The document discusses the impacts of climate change on crop yields based on several studies. It finds that increases in temperature, dry periods, and extreme rainfall negatively affect crop yields. It then proposes using adaptation and mitigation strategies developed through modeling to address these impacts. Specifically, it suggests selecting climate change scenarios to simulate, developing a global crop response model, and conducting localized "virtual experiments" to identify adapted crop varieties and traits.

1. Carlos Navarro, Patricia Moreno, Maputo, Mozambique IIAM
26/02/2013
Julián Ramírez, Osana Bonilla, Andy Jarvis

2. Important impact assessment of climate
change in crop yield (Craufurd et al., 2011).
Increases in temperature, dry and extreme
rainfall have a negative effect in crop yield
(Craufurd et al., 2011; Auffhammer et al., 2012).
Generation of adaptation and mitigation
strategies through ex ante modeling (Craufurd et al.,
2011).
Figure 1. Effect of minimum temperature increase on rice
yield variables (Peng et al., 2004).

3. Scenarios selection
• Quality check: simulations with hindcasts vs. historical climate
• Present, 2030 pessimistic, 2030 optimistic?
• Establishment of operational scenario database
Global study
• Simple model
• Spatial gridded approach
• Global mapping of crop response to CC
Zoom-ins: Virtual experiments
• Models G*E*M
• Model calibration for kay varietal types
• Identification of crop parameters ranges
• Zooming in on TPEs for each crop
• Sensitivity analyses: Trait variation vs. environment
• Ideotype composition for adapted crops
Dingkuhn, 2011

4. • Model predicting the potential
distribution of a species
 Maxent use the principle of
the maximum entropy
 Maxent use only presence
point of specific species and
environmental variables
• One of the most accurate
model for the prediction of
shifts in suitable growth ranges
of species

5. MaxEnt Application on Kenyan coffee
Main coffee-producing areas in Kenya are located in
two areas:
- the central region around Mount Kanya
- in the Rift Valley in the west
- The most suitable areas: in the higher areas of
Bungoma, Embu, Kericho, Kiambu, Kirinyaga, Kisii,
Machakos, Meru, Muranga, Nithi, Nyamira, Nyeri
and Trans-Nzoia
New markets
Management
Alternatives
to tea

6. • So, how does it work?
It evaluates on monthly basis if there
are adequate climatic conditions
within a growing season for
…and calculates the climatic suitability of the
temperature and precipitation…
resulting interaction between rainfall and
temperature…

7. Kiling temperature (°C) Tkill(initial) 2.2
Minimum absolute temperature (°C) Tmin 10.0
Minimum optimum temperature (°C) Topmin 14.6
Maximum optimum temperature (°C) Topmax 30.0
Maximum absolute temperature (°C) Tmax 40.0
Growing season (days) (average) 150
Minimum absolute rainfall (mm) Pmin 60
Minimum optimum rainfall (mm) Popmin 360
Maximum optimum rainfall (mm) Popmax 1500
Maximum absoluterainfall (mm) Pmax 3000

8. Changes in climate affect the adaptability of crops…
There will be
winners…
Number of crops with more than 5% gain
…But much
more losers in
developing
countries
Number of crops with more than 5% loss

9. Kiling temperature (°C) 0 Growing season (days) 240
Minimum absolute temperature (°C) 15 Minimum absolute rainfall (mm) 300
Minimum optimum temperature (°C) 22 Minimum optimum rainfall (mm) 800
Maximum optimum temperature (°C) 32 Maximum optimum rainfall (mm) 2200
Maximum absolute temperature (°C) 45 Maximum absolute rainfall (mm) 2800

10. 2030s SRES-A1B
2030s SRES-A1B

11. Cassava – an exception to the rule?
•For example, US maize, soy, cotton yields fall rapidly when exposed
to temperatures >30˚C
•In many cases, roughly 6-10% yield loss per degree
Schlenker and Roberts 2009 PNAS

12. What will this mean for cassava?

13. Cassava suitability change compared
with other staples
• Cassava consistently outperforms other
staples in terms of changes in suitability

15. Genetic breeding as strategy of
adaptation and mitigation of
climate change
5%
(Craufurd et al., 2011).
Evaluation of sensitivity and threshold
variations of adaptation (Jarvis et al., 2012).
0.5 °C
Figure 3. Diagram of model used to evaluate
suitability (Ramirez-Villegas et al., 2011).
Simulate possible scenarios of
breeding.
Different suitability predictions 
Quantify possible benefits
Figure 4. Potential benefits of a new combination of
parameters reflecting scenarios of cassava improvement (Jarvis
et al., 2012).

16. AND Andean Region EAS East Asia NEU North Europe WAF West Africa
BRA Brazil EAF East Africa SAF South Africa WEU West Europe
CAC Cen. America and Caribean EEU East Europe SAH Sahel OCE Oceania
CAF Central Africa WAS West Asia SAS South Asia SAM South Latin America
CAS Central Asia NAF North Africa SEA Southeast Asia
CEU Central Europe NAM North America SEU South Europe
Change in Suitable Area Overall Suitability Change PIA/NIA ratio