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Osana Bonilla-Findji, Andy Jarvis, Marie Quinney,Todd Rosenstock,
Christine Lamanna, Lini Wollemberg, Meryl Richard, Sonja Vermulen,
Dhanush Dinesh
Metrics for CSA
increasing programming
effectiveness and outcome tracking
GACSA’s joint workshop ‘Metrics for Climate-Smart Agriculture’
Rome, FAO HQ, June 15th 2017
ccafs.cgiar.org
CSA: Agriculture and Food systems that
deliver outcomes
Issues with metrics
reduced
emissions
resilience/
adaptation
food
security/
productivity
nutrition
outcomes
costly to
quantify
agreement
on core
indicators
 CSA is emerging as a mechanism for coherent and coordinated action
 Measuring processes or Outcomes?
Standard of measurements
That show progress
500 million farmers globally
25 million by 2025
Global momentum still building for CSA
6 million by 2021
CSA Complexity & Uncertainty
Complexity stemming from diversity of:
1. Interventions (micro to macro level)
2. Farming systems and households
3. Objectives/ potential outcomes of success (ranging from increased soil carbon to
maternal dietary diversity), with trade-offs possible.
Uncertainty and Context Specificity:
Lack of information and data
• Outcomes = CSA practices x social-environmental
context
• Need to identify where biggest wins occur across
landscapes/regions
• Gradient of CSA-ness
• CSA today, may not be CSA tomorrow
Review CSA metrics : What's there ?
High demand for useful/actionable tools to design, prioritize and
monitor useful interventions and investments: Paris Workshop 2015.
0
50
100
150
200
Indicators types (%)
Readiness Process Outcome
0
50
100
150
200
Indicators relative contribution to CSA
objectives (%)
Productivity Adaptation Mitigation
- Most agencies covered all the types of indicators but focused on Process and Outcomes
- Mostly covering Adaptation pillar (81%) and Productivity (40%).
Adaptation => Guidance needed
- Largely geared towards risk management
- Multidimensional nature of resilience (economic, financial social) and
capacities (absortive, adaptive, transformative) often not factored
- Measure potential adaptation rather than actual adaptation
- Lack ability to track changes over time (e.g lean season)
Mitigation
Significant lack of indicators related to mitigation outcomes
Global CSA indicators database:
 378 relevant indicators (USAID, DFID, World Bank, IFAD, GIZ, FAO, CCAFS)
 Revisited thought the “CSA lenses” and Classified
* Readiness/Enabling environment
* Process (outputs)
* Outcome indicators
What to measure and how?
Some CCAFS metrics tools
CSA
Evidence
building
CSA targeting/
Programming
CSA
Measures
and
Monitoring
- Compendium of CSA (Practice)
- CSA Calculator (Farm)
- Diverse Prioritization tools and approaches
- Risk-household-option modelling (RhOMIS)
- CSA programming and Indicator Tool
- SHAMBA (Small-Holder Ag. Monitoring and
Baseline Assessment
- CCAFS Mitigation Option Tool (MOT)
$
CSA Compendium
Rosenstock et al. 2016
Systematic review
• 1,200 peer reviewed
studies for Africa
• 100,000+ comparisons to
baselines,
• 70 practices
• 55 outcome indicators
Largest meta analysis to date
♦ What is the evidence base for field interventions’ potential impact on three pillars?
 Outcomes vary (crop, farming system, agroecology…)
 There is no best-bet practice across all outcomes and
systems
 Prioritization of outcomes and tailoring is key
Integration with other initiatives: WB, USAID, World Bank,
GIZ, Care, CRS, Oxfam, World Vision, Concern, NEPAD,
COMESA, national governments in multiple African Countries
Evidence building
♦ Farm model allowing a prospective assessment of synergies &
trade-offs related to implementation of different CSA options and
outcomes that can be expected (three pillars).
