation, geographic

information systems and Plasmodium falciparum malaria in sub-Saharan Africa. Advances in Parasitology 47: 173-215.

Hay S.I., Rogers D.J., Toomer J.F. and Snow R.W. 2000. Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, internet access and review. Transactions of the Royal Society of Tropical Medicine and Hygiene 94 (2): 113-127.

Rogers D.J., Randolph S.E., Snow R.W. and Hay S.I. 2002. Satellite imagery in the study and forecast of malaria. Nature 415(6872): 710-715.

Snow R.W., Craig M., Deichmann U. and Marsh K. 1999. Estimating mortality, morbidity and disability due to malaria among Africa's non-pregnant population. Bulletin of the World Health Organization 77(8): 624-640.

U.N. 2002. World urbanization prospects: the 2001 revision. Data tables and highlights. New York, United Nations: 182pp.

Mapping poverty and inequality

Gero CarlettoDevelopment Research Group, World Bank


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Poverty maps, spatial descriptions of the distribution of poverty in any given country, are of greatest use to policy-makers and researchers when they are finely disaggregated, i.e. when they represent small geographic units, such as cities, towns, or villages. Many developing country policymakers use poverty maps when planning public investments in education, health, sanitation, water, transport, and other sectors. Social Funds often use poverty maps because geographically targeted investments are thought to reach many poor citizens and have spillover effects enhancing productivity in depressed areas.

Unfortunately, almost all household surveys are too small to be representative at such fine levels of disaggregation, and most census data (or other large data sets) do not contain the detailed information needed to calculate monetary indicators of poverty or inequality. In general, there is a tradeoff between the number of observations in a dataset and the information content, because collecting a lot of information for a very large sample is prohibitively expensive and is likely to have negative effects on data quality.

How can this obstacle be overcome? One way is to apply econometric techniques to combine sample survey data with census data for prediction of consumption-based poverty indicators using all households covered by the census. Since policymakers in many countries are familiar with poverty and inequality indicators (e.g. the Foster-Greer-Thorbecke measures, the Gini coefficient, etc.) that are regularly reported in country poverty profiles using household surveys, this method is appealing because it produces estimates of these same measures, as well as an indication of the degree of statistical precision of these estimates, for smaller administrative units. Poverty maps constructed in this fashion are more likely to be put to practical use, because the statistical underpinnings of the methodology makes them more credible and more readily endorsed than the more commonly found maps based on ad-hoc methods (e.g. maps based on Basic Needs Indicators).

The basic methodology is quite simple and involves the following stages:

Zero stage: (a) Establish comparability of data sources; (b) identify common variables and (c) understand sampling strategy.

First stage: Estimate a model of (log) per capita consumption in the household survey based on common variables found in both the census and the survey. The model estimation thus provides an empirical weighting scheme.

Second stage: Take parameter estimates to census, predict consumption, and estimate poverty and inequality for each population of interest. It is particularly important to gauge the precision of the poverty estimates by computing standard errors. Empirical evidence to date has shown that standard errors increase with the level of disaggregation and tend to “explode” at cluster sizes below a certain threshold (about 1,000 households for poverty estimates).The maps shown in Figure 1 give a visual representation of what can be accomplished by applying the described methodology. While the original survey would only allow estimates at the regional level, maps showing the heterogeneity in poverty levels at the district, cantón, and parroquia (not shown) levels could be produced by combining the survey data with the census information.


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Figure 1: Maps showing impact of disaggregation on spatial heterogeneity of poverty

A small team of researchers in the World Bank’s “poverty cluster” of the Development Economics Research Group (DECRG-PO) have now developed a methodology along these lines and have supported its implementation, or are in the process of doing so, in Ecuador, Bolivia, Panama, Nicaragua, Guatemala, Mexico, South Africa, Madagascar, Mozambique, Malawi, Tanzania, Kenya, Uganda, Zambia, Morocco, Bulgaria, Albania, Armenia, Thailand, Cambodia, Laos, Vietnam, Indonesia, Papua New Guinea and China. The team provides technical assistance, capacity building, and various free software tools to statistical institutes in developing countries on demand, conditional upon availability of suitable data (a recent census and a LSMS-type household survey). Furthermore, the team is currently working on developing a “how-to manual” for poverty mapping, platform-independent software tools, and a training course aimed at staff from statistical institutes and researchers from developing countries. A training course combined with user-friendly tools and technical assistance should make poverty maps accessible for a wide range of countries in the near future. Much of the work on “Poverty Mapping” has been done in collaboration with researchers at the International Food Policy Research Institute (IFPRI), the World Food Program, the International Livestock Research Institute, and also Macro International. Financial and logistical support has also come from the Bank Netherlands Partnership Program, the Department for International Development and the Amsterdam Institute of International Development. Interested parties are encouraged to visit (http://econ.worldbank.org/programs/2473/topic/14460/) for further information.

http://econ.worldbank.org/programs/2473/topic/14460/

Poverty, intervention and livelihoods mapping:Some examples from ILRI’s research in Kenya

Patti Kristjanson and Russ KruskaSystems Analysis and Impact Assessment,

International Livestock Research Institute, Nairobi, Kenya

Poverty, intervention and livelihoods mapping: Some examples from ILRI’s research in Kenya

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Introduction The International Livestock Research Institute (ILRI) has been pursuing several different avenues of research aimed at better understanding the links between livestock and poverty. The approaches are at different scales and use multiple methods to try to get at this complex issue. This paper briefly summarizes several of these approaches (note: it does not attempt to cover all the research at ILRI that addresses poverty and livestock-related issues).

Poverty maps and analyses using these maps A research collaboration between ILRI, Kenya’s Central Bureau of Statistics, Rockefeller and World Bank researchers has resulted in the first high resolution poverty maps for Kenya (maps for Uganda will also soon be available). Estimates of the percentage of the population living below the poverty line at the sub-location (hundreds of households) and location-level (thousands of households) are the result of this teamwork. This research is now entering the dissemination and use stage, with exciting possibilities for using the results directly for policy targeting and better understanding spatial patterns of poverty in Kenya, but also for the further research opportunities that have been opened up to analyze the underlying determinants of these patterns.

On of these research opportunities involves spatial analysis using high resolution poverty maps for examining poverty-environment linkages and improving poverty reduction strategies and targeting in Kenya. Because locations/sub-locations are relatively small areas compared to districts, spatial factors like soil, climate and even market access among households within a location/sub-location are relatively homogenous. Such high resolution data for a welfare measure will allow an investigation of the links between geographic characteristics and poverty; an analysis that needs to be done at a scale that provides enough variation in critical spatial variables such as temperature, altitude, access to markets, ethnicity, etc. to examine the importance of these factors in determining land-use patterns and relative poverty levels. Thus if such ‘map-able’ geographic and socioeconomic factors are in fact largely determining the degree of expenditure/welfare levels, due to lack of variation this might not show up in the results of typical household-level studies that are confined to a small area. At the same time, household-level census or survey data that can be aggregated to the same broader level of observation (e.g. location) can be used to capture “traditional” determinants of poverty, such as level of education. The premise behind this spatial analytical approach, therefore, is that apart from traditional household-level factors, spatial factors are likely to be crucial in understanding levels of poverty at the broader landscape or community-level. Using the current GIS database developed at ILRI, it will be possible to evaluate climatic characteristics (both level and variability for rainfall), soil and slopes, allowing a more in-depth examination of the linkages between poverty and environmental degradation than was previously possible (both the high resolution poverty data and new remote sensing data on land cover are now available). This research will also build on previous ILRI analysis that shows an important ‘piece of the puzzle’ is degree of market access (Staal et al., 2002). ILRI has developed a very detailed road network database for the Kenyan Highlands that has been successfully used in the past to capture market access (see Figure 1), and this will be extended in order to cover the whole country.


