Carlo Azzarri, Research Fellow, Environment and Production Technology Division, IFPRI, USA

1. Trade, Climate Change, and
Climate-Smart Agriculture
Beliyou Haile, Carlo Azzarri, Jawoo Koo, Alessandro De Pinto
IFPRI

2. Background/1
• Climate change predictions suggest an overall warming
trend and higher incidence of extreme weather events, with
spatial heterogeneity by altitude (Serdeczny et al. 2017)
• Changes expected to impact agricultural productivity and the
availability of productive resources, especially in Sub-
Saharan Africa (SSA) (Knox et al. 2012; Müller and Robertson
2014)
• Heavy reliance on rainfed agriculture, with institutional and
market failures limiting the set of coping and adaptation
strategies

3. Background/2
• Climate-smart agriculture (CSA) among the approaches
promoted to enhance productivity while reducing GHG
emissions and increasing carbon sequestration (Campbell et al.
2014; Huang, Lampe, Tongeren 2011), but…
• …highly context- and location-specific (McCarthy, Lipper, and
Branca 2011), and short-term productivity may even decrease
under CSA, with more stable and often increasing yields
observed over time (Corbeels et al. 2014; Pittelkow et al.
2015)
• Trade could play a crucial role in the global effort to eradicate
extreme poverty and promote inclusive and sustainable
development (a cross-cutting issue under SDG 17)

4. Background/3
• However, Africa accounts for a small share of the global
commodity trade and is associated to one of the lowest
intra-regional trade, despite the regional economic
communities (RECs)
• 16% (Africa) versus 17% (South and Central America), 42%
(North America), 62 % (EU), and 64 % (Asia) (Davis 2016;
Khandelwal 2005; Tamiotti et al. 2009)
• Various initiatives aim to increase regional commodity trade
(e.g., Malabo Declaration for tripling of intra-Africa
agricultural commodity trade in 2025)

5. GHG emissions, changes in the
distribution of agricultural production,
increased consumption of tradeable goods
Climate change
Rising temperature,
weather variability,
extreme weather
events, adaptation
and mitigation
policies
Agricultural
commodity trade
Export and import,
trade policies
Agricultural production
Crop and livestock productivity,
availability of productive resources,
physical infrastructure, agricultural
policies
Domestic
consumption
Changes in agricultural comparative advantage,
increased transportation costs
“Sandwich” framework: climate
change, agriculture, and trade

6. • Examine the role of CSA in mitigating the negative effects of
climate change in SSA
• How would CSA adoption affect yields and total
agricultural commodity flow (2018‒2025)?
• Disaggregated analysis by the three Regional Economic
Communities (RECs): COMESA, ECOWAS, SADC
• Focus on maize and rice, as well as ag net exports
Objectives

7. CSA practice Definition Crop
No tillage Minimal or no soil disturbance, often in
combination with residue retention, crop
rotation, and use of cover crops
Maize
Integrated soil
fertility management
Combination of chemical fertilizers, crop
residues, and manure or compost
Maize
Alternative wetting and
drying
Repeated interruptions of flooding during
the season, causing water to decline as the
upper soil layer dries out before subsequent
reflooding
Rice
Urea deep placement Strategic burial of urea “supergranules” near
the root zones of crop plants
Rice
Objectives

8. • Maize and rice farmers have the option to choose among a
portfolio of the four CSA practices under consideration
• Farmers have complete information about potential yields of
each practice and can choose the one that provides the
highest yield for their agro-ecological condition– “best option”
• Economic and weather patterns during 2018–2025 will be
similar to those for the period 2003–2010
Assumptions

9. • FAO: time series (1993–2010) country-level data on gross value of
agricultural production (in purchasing power parity -PPP- constant
2004–2006 international dollar); value of agricultural trade (US
dollars) (FAOSTAT 2017)
• World Bank: population and GDP per capita (constant 2011
international dollar) (World Bank 2017)
• US (NASA) AgMERRA: time series of site-specific weather data
(Ruane, Goldberg, and Chryssanthacopoulos 2015)
• Global High-Resolution Soil Profile Database soil data (IRI et al.
2015)
• Spatial Production Allocation Model (SPAM): distribution of
maize and rice production (IFPRI and IIASA 2016)
Data

10. Historical per capita gross production
value and per capita GDP growth rate
• Steady  in per capita production: ECOWAS > SADC > COMESA
• Variable growth rate, also linked to weather variability?

11. Agricultural net exports and growth
rate
• Widening agricultural commodity trade deficit over time (after 2002-3)
• Possibly due to strong demand for prepared foods, dairy, poultry, and
vegetables (USDA 2014) linked to urbanization?

