Page 1
for Xe (
28
). The difference in chemical shifts for
H
2
O in this study is 0.11 ppm (fig. S32), which
is consistent with the size of an H
2
O molecule
between that of H
2
and Ar. No difference in
chemical shift of the
13
C NMR was observed
among H
2
O@C
60
, HDO@C
60
, and D
2
O@C
60
.
Separations of He@C
60
, H
2
@C
60
, Ar@C
60
,
Kr@C
60
, and Xe@C
60
from empty C
60
were
usually difficult and possible only when an HPLC
equipped with Buckyprep [3-(1-pyrenyl)propylsilyl]
or PYE [2-(1-pyrenyl)ethylsilyl] column(s) was
used with many time-consuming recycles. In con-
trast, H
2
O@C
60
was easily separated from emp-
ty C
60
by the single-stage HPLC (Buckyprep,
toluene), with retention times of 7.93 and 8.28
min for empty C
60
and H
2
O@C
60
, respectively
(fig. S33). It is believed that
p
-
p
interaction of
the fullerene cage with pyrene moieties attached
to the silica gel surface is important for the sep-
aration, and the presence of the encapsulated
H
2
O molecule might influence such interactions.
One of the characteristic properties of an H
2
O
molecule is its high dipole moment, whereas
C
60
with the
I
h
symmetry does not have the
dipole. Thus, we expect that H
2
O@C
60
should
be a polar molecule. The density functional the-
ory (DFT) calculations at the M06-2X/6-311G(2d,
p) level of theory (
29
) with basis set super-
position error correction during structural opti-
mization showed that the dipole moments of
H
2
O, C
60
, and H
2
O@C
60
are 2.02, 0.00, and 2.03
Debye, respectively.
Infrared spectroscopy is a useful method to
study the properties of water (
30
). However, the
vibrational frequency analysis of H
2
O@C
60
by
the DFT calculations suggested that the sym-
metric and asymmetric stretching modes of the
H
2
O molecule inside C
60
(3810 and 3894 cm
–
1
)
should be very weak (
15
). It was difficult to see
spectral features clearly in the observed Fourier
transform infrared spectrum (diffuse reflectance
spectroscopy, KBr, fig. S34), probably because
of the shielding effect of the dipole (
30
). The
observed peaks for H
2
O@C
60
were 1429.3, 1182.4,
576.7, and 526.6 cm
–
1
, which were exactly the
same as those of empty C
60
.
Upon cyclic voltammetry in ODCB with 0.1 M
Bu
4
NPF
6
as a supporting electrolyte, an irrever-
sible oxidation peak and four quasi-reversible
reduction waves were observed (fig. S35), and the
redox potentials were determined by differential
pulse voltammetry (fig. S36) as +1.32,
–
1.08,
–
1.46,
–
1.91, and
–
2.38 V versus a ferrocene/ferrocenium
couple within the potential window. These val-
ues were almost the same as those of empty C
60
(+1.32,
–
1.08,
–
1.47,
–
1.92, and
–
2.39 V) under
the same conditions. This result indicated that
the single molecule of H
2
O is electrochemical-
ly stable under the hydrophobic environment
inside C
60
.
The H
2
O@C
60
molecule, which can be con-
sidered as
“
wet fullerene
”
or
“
polar C
60
,
”
should
allow the study of the intrinsic properties of a
single molecule of H
2
O, such as ortho and para
conversion (
31
). When this synthetic methodol-
ogy is expanded to other small molecules into
C
60
, C
70
, and higher fullerenes, research work
on the isolated molecule or the control of phys-
ical properties of outer fullerene cages will pro-
gress in the near future.
References and Notes
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Y. Murata,
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, 298 (2010).
15. Materials and methods are available as supporting
material on
Science
Online.
16. C. J. Andres, N. Spetseris, J. R. Norton, A. I. Meyers,
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36
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J. Org. Chem.
36
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553 (1971).
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J. Am. Chem. Soc.
89
, 150
(1967).
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9
, 4881 (1968).
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et al
.,
Chem. Commun.
2002
, 1352 (2002).
22. Y. Kohama
et al
.,
Phys. Rev. Lett.
103
, 073001 (2009).
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et al
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Nat. Chem.
2
, 678 (2010).
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R. Shimshi, A. Khong,
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271
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25. Y. Morinaka, F. Tanabe, M. Murata, Y. Murata,
K. Komatsu,
Chem. Commun.
