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Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Keynote Pablo Tittonell
 

Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Keynote Pablo Tittonell

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Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Keynote Pablo Tittonell Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Keynote Pablo Tittonell Presentation Transcript

  • Jeroen Groot, 26 March 2012Quantitative trade-offs analysisin agricultural systems Fields, farms and territoriesPablo TittonellFarming Systems Ecology – Wageningen University, The NetherlandsPablo.tittonell@wur.nlwww.facebook.com/FSE.WageningenUR Analysis of Trade-offs in Agricultural Systems Wageningen 19 February 2013
  • Outline 1. What are trade-offs? 2. How to quantify them? 3. Examples i. Measurements and data ii. Output of a dynamic household model iii. Pareto optimisation through evolutionary design iv. Inverse dynamic modelling (global search alg.) v. Agent-based systems and games
  • What are trade-offs? Situations in which two or more competing/ conflicting objectives must be simultaneously satisfied to a certain degree Objective B B1” Complementarity B1 Substitution B1’ Objective A A1Tittonell (2013) Chapter on Trade-offs evaluation, CIALCA Conf., Earthscan, in press.
  • Tradeoffs analysis Objective B B1 A1 A2 A3 Objective A
  • Services écosystemiques: biodiversité et séquestration de C A Vihiga B Siaya Aboveground C stock (Mg ha-1) 40 40 homegarden annual crop permanent crop 30 30 pasture A) Trees 20 B) Hedgerows 20 40 20 Delta C stock (Mg farm-1) Vihiga 10 Vihiga 10 Siaya l Siaya ntia 30 p ote 0 15 0 tion 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 stra que C-se C 10 Vihiga D Siaya C-sequestration potential 20 Aboveground C density (kg m-2) 8 8 Windrow Individual tree Woodlot 6 6 10 5 4 4 0 0 0 5 10 15 2 20 0 5 10 2 15 20 it wt Current aboveground C stock (Mg farm-1) 0 0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 g Homegarden index Shannon b lh Food crop hh wlt mh Pasture t e Cash crop Slop Woodlot Henry et al. (2009), Agriculture Ecosystems and Environment 129
  • Quantifying trade-offs Absolute change Relative change ΔB” ΔB” ΔB’ < ΔB’ ΔA ΔA B0” B0’ Objective B < ΔA ΔA B1”- B0” < B1’- B0’ A0 A0 B0” ΔA ΔA ΔB” B1” Complementarity B0’ ΔB’ Substitution B1’ Objective A A0 A1 ΔA Opportunity costs, shadow prices, payment for environmental services, etc.Tittonell (2013) Chapter on Trade-offs evaluation,relative sensitivity, preference Elasticity of substitution, partial CIALCA Conf., Earthscan, in press. rate, etc.
  • Mapping trade-offsObjective: 400 Alternative IIncrease 350incomes 300 Alternative II Gross margin ($ ha-1) 250 Complementarity Alternative III 200 Current 150 100 Substitution 50 Alternative IV 0 20 25 30 35 40 45 Objective: Maintain soil Soil organic matter (t ha-1) Modelling: fertility • To generate ‘clouds’ of alternative solutions • To delineate ‘frontiers’ of possibilities Management strategies Objective Indicator Current Alternative I Alternative II To maintain Soil organic matter (t ha-1) 40 28 36 soil fertility To increase Gross margin ($ ha-1) 180 360 280 net incomes Cost of maintaining soil C: 15 $ t-1 25 $ t-1Tittonell (2013) Chapter on Trade-offs evaluation, CIALCA Conf., Earthscan, in press.
  • Quantitative trade-offs analysis: methods 1. Ad-hoc analysis 1.1 - By looking at data 1.2 - By formalising a problem (discussion, expert knowledge, etc.) 1.3 - By looking at the output of a dynamic model 2. Multi-objective ‘compromising’ using models 2.1 - Using optimisation models (e.g., linear programming) 2.2 - Using search algorithms (e.g., inverse modelling) 2.3 – Agent based-systemsTittonell (2013) Chapter on Trade-offs evaluation, CIALCA Conf., Earthscan, in press.
