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ARTICLE
Received 1 Sep 2014
|
Accepted 28 Nov 2014
|
Published 22 Jan 2015
Climate variation explains a third of global crop
yield variability
Deepak K. Ray
1
, James S. Gerber
1
, Graham K. MacDonald
1
& Paul C. West
1
Many studies have examined the role of mean climate change in agriculture, but an under-
standing of the influence of inter-annual climate variations on crop yields in different regions
remains elusive. We use detailed crop statistics time series for
B
13,500 political units to
examine how recent climate variability led to variations in maize, rice, wheat and soybean
crop yields worldwide. While some areas show no significant influence of climate variability,
in substantial areas of the global breadbaskets,
4
60% of the yield variability can be
explained by climate variability. Globally, climate variability accounts for roughly a third
(
B
32–39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in
the relationship between climate variability and crop yield variability, highlighting where
variations in temperature, precipitation or their interaction explain yield variability. We dis-
cuss key drivers for the observed variations to target further research and policy interventions
geared towards buffering future crop production from climate variability.
DOI: 10.1038/ncomms6989
OPEN
1
Institute on the Environment (IonE), University of Minnesota, Saint Paul, Minnesota 55108, USA. Correspondence and requests for materials should be
addressed to D.K.R. (email: dray@umn.edu).
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H
ow mean historical and future climate change affects crop
yields has received a great deal of attention
1–5
. However,
how variations in climate impact crop yield, and how they
vary over time, has received less attention
6,7
. This is important
both to help us understand how climate and crop yields are linked
over time and also for ensuring future food security. In particular,
low-yield variability leads to stable farmer incomes
8–10
and food
supply
1,11
, and prevents price spikes that have disproportionate
adverse impacts on the globally food-insecure who are mostly
farmers
12,13
. In this study, we ask how much of the year-to-year
variability in observed crop yields is associated with variations in
climate across global croplands? Further, we investigate which
climatic variables—those related to warmth and growing season
length, or those related to rainfall and moisture availability—best
explain variations in yield across the world?
Previous analyses that have examined how crop yields and
climate were related
3,14–17
have typically used national and
regional data. For example, global studies are typically at the
country scale
1
, and provide little insight on the spatial patterns of
the within-country impacts. In contrast, analysis at the
subnational
16
, sub-subnational
2
or local sites is available for
specific countries only, and thus, provide little insight on global
patterns. Our study uses newly available temporal geospatial data
on crop harvested area and yields of four major crops (maize,
rice, wheat and soybean) across 13,500 different political units of
the world
12,13
—a major (
4
50x) increase in the level of spatial
detail from previous analyses that examined how crop yields and
climate were related
3,14–17
. Similar to other studies
1–2
, we
examine the recent historical period (1979–2008) but across
these 13,500 different political units of the world
12,13
. The
increased spatial resolution helps to identify where, and how
strongly, climate variability is correlated with variations in crop
yield in each of these political units. Given multiple breadbaskets
across the globe and globally traded commodities, our study
provides a consistent investigation of the differences both within
and across regions.
To examine how observed variations in yields were related to
climate variations, we used the Climate Research Unit’s (CRU)
18
gridded monthly data, and then re-mapped the data to the
B
13,500 political units where yield was measured. We explored a
range of statistical models relating observed de-trended variations
in temperature and precipitation during a crop’s growing season
and annual conditions to the observed de-trended variations in
yields at each political unit. Next we selected the ‘best-fit’ model,
and then conducted
F
-tests to determine the goodness-of-fit of
the selected model against the null model that assumes random
climate variability. We conducted this analysis at each of the
tracked 13,500 political units to draw conclusions on how much
of the crop yield variability was explained by climate variability.
Different aspects of climate variability—temperature, precipi-
tation, and the interaction of the two—may affect crop growth
and resultant productivity disproportionately. We classified how
yield variability was related to either normal or extreme
fluctuations in temperature or precipitation variability—or their
interactions. Here, linear and squared terms represent normal
and extreme variation, respectively, for example
1,5,16
. The ‘best-
fit’ model at each political unit was classified into one of seven
broad categories and then mapped globally: models where the
yield variability was explained by (i) normal temperature or
(ii) normal precipitation variations, but not both; models where
the yield variability was explained by (iii) normal and extreme
temperature or (iv) normal and extreme precipitation variations,
but not both; (v) where yield variability was explained by extreme
temperature or (vi) extreme precipitation variations, but not
both; and (vii) temperature and precipitation terms and their
combinations due to interactions between temperature and
precipitation.
We
further
developed
reduced
models
of
temperature and precipitation and mapped them at each
political unit. The resulting global maps, which identify where
and to what degree normal and extreme climate variability
explains yield variability, and quantifies them, can be used to
target research into causal relations between yield and climate
variability, and eventually policy interventions to stabilize farmer
incomes and food supply.
Averaged globally over areas with significant relationships, we
find that 32–39% of the maize, rice, wheat and soybean year-to-
year yield variability was explained by climate variability. This
translates into climate explained annual production fluctuations
of
B
22 million tons,
B
3 million tons,
B
9 million tons and
B
2
million tons for maize, rice, wheat and soybean, respectively.
