Meine van Noordwijk

1. Meine van Noordwijk ICRAF, Indonesia

2. Ecological intensification (closing efficiency gaps) within a multifunctional landscape perspective is possible We only need to learn how to do it better Conventional agricultural intensification (closing yield gaps) is a sine qua non for humanity & for saving our last forests We only need to learn how to achieve it in better ways Red herrings Discordant information Black Swan Paradigm shift

3. Red herrings? In this talk • The agriculture  forest dichotomy is artificial: trees are a common (& essential?) part of many farmed landscapes • The forest (or tree cover) transition affects climate at many scales (micro-, hydro-, carboclimate) • Sustainability is a social construct, across 3 temporal scales • 5 scales of economics are needed for sustainagility • The tradeoff between ‘green’ and ‘growth’ is our primary challenge: simultaneously closing yield & efficiency gaps • Outsourcing staple food is key to forest-based livelihoods • Landscape-level tree cover enhances nutritional diversity • New landscape-scale performance metrics are needed, building on land equivalence ratio’s and foot prints Black Swans? Your call

4. Agroforestry Agro- vs Fores- vs vs Tree Natural vegetation, biodiversity, wilderness Culture, control, food security, profitability Product value chains Crop fields, pasture Natural forest Tree crops Integrate Segregate Knowledge, Attitude, Skills & Aspirations

5. The logarithm of human population density is a good predictor of the fraction of land area reported as forest (across different forest types) We can identify countries that have more than 10% extra, or more than 10% forest deficit relative to what is expected for their population density

6. A Boundary-spanning Object…

7. Sumber Jaya (Lampung, Indonesia) Coffee agroforestry in a contested watershed Bungo (Jambi, Indonesia) Tapping rubber in biodiversity rich agroforest

10. Figure 2b. Global percentage of tree cover in the years 2008–2010 (averaged) % Tree cover FractionofAgland Small increase

11. 800 (+100) million people live in 9.5 (+0.5) million km2 of agricultural lands with >10% tree cover 180 (+20) million people live in 3.5 (+0.2) million km2 of agricultural lands with >30% tree cover CumulativeareaCumulativepopulation Zomer et al. 2014 (in prep.)

12. S, N&C, W Asia and N.Africa C.America, Oceania, SE Asia, South America Zomer et al. 2014 (in prep.)

13. Dry  Wet Latin America has more tree cover for a similar climate

14. Zomer et al. 2014 (in prep.) In SE and S Asia more people less tree cover in agricultural areas Weak trend to “more people more tree cover” Central America is different story….

15. http://www.worldagroforestry.org/d ownloads/publications/PDFs/WP130 54.PDF Agroforestry supports food and nutritional security through: (1)the direct provision of tree foods such as fruits and leafy vegetables and by supporting staple crop production; (2) by raising farmers’ incomes through the sale of tree products and surplus staples; (3) by providing fuels for cooking; and (4) by supporting various ecosystem services such as pollination that are essential for the production of some food plants.

17. Blue water streamflow Rainfall triggering conditions Precipi- tation Rainbow water Atmospheric transport Soil & ground- water buffering GreenwaterET evapotranspiration Precipi- tation GreenwaterET evapotranspiration Blue water streamflow Soil & ground- water buffering

18. Land with diverse tree cover and carbon-rich soil Adaptive management capacity of empowered local communities Enhanced carbon sinks, avoided losses Sustainable forest management Avoidance of soil C loss (peat & mineral soils) Increased A & F productivity (Agro)biodiversity conservation Agroforestry Afforestation, reforestation Soil and water conservation Ecosystem service value realization Decentralization of NRM decisions Centralization of NRM decisions Increased efficiency of Ag input use Rules need to evolve from ‘additionality’ tests on sepa- rate funding streams via ‘complementarity’ to full ‘synergy’ Mitiga- daptation Lalisa et al. soon to be submitted

20. Bernard F, van Noordwijk M, Luedeling E, Villamor GB, Gudeta S, Namirembe S. 2014? Social actors and unsustainability of agriculture. Current Opinion in Environmental Sustainability 6, 155-161

23. 5 scales of economics Individual & household decisions on scarce resources National scale decisions on scarce resources Environmental economics: internalizing externalities of individual decisions for common goods Ecological economics: planetary boundaries put hard constraints Behavioural economics: really internalizing externalities at emotional core of decision making Annu. Rev. Environ. Resour. 37, 389- 420

