Climate change, crop yields, and undernutrition: development of a model to quantify the impact of climate scenarios on child undernutrition.

Abstract

Global climate change is anticipated to reduce future cereal yields and threaten food security, thus potentially increasing the risk of undernutrition. The causation of undernutrition is complex, and there is a need to develop models that better quantify the potential impacts of climate change on population health.
We developed a model for estimating future undernutrition that accounts for food and nonfood (socioeconomic) causes and can be linked to available regional scenario data. We estimated child stunting attributable to climate change in five regions in South Asia and sub-Saharan Africa (SSA) in 2050.
We used current national food availability and undernutrition data to parameterize and validate a global model, using a process-driven approach based on estimations of the physiological relationship between a lack of food and stunting. We estimated stunting in 2050 using published modeled national calorie availability under two climate scenarios and a reference scenario (no climate change).
We estimated that climate change will lead to a relative increase in moderate stunting of 1-29% in 2050 compared with a future without climate change. Climate change will have a greater impact on rates of severe stunting, which we estimated will increase by 23% (central SSA) to 62% (South Asia).
Climate change is likely to impair future efforts to reduce child malnutrition in South Asia and SSA, even when economic growth is taken into account. Our model suggests that to reduce and prevent future undernutrition, it is necessary to both increase food access and improve socioeconomic conditions, as well as reduce greenhouse gas emissions.

Full text

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Environmental Health Perspectives • volume 119 | number 12 | December 2011
1817
Research | Children’s Health
Hunger and under nutrition are pervasive,
thought to be worsening in absolute terms,
and are major contributors to global ill health
[Black et al. 2008; Food and Agricultural
Organization of the United Nations (FAO)
2009]. More than one billion people are under­
nourished (FAO 2009), and about a third of
the burden of disease in children < 5 years of
age is attributable to under nutrition (Black
et al. 2008). Economic growth is anticipated
by many to reduce future under nutrition
(Smith and Haddad 2002), although recent
observations do not support this assumption
(Subramanyam et al. 2011).
Global food security depends on a range
of factors (Schmidhuber and Tubiello 2007),
with cereal production playing a major role
(Parry et al. 2009). Data suggest that global
per capita cereal production plateaued during
the 1980s and has since declined (Magdoff and
Tokar 2010), despite production increases in
some regions (FAO 2011). Further, with eco­
nomic growth, dietary preferences tend toward
greater meat consumption, placing greater
demands on cereal production to provide ani­
mal feed (Msangi and Rosegrant 2011).
Concern is growing that efforts to reduce
under nutrition in the coming decades may be
threatened by global climate change (Nelson
et al. 2010; Parry et al. 2009; Schmidhuber and
Tubiello 2007). Scientific assessments indicate
that warming will have an overall negative
impact on major cereal yields in low­latitude
areas, although yields may increase in some
high­latitude areas (Easterling et al. 2007).
Climate change could place an additional
5–170 million people “at risk of hunger” by
the 2080s (Parry et al. 1999, 2004; Rosenzweig
and Parry 1994). Food security is now one of
the leading concerns associated with anthropo­
genic climate change (Parry et al. 2009).
A number of terms are used to describe hun­
ger and under nutrition. “Undernourishment” is
not a health outcome per se; it is a theoreti­
cal model­based estimate of access to calories
developed by the FAO and is defined as the
proportion of people “whose dietary energy
consumption is continuously below a minimum
dietary energy requirement for maintaining a
healthy life and carrying out light physical activ­
ity with an acceptable minimum body­weight
for attained­height” (FAO 2010). That is, it has
one final cause: a lack of food. “At risk of hun­
ger” is synonymous with under nourishment.
“Undernutrition” refers to a physical state
and is measured using (among other things)
anthropometric indices such as stunting
(height­for­age) and underweight (weight­for­
age) [World Health Organization (WHO)
2010]. A lack of food—that is, under­
nourishment—is one of the many causes
of under nutrition, which also include poor
water and sanitation provision, low levels of
women’s education, repeated episodes of infec­
tious diseases, and low birth weight [United
Nations Children’s Fund (UNICEF 1990); for
more details on causes, see Black et al. 2008;
UNICEF 1990]. Checkley et al. (2008), for
example, estimated that 25% [95% confidence
interval (CI): 8, 38%] of irreversible stunting
at 24 months of age could be attributed to
having had five or more episodes of diarrhea.
