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Influence of extreme weather disasters on global crop production

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In recent years, several extreme weather disasters have partially or completely damaged regional crop production. While detailed regional accounts of the effects of extreme weather disasters exist, the global scale effects of droughts, floods and extreme temperature on crop production are yet to be quantified. Here we estimate for the first time, to our knowledge, national cereal production losses across the globe resulting from reported extreme weather disasters during 1964-2007. We show that droughts and extreme heat significantly reduced national cereal production by 9-10%, whereas our analysis could not identify an effect from floods and extreme cold in the national data. Analysing the underlying processes, we find that production losses due to droughts were associated with a reduction in both harvested area and yields, whereas extreme heat mainly decreased cereal yields. Furthermore, the results highlight ∼7% greater production damage from more recent droughts and 8-11% more damage in developed countries than in developing ones. Our findings may help to guide agricultural priorities in international disaster risk reduction and adaptation efforts.
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84 | NATURE | VOL 529 | 7 JANUARY 2016
LETTER doi:10.1038/nature16467
Influence of extreme weather disasters on global
crop production
Corey Lesk1, Pedram Rowhani2 & Navin Ramankutty1,3
In recent years, several extreme weather disasters have partially or
completely damaged regional crop production
1–5
. While detailed
regional accounts of the effects of extreme weather disasters exist,
the global scale effects of droughts, floods and extreme temperature
on crop production are yet to be quantified. Here we estimate for the
first time, to our knowledge, national cereal production losses across
the globe resulting from reported extreme weather disasters during
1964–2007. We show that droughts and extreme heat significantly
reduced national cereal production by 9–10%, whereas our analysis
could not identify an effect from floods and extreme cold in the
national data. Analysing the underlying processes, we find that
production losses due to droughts were associated with a reduction
in both harvested area and yields, whereas extreme heat mainly
decreased cereal yields. Furthermore, the results highlight ~7%
greater production damage from more recent droughts and 8–11%
more damage in developed countries than in developing ones. Our
findings may help to guide agricultural priorities in international
disaster risk reduction and adaptation efforts.
In many regions of the world, there have been considerable changes
in the nature of droughts, floods and extreme temperature events since
the middle of the twentieth century6–8. Over agricultural areas, disasters
arising from extreme weather can cause marked damage to crops and
food system infrastructure, with the potential to destabilize food sys-
tems and threaten local to global food security. In recent years, nearly
one-quarter of all damage and losses from climate-related disasters has
been in the agricultural sector in developing countries
9
. With such dis-
asters expected to become more common in the future
1,6,7
, policymak-
ers need robust scientific information to develop effective disaster risk
management and adaptation interventions (for example, infrastructure,
technology, management and insurance) to protect the most vulnerable
populations and to ensure global food security.
Whether an extreme weather event results in a disaster depends not
only on the severity of the event itself, but also on the vulnerability and
exposure of the human and natural systems that experience it
6
. Past
research has addressed agricultural effects of specific weather extremes
with fixed definitions, such as degree days above some threshold10–15.
This approach probably underestimates the crop effects of extreme
weather disasters (EWDs), because similar extreme weather events may
have differing effects depending on the vulnerability of the exposed
system.
In this study, we address this bias by using a disaster data set com-
piled based on human impact. In addition, we attend to two further
limitations of previous work on extreme weather and agriculture. First,
several regional empirical studies have highlighted the adverse effects
of extreme heat events on crop yields10–13, and global modelling efforts
have estimated future crop yield declines due to increasing extreme
heat stress14,15. But this emphasis on crop yields offers an incomplete
picture of crop production because of the potential for compensation
or compounding of yield impacts by changes in harvested area16;
and because crop production (and not yields)—together with access
and utilization—determines food security2,4,7,17,18. Second, we seek
to investigate the agricultural effects of often-overlooked extreme
weather events, namely floods and extreme cold
2,3
. Thus, our study is
the first, to our knowledge, that takes an empirical approach to estimat-
ing the influence of EWDs on crop area, yields and production at the
global scale.
We use a statistical method, superposed epoch analysis19 (also known
as compositing, see Methods), to estimate average national per-disaster
cereal production losses (Food and Agriculture Organization of the
United Nations (FAO), http://faostat3.fao.org) across the globe due
to reported droughts, floods and extreme temperature disasters from
1964 to 2007. Furthermore, we estimate the effects on cereal yield and
harvested area separately to identify processes leading to production
losses. On the basis of ~2,800 reported extreme hydro-meteorological
disasters collated by the Emergency Events Database (EM-DAT, http://
www.emdat.be/database), we find that national cereal production
during a drought was significantly reduced by 10.1% on average (95%
confidence interval 9.9–10.2%), while years with extreme heat led to
national production deficits of 9.1% (8.4–9.5%; Fig. 1a, b and Extended
Data Table 1). These production deficits were equivalent to roughly
6 years of production growth; however, no significant lasting effects
were noted in the years after the disasters. Estimated mean production
losses were driven mainly by a preponderance of disasters with moder-
ate effects on crops, as opposed to a few extreme cases (Extended Data
Fig. 1), and were not strongly influenced by sample size (see Extended
Data Fig. 2, Extended Data Table 2 and Supplementary Discussion).