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Figure 1: Mapped distance to the nearest good-quality road for Kenya Highlands

The analysis will combine data from different sources, evaluated at the lowest or second lowest administrative level in Kenya (i.e. sub-location or location). The variable of interest (main dependent variable) is the level of poverty, defined as the percentage of households falling below a specific poverty line. Two main sets of explanatory variables are identified: socio-economic factors available through census data, and spatial factors derived from existing GIS layers, both purely biophysical (e.g. altitude, soil type) as well as some with socio-economic aspects (e.g. market access). The combined use of these rich datasets will allow us to disentangle the influence of environmental conditions and socio-economic characteristics on the level of poverty and will lead to important policy recommendations as well as information that can be used to improve targeting of research and development (and policy) interventions. For example, controlling for agro-climatic characteristics, if socio-economic factors like market access turn out to significantly influence the level and distribution of poverty, strong policy recommendations in terms of road infrastructure and/or marketing channels can be derived.

Poverty intervention mapping and development of new tools The new poverty maps also open up opportunities for developing maps and tools aimed at improved targeting of research investments towards interventions that reach the poorest segments of society. First, a good understanding of where particular livestock-related interventions (technologies, strategies, policies) are feasible and relevant is needed. This is done by first developing an inventory of interventions and characterizing them. Second, an exploration of the degree to which mapping the areas where the range of biophysical and socioeconomic factors relevant to/required by the particular intervention (e.g. amount of rainfall, proximity to markets) can be undertaken. Figure 2 shows an example using Calliandra, a fodder tree that provides high quality feed for dairy cows. The so-called ‘natural domain’ was defined as areas with: elevation between 12000 and 2500 metres, annual precipitation greater than 1000mm, and AEZ aones UH1, LH1, and UM1-4. The ‘socio-economic domain’ included


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areas with: human population density greater than 250 persons/km2, density of dairy cows greater than 50 grade cows/ km2, and distance to the main road of less than 5 km. Combining the two gives a natural and socio-economic recommendation domain that gives a fairly good idea of feasible areas for this technology. This can be followed up with ground-truthing and more targeted surveys to get more details as to actual use. Overlaying this with the poverty maps will also give an indication if the intervention will be relevant in the poorest areas or not.

Figure 2: Natural and socio-economic recommendation domain for Calliandra in central Kenya

While maps such as this are useful for some users, even more useful for others will be a web/CD-based ‘policy intervention’ tool that will allow the user to define the relevant ranges and develop their own domains and policy-related queries, which is the goal of a new ILRI activity funded by the United Kingdom Department for International Development.

Livelihood maps Recognizing that expenditure-based poverty estimates do not necessarily capture the importance of the five types of assets (physical, natural, social, financial and human – see Table 1) that may largely determine the livelihood options facing poor households, ILRI is collaborating with Fodo and Agriculture Organisation (FAO) and Food Insecurity and Vulnerability Information and Mapping Systems (FIVIMS) on a livelihoods mapping project, commencing with Kajiado District in Kenya. This project is exploring what is


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‘map-able’ with respect to these five types of assets. We will attempt to capture the idea of what are the relevant catchment areas for schools, health facilities, agricultural services (crops and livestock), access to water, communal land for grazing, etc., for example, by using travel times to reach these facilities/services (e.g. areas within a 1 hour, 3 hour, 5 hour walk to obtain drinking water, reach a school, police station, health facility, etc.). This will allow us to identify areas that are not serviced, under-serviced, or sufficiently serviced with respect to the above categories. We could also look at the relationship between different types of services (e.g. do they all clump together? Are most found only in large towns?). Another objective is to identify areas high versus low in physical capital, natural capital, etc. and compare to areas of high versus low poverty (using our poverty maps).

Table 1: Examples of the 5 types of livelihoods assets to map: Locations and catchment areas

Physical capital: markets, roads, water, electricity, telephones, livestock dips

Natural capital: agricultural potential: rainfall (amounts and timing), soils, water sources, communal forage land, forests

Human capital: agricultural services, including skilled people, e.g. vets, other service providers, health facilities/services, human disease incidence and sickness rates, availability of labour

Social capital: Information networks/groups, churches, community-based organizations, NGOs

Financial capital: Credit, livestock, off-farm income options (e.g. mining sites, tourism)

Community and household-level surveys The approaches above can give us a better understanding of some of the geographical determinants of poverty as well as some of the underlying factors determining households’ livelihood options. None of them really get at the underlying processes of poverty, however. ILRI has just completed a pilot test study in western Kenya that was aimed at testing a method for better understanding pathways out of and into poverty and the role that livestock play. The method involves participatory community-level discussions using a facilitative approach, followed by household-level discussions. The use of a simple diagnostic tool makes this approach analytical, yet thoroughly participatory in nature. The results of the pilot test were interesting and encouraging, and the plan is to broaden out the study to cover 40 villages in Kenya and 40 in Uganda. Villages will be chosen using GIS techniques to stratify them according to several criteria that are important in defining the range of livelihood options available to villagers, including degree of market access and agricultural potential.

This project will generate knowledge as to what major factors are responsible for households’ declines into poverty and the various reasons why other households have managed to boost themselves out of poverty (as defined by the communities themselves, rather than a government-defined poverty line that means little to them). These reasons are likely to vary significantly in different types of villages in different regions. For example, human disease and funeral-related costs are likely to be a more important factor explaining declines into poverty in drier areas near Lake Victoria, as we saw in the pilot project, whereas livestock ownership is likely to be a more critical factor in the higher rainfall and altitude areas of western Kenya. This information will allow the development at the regional or national level of sector-specific investments and policies for particular areas and certain types of communities. For example, if the most important reason within a particular district for declines into poverty is the non-availability of consumer credit, a possible solution lies in working with communities to develop an appropriate micro-credit scheme. If, however, in another district, it is found to be a particular disease, a community-driven disease eradication program may do more to prevent needless declines into poverty than will a credit scheme. More


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contextualized and precise knowledge about poverty will be generated, and better targeted programs can be formulated to utilize scarce development resources more effectively.

References Staal S.J., Baltenweck I., Waithaka M.M., de Wolff T. and Njoroge L. 2002. Location

and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya. Agricultural Economics 27: 295-315.

Poverty, ecosystems, and vulnerability: Spatial data issues

Norbert HenningerWorld Resources Institute


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Introduction A number of obstacles have to be overcome when compiling spatial information for global livestock and poverty mapping and developing pro-poor livestock policies:

! Economic interests mostly drive international and national data collection efforts. While international data on important security issues or high-value commodities are collected frequently, data relevant to the livelihoods of poor livestock keepers are generally more ad hoc, less reliable, or lacking, except for selected case studies and research activities.