Although it can be argued that under nutrition
itself is not a health outcome, under nutrition
can be directly linked to increased risk of
death and poor health (Black et al. 2008).
Additionally, child under nutrition has long­
term consequences for the health and earning
potential of adults (Victora et al. 2008).
To quantify future health burdens, it is
preferable to model under nutrition (which
refers to a physical state and accounts for com­
plex causation) rather than under nourishment
(which is a theoretical concept). They are often
poorly correlated (Klasen 2006; Svedberg
2002) and this suggests that under nourishment
is a poor proxy for under nutrition. The WHO
concluded that (using a number of simplify­
ing assumptions) under nutrition represented
a significant proportion of the total burden
of disease estimated to be attributable to cli­
mate change in 2000 (McMichael et al. 2004).
Only one group has provided more recent
quantitative estimates of future under nutrition
attributable to climate change. Nelson et al.
(2009) reported that, for two climate sce­
narios, climate change may increase under­
weight in children < 5 years of age by around
Address correspondence to S. Kovats, Department
of Social and Environmental Health Research,
London School of Hygiene and Tropical Medicine,
15­17 Tavistock Place, London, WC1H 9SH UK.
Telephone: 44 0 20 7927 2962. E­mail: sari.kovats@
lshtm.ac.uk
Supplemental Material is available online (http://
dx.doi.org/10.1289/ehp.1003311).
We thank G. Nelson and his colleagues at the
International Food Policy Research Institute for
kindly providing projections of calorie availability,
and A. Dangour and G. Nelson for providing com­
ments on earlier drafts of the manuscript. We also
thank the anonymous reviewers whose comments
led to many improvements in the paper.
This project was funded by the Natural Environment
Research Council under the Quantifying and
Understanding the Earth System (QUEST) project
(contract NE/E001874/1).
The authors declare they have no actual or poten­
tial competing financial interests.
Received 7 December 2010; accepted 15 August
2011.
Climate Change, Crop Yields, and Undernutrition: Development of a Model
to Quantify the Impact of Climate Scenarios on Child Undernutrition
Simon J. Lloyd, R. Sari Kovats, and Zaid Chalabi
Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, United Kingdom
Background: Global climate change is anticipated to reduce future cereal yields and threaten food
security, thus potentially increasing the risk of under nutrition. The causation of under nutrition is
complex, and there is a need to develop models that better quantify the potential impacts of climate
change on population health.
oBjectives: We developed a model for estimating future under nutrition that accounts for food and
non food (socioeconomic) causes and can be linked to available regional scenario data. We estimated
child stunting attributable to climate change in five regions in South Asia and sub-Saharan Africa
(SSA) in 2050.
Methods: We used current national food availability and under nutrition data to parameterize and
validate a global model, using a process-driven approach based on estimations of the physiological
relationship between a lack of food and stunting. We estimated stunting in 2050 using published
modeled national calorie availability under two climate scenarios and a reference scenario (no cli-
mate change).
results: We estimated that climate change will lead to a relative increase in moderate stunting of
1–29% in 2050 compared with a future without climate change. Climate change will have a greater
impact on rates of severe stunting, which we estimated will increase by 23% (central SSA) to 62%
(South Asia).
conclusions: Climate change is likely to impair future efforts to reduce child malnutrition in
South Asia and SSA, even when economic growth is taken into account. Our model suggests that to
reduce and prevent future under nutrition, it is necessary to both increase food access and improve
socioeconomic conditions, as well as reduce greenhouse gas emissions.
key words: cereal crops, climate change, Monte Carlo simulation, quantitative model, under-
nourishment, under nutrition. Environ Health Perspect 119:1817–1823 (2011). http://dx.doi.
org/10.1289/ehp.1003311 [Online 15 August 2011]

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Lloyd et al.