  • Ad hoc analysis
  • (i) Trade-offs analysis: data Biomass allocation at village scale Burkina Faso ( Andrieu et al., subm.) Demand for residues across regions Conservation field/farm scale Trade-offs at agriculture • No-till 3 India-1 High • Rotation Bangladesh • Soil cover 2 Kenya tlu ha-1 Ethiopia-2 Ethiopia-1 Medium India-2 Smallholder 1 Zimbabwe Niger-2 agriculture: crop Mozambique Nigeria Malawi Low residues are in high Niger-1 0 0 demand 2 4 6 8 10 12 Naudin et al. ha-1 persons 2011 Valbuena et al. 2012
  • Semi-quantitative trade-offs analysis Fuzzy cognitive mapping Lopez-Ridaura, 2002 Management INCOME EROSION BIO- LEACHING INCOME DIVERSITY VARIABILITY alternatives Input-intensive Organic farming Integrated farming Traditional practices LOW MEDIUM HIGH Kok, 2008
  • Napier grass production (t farm-1 Napier grass FArm-scale Resource Management SIMulator(ii) Dynamic household model Maize Maize production (t farm-1) 60 8 NUANCES CROP FIELD SOIL 50 Potential, water- and Soil C dynamics CLIMATE nutrient-limited yields 6 Water, N, P and K 40 Actual variability Weed competition availability Scenarios 30 4 MARKET HOUSEHOLD FactorsSweet potato field 1 Maize field 3 Objectives & decisions Products 20 (0.18 ha) (0.24 ha) 2 Investment, allocation and expenditure 10 COMMONLAND Labour availability Rangeland 0 Woodlots 0 LIVSIM 20 40 HEAPSIM 60 80 100 120 Napier grass Feed supply and OFF-FARM field 2 (0.15) demand Productivity Soil Manure & qualityand-1Napier B of storage collection, ha ) organic C (t Maize Employment Milk, meat, traction Remittances and manure 1 1 Effects on soil fertility FARMSIM Relative Napier grass yield Maize field 2 0.8 0.8 (0.25 ha) A Relative maize yield 10 70 Napier grass production (t farm-1) Napier grass Napier grass production 0.6 Maize 60 0.6 Maize production (t farm-1) 8 Maize Napier grass field 1 Manure 50 0.4 0.4 field 1(0.06 ha) (0.15 ha) allocation 6 40 strategies 0.2 0.2 (10 year 4 Maize production 30 Manure heap simulations) 20 0 0 2 1 2 3 4 5 6 7 8 9 10 10 Even spread Concentration Homestead 2 cows 0 Manure allocation strategy 0 20 40 60 80 100 120Rowe et al., 2006; Titttonell et al., 2007; 2009; Van Wijk et al., 2009; Rufino et al., 2011; )Zingore et al., 2011 Soil organic C (t ha-1
  • Multi-objective ‘compromising’
  • (iii) Pareto optimisation: evolutionary design Evolutionary model generate by allocating land-use activities evolutionary algorithm Obj. 1 evaluate Pareto-ranking for multiple indicators 1 2 1 2 1 2 1 2 rank & select using non-weighting dominated Pareto-based methods Farm IMAGES Obj. 2 Landscape IMAGES Groot & Rossing (2011)
  • (iii) Pareto optimisation: evolutionary design Evolutionary model generate by allocating land-use activities evolutionary algorithm Obj. 1 evaluate Evolutionary algorithm for multiple indicators rank & select using non-weighting Pareto-based methods Farm IMAGES Obj. 2 Landscape IMAGES Groot & Rossing (2011)
  • Intensification pathways at farm scale Productivity per animal a. Trade-offs 2400 2000 Labor balance (h) Groot et al., 2012. Agricultural Systems. 1600 Productivity per unit labour 1200 800 400 0 -20 0 20 40 60 80 500 500 Organic matter balance (kg/ha) b. c. 250 250 0 0 -250 -250 -500 -500 -20 0 20 40 60 80 0 400 800 1200 1600 2000 2400 80 80 80 d. e. f. Soil nitrogen loss (kg/ha) 70 70 70 60 60 60 50 50 50 40 40 40 30 30 30 20 20 20 10 10 10 0 0 0 -20 0 20 40 60 80 0 400 800 1200 1600 2000 2400 -500 -250 0 250 500Cortez-Arriola et al., subm. Operating profit (k€) Ecological services Ecological services (h) Labor balance Organic matter balance (kg/ha)
