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![Page 1: Evidence and risk-based planning for a climate-smart ... · Evidence and risk-based planning for a climate-smart agriculture Julian Ramirez-Villegas Christine Lamanna, Mark van Wijk,](https://reader033.vdocuments.us/reader033/viewer/2022042804/5f52c19c5d84366af72d23f8/html5/thumbnails/1.jpg)
Evidence and risk-based planning for a climate-smart agriculture
Julian Ramirez-VillegasChristine Lamanna, Mark van Wijk, Caitlin Corner-Dolloff, Todd Rosenstock, and Evan Girvetz
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Contents
• What is climate-smart agriculture?
• Why CSA?
– Food security
– Impacts and adaptation
– Mitigation
• But… a lack of evidence base?
• Risks-households-options (RHO) modelling for evidence-based CSA planning
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What is climate-smart agriculture?
CSA…
• Improves food security
• Enhances adaptive capacity and resilience
• Reduces agriculture’s burden on the climate system
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Why CSA? Food security
Frelat et al. (2016)
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Why CSA? Food security
Frelat et al. (2016)
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Why CSA? Climate change impacts and adaptation
Climate Action Tracker (2016) NASA (2016)
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Climate change impacts and adaptation
Porter et al. (2014)
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Transformational adaptation needs at higher levels of global warming
Rippke; Ramirez-Villegas et al. (2016) Nat. Clim. Chang.
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`
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Climate Action Tracker (2016)
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Ramirez-Villegas, J. (unpublished)
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Climate change: 1.5 vs. 2 ºC
Ramirez-Villegas and Challinor (unpublished)
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2013
13
Agriculture-related activities are 19-29% of global greenhouse gas
emissions (2010)
Agriculture production (e.g., fertilizers, rice,
livestock, energy)
Land-use change and forestry including drained peatlands
Industrial processes
Waste
Percent, 100% = 50 gigatonnes CO2e per year
Non-Ag Energy
70
11
4 2
Why CSA? Mitigation
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But… a lack of evidence base?
• What is CSA, where, and why? –A large compendium of practices shows many studies assess ≥ 1 CSA pillar
Rosenstock et al. (in prep.)
Random sample of 815 studies
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-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
-0.5 -0.3 -0.1 0.1 0.3 0.5
Food securityA
dap
tati
on
6% 16%
46% 32%SynergiesTradeoffs
Tradeoffs
Mean effect from random sample of 130 studies (55 comparisons) Rosenstock et al. (in prep.)
We can start to understand synergies and tradeoffs
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Random sample of 815 studies
So, we don’t really know what is CSA, do we? Need a new paradigm for research
Rosenstock et al. (in prep.)
But… only a few studies consider the 3 pillars (!)
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CSA Plan
1. Diagnosis and foresight
2. Prioritization
3. Program design
4. M & E
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Risks-Households-Options (RHO) modelling for CSA planning
Lamanna; Ramirez-Villegas et al. (2015)
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Modelling approach
1. Use household survey (World Bank LSMS, CCAFS) to model yields at household scale (process-based or empirical models)
2. Quantify frequency and intensity of impacts of biophysical risks and vulnerabilities (e.g. soil fertility, drought spell length) on food availability
3. Use CSA compendium to identify promising CSA practices
4. Simulate CSA practice impact on food availability
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Household survey data• Frelat et al. (2016) gathered data from 93 survey
sites, 17 countries and >13,000 hh
• LSMS-ISA (World Bank)—8 countries in SSA, eg. Niger
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Climate change related risks –risk profiles
• Household survey data to understand climate vs. other risks (e.g. pest / disease)
• Crop-climate modelling to understand key climate vulnerability factors
Ramirez-Villegas and Challinor (in revision)
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Playing out CSA practice prioritizationEf
fect
Siz
e (l
og-
scal
e)
Adaptation ProductivityLamanna et al. (in prep.)
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Risk-based CSA prioritization in Niger: preliminary results
World Bank LSMS study sites
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Contributors to household food availability
Lamanna, Ramirez-Villegas et al. (2015)
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Household food availability in Niger
Niger –contribution to household food availability from different farming system types
Analysis: Mark van Wijk
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Simple indicator of food security: contribution maps
Analysis: Robert Hijmans, UC Davis
Crop/livestock contributions to food availability vary geographically
• Marked difference between sudano-sahelian zone and sahel-saharan zone
• Millets grown ~everywhere
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Risks amongst households
• First, used the LSMS database to characteriserisks to which HHs are exposed
• 90 % HHs reported some harvest loss
• 65 % of these reported drought as the cause
• Average loss to drought was 78 %
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Crop modelling: initial results (millet)
• Used a maximin latin hypercube approach to determine realistic management scenarios, based on prescribed durations and observed yields.
Only limited management scenarios represent high yielding households
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Crop modelling –next steps
• Simulate historical (1980-2010) yields for each household
• Deconstruct ”drought” through sensitivity analysis and environmental classification
• Assess drought vs. heat stress under future climate scenarios
Heinemann et al. (2015)
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CSA compendium analysis: initial results
Analysis: Todd Rosenstock and Mark van Wijk
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We learned that…• This preliminary analysis suggests priority
investments need to address food insecurity with particular focus on cereal-based households across the Sahelian zone.
• There is potential in the use of a crop model to disentangle “drought” –we’ll keep working on that
• The CSA Compendium is a useful yet incomplete source of information… we need to change the way we do field experiments
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Generating the field-scale evidence base that links up to modelling
Campbell et al. (under review)