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volume 119 | number 12 | December 2011 • Environmental Health Perspectives
20% by 2050. Underweight was estimated
using an equation developed by Smith and
Haddad (2000), which is driven by per capita
calorie availability and socioeconomic indica­
tors: the ratio of female to male life expectancy,
female enrollment in secondary education,
and access to improved water supply. Future
per capita calorie availability was estimated by
modeling crop yield and global food trade.
All other non climate factors were assumed to
stay constant over time (i.e., unchanged from
baseline values). These assumptions are likely
to have led to an overestimate of the future
burden attributable to climate change because
this approach assumes that living conditions
in countries will improve little over the next
40 years. This is not consistent with historical
trends; between 1970 and 1995, 43% of the
reduction in child underweight has been attrib­
uted to improved female education, compared
with 26% for increased food availability and
19% from improved water access (Smith and
Haddad 2000).
More recently, the same group produced
updated estimates for a broader range of sce­
narios using a similar strategy (Nelson et al.
2010). Based on expert opinion, the socioeco­
nomic variables driving the underweight model
were varied with time but were considered
constant across three socioeconomic scenarios
broadly representing pessimistic, business­as­
usual, and optimistic economic growth.
Despite the importance of socioeconomic
influences on health, the data currently avail­
able for climate impact studies are largely lim­
ited to population and gross domestic product
(GDP) projections that were created for esti­
mating future greenhouse gas emission concen­
trations. At present, any modeling efforts must
work within these constraints. However, atten­
tion is now being focused on creating a wider
range of plausible socioeconomic scenarios for
climate impact assessments (Moss et al. 2010).
We developed a parsimonious model for
estimating future under nutrition attributable
to global climate change, specifically due to its
impacts on crop productivity. We then esti­
mated the future impact of climate scenarios
on under nutrition in children for five world
regions in Africa and Asia in 2050 using previ­
ously published estimates of climate change–
attributable changes in calorie availability from
Nelson et al. (2009). [The more recent esti­
mates (Nelson et al. 2010) are not included in
our assessment because they were released after
the completion of our project.]
Materials and Methods
We first describe the development and fit­
ting of a model for estimating the prevalence
of stunting. Second, we outline the process
of estimating the proportion under nourished
(PoU) using per capita calorie availability esti­
mates from Nelson et al. (2009). Finally, we
discuss the simulation process for estimating
future under nutrition attributable to global
climate change.
Model development. Our outcome of
interest is stunting in children < 5 years of age,
because this best captures the impact of condi­
tions over the long term (Black et al. 2008).
Children are considered moderately stunted
if they are > 2 SDs below the mean expected
height­for­age and severely stunted if > 3 SDs
below the mean (de Onis and Blossner 2003).
Scenario data are limited essentially to
future food availability and per capita GDP,
and many causes of stunting cannot be explic­
itly modeled. We considered stunting to have
two main causes, which we refer to as “food
causes” and “non food causes.” Food causes
are represented as PoU, which accounts for
climate change effects on calorie availability
(via changes in crop productivity) and food
access. [Stunting has food causes other than
calories, e.g., micronutrient deficiencies (Black
et al. 2008), but these are not represented in
PoU, nor are they modeled in climate­crop
models.] Non food causes are represented as
a “black box cluster” of socioeconomic fac­
tors acting at various levels and represent the
wide range of social and demographic causes
of stunting, such as low female literacy and
poor health care access (Frongillo et al. 1997).
Non food causes are modeled using per capita
GDP and the Gini coefficient for income dis­
tribution to generate a “development score,”
as described below.
The conceptual model is represented by
two general equations:

for every i, j; k = 2, 3,
yijk = αk + βk xij + γk wij + θk xijwij [1]

for every i, j; k = 1,
yij1 = 1 – yij2 – yij3
[2]
where yijk is the proportion of children
< 5 years of age stunted in country i, in region
j, at level k, where k is 1 if no/mild stunting,
2 if moderate stunting, or 3 if severe stunting;
xij is food causes of stunting, represented by
the PoU in country i, in region j; and wij is
non food causes of stunting, represented by the
“development score” (defined below) in coun­
try i, in region j. The parameters αk, βk, γk,
and θk are to be determined: βk is the physi­
ological relation between under nourishment
and stunting (details given below), γk relates
the development score to stunting, θk relates
the interaction between under nourishment
and the development score to stunting, and αk
is the regression constant.