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, 4532 (2010).
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et al
.,
Chem. Commun.
2006
, 912 (2006).
27. K. Yamamoto
et al
.,
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121
, 1591 (1999).
28. M. S. Syamala, R. J. Cross, M. Saunders,
J. Am. Chem.
Soc.
124
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29. Y. Zhao, D. G. Truhlar,
Theor. Chem. Acc.
120
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Int. J. Quantum Chem.
109
, 2080
(2009).
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296
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Acknowledgments:
This work was supported by the Ministry of
Education, Culture, Sports, Science and Technology (MEXT)
project on Integrated Research on Chemical Synthesis,
Grants-in-Aid for Scientific Research on Innovative Areas
(20108003, pi-Space) and for Scientific Research (A)
(23241032), and the Global COE Program Integrated
Materials Science (B-09) from MEXT, Japan. The structural
coordinates have been deposited with the Cambridge
Data Bank. The accession numbers for the three x-ray
structures are as follows:
5a
·(
o
-xylene)
2.5
, CCDC
828041; H
2
O@C
60
·(NiOEP)
2
, CCDC 828029; empty
C
60
·(NiOEP)
2
, CCDC 828028.
Supporting Online Material
www.sciencemag.org/cgi/content/full/333/6042/613/DC1
Materials and Methods
SOM Text
Figs. S1 to S36
Tables S1 to S4
References (
32
,
33
)
31 March 2011; accepted 7 June 2011
10.1126/science.1206376
Climate Trends and Global Crop
Production Since 1980
David B. Lobell,
1
*
Wolfram Schlenker,
2,3
Justin Costa-Roberts
1
Efforts to anticipate how climate change will affect future food availability can benefit from
understanding the impacts of changes to date. We found that in the cropping regions and
growing seasons of most countries, with the important exception of the United States, temperature
trends from 1980 to 2008 exceeded one standard deviation of historic year-to-year variability.
Models that link yields of the four largest commodity crops to weather indicate that global maize
and wheat production declined by 3.8 and 5.5%, respectively, relative to a counterfactual
without climate trends. For soybeans and rice, winners and losers largely balanced out. Climate
trends were large enough in some countries to offset a significant portion of the increases in
average yields that arose from technology, carbon dioxide fertilization, and other factors.
I
nflation-adjusted prices for food have shown
a downward trend over the last century as
increases in supply have outpaced demand.
More recently, food prices have increased rapidly,
and many observers have attributed this in part
to weather episodes such as the prolonged drought
in Australia or the heat waves and wildfires in
Russia. However, efforts to model the effects
of climate on prices or food availability, even
for individual countries, must consider effects
throughout the world, given that agricultural
commodities are traded worldwide and that world
market prices are determined by global supply
and demand (
1
–
3
).
Global average temperatures have risen by
roughly 0.13°C per decade since 1950 (
4
), yet the
1
Department of Environmental Earth System Science and
Program on Food Security and the Environment, Stanford
University, Stanford, CA 94305, USA.
2
Department of Eco-
nomics and School of International and Public Affairs, Co-
lumbia University, New York, NY 10027, USA.
3
National
Bureau of Economic Research, New York, NY 10016, USA.
*To whom correspondence should be addressed. E-mail:
dlobell@stanford.edu
29 JULY 2011 VOL 333
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impact this has had on agriculture is not well
understood (
5
). An even faster pace of roughly
0.2°C per decade of global warming is expected
over the next two to three decades, with sub-
stantially larger trends likely for cultivated land
areas (
4
). Understanding the impacts of past trends
can help us to gauge the importance of near-term
climate change for supplies of key food com-
modities. In addition, identifying the particular
crops and regions that have been most affected
by recent trends would assist efforts to measure
and analyze ongoing efforts to adapt.
We developed a database of yield response
models to evaluate the impact of these recent
climate trends on major crop yields at the country
scale for the period 1980
–
2008. Publicly availa-
ble data sets on crop production, crop locations,
growing seasons, and monthly temperature (T)
and precipitation (P) were combined in a panel
analysis of four crops (maize, wheat, rice, and
soybeans) for all countries in the world (
6
). These
four crops constitute roughly 75% of the calories
that humans directly or indirectly consume (
7
).