27. Swiddens as starting point of rotan gardens in Central Kalimantan – PhD research Viola Bizard Pictures:ViolaBizard

28. 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management A and F are inseparable aspects of a land use system that provides the 4 functions e.g. swidden/ fallow cycles I. Swidden-rubber- rattan system in Katingan, Kalimantan van Noordwijk, M, Minang P A, Hairiah K, 2014. Shifting cultivation in an era of climate change, In: M.Cairns (Ed). A growing forest of voices. Earthscan, UK; in press)

29. 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management A and F are inseparable aspects of a land use system that provides the 4 functions 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management A and F are segregated parts of landscapes, both providing for the 4 functions (with + & - interac- tions) e.g. swidden/ fallow cycles I. II. e.g. Green revolution + National Parrks

30. 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management A and F are inseparable aspects of a land use system that provides the 4 functions 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management agroforestry A and F are segregated parts of landscapes, both providing for the 4 functions (with + & - interac- tions) A and F are connected through af intermediary land use, jointly providing the 4 functions e.g. swidden/ fallow cycles I. II. III. e.g. green revolution e.g. inte- grated LU planning Lubuk Beringen, Jambi. Sumatra, Indonesia

32. 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management A and F are inseparable aspects of a land use system that provides the 4 functions 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management agroforestry 1. Rural income 2. Food production 3. Nutrition & health 4. Natural resource management A and F are segregated parts of landscapes, both providing for the 4 functions (with + & - interac- tions) A and F are connected through af intermediary land use, jointly providing the 4 functions F facilitates and supports intensive af+A landscapes to provide the 4 functions e.g. swidden/ fallow cycles I. II. III. IV. e.g. green revolution e.g. inte- grated LU planning e.g. ecological intensification

33. Higher order mosaic where both sparing and sharing are combined, both driven by caring Segregate // land sparing Integrate // land sharing van Noordwijk M, Tata H L, Xu J, Dewi S and Minang P, 2012. Segregate or integrate for multifunctionality and sustained change through landscape agroforestry involving rubber in Indonesia and China.. In: Agroforestry: The Future of Global Landuse. Nair PKR and Garrity DP (eds.), Springer, The Netherlands. pp 69-

36. Luang Prabangh, Mekong river (Lao PDR)

37. Wageningen river Rhine (Netherlands) Even some forest-derived mushrooms Similar diversity of products to the consumer, but accessed from all overr the world

38. Biodiversity paradox: Urban consumers have more and more choice of foods, derived from farms that get less and less diverse

40. Payments for environmental services (PES), or non-provisioning ecosystem services, target alignment of micro- economic incentives for land users with meso- and macro-economic societal costs and benefits of their choices across stakeholders and scales They can interfere with or comple- ment social norms and rights-based approaches at generic (land use planning) and in-dividual (tenure, use rights) levels, and with macro-economic policies influencing the drivers to which individual agents respond. PES concepts need to adapt. Multiple para- digms have emerged within the broad PES domain. Forms of “co-investment in stewardship” alongside rights are the preferred entry point

41. Providers, Beneficiaries, ‘Sellers’ ‘Buyers’ Intermediary, Broker From: $$  ES Annu. Rev. Environ. Resour. 37, 389- 420

42. From: $$  ES to also Res-&Coo pect pera- tion Intermediary, Broker Providers, Beneficiaries, ‘Sellers’ ‘Buyers’ ‘Co-investors’ ‘Co- investors’ van Noordwijk M, Leimona B, Jindal R, Villamor G B, Vardhan M, Namirembe S, Catacutan D, Kerr J, Minang PA, Tomich TP, 2012. Payments for Environmental Services: evolution towards efficient and fair incentives for multifunctional landscapes. Annu. Rev. Environ. Resour. 37, 389-420

43. Scenario evaluation

44. Patterns of tree cover change Actors/drivers SmallLarge scale Stakeholder feed- back options (~scale) Ecosystem Service & social conse- quences (~scale) Direct benefits Drivers & leverage points: motivation & knowledge, investment, rules (~scale)

45. Landscapes Landscape approaches are • Landscapes are not ‘just’ mosaics of multiple land covers and land uses, • They are a space within which livelihoods used to run their course, • They include aspects of identity, pride and concern and have (some) social coherence • Attempts to reconcile local and external perspectives on desirable landscape outcomes • Usually have a ‘negotiation’ dimension within a ‘learning landscape’ context