Equation 1 is a bilinear model because it
is a linear function of the independent vari­
ables (xij and wij) and their product (xijwij).
After estimating moderate (yij2) and severe
(yij3) stunting, we estimated the proportion
not or mildly stunted (yij1) as described in
Equation 2.
The “development score” is an indicator of
the non food causes of stunting. It is driven by
country­level projections of future per capita
GDP and the baseline (i.e., most recent esti­
mate available) Gini coefficient (because no
projections were available). The development
score is scaled from 0 to 1; it equals 0 when
socioeconomic conditions are optimal (in terms
of avoiding under nutrition) and all under­
nutrition is attributable to food causes, and it
equals 1 when non food causes are at their cur­
rent (baseline) global maximum [for additional
information on development score calculations,
see Supplemental Material, Annex 1 (http://
dx.doi.org/10.1289/ehp.1003311)].
To parameterize the equations, we assem­
bled a global data set obtaining country­level
under nourishment estimates from the FAO
(FAO 2010), per capita GDP and Gini data
from the World Bank Development Indicators
(WBDI) database (World Bank 2010),
and stunting data from the WHO’s Global
Database on Child Growth and Malnutrition
(WHO 2010).
Stunting data were matched to under­
nourishment data to within a 1­year period.
Per capita GDP and Gini coefficient estimates
were matched as closely as possible to the
stunting data year. The data set covered the
period 1988–2008 and contained 186 records
with complete data. Countries were included
in the data set more than once if they had data
for multiple years.
Fitting the model. We decided, a priori,
to use a process­driven (theory­based) rather
than a standard data­driven (statistical)
approach to develop and parameterize the
model equations. The purpose of the model is
to describe plausible futures, so we designed it
to be driven as much as possible by relation­
ships that will be stable over time.
Of the two model variables, we assumed
that food causes have a more stable relation­
ship with stunting than do non food causes
because food causes are physiologically related
to stunting, and it is reasonable to assume
that this relationship will hold over the
next 50 years. In contrast, we assumed that
non food causes—which we modeled using
per capita GDP and the Gini coefficient—do
not necessarily have a stable relationship with
stunting because the relationship is mediated,
at least partly, by social and political factors
that may change over time. Therefore, when
fitting our model, we first quantified the rela­
tionship between stunting and food causes and
then considered socioeconomic factors.
We assumed that if someone had insuffi­
cient food, and non food causes of stunting were
absent (i.e., socioeconomic conditions were
optimal in terms of avoiding under nutrition),
there would be a predictable risk of stunting;

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Climate change, crop yields, and future undernutrition
Environmental Health Perspectives • volume 119 | number 12 | December 2011
1819
that is, we assumed the relationship between
food intake and stunting is physiologically
determined and holds globally. This assumption
is supported by ample evidence that, at least
until 6 years of age, all adequately nourished
and optimally cared for children will have simi­
lar, predictable growth rates (WHO 2006). In
addition to this food intake–related burden, if
socioeconomic conditions are poor, there is an
additional risk of stunting from non food causes
and their interaction with food causes, for
example, high rates of diarrhea associated with
inadequate sanitation. We do not consider it
probable that a country will lack sufficient food
but otherwise have “optimal” socio economic
conditions; our conception is theoretical.
Using the data set, we estimated the pre­
dictable but unknown physiologically based
relationship between under nourishment and
stunting at level k (βk) as
?βk = mini,j {yijk/xij; i = 1…, j = 1…}. [3]
(The operator mini,j{∙} means the minimum
of the argument in {∙}.) This minimum pro­
portion was obtained by finding the mini­
mum value of the ratio of yijk to xij among all
the countries in all regions, where, as defined
above, yijk represents the proportion stunted
< 5 years of age in country i, in region j, and
stunting level k; and xij represents the pro­
portion of the population under nourished in
county i, in region j. Because it is unlikely
that all stunting in a country is caused by
food causes alone, our estimate of βk will be
an overestimate of the purely physiological
relationship between food and stunting. In
practice, because the minimum observed
value may be too low because of data errors,
we chose to use the 5th percentile of the
distribution of yijk/xij as the best estimate
of βk and used the 1st and 10th percentiles
as the boundaries of its plausible range (see
“Estimating future stunting,” below).