Main indicators of the CSA calculator
Pilar ASAC Indicador Calculation
Productivity Caloric ratio of the farm (%) Caloric supply/Caloric demand x 100
Fodder ratio of the farm (%) Fodder supply/Fodder demand x 100
Cost benefit ratio (%) Benefit/Cost x 100
Adaptation Biodiversity index (%) Assessment based on Gobbi, J.,
Casasola, F., 2003.
Water balance (%) Water supply/water demand x 100
Nutrient balance (%) Nutrient supply/nutrient demand x 100
Mitigation Emission/Sequestration of CO2 CoolFarmTool
Measurement & Monitoring
CSA Calculator of farm performance
(Synergies and Trade-offs)
Users: Piloting ongoing in
CCAFS CSV monitoring
Evidence building
Risk-household-option modelling (RhOMIS)
http://rhomis.net/blog/
CSA Programming
 Captures information on farm productivity and
practices, nutrition, food security, gender
equity, climate and poverty
 Up to 20 important performance and welfare
indicators + key farm level drivers, livelihood
data and management decisions
 Compare changes in farming practice and
livelihoods over time, stated plans for the
future, and farmers intrinsic values and attitudes
 Identifies ‘positive deviant’ farmers
Users: tested and adapted for diverse systems in more
than 7,000 households across the global tropics
Implementations by 10 projects, 15 diverse farming
systems on 4 continents. Lutheran World Relief (Kenya);
TreeAID (northern Ghana), CONABIO (in Mexico)
Measurement & Monitoring
Citation: Hammond, J. et al. 2017
♦ Household-level Survey and Analytical engine for characterizing,
targeting and monitoring agricultural performance and the effectiveness
of ongoing out-scaling efforts
Mitigation measurement tools
Mitigation option tool (MOT) Small Holder Agriculture
Monitoring and Baseline
Assessment (SHAMBA)
 Provides fast, accessible, and
reliable information to make
informed decisions about
emissions reductions
 Assesses GHG emissions estimates
and Ranks the most effective
mitigation options for dozens of
different crops and livestock systems
 Low input data requirements
 Runs in Excel bringing together
several empirical models
• Estimates GHG emissions or
removals resulting from a change in
land management practices
(biomass burning, plant nitrogen inputs
to soils, and fertiliser use)
• Models a baseline scenario (where
land management activities continue
as business as usual) and a CSA
intervention scenario
• Set to calculate estimates on a yearly
basis, in tonnes of CO2e per hectare.
Measurement & Monitoring
Case study : working with the public
sector on metrics (USAID)
CSA Programming and Indicator tool (CCAFS)
https://ccafs.cgiar.org/csa-programming-and-indicator-tool#.WT7e7usrJhE
CSA Programming Measurement & Monitoring
• Provide common framework to guide for
agriculturally focused programs/donors on the
design of CSA interventions
• Provide a robust and transparent process to
examine to which extent a specific program
addresses the three CSA pillars
• Support the selection of appropriate
indicators to measure progress and monitor
impact
Productivity
Adaptation
Mitigation
To increase the effectiveness of CSA interventions we need:
 How are agencies currently addressing CSA or how they can make their future
programing process more climate-smart
Case study : working with the public