47. RaCSA REPEAT ROSAQ BERES FERVA RESFA RUPES RPG Con$erv MuScaPES RaTA RISNA ASSAPP WNoTree Tradeoff matrix REDD Abacus SP LUWES RABA QBSur Ecor CASAVA TALAS RAFT LUPA GRoLUV RMA WhichTree ? G-TreeFarm NotAnyTre e CoolTree FBA SLIM AKT5 Treesilience LUCIA Polyscape Adopt&Learn FALLOW RHA GenRiver SpatRain FlowPer RaLMA PaWaMo Lives Landscapes Transformation PaPOLD PaLA ALUCT and DriLUC Agroforestry Water Biodiversity Carbon Tradeoffs&change integration

51. http://worldagroforestry.org/sea/publication ?do=view_pub_detail&pub_no=BK0170-13

55. Natural vegetation, biodiversity, wilderness Culture, control, food security, profitability Product value chains Crop fields, pasture Natural forest Tree crops Integrate Segregate Agroforestry Agro- vs Fores- vs vs Tree Fig. 0.2

58. Geological history, patterns and current activity Global climate systems based on oceans, land and atmosphere Flora and fauna and its biogeography Landforms,vegetation,ecosystems,hydrology A. Initial human land use B. Late-stage human land use Land use is predictable from ‘reading the landscape’ Land use dominates over original terrain features C. The transition is predictable

60. Fig. 0.5

64. B. Accepting an issue as part of a policy agenda: ‘How big is it? What are symptoms, what are the underlying causes? Who can be blamed? Costs?’ A. Entry phase of ‘new’ issue into public debate: ‘is there a problem?’ C. Identifying and negotiating solutions: ‘What can be done about it? What is lowest cost solution? Who’ll have to pay? Will it work? Why didn’t it work before? D. Reaching agreements that are implementable: ‘How do solutions for this issue interact with other con-current negotiations? How can deals be made that are implementable and meet minimum targets’ E. Implementing, eva- luating and not-forgetting: ‘Are agreements implemen- ted and working? What next generation issues are emerging?’ Funding peaks Fig.0.9

66. Regional networks of ‘learning landscapes’: variable methods aimed at supporting local resource access, value-chain development, local institutions and/or reform of (sub) national regulations ‘Extractive science’: standardized methods for advancement of disciplinary knowledge and academic publications as international public goods ‘Locally owned’ learning that can but doesn’t have to include participation by scientists or development agents Global network of ‘sentinel landscapes’: aimed at long-term socio-ecological monitoring using standardized methods, science-led, aimed at informing international policy arenas Fig. 0.10

67. Product- oriented research Process-oriented multistakeholder discussion tools Qualitative Dynamic and spatial Trade-offs understood Value compensation Operating mechanisms Spatially explicit freeand prior informed consent Fig. 0.11 Nesting of landscapein (inter-) national action plans Focus on external learning Focus on local learning Boundary objects created

68. RESFA redd feasibility appraisals Tradeoff ranking of options for land use change FERVA arguments for fairness & efficiency OpCost analysis with ABACUS RUPES-game with local stakeholders Product oriented research Process-oriented multistakeholder discussion tools Quali- tative Dynamic + spatial Tradeoffs Value-compensated tradeoffs Operating mechanisms Realistic, Conditional Voluntary Spatially explicit C-compensated land use planning for REDD+ game TALaS scenario analysis with FALLOW FPIC NAMA Rel/Rl LAAMA- NSS FlowPer & Ecor predictors of co- benefits ABACUS = abatement cost curve calculator; ASB matrix = land use systems & their key attributes; ΔLU = land use change; Ecor = Ecological corridors; FALLOW = Forest, Agriculture, Low-value Lands or Waster model; FERVA = Fair & Efficient REDD Valuechain Analysis; FlowPer = Flow Persisytence model; FPIC = Free and Prior Informed Consent; LAAMA = Locally Appropriate Adaptation and Mitigation Actions; NAMA = Nationally Appropriate Mitigation Actions; OpCost = Opportunity Cost analysis scheme; NSS = Negotiation Support System; RACSA = Rapid C stock appraisal; RATA = Rapid Tenure Claim Appraisal; REDD+ = Reducing Emissions from Deforestation and Degradation; REL/RL = reference (emission) level; RUPES = Rewarding Upland Poor for the Environmental Services they provide; TALaS = Tradeoff Analysis for Landuse Scenarios ΔLU maps ASB matrix RATA RACSA