Our spatially detailed assessment of the relationship between
climate variability and yield variability shows distinct spatial
patterns in the relative effects of temperature, precipitation and
their interaction within and across regions.
Results
Yield variability
. We first establish where and by how much crop
yields varied within countries and then identify how much of the
year-to-year variation in crop yields was explained by year-to-
year variations in climate.
In general the coefficient of variation, or yield variability
normalized by mean yields (Fig. 1), has been lower in the top crop
production regions of the world on account of their higher yields
(Supplementary Fig. 1) and conversely higher in the areas of
lower yields and of less consequence to global crop production
but exceptions such as the Australian wheat belt exist. Over the
last three decades, maize yields had a global average variability of
B
0.9 tons/ha/year (s.d.), which corresponds to
B
22% of the
global average yields of
B
4 tons/ha/year (Supplementary Fig. 1).
The highest coefficient of variation—which indicates greatest
relative variability—in maize yields was in areas outside the core
maize grain belts, including northeastern Brazil and in parts of
Africa, India, northeast Mexico and the southeast United States
(Fig. 1). The global average rice yield variability (standard
deviation) was
B
0.5 tons/ha/year (or
B
13% of average rice
yields). The coefficient of variation in rice yields was similarly
higher in more marginal rice-producing regions such as north-
eastern Brazil and central India. In contrast, some wheat regions
with high coefficient of variation in yields such as in Australia and
the Great Plains states of the United States (U.S.) are key global
wheat breadbaskets (Fig. 1). The global average wheat yield
variability (s.d.) was 0.4 tons/ha/year (
B
17% of average yields
over the study period). In the top soybean production areas of the
world such as in the Midwestern U.S. and Latin American
countries, the coefficient of variation was low.
Climate explained yield variability
. Not all crop growing regions
showed statistically significant influence of year-to-year variations
in climate on crop yield variability as determined from con-
ducting
F
-tests (using a threshold of
P
¼
0.1;
B
13,500 political
units
30 years sample size, Fig. 2). However, the vast majority
of crop harvesting regions did experience the influence of climate
variability on crop yields:
B
70% of maize harvesting regions,
B
53% of rice harvesting regions,
B
79% of wheat harvesting
regions and
B
67% of soybean harvesting regions. The percentage
of global total average production harvested over these regions
and thus influenced by climate variability was
B
78% of maize,
B
52% of rice,
B
75% of wheat and
B
67% of soybean. In specific
locations, within the top global crop production regions, climate
variability accounted for
4
60% of the variability in a crop’s yield,
though there were also political units where climate impacts have
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been statistically insignificant (Fig. 2). Where and how much of a
crop’s yield varied on account of climate has been highly location-
and crop–specific, and we describe this in greater detail in the
subsequent sections.
Averaged globally over areas with significant relationships, we
find that 32–39% of the maize, rice, wheat and soybean year-to-
year yield variability was explained by climate variability
(Supplementary Data). Climate variability in general explains
rice yield variability the least.
Regional variations in maize
. Approximately 75% of the global
maize production is concentrated in
B
57% of the harvested areas
comprising the American Midwestern region, central Mexico,
southern Brazil, the maize belts of Argentina and China, parts of
Western Europe and South Africa, and some areas of India and
Indonesia. In these major maize grain belts,
B
41% of the total
year-to-year yield variability (0.8 tons/ha/year) was explained by
inter-annual climate variability. Approximately 50% of global
maize production is concentrated in a proportionally even smaller
B
31% of high yielding maize belt comprising primarily two
regions—the American Midwest and the Chinese Corn Belt—and
in these two regions
B
42% of the corresponding yield variability
(0.9 tons/ha/year) was explained by climate variability.
In some specific political units within these maize bread-
baskets, more than 60% of the yield variability has historically
been related to climate variability including numerous counties of
the U.S. Midwestern states, and in Shanxi, Hebei and Shandong
provinces of China (Fig. 2a); political units with
4
75% of the
yield variability explained by climate are also present, for
example, many counties of Midwestern U.S.
When averaged over all the statistically significant maize
harvested areas with climate variability impacts globally 39% of
the yield variability was explained and in the top ten global maize
producing nations we find the following (Supplementary Data):
in the United States, France and Italy 41–49% of the observed
maize yield variability can be explained by climate variability,
whereas in South Africa it was
B
50%, and in Argentina and
China it was 32 and 44%, respectively (Fig. 2a and see
Supplementary Data).
In the upper and eastern Midwest of the United States and
Canada extreme temperature variability was more important,
whereas in the central and western parts of Midwestern U.S.
extreme precipitation variability explained maize yield variability
in more counties (Fig. 3a); overall temperature variability was
more important for explaining maize yields in the upper and
eastern Midwest of the United States and precipitation variability
was more important in the central and western Midwest
(Supplementary Figs 2 and 3). Temperature variability influenced
maize yield variability more in some colder countries such as in
Canada, but also in some warmer countries such as Spain and
Italy with within-country variations.