Time series of average growing-season T and
P revealed significant positive trends in temper-
ature since 1980 for nearly all major growing re-
gions of maize, wheat, rice, and soybeans (Fig. 1,
Fig. 2, and figs. S1 to S4). To put the magni-
tude of trends in context, we normalized them by
the historical standard deviation (
s
) of year-to-
year fluctuations (i.e., a T trend of 1.0 means that
temperatures at the end of the period were 1.0
s
higher than at the beginning of the period). A
notable exception to the warming pattern is the
United States, which accounts for ~40% of global
maize and soybean production and experienced a
slight cooling over the period (Fig. 1). Overall,
65% of countries experienced T trends in growing
regions of at least 1
s
for maize and rice, with the
number slightly higher (75%) for wheat and
lower (53%) for soybean. Roughly one-fourth of
all countries experienced trends of more than 2
s
for each crop (Fig. 2). This distribution of trends
stands in marked contrast to the 20 years before
1980, for which trends were evenly distributed
about zero (Fig. 1). Precipitation trends were more
mixed across regions and were significantly smaller
relative to historical variability in most places. The
number of countries with extreme trends reflected
the number expected by chance (Fig. 2), indicat-
ing no consistent global shift in growing-season
average P.
Translating these climate trends into potential
yield impacts required models of yield response.
We used regression analysis of historical data to
relate past yield outcomes to weather realizations.
All of the resulting models include T and P, their
squares, country-specific intercepts to account for
spatial variations in crop management and soil
quality, and country-specific time trends to ac-
count for yield growth due to technology gains
(
6
). Because our models are nonlinear, both year-
to-year variations in historical weather as well as
the average climate are used for the identification
of the coefficients (unlike a linear panel, which
only uses deviations from the average). However,
we do not directly estimate the full set of adapta-
tion possibilities that might occur in the long term
under climate change (
8
). For this reason, we pre-
fer to view these not as predictions of actual im-
pacts, but rather as a useful measure of the pace
of climate change in the context of agriculture.
The greater the estimated impacts, the faster any
adaptation or action to raise yields would have to
occur to offset potential losses.
The models exhibited statistically significant
sensitivities to T and P that are consistent with
process-based crop models and the broader agro-
nomic literature (figs. S6 and S7). Given the hill-
shaped yield-temperature function, predicted
decreases are larger the warmer a country is to
begin with. A 1°C rise tends to lower yields by up
to 10% except in high-latitude countries, where
rice in particular gains from warming. Precipita-
tion increases yields for nearly all crops and
countries, up to a point at which further rainfall
becomes harmful. Tests of alternate climate
data sets and groupings of countries identified
some important differences, but responses for
most countries were robust to these model
choices (fig. S8).
To estimate yield impacts of climate trends,
we used the statistical models to predict annual
yields for four scenarios of historical T and P: (i)
actual T and actual P for each country for the
period 1980
–
2008, (ii) actual T and detrended P,
(iii) detrended T and actual P, and (iv) detrended
T and detrended P. Trends in the difference be-
tween (iv) and (i) were used to quantify the im-
pact of historical climate trends, whereas (ii) and
(iii) were used to determine the relative contri-
bution of T and P to overall impacts.
At the global scale, maize and wheat exhib-
ited negative impacts for several major producers
and global net loss of 3.8% and 5.5%, respectively,
Fig. 1.
(
A
and
B
) Maps of the 1980
–
2008 linear
trend in temperature (A) and precipitation (B)
for the growing season of the predominant crop
(among maize, wheat, rice, and soybean) in each
0.5 × 0.5 grid cell. Trends are expressed as the
ratio of the total trend for the 29-year period (e.g.,
°C per 29 years) divided by the historical standard
deviation for the period 1960
–
2000. For clarity,
only cells with at least 1% area covered by maize,
wheat, rice, or soybean are shown. Temperature
trends exceed more than twice the historical stan-
dard deviation in many locations, whereas precip-
itation trends have been smaller.
A
B
60°N
40°N
20°N
0°
20°S
40°S
60°N
40°N
20°N
0°
20°S
40°S
100°W
50°W
0°
50°E
100°E
150°E
100°W
50°W
0°
50°E
100°E
150°E
3
2
0
1
-1
-2
-3
3
2
0
1
-1
-2
-3
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relative to what would have been achieved without
the climate trends in 1980
–
2008 (Fig. 3 and Table
1). In absolute terms, these equal the annual pro-
duction of maize in Mexico (23 MT) and wheat
in France (33 MT), respectively. The net impact
on rice and soybean production was insignif-
icant, with gains in some countries balancing losses
in others. Among the largest country-specific
losses was wheat in Russia (almost 15%), where-
as the country with the largest overall share of
crop production (United States) showed no effect
because of the lack of significant climate trends.