During 1964–2007, these estimated EWD effects represent a loss of
1,820 million Mg due to droughts (approximately equal to the global
maize and wheat production in 2013), and 1,190 million Mg due to
extreme heat disasters (more than the global 2013 maize harvest). Over
2000–2007 (the period with the most complete disaster reporting com-
pared with earlier decades), 6.2% of total global cereal production was
lost due to EWDs relative to an estimated counterfactual global produc-
tion without EWD effects (3.0% to extreme heat and 3.2% to drought).
Cereal yield declines during EWDs were 5.1% (4.9–5.2%) and 7.6%
(7.0–8.1%) for drought and extreme heat, respectively (Fig. 2a). The
harvested area dropped 4.1% (4.0–4.3%) during droughts, but was not
significantly affected by extreme heat (Fig. 2b). This may be due to the
shorter duration of extreme heat relative to droughts—while approx-
imately one-third of droughts in this study spanned several years, all
extreme heat disasters took place within a single year. Droughts may
thus be more likely to last long enough to cause complete crop failure
and discourage planting, while extreme heat disasters, especially out-
side key crop developmental stages, may affect crop growth and reduce
yields without critically damaging harvests.
Our estimated yield deficits from EWDs cannot be directly com-
pared to previous studies of the impact of seasonal mean climate trends
over the same period20 (see Supplementary Discussion). However, we
derived a comparable measure to that reported previously21, and esti-
mated a yield sensitivity of 6–7% per 1 °C increase in seasonal mean
1Department of Geography, McGill University, Montreal H3A 0B9, Canada. 2Department of Geography, University of Sussex, Brighton BN1 9QJ, UK. 3Liu Institute for Global Issues and Institute for
Resources, Environment and Sustainability, University of British Columbia, Vancouver V6T 1Z2, Canada.
© 2016 Macmillan Publishers Limited. All rights reserved
7 JANUARY 2016 | VOL 529 | NATURE | 85
Letter reSeArCH
weather associated with extreme heat disasters, which suggests that our
observed extreme heat effects are not necessarily independent from
those detected in studies examining changes in seasonal temperatures
(Extended Data Fig. 3). Methodological differences and uncertainties
prevent us from drawing strong conclusions based on this comparison.
Our drought impacts, however, seem to be independent of previous
estimates that used seasonal weather anomalies (see Supplementary
Discussion).
Our results do not show significant production effects from extreme
cold and floods (Fig. 1c, d). A potential explanation for this is that
floods tend to occur in the spring in temperate regions as a result of
snowmelt, and cold weather susceptibility in most agricultural regions
is highest outside the growing season, which may render a sizeable
portion of the flood and extreme cold disasters analysed in this study
agriculturally irrelevant. The estimated lack of response may also be
an artefact of the spatial dimension of these disasters. While drought
and extreme temperature affect broad regions, floods are a function
of both weather and topography, and can be highly localized within
a country22. Since this study uses country-level agricultural statistics,
one may speculate that a more noticeable flood effect on sub-national
production is masked at the national scale.
Several additional analyses offer more detailed insights into the
effects of these EWDs on cereal production. Cereals in the more tech-
nically developed agricultural systems of North America, Europe and
Australasia suffered most from droughts, facing on average a 19.9%
production deficit compared to 12.1% in Asia, 9.2% in Africa, and no
significant effect in Latin America and the Caribbean (overall differ-
ence in means P = 0.02; Fig. 3a and Extended Data Tables 3 and 4). This
more severe production impact in the developed nations was driven
by a substantial yield deficit of 15.9%, with no significant reduction in
harvested area (Fig. 3b, c). We see three possible explanations for this
pattern. First, it may arise from a tendency among lower-income coun-
tries to encompass diverse crops and management across many small
fields, which may allow for some fields to resist drought better than
others. This might reduce the national drought sensitivity compared
to higher-income countries, where large-scale monocultures are more
dominant. Second, lower-income countries may better resist drought
because smallholders tend to use risk-minimizing strategies compared
to the yield-maximizing ones prevalent in higher-income countries.