  • (iv) Inverse modelling • A spatially heterogeneous farm Trade-offs between objectives 200 • A limited availability of cash 25 180 10000 KSh • A limited availability of labour Farm farm scale (kg) 24 5000 KSh 2000 KSh • Objectives: maximise food 23 Relative investment in erosion control erosionfarm scale (t) 160 Farm erosion at loss [tons] N losses at N loss [kg] production, minimise N 22 losses, etc… 21 140 Simulated management decisions 20 A 120 B Soil soil 0.8Profile 0.8 19 Relative investment in weeding 2000 KSh 100 2000 KSh Homestead 18 Napier grass 5000 KSh 5000 KSh Compound 0.6 10000 KSh 0.6 17 10000 KSh fields Home garden 80 0 1000 1 2000 2 3000 3 4000 4 5000 5 6000 6 7000 7 8000 8 Maize fieldsLiving fence Woodlot 16 Farm grain yield [kg] 0 1000 2000 3000 4000 5000 Tea 0 1 Farm-scale3maize4grain 5 2 production (tones)8000 6000 6 7000 7 8 0.4 Maize 0.4 Farm grain yield [kg] Farm-scale maize grain production (t)Layout 0.2 0.2 Maize 1 Sweet Maize 2 potato Maize 5 (+) (-) (+/-) 0 Maize 6 Woodlot 0.0 Maize 4 (-) 0 0.2 (+/-) 0.4 Tea 0.6 0.8 0.0 0.2 0.4 0.6 0.8 1.0 Maize 3 Home Relative investment in N fertiliser (+) RelativeTittonell et al. (2007), Agricultural Systems 95 investment in land preparation garden
  • Agent-based systems
  • (v) Agent-based systemsMulti-scale –trade-offs around crop residue biomass herd A village territory representation of the multi-agent modeltypes, communal Figure 2 Schematic of 100 Km2, 4 farmuse in the Zambezi valley Results at village scale Baudron, Delmotte, Herrera, Corbeels, Tittonell 5.5 0 Average change of total soil organic carbon 5 0 kg N ha-1 (a)Intensification through conservation agriculture to preserve habitats and biodiversity -2 0 kg N ha-1 (b) 4.5 20 kg N ha-1 20 kg N ha-1 -4 Average mulch cover (t ha-1) 4 100 kg N ha-1 100 kg N ha-1 in the 0-20 cm ( t ha-1) -6 3.5 3 -8 2.5 -10 2 -12 1.5 -14 1 0.5 -16 0 -18 0 100 200 300 400 0 100 200 300 400 Cattle density (head km-2) Cattle density (heads km-2)
  • Simulation and gaming - Mexico Mapa de la Reserva de la Biosfera de la Sepultura. Fuente: CONANPSimulation and gaming for improving local adaptive capacity; The case of a buffer-zone community in Mexico E.N. Speelman (2008-2013) Supervisory team J.C.J. Groot, L.E. Garcia-Barrios, P. Tittonell
  • A methodological framework Landscapes COMPASSAttic LandscapeIMAGES ActorIMAGES Agro-ecosystem diversity, Trajectories and Trade-offs for Intensification of Cereal-based systems Economic Spatial Land use results coherence systems Farms Nutrient Landscape Collective losses metrics decisions Diego Valbuena (WUR) Bruno Gerard (CIMMYT) Nutrient Jeroen Groot (WUR) Water Feed FarmIMAGES balance balance balanceFields, landscape elements Santiago Lopez Ridaura (CIMMYT) FarmDESIGN FarmSTEPS Labor balance Fred Baudron (CIMMYT) Economic results Nutrient losses FarmDANCES Andy McDonald (CIMMYT) Tim Krupnik (CIMMYT) Felix Bianchi (WUR) Katrien Descheemaker (WUR) Nutrient Organic Soil Water FieldIMAGES Pablo Tittonell (WUR) balance matter erosion balance NDICEA Crop yield Nutrient Nutrient Plant RotSOM ROTAT 3 PhD started in 2013 uptake losses diversity A Cimmyt-Wageningen collaboration in the context ofSimulation – Groot and Wheat Co-innovation and Modeling Platform for Agro-ecoSystem the CRP Maize et al., 2012
  • Summary Trade-offs: situations in which two or more competing/ conflicting objectives must be simultaneously satisfied to a certain degree Quantifying slopes, opportunity costs or substitution rates not always enough – models can be used to map-out tradeoffs, to explore a wider range of options and possibility frontiers Model-aided trade-offs analysis: 1. Dynamic household models (no formal optimisation) 2. Optimisation through linear programming 3. Pareto optimisation through evolutionary algorithms 4. Agent-based systems How to scale? Models typically work for single ‘representative’ farms; typologies, distribution of farm population, etc. How to choose? Objective algortithms can always be calculated, but they cannot replace the insihgt to be gained by involving the actor; combinations of both aproaches are possible