The majority of impacts were driven by trends
in T rather than P (Fig. 3). Precipitation is an im-
portant driver of interannual variability of yields,
and indeed our models often predict a compara-
ble yield change for a 1
s
change in P or T (fig.
S7). However, the magnitude of recent T trends
Fig. 2.
(
A
to
D
) Frequency dis-
tributions of country-level growing-
season temperature trends (top)
and precipitation trends (bottom)
for the periods 1960
–
1980 (left)
and 1980
–
2008 (right) for four
major crops, with trends ex-
pressed as the total trend for the
period (e.g., °C per 29 years), di-
vided by the historical standard
deviation for the period 1960
–
2000. The null distribution (de-
rived from 10,000 runs with
simulated random noise) is shown
by gray line, reflecting the fre-
quency of different trends ex-
pected by chance. The distribution
across countries of precipitation
trends for both 1960
–
1980 and
1980
–
2008, as well as temper-
ature trends for 1960
–
1980, do
not appear different from what
would be expected from random
variation. In contrast, the tem-
perature distribution for 1980
–
2008 is shifted relative to the null
distribution, with temperature
trends often two or more times
the historical standard deviation.
−3
−2
−1
0
1
2
3
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Density
Temperature trend (1960−1980, # of SD)
−3
−2
−1
0
1
2
3
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Density
Precipitation trend (1960−1980, # of SD)
−3
−2
−1
0
1
2
3
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Density
Temperature trend (1980−2008, # of SD)
−3
−2
−1
0
1
2
3
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Density
Precipitation trend (1980−2008, # of SD)
A
B
C
D
Maize
Rice
Wheat
Soy
Global
India
France
Brazil
China
United States
−20
−10
0
10
20
Temp
Precip
Maize
Global
Vietnam
Bangladesh
Indonesia
India
China
−20
−10
0
10
20
Rice
Global
France
Russia
United States
India
China
−20
−10
0
10
20
Wheat
Global
Paraguay
China
Argentina
Brazil
United States
−20
−10
0
10
20
Soy
% Yield Impact
% Yield Impact
A
C
B
D
Fig. 3.
(
A
to
D
) Estimated net impact of climate trends for 1980
–
2008 on
crop yields for major producers and for global production. Values are ex-
pressed as percent of average yield. Gray bars show median estimate; error
bars show 5% to 95% confidence interval from bootstrap resampling with
500 replicates. Red and blue dots show median estimate of impact for T
trend and P trend, respectively.
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(Fig. 2) is larger than those for P in most situa-
tions. This finding is consistent with models of
future yield impacts of climate change, which
indicate that changes in T are more important
than changes in P, at least at the national and
regional scales (
9
,
10
).
Prior studies for individual countries and at the
global scale also found that recent trends have de-
pressed maize and wheat yields (
5
). For example,
a recent study of wheat yields in France suggests
that climate is an important factor contributing to
stagnation of yields since 1990 (
11
). Similarly,
warming trends in India have a well-understood
negative effect on yields and are thought to explain
part of the slowdown in recent yield gains (
12
,
13
).
For rice, the lack of significant impacts is consistent
with a recent study of rice in Asia, which showed
that past changes in average T had small effects at
large scales, in part because of opposing influence
of nighttime and daytime temperatures, and in part
because of opposing climate trends in different
countries (
14
). The trends reported in the current
study are dependent on the time period used,
1980
–
2008, but adjustments to this time period
did not qualitatively affect the results (fig. S9). Sep-
arating the effects of maximum and minimum
temperature, or different treatments of the time
trend, also did not qualitatively alter the conclu-
sions (figs. S10 and S11).
Climate is only one factor likely to shape the
future (or past) of food supply. It is therefore
important to assess how these impacts of climate
trends compare to other factors over the same time
period. As one measure, we divided the climate-
induced yield trend by the overall yield trend for
1980
–
2008 in each country (Fig. 4). We empha-
size that this is a simple metric of the importance
of climate relative to all other factors, and does
not address the overall pace of yield growth, nor
does it separately attribute yield growth to the
many technological and environmental factors
that influence trends. However, it provides a useful
measure of the relative importance of climate, with
values of
–
0.1 indicating that 10 years of climate
trend is equivalent to a setback of roughly 1 year
of technology gains.