Once the above relationship was found,
one­fifth of the data set (37 records) was ran­
domly selected and reserved for model valida­
tion; the remainder (149 records) was used to
parameterize the equations. (To obtain the
best possible estimate, and considering that
our method of estimation provides a rough
approximation, we used the entire data set to
estimate βk.)
We parameterized the equations in a step­
wise manner. In the first step, we used βk to
attribute a proportion of stunting to food causes
in all countries in the parameterization data set:

for every i, j, k,
rijk = βk xij [4]
where rijk is the proportion of stunting
attributable to food causes in country i, in
region j, at level k.
In the second step, we attributed the
remaining proportion of stunting to non food
causes and the interaction between food and
non food causes:

for every i, j, k,
sijk = yijk – rijk [5]
where sijk is the proportion of stunting attrib­
utable to non food causes and the interaction
between food and non food causes in coun­
try i, in region j, at level k. We then used lin­
ear methods to estimate the parameters (αk,
γk, θk) of the bilinear model:

for every i, j, k.
sijk = αk + γk wij + θk xijwij [6]
The model was validated by comparing
levels of stunting predicted by the model to
observed stunting in the reserved portion of
the data set (37 records).
For αk, γk, and θk we used the standard
errors of the estimates to describe the plau­
sible range of their true values. We carried out
our analysis with Stata (version 11; StataCorp,
College Station, TX, USA).
Estimating future population under­
nourished. The model required estimates of
future PoU with and without climate change.
Calculation of PoU requires data for a) the coef­
ficient of variation for within­population calorie
distribution, b) the average minimum calorie
requirements to avoid under nourishment in
the population, and c) per capita calorie avail­
ability (FAO 2003). Because projection data
for a) and b) are not available, we assumed they
remain at baseline levels. For c), we used esti­
mates made by Nelson et al. (2009) for futures
with and without climate change. The future
without climate change (reference scenario)
was represented with the 1950–2000 climate.
The two climate change scenarios were derived
from two climate models [the National Centre
for Atmospheric Research (NCAR) model and
the Commonwealth Scientific and Industrial
Research Organisation (CSIRO) model]
forced by a medium­high emissions scenario
[the Intergovernmental Panel on Climate
Change A2 scenario from the Special Report on
Emissions Scenarios; see Nakicenovic and Swart
(2000)]. The two climate scenarios were used to
address uncertainty in the climate system; the
NCAR model is warmer and wetter than the
CSIRO model. The global average increases in
maximum temperature and precipi tation over
land by 2050 were 1.9°C and 10%, and 1.2°C
and 2% for the NCAR and CSIRO scenarios,
respectively. For details of the assumptions in
the crop modeling (e.g., carbon dioxide fertil­
ization, irrigation, and adaptation responses),
extrapolations to other food groups, and the
trade model, see Nelson et al. (2009). For
additional information on PoU estimation, see
Supplemental Material, Annex 2 (http://dx.doi.
org/10.1289/ehp.1003311).
Estimating future stunting. The principal
input to our simulation model was future
country­level PoU derived from Nelson et al.
(2009). We ensured within­scenario consis­
tency by using the same GDP (G. Nelson,
International Food Policy Research Institute,
personal communication) and population
projections [United Nations medium variant,
2006 revision (United Nations 2007)] used in
the calorie availability projections. Our esti­
mates of the Gini coefficient were the most
recent estimates available from the WBDI
(World Bank 2010).
To account for parameter uncertainty, we
used a standard Monte Carlo approach. Each
of αk, γk, and θk were assumed to be nor­
mally distributed about their point estimates
as defined by their respective standard errors.
