Herrero - General Intro - Modeling Workshop - Amsterdam_2012-04-23Presentation Transcript
Some kinds of questions CCAFS would like to answer Mario HerreroFarm-household Modeling with a focus on Food security, Climate change adaptation, Risk management and Mitigation: a way forward Amsterdam 23-25 April 2012
Background– CCAFS currently funding a large household data collection exercise in the CCAFS regions– Focus: studying adaptation, risk management and mitigation– Additional undergoing work on developing regional socio- economic scenarios–– Need to supplement this body of work with household modeling studies for identifying key options, targeting strategies to specific systems etc
..and for linking to current knowledge on climate change impacts….
An example of climate-induced livelihood transitions 20º Areas where cropping of an indicator cereal may 0º become unviable between now and 2050 and where farmers may have to rely more on livestock as a livelihood -20º strategyJones & Thornton (2008) 0º 20º 40º
A game of winners and losers…Simulated percentage maize production changes to 2030 and 2050, bycountry and system Mixed Mixed Mixed National rainfed rainfed rainfed Production temperate humid arid 2030 2050 2030 2050 2030 2050 2030 2050 Burundi 9.1 9.1 14.4 18.1 -1.8 -8.8 - - Kenya 15.0 17.8 33.3 46.5 -4.6 -9.8 -1.1 -8.4 Rwanda 10.8 14.9 13.4 18.8 5.4 3.6 1.1 2.7 Tanzania -3.1 -8.1 7.5 8.7 -1.6 -6.4 -5.1 -11.1 Uganda -2.2 -8.6 4.9 3.1 -4.6 -12.9 -1.1 -6.3 Mean of 4 combinations of GCM and emissions scenario Winners Losers Thornton et al. (2010)
There are always trade-offs income 1 0.5 external inputs food security 0 water use GHG mixed pastoral
Monthly calendar of different activities of the system Wa, Upper West, Ghana Dry Rainy Dry Weather calendar Groundnuts Yams Cropping calendar Sorghum Cut & Crop Critical Grazing Feeding calendar Carry residue Food security Energy Prot. & Ene. Family’s deficit deficit nutritionHigh Very High High Lo High Low Low Cash demands high w J F M A M J J A S O N D Gonzalez-Estrada et al. 2006
...from global assessment to assessing household level impacts...A necessary link to design adaptation, risk management and mitigation options
Adaptation options will dependlargely on the how we shape theworld• Several options exist though largely dependent on our vision of world development and how it plays out in different regions• essential to link it to scenarios of change• Different paradigms of agricultural development (industrial vs pro-poor smallholders, large vs family farms)• Globalisation and trade patterns• Consumption patterns• Carbon constraints• Roles and incentives for technology adoption• Growth in other sectors• Power relationships
What are the options? • Sustainable intensification / extensification • Income / livelihood diversification • Better risk management • More transformative change (e.g. exit from agriculture) All require a mixture of technology & supporting policies and investments No single path best: mixtures required in different parts of the world
Addressing adaptation at multiple scales Herrero et al, Science (2010)
Site targeting Participatory modelling Policy-making Ecoregion • Systems’ classification Farms A B C • Selection of farmsDissemination & • Longitudinal dataimplementation Case studies • Participatory methods • Key informants Range of interventions to • Participatory appraisals test for each system • Recommendation domains (filtering) • Toolboxes of interventions • Farmers / NARS Testing Scenario formulation • IMPACT & Household options in the (Farm and policy level) model field • Sensitivity analyses Selection of a fewer • Stakeholder workshops range of options • Participatory appraisals (Herrero, 1999)
Some questions• Can we identify robust adaptation options that cut across systems and socio-economic scenarios?• Can we identify key trade-offs for each system?• Are there adaptation – mitigation synergies?• What is the role of farming diversity in adaptation?• Can we upscale the strategies to quantify investment needs in adaptation?• Can the upscaling exercise also link to regional modeling work?