sector on metrics (USAID)
CSA Programming and Indicator tool
https://ccafs.cgiar.org/csa-programming-and-indicator-tool#.WT7e7usrJhE
CSA Programming Measurement & Monitoring
CSA Indicators data base = initial input
Step 1
Questions to be
addressed & intentionality
of desired outcomes
CSA
Programming
Measurement &
Monitoring
CSA
Programming
Measurement &
Monitoring
Results summary and visualisation3
Shared frameworks it enables to
programs/ donors :
 To Assess scope
 Compares intentionality
 Support indicators identification
CSA
Programming
Measurement &
Monitoring
Case study : working with the private
sector on metrics
CSA Programming Measurement & Monitoring
PRODUCTIVITY
OUTCOME: 50%
more nutritious
food available
ACTIVITYe.g.raiseyields
RESILIENCE, INCOMES
& LIVELIHOODS
OUTCOME: climate
resilient agricultural
landscapes and
farming communities
ACTIVITYe.g.skillstransfer
MITIGATION
OUTCOME: food GHG
emissions 30% lower &
land use change
emissions eliminated
ACTIVITY:e.g.haltforestconversion
Source: Vermeulen & Frid-
Nielsen, 2017
Basic framework for measurement
INPUTS &
FARMING
PROCESSING &
LOGISTICS
STORAGE &
TRANSPORT
TRADING &
PURCHASE
GLOBAL INDICATORS
(some are OUTCOME indicators and some are ACTIVITY indicators)
COMPANY INDICATORS
(some are OUTCOME indicators and some are ACTIVITY indicators)
Enabling and regulatory environment
Companies included in the analysis
Company Sub-sector
Coca-Cola Food processing
CP Foods Food processing
Diageo Food processing
DuPont Agricultural inputs
Kellogg Food processing
Monsanto Agricultural inputs
Olam Food processing
PepsiCo Food processing
Starbucks Retail
Syngenta Agricultural inputs
Tyson Foods Food processing
Unilever Food processing
Yara Agricultural inputs
Source: Vermeulen & Frid-Nielsen, 2017
Results
Productivity
• On track to produce enough food to meet the demand for 50% more
food by 2030.
• No direct evidence that this food will be equally or more nutritious
Resilience
• No relevant global data that match the indicators that companies use
for resilience.
• Need to provide quantitative information on indicators that cover both
activities (e.g. training, on-farm agroecological practices) and
outcomes (e.g. incomes, women’s share of assets and decisions).
Mitigation
• The 2030 goal of 30% emissions reductions compared to 2010 will
not be met unless the trend 2010-30 is changed.
• Companies generally reduced the intensity of their own operations,
but reporting on Scope 3 emissions is not currently pervasive enough.
Source: Vermeulen & Frid-Nielsen, 2017
Next steps/opportunities
• Building CSA metrics into regular practice
• Considering how any activity or intervention will lead to desired
outcomes for productivity, resilience and mitigation
• Road-test countries and regions provide an innovative opportunity
to test and measure how scale effects and trade-offs can be
managed
• Lot more work needs to be done – on measurement and action
CSA M&E challenges
Indicator overload
• SDGs - 230 indicators
• USAID 500 indicators
• UNICEF 300+
Figures and graphic courtesy
of T. Rosenstock
CSA M&E challenges
• Multi-objective complexity
• Heterogeneity, complexity, and diversity of indicators
• Maintaining simplicity and cost effectiveness
• Logistical, statistical, technical and practical challenges.
