Climate Smart Agriculture for an Inter-Dependent World: From Dialogue to Action with the Aid of Science
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Presentation made in NIFA USDA on 10th April 2013 in Washington DC on scaling out climate smart agriculture in the developing world.
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Climate Smart Agriculture for an Inter-Dependent World: From Dialogue to Action with the Aid of Science
- 1. Led by Climate Smart Agriculture for an Inter- Dependent World: From Dialogue to Action with the Aid of Science Andy Jarvis Director of Decision and Policy Analysis (DAPA) Theme Leader, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) 1
- 2. Leb Led by Climate Change, Agriculture and Food Security (CCAFS) CGIAR Research Program 1 January 2013 2
- 3. Leb Led by Global alliance 15 CG centers and ~70 regional offices Lead center - CIAT 1 January 2013 3
- 4. Liderado Led by por Objectives Identify and develop pro-poor adaptation and mitigation practices, technologies and policies for agriculture and food systems. Support the inclusion of agricultural issues in climate change policies, and of climate issues in agricultural policies, at all levels. Commit to data availability, cross-center cooperation, and making an impact on both the global and regional level. 1 January 2013 4
- 5. Led by CCAFS Framework Adapting Agriculture to Climate Variability and Change Technologies, practices, partnerships and policies for: Improved 1. Adaptation to Progressive Climate Environmental Improved Change Health Rural 2. Adaptation through Managing Livelihoods Climate Risk Improved 3. Pro-poor Climate Change Mitigation Food Security 4. Integration for Decision Making • Linking Knowledge with Action • Assembling Data and Tools for Analysis and Planning • Refining Frameworks for Policy Analysis Enhanced adaptive capacity in agricultural, natural resource management, and food systems 1 January 2013 5
- 6. Led by Place-based field work Sur de Asia: Lider Regional Pramod Aggarwal Africa del Oeste Lider Regional Robert Zougmoré Africa del Este Lider Regional James Kinyangi Latinoamerica: Lider Regional Ana Maria Loboguerrero 1 January 2013 6
- 7. Led by Urgency and magnitude 7
- 8. Led by Historical impacts on food security Observed changes in growing season temperature for crop growing regions,1980-2008. Lobell et al (2011) % Yield impact for wheat 8
- 9. Led by Our ability to grow food in 2050 Average projected % change in suitability for 50 crops, to 2050 9
- 10. Led by The need for more food In order to meet global demands, we will need 60-70% more food by 2050. 10
- 11. Led by Livestock products: Developing countries are hungry for more. •Growth in animal product consumption has increased more than any other commodity group.1 •Greatest increases in S and SE Asia, Latin America. -Overall meat consumption in China has quadrupled since 1980 to 119 lbs/person/yr. 2 •Economic and population growth, rising per capita Photo by: CGIAR incomes, urbanization 11
- 12. 3 Livestock and GHG Led by •30-45% of earth’s terrestrial surface is pasture - 80% of all agricultural land •1/3 arable land used for feed crop production •70% of previously forested land in the Amazon = pasture 12 Source: Erb et al. (2007)
- 13. Arable land per person will decrease Led by The arable land on the earth is ~3% or 1.5 billion ha Year 1950 2000 2050 • World Population • 2,500,000,000 6,1000,000 9,000,000 • Arable land • 0.52 ha • 0.25 ha • 0.16 ha 13
- 14. 2 Livestock and GHG Led by •Livestock alone is 10-18% of all global 3 anthropogenic GHG -Other estimates as high as 51%4,5 •Range arises from methodological differences -Inventories vs. life cycle assessments, Attribution of land use to livestock, Omissions, misallocations Range of GHG intensities for livestock commodities 200 180 •Highest variation occurs for kg CO2 eq/kg animal protein 160 beef, due to variety of 140 120 production systems. 100 80 •Ruminants require more 60 fossil energy use, emit more 40 20 CH4 per animal.6 0 Pig Poultry Beef Milk Eggs Source: de Vries and de Boer (2009) 14
- 15. Led by A wicked problem 15
- 16. Led by Let’s talk about Wicked Solutions wick·ed (w k d) adj. wick·ed·er, wick·ed·est 1. Evil by nature and in practice: "this wicked man Hitler, the repository and embodiment of many forms of soul-destroying hatred"(Winston S. Churchill). 2. Playfully malicious or mischievous: a wicked prank; a critic's wicked wit. 3. Severe and distressing: a wicked cough; a wicked gash; wicked driving conditions. 4. Highly offensive; obnoxious: a wicked stench. 5. Slang Strikingly good, effective, or skillful 16
- 17. Led by Transformation in agriculture 17
- 18. Led by Incremental adaptation • Farmers are adapting all the time • But the questions remains if it is at a rate that is fast enough • And if the incremental adjustments are in the right direction to enable the systematic adjustment • How we can speeden up incremental adaptation? 18
