Climate-smart agriculture (CSA): Panacea, propaganda or paradigm shift? • We are conducting a systematic review and meta-analysis to evaluate the evidence base for CSA. • More than 144,500 abstracts of journal articles have been reviewed, of which, 6,741 papers met our inclusion/ exclusion criteria making this the largest agricultural meta- analysis attempted. • Geographic clustering of research and a lack of co-located multi-objective research leaves gaps in the evidence base and dictates the need for new paradigm for CSA research. • Evidence of variable impacts of practices on CSA objectives and synergies and tradeoffs between objectives indicates the need for careful selection of practices when scaling up CSA. • Data will be publically available in a Web-based database later in 2015. Todd S. Rosenstock 1, 2 , Christine Lamanna 1 , Katherine L. Tully 3 , Caitlin Corner-Dolloff 4 Miguel Lazaro 4 , Sabrina Chesterman 1 , Patrick R. Bell 5 , Evan H. Girvetz 2, 6 Main Messages -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 Productivity Adaptive capacity 6% 16% 46% 32% Synergies Tradeoffs Tradeoffs How do the most common farm-level CSA management practices/technologies affect food production, resilience/adaptive capacity, and mitigation in farming systems of developing countries? What we are doing? Select practices (N) 1 Select indicators (N) 2 Productivity (11) Resilience/ Adaptive Capacity (23) Mitigation (9) Yield Species richness CO 2 , N 2 O, CH 4 fluxes Net returns Nutrient use/feed conversion efficiency Carbon in above or belowground pools Net present value Water use efficiency Emissions intensity Returns to labor Gender disaggregated labor Woody biomass consumption Geographic & topical clustering of research Search and data extraction 3 Key word search Abstract review Full text review 144,567 papers 16,254 papers 6,741 papers Data extract- ion Analysis 4 Standard meta-analytical approach: Response ratios (RR) and Effect sizes (ES). RR = ln(mean(X T )/mean(X C )). ES = weighted mean of RRs based on number of reps. ● ● ● −0.5 0.0 0.5 Effect size Agroforestry Leguminous agroforestry Inorganic fertilizer Diet management Increasing protein Alternative feeds Next step: Searchable internet-based database Variability, synergies and tradeoffs Financial support 1 World Agroforestry Centre (ICRAF), Nairobi, Kenya, 2 CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS), 3 University of Maryland, College Park, USA, 4 International Centre for Tropical Agriculture (CIAT), Cali, Colombia, 5 The Ohio State University (Ohio), Columbus, Ohio, 6 International Centre for Tropical Agriculture (CIAT), Nairobi, Kenya contact: [email protected] Left. Effect of select aggregate management measures on yield (ln 0.5 ≅ Δ60% between CSA and control). Figure shows clear benefits of select CSA but variability, within and among practices, in effect size suggests potential for context-specific outcomes. Based on random sample of 130 studies. Agroforestry (14) Agronomy (36) Livestock & aquaculture (17) Right. Potential synergies and trade- offs from CSA from co-located research. In this graph, based on comparisons from a randomly selected sample of 55 studies, more than 60% showed trade-offs among adaptive capacity and productivity, versus 32% showing synergies. Contain data for ≥ 1 CSA objective Contain data for ≥ 2 CSA objectives Contain data for All 3 CSA objectives Only 1% of studies contain data relevant to all three CSA’s three objectives from co-located research. Research is geographically clustered around highly research locations, leaving potentially significant gaps in knowledge base. Based on 815 randomly selected studies Climate-Smart Agriculture Decision Support Platform Home Where we work Database Analytical Tools Keywords Region Agroecological zone Country Sub-Saharan Africa Tanzania Sub-humid Threats Practice Farming system Mixed maize Drought Intercropping CSA objective X X X Productivity Mitigation Adaptation We thank C Champalle, A-S Eyrich, W English, H Strom, A Madalinska, S MacFadridge, A Poultouchidou, A Akinleye, and A Kerr for their technical support.
Climate Smart Agriculture: Panacea, Propaganda or Paradigm Shift?
Jul 15, 2015
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Climate-smart agriculture (CSA): Panacea, propaganda or paradigm shift?