Third, the pattern may relate to generally lower fair-weather yields in
lower-income countries. In Asia, we found a significant reduction of
8.8% in harvested area during droughts with no corresponding yield
deficit, suggesting that this region has a greater tendency for total crop
failure in the event of a drought rather than harvesting with reduced
yields
16
. The production effects in Africa did not correspond to signif-
icant deficits in either yield or harvested area.
While the production of all three crops was similarly affected by
droughts (5–6% deficit each; Fig. 4a, Extended Data Tables 2 and 5),
only maize was significantly affected by extreme heat (11.7% deficit,
P = 0.01) (Fig. 4d). Maize was also the only crop with significant yield
Drought (n = 222) Extreme heat (n = 32)
Extreme cold (n = 51)
Flood (n = 756)
Normalized compositeNormalized composite
ab
cd
1.2
1.1
1.0
0.9
0.8
1.2
1.1
1.0
0.9
0.8
1.2
1.1
1.0
0.9
0.8
10.1% 9.1%
Year from event Year from event
1.2
1.1
1.0
0.9
0.8 –3 –2 –1 0123 –3 –2 –1 01
23
Figure 1 | Influence of EWDs on national cereal production.
ad, Normalized production composites for drought (n = 222)
(a), extreme heat (n = 32) (b), flood (n = 756) (c) and extreme cold
disasters (n = 51) (d) over 7-year windows centred on the disaster year
(blue lines). Box plots depict the distributions of 1,000 false-disaster
control composites, with red crosses denoting extreme outliers, and red
dashes denoting medians. Production during drought and extreme heat
years was 10.1% and 9.1% below the control mean, respectively, whereas
no significant production signal was detected for floods or extreme cold.
Production resumed normal levels immediately after drought and extreme
heat. The increasing trend in production over the 7-year window reflects
the observed growth trend.
Year from event
ab
Year from event
Drought (n = 222) Extreme heat (n = 32)
Yield
Harvested area
123
0–1–2
–3
0.94
1.02
1.04
1.06
0.98
0.96
0.92
Normalized composite
12
3
0–1–2
–3
0.94
1.02
1.04
1.06
0.98
0.96
0.92
1.00
1.00
*
*
*
Figure 2 | Influence of EWDs on national cereal yields and harvested
area. a, b, Yield (blue) and harvested area (red) composites for drought
(n = 222) (a) and extreme heat (n = 32) (b), with significant points (those
lying beyond the control box plot whiskers) marked by asterisks (box plots
not shown for clarity). Drought was associated with significant deficits
in both yield and harvested area (5.1 and 4.1%), whereas extreme heat
revealed only significant yield impacts of 7.6% with no significant effect on
harvested area.
© 2016 Macmillan Publishers Limited. All rights reserved
86 | NATURE | VOL 529 | 7 JANUARY 2016
Letter
reSeArCH
effects (12.4%, P = 0.002) (Fig. 4b, e). We are hesitant to draw strong
conclusions based on this difference, as it may be due to differing var-
iance as well as mean (see Extended Data Table 6 and Supplementary
Discussion). Furthermore, it may reflect the fact that maize is generally
grown during summer months, which have the highest probabilities of
extreme heat as defined in EM-DAT, while wheat is grown during the
spring. Disaster data with monthly or daily resolution would enable us
to investigate whether this apparent susceptibility of maize is a result
of differing growing season.
Finally, more recent droughts (1985–2007) caused cereal produc-
tion losses averaging 13.7%, greater than the estimated 6.7% during
earlier droughts (1964–1984) (P = 0.008, Fig. 5), which may be due
to any combination of rising drought severity (although whether
drought severity has increased globally is presently debated)23–26,
increasing vulnerability
6,27
and exposure to drought
6
, and/or chang-
ing reporting dynamics (Extended Data Fig. 4). Sample size limita-
tions prevented us from repeating a regional and temporal analysis
for extreme heat.
Some limitations of our analyses are worth noting. First, we mainly
focus on four principal types of EWDs, but follow-up studies should
include tropical storms and extreme precipitation and wind events,
especially since they may have an increasingly important effect on
agriculture in the context of climate change
28
. Second, our estimates
are biased towards more recent disasters as they are more abundantly
reported in EM-DAT than older ones (see Extended Data Fig. 4 and
Supplementary Discussion). Third, we use EWDs from the EM-DAT
database, which collates disasters based on several criteria for sub-
stantial human impact (Methods). We may be underestimating the
true effects of EWDs if disasters are included mainly based on urban
impacts, or if extreme events occurring in sparsely populated areas are
less likely to qualify as disasters. Finally, since we observe agricultural
impacts at the national level, more notable local and regional effects of
disasters may be muted (but conversely, finding a signal at the national
level highlights the substantial influence of droughts and extreme heat).