- 19. Led by Where do we work? CCAFS sites Main crops Main livestock (forages) Maize Beans Wheat Beef cattle Goats Borana(ET) (96.6%) (86.4%) (33.1%) (93.2%) (77.8%) Maize Sorghum Beans Goats Chicken/hens Nyando (KE) (99.2%) (73.3%) (34.4%) (66.9%) (61.2%) Maize Beans Tomatoes Chicken/hens Dairy cows Usambara (TZ) (87.1%) (75%) (29%) (82.1%) (56.4%) Sweet Albertine Cassava Beans Chicken/hens potatoes Pigs (63.1%) Rift (UG) (78.6%) (68.4%) (82.5%) (59.8%) 19
- 20. Leb Led by Lushoto (Tanzania) 100 90 80 70 60 50 40 30 20 10 0 1 January 2013 20
- 21. Led by Lushoto (Tanzania) Weather reasons for adapting Changes in land use and crop management a) More erratic rainfall - introduction of new, higher yielding crop varieties of maize, beans b) ↘ overall rainfall (88%) and tomatoes c) ↗ amount of rainfall (39%) d) more frequent droughts (71%) - switching to disease resistant varieties of cassava, bananas and e) earlier start of the rains 77%) maize f) Later start of rains (65%) Drivers • Availability of high yielding varieties more resistant to pest and diseases • More profitable market prices. • Less productive land 21
- 22. Led by Overall, men and women tend to report that they themselves do most of the tasks Gender Division of Labor Women’s Reporting Men’s Reporting Men Women Boys Girls Examples: Spraying was reported as a men’s task, and Weeding mainly as a women’s task 22
- 23. Led by Decision-Making Across all 4 sites: Women report that men make most decisions Men report more decisions are taken jointly Women’s Reporting Men’s Reporting Men Women Together Example: Nyando, Kenya 23
- 24. Led by Persons and items distribution Rash model (Campell, 1963): Attitude towards change = number + difficulty of change made 24
- 25. Led by Determinants of the degree of adaptation – Poisson regression model Variable Coefficient P-value Lnage -0.259 0.034** Help 0.281 0.019** Years of schooling 0.025 0.014** Ln total asset value 0.060 0.096* Government influence 0.364 0.002*** Less land productivity 0.164 0.060* Ability to hire farm labour 0.231 0.031** Constant 2.135 0.002*** Wald chi2(20)=104.63; p=0.000 Alpha = 0.12 N=131 Dependent variable = number of adaptation strategies undertaken 25
- 26. Led by Systemic adaptation • Supports incremental adaptation • But also ensures that the direction farmers take is along the correct trajectory • Involves design of suitable policies • Incentivizing the changes that are needed • And in some cases, overcoming technological constraints • E.g. breeding for a 2030 world 26
- 27. Led by Why do we need breeding? For starters, we have novel climates 27
- 28. Led by Crops biologically at tipping points •For example, US maize, soy, cotton yields fall rapidly when exposed to temperatures >30˚C •In many cases, roughly 6-10% yield loss per degree Schlenker and Roberts 2009 PNAS 28
- 29. Led by Area harvested Current bean suitability Bean The most important food legume in tropical Latin America and East and southern Africa 29
- 30. Changes in Beans Led by Suitability • Average global area of suitability for growing beans may be reduced by 6.6% by 2020 • But wide range of change in suitability from -87% to +66% across regions. 30
- 31. Which climatic constraint affects the most beans? Led by Major climate constraints: heat stress drought stress 31
- 32. Led by Transformational change • Different livelihood systems for rural communities • Different structural make-up of the agricultural and food system at national and regional scales • Crucial to plan for transformational change, and not wait until it happens • One example where it is needed…. 33
- 33. Led by Suitability in Cauca Significant changes to 2020, drastic changes to 2050 The Cauca case: reduced coffeee growing area and MECETA changes in geographic distribution. Some new opportunities. 34
- 34. Adaptation entry points in maize- Led by bean systems 35
- 35. Silvopastoral systems: Agosto 15, 2008 Led by A mini-revolution in Colombia and Central America Piedemonte llanero Estado inicial: Julio 17, 2007 13 meses Octubre 22, 2008 36 15 meses
- 36. Farms of the future Led by The Concept Three ongoing pilots 37
- 37. Farms of in Tanzania FOTF the future Led by Journey to Yamba’s plausible futures Analogue study Tour Villages visited Starting point Lushoto Mbuzii Yamba Kinole Morogoro Mwitikilwa -Market value chain social -Weather station visit enterprise visit - Bean trial visit - Input supply Stockists Njombe - Tree nursery visit Nyombo Sepukila Village: -Matengo pits: Traditional soil and water conservation technique -Coffee nursery -Stoves Masasi Village: -Water source Mbinga -Fish pond -Biogas Mtama Village: - Bee keeping 38
- 38. Led by From dialogue to action……. 39
- 39. Led by Scalable climate smart technologies…. 40
- 40. Led by 41
- 41. Led by Which system is more sustainable? 42
- 42. Led by A MAC style prioritisation framework for CSA? 43