• M&E at different scales of impact
• Multi-institutional coordination
Source: Rosenstock & Corner-Dolloff, 2015
A way forward for GACSA
A common approach to metrics to CSA which takes into
account:
• Rigour Vs Simplicity
• Cost effectiveness
• Harmonisation Vs flexibility
• Role of ICT across scales
• Continual feedback for adaptive programming and monitoring
Further reading
• CSA Programming and Indicator Tool (https://ccafs.cgiar.org/csa-programming-and-indicator-tool)
• Info note on livestock MRV (http://hdl.handle.net/10568/80890)
• Measuring Progress Towards the WBCSD Statement of Ambition on Climate-Smart Agriculture: Improving
Businesses’ Ability to Trace, Measure and Monitor CSA (http://hdl.handle.net/10568/80652)
• Monitoring, evaluation and learning for CSA (https://csa.guide/csa/monitoring-evaluation-and-learning)
• National level indicators for gender, poverty, food security, nutrition and health in Climate-Smart Agriculture (CSA)
activities (https://ccafs.cgiar.org/publications/national-level-indicators-gender-poverty-food-security-nutrition-and-
health-climate#.WSh81OsrJhE)
• A Monitoring Instrument for Resilience (https://cgspace.cgiar.org/handle/10568/56757)
• Info Note: A rapid, cost-effective and flexible tool for farm household characterisation, targeting interventions and
monitoring progress towards climate-smart agriculture http://rhomis.net/blog/wp-content/uploads/2016/09/CCAFS-
info-note-VanWijkHammond-etal-final.pdf
• Info Note: Surveillance of Climate-smart Agriculture for Nutrition (SCAN): Innovations for monitoring at scale :
https://cgspace.cgiar.org/handle/10568/79903
• Journal paper: When less is more: Innovations for tracking progress toward global targets :
https://cgspace.cgiar.org/handle/10568/80425
• Info Note: What is the scientific basis for climate-smart agriculture? https://ccafs.cgiar.org/publications/what-scientific-
basis-climate-smart-agriculture
• Evidence-based opportunities for out-scaling climate-smart agriculture in East Africa:
https://cgspace.cgiar.org/handle/10568/77180
• CSA X-Rays: Conservation agriculture in East and Southern Africa main messages
(https://cgspace.cgiar.org/handle/10568/79900)
• CCAFS Mitigation Option Tool: https://ccafs.cgiar.org/es/mitigation-option-tool-agriculture#.WUGSU-t97IU
• SHAMBA Tool https://ccafs.cgiar.org/es/small-holder-agriculture-monitoring-and-baseline-assessment-
tool#.WUGO2ut97IU
Thank you
ccafs.cgiar.org
Questions
Answers
&
Risks-Households-Options (RHO) integrated modeling
system
CSA Programming
Applications/users to date
Niger case study using the HH-level data from
the World Bank’s Living Standards
Measurement Survey
 Integrates available datasets
combined field level and
modeling information + CSA
evidence
♦ Three-step (cost effective) approach that
integrates information on
- HH characteristics (Survey datasets),
- risk exposure levels (Crop modeling)
- for determining best bet CSA practices
to support efficient and relevant CSA
programming
♦ Ultimately addresses Food Security under CC
♦ Supports building robust portfolio of best bet
CSA options that can be implemented, using
currently available data!
Citation: Lamanna et al 2015

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Metrics for CSA: increasing programming effectiveness and outcome tracking

  • 1. Osana Bonilla-Findji, Andy Jarvis, Marie Quinney,Todd Rosenstock, Christine Lamanna, Lini Wollemberg, Meryl Richard, Sonja Vermulen, Dhanush Dinesh Metrics for CSA increasing programming effectiveness and outcome tracking GACSA’s joint workshop ‘Metrics for Climate-Smart Agriculture’ Rome, FAO HQ, June 15th 2017 ccafs.cgiar.org
  • 2. CSA: Agriculture and Food systems that deliver outcomes Issues with metrics reduced emissions resilience/ adaptation food security/ productivity nutrition outcomes costly to quantify agreement on core indicators  CSA is emerging as a mechanism for coherent and coordinated action  Measuring processes or Outcomes? Standard of measurements That show progress
  • 3. 500 million farmers globally 25 million by 2025 Global momentum still building for CSA 6 million by 2021