Future studies may arrive at a more detailed estimate by using subna-
tional agricultural data, localizing the reported disasters within nations,
selecting disasters taking place during the growing season, and con-
trolling for severity of disasters. Linking the definitions of EWDs used
in this study with statistical meteorological definitions will also enable
a forecasting of future impacts.
Overall, there are four main conclusions from our study. First, over
the period 1964–2007, drought and extreme heat substantially damaged
national agricultural production across the globe. Within the frame-
work of this study, no effect on agriculture was identified from floods
and extreme cold. Second, drought reduced cereal yield and completely
damaged crops, whereas extreme heat only affected yield, reflecting
clear differences in the processes leading to overall production effects.
Third, this study highlights an important temporal dimension to these
impacts. While the damage to cereal production is considerable, this
effect is only short term, as agricultural output rebounds and continues
its growth trend after the disaster. Furthermore, we show that recent
droughts had a larger effect on cereal production than earlier ones.
Production Yield Harvested area
Drought
abc
d
ef
1
–3 230
–1
–2
Normalized composite
1.1
1.0
0.9
Extrem
e
heat
1.2
1.1
1.0
0.9
0.8
1.1
1.0
0.9
1.2
1.1
1.0
0.9
0.8
1.1
1.0
0.9
1.2
1.1
1.0
0.9
0.8
1
–3 230
–1
–2 1
–3 2
3
0
–1
–2
P = 0.61 P = 0.68 P = 0.65
P = 0.01 P = 0.002 P = 0.18
Year from event Year from event Year from event
Maize (n = 28)
Rice (n = 16)
Wheat (n = 32)
Maize (n = 208)
Rice (n = 171)
Wheat (n = 234)
**
*
*
*
*
Figure 4 | The influence of drought and extreme heat on maize, rice and
wheat. af, Drought and extreme heat composites of production (a, d),
yield (b, e) and harvested area (c, f) for maize (blue), rice (red) and wheat
(green), with significant points (those lying beyond the control box plot
whiskers) marked by asterisks (box plots not shown for clarity). P values
reflect significant differences between crops in disaster-year response
(Kruskal–Wallis test). Maize production (n = 28) responds more (P = 0.01)
to extreme heat than wheat (n = 32) and rice (n = 16), an effect driven by a
substantial yield deficit (P = 0.002). For drought data, maize (n = 208), rice
(n = 171) and wheat (n = 234).
Production Yield Harvested area
Year from event Year from eventYear from event
1–3 2 30–1–2 1–3 2
3
0–1–2
P = 0.02 P = 0.002 P = 0.13
abc
Normalized composite
1–3 2 30–1–2
1.10
1.05
1.00
0.95
0.90
0.85
0.80
1.10
1.05
1.00
0.95
0.90
0.85
0.80
1.10
1.05
1.00
0.95
0.90
0.85
0.80
Eur., N.Am., Aus. (n = 28)
Asia (n = 32)
Africa (n = 125)
L.Am. & Carib. (n = 37)
**
*
*
*
Figure 3 | A regional analysis of the influence of drought. ac, Regional
composites of production (a), yield (b) and harvested area (c) for
drought, with significant points (those lying beyond the control box plot
whiskers) marked by asterisks (box plots not shown for clarity). P values
reflect significant differences between regions in drought-year response
(Kruskal–Wallis test). The drought-year normalized production is 7.8%
and 10.7% lower (P = 0.02) in developed Western countries (n = 28)
than in Asia (n = 32) and Africa (n = 125) (a), a difference driven by a
significantly greater yield deficit (P = 0.002) (b). Meanwhile, the Latin
America (L.Am) and Caribbean (Carib.) region (n = 37) exhibits no
significant response to drought. Aus., Australasia; Eur, Europe; N.Am.,
North America.
© 2016 Macmillan Publishers Limited. All rights reserved
7 JANUARY 2016 | VOL 529 | NATURE | 87
Letter reSeArCH
Finally, our regional and crop-specific analysis finds that developed
nations suffer most from these EWDs.
Present climate projections suggest that extreme heat events will be
increasingly common and severe in the future
1
. Droughts are likely
to become more frequent in some regions, although considerable
uncertainty persists in the projections
6
. This study, by highlighting
the important historical effects of EWDs on agriculture, emphasizes
the urgency with which the global cereal production system must
adapt to extremes in a changing climate. Understanding the key pro-
cesses leading to such crop losses enables an informed prioritiza-
tion of disaster risk reduction and adaptation interventions to better
protect the most vulnerable farming systems and the populations
dependent on them.
Online Content Methods, along with any additional Extended Data display items and
Source Data, are available in the online version of the paper; references unique to
these sections appear only in the online paper.
