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Supporting the vulnerable:

Supporting the vulnerable: Increasing the adaptive capacity of agro-pastoralists to climatic change in West and Southern Africa using a transdisciplinary research approach. BMZ CC projects meeting, May 2010. Who are we?. ILRI

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Supporting the vulnerable:

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  1. Supporting the vulnerable: Increasing the adaptive capacity of agro-pastoralists to climatic change in West and Southern Africa using a transdisciplinary research approach BMZ CC projects meeting, May 2010

  2. Who are we? • ILRI • Boni Moyo (Moza office), Augustine Ayantunde (Mali office), An Notenbaert, Philip Thornton and Mario Herrero • PIK • Christoph Muller, Hermann Lotze-Campen, Alex Popp • DITSL • Brigitte Kauffmann • IER, Mali: Lassine Diarra • IIAM, Mozambique: Xoares Xerinda • AU-IBAR: Simplice Nouala

  3. Identifying and documenting local adaptive responses Adaptation to Climate change Estimation of climate change impacts Promoting active learning by communities 5 Revitalising the policy environment to increase access to adaptation options Dissemination and capacity building

  4. Estimation and documentation of the effects of climate variability and change on primary productivity of crops, rangelands and livestock, and associated livelihoods impacts under different scenarios (PIK, ILRI) • Activities • Assemble climate change scenarios • Define a systems classification framework incorporating crops and livestock and intensification (market access, agricultural potential, …) • Identify key impacts on primary productivity of crops and livestock under different scenarios by system • Identify and characterise areas of reduced primary productivity (crops and rangelands), overlaid with information on poverty levels and livelihoods to identify sites where productivity reductions may have serious repercussions on smallholder wellbeing • Sensitivity analysis on uncertainties related to climate variability on selected hotspots

  5. Assemble climate change scenarios Key impacts on primary productivity Identify sites where productivity reductions may have serious repercussions on smallholder wellbeing Multiple models – multiple scenarios - GLOBAL

  6. AOGCMs used in the downscaling work Randall et al. (2007)

  7. Scheme of the down-scaling analysis WorldClim CRU etc Weather typing Observed climate grid at resolution of choice MarkSim stochastic weather generator Generate daily data characteristic of a chosen “year” (time-slice) from 2000-2099 Applications Jones et al 2009

  8. Applications • Daily data that are characteristic (to some extent) of the climatology of future time slices: • Rainfall • Maximum temp • Minimum temp • With these, can derive or estimate other variables: • Daily: Solar radiation (a function of Tmax, Tmin, lat, long) • Seasonal: Length of growing period, season start date, duration, ending date (simple water balance, soil data) • Drive vegetation, crop, livestock models …

  9. IPCC (2007) AR4 data: Mozambique • Historical • Mean annual temperature up by 0.6°C since 1960 (0.13°C per decade) • Increasing frequency of ‘hot’ (10%) days and nights in all seasons • Mean annual rainfall down at a rate of 2.5mm per month (3.1%) per decade since 1960 (mostly due to decreases in DJF rainfall)

  10. IPCC (2007) AR4 data: Mozambique • Future • • Mean annual temp up by 1.0-2.8°C by 2060s, 1.4-4.6°C by 2090s – more rapid warming in interior • • Few substantial changes in annual rainfall projected (model range is large, straddles negative and positive). Decreases in dry season (JJA and SON), offset partially by increases in wet season (DJF) • Potential changes in intensity and tracks of tropical cyclones very uncertain. No agreement in projected changes in the amplitude of future El Niño events • • Sea-level rises to 2090s: 0.13 - 0.43m (B1); 0.16 - 0.53m (A1B); 0.18 - 0.56m (A2)

  11. CNR CSI Average annual temperature changes (°C*10) from 2000 to 2050, A2 ECH MIR

  12. CNR CSI Annual rainfall changes (mm) from 2000 to 2050, A2 ECH MIR

  13. CNR CSI Length of growing period change (days per year) from 2000 to 2050, A2 ECH MIR

  14. Integrating livestock in global models • Model of agricultural productivity: LPJmL • Grassland management systems • Grassland productivity • Carbon & water dynamics • Model of land-use dynamics: MAgPIE • Feed mixes & energy requirements • Production costs • Intensification vs. expansion • Resource usage • Environmental impacts (GHG emissions)

  15. Grassland productivity From cows to grass • Grassland that sustains livestock for milk and/or meat • can be managed intensive or extensive • can be temporal or permanent • can be irrigated or rain-fed • can be ...  differs in yield • Question: How much grassland is needed to feed livestock? • Approach: • Combination ofCA: cattle abundances per livestock system EC: energy demand of dairy and beef cattleEG: energy content of temperate and tropical grassesGA: grassland areas • grassland yield = CA*EC/EG/GA Rolinsky et al., in prep.

