Systems modeling is as essential component of systems research - past and planned cases in Africa

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AusAID Food Security through Rural Development Initiative,Farming systems research and modelling,Agricultural Production Systems Simulator

AusAID Food Security through Rural Development Initiative,Farming systems research and modelling,Agricultural Production Systems Simulator

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  • 1. Systems modeling is as essential component of systems research - past and planned cases in AfricaPeter CarberryCSIRO Sustainable Agriculture Flagship
  • 2. Outline• AusAID Food Security through Rural Development Initiative• Farming systems research• Farming systems modelling• Current status
  • 3. Food Security through Rural DevelopmentInitiative• Australian Government commitment • $464 million to encourage greater food security globally • $100 million for Africa • 4 years (but re-occurring)• African Food Security Initiative 1. Increasing agricultural productivity through increased investments in agricultural research and development; 2. Improving rural livelihoods by helping to address market failures that hinder economic development in rural areas; and 3. Building community resilience by supporting social protection mechanisms that enable vulnerable people to withstand shocks.• Administered through AusAID
  • 4. Agricultural research and development inAfrica1. Sustainable Intensification of Maize-Legume Farming Systems for Food Security in Eastern and Southern Africa (SIMLESA) ($20M) • Funding via ACIAR’s project development process • CIMMYT led • Australian partners2. AusAID/CSIRO African Food Security Initiative ($30M) • AusAID support for CAADP Pillar 4 • CSIRO led • African partners • CORAF in West Africa • BecA in East Africa
  • 5. SIMLESA collaboration SIMLESA, 2010 4 PhD scholarships granted University of Sydney (Risk management)African Universities University of Western Sydney (Socio economics) IRI AusAid Charles Sturt University (Policy & innovation) TIAR CSIRO University of Southern Queensland (Climate change)
  • 6. SIMLESA aimsAims at increasing farm-household food security and productivity, in the context ofclimate risk and change, through the development of more resilient, profitable and sustainable maize-legume farming systems Socio-economic More productive, Improved range characterization resilient and of maize and sustainable legume varieties Input and output smallholder available for value chain maize-legume smallholders practices, tactics Whole farm and strategies resource allocations Scaling out and capacity building 30% increase in maize yields and 30% reduction in risk 500,000 households over the next 10 years
  • 7. AusAID/CSIRO African Food Security Initiative• CORAF/WECARD • Dakar, Senegal • 22 member States • Mandated to coordinate the implementation of CAADP Pillar IV in West and Central Africa• BecA • Nairobi, Kenya • Managed by ILRI (International Livestock Research Institute) • Shared agricultural research and biosciences development platform
  • 8. Pillar I - Land ManagementNEPAD CAADP Pillar II - Rural Infrastructure Pillar III - Food Supply Agricultural Research CAADP Pillar IV and DisseminationWCA RECs FARA FAAP NASRO CORAF/WECARD ASARECA CARDESA Strategic • EmpowermentNational Plans • Pluralism in delivery Plans of • Subsidiarity Operational • Evidence-based approaches ARI • Sustainability Univ. Plans • Integration ofMinistries Research/extension Programmes • MIS NGO CSO • Cost sharing Projects • Integration of gender
  • 9. Research agenda and current/futureprojects• BecA: • Food and Nutrition Science (FANS) scoping study • Analyse food systems (farm to consumption) • Identify opportunities within value-chains • Animal health • Pest des Petits Ruminants (PPR) disease control • African swine fever • Capacity building/Challenge Fund• CORAF/WECARD: • 5 projects being developed • 3 farming systems focus in the sub-humid to semi-arid zone • Seeds systems project – based in Mali • Animal Health Project – Ticks and tick-borne diseases