  • 4. CSA Complexity & Uncertainty Complexity stemming from diversity of: 1. Interventions (micro to macro level) 2. Farming systems and households 3. Objectives/ potential outcomes of success (ranging from increased soil carbon to maternal dietary diversity), with trade-offs possible. Uncertainty and Context Specificity: Lack of information and data • Outcomes = CSA practices x social-environmental context • Need to identify where biggest wins occur across landscapes/regions • Gradient of CSA-ness • CSA today, may not be CSA tomorrow
  • 5. Review CSA metrics : What's there ? High demand for useful/actionable tools to design, prioritize and monitor useful interventions and investments: Paris Workshop 2015. 0 50 100 150 200 Indicators types (%) Readiness Process Outcome 0 50 100 150 200 Indicators relative contribution to CSA objectives (%) Productivity Adaptation Mitigation - Most agencies covered all the types of indicators but focused on Process and Outcomes - Mostly covering Adaptation pillar (81%) and Productivity (40%). Adaptation => Guidance needed - Largely geared towards risk management - Multidimensional nature of resilience (economic, financial social) and capacities (absortive, adaptive, transformative) often not factored - Measure potential adaptation rather than actual adaptation - Lack ability to track changes over time (e.g lean season) Mitigation Significant lack of indicators related to mitigation outcomes Global CSA indicators database:  378 relevant indicators (USAID, DFID, World Bank, IFAD, GIZ, FAO, CCAFS)  Revisited thought the “CSA lenses” and Classified * Readiness/Enabling environment * Process (outputs) * Outcome indicators
  • 6. What to measure and how? Some CCAFS metrics tools CSA Evidence building CSA targeting/ Programming CSA Measures and Monitoring - Compendium of CSA (Practice) - CSA Calculator (Farm) - Diverse Prioritization tools and approaches - Risk-household-option modelling (RhOMIS) - CSA programming and Indicator Tool - SHAMBA (Small-Holder Ag. Monitoring and Baseline Assessment - CCAFS Mitigation Option Tool (MOT) $
  • 7. CSA Compendium Rosenstock et al. 2016 Systematic review • 1,200 peer reviewed studies for Africa • 100,000+ comparisons to baselines, • 70 practices • 55 outcome indicators Largest meta analysis to date ♦ What is the evidence base for field interventions’ potential impact on three pillars?  Outcomes vary (crop, farming system, agroecology…)  There is no best-bet practice across all outcomes and systems  Prioritization of outcomes and tailoring is key Integration with other initiatives: WB, USAID, World Bank, GIZ, Care, CRS, Oxfam, World Vision, Concern, NEPAD, COMESA, national governments in multiple African Countries Evidence building
  • 8. ♦ Farm model allowing a prospective assessment of synergies & trade-offs related to implementation of different CSA options and outcomes that can be expected (three pillars). Main indicators of the CSA calculator Pilar ASAC Indicador Calculation Productivity Caloric ratio of the farm (%) Caloric supply/Caloric demand x 100 Fodder ratio of the farm (%) Fodder supply/Fodder demand x 100 Cost benefit ratio (%) Benefit/Cost x 100 Adaptation Biodiversity index (%) Assessment based on Gobbi, J., Casasola, F., 2003. Water balance (%) Water supply/water demand x 100 Nutrient balance (%) Nutrient supply/nutrient demand x 100 Mitigation Emission/Sequestration of CO2 CoolFarmTool Measurement & Monitoring CSA Calculator of farm performance (Synergies and Trade-offs) Users: Piloting ongoing in CCAFS CSV monitoring Evidence building
  • 9. Risk-household-option modelling (RhOMIS) http://rhomis.net/blog/ CSA Programming  Captures information on farm productivity and practices, nutrition, food security, gender equity, climate and poverty  Up to 20 important performance and welfare indicators + key farm level drivers, livelihood data and management decisions  Compare changes in farming practice and livelihoods over time, stated plans for the future, and farmers intrinsic values and attitudes  Identifies ‘positive deviant’ farmers Users: tested and adapted for diverse systems in more than 7,000 households across the global tropics Implementations by 10 projects, 15 diverse farming systems on 4 continents. Lutheran World Relief (Kenya); TreeAID (northern Ghana), CONABIO (in Mexico) Measurement & Monitoring Citation: Hammond, J. et al. 2017 ♦ Household-level Survey and Analytical engine for characterizing, targeting and monitoring agricultural performance and the effectiveness of ongoing out-scaling efforts