  16. Rolinsky et al., in prep. Grassland Assessment From cows to grass CA: worldwide abundance of cattle in livestock systems (ILRI data) EC: Feed requirement per cattle in unit grass DW (Calc. by Wirsenius, 2000, and Bouwman, 2005) EG: Energy content of grass for maintenance (NEm), growth (NEg) and lactation (NEl) (Wirsenius 2000) GA: FAO data grassland yield = CA*EC/EG/GA T DW/ha Assess-ment of yield and area

  17. Grassland productivity Smit et al., 2008 LPJmL “observations” Requirements

  18. Next steps: • Harmonization of input data: area, management system, livestock numbers • Implementation of different grassland management systems in LPJmL (so far, intensive mowing only) • Calibration of grassland productivity

  19. Land-use dynamics: trade-offs in livestock • Livestock production systems differ in • Feed mixes (raw material, compound, crop residues) • Conversion efficiencies (feed -> livestock) • Resource demand (water, land, inputs) • Production costs • Environmental impacts (resource use, GHG) • Livestock in future food supply • Competition for resources (land, water) • Impacts on markets (food/resource prices) • “side-effects”: GHG emissions, C sequestration

  20. Livestock in MAgPIE • Feeding technologies play a crucial role for conversion efficiencies and environmental impacts per unit output • For ten world regions, we define the feeding technology for five livestock subsectors as a set of the following parameters: • productivity index p (products per producing animals and per animals in stock) • feed energy requirements per unit output [f(p)] • required nutrient density of the feed mix [f(p)] • Feed mix • GHG emissions per unit output. • All data consistent with FAO

  21. Livestock Module Demand for livestock products (exogenous) Residues Harvest index, recovery rates, assignment rates for feed Total feed demand (ME, Nel, Nem, Neg) FAO-FBS Feed use Feed conversion efficiency (ME, Nel, Nem, Neg) Nutrient density requirements (Nel, NEm) Productivity (endogenous) • Work in progress: • Production costs • Elasticity of production systems • Livestock Feed Mix • Feed assignment to different livestock systems • Grazed Biomass Weindl et al., in prep.

  22. Assemble climate change scenarios Key impacts on primary productivity Identify sites where productivity reductions may have serious repercussions on smallholder wellbeing • 1.Combining Productivity impacts with “vulnerability” - example • 2. Different future vulnerability models and scenarios

  23. Net Primary Production • Maize - Managed • Rice grass • Temp.cereals • Trop.cereals LPJmL Pastoral / Agro-pastoral Mixed Stover: DM ME Grass: DM ME Indices = Feed + Livestock Units Small Ruminants FAO & Impact = Cattle + Vulnerability ~ Exposure - Coping Changes from 2000 to 2050 Thornton et al. Vulnerability Index = Feed resource impacts of CC (in terms of changes of feed availability ME/LU/day)

  24. From NPP of the CFTs to Feed

  25. Livestock Projections 2000 2050 FAO gridded livestock of the world IMPACT projections

  26. Combination

  27. Changing vulnerability 1 framework 1 scenario Multitude of Frameworks & scenarios

  28. Inventory, documentation and dissemination of past, present and possible future agro-pastoralists coping mechanisms to deal with climate variability (ILRI DITSL IAM IER) • 2.1. Household surveys conducted and risk profiles developed (IIAM/IER/ILRI/Kassel) • 2.2. Documentation and inventory completed of how agro-pastoralists used to cope with climate variability (Kassel/ILRI/IIAM/IER) • 2.3. Historical analysis (Kassel/ILRI/IIAM/IER) • 2.4. Conduct a review of adaptation options to climate variability and change that are currently being promoted and tried by institutions in the selected regions. (ILRI/Kassel/IIAM/IER) • 2.5. Gap analysis: identification of options that smallholders would like to pursue but are not able to, and documentation of the reasons why. (ILRI/Kassel/IIAM/IER)

  29. Household surveys conducted to identify livelihood strategies and risk profiles of agro-pastoralists in selected hotspot areas Case study sites in The Mopti region of Mali Gaza province in Mozambique