! The media and the international development literature rarely highlight issues about or relevant to poor livestock keepers. How often do we see a woman feeding guinea fowl, as depicted in the Pro-Poor Livestock Policy Initiative (PPLPI) logo, on the front page of a newspaper?

Developing spatial information systems for the PPLPI, however, is not a futile task because of the following encouraging trends:

! We are at an important transition point in global data compilation and analyses. Satellite data, household surveys, and detailed sub-national data, are more readily available in electronic and geo-referenced formats. Global data compilation is moving beyond country estimates. For example, global demographic data are now available for 150,000 sub-national units. With some additional resources and the right partnerships, we could compile production data for selected crops, human wellbeing, and poverty for sub-national units with relative comprehensive international coverage.

! A network of entrepreneurial researchers equipped with e-mail, online data access, and some flexible funding are driving some of these innovative efforts to compile sub-national global data. For example, the Gridded Population of the World begun as an effort to compile sub-national demographic data for a dissertation on technology transfer and use. A global model developing water demand scenarios drove the most recent compilation of a world map of irrigated areas, relying on many dispersed sources.

The PPLPI’s quest to develop spatial information for global livestock and poverty mapping could learn from and built on these activities. PPLPI has an opportunity, some would say responsibility, to change the way we collect, compile, and distribute international data, especially those data that are relevant for developing pro-poor livestock policies. Four principles, gleaned from compiling spatial data for Africa, geo-referencing household surveys, examining the use of poverty maps, and developing an indicator strategy for the Millennium Ecosystem Assessment, could guide PPLPI’s process of developing spatial information:

! Work at multiple scales ! Decide to map at both global and more local scales. This will allow limiting the

global maps to issues that can be and should be done at that scale within the existing time frame and available resources. For example, global maps are a great tool to raise awareness, to test a concept, to examine large macro-scale changes, and to cover issues of trade and international equity. More local poverty and livestock maps can help in targeting projects, in examining congruence between poverty and livestock production systems, and in developing specific national policies.

! Have the courage to share components and modules ! Any global mapping efforts usually requires integrating multiple data sets, often

combined with expert opinion or modeling to fill data gaps or develop aggregate indexes. Too often researchers only share the final maps or results. However, by


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distributing all input data (including expert evaluations) other analysts will be able to redo the analysis, create their own maps, or apply different weighting schemes. Moreover, selected input data sets by themselves could be useful for analysts and applications in other sectors.

! Ensure wide availability of data and final products ! Selecting different distribution channels will ensure that spatial information and

the derived products are used by researchers, policymakers, and students. ! Track the use of data and maps very closely ! It is worthwhile to earmark some resources to track use and applications. How are

multilateral and bilateral development agencies applying spatial data? Which decisions are being influenced and how is resource allocation changing? Are regional and national institutions using the data or modifying the approach on their own?

The PowerPoint presentation associated with this paper includes examples of global mapping efforts related to poverty, ecosystems, and vulnerability, but because of time limitations only spatial data issues related to poverty and human well-being were discussed.

Opportunities for mapping poverty and human well-being We are still a number of years away from producing a globally consistent high-resolution poverty map that could be used for geographic targeting or detailed spatial analyses, because of data availability and comparability reasons. Sub-national data on human well-being and poverty are dispersed among many different international and national institutions and are not readily accessible to analysts serving decision-makers.

PPLPI could support compilation of a geo-referenced database of human well-being and poverty indicators at sub-national level by bringing together some of these dispersed efforts, thus significantly reducing access costs for analysts and policymakers.

There is general agreement that poverty, a pronounced deprivation of well-being, is multidimensional, meaning a condition characterized not only by lack of income and material goods, but also by lack of opportunity to learn and to participate in decision-making. International data for these different dimensions, for example on income, expenditure, health, and education, are generally available. Most of the time, these data are country-level estimates with a possible dissagregation into urban and rural areas. In addition to this limited spatial resolution, not all indicators are standardized or updated annually for all countries, limiting international and trend analyses.

To map poverty and other human wellbeing measures, the PPLPI could rely on five major sources that would yield improvements in resolution and coverage: Demographic and Health Surveys (DHS), nutrition surveys (e.g., anthropometric data from the Global Database on Child Growth and Malnutrition), sub-national Human Development Indexes (and other basic needs indexes), poverty estimates from household surveys (e.g., Living Standard Measurement Surveys), and poverty estimates and other variables (e.g., nutrition indicators) derived from small area estimation.

Demographic and health surveys Demographic and Health Surveys can provide indicators related to different poverty dimensions: housing characteristics, household assets (“Wealth Index”), high-risk births and family planning, early childhood mortality, child nutrition (anthropometry), and school enrollment. By working with the original data providers, PPLPI could improve resolution from country-level to broad sub-national regions, for example from


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26 countries to 195 DHS regions in Africa. This would require producing an inventory of DHS regions and map boundaries, extracting selected variables from country reports, and calculating other variables from micro data. PPLPI could produce medium resolution maps that are internationally comparable, because most variables (questions in survey) are standardized.

Using sub-national DHS data has the following limitations: Countries do not conduct these surveys annually or in the same year, raising the question of comparability over time. While surveys cover most developing countries, significant gaps in global coverage remain, for example data are not available for China, Mongolia, Argentina, Chile, Uruguay, selected countries in North Africa and Middle East, Russia, and most OECD countries.

Nutrition surveys PPLPI could produce sub-national maps, at medium resolution and internationally comparable, from national nutrition surveys. For example, the Global Database on Child Growth and Malnutrition is one international source with data for 200 countries. Most of the countries have nationally representative samples with sub-national estimates covering the 1990s. Major variables include stunting (low height-for-age), wasting (low weight-for-height), and underweight (low weight-for-age), typically expressed as a percentage of the population of children under five years.

There are two limitations with these data. Access to the data will require extensive negotiation and partnership building (data are publicly available only in a locked PDF files). Anthropometric data on wasting and underweight are very time sensitive, fluctuating with the seasons the surveys are conducted.

Sub-national human development indices During the past decade, UNDP has produced national Human Development Reports for most developing countries. Many of these reports contain sub-national estimates of the Human Development Index and other human wellbeing measures. CIESIN and UNDP have now completed a data catalog of available sub-national indicators for 52 countries. The variables cover many dimensions of poverty and are relevant to the Millennium Development Goals. PPLPI could work with these institutions and map these data, which are generally comparable in resolution to the DHS and nutrition data.

Poverty estimates from household surveys The Global Poverty and Inequality Database is one important international source of sub-national poverty measures. It covers 83 developing countries and is based on 265 national sample surveys conducted between1987 and 1998. Surveys provide different poverty measures such as headcount, poverty gap, and squared poverty gap. About 40 percent base their welfare indicator on income, while 60 percent use expenditures. These surveys represent 90 percent of the 1998 world population.

PPLPI could produce global or regional maps of poverty at medium resolution, using these surveys. One major challenge for international analyses lies in the comparability of poverty measures. The maps will not be comparable: Countries use different welfare definitions (income or expenditures that include food and non-food items, sometimes own production, food gifts and in-kind transfers, or humanitarian assistance), poverty lines, and make adjustments made for regions, over time (e.g., consumer price index), household size, and economies of scale. To increase greater comparability, for example in a region, PPLPI could encourage concerted research efforts to reconcile and adjust national poverty lines or poverty estimates.