  • 10. Mitigation measurement tools Mitigation option tool (MOT) Small Holder Agriculture Monitoring and Baseline Assessment (SHAMBA)  Provides fast, accessible, and reliable information to make informed decisions about emissions reductions  Assesses GHG emissions estimates and Ranks the most effective mitigation options for dozens of different crops and livestock systems  Low input data requirements  Runs in Excel bringing together several empirical models • Estimates GHG emissions or removals resulting from a change in land management practices (biomass burning, plant nitrogen inputs to soils, and fertiliser use) • Models a baseline scenario (where land management activities continue as business as usual) and a CSA intervention scenario • Set to calculate estimates on a yearly basis, in tonnes of CO2e per hectare. Measurement & Monitoring
  • 11. Case study : working with the public sector on metrics (USAID) CSA Programming and Indicator tool (CCAFS) https://ccafs.cgiar.org/csa-programming-and-indicator-tool#.WT7e7usrJhE CSA Programming Measurement & Monitoring • Provide common framework to guide for agriculturally focused programs/donors on the design of CSA interventions • Provide a robust and transparent process to examine to which extent a specific program addresses the three CSA pillars • Support the selection of appropriate indicators to measure progress and monitor impact Productivity Adaptation Mitigation To increase the effectiveness of CSA interventions we need:  How are agencies currently addressing CSA or how they can make their future programing process more climate-smart
  • 12. Case study : working with the public sector on metrics (USAID) CSA Programming and Indicator tool https://ccafs.cgiar.org/csa-programming-and-indicator-tool#.WT7e7usrJhE CSA Programming Measurement & Monitoring CSA Indicators data base = initial input
  • 13. Step 1 Questions to be addressed & intentionality of desired outcomes CSA Programming Measurement & Monitoring
  • 15. Results summary and visualisation3 Shared frameworks it enables to programs/ donors :  To Assess scope  Compares intentionality  Support indicators identification CSA Programming Measurement & Monitoring
  • 16. Case study : working with the private sector on metrics CSA Programming Measurement & Monitoring PRODUCTIVITY OUTCOME: 50% more nutritious food available ACTIVITYe.g.raiseyields RESILIENCE, INCOMES & LIVELIHOODS OUTCOME: climate resilient agricultural landscapes and farming communities ACTIVITYe.g.skillstransfer MITIGATION OUTCOME: food GHG emissions 30% lower & land use change emissions eliminated ACTIVITY:e.g.haltforestconversion Source: Vermeulen & Frid- Nielsen, 2017
  • 17. Basic framework for measurement INPUTS & FARMING PROCESSING & LOGISTICS STORAGE & TRANSPORT TRADING & PURCHASE GLOBAL INDICATORS (some are OUTCOME indicators and some are ACTIVITY indicators) COMPANY INDICATORS (some are OUTCOME indicators and some are ACTIVITY indicators) Enabling and regulatory environment
  • 18. Companies included in the analysis Company Sub-sector Coca-Cola Food processing CP Foods Food processing Diageo Food processing DuPont Agricultural inputs Kellogg Food processing Monsanto Agricultural inputs Olam Food processing PepsiCo Food processing Starbucks Retail Syngenta Agricultural inputs Tyson Foods Food processing Unilever Food processing Yara Agricultural inputs Source: Vermeulen & Frid-Nielsen, 2017
  • 19. Results Productivity • On track to produce enough food to meet the demand for 50% more food by 2030. • No direct evidence that this food will be equally or more nutritious Resilience • No relevant global data that match the indicators that companies use for resilience. • Need to provide quantitative information on indicators that cover both activities (e.g. training, on-farm agroecological practices) and outcomes (e.g. incomes, women’s share of assets and decisions). Mitigation • The 2030 goal of 30% emissions reductions compared to 2010 will not be met unless the trend 2010-30 is changed. • Companies generally reduced the intensity of their own operations, but reporting on Scope 3 emissions is not currently pervasive enough. Source: Vermeulen & Frid-Nielsen, 2017
