Is Cassava the Answer to African Climate Change Adaptation?
IS CASSAVA THE ANSWER TO AFRICAN CLIMATE CHANGE ADAPTATION?
Research Area on Decision and Policy Analysis (DAPA)
Climatic changes for cassavagrowing regions
Annual mean temperature change (°C)
Predicted changes in climates as average
of 24 GCMs (and uncertainties expressed
as standard deviations), by Africa regions.
CAF: Central Africa SAF: South Africa
EAF: East Africa
NAF: North Africa WAF: West Africa
Impact assessment methods are sensitive to uncertainties in climate data, and hence these must be
considered when assessing crop responses to combinations of increasing temperatures, varied
precipitation patterns and increased CO2 concentrations. There was a relatively strong disagreement in
GCM signals even for temperature and the uncertainty related to GCMs in rainfall was high.
In CAF, there were very little
increases (<1% for all crops
except potato and beans, which
were predicted a substantial
Cassava production is located in
WAF, EAF, CAF
Countries with increases below
1.5 °C where the most production
overall increase in yearly
predicted rainfall in the SAH.
Whilst most areas in Africa
experience decreases in
overall suitability of the
additional crops modeled,
outperformed or (in the
worst case) equaled the
average of these crops.
Impacts of climate change on cassava suitability in
sub-regions of Africa (Average change in suitability)
Conversely, for other major food
staples, we found that they are
all projected to experience
negative impacts, with the
greatest impacts for:
The EcoCrop model was used in
assessing the impacts of climate change.
It evaluates on a monthly basis if there
are adequate climatic conditions within a
growing season for temperature and
precipitation and calculates the climatic
suitability of the resulting interaction
between rainfall and temperature.
Predicted changes in
cassava suitability as
average of 24GCMs
Responses in SAF were observed
positive only for cassava, millet,
and banana (5% each).
Impacts of CC on cassava climate suitability
In EAF, however, cassava
showed the greatest
potential compared to all
other crops (10%), whereas
beans and potatoes were
the most affected.
In WAF, large negative impacts were
predicted for potato (-15%,), beans
(-20%), and banana (-13%,), whereas
millet, maize, and cassava were
predicted to remain the same. Sorghum
showed positive impacts (10%)
In SAH, responses were
similar to those found in
area with gain
Overall suitability change (%)
Discrimination of areas according to gains and losses using
the mean change in suitability of all crops but cassava. Typology:
C: cassava, O: other crops; G: increase in suitability, L: decrease
in suitability. For instance, “C:G, O:L” indicates that in that area
cassava (C) increases in suitability (G), and all other crops in
average (O) decrease suitability (L)
Boxplots are combinations of GCM-by-country predictions. Thick
black vertical lines are the median, boxes show the first and third
quartile and whiskers extend 5% and 95% of the distributions
area with loss
area with loss
In NAF, moderate negative impacts were
predicted for beans (-4%), and potato (-4%),
slight positive changes for millet and banana.
The GCMs predicts
between 1.2 and
°C, and changes in
between -39 to +64
mm/year across all
Total annual rainfall change (mm)
Cassava reacted very well to the predicted future climate
conditions compared to other crops.
Predicted changes in suitability of other staples (average among crops and GCMs)
We use downscaled
projections of 24
Models (GCMs) for the
scenario by 2030s
Impacts of CC on other staple crops
Impacts on pests and diseases
area with gain
Variation amongst individual GCM predictions was
significant and predicted impacts with very high certainty
(>80% GCMs predicting changes in the same direction).
Changes in cassava climatic suitability by
2030s as predicted by EcoCrop indicate
increases in the vast majority of areas, and
especially seem to occur in a greater
proportion over currently cropped areas and
where the most significant production is
Boxplots are combinations of GCM-by-country
predictions. Thick black vertical lines are the median,
boxes show the first and third quartile and whiskers
extend 5% and 95% of the distributions
Overall suitability change (%)
The most severe impacts were observed in WAF and the SAH,
where predicted changes were negative in ~80% of the countries.
3.3 km2 negatively
5.5 km positively
DR of the Congo
is the projected change in
suitability across the
continent. Cassava is actually
positively impacted in many
areas of Africa.
inhibit plant growth
and reduce leaf
biomass and roots
yield but rarely kill
drought, but this
depends on the
Cassava mosaic disease
Cassava brown streak disease
Abiotic breeding priorities
Many cassava-growing regions (>80% of area) are not abiotically
constrained in 2030, meaning that they are unlikely to benefit from crop
improvement for abiotic traits.
Some cassava agroclimatological data
optimally in the
although it can
of up to 38°C.
Area likely to become pest and
disease free (million km2)
Overall suitability change
Area predicted to improve climate
suitability (million km2)
regions (million km2)
Current climatically suitable area
Countries with the highest harvest area and
overall suitability change (% ± SD)
We then examined the challenges that cassava will likely face from pests and diseases
through the use of ecological niche modeling for cassava mosaic disease, whitefly, brown
streak disease, and cassava mealybug. The findings show that the geographic distribution
of these pests and diseases are projected to change, with both new areas opening up and
areas where the pests and diseases are likely to move away or reduce in pressure.
Increased drought tolerance could bring benefits to nearly 30% of
cassava-growing regions in EAF, SAF, and SAH.
Cold tolerance is also a priority despite the projected warmer climates.
This is largely because of constraints in high-elevation regions of EAF or
in low latitudinal regions in SAF where seasonal temperatures during
winter pose a constraint for cassava development.
Designed by Carlos Navarro-Racines (CIAT-CCAFS 2013) e-mail:firstname.lastname@example.org