12. • Simulate yields for the best CSA option using Decision
Support System for Agrotechnology Transfer (DSSAT)
(Hoogenboom et al. 2015; Jones et al. 2003)
𝑌𝑖𝑒𝑙𝑑 𝑐𝑡
𝑗
= 𝑓(𝐶𝑆𝐴 𝑐𝑡
𝑗
, Wct) (1)
j=maize or rice; c=country; t=2018,…2025; W=biophysical
variables
• Compute simulated total agricultural production value using
PPP conversions from FAOSTAT (prod. value/quantity)
𝑌𝑐𝑡
𝑗
= 𝑔( 𝑌𝑖𝑒𝑙𝑑 𝑐𝑡
𝑗
*PPP, .) (2)
Method/1

13. Historical and simulated yield (eq. #1)
for best CSA option (ECOWAS)

14. Historical and simulated yield (eq. #1)
for best CSA option (COMESA)

15. Historical and simulated yield (eq. #1)
for best CSA option (SADC)

16. • Estimate a statistical model of agricultural commodity trade
using historical data (OLS, RE)
𝑁𝑋𝑐𝑡 = 𝛼0 + 𝛼1 𝑌𝑐𝑡 + 𝜦′ 𝒁 𝑐(𝑡) + 𝛾𝑡 + 𝜀 𝑐𝑡 (3)
t=1993,…2010; c=country; NX=net exports (exports minus
imports) (millions of current US$); Y=agricultural production value
(‘000 of constant international dollars); Z=matrix of time-varying or
time-invariant factors that could affect net exports
• Calculate predicted NX using simulated production values and
parameter estimates from (3) and 𝑌𝑐𝑡
𝑗
from (2)
𝑁𝑋𝑐𝑡
𝑗
= 𝛼0 + 𝛼1 𝑌𝑐𝑡
𝑗
+ 𝜦′ 𝒁 𝑐(𝑡) + 𝛾𝑡 (4)
Method/2

17. Regression results on historical
data (eq. #3)
• 1%  in gross agricultural production value  agricultural NX ~ $ 4 million
Dependent variable: agricultural net exports (millions of US $) Coef. Std. err. Coef. Std. err.
Log. gross production value (thousands of constant 2004–2006
international $)
410.13*** 139.150 447.689** 180.352
Population (millions) -0.000 0.000 -0.000** 0.000
Per capita gross domestic product (2011 international $) 0.017 0.020 -0.072 0.069
Import value index (2004–2006 = 100) -2.085 1.747 -2.264* 1.302
Export value index (2004–2006 = 100) 0.962 0.917 1.494** 0.658
Total cereal area harvested (millions of hectares) -104.634** 43.684 36.998 60.310
Linear time trend -4.552 7.179 8.218 7.600
Constant 3,862.029 14,108.674 -21,845.842 15,385.298
Number of observations (N*T)
Adjusted R-squared
R-squared within
R-squared between
R-squared overall
Chi-squared
F-statistic
Panel-level std. dev.
Rho
Log-likelihood
*** p < 0.01, ** p < 0.05, * p < 0.1. n.a. = not applicable
0.407 n.a.
OLS Random-effects
450 450
n.a. 0.367
n.a. 0.224
n.a. 0.228
n.a. 0.767
-3,487.62 n.a.
n.a. 13.104
3.959 n.a.
n.a. 520.316

18. Simulated (eq. #4) net exports (S
NX) for best CSA option: maize
• Relative to trends during 2003‒2010:
• Higher S NX (trade surplus) for SADC
• Reduced (increased) trade deficit for COMESA (ECOWAS)

19. Simulated (eq. #4) net exports (S
NX) for best CSA option: rice
• Relative to trends during 2003‒2010:
• Higher S NX (trade surplus) for both SADC and COMESA
• Widened trade deficit for ECOWAS

20. Conclusions/1
• Dominance of agricultural commodities in SSA’s exports →
agro-climatic changes could significantly affect the ability to
fully benefit from trade
• Also due to the greater reliance on imports in spite of
increasing production
• Opportunity to combine crop modeling and econometric
analysis to simulate the effects of CSA on maize and rice
yields, as well as on agric. net exports

21. Conclusions/2
• Our study shows that CSA significantly increases both yields and
agricultural trade flow, suggesting a potential role for CSA in
improving resilience and spreading out agricultural production
risks
• However, responses of trade flows to CSA adoption by RECs are
heterogeneous, and…
• …further research is needed on the drivers of those responses,
also relaxing the assumption of complete information, perfect
foresight, and by different climate scenarios

22. Obrigado!