How do the most common farm - level CSA management practices affect food production, resilience capacity and mitigation in farming systems of developing countries.

1. 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
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0.5
-0.5 -0.3 -0.1 0.1 0.3 0.5
Productivity
Adaptivecapacity
6%
16%
46% 32%
SynergiesTradeoffs
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?
Selectpractices(N)
1
Selectindicators(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
Searchanddataextraction
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(XT)/mean(XC)).
ES = weighted mean of RRs based on number of reps.
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Alternative feeds
Increasing protein
Diet management
Inorganic fertilizer
Leguminous AF
Agroforestry (AF)
−0.5 0.0 0.5
Effect size
CSA
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: t.rosenstock@cgiar.org
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 XXProductivity MitigationAdaptation
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.