The ratio exhibits wide variation across coun-
tries because of differences in both the growth
rate of average yields and climate impacts (Fig. 4).
Cases where negative climate impacts represent
a large fraction of overall yield gains include
wheat in Russia, Turkey, and Mexico, and maize
in China. Rice production in high-latitude regions
appears to have benefited from warming, but lat-
itudinal gradients are not apparent for other
crops. Although temperate systems tend to be hurt
less from a given amount of warming in many
model assessments (
15
), in reality these systems
often have much lower nonclimatic constraints
—
for instance, their sensitivity to weather may be
increased by high rates of fertilizer use (
9
). More-
over, temperate systems tend to warm more quickly
than tropics (
16
), and several high-latitude grow-
ing regions have seen substantial warming since
1980 (Fig. 1).
Any model has its limitations, and we recognize
a few caveats that are common to statistical models.
Our approach may be overly pessimistic because
it does not fully incorporate long-term adaptations
Table 1.
Median estimates of global impacts of temperature and precipitation trends, 1980
–
2008, on
average yields for four major crops. Estimates of the 47 ppm increase in CO
2
over the time period were
derived from data in (
21
). Values in parentheses show 5% to 95% confidence interval estimated by
bootstrap resampling over all samples.
Crop
Global
production,
1998
–
2002
average
(millions of
metric tons)
Global yield
impact of
temperature
trends (%)
Global yield
impact of
precipitation
trends (%)
Subtotal
Global yield
impact of
CO
2
trends (%)
Total
Maize
607
–
3.1
(
–
4.9,
–
1.4)
–
0.7
(
–
1.2, 0.2)
–
3.8
(
–
5.8,
–
1.9)
0.0
–
3.8
Rice
591
0.1
(
–
0.9, 1.2)
–
0.2
(
–
1.0, 0.5)
–
0.1
(
–
1.6, 1.4)
3.0
2.9
Wheat
586
–
4.9
(
–
7.2,
–
2.8)
–
0.6
(
–
1.3, 0.1)
–
5.5
(
–
8.0,
–
3.3)
3.0
–
2.5
Soybean
168
–
0.8
(
–
3.8,1.9)
–
0.9
(
–
1.5,
–
0.2)
–
1.7
(
–
4.9, 1.2)
3.0
1.3
Fig. 4.
(
A
to
D
) Estimated net
impact of climate trends for
1980
–
2008 on crop yields by
country, divided by the overall
yield trend per year for 1980
–
2008. Values represent the
climate effect in the equivalent
number of years of overall yield
gains. Negative values indi-
cate that the climate trend slowed
yield trends, and positive val-
ues indicate that the climate
trend sped up yield trends, rel-
ative to what would have oc-
curred without trends in climate.
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that may occur once farmers adjust their expec-
tations of future climate. Examples of this would
include expansion of crop area into cooler regions,
switches to new varieties (
17
), or shifts toward
earlier planting dates, although there is little evi-
dence that the latter is happening beyond what is
expected from historical responses to warm years
(
18
). Moreover, the incentives to innovate have
been limited in most of our sample period because
prices have been low. On the other hand, our es-
timates may be overly optimistic because data lim-
itations prevent us from explicitly modeling effects
of extreme temperature or precipitation events with-
in the growing season, which can have dispro-
portionately large impacts on final yields (
19
).
For example, although we captured the decline in
growing-season total precipitation for wheat in
India, there has also been a trend toward heavy
rain events as an increased fraction of total rain-
fall, which is likely harmful to wheat yields (
20
).
Finally, we note that our study does not esti-
mate the direct effect of elevated CO
2
on crop
yields that are captured in the smooth time trends.