Received 29 April; accepted 16 November 2015.
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Supplementary Information is available in the online version of the paper.
Acknowledgements We thank R. Below, who is in charge of the EM-DAT project
at the Centre for Research on the Epidemiology of Disasters, for sharing the
data. We thank C. Champalle for testing the original idea using data over East
Africa in a class project. This research was supported by a Discovery Grant from
the Natural Science and Engineering Research Council of Canada to N.R.
Author Contributions This research was designed and coordinated by N.R. All
authors performed analyses, discussed the results, and wrote the manuscript.
Author Information Reprints and permissions information is available at
www.nature.com/reprints. The authors declare no competing financial
interests. Readers are welcome to comment on the online version of the paper.
Correspondence and requests for materials should be addressed to
N.R. (navin.ramankutty@ubc.ca).
ab
Earlier (1964–1984)
n = 126
Later (1985–2007)
n = 121
Year from event Year from event
1.1
1.0
0.9
1.1
1.0
0.9
–3 –2 –1 0123 –3 –2 –1 01
23
Normalized composite
Figure 5 | A temporal analysis of the influence of drought.
a, b, Production composites for earlier (1964–1984, n = 126) (a) and
later (1985–2007, n = 121) (b) droughts, with boxplots of 100 respective
control composites. In later instances, mean drought-year production
losses were greater (13.7%) than in earlier instances (6.7%; P = 0.008,
Kruskal–Wallis test).
© 2016 Macmillan Publishers Limited. All rights reserved
Letter
reSeArCH
METHODS
Superposed epoch analysis (SEA) is used to isolate an average EWD response
signal using time series of national agricultural production data and EWDs. SEA is
a statistical approach that has been used to enhance the signal (that is, influence of
particular events) in time-series data, while reducing noise due to extraneous vari-
ables19. The EWDs are compiled from the Emergency Events Database (EM-DAT;
http://www.emdat.be/database) and consist of 2,184 floods, 497 droughts, 138
extreme heat and 194 extreme cold disasters from 177 countries over the period
1964–2007. EM-DAT collects information on a reported disaster if at least ten peo-
ple died, a state of emergency was declared, international assistance was called, or
at least 100 people were injured, made homeless or required immediate assistance.
Disaster reports are gathered from various organizations including United Nations
agencies, governments, and the International Federation of Red Cross and Red
Crescent Societies
8
. The agricultural data consist of country-level total production,
average yield, and total harvested area data for 16 cereals (http://faostat3.fao.org)
covering the 177 countries in the set of EWDs from 1961 to 2010.
From the time series of agricultural data, we extracted shorter sets of time series
using a 7-year window centred on the year of occurrence of each EWD, with 3 years
of data preceding and following each EWD. The data were normalized to the aver-
age of the 3 years preceding and following the event to remove the absolute magni
-
tude of national data from the signal. For multi-year droughts, we averaged across
all drought years to produce a single disaster year datum. For a 3-year drought,
for example, the 7-year window became a 9-year window with seven data points
(with the middle 3 years being averaged and assigned to year 0). The 7-year sets of
EWD time series were then centred on the disaster year and averaged year-wise to
yield single composited time-series of production, yield and harvested area for each
EWD type (a total of 12 composited time series). The averaging thus strengthens
the signal at the central year of EWD occurrence, while also cancelling the noise
in the non-disaster years preceding and following the event.
During compositing, points on individual time series co-occurring with another
disaster in the set were excluded from the mean. This procedure resulted in variable
sample size across the 7 years of the composites. For brevity, we have presented
mean sample sizes across all years; complete tabulated sample sizes are displayed in
Extended Data Tables 2 and 4. Our composited mean estimate does not seem to be
influenced by outliers (see Extended Data Fig. 1 and Supplementary Discussion).
The signal-to-noise strength will certainly depend on the sample size, and we
performed an analysis to estimate the influence of sample size (see Extended Data
Tables 2 and 4, Extended Data Fig. 2 and Supplementary Discussion).
In addition to average per-disaster estimates, we also calculated aggregate pro-
duction losses over specific time periods. For each extreme heat or drought, we
first applied the average per-disaster percentage loss estimate (different values for
extreme heat or drought) to the average national production across the six adjacent
non-disaster years. We then computed the aggregate drought or heat-related global
production loss for each year by summing the production losses for each disaster
over the given time period. We estimated the percentage of global production lost
to the EWDs relative to an estimated counterfactual global production in a world
without EWDs (the latter being the sum of observed global production plus the
estimated production loss).