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Poverty estimates based on small area estimation Recent progress in developing sophisticated statistical methods of mapping poverty based on econometric techniques (sometimes referred to as small area estimation) are increasingly becoming available, providing high-resolution poverty measures, and in one case, Cambodia, also nutrition indicators. In early 2003, 10 countries had completed their estimation (Ecuador, Panama, Nicaragua, Guatemala, South Africa, Malawi, Madagascar, and Mozambique, Vietnam, and Cambodia), 13 countries had ongoing activities at various stages (Mexico, Bolivia, Kenya, Uganda, Tanzania, Morocco, Albania, Bulgaria, China, Indonesia, Laos, Papua New Guinea, and Thailand), and four countries were contemplating to use small area estimation (Zambia, Turkey, Philippines, and India).

These maps have the advantage of providing estimates at high resolution, making them useful for spatial analyses and policy making in a country. Their international use, however, is still limited, because of different national poverty rates. PPLPI could support these efforts in selected regions, for example by encouraging research to reconcile national poverty lines or to provide resources to accelerate efforts in selected PPLPI focus countries.

SESSION III:

LINKING LIVESTOCK AND POVERTY

Understanding how livestock can contribute to poverty reduction:

What should we be mapping?

Brian Perry and Tom RandolphInternational Livestock Research Institute (ILRI),

Nairobi, Kenya

Understanding how livestock can contribute to poverty reduction: What should we be mapping?

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Introduction The last few years have seen an increasing interest by public sector investors in livestock research and development towards targeting their financial resources at policies, strategies and activities that can be shown to reduce poverty. This has led to a complementary increase in research activity to try and define what those policies might be, and what are the mechanisms by which they may operate.

There is of course more than one way of reducing poverty, and besides, no single measure is likely to be successful. For years many development programmes have been designed to promote economic growth, usually at a national level. However, while this is a goal that may provide an environment conducive for poverty reduction policies and strategies, it does not in itself necessarily reduce poverty. A number of ex post impact assessments, and the rise of other schools of thought, have resulted in greater emphasis being paid over recent years by some investors to issues of equity, rather than, or in partnership with, economic growth. This shift has resulted in a greater focus on policies that target more directly the poor themselves, and the assets, physical, material, and social, which comprise their livelihoods.

Outcomes of this have been policies that concentrate efforts into securing the assets of poor people by focussing on the multiple components of rural livelihoods, and have resulted in a many very valuable analyses of the different dimensions of poverty (see for example Carney, 1998). Dare we say that livestock scientists have been latecomers to this field? Livestock play a very important, and often complicated role in the lives of rural (and urban) poor, but strangely enough they are hardly mentioned in the majority of studies of poverty in the developing world. This vacuum in our understanding has precipitated a recent flurry of activity, possibly the most prominent of which is the work of Thornton et al (2002), in which the first attempt was made on a global scale to try and understand the possible linkages between the distributions of livestock in different production systems, and the distribution of poverty, expressing the results as a series of maps and figures. This has indeed been a significant move forward, and presents data in cartographic format that allows us to see broad categories of associations between livestock and poverty that will help underpin certain decisions on resource allocation. Or will they? Like so many areas of scientific progress, these maps raise more questions than they answer.

The development of poverty reduction policies Returning to the two key elements of poverty reduction, economic growth and equity promotion, it appears to us that there are few empirical studies on the relative merit of optimal combinations of these broad policies in livestock development. Indeed, in a recent study we have been part of on the impact of a foot-and-mouth disease (FMD) control policy in southern Africa on poverty reduction, we found a catalogue of complexities, inconsistencies and confusion (Perry et al., 2003). Firstly, the complexities; just defining poverty is complex, let alone understanding its multiple causes in different settings and evaluating methods of reducing it. Secondly, the inconsistencies; these mainly relate to agendas contained in policies for national development, in which poverty reduction features to a greater or lesser extent. We experienced significant inconsistencies between stated overall national development policies, and specific agendas in the livestock, and animal health, sectors, for example, in which it was apparent that the effects of proposed agendas on specific elements of poverty reduction had not been identified, let alone quantified and compared with other options. This is possibly not surprising, but demonstrates that there is clearly a lot of room for greater understanding of the specifics of how livestock can contribute to poverty reduction. Thirdly, the confusion; this relates mainly to the failure, by some policy makers, to define policy objectives clearly,


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leading to confusion in the selection of appropriate economic and social methodologies, and in synthesising the results of different analysis techniques, in order to arrive at a valuation of the overall efficacy of a policy in reducing poverty. In our example, the strategies that effectively controlled FMD and permitted access to export markets produced positive economic returns at a national level, and export bans imposed as a result of disease outbreaks had negative economic impacts on most sectors of society. This suggests FMD control makes positive contributions to poverty reduction. But given that very few of the poor actively participate in cattle marketing for export, and increasing offtake for slaughter directly conflicts with their wealth storing strategy, the direct returns to the poor are minimal. This suggests that FMD control makes minimal contributions to poverty reduction. Could there be a weighting that would allow these two contrasting example indicators to be combined into a single value that would produce a definitive assessment of the programme? Would it be essential to compare the results with other potential investment options for poverty reduction, in the health, livestock, or other sectors, and how could this be feasibly achieved?

This example illustrates some of the complexities in the understanding of which policies might have greatest impact on poverty, and how we might be able to measure the effects they promote or achieve.

The spatial aspects of poverty reduction Overlays of the distributions poverty and livestock production systems provide a fundamental data ingredient necessary for the understanding of poverty reduction options. But the products are generally restricted to answering the following questions:

! What is the distribution of the numbers of poor associated with livestock? ! How do the numbers of poor vary by country, region and production system? ! How are these numbers likely to change with population growth and migration? ! How might climate change (and other global phenomena) affect these

distributions?

The theme of this meeting has been to discuss ways to improve the quality of answers to these questions, through better estimates and understanding of both poverty and livestock populations, for example by increasing the resolution at which data can be made available. However, while this is without doubt an essential process, we wonder whether there is a greater need to move on to improve our understanding of potential outcomes of poverty reduction policies, and how these might vary by policy, region, production system, etc. This infers extending the use of ex ante predictions, currently being used to evaluate the effect of climate change on production system distribution, to evaluate and predict the possible outcomes of different policy options, which will provide greater value in decision support than just knowing where the poor and their livestock are, and how many of them there are.

The big question then arises as to which elements of poverty reduction options and their outcomes can be effectively analysed and displayed spatially, and how these might be most effectively integrated with key non-spatial elements.


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The need to understand the spatial aspects of processes The use of GIS and other spatial tools to evaluate processes in a predictive way, rather than human and animal populations, will require an understanding of what processes are most relevant to poverty reduction, what indicators can be used to understand change in those processes, and which of these can be effectively analysed and displayed spatially.