βk was assumed to be uniformly distributed
between the 1st and 10th percentiles of the
distribution of yijk/xij. This method produced
probability density functions (PDFs) of future
stunting.
We aimed to base each PDF on 100,000
estimates. We selected the first 100,000 esti­
mates that were > 0 and < 1. By rejecting
low and high estimates, we potentially intro­
duced an upward or downward bias; to assess
this, we quantified the proportion of rejected
results [see Supplemental Material, Table 1
(http://dx.doi.org/10.1289/ehp.1003311)].
Final estimates were produced at the
regional level for South Asia and four regions
in sub­Saharan Africa [SSA; central, east, south,
and west; see Supplemental Material, Table 2
(http://dx.doi.org/10.1289/ehp.1003311)].
We aggregated stunting from the country to
regional level using population weighting. We
ran the simulation using MATLAB (version
2009b; MathWorks, Natick, MA, USA).
Results
Model development and parameters. Table 1
summarizes the data set used to parameter­
ize our model. The correlation coefficients
between stunting and PoU were 0.16 and
0.19 for moderate and severe stunting, respec­
tively. For univariate analysis of stunting and
the development score, R2 was 0.40 for mod­
erate stunting and 0.45 for severe stunting;
when PoU was added to these models, R2
was unchanged. That is, using a data­driven
approach, including PoU as an explanatory
variable would not improve the model fit to
estimate stunting in the present compared
with using the development score alone. This
supported our approach using a theory­based
model that accounts for both food access and
socioeconomic conditions.
The model parameter estimates are shown
in Table 2. The β parameter is an estimate
of the assumed physiological relationship

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Lloyd et al.
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volume 119 | number 12 | December 2011 • Environmental Health Perspectives
between a lack of food and stunting. Thus,
the central estimate of β = 0.35 for moder­
ate stunting suggests that for every 1% of the
population who are under nourished, on aver­
age 0.35% of children < 5 years of age will
be moderately stunted. Using the validation
data set, the predicted and observed values are
well correlated, with correlation coefficients of
0.78, 0.66, and 0.80 for no/mild, moderate,
and severe stunting, respectively [for scatter­
plots, see Supplemental Material, Figure 1
(http://dx.doi.org/10.1289/ehp.1003311)].
Estimates of future proportions under­
nourished. The proportions of regional pop­
ulations projected to be under nourished in
2050 are shown in Table 3. Countries for
which complete data were not available were
excluded [see Supplemental Material, Table 2
(http://dx.doi.org/10.1289/ehp.1003311)].
The estimates suggest that climate change will
increase PoU compared with a future without
climate change, and also that climate change
and population growth will increase it to
above current levels in all regions.
Projections of stunting in 2050. We estimate
that climate change will increase stunting in all
regions (Table 3), with severe stunting increas­
ing by 30–50%. The estimated relative change
in stunting was smaller than the estimated rela­
tive change in under nourishment. Figure 1
shows the uncertainty in the stunting estimates
as histograms of probabilistic outcomes derived
from the Monte Carlo simulation.
We compared our stunting estimates
with underweight estimates made by Nelson
et al. (2009) (Table 4). The results are not
directly comparable, but we have assumed
that the ratio of underweight to stunting at
baseline remains constant in the future. The
final column shows this ratio as a regional,
population­weighted average calculated using
the most recent estimates of underweight and
stunting (FAO 2010).
Discussion
We have developed the first global model to
estimate the impact of climate change on future
stunting—a more relevant outcome measure
for human population health than “population
at risk of hunger” (i.e., under nourishment) or
underweight. Additionally, our model distin­
guishes moderate from severe stunting, which
bring substantially different health risks (Black
et al. 2008). Based on our conservative assump­
tions, the model suggests that climate change
will have significant effects on future under­
nutrition, even when the beneficial effects of
economic growth are taken into account. This
is particularly so for severe stunting, with a
62% increase in South Asia and a 55% increase
in east and south SSA. The health implica­
tions of this are large: according to Black et al.
Table 1. Summary of the data used to parameterize the model.