The significance-testing procedure involved setting up a ‘control’ estimate by
randomly resampling the agricultural data using sets of fictitious disasters with
randomly generated years and countries of occurrence. The fictitious EWD time
series were averaged as for the true ones to yield composited ‘control’ time series,
and the entire process was repeated 1,000 times. We quantified EWD-year deficits
in production, yield and harvested area by subtracting the true EWD time series
from the mean of the controls. Excluding randomly generated disasters that hap-
pened to be real disasters systematically raised the impact estimates by ~1%; to
present a more conservative and rigorous detection of the disaster signal, we elected
not to exclude such pseudo-disasters. Note that we chose not to de-trend the time
series before compositing to remove technology-driven growth, but rather simply
estimate the disaster signal as difference from control (see Fig. 1). We estimated the
95% confidence intervals for our point estimates of impacts using an approach sim
-
ilar to a delete-one jackknife resampling method (see Supplementary Discussion).
The percentage significance of each estimate of the EWD composites relative
to control was estimated as the percentage of 1,000 control points less than the
EWD composite estimate for each year. Points with estimated significance of
<0.5% or >99.5% were considered significant deficits and surpluses, respectively,
corresponding to a two-tailed 99% confidence level. While we chose a two-tailed
approach for robustness, we found no significant surpluses. The significant points
appear as asterisks in Figs 2–4, while for Figs 1 and 5 we present the EWD compos-
ites with the distribution of controls represented as an array of box-and-whisker
plots for a visual representation of significance. The complete tabulated percentage
significance values are presented in Extended Data Tables 1, 3 and 5.
The earlier-versus-later analysis for droughts was performed by applying the
SEA procedure to the set of droughts divided roughly equally into earlier and later
halves. Similarly, the regional analysis was conducted by repeating SEA for full set
of disasters divided into four regional groupings, and the by-crop composites were
obtained by repeating SEA on the full disaster sets using crop-specific agricultural
data from the FAO (http://faostat3.fao.org). Statistical significance of differences
between crop-specific, regional and earlier-versus-later composites was assessed
using the Kruskal–Wallis test. We applied a quadratic transformation to the data
for comparison to equalize variance between groups (verified using Levene’s test),
and used non-parametric tests when comparing groups as normal assumptions
were not met (see Supplementary Discussion).
Code availability. All the core programs including codes to perform superposed
epoch analysis and the various statistics described in this paper are available on
Github (https://github.com/nramankutty/SEA-code).
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Letter reSeArCH
Extended Data Figure 1 | Distributions of individual responses to
drought and extreme heat. af, Histograms of disaster-year differences
from means of 1,000 resampled controls for drought (n = 222) (ac) and
extreme heat (n = 32) (df). A preponderance of moderately negative
values (falling towards the right of the red shaded areas) underlies the
negative mean disaster year signals, with a limited influence of extreme
cases (those at the left of the red shaded areas).
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Letter
reSeArCH
Extended Data Figure 2 | The influence of sample size on estimated
disaster effects. a, b, Estimated mean 16-cereal aggregated production
deficit for extreme heat (a) and drought (b) in 200 sub-samples with size
of (1, 2, …, n) (points). Dotted grey line shows the final estimated mean
production deficit (9.1% for extreme heat, 10.1% for drought). Most of the
initial variability at low sample sizes dissipates into the mean at well below
the actual sample size (n = 39 for extreme heat, n = 247 for drought).
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Letter reSeArCH
Extended Data Figure 3 | Seasonal weather anomalies of drought and
extreme heat disasters in EM-DAT. ac, Normalized composite mean
growing season temperature for extreme heat (n = 32) (a) and drought
(n = 222) (b), and total precipitation for drought (c). Box plots depict the
distributions of 1,000 false-disaster control composites, with red crosses
denoting extreme outliers and red dashes denoting medians. Years with
extreme heat correspond to seasonal temperature anomalies of 1.2 °C,
while drought years have only 0.15 °C warmer temperatures, with no
significant precipitation anomaly.
© 2016 Macmillan Publishers Limited. All rights reserved
Letter
reSeArCH
Extended Data Figure 4 | Time series of the number of extreme heat and
drought disasters per year from the EM-DAT database. The EM-DAT
database is based on a compilation of disaster reports gathered from various
organizations including United Nations agencies, governments and the
International Federation of Red Cross and Red Crescent Societies. The time
series of reported disasters per year exhibits an increasing trend, probably
the result of more complete disaster reporting in more recent decades with a
possible contribution from increasing disaster incidence. There is also large
inter-annual variability in the number of disasters.
© 2016 Macmillan Publishers Limited. All rights reserved
Letter reSeArCH
Extended Data Table 1 | Statistical significance of 16-cereal aggregate analysis
Percentage of points on control composites less than EWD composites for 16-cereal aggregate, 1,000 control replicates total.
