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Evaluation approaches and tools for assessing
agricultural vulnerability/resilience to climate
change in regional contexts (assessments,
multi-scale models, place-based analysis with
stakeholders)
Andy Jarvis
Theme Leader, Adapting to Progressive Climate
Change




                                                         1
Led by




A wicked problem




                            2
Led by




Landscape-scale research on food
security, natural resources, policy and
governance to achieve agricultural
resilience to climate change.

     Talk about a wicked scale!


                                            3
Led by




Evaluation approaches and tools for
assessing agricultural vulnerability/resilience
to climate change in regional contexts
(assessments, multi-scale models, place-
based analysis with stakeholders)

…a particularly malicious title



                                                  4
Led by




I’m not a wicked scientist…




                                       5
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




                                                                                          6
Led by




What do we know?




                            7
Leb
                    Led by




      Lushoto (Tanzania)
100
90
80
70
60
50
40
30
20
10
  0




                        1 January 2013
                                     8
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




                                                                                                                             9
Leb
                                                                                      Led by




Weather related reasons for Δ      Crop/ mngt change

↘ overall rainfall (88%)              Adoption of shorter cycle and drought tolerant crop
↗ erratic rainfall (75%)               varieties
↗ frequent droughts (71%)
Earlier start of the rains (77%)      Planting earlier (maize)
                                      Switching to disease resistant varieties (maize cassava,
                                       bananas)


Later start of rains (65%)            Planting crops later (beans and cassava)


↗ overall rain (39%)                  Maize, beans and tree based crops (peaches, apples and
                                       coffee) planted years to utilize the increased moisture




                                                                                          1 January 2013
                                                                                                      10
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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



                                                                              11
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


                                                              12
Led by




Let’s ask the scientist?

What happens to staples in Africa?




                                              13
Led by

Impacts on staples
 in SSA




                 Crops affected differently.
               Regional differences in impacts.
                    We have uncertainty.

                                                           14
Led by

Cassava gaining
while other fail




                            15
Led by




Cassava’s role as a
substitution crop
Cassava as a fallback crop under an uncertain
climate (risk management)
Cassava as the substitution crop for other staples
more sensitive to heat and drought
What are the socio-cultural constraints to a shift in
staple, and how can this shift be most effectively
made?

                                                        16
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If I were a policy maker or decision
maker…………I’d be confused




                                                17
Led by




Wicked solutions across scales and disciplines


•   Global and regional scale problem diagnosis feeding into local
    and national lead solutions

•   Some examples:

     •   Vulnerability assessment identifying entry points

     •   Local learning processes

     •   Towards better national level plans and strategies




                                                                              18
Adaptation entry points in maize-   Led by




bean systems




                                             19
Farms of the future          Led by



    The Concept
      Three ongoing pilots




                                      20
Yamba analogue map + Study Tour Itinerary + Activities -
   Zoom       Farms of the future                          Led by



    Taking theto Yamba’sconcept tofutures
      Journey analogue plausible the field
                                                  Lushoto
                                                  CCAFS site
Tanzania

                                                            Morogoro



                                                   -Weather sttin visit
                                     Mwitikilwa    - Bean trial visit
                                                   - Tree nursery visit
                        Njombe


                       Nyombo




                        Mbinga

                                                                          21
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                         22
Led by

   Persons and items distribution




Rash model (Campell, 1963): Attitude towards change = number + difficulty of change made
                                                                                     23
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
                                                                                       24
Led by




Scalable climate smart technologies….




                                                 25
Led by




         26
Led by




Scalable climate smart technologies….




                                            27
Led by




A MAC style prioritisation
framework for CSA?