- 43. Led by Uptake of sustainable agricultural practices Innovation / Pre-investment Implementation at Identification of (eg, development scale / practices funds, climate Establishment of finance) institutions Demonstration of financial / Policy shifts and large- commercial viability scale changes in and sustainability practices, livelihoods Demonstration of outcomes and environmental agro-economic and sustainability impacts potential Time 44
- 44. Led by Wicked solutions for climate smart agriculture • Identifying viable practices, technologies • Collating costs and benefits for establishment, target domains • Prioritisation and screening approaches • Ensuring the enabling environment • Piloting and outscaling • The challenge is very big – reducing emissions from agriculture, ensuring adaptation 45
- 45. Led by CIAT: Science to Cultivate Change Website: www.ciat.cgiar.org Follow us: http://twitter.com/ciat_ Blog: www.ciatnews.cgiar.org/en/ http://www.facebook.com/ciat.ecoefficient 46
Editor's Notes
- For Lobell map: Values show the linear trend in temperature for the main crop grown in that grid cell, and for the months in which that crop is grown. Values indicate the trend in terms of multiples of the standard deviation of historical year-to-year variation. ** A 1˚C rise tended to lower yields by up to 10% except in high latitude countries, where in particular rice gains from warming.** In India, warming may explain the recently slowing of yield gains. For yield graph: Estimated net impact of climate trends for 1980-2008 on crop yields for major producers and for global production. Values are expressed as percent of average yield. Gray bars show median estimate and error bars show 5-95% confidence interval from bootstrap resampling with 500 replicates. Red and blue dots show median estimate of impact for T trend and P trend, respectively. **At the global scale, maize and wheat exhibited negative impacts for several major producers and global net loss of 3.8% and 5.5% relative to what would have been achieved without the climate trends in 1980-2008. In absolute terms, these equal the annual production of maize in Mexico (23 MT) and wheat in France (33 MT), respectively.Source:Climate Trends and Global Crop Production Since 1980David B. Lobell1,*, Wolfram Schlenker2,3, and Justin Costa-Roberts1Science magazine
- Why focus on Food securityAnd climate change has to be set in the context of growing populations and changing diets60-70% more food will be needed by 2050 because of population growth and changing diets – and this is in a context where climate change will make agriculture more difficult.
- Distribution change is the other side of the “land-use change” coin – i.e. the distribution of coffee / maize / apple production across the world or across a region changes (i.e. it is exactly the same as the “farmers moving” in the previous slide)But perhaps leave out this slide as the previous one covers itHowden SM, Crimp S, Nelson R (2010) Australian agriculture in a climate of change. In ‘Managing Climate Change. Papers from the Greenhouse 2009 Conference’. (Eds I Jubb, P Holper, W Cai) pp. 101–112. (CSIRO: Melbourne) CCAFS does not have a copy of this conference paper
- nwcrpIntroduced a new cropnwvarIntroduced a new variety of cropshcyIntroduced a short cycle varietylgcyIntroduced a long cycle varietydrtlIntroduced a drought tolerant varietyfdtlIntroduced a flood tolerant varietydstlIntroduced a disease tolerant varietypsrsIntroduced a pest resistant varietyexarExpanded cropping areardarReduced cropping areastirStarted irrigationspbrStopped burningincrIntroduced intercroppingcrcvIntroduced cover cropsmcctIntroduced micro-catchmentsbundIntroduced bunds / ridgesmulcIntroduced mulchingterrIntroduced terracesstlnIntroduced stone lininghedgIntroduced hedgesctplIntroduced contour ploughingrotaIntroduced crop rotationelppIntroduced early land preparationelptIntroduced early plantingltptIntroduced late plantingmnftStarted using or increased use of mineral fertilizermncpStarted using or increased use of mineral fertilizerumphStarted using pesticides / herbicidesumipIntroduced integrated pest managementumcmIntroduced integrated crop management
- Nos encontramos con el modelo de los cuatro países y se asigna el resultado (en este caso las diferencias entre la producciones actuales y futuras (2020) la producción de frijol) para Centroamérica.Como podemos ver, hay zonas donde la producción se reducirá drásticamente, mientras que otros están mejorando su potencial de producción. Los cambios ya descritos en las condiciones del clima y sus interacciones con las condiciones de ubicación específica determinaran la producción del cultivo. El estrés por calor, la sequía y las altas temperaturas en noche son los principales culpables de estos resultados. Esto es ampliamente sostenido por evidencia científica. Algunas de las conclusiones generales son:Frijol:Temperaturas> 28/18 C (día / noche) decrecimiento en la producción de biomasa, seed-set, el numero y tamaño de las semillas (menos vainas por planta, menos semillas por vaina, peso menor