70. Aspirations Action Knowledge Power Fig. 0.12

73. What are the drivers of current human activity and what are levers (regulatory framework, economic incentives, motivation) for modifying future change? How does tree cover vary in the landscape (patterns along a typical cross-section, main gradients), and how has it decreased and increased over time? Who makes a living here, what is ethnic identity, historical origin, migrational history, claims to land use rights, role in main value chains, what are key power relations? How do ecosystem services (provisioning, regu- lating, cultural/religious, supporting) depend on tree cover and the spatial organization of the landscape? Which land use patterns with or without trees are prominent in the landscape and provide the basis for local lives and livelihoods? What value chains are based on these land uses? Who is affected by or benefits from the changes in tree co-ver and associated ecosystem services? How are stakehol-ders organized and empo-wered to influence the drivers? Fig. 0.14

75. I. Initial appraisal of context V. Process of negotiated change II. Lives, land use & livelihoods III. Landscapes, ecosystem services, tradeoffs IV. Transforma- tions, gover- nance, rights Fig. 0.14

77. What are the drivers of current human activity and what are levers (regulatory framework, economic incentives, motivation) for modifying future change? How does tree cover vary in the landscape (patterns along a typical cross-section, main gradients), and how has it decreased and increased over time? Who makes a living here, what is ethnic identity, historical origin, migrational history, claims to land use rights, role in main value chains, what are key power relations? How do ecosystem services (provisioning, regu- lating, cultural/religious, supporting) depend on tree cover and the spatial organization of the landscape? Which land use patterns with or without trees are prominent in the landscape and provide the basis for local lives and livelihoods? What value chains are based on these land uses? Who is affected by or benefits from the changes in tree co-ver and associated ecosystem services? How are stakehol-ders organized and empo-wered to influence the drivers? Section I

78. Citation Van Noordwijk M, Lusiana B, Leimona B, Dewi S, Wulandari D, eds. 2013. Negotiation-support toolkit for learning landscapes. Bogor, Indonesia: World Agroforestry Centre (ICRAF) Southeast Asia Regional Program.

79. Back to main menu

81. Geological history, patterns and current activity Global climate systems based on oceans, land and atmosphere Flora and fauna and its biogeography Landforms,vegetation,ecosystems,hydrology A. Initial human land use B. Late-stage human land use Land use is predictable from ‘reading the landscape’ Land use dominates over original terrain features C. The transition is predictable

99. What are the drivers of current human activity and what are levers (regulatory framework, economic incentives, motivation) for modifying future change? How does tree cover vary in the landscape (patterns along a typical cross-section, main gradients), and how has it decreased and increased over time? Who makes a living here, what is ethnic identity, historical origin, migrational history, claims to land use rights, role in main value chains, what are key power relations? How do ecosystem services (provisioning, regu- lating, cultural/religious, supporting) depend on tree cover and the spatial organization of the landscape? Which land use patterns with or without trees are prominent in the landscape and provide the basis for local lives and livelihoods? What value chains are based on these land uses? Who is affected by or benefits from the changes in tree co-ver and associated ecosystem services? How are stakehol-ders organized and empo-wered to influence the drivers?

268. ABACUS NPV, $/Ha Carbonstock,tC/Ha Slope indicates emissions per gain in $/ha Tradeoff at land use system level opportunitycost,$/tCO2e,  Cumulative emissions Emission reduction poten- tial for given C price Opportunity cost at landcape scale Rural income (increasing) Rural income  (declining) C stock (increasing) C stock (decreasing) Dynamic land use scenario model Agents with variation in resource base, motivation, live- lihood stra- tegies. interacting with rules & policies Agent-based land use change model ASB tradeoff Matrix e.g. FALLOW scenarios I II III IV Four levels of analyzing opportunity costs

277. Δ C in the Landscape Land use change Difference in C stock for any type of change =SUM X “Emission factor” as difference in time- averaged C stock: ton C ha-1 “Activity data” in the form of land use change for any type of transition: ha y-1 Change in C stock of the landscape: ton C yr-1 over all pairwise land use change combinations

Meine van Noordwijk

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