  • 10. Australian RDE expertise highly relevant to dryland agriculture in Africa• Similarity in environments • Shared constraints to biological productivity• Agricultural research and extension • Systems science and modelling • Conservation agriculture • Livestock/crop integration • Water management (droughts and flooding) • Eucalypt and Acacia based forestry • Participatory extension models including private sector and NGOs• Natural resource management • Community based approaches to NRM • Rangelands and biodiversity science• Climate science • Seasonal climate forecasting• Economic and policy research • Analysis of market and trade liberalisation• Research infrastructure and institutional arrangements • Collaborative research centres • Australian scientists with overseas research experience
  • 11. Farming Systems Research – Collinson 1982 On-farm research Operations research On-station research
  • 12. Rigour v’s relevance? High Control Envir. expts. On-station expts. Data Simulations integrity On-farm / Rigour trials On-farm demonstrations Surveys Case studies SCIENCE Archives Stories NONSCIENCE Myths Low Personal opinion Legends Low High Currency / Relevance Bonoma 1985, Crookston 1994
  • 13. A shifting emphasis in research? High Control Envir. expts. On-station expts. Data Simulations integrity On-farm / Rigour trials On-farm demonstrations Surveys Case studies SCIENCE Archives Stories NONSCIENCE Myths Low Personal opinion Legends Low High Currency / Relevance Bonoma 1985, Crookston 1994
  • 14. Participatory research (1994) • Honest in admitting to being long on rhetoric and short on achievements • High time costs of participation • Dependence on qualitative data • Difficulties in data & analysis • Poor evaluation • Difficulties in publicationRhetoric and reality • Lack of recognition & rewards • 11 case studies … on-ground impacts mostly promises Okali, Sumberg and Farrington, 1994
  • 15. Participatory research (2003)• Now lots of participatory methodologies promoted (>30)• The promise of bringing “practical reality to bear on generalised concepts”• Problems of evaluation, data analysis & publication persist• A call for organisational learning & change Uniting science &• 23 case studies … the rhetoric participation continues? Pound, Snapp, McDougall and Braun (eds) 2003
  • 16. A preferred shift in emphasis? High Control Envir. expts. On-station expts. Data Simulations integrity On-farm / Rigour trials On-farm demonstrations Surveys Case studies SCIENCE Archives Stories NONSCIENCE Myths Low Personal opinion Legends Low High Currency / Relevance
  • 17. A proposed methodology High Participatory Action Research & systems Control Envir. expts. modelling On-station expts. Data Simulations integrity On-farm / Rigour trials On-farm demonstrations Surveys Case studies SCIENCE Archives Stories NONSCIENCE Myths Low Personal opinion Legends Low High Currency / RelevanceCarberry, P.S., 2001. Are science rigour and industry relevance both achievable inparticipatory action research? Agricultural Science, 14:22-28
  • 18. APSRU’s systems approachFarming Systems Research – Collinson 1982 APSRU Strategic Plan 1991 -1995
  • 19. Agricultural Production SystemsSimulator (APSIM) www.apsim.info The soil provides a central focus, crops, seasons and managers come and go, finding the soil in one state and leaving it in another Simulates:  mechanistic growth of crops, pastures, trees, weeds ...  dynamics of populations (eg. weed seedbank)  key soil processes (water, solutes, N, P, carbon, pH)  surface residue dynamics & erosion  dryland or irrigated systems  range of management options  crop rotations + fallowing + mixtures  short or long term effects  one or two (multi-point) dimensions  high software engineering standards  language independent (VENSIM™ module maker)  now includes pests nor diseases  links to livestock modules