Some questions (2)• Can we identify risk management strategies for crop/livestock and livestock systems• What are the impacts of consecutive dry seasons of farmers ability to cope with climate change?• Can we model household level vulnerability or some proxy indicator?• What are the key impacts of climate variability on trade- offs between the different indicators?• Can we mitigate climate change under climate variability? How? Which GHG easier? For which system?• What are the costs of managing risk?
Some questions (3)• What is the potential contribution of smallholder systems to climate change mitigation?• What are key mitigation strategies for different systems? Again, can we identify robust ones that cut across scenarios and systems?• Economics of household level mitigation strategies• Is sustainable intensifccation the ley to GHG mitigation for smallholders?
For discussion• Human dimensions in the models: what can we really capture• How do we deal with systems transitions into the future (still very static?)• Proxies for vulnerability at the household level?• Can we really deal with heterogeneous systems?• What do we need to do to really succeed at multi-scale assessment (from global to household and back)
Exploring adaptation – mitigation synergies Herrero et al forthcoming
Milk production and diets for cattle in the 6 districts of Kenya District Milk per Rangeland Maize Cut and Roadside Grain cow (kg/yr) grazing stover carry weeds supplements fodder Garissa 275 X Gem 548 X X X X X Mbeere S 860 X X X X X Njoro 1256 X X X X X Mukurweni 2089 X X X Othaya 2035 X Siaya 706 X
Manure and methane production for the baseline diets in the six districts District Energy Manure per Methane Methane density of animal (kg/yr) production produced per lt the diet (CO2 of milk (MJ ME/kg eq/lactation) (CO2 eq/lt) DM) Garissa 8.4 693 796 2.37 Gem 9.3 730 780 1.42 Mbeere S 9.6 693 824 1.12 Njoro 9.9 693 863 0.72 Mukurweni 10.5 657 936 0.47 Othaya 10.5 657 936 0.47 Siaya 9.4 730 838 1.14
Most common new feeds appearing in the last 10 years and the scenarios simulated District Main new feed Scenarios of use Garissa Prosopis spp. 1.5 kg offered in the diet 3 kg offered in the diet Gem Desmodium 1 kg offered in the diet instead of stover 2 kg offered in the diet instead of stover Mbeere S Napier grass 2 kg offered in the diet instead of stover 3 kg offered in the diet instead of stover Njoro Hay 1 kg offered in the diet instead of stover 2 kg offered in the diet instead of stover Mukurweni Desmodium 1 kg offered in the diet instead of stover 2 kg offered in the diet instead of stover Othaya Hay 2 kg offered in the diet instead of stover 4 kg offered in the diet instead of stover Siaya Napier grass 2 kg offered in the diet instead of stover 3 kg offered in the diet instead of stover
Impact of alternative feeding strategies on milk, manure and methane production (% change) District Scenario Milk production Manure Methane Methane per production production kg milk Garissa Prosopis 1.5 kg 64 0 -2 -40 3 kg 136 0 -5 -60 Gem Desmodium 1 kg 21 5 -3 -20 2 kg 36 10 0 -26 Mbeere Napier grass 2 kg 12 11 3 -8 3 kg 17 16 2 -12 Njoro Hay 1 kg 18 -5 6 -10 2 kg 49 -5 18 -21 Mukurweni Desmodium 1 kg 9 11 2 -7 2 kg 8 11 0 -7 Othaya Hay 2 kg 9 11 2 -7 4 kg 8 11 0 -7 Siaya Napier grass 2 kg 42 0 12 -21 3 kg 79 10 16 -35 6 districts Average 36 6 4 -20
Research opportunities exploring livelihood and systems transitions Scenarios (global, regional, household) trade-offs adaptation-mitigation synergies ( different systems: rangelands, mixed)….carbon markets ==================================================== Farms of the future / analogue Impact work (Chase) DSS on adaptation costs and priority options Comission of a paper on breeding strategies of livestock and climate change (Karen to liase with James, concept note) Adaptation: tweaking, structural, transformative