                                      28
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                                       29
Led by




     Wicked solutions across scales and
                disciplines
• Local, landscape, national, regional and global scale – make
  sense of the trade-offs and drivers between these
• Science stitching pieces together, without getting lost in the
  complexity –hopping between disciplines and scales
• Plug scientific insights into the policy environment to
  achieve wide-scale climate smart agricultural adoption




                                                               30

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Wicked Solutions to Climate Smart Agriculture

  • 1. Led by Evaluation approaches and tools for assessing agricultural vulnerability/resilience to climate change in regional contexts (assessments, multi-scale models, place-based analysis with stakeholders) Andy Jarvis Theme Leader, Adapting to Progressive Climate Change 1
  • 2. Led by A wicked problem 2
  • 3. Led by Landscape-scale research on food security, natural resources, policy and governance to achieve agricultural resilience to climate change. Talk about a wicked scale! 3
  • 4. Led by Evaluation approaches and tools for assessing agricultural vulnerability/resilience to climate change in regional contexts (assessments, multi-scale models, place- based analysis with stakeholders) …a particularly malicious title 4
  • 5. Led by I’m not a wicked scientist… 5
  • 6. 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 6
  • 7. Led by What do we know? 7
  • 8. Leb Led by Lushoto (Tanzania) 100 90 80 70 60 50 40 30 20 10 0 1 January 2013 8
  • 9. 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 9
  • 10. Leb Led by Weather related reasons for Δ Crop/ mngt change ↘ overall rainfall (88%)  Adoption of shorter cycle and drought tolerant crop ↗ erratic rainfall (75%) varieties ↗ frequent droughts (71%) Earlier start of the rains (77%)  Planting earlier (maize)  Switching to disease resistant varieties (maize cassava, bananas) Later start of rains (65%)  Planting crops later (beans and cassava) ↗ overall rain (39%)  Maize, beans and tree based crops (peaches, apples and coffee) planted years to utilize the increased moisture 1 January 2013 10
  • 11. 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 11
  • 12. 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 12
  • 13. Led by Let’s ask the scientist? What happens to staples in Africa? 13
  • 14. Led by Impacts on staples in SSA Crops affected differently. Regional differences in impacts. We have uncertainty. 14
  • 16. Led by Cassava’s role as a substitution crop Cassava as a fallback crop under an uncertain climate (risk management) Cassava as the substitution crop for other staples more sensitive to heat and drought What are the socio-cultural constraints to a shift in staple, and how can this shift be most effectively made? 16
  • 17. Led by If I were a policy maker or decision maker…………I’d be confused 17
  • 18. Led by Wicked solutions across scales and disciplines • Global and regional scale problem diagnosis feeding into local and national lead solutions • Some examples: • Vulnerability assessment identifying entry points • Local learning processes • Towards better national level plans and strategies 18
  • 19. Adaptation entry points in maize- Led by bean systems 19
  • 20. Farms of the future Led by The Concept Three ongoing pilots 20
  • 21. Yamba analogue map + Study Tour Itinerary + Activities - Zoom Farms of the future Led by Taking theto Yamba’sconcept tofutures Journey analogue plausible the field Lushoto CCAFS site Tanzania Morogoro -Weather sttin visit Mwitikilwa - Bean trial visit - Tree nursery visit Njombe Nyombo Mbinga 21
  • 22. 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 22
  • 23. Led by Persons and items distribution Rash model (Campell, 1963): Attitude towards change = number + difficulty of change made 23
  • 24. 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 24
  • 25. Led by Scalable climate smart technologies…. 25
  • 26. Led by 26
  • 27. Led by Scalable climate smart technologies…. 27
  • 28. Led by A MAC style prioritisation framework for CSA? 28
  • 29. 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 29
  • 30. Led by Wicked solutions across scales and disciplines • Local, landscape, national, regional and global scale – make sense of the trade-offs and drivers between these • Science stitching pieces together, without getting lost in the complexity –hopping between disciplines and scales • Plug scientific insights into the policy environment to achieve wide-scale climate smart agricultural adoption 30

Editor's Notes

  1. 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)  
  2. 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.
  3. African pilots …Selection of a set of analogue villages to visit on the way to Mbinga
  4. Analogue tourParticipatory videos
  5. 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
  6. 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).