  30. Household surveys conducted to identify livelihood strategies and risk profiles of agro-pastoralists in selected hotspot areas In each country: • 12 villages * 12 surveys = 144 households • Criteria for selection of villages: • Distance to market: “far” from market had been defined as more than 10 km. To divide villages where production is more versus less market orientated. • Distance to the river: “far” from the river had been defined as more than 5 km. To divide villages where access to fodder and water during the dry season is or is not a constraint in dry season • 5 modules: • Livelihoods • Herd dynamics • Livestock management • Welfare outcomes • Vulnerability

  31. Vulnerability of agro-pastoralists in Mali and Mozambique Household concerns Mali Mozambique

  32. Coping by agro-pastoralists in Mali Not enough pasture Not enough food Animal Health Crop Failure

  33. Coping by agro-pastoralists in Mozambique

  34. Estimating household’s vulnerability Based on the HH’s assessment of impact of the concerns they are facing: Vulnerability = V = ∑ wi * Ii with n = number of concerns wi = weight of concern i Ii = impact (1: worse / 0: same / -1: better) n i=1

  35. Factors influencing vulnerability (Mozambique) Amongst others: Distance to market Gender Age Income diversity Number of animals

  36. Source: Satao, M. (MSc thesis in preparation) Example from Mali: Changes in livestock production during the past 20 years Reasons for changes: • Decrease in: • contribution of livestock to the household needs • animal numbers, total and also per household • condition of livestock • Increase in: • concentrate feeding • production costs (concentrate) • transhumance period • animal numbers (in the Delta) • contribution of livestock to the household needs (Delta) • increase in population • increase in needs • reduction of pasture area • increase in cropping area • loss of interest of young people in transhumance • improvement of climatic conditions (Delta) Major current constraints: Lack of pasture Lack of water Diseases High price of concentrate (Summary of data collected in livelihood analyses and timelines in 4 villages)

  37. Source: Satao, M. (MSc thesis in preparation) Example from Mali: Changes in crop production during the past 20 years Reasons for changes: • Extension of crop production to all members of villages • Increase in: • contribution of crop production to household needs • number of fields • field size • degree of mechanization • occupation of family members • Decrease in: • in soil productivity • Increase in population size • Droughts let to drop in livestock production • Reduction in animal numbers and contribution of livestock • Search for food security • Increase in rainfall and inundation since 1992 (Delta) Major current constraints: Insufficient and irregular rainfall Pest Reduction in production Crop damage through livestock(Delta) Insufficient manure (Summary of data collected in livelihood analyses and timelines in 4 villages)

  38. Source: Werner M. (MSc thesis in preparation) Example from Mali - Adaptation strategy: Avoid crop failure through the choice of varieties Source: pairwise ranking Nérékoro, 3 participants, 06/03/2009

  39. Source: Werner M. (MSc thesis in preparation) Example from Mali - Adaptation strategy: Factors influencing decision of the choice of varieties Source: feedback seminarNérékoro, 18 participants, 14/05/2009

  40. Policy entry points for supporting the implementation of priority livestock-based adaptation options in agro-pastoral systems identified and discussed with key stakeholders (AU-IBAR leading) • Activities • Stock-taking to assess country commitment and institutional capacity to implement climate change adaptation strategies • Analysis of key actors and their linkages as well as the interactions between policies and actors to identify policy, institutional and organizational entry points and evaluate institutional capacity to support CC projects • Policy and institutional analysis to identify and evaluate promising policy and institutional options that facilitate climate change adaptation strategies • Establish regional policy dialogues on adaptation to CC

  41. Policy entry points for supporting the implementation of priority livestock-based adaptation options in agro-pastoral systems identified and discussed with key stakeholders (AU-IBAR leading) • AU-IBAR have reviewed the NAPAs (National Adaptation Programmes of Action – see UNFCCC website) for Mali and Mozambique • The first policy forum is planned for May 2010 ‘Ministerial rounds on cliamte change a livestock systems’

  42. Dissemination • Activities • Dissemination of information on implementing and managing the high-priority adaptation options identified using community facilitators, mobile extension teams, and comic books • 1 PhD and 1 post doc (at PIK) together with other sources of funding • 5 MSc students (DITSL – Kassel and local) • 1 short film on climate change and adaptation strategies for pastoralists and agro-pastoralists produced • Development of tools for training national partners in targeting analyses and community participatory research techniques in relation to risk management and adaptation

  43. Dissemination • Papers, reports, conference presentations, other dissemination activities, in an updatable list • PNAS Special Issue on Livestock and Global Change (2 papers: 1 PIK- led, 1 ILRI-led) • Side event at COP15 • The Climate Change film is on the way. Filming has taken place in a range of places in Africa, and the script is being finalised and edited

  44. THANKS!

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