There are various candidate processes, and at least two sets have been mentioned in this meeting. Joachim Otte, in his introduction, described the broader evolutionary processes as access rights to land, market integration and asset conservation/protection. John Dixon identified intensification, diversification, herd size growth, increase in off farm income, and exit from livestock/farming as the 5 key processes. For the purposes of illustration only, we discuss here the processes identified by Perry et al. (2002) for evaluating how different constraints (in this case diseases) influence poverty, as examples of processes in poverty reduction, and examine potential spatial indicators that might be of value in understanding and measuring such processes. These were:

! Securing the assets of the poor ! Improving marketing opportunities for livestock products ! Improving animal productivity to promote intensification of livestock activities

Securing the assets of the poor Assets of the poor associated with their livestock include financial, human, social, natural and physical capital. Taking processes affected by changes in improvements in animal health as an example, indicators might include the following: livestock holdings per household (with poultry likely a key indicator in many systems), livestock mortality and morbidity, animal health expenditures on inputs, zoonotic disease incidence and morbidity, child physical and mental development, protein status, purchases of meat and milk, medical care expenses, social network intensity, soil fertility, traction capacity, etc. Some of these clearly lend themselves to spatial analysis, but most of the categories are of data not generally sought, and thus might require collection and evaluation on a case study basis. In addition, there may be surrogates for some of the data categories available that would be acceptable. In addition, it will be very important to understand the value of such indicators in quantifying the dynamics of poverty.

Improving marketing opportunities for livestock products Indicators of changing market opportunities are multiple, and can exist at different levels, depending on the scale of marketing practices. First on the list is simply the location of markets and market pathways, a fundamental need for which data are often absent. Staal et al (2002) have evaluated the use of different spatial variables in analysing the uptake of dairy technology in Kenya, and concluded that location, with all its dimensions of market access, demographics and agro-climate remain key to understanding technology use. The same is likely to be the case with policies, but the associations more complex.

Building on this is the need for product price data, for livestock products that are relevant to target groups of poor. This might be local chicken meat under some circumstances, or variations in regional prices of milk. Price data have previously been used in a spatial context with varying success, but it has not, to our knowledge, been used as an outcome indicator of a poverty reduction intervention or policy. Examples include price surface maps. In Laos, the Ministry of Agriculture collects monthly commodity price data from provincial markets across the country. These data have been used to generate such maps. Shaded surfaces and price contours allow the user


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to easily determine the relative price difference between points, and the superimposed layer for major roads indicates how easily animals might be moved from one place to another (from Cameron, 1998). Further precision and relevance to poverty reduction process might be achieved by disaggregating price data if there are significant variations in prices by product quality, if such variations are associated with particular income groupings. Staal et al (2000) demonstrated the importance of road types, distance from markets and market infrastructure on farm-gate milk prices in the smallholder dairy sector of highland Kenya.

Another indicator that has potential as a spatial indicator of changes in marketing intensities is the “offtake rate”, the proportion of a cattle herd (usually at administrative boundary level such as district, provincial, national, but can also be by sale pen) that is sold into the beef marketing system. This indicator has been widely used in southern Africa, but never applied spatially, although it would seem that it could be reasonably straightforward. In a study in Zambia, Perry et al (1985) calculated commercial sale offtake rates for cattle at the district level for seven districts ranging from 2-7%, indicating a substantial spatial variation. This study was not repeated, so the added dimension of temporal variation was not studied. In Zimbabwe and other countries on southern Africa, sales pens are located at strategic sites throughout communal farming areas, and sales data are available, and could potentially provide higher spatial resolution. In Namibia, offtake rates of up to 15% have reportedly been achieved in the north of the country, as a result of aggressive marketing and the construction of export quality abattoirs (Grobler, 2002, cited by Perry et al., 2003), but like the Zambia study, temporal data documenting change in offtake over time as a result of these interventions are not available. Difficulties with this indicator might arise in its use and interpretation in the context of poverty reduction. A recent study in Zimbabwe concluded that less than 2% of communal cattle farmers had herd sizes adequate for cattle marketing, on top of their livelihood obligations for wealth storing, traction, etc. This suggests that policies aimed at increasing cattle marketing under conditions, when most households are using cattle primarily for other livelihoods functions, may not be “pro-poor” (Perry et al., 2003).

Developing these concepts further, there are various combinations of spatial indicators that might be of value as indicators of poverty reduction processes. For example, spatial equilibrium models could be used to analyse supply and demand, identifying where there are situations of increasing population and income growth (so increasing demand) that coincide with opportunities for improved transportation and marketing, that might offer particular incentives for local producers to intensify. This illustrates the likely overlap between the processes of enhancing market access and improving productivity through intensification.

Improving animal productivity to promote intensification of livestock activities Increasing productivity is the classic pathway for intensification of farming systems by which households increase the value of outputs for their inputs, involving upgrading of an existing enterprise through a more productive management technique, or adopting a wholly new, more productive, livestock activity (Perry et al., 2002). Key indicators for this process will be measures of the use of inputs, such as animal feeds and services. For example, in the smallholder dairy sector of the developing world, this might include purchase of concentrates and other feeds, animal health services, and artificial insemination services, for example.

Other processes John Dixon identified “exit from farming” as an important process that might result from decreasing poverty, and this example illustrates the complexity of indicator selection. In the field of smallholder dairying, for example, this could presumably be measured by changing numbers of households in dairying, with a decrease indicating a reduction in poverty. However, at the same time a reduction of poverty might also be


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measured by the rate of increased adoption of dairy animals by smallholders, making the interpretation of data on dairy cows per household rather complex.

The role of spatial data and analyses in ex ante modelling of livestock policy options

As many have said before, central to predictive modelling of policy options is an understanding of what constitutes poverty, what are the mechanisms for reducing it, and how the implementation of such mechanisms can be evaluated. Here we return to the complexities and confusions! Impact assessment methodologies to evaluate interventions, strategies and policies aimed at reducing poverty are many and various, and operate at different levels. In the case study example we cited earlier of FMD control in southern Africa, methods used included a national benefit cost analysis of alternative intervention scenarios to evaluate the economic growth component (incorporating a computable general equilibrium [CGE] model that evaluated how income levels across different sectors of the economy would adjust to a loss of revenue from beef export bans), and a livelihoods analysis to evaluate the equity component. The multiple study outputs included:

! Benefit: cost ratios for different intervention scenarios ! “Multiplier effect” values to understand the implications of beef export bans on

different sectors of the economy ! Price effect values to understand the effects of export market access on beef

prices for different types of cattle producers (and to a lesser extent consumers) ! Qualitative direct and indirect impacts of FMD and its control on the livelihoods of

the poor, particularly poor livestock keepers

However, within these outputs, spatial analysis played a limited (but valuable) role. Nevertheless, it could have played a more prominent role had more of the necessary data for such analyses been geo-referenced, although in the end it would still have likely been a support role to econometric and livelihoods analyses. We conclude that it is by teasing out the needs of such multi-disciplinary impact assessments of poverty reducing policies, and identifying those that require data with spatial attributes, that GIS will best serve the development of effective policies for poverty reduction.

In the group discussion that follows, it is suggested that particular emphasis be placed on identifying key spatial data variables that might be essential for describing and measuring selected processes in poverty reduction at different levels of resolution.


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References Cameron A.R. 1998. Active surveillance and GIS as components of an animal health

information system for developing countries – Thailand and Laos as examples. PhD Thesis, University of Queensland. Brisbane, Australia.

Carney D. (ed). 1998. Sustainable rural livelihoods: What contribution can we make? DFID (Department for International Development), London, UK. 218 pp.

Perry B.D., Mwanaumo B., Schels H.F., Eicher E. and Zaman M.R. 1984. A study of health and productivity of traditionally managed cattle in Zambia. Preventive Veterinary Medicine 2, 633-653.