No.
observations
149
9
12
8
12
Children stunteda (%)
Moderate
19 (3–30)
8 (3–14)
19 (13–27)
26 (22–30)
22 (11–27)
Undernourisheda
(%)
24 (5–70)
12 (5–27)
19 (5–52)
22 (16–26)
21 (9–41)
Per capita GDPa
(2000 US$)
897 (81–5,513)
2,398 (942–3,688)
2,051 (633–5,513)
364 (207–589)
729 (232–1,958)
Region
Global
Caribbean
Central America
South Asia
Southeast Asia
SSA
Central
East
South
West
Other regions
Data are shown globally (for all those countries for which data were available) and for regions defined for the Global
Burden of Disease Study (Harvard University et al. 2009).
aValues are regional means (minimum–maximum); numbers are based on records from between 1991 and 2008. bThe Gini
coefficient ranges from 0, where there is perfect income equality, to 1, where all income goes to one person.
Severe
16 (1–36)
4 (1–8)
12 (4–29)
26 (2–35)
18 (3–33)
Ginia,b
0.45 (0.17–0.74)
0.47 (0.4–0.53)
0.53 (0.49–0.58)
0.38 (0.3–0.47)
0.4 (0.33–0.44)
5 21 (16–26)
24 (14–29)
30 (19–23)
20 (13–25)
16 (6–23)
24 (15–35)
23 (12–34)
14 (9–30)
19 (7–30)
16 (6–23)
49 (21–76)
36 (15–62)
29 (14–46)
24 (8–51)
18 (5–58)
309 (81–578)
286 (110–757)
1,298 (415–2,599)
315 (138–684)
1,249 (206–3,975)
0.51 (0.44–0.61)
0.43 (0.3–0.6)
0.60 (0.5–0.74)
0.43 (0.36–0.53)
0.43 (0.17–0.62)
23
8
35
37
Table 2. Central estimates and plausible ranges of model parameters.
Level of stunting
βk
Moderate (k = 2) 0.35 (0.20–0.44)
Severe (k = 3)0.18 (0.11–0.28)
βk is the physiological relation between undernourishment and stunting [5th percentile (1st–10th percentile)]; αk is the
regression constant, γk relates the development score to stunting, and θk relates the interaction between undernourish-
ment and the development score to stunting (regression estimate ± SE).
αk
γk
θk
–0.052 ± 0.021
–0.013 ± 0.014
0.26 ± 0.028
0.34 ± 0.044
–0.43 ± 0.041
–0.18 ± 0.064
Table 3. Estimates of undernourishment and stunting at baseline (present) and in 2050 with and without climate change (CC).
Percent undernourisheda
Percent relative
increase in
PoU under
climate changeb
RegionBaselineNo CCNCARCSIRO
South Asia22153029
Percent stunted (mean ± SD) of the PDFsa,c
Percent relative
increase in
stunting under
climate changed
29
61
2050
Stunting level
Moderate
Severe
2050
NCAR
14.6 ± 2.6
4.8 ± 1.7
Baseline
23
19
No CC
11.2 ± 1.8
2.9 ± 1.2
CSIRO
14.3 ± 2.5
4.6 ± 1.6
97
SSA
Central6553818052Moderate
Severe
Moderate
Severe
Moderate
Severe
Moderate
Severe
20
20
22
18
16
12
17
16
19.9 ± 4.7
16.8 ± 5.6
19.3 ± 2.9
9.7 ± 1.9
17.1 ± 3.0
8.8 ± 3.3
17.0 ± 2.2
6.8 ± 1.6
20.1 ± 5.7
22.1 ± 6.1
21.1 ± 4.6
15.0 ± 2.3
21.0 ± 4.8
13.6 ± 4.0
18.6 ± 2.9
9.3 ± 1.8
20.1 ± 5.7
22.0 ± 6.1
21.1 ± 4.5
15.0 ± 2.3
21.0 ± 4.8
13.6 ± 4.0
18.5 ± 2.9
9.2 ± 1.8
1
31
East352452521169
55
23
55
9
36
South
3233606082
West15122929142
aBaseline undernourishment and stunting data are from FAO (2010) and are calculated as population-weighted averages using the most recent data available; countries without data are excluded.