• We are conducting a systematic review and meta-analysis to evaluate the evidence base for CSA.
• More than 144,500 abstracts of journal articles have been reviewed, of which, 6,741 papers met our inclusion/exclusion criteria making this the largest agricultural meta-analysis attempted.
• Geographic clustering of research and a lack of co-located multi-objective research leaves gaps in the evidence base and dictates the need for new paradigm for CSA research.
• Evidence of variable impacts of practices on CSA objectives and synergies and tradeoffs between objectives indicates the need for careful selection of practices when scaling up CSA.
• Data will be publically available in a Web-based database later in 2015.
Todd S. Rosenstock1, 2, Christine Lamanna1, Katherine L. Tully3, Caitlin Corner-Dolloff4 Miguel Lazaro4, Sabrina Chesterman1, Patrick R. Bell5, Evan H. Girvetz2, 6
Main Messages
-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
Productivity
Ada
ptiv
e ca
paci
ty
6% 16%
46% 32% Synergies Tradeoffs
Tradeoffs
How do the most common farm-level CSA management practices/technologies affect food production, resilience/adaptive capacity, and mitigation in farming systems of developing countries?
What we are doing?
Sele
ct p
ract
ices
(N)
1
Sele
ct in
dica
tors
(N)
2
Productivity (11)
Resilience/ Adaptive Capacity (23)
Mitigation (9)
Yield Species richness CO2, N2O, CH4 fluxes
Net returns Nutrient use/feed conversion efficiency Carbon in above or belowground pools
Net present value Water use efficiency Emissions intensity
Returns to labor Gender disaggregated labor Woody biomass consumption
Geographic & topical clustering of research
Sear
ch a
nd d
ata
extr
acti
on
3
Key word search
Abstract review
Full text review
144,567 papers
16,254 papers
6,741 papers
Data extract-
ion
Ana
lysi
s
4
Standard meta-analytical approach: Response ratios (RR) and Effect sizes (ES). RR = ln(mean(XT)/mean(XC)). ES = weighted mean of RRs based on number of reps.
●
●
●
●●●
Alternative feeds
Increasing protein
Diet management
Inorganic fertilizer
Leguminous AF
Agroforestry (AF)
−0.5 0.0 0.5Effect size
CS
A
Agroforestry
Leguminous agroforestry
Inorganic fertilizer
Diet management
Increasing protein
Alternative feeds
Next step: Searchable internet-based database
Variability, synergies and tradeoffs
Financial support
1World Agroforestry Centre (ICRAF), Nairobi, Kenya, 2CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS), 3University of Maryland, College Park, USA, 4International Centre for Tropical Agriculture (CIAT), Cali, Colombia, 5The Ohio State University (Ohio), Columbus, Ohio, 6International Centre for Tropical Agriculture (CIAT), Nairobi, Kenya contact: [email protected]
Left. Effect of select aggregate management measures on yield (ln 0.5 ≅ Δ60% between CSA and control). Figure shows clear benefits of select CSA but variability, within and among practices, in effect size suggests potential for context-specific outcomes. Based on random sample of 130 studies.
Agroforestry (14) Agronomy (36) Livestock & aquaculture (17)
Right. Potential synergies and trade-offs from CSA from co-located research. In this graph, based on comparisons from a randomly selected sample of 55 studies, more than 60% showed trade-offs among adaptive capacity and productivity, versus 32% showing synergies.
Contain data for ≥ 1 CSA objective
Contain data for ≥ 2 CSA objectives
Contain data for All 3 CSA objectives
Only 1% of studies contain data relevant to all three CSA’s three objectives from co-located research.
Research is geographically clustered around highly research locations, leaving potentially significant gaps in knowledge base.
Based on 815 randomly selected studies
Climate-Smart Agriculture Decision Support Platform
Home Where we work Database Analytical Tools
Keywords
Region
Agroecological zone
Country
Sub-Saharan Africa
Tanzania
Sub-humid
Threats
Practice
Farming system Mixed maize
Drought
Intercropping
CSA objective X X X Productivity Mitigation Adaptation
We thank C Champalle, A-S Eyrich, W English, H Strom, A Madalinska, S MacFadridge, A Poultouchidou, A Akinleye, and A Kerr for their technical support.
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