Perry B.D., Randolph T.F., McDermott J.J., Sones K.R. and Thornton P.K. 2002. Investing in animal health research to alleviate poverty. International Livestock Research Institute (ILRI), Nairobi, Kenya, 140 pp plus CD-ROM.

Perry B.D., Randolph T.F., Ashley S., Chimedza R., Forman T., Morrison J., Poulton C., Sibanda L., Stevens C., Tebele N. and Yngstrom I. 2003. The impact and poverty reduction implications of foot and mouth disease control in southern Africa - with special reference to Zimbabwe. International Livestock Research Institute (ILRI), Nairobi, Kenya. 138 pp plus CD-ROM.

Staal S.J., Delgado C., Baltenweck I. and Kruska R. 2000. Spatial aspects of producer milk price formulation in Kenya: a joint household-GIS approach. Contributed paper, IAAE meeting, Berlin.

Staal S.J., Baltenweck I., Waithaka M.M., de Wolff T. and Njoroge L. 2002. Location and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya. Agricultural Economics 27: 295-315.

Thornton P.K., Kruska R.L., Henninger N., Kristjanson P.M., Reid R.S., Atieno F., Odero A. and Ndegwa T. 2002. Mapping poverty and livestock in developing countries. ILRI (International Livestock Research Institute), Nairobi, Kenya. 132 pp.

Summary of group discussions

Dirk PfeifferRapporteur

The Royal Veterinary CollegeLondon, UK


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Amongst a wide range of other activities, the “Pro-Poor Livestock Policy Facility” (PPLPF) recently established by FAO will develop spatial information systems in order to contribute towards addressing a number of project objectives:

! Characterise and target the different strata of livestock-dependent poor ! Understand the factors that affect poor people’s livestock-related livelihoods ! Evaluate livestock policy options ! Facilitate decision support by stakeholders in livestock policy making

The link between livestock and poverty needs to be better understood in order to develop and promote livestock policies that are of benefit to the poor. This workshop was used by the PPLPF to draw on international expertise with respect to spatial and non-spatial data relating to the livestock – poverty relationship. In addition, the potential for forming alliances with groups sharing similar objectives and interests, both within and outside the FAO, was explored.

During the workshop, separate group discussions were held for each of the three workshop sub-topics ‘Livestock distribution and production systems’, ‘Population, welfare & vulnerability’ and ‘Linking livestock to poverty’. The discussions about the first two topics were held after dividing the participants into two groups. The third topic was discussed with the whole group. The objectives of the discussions were defined as follows:

! To provide an inventory of on-going and planned activities; ! To identify common goals and objectives; ! To discuss ways to achieve the common goals and objectives; ! To identify groups of collaborators

A summary of the conclusions reached during the group discussions is presented in the following.

Livestock distribution and production systems

Inventory of on-going and planned activities Geo-referenced data sources have been and are being collated for livestock distributions and systems (see http://ergodd.zoo.ox.ac.uk/agaagdat/index.htm ). FAO and ERGO, with input from ILRI, have produced data layers of the distributions of cattle, sheep and goats, pigs and poultry, over much of the Globe, at a resolution of 3 minutes (c. 5 km) with the immediate aim of producing globally standardised data sets at 5 minute resolution (c. 8 km), and eventually at a resolution of 30 seconds (c. 1 km). Systems data layers are available defining livelihoods, farming and livestock production systems. Land-use data layers are available for pasture production and suitability as well as grassland species adaptation.

Household survey information which has the potential to contribute towards refined system classifications is being centrally collated by the World Bank for Africa.1

Common goals and objectives As a priority the classification system which had been adapted by Thornton et al. (2002) from Sere and Steinfeld (1996) should be linked with the World Bank/FAO farming systems classification (Dixon et al. 2001), so that it will be possible to convert information from one to the other. It was also recognised that more refined classifications need to be developed, since the continuously improving quality and resolution of geo-referenced data sources will allow more accurate systems definition.

1 http://www4.worldbank.org/afr/poverty/databank/default.cfm , and http://wbln0018.worldbank.org/dg/povertys.nsf

http://ergodd.zoo.ox.ac.uk/agaagdat/index.htm

http://www4.worldbank.org/afr/poverty/databank/default.cfm

http://wbln0018.worldbank.org/dg/povertys.nsf


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The characteristics of household survey data need to be better understood, for example with respect to quality, geo-referencing and information detail relating to livestock. Amongst other possibilities, this data should allow improved definition of landless / peri-urban farming systems. The small area estimation methods required to allow meaningful integration of household survey information with production/farming system classification need to be developed, such as has been done by World Bank with census data for the purpose of poverty mapping.2

Human population density mapping is a fundamental component of poverty mapping, and its accuracy can be increased through geo-referencing of human settlement locations.

It is an essential pre-requisite that all data sources need to be accompanied by meta-data which clearly defines source, resolution, date of data collection etc. The statistical uncertainty of collected information should be stated where possible (this is standard procedure with survey data). With predictions based on statistical models, confidence limits should be presented, and particular care has to be taken when using these derived maps as the basis for further maps, since the errors inherent in each individual data source can produced unpredictable results in the derived maps (process called error propagation). Since the objective is to make more data more widely available, it is necessary to generate criteria that will allow standardised description of data quality. Ideally, an inventory of data sources should be developed which includes data quality indices based on these standardised criteria. Validation of generic and derived data layers needs to become an integral part of development.

The information coded in the data layers has to be relevant to the various end users, and it has to be possible to tailor the information to their particular needs.

The most important data layers need to be defined that will be used by groups working in poverty alleviation, and agreement needs to be reached on the required scale and resolution of the data layers.

Ways to achieve the common goals and objectives The generic data layers which are used to derive specific system classifications need to be made available in addition to any derived (ie classified) information, so that tailored classifications can be produced for specific projects. The derived maps need to be accompanied by the criteria used for classification. If the rules used to create classifications are published with the meta-data accompanying the respective geographic data layers, it will be possible to compare and develop conversions between the systems.

Groups creating data layers relating to livestock and production system distribution need to do so in consultation with users of the information including governmental organisation and NGOs, as well as researchers and developers.

The remote sensing community has long experience with deriving and validating geographic data layers, including with methods for exploring the effects of error propagation. Some of these methods will be appropriate for livestock and production/farming system classification.

Groups of collaborators Discussions to ensure compatibility of classification systems are underway between ERGO, FAO, ILRI and IFPRI. Discussions with end-users of the derived information are on-going, but may need to be widened.

Groups working in remote sensing at FAO and other organisations need to be approached for collaboration.

2 http://econ.worldbank.org/programs/poverty/topic/14460/library/doc?id=14462

http://econ.worldbank.org/programs/poverty/topic/14460/library/doc?id=14462


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Collaboration with groups currently using household survey data should also be sought.

Population, welfare & vulnerability

Inventory of on-going and planned activities FIVIMS maintains a list of databases which also includes information about population, welfare and vulnerability.3 These data sources include for example Demographic and Health Surveys (DHS) funded by USAID and implemented by Macro International Inc., Living Standards Measurement Study (LSMS) household surveys conducted by World Bank. It is important to recognise that the resolution of the population, welfare and vulnerability data will vary between target groups, eg between poor livestock keepers and poor consumers.