  • 20. Example APSIM applications• cereal-legume rotations (Probert et • climate change impacts (Howden et al., 1999) al.1995)• ley farming systems (Carberry et al. • agribusiness value chain (Brennan et al., 2000) 1996)• intercropping systems (Carberry et al. • tree windbreak systems (Meinke et al. 2001) 1996) • deep drainage assessment (Keating et al., 2001)• alley farming systems (Nelson et al. 1998) • soil acidification (Verburg et al., 2001)• drought policy formation (Keating & • risk assessment of GMO (Smith et al. 2001) Meinke 1998) • effluent irrigation (Brennan et al., 2002)• erosion impacts (Connolly et al. 1998) • agroforestry systems (Huth et al., 2002)• genetic trait identification (Robertson et • crop-weed competition (Keating et al. 1999) al. 1999)• seasonal climate forecasting (Hammer • smallholder farming systems (Carberry, 2004) et al. 1999)• on-farm trial analyses (Robertson et al. • biodiversity assessment (Huth et al., 2008) 1999)
  • 21. Need examples of successful PARachieving science innovation & impact … 17 years of action research
  • 22. ACIAR Kenyan Project (1985-1992)
  • 23. Modelling was a “new frontier” in 1985
  • 24. Observed grain yields – average 1990-1999 3.00 3 -Average grain yirld (t/ha) 2.50 2.5 2.00 2 1.50 1.5 1.00 1 0.50 0.5 0.00 0 lo w al Stepc1 p ro ulc h utStep 2 ge Step 3nputStep 4 fal itio n er m inp ti lla i e d Int m d gh a r Tra C 50 % diu ce Hi AB B D Me ed u G E F R Predicted averages : 1957 – 1988 Keating, Wafula & Watiki 1990, Climatic Risk Symposium
  • 25. Example - Linking Logics workshop• Initiative of ICRISAT / CIMMYT• October 2001, Zimbabwe• Hosted at Jusanani carpentry workshop owned by farmer Richard Ndimande• Mkubazi School, PO Box 44, Tsholotsho.• Between 30-40 farmers attended each day
  • 26. Peter Carberry, Christy Gladwin and Steve Twomlow, 2004. LinkingSimulation Modelling to Participatory Research in Smallholder FarmingSystems. ACIAR Proceedings No. 114. pp32-46 What worked? Why did farmers give the simulations credibility? What indicators of impact? How to scale up? Follow-on project with ICRISAT - Using systems simulation to enhance the effectiveness of agricultural change agents in the southern African semi-arid tropics (SAT)
  • 27. Forward projections – with APSIM
  • 28. New initiatives informed by past efforts
  • 29. Systems simulation across different scales gene crop farm catchment regionQTL map for Sorghum Broadened spatial scale
  • 30. … genes in environments Farming system Crop Plant Traits Traits Cell Traits Nucleus enzymes Genes YIELD substrate products • Interaction across scales: – Environment signals Profitability – Gene responses = trait expressionHammer, G., Sinclair, T., Chapman, S. and van Oosterom, E. (2004). On systems thinking, systemsbiology and the in silico plant. Plant Physiology 134: 909-911.
  • 31. … analysing decisions at the whole farm level APSFARMAPSFARM Farm ManagerAPSIM Paddock Manager Report Climate Forecast Livestock E Economics N Pastures Soilwat G Crops I N SoilNSurface Residue E Irrigation ErosionRodriguez et al., 2009
  • 32. Systems simulation of the normal, novel & natural The normal, the novel & the naturalGrowth, water use and life form analysis of cropping, novel agroforestry systems and native woodland Increased boundaries to the biophysical system
  • 33. … below ground dynamics in multi-point agroforestry systems Soil Water Extraction 50 m 1.5 mNeil Huth 2007
  • 34. Combining bio-physical & ecological models Habitat complexity 9 score Habitat Complexity Score 6 Ecologic al model 3 Trees Only Trees + Grass Trees + Grass + Shrubs Edge Trees Only 0 0 10 20 30 40 Woodlot Age (years) Species richness 50 0 to 5 5 to 10 APSIM 40 10 to 15 15 30 Species Tradeoffs in 20 economic & ecological 10 performance 0 1 10 100 1000Neil Huth 2009 Area (ha)
  • 35. In summary• AusAID Food Security through Rural Development Initiative represents a significant re-entry of Australia in supporting African RDE• Significant that agriculture RDE is a key delivery platform• Systems modelling will be a key contribution from Australia