Alessandro (Alex) De Pinto, Senior Research Fellow, Environment and Production Technology Division, IFPRI, USA

1. The Effects of Widespread Adoption
of Climate-Smart Agriculture in
Africa South of the Sahara Under
Changing Climate Regimes
A l e s s a n d r o D e P i n t o , H o Y o u n g
K w o n , N i c o l a C e n a c c h i , a n d
S h a h n i l a D u n s t o n
ENVIRONMENT AND PRODUCTION TECHNOLOGY DIVISION
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE,
WASHINGTON, DC

2. The Background
• CSA is expected to address climate-related risks by
simultaneously considering three main objectives
and by fully accounting for the trade-offs and
synergies among them (Rosenstock et al. 2016).
• There is a general consensus that CSA, albeit with
limits (Wheeler and von Braun 2013, Taylor 2017),
helps to advance the discussion on future
agricultural production under a significantly
different climate environment.

3. The Background
• The literature has focused on technical aspects
related to economic feasibility (Sain et al. 2017),
the emission reduction and adaptation benefits (de
Nijs et al. 2015), and the local-level impacts of
CSA(Zougmoré et al. 2016).
• Many operational aspects of CSA are still under
investigation.

4. Purpose of this Study
• No study has produced a comprehensive
analysis of the effects that widespread
adoption of CSA practices and technologies
may have on the production of key crops, on
GHG emissions, and on key food security
metrics, regionally or globally.
• Objective: ex-ante analysis of potential broad
benefits of a widespread adoption of CSA
practices, focus on Africa south of the Sahara
(SSA)

5. Simulation scenarios
• The ex-ante assessment is performed by linking
spatially-disaggregated data from three models:
the Spatial Production Allocation Model (SPAM),
Decision Support System for Agrotechnology
Transfer (DSSAT), and the International Model for
Policy Analysis of Agricultural Commodities and
Trade (IMPACT).

6. Simulation scenarios
Spatial Production
Allocation Model (SPAM)
Decision Support System for
Agrotechnology Transfer (DSSAT)
Location of crop production
(soil conditions)
Yields and
Yield changes
International Model for Policy Analysis of
Agricultural Commodities and Trade (IMPACT)
Changes in total production
Changes in prices
Changes in calorie availability

7. Simulation scenarios
• The evaluation compares scenarios in presence of
climate change in which these practices are adopted
against a plausible BAU scenario that assumes that
current practices are retained by farmers. Period
considered 2010 – 2050.
• Climate change projections are generated using two
global circulations models (GCMs), GFDL-ESM2M
and HadGEM2-ES, under a Representative
Concentration Pathway (RCP) of 8.5. The GFDL
climate change scenario can be considered as drier
and cooler compared to HadGEM .

8. Simulation scenarios
• The analysis focuses on three widely grown crops:
wheat (Triticum aestivum), maize (Zea mays), and
rice (Oryza sativa).
• They represent about 41 percent of the global
harvested area and 20 percent of the harvested
area in SSA.
• They also represent about 64 percent of GHG
emissions generated by crop production globally
(Carlson et al. 2016).

9. Climate-Smart Alternatives
Technology Definition Crop
No tillage (NT) Minimal or no soil disturbance; often used in
combination with residue retention, crop
rotation, and cover crops
Maize, wheat
Integrated soil fertility
management (ISFM)
Combination of chemical fertilizers, crop
residues, and manure or compost
Maize, wheat
Alternate wetting and
drying (AWD)
Repeated interruptions of flooding during the
season, causing the water to decline as the
upper soil layer dries out before subsequent
reflooding
Rice
Urea deep placement
(UDP)
Strategic burial of urea “supergranules” near
the root zones of crop plants
Rice
Source: Authors

10. Adoption of Alternative
Technologies
• The alternatives to the BAU scenario assume that
farmers are offered a portfolio of alternatives from
which to choose.
• Two alternative scenarios
• CSA technologies are adopted when they return a yield
gain over current practices (BAU scenario)
• CSA practices are adopted when they generate higher
yields and reduce emission intensity
• Simulations assume 100% adoption rate therefore
results must be interpreted as an upper-bound of
the possible effects.

11. Results

12. Results: Effects on Production
Source: Authors

13. Results: Effects on Prices
Population at risk of hunger in SSA is projected to
decrease by between 1.8 and 2.5 percent,
Decrease in undernourished children younger than five
years ranges between 0.2 and 0.3 percent (equivalent to
approximately 100,000 children)
Scenario Maize
(GFDL / HadGEM)
Wheat
(GFDL / HadGEM)
Rice
(GFDL /
HadGEM)
Adoption rate of CSA practices predicated
on increased yields
-2.80% / -3.00% -1.30% / -2.00% -3.20% / -3.40%
Adoption rate of CSA practices predicated
on increased yields and reduction of
emission intensity
-2.70% / -3.00% -1.20% / -1.80% -3.20% / -3.30%
Percentage change in 2050 world prices under two scenarios, compared with business-
as-usual
Source: Authors

14. Results: Changes in Emissions
• The soil organic carbon concentration, which increases not
only fertility but also soil water retention, is estimated to
increase by an average of 0.16–0.17 tons/ha-1 year-1 over
BAU.
• Total GHG emissions remain basically unchanged or
decrease minimally, at an estimated +0.01 tons/ha-1 year-1
when only yields increases are considered.
• By enforcing a reduction of emission intensity, it is possible
to reduce GHG emissions by more than 200 million tons of
CO2 equivalent, equivalent to an average per-hectare yearly
reduction of approximately 0.17 tons /ha-1 year-1 of CO2
equivalent.

15. Results
Source: Authors
Tradeoffs

16. Conclusion
• Widespread adoption of CSA practices has a
positive effect on production with a consequent
reduction in prices and decrease in the number
of people at risk of hunger and the number of
children younger than five years at risk of
malnutrition.
• Soil organic carbon appears to grow, compared
with the BAU scenario, indicating that
productivity can be increased while making
production more sustainable than it is with
current practices.

17. Conclusion
• The effects on GHG emissions are mixed
• GHG emission reduction are highly context- and
location-specific. CSA practices alone do not
assure a reduction in emissions.
• Result indicate that the reduction of GHG
emissions is compatible with increased
productivity. Results are dependent on how
feasible it is to enforce and control the actual
achievement of in-the-field emission intensity
reductions.

18. Conclusion
• Effects of CSA practices is highly dependent on how
widely adopted they are. Lower adoptions than
simulated (e.g. 50%, 25%) would make the effects
marginal.
• This points to the importance of barriers to adoption
and the incentives and policies that must be in place
to overcome them.
• GHG emissions: we must go beyond the field and the
farm and think about the relationships with other
carbon-rich environments, think about systems (e.g.
agroforestry, silvopastoral), value chains, etc.

19. Thank You
A l e x D e P i n t o : a . d e p i n t o @ c g i a r . o r g
SENIOR RESEARCH FELLOW
ENVIRONMENT AND PRODUCTION TECHNOLOGY DIVISION
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE, WASHINGTON, DC