Spatial data becomes more useful for policy making the more dis-aggregated it is. Ideally it represents geographic units as small as individual cities and villages. The Development Economics Research Group, Poverty Cluster (DECRG-PO) of World Bank have developed a methodology based on combining recent census and LSMS-type household survey data to estimate welfare indicators for small areas and piloted it with success in several countries, such as Ecuador, Madagascar, Nicaragua, and South Africa4. Small area estimation conducted by the World Bank and other organisations is ongoing for a number of PPLPF countries (e.g. Kenya, Uganda, Ecuador).

Common goals and objectives An inventory of Demographic and Health Surveys (DHS), nutrition surveys (WHO - anthropometry) and Human Development Indicators (UNDP) should be created considering their relevance to the livestock – poverty link. Poverty and nutrition indicators have to be generated from household surveys (eg LSMS household surveys), in collaboration with World Bank. All groups will benefit from the availability of an inventory of human welfare data that will have a particular emphasis on comparability.

Maps describing global, regional and small area level poverty with a specific livestock focus have to be generated. Global maps are suitable as an advocacy tool for obtaining funding from donors. Higher resolution national maps are likely to be more useful for analysis, and policy development. Small area estimation methods will be needed to generate higher resolution maps.

At the global scale, mapped poverty estimates should be presented by showing the bottom quintiles of global poverty estimates, in addition to the current use of single cut-offs for example at a number of less than $1. If this is not done, a large number of poor above that level will be ignored.

The databases should be developed using a hierarchical process representing different scales of spatial aggregation and resolution. In addition, focussed regional / local projects (hubs) should be supported where different groups can work together to investigate and generate cases of what can be done with this data.

Within the projects, specific effort should be directed towards achieving better definition of poverty within systems. This includes an improved understanding of the association between poverty and livestock systems. Peri-urban and landless systems also need to be considered.

The role of agricultural and population census information, particularly in the context of small area estimation, has to be assessed. This relates to the data quality and resolution, but also the development of mechanisms of combining this data with household survey information.

3 http://www.fivims.net/index.jsp 4 http://econ.worldbank.org/programs/poverty/topic/14460/

http://www.fivims.net/index.jsp

http://econ.worldbank.org/programs/poverty/topic/14460/


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Small areas (village resolution) suitable for livestock interventions need to be defined considering livestock and farming system classification data. Participatory rural appraisal methods should be used to identify methods suitable in the local context as well as to assess impact.

The data sources need to be used to identify factors affecting risk, so that processes likely to lead to poverty reduction can be described. This may include data layers, for example, on climate vulnerability, land degradation, animal health, and genotype. With this information, it will be possible to create maps describing suitability for specific interventions.

Ways to achieve the common goals and objectives To achieve comparable poverty typology, case studies should be set up with particular emphasis on integration of different data sources. But it has to be realised that the spatial dimension of this data will be difficult to define and manage consistently. Measurement of poverty is inherently linked to household data, and all groups should work together on exploring the possibilities resulting from using different derivations of this information. But coordinated action is important since otherwise derived parameters generated by different groups may become difficult to compare.

Groups of collaborators The groups present at the workshop plus Demographic and Health Surveys (DHS) were suggested as collaborators.

Linking livestock to poverty

Inventory of on-going and planned activities It was considered likely that only little data suitable for addressing the link between livestock and poverty is currently available.

Common goals and objectives An inventory of micro-studies / policies / interventions on livestock-related issues and priorities is required. This can then be used to develop and test specific hypotheses about specific processes using statistical analysis, for example whether the uptake of dairying will lead to an improvement of the situation of the poor. The hypothesised cause-effect relationships need to be further examined and published. This will lead to a better understanding of the impacts of policies/interventions on the poor, and it will then be possible to learn from failures as well as successes. The systematic review methodology may be suitable for assessments involving synthesis of findings obtained from different sources (Egger and Smith 2001; Otte and Chilonda 2002). Specifically, the impacts of poverty reduction policies have to be evaluated since these may have important effects (positive or negative) on farming systems dynamics. In addition to systematic reviews, this may require statistical modelling of data currently available in FAO and other databases about economics, welfare, poverty, health and production systems.

Better knowledge of the link between community and household-level livestock- poverty issues is needed to decide what to do in order to improve the situation of the poor in a sustainable and socially/culturally appropriate fashion.

In order to allow more targeted and coordinated future data collection it will be desirable to determine what types of policies are under consideration.


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Ways to achieve the common goals and objectives The hubs established by PPLPF should be used to assess the impact of livestock policies on poverty in more detail. This will need deciding on some specific locally relevant case studies as soon as possible.

Geo-referenced indicators of temporal change have to be developed which will allow the poverty reduction processes resulting from livestock-centred policies to be defined. Linkages with short to medium term demographic changes such as urbanisation and population increases need to be considered. Drivers of change such as water availability, climate change, AIDS, demand growth and food safety standards need to be identified and quantified.

While the emphasis of this workshop was on spatial data, it needs to be remembered that not everything that is to be described or analysed in the context of the link between livestock and poverty is spatially heterogeneous. But spatial analysis methods are very effective for identifying areas suitable for particular development activities. Data- (eg statistical models) as well as knowledge-driven (multi-criteria decision making) models can be used for this task.

Specific efforts are required to generate increased resolution data, and as mentioned above small area estimation where household survey data is combined with census data should be investigated with respect to its accuracy. Subsequent analyses need to be conducted with a specific livestock focus, considering intra-household activities, responsibilities and dynamics.

Marketing pathways and access have to be described, and market prices need to be recorded, so that economic factors can be taken into account. An appropriate assessment of suitability of specific policies will also require an understanding of demand and market opportunities.

Groups of collaborators The groups present at the workshop would be interested in collaborations, but the marketing and economic aspect of the suggested goals and objectives may require approaching additional groups with specific strengths in this area.

References Dixon J. and Gulliver A. 2001. Farming systems and poverty – Improving farmer’s

livelihoods in a changing world. FAO, Rome, Italy and World Bank, Washington DC, USA. 412pp.

Egger M. and Smith G.D. 2001. Principles of and procedures for systematic reviews. In: M. Egger, G.D. Smith and D.G. Altman (eds.) Systematic reviews in health care: Meta-analysis in context. BMJ Publishing Group, London, UK. pp. 23-42.

Otte M.J. and Chilonda P. 2002. Cattle and small ruminant production systems in sub-saharan Africa. Food and Agriculture Organization of the United Nations, Rome, Italy. 98pp.

Sere C. and Steinfeld H. 1996. World livestock production systems: current status, issues and trends. Animal production and health paper No127. FAO, Rome, Italy.