© 2016 Macmillan Publishers Limited. All rights reserved
Letter
reSeArCH
Extended Data Table 2 | Sample sizes for individual crop and 16-cereal aggregate analyses
© 2016 Macmillan Publishers Limited. All rights reserved
Letter reSeArCH
Extended Data Table 3 | Statistical significance of regional analysis
Percentage of points on control composites less than EWD composites for 16-cereal aggregate by region, 1,000 control replicates total.
© 2016 Macmillan Publishers Limited. All rights reserved
Letter
reSeArCH
Extended Data Table 4 | Sample sizes for regional analysis
© 2016 Macmillan Publishers Limited. All rights reserved
Letter reSeArCH
Extended Data Table 5 | Statistical significance of individual crop analysis
Percentage of points on control composites less than EWD composites for individual crop analysis, 1,000 control replicates total.
© 2016 Macmillan Publishers Limited. All rights reserved
Letter
reSeArCH
Extended Data Table 6 | Kruskal–Wallis assumptions test results for group comparison analyses
© 2016 Macmillan Publishers Limited. All rights reserved
... Drought is considered the most devastating natural disaster for the economy (Dahlmann et al., 2019;Elijido-Ten & Clarkson, 2019;Hong et al., 2019;Lesk et al., 2016) and the most common natural disaster in the U.S. (Ding et al., 2011;Williams et al., 2020). 3 Recent academic research has documented that drought materially hikes up a firm's implied cost of equity (Huynh et al., 2020) and the cost of private debt (Do et al., 2021), and also increases audit fees . ...
... Williams et al. (2020) have reported that the U.S. is in the grips of a natural "megadrought" that began in year 2000 and is still continuing as a result of the climate crisis. There is considerable evidence demonstrating the adverse impacts of drought on economy (see, for example, Howitt et al., 2015;Lesk et al., 2016). In addition, the impacts resulting from drought have larger geographical dispersions compared to damages caused by other natural disasters (Wilhite, 2000). ...
... 9 Recent studies provide evidence of how drought brings about significant economic impacts at macro and individual firm levels. Lesk et al. (2016) examine severe weather disasters on global crop production and find that drought and extreme heat substantially damage agricultural production across the globe. Research also shows that drought is associated with an increase in a firm's cost of equity capital (Huynh et al., 2020), higher cost of debt (Do et al., 2021), and higher audit fees . ...
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Natural disaster events such as drought affect the broader economy and inflict adverse consequences for firms because of spill-over effects in an integrated economy. Contrary to the expectation that firms would engage in higher levels of corporate tax avoidance strategies when they experience a negative cash flow shock, we document consistent evidence that firms engage in less corporate tax avoidance when their headquarter states experience drought. Reduced tax avoidance is more pronounced among firms with higher CSR performance and among firms operating in states that experience a GDP decline. Collectively, the findings of our study demonstrate prosocial and ethical behavior of U.S. firms when they experience natural disaster events.
... Extreme weather events like droughts, waterlogging due to heavy rain, heat waves and frosts frequently cause severe crop yield and income losses on a global scale (Lesk et al., 2016;Lobell et al., 2011;Powell & Reinhard, 2016;Schlenker & Roberts, 2009). This also applies to European agriculture (e.g. ...
... Previous research identified that extreme weather events cause severe crop yield losses (e.g., Lesk et al., 2016;Lobell et al., 2011;Powell & Reinhard, 2016;Schlenker & Roberts, 2009). These analyses usually focus on specific crops and the extreme weather events to which they are particularly susceptible (Albers et al., 2017;Lobell et al., 2013;Lüttger & Feike, 2018;Mäkinen et al., 2018;Tack et al., 2015). ...
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Extreme weather events frequently cause severe crop yield losses, affecting food security and farmers’ incomes. In this paper, we aim to provide a holistic assessment of these impacts across various extreme weather events and multiple crops. More specifically, we estimate and compare the impact of frost, heat, drought and waterlogging on yields of winter wheat, winter barley, winter rapeseed and grain maize production in Germany. We analyse 423,815 farm-level yield observations between 1995 and 2019, and account for extreme weather conditions within critical phenological phases. Furthermore, we monetarize historical yield losses due to extreme weather events on a spatially disaggregated level. We find that drought is a main driver for farm-level grain yield and monetary losses in German agriculture. For instance, a single drought day can reduce winter wheat yields by up to 0.36%. It is estimated that during the period 1995–2019, summer drought led to yield losses in winter wheat, which, on average, caused annual revenues to sink by over 23 million Euro across Germany. We find that the impacts of extreme weather events vary considerably across space and time. For example, only the most important winter rapeseed production region in the North of Germany was prone to winter rapeseed yield losses due to heat during flowering. Moreover, waterlogging and frost are generally less relevant from an economic point of view, but can nevertheless cause crop- and regional-specific damage. Our analysis provides stakeholders with information for weather-related risk management and adaptation strategies.