  • 20. Next steps/opportunities • Building CSA metrics into regular practice • Considering how any activity or intervention will lead to desired outcomes for productivity, resilience and mitigation • Road-test countries and regions provide an innovative opportunity to test and measure how scale effects and trade-offs can be managed • Lot more work needs to be done – on measurement and action
  • 22. Indicator overload • SDGs - 230 indicators • USAID 500 indicators • UNICEF 300+ Figures and graphic courtesy of T. Rosenstock
  • 23. CSA M&E challenges • Multi-objective complexity • Heterogeneity, complexity, and diversity of indicators • Maintaining simplicity and cost effectiveness • Logistical, statistical, technical and practical challenges. • M&E at different scales of impact • Multi-institutional coordination Source: Rosenstock & Corner-Dolloff, 2015
  • 24. A way forward for GACSA A common approach to metrics to CSA which takes into account: • Rigour Vs Simplicity • Cost effectiveness • Harmonisation Vs flexibility • Role of ICT across scales • Continual feedback for adaptive programming and monitoring
  • 25. Further reading • CSA Programming and Indicator Tool (https://ccafs.cgiar.org/csa-programming-and-indicator-tool) • Info note on livestock MRV (http://hdl.handle.net/10568/80890) • Measuring Progress Towards the WBCSD Statement of Ambition on Climate-Smart Agriculture: Improving Businesses’ Ability to Trace, Measure and Monitor CSA (http://hdl.handle.net/10568/80652) • Monitoring, evaluation and learning for CSA (https://csa.guide/csa/monitoring-evaluation-and-learning) • National level indicators for gender, poverty, food security, nutrition and health in Climate-Smart Agriculture (CSA) activities (https://ccafs.cgiar.org/publications/national-level-indicators-gender-poverty-food-security-nutrition-and- health-climate#.WSh81OsrJhE) • A Monitoring Instrument for Resilience (https://cgspace.cgiar.org/handle/10568/56757) • Info Note: A rapid, cost-effective and flexible tool for farm household characterisation, targeting interventions and monitoring progress towards climate-smart agriculture http://rhomis.net/blog/wp-content/uploads/2016/09/CCAFS- info-note-VanWijkHammond-etal-final.pdf • Info Note: Surveillance of Climate-smart Agriculture for Nutrition (SCAN): Innovations for monitoring at scale : https://cgspace.cgiar.org/handle/10568/79903 • Journal paper: When less is more: Innovations for tracking progress toward global targets : https://cgspace.cgiar.org/handle/10568/80425 • Info Note: What is the scientific basis for climate-smart agriculture? https://ccafs.cgiar.org/publications/what-scientific- basis-climate-smart-agriculture • Evidence-based opportunities for out-scaling climate-smart agriculture in East Africa: https://cgspace.cgiar.org/handle/10568/77180 • CSA X-Rays: Conservation agriculture in East and Southern Africa main messages (https://cgspace.cgiar.org/handle/10568/79900) • CCAFS Mitigation Option Tool: https://ccafs.cgiar.org/es/mitigation-option-tool-agriculture#.WUGSU-t97IU • SHAMBA Tool https://ccafs.cgiar.org/es/small-holder-agriculture-monitoring-and-baseline-assessment- tool#.WUGO2ut97IU
  • 28. Risks-Households-Options (RHO) integrated modeling system CSA Programming Applications/users to date Niger case study using the HH-level data from the World Bank’s Living Standards Measurement Survey  Integrates available datasets combined field level and modeling information + CSA evidence ♦ Three-step (cost effective) approach that integrates information on - HH characteristics (Survey datasets), - risk exposure levels (Crop modeling) - for determining best bet CSA practices to support efficient and relevant CSA programming ♦ Ultimately addresses Food Security under CC ♦ Supports building robust portfolio of best bet CSA options that can be implemented, using currently available data! Citation: Lamanna et al 2015