Contact details of meeting participants


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Address FAO (SDRN) Viale delle Terme di Caracalla 00100 Roma, Italia

Telephone + 39 06 570 55510 Fax + 39 06 570 53369 Email [email protected]

Ergin Ataman Environment and Natural Resources Service

Internet http://www.fao.org/sd/EIdirect/GIS/EIgis000.htm

Address FAO (AGPC)

Viale delle Terme di Caracalla 00100 Roma, Italia


Caterina Batello Crop and Grassland Service

Internet http://www.fao.org/WAICENT/FAOINFO/AGRICULT/AGP/AGPC/doc/pasture/pasture.htm

Address World Bank

1818 H street NW Washington, DC 20433, USA


Gero Carletto Development Research Group

Internet http://www.worldbank.org/lsms

Address FAO (AGAL) Viale delle Terme di Caracalla 00100 Roma, Italia


Pius Chilonda Livestock Information, Sector Analysis and Policy Branch (Pro-Poor Livestock Policy Facility)

Internet http://www.fao.org/ag/againfo/projects/en/pplpi/home.html

Address FAO (AGAL)



Katinka DeBalogh Livestock Information, Sector Analysis and Policy Branch (Pro-Poor Livestock Policy Facility)


Address FAO (ESAE)



Ben Davis Agricultural Sector in Economic Development Service

Internet http://www.fao.org/es/esa

http://www.fao.org/sd/EIdirect/GIS/EIgis000.h

http://www.fao.org/WAICENT/FAOINFO/AGRIC

http://www.worldbank.org/lsms

http://www.fao.org/ag/againfo/projects/en/p


http://www.fao.org/es/esa


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Address FAO (AGSF) Viale delle Terme di Caracalla 00100 Roma, Italia


John Dixon Agricultural Management, Marketing and Finance Service

Internet http://www.fao.org/farmingsystems

Address International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria

Telephone + 43 2236 807 292 Fax + 432236 71313 Email [email protected]

Guenther Fischer

Internet http://www.iiasa.ac.at/Research/LUC

Address FAO (AGD) Viale delle Terme di Caracalla 00100 Roma, Italia


Louise Fresco Agriculture Department (Assistant Director-General)

Internet http://www.fao.org/ag

Address FAO (AGLL) Viale delle Terme di Caracalla 00100 Roma, Italia


Hubert George Land and Plant Nutrition Management Service

Internet http://www.fao.org/ag/agl/agll/default.stm



Pierre Gerber Livestock Information, Sector Analysis and Policy Branch (Livestock, Environment and Development Initiative)

Internet http://www.lead.virtualcentre.org

Address Department of Zoology University of Oxford, South Parks Road Oxford, OX1 3PS, U.K.

Telephone + 44 1865 271243 Fax + 44 1865 271243 Email [email protected]

Simon Hay TALA Research Group

Internet http://www.tala.ox.ac.uk

http://www.fao.org/farmingsystems

http://www.iiasa.ac.at/Research/LUC

http://www.fao.org/ag

http://www.fao.org/ag/agl/agll/default.stm

http://www.lead.virtualcentre.org

http://www.tala.ox.ac.uk


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Address World Resources Institute 10 G Street, NE Washington, DC 20002, USA


Norbert Henninger

Internet http://www.wri.org

Address FAO (AGAD) Viale delle Terme di Caracalla 00100 Roma, Italia


Samuel Jutzi Animal Production and Health Division (Director)

Internet http://www.fao.org/ag/aga/index_en.htm

Address International Livestock Research Institute PO Box 30709 00100 Nairobi, Kenya

Telephone + 254 2 630743 (ext. 4819) Fax + 254 2 631499 Email [email protected]

Patti Kristjanson Systems Analysis and Impact Assessment

Internet http://www.cgiar.org/ilri

Address International Livestock Research Institute PO Box 30709 00100 Nairobi, Kenya


Russ Kruska


Address FAO (AGAH) Viale delle Terme di Caracalla 00100 Roma, Italia


Juan Lubroth Animal Health Service (Infectious Disease Group / EMPRES)

Internet http://www.fao.org/EMPRES

Address FAO (AGAP) Viale delle Terme di Caracalla 00100 Roma, Italia


Simon Mack Animal Production Service


http://www.wri.org

http://www.fao.org/ag/aga/index_en.htm

http://www.cgiar.org/ilri


http://www.fao.org/EMPRES



87



Antonella Miceli Livestock Information, Sector Analysis and Policy Branch (Pro-Poor Livestock Policy Facility)


Address FAO (ESCB)



Nancy Morgan Basic Foodstuffs Service (Pro-Poor Livestock Policy Facility)


Address FAO (AGLL)



Freddy Nachtergaele Land and Plant Nutrition Management Service

Internet http://www.fao.org/ag/agl/agll/prtsoil.stm

Address School of Geography University of Leeds Leeds, W. Yorkshire, LS2 9JT, UK


Andy Nelson Centre for Computational Geography

Internet http://www.ccg.leeds.ac.uk



Joachim Otte Livestock Information, Sector Analysis and Policy Branch (Pro-Poor Livestock Policy Facility)


Address International Livestock Research Institute

PO Box 30709 00100 Nairobi, Kenya


Brian Perry




http://www.fao.org/ag/agl/agll/prtsoil.stm

http://www.ccg.leeds.ac.uk




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Address Royal Veterinary College Hawkshead Lane North Mymms, Hatfield Hertfordshire AL9 7TA, UK


Dirk Pfeiffer Epidemiology Division

Internet http://www.rvc.ac.uk

Address FAO (AGPC) Viale delle Terme di Caracalla 00100 Roma, Italia


Steve Reynolds Crop and Grassland Service

Internet http://www.fao.org/WAICENT/FAOINFO/AGRICULT/AGP/AGPC/doc/pasture/pasture.htm

Address FAO (AGAL)



Ana Riviere-Cinnamond Livestock Information, Sector Analysis and Policy Branch (Pro-Poor Livestock Policy Facility)


Address FAO (AGAL)



Tim Robinson Livestock Information, Sector Analysis and Policy Branch (Pro-Poor Livestock Policy Facility)


Address FAO (AGAH)



Donal Sammin Animal Health Service


Address FAO (AGAH) Viale delle Terme di Caracalla 00100 Roma, Italia


Jan Slingenberg Animal Health Service


http://www.rvc.ac.uk

http://www.fao.org/WAICENT/FAOINFO/AGRIC






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Telephone + 39 06 570 54751 (office) + 39 348 257 3293

Fax + 39 06 570 55759 Email [email protected]

Henning Steinfeld Livestock Information, Sector Analysis and Policy Branch (Chief)

Internet http://www.fao.org/lead

Address 16 Mentone Terrace Edinburgh EH9 2DF Scotland, UK


Philip Thornton

Internet www.cgiar.org/ilri



Karen Tibbo Livestock Information, Sector Analysis and Policy Branch (Pro-Poor Livestock Policy Facility / Livestock, Environment and Development Initiative) Internet http://www.fao.org/ag/againfo/projects/en/p

plpi/home.html http://www.lead.virtualcentre.org

Address International Institute for Applied Systems

Analysis A-2361 Laxenburg, Austria

Telephone + 43 2236 807 558 Fax + 43 2236 71313 Email [email protected]

Harrij van Velthuizen Land Resources Ecologist

Internet http://www.iiasa.ac.at/Research/LUC

Address Environmental Research Group Oxford P.O. Box 346 Oxford, OX1 3QE, UK

Telephone + 44 1865 271257 and + 44 1608 811258 Fax + 44 1865 310447 Email [email protected]

Willy Wint

Internet http://ergodd.zoo.ox.ac.uk

Address International Food Policy Research Institute 2033 K Street, NW Washington, DC 20006-1002, USA


Stanley Wood Environment and Production Technology Division

Internet http://www.ifpri.org

http://www.fao.org/lead


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global livestock and poverty mapping meeting · overview of the pro-poor livestock policy...

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