Atmospheric CO
2
concentrations at Mauna Loa,
Hawaii have increased from 339 ppm in 1980 to
386 ppm in 2008 (www.esrl.noaa.gov/gmd/ccgg/
trends). Free-air CO
2
enrichment experiments for
C
3
crops (i.e., wheat, rice, and soybean) show an
average yield increase of 14% in 583 ppm CO
2
relative to 367 ppm CO
2
[i.e., 0.065% increase
per ppm (
21
)]. This suggests that the 47 ppm in-
crease since 1980 would have boosted yields by
roughly 3%. Impacts of higher CO
2
on maize
were likely much smaller because its C
4
photo-
synthetic pathway is unresponsive to elevated
CO
2
(
22
). Thus, the net effects of higher CO
2
and
climate change since 1980 have likely been slight-
ly positive for rice and soybean, and negative for
wheat and maize (Table 1).
The fact that climate impacts often exceed 10%
of the rate of yield change indicates that climate
changes are already exerting a considerable drag on
yield growth. To further put this in perspective, we
have calculated the impact of climate trends on
global prices using recent estimates of price elas-
ticities for global supply and demand of calories
(
23
). The estimated changes in crop production ex-
cluding and including CO
2
fertilization (subtotal
and total columns in Table 1, respectively) trans-
late into average commodity price increases of
18.9% and 6.4% when we use the same bootstrap
procedure as used in table 3 of (
22
).
Our study considers production of four major
commodities at national scales. There are many
important questions at subnational scales that our
models cannot address, many important foods
beyond the four modeled here, and many
important factors other than food production that
determine food security. Nonetheless, we con-
tend that periodic assessments of how climate
trends are affecting global food production can
provide some useful insights for scientists and
policy makers. This type of analysis should be
accompanied by studies that evaluate the true pace
and effectiveness of adaptation responses around
the world, particularly for wheat and maize. By
identifying countries where the pace of climate
change and associated yield pressures are espe-
cially fast, our study should facilitate these future
analyses. Without successful adaptation, and given
the persistent rise in demand for maize and wheat,
the sizable yield setback from climate change is
likely incurring large economic and health costs.
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Acknowledgments:
We thank five anonymous reviewers for
helpful comments. Supported by a grant from the
Rockefeller Foundation, NASA new investigator grant
NNX08AV25G (D.B.L.), and NSF grant SES-0962625.
Supporting Online Material
www.sciencemag.org/cgi/content/full/science.1204531/DC1
Materials and Methods
Figs. S1 to S11
References
18 February 2011; accepted 22 April 2011
Published online 5 May 2011;
10.1126/science.1204531
Sr-Nd-Pb Isotope Evidence for
Ice-Sheet Presence on Southern
Greenland During the Last Interglacial
Elizabeth J. Colville,
1
Anders E. Carlson,
1
*
Brian L. Beard,
1
Robert G. Hatfield,
2
Joseph S. Stoner,
2
Alberto V. Reyes,
1
David J. Ullman
1
To ascertain the response of the southern Greenland Ice Sheet (GIS) to a boreal summer climate warmer
than at present, we explored whether southern Greenland was deglaciated during the Last Interglacial
(LIG), using the Sr-Nd-Pb isotope ratios of silt-sized sediment discharged from southern Greenland. Our
isotope data indicate that no single southern Greenland geologic terrane was completely deglaciated during
the LIG, similar to the Holocene. Differences in sediment sources during the LIG relative to the early
Holocene denote, however, greater southern GIS retreat during the LIG. These results allow the evaluation
of a suite of GIS models and are consistent with a GIS contribution of 1.6 to 2.2 meters to the
≥
4-meter
LIG sea-level highstand, requiring a significant sea-level contribution from the Antarctic Ice Sheet.
T
he response of ice sheets to climate change is
the largest source of uncertainty in predicting
future sea-level rise (
1
). In the case of the
Greenland Ice Sheet (GIS), observations of mass
changes are restricted to the past few decades, pro-
viding limited context with which to assess present
changes and future model predictions (
1
). An alter-
native approach for assessing the GIS response to
climate change is to use the geologic record of the
GIS during earlier climate periods that were nat-
urally warmer than the present in the boreal sum-
mer (
2
,
3
), the season that affects GIS ablation.
1
Department of Geoscience, University of Wisconsin, Madison,
WI 53706, USA.
2
College of Oceanic and Atmospheric Sciences,
Oregon State University, Corvallis, OR 97331, USA.
*To whom correspondence should be addressed. E-mail:
acarlson@geology.wisc.edu
29 JULY 2011 VOL 333
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Climate Trends and Global Crop Production Since 1980
David B. Lobell, Wolfram Schlenker, and Justin Costa-Roberts
Science 333 (6042), . DOI: 10.1126/science.1204531
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