“No CC” is the reference scenario (i.e. future without climate change); “NCAR” and “CSIRO” are futures under climate change scenarios based on the NCAR and CSIRO models respectively.
bCompared with a future with no climate change; estimate based on average estimates from NCAR and CSIRO. For example, for South Asia the calculation was:
3029+
-
fp
.
15
2
110097
#
=
cEmpirically derived PDF, derived from the Monte Carlo simulations. dCompared with a future with no climate change; estimate based on average of the mean of the estimates from
NCAR and CSIRO. For example, for moderate stunting in South Asia the calculation was:
11.2
2
14.614.3
110029
#
+
=
-
fp
.

Page 5

Climate change, crop yields, and future undernutrition
Environmental Health Perspectives • volume 119 | number 12 | December 2011
1821
(2008), moderate stunting increases the risk of
all­cause death 1.6 times (95% CI: 1.3, 2.2)
and severe stunting increases the risk 4.1 times
(95% CI: 2.6, 6.4).
Comparing our results with those of
Nelson et al. (2009) should be done cau­
tiously because the outcome measures are dif­
ferent. Our estimates for stunting are lower
than estimates from Nelson et al. (2009) for
underweight in both South Asia and SSA
(Table 4). Our estimates for SSA are closer
but still lower. It is likely these differences are
largely explained by how the models account
for socioeconomic conditions. Nelson et al.
(2009) estimated underweight using a complex
model that accounted for many socio economic
factors, but because of a lack of data, all the
factors (except for food access) were held at
baseline levels. Our stunting equation repre­
sents socioeconomics more simply but is able
to account for expected changes over the next
40 years. World Bank projections suggest that
in South Asia, GDP will increase nine times
between 2005 and 2050—an absolute increase
of about $7,000 billion (year 2000 US$); in
SSA the figures are five times and $1,700 bil­
lion. Hence, allowing for these changes results
in lower future stunting estimates, with a
greater reduction in South Asia.
Model approximations and assumptions.
We used a theory­based rather than statisti­
cally based approach to modeling. Although
we accept that a statistical approach would
Figure 1. Histograms proportional to the PDFs for the proportion estimated to be stunted in 2050, by region: SSA, C (central); SSA, E (east); SSA, S (south); SSA,
W (west). Histograms were derived from 100,000 Monte Carlo runs. The x-axes are proportion stunted at a given level; the y-axes are number of estimates. The
curves are blue for no climate change, green for NCAR, and red for CSIRO. There is large overlap of the NCAR and CSIRO curves.
00.51
0
2,000
4,000
0 0.51
0
2,000
4,000
0 0.5
1
0
2,000
4,000
0 0.51
0
2,000
4,000
00.51
0
2,000
4,000
0 0.5
1
0
2,000
4,000
00.51
0
2,000
4,000
00.51
0
0
2,000
4,000
00.5
1
0
2,000
4,000
00.51
0
2,000
4,000
00.51
0
0
2,000
4,000
00.51
0
2,000
4,000
00.51
0
2,000
4,000
00.51
0
2,000
4,000
00.51
0
2,000
4,000
South Asia
SSA, C
SSA, E
SSA, S
SSA, W
No/mild stuntingModerate stuntingSevere stunting
Table 4. Model estimates of numbers of children affected by undernutrition in 2050: underweight and
stunting.
Millions of
children affected by
undernutrition in 2050
No CCNCAR
52
20
42
45
Additional millions
of children affected
by undernutrition
with climate change
No CC
7
7
10
9
Baseline
ratio of
underweight
to stuntinga
1.1
Region
South Asia
Outcome
Underweightb
Stuntingc
Underweightb
Stuntingc
CSIRO
59
26
52
54
NCAR
7
6
10
9
59
27
52
54
SSA
0.7
aCalculated as [(moderate + severe underweight)/(moderate + severe stunting)] using data for the present (FAO 2010)
and as a regional, population-weighted average. bUnderweight estimates for 2050 are from Nelson et al. (2009). cStunt-
ing estimates are the sum of the numbers moderately and severely stunted, based on the mean estimates of the empiri-
cally derived PDFs.