en las semillas)Niveles elevados de CO2 también decrece seed-setNiveles elevados de CO2 aumentaron la biomasa, pero los beneficios de los niveles elevados de CO2 disminuye con aumento de las temperaturas maíz:La tensión alta temperatura disminuye la polinización y la producción de semillas de maíz, causada principalmente por la disminución en la viabilidad del polen y receptividad del estigmaLa tensión alta temperatura disminuye la semilla-set y los números del núcleo por planta.La tensión alta temperatura también afecta negativamente la calidad del núcleo y la densidad (proteínas, enzimas)Etapas reproductivas (el desarrollo del polen, floración, llenado de los granos antes de tiempo) son relativamente más sensibles a la sequía, la sequía disminuye el número y el peso seco del núcleo. El maíz necesita 50% del agua en el período de10 días antes y 20 días después de la floración inicial. A pesar de subrayar lo suficiente la temperatura del agua afecta el desarrollo del polen.El estrés hídrico reduce el número y tamaño de granos.Las temperaturas más altas en la noche significa mayores pérdidas de la respiración por lo tanto la pérdidas de biomasa y de rendimiento.Con los resultados DSSAT ahora podemos identificar los diferentes tipos de ámbitos de intervención en la región (siguiente diapositiva)
- The use of climate analogues for locating future climates today can ground models in field-based realities, significantly enhancing our knowledge of adaptation capacity and supporting the identification of appropriate interventions.Building and testing a methodology to study farmer’s social, cultural and gender specific barriers for enabling behavioral change and improve adaptive capacity.
- Analogue tourParticipatory videos
- Scaling up climate-smart agriculture: investment needs from innovation to implementation at scale. The set of sustainable agricultural practices that can improve adaptation, mitigation and livelihoods is highly diverse, varying by region and farming system. Many such practices are already well-known and others are yet to be invented or brought into general awareness. The process by which sustainable agricultural practices are taken up in specific farm regions and commodity sectors will be idiosyncratic, controlled by factors such as type and level of investment, availability of relevant knowledge and infrastructure, and the institutional and policy context. The type and amount of public and private sector investment varies country to country although, in general, investment in agriculture is low in low-income countries and higher in wealthier countries (where selection of agricultural practices is driven by a complex mixture of policy and market signals). The role of farmers’ organizations and agribusinesses is also highly variable by country and region. This schematic depicts the general sequence of investments, transitions and outcomes on the path to widespread adoption of agriculture practices that achieve adaptation, mitigation and livelihood objectives. Each phase in this general sequence has distinct incentives, knowledge requirements, risk tolerances, success metrics and expectations about return on investment. The purpose of this conceptual framework is to challenge funders, researchers, practitioners and other actors to clearly understand the precursors, partnerships and institutions required for investments to result in broad uptake of sustainable practices. It can also be used by those currently operating in one or more of these phases to clarify their role, objectives, progress and likely outcomes. Major phases include: (1) Innovation / identification of sustainable practices through adaptive farmer-driven research designed to achieve robust understanding of biophysical and socio-economic dynamics and outcomes relevant to incomes and environmental services. (2) Pre-investment (eg, climate finance, agricultural development funds) focused on ”real world” testing and operationalizing of sustainable practices through public-private partnerships designed to understand risks (eg, ROI lag time), barriers (eg, land tenure, subsidies) and necessary institutions (eg, managing financial flows, Extension) and infrastructure (eg, seed systems, monitoring). (3) Implementation of sustainable agricultural practices at scale, based on robust ROI, and establishment of public and private sector institutions to build capacity (eg, local farm associations and agribusinesses), provide oversight (eg, quality control for implementation and financing) and manage risk (eg, insurance or safety net programs), coupled with harmonization of the policy context (eg, re-orientation of subsidy programs). To meet urgent new challenges, stronger institutional mechanisms are needed (eg, to mitigate risks associated with innovation) and the research enterprise must evolve much more rapidly and develop better connectivity across research institutions, Extension and farmers (eg, through mandates for farmer-oriented research).