... It has a pleasant taste and many health-associated characteristics such as antioxidant, anticoagulant, antitumor, immunomodulatory, and cholesterollowering properties (Sekara et al., 2015). It is expected that the duration and severity of droughts will increase, resulting in adverse effects on agriculture and causing significant declines in crop production on a global scale (Lesk et al., 2016). Thus, it is important to improve drought tolerance in organisms for global food security and necessary to clarify the physiological and molecular mechanisms of dessication tolerance. ...
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Drought stress is one of the main factors influencing the growth and development of an organism. Auricularia fibrillifera has strong dessication resistance. In A. fibrillifera under dessication-stress, the melanin content of fruiting bodies elevated significantly by >10-fold compared with the control. Folate content also increased sharply but decreased significantly after rehydration, and amino acid and biotin levels increased by 40.11 and 22.14%, respectively. In proteomic analysis, 1,572 and 21 differentially abundant proteins (DAPs) were identified under dessication-stress and rehydration, respectively. A large number of DAPs were annotated in “amino acid metabolism,” “carbohydrate metabolism,” and “translation” pathways, and the DAPs related to osmotic regulation and antioxidant enzymes were significantly increased in abundance. Transcriptome-proteome association analysis showed that most DAPs (30) were annotated in the “biosynthesis of antibiotics” pathway. DAPs and corresponding differentially expressed genes were all up-regulated in the “biotin biosynthesis” pathway and associated with “folate biosynthesis” and “phenylalanine, tyrosine, and tryptophan biosynthesis.” In the analysis of protein–protein interactions, the DAPs annotated in the “phenylalanine, tyrosine, and tryptophan biosynthesis” pathway had the strongest interactions with other DAPs. These enriched pathways could enhance amino acid, folate, biotin, and melanin levels during desiccation stress, which is consistent with the physiological data (amino acid, folate, biotin, and melanin contents). In addition, many DAPs related to the cytoskeleton were significantly increased in abundance under dessication-stress. Physiological and transcriptome data were in agreement with proteomic results. This work provides valuable insight into the dessication-tolerant mechanisms of A. fibrillifera.
... Heatwaves, floods, droughts: each of these types of events can propagate and cause e.g. crop failures (Lesk et al., 2016), further endangering human lives. Additionally, these events may occur simultaneously, in rapid succession, precondition each other or occur over a large area, all of which are discussed in the literature as 1 Figure 1.1: Projected changes in the intensity and frequency of extreme precipitation over land, from the Summary for Policymakers of the AR6 WGI report (IPCC, 2021a). ...
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Climate-driven extremes have become more common during the past few years. Even on the small scale of Belgium, it is clear that climate change exacerbates many extremes, such as droughts or floods. Consequently, the question arises how extremes will evolve under further climate change. By coupling different models, scientists can answer this question ever more precisely and with more certainty. Yet, there is much research yet to do, as there are many subquestions currently unanswered or with only an uncertain answer. Mathematical methods help to answer the remaining questions or to refine the answers. Stochastic methods are a very useful type of mathematical methods, as allow for expressing climate and weather in function of probability. This bypasses the complexity that can be found everywhere in Earth’s weather system. In this dissertation, two groups of stochastic methods are discussed which are applied later in the impact modelling chain. On the one hand, bias-adjusting methods allow for adjusting the biases in climate models. However, these methods are themselves affected by climate change. On the other hand, stochastic weather generators allow for extrapolating time series and thus a better extreme value inference. Both groups of methods can be of importance when studying climate impacts, but can simultaneously increase uncertainty. Although there are currently many challenges, such as heatwaves, droughts and floods, only the latter is focused on in this PhD. River floods have been shown to be an important challenge, whether it is on the global, European or Belgian scale. For example, the floods of July 2021 illustrated how our country is currently poorly adapted to such extremes. By focusing on extreme precipitation and floods, the problems discussed in this PhD and the resulting uncertainty on climate change impact, can be situated within a tangible framework.
... Global crop production could lower by 9-10%, which would worsen 4 1.1. Human and water the stability of global food security (Lesk et al., 2016). The increasing risks of these events cannot been seen as purely natural hazards anymore (Cai et al., 2014;Tabari, 2020). ...
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... Climate change and erratic weather patterns pose huge threats to global food security (Ehrlich and Harte, 2015;Lesk et al., 2016). Food production, however, needs to increase 60-70% to meet the growing global population in the 21st century (Bailey-Serres et al., 2019;Mittler, 2006;Zhu, 2016). ...
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