Regional variations in rice
. Approximately 75% of global rice
production was from China, India and Indonesia. Averaged over
all rice-harvesting areas with statistically significant climate
influence (around 52% of global rice harvested areas), we estimate
that yields have varied by
B
0.1 tons/ha/year. Year-to-year
climate variability explains
B
32% of rice yield variability globally
(Fig. 2b) with precipitation variability explaining more of the
variability in South Asia and temperature variability more of the
0.05
0
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Coefficient of variance of yield
Maize
Rice
Soybean
Wheat
0.05
0
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Coefficient of variance of yield
0.05
0
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Coefficient of variance of yield
0.05
0
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Coefficient of variance of yield
Figure 1 | Coefficient of variation of crop yields over the entire study period.
The ratio of the s.d. of yield over the 30-year period to the average yield
over the same period. (
a
) maize, (
b
) rice, (
c
) wheat, (
d
) soybean (sample size of
B
13,500 political units
30 years per crop). White areas indicate where
the crop is not harvested or analysed. Details on crop yields are given in reference
13
.
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No effect
>0.00–0.15
Maize
Rice
Soybean
Wheat
>0.15–0.30
>0.30–0.45
>0.45–0.60
>0.60–0.75
>0.75
No effect
>0.00–0.15
>0.15–0.30
>0.30–0.45
>0.45–0.60
>0.60–0.75
>0.75
No effect
>0.00–0.15
>0.15–0.30
>0.30–0.45
>0.45–0.60
>0.60–0.75
>0.75
No effect
>0.00–0.15
>0.15–0.30
>0.30–0.45
>0.45–0.60
>0.60–0.75
>0.75
Figure 2 | Total crop yield variability explained due to climate variability over the last three decades.
A value of 1.0 implies that the entire variability in
observed yields was explained by climate variability (coefficient of determination metric; sample size of
B
13,500 political units
30 years per crop).
Similarly a value of 0.30–0.45 implies 30–45% of the variability in yields was explained by climate variability. We cutoff the range at 0.75 (or 75%)
and above to a single categorical colour. No effect implies that at the
P
¼
0.10 level, there was no statistical difference between the best fit model and
the null model in the political unit. White areas indicate where the crop is not harvested or analysed. (
a
) maize, (
b
) rice, (
c
) wheat, (
d
) soybean.
Normal T
Maize
Wheat
Rice
Soybean
Normal P
Normal + extreme T
Normal + extreme P
Extreme T
Extreme P
Complex
Normal T
Normal P
Normal + extreme T
Normal + extreme P
Extreme T
Extreme P
Complex
Normal T
Normal P
Normal + extreme T
Normal + extreme P
Extreme T
Extreme P
Complex
Normal T
Normal P
Normal + extreme T
Normal + extreme P
Extreme T
Extreme P
Complex
Figure 3 | Selected models explaining crop yield variability classified into seven categories of temperature and precipitation variations.
White areas
indicate where the crop is not harvested or analysed. (
a
) maize, (
b
) rice, (
c
) wheat, (
d
) soybean. Regions where models with only normal temperature (T)
terms are selected are shown in yellow colour; regions where models with normal and extreme temperature (T
2
) terms are selected are shown in tan
colour; regions where models with only extreme temperature terms are selected are shown in red colours. Similarly, regions where models with
normal, normal and extreme, and only extreme precipitation (P) terms are selected are shown in the maps with different shades of blue. Regions where
models with both temperature, precipitation and their interactions terms were selected are shown in purple colour.
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variability in Southeast and East Asia (Fig. 3b and Supplementary
Figs 2 and 3). In some key rice producing nations, however,
climate variability was more important: in 80% of the rice har-
vested areas in Japan climate variability was statistically sig-
nificant. Averaged over these areas we find that
B
79% of the rice
yield variability (
B
0.2 tons/ha/year) was explained, whereas, in
South Korea,
B
47% of the yield variability was explained by
climate variability (Fig. 2b). In both countries temperature
variability was more important (Supplementary Figs 2 and 3). In
India, China, Indonesia, Thailand, Brazil, Cambodia, Peru and
Spain, 25–38% of the yield variability was explained by climate
variability (Supplementary Data). As with maize, there are
specific regions where
4
60% of yield variability was explained by
climate variability such as in the central Indian states of Madhya
Pradesh, Chhattisgarh and Karnataka (Fig. 2b).
Regional variations in wheat
. Approximately 75% of global
wheat production came from
B
66% of the harvested lands in the
United States, Canada, Argentina, Europe, North Africa, India,
China and Australia. In these highly productive wheat belts,
B
36% of the year-to-year yield variability was explained by
climate variability (Fig. 2c). Approximately 34–45% of the wheat
yield variability in the United States, Canada, United Kingdom,
Turkey, Australia and Argentina was explained by climate
variability. To give an indication of the magnitude of this effect,
the climate driven variability in the United States wheat yields
equates to, on average, more than half the entire annual
production of wheat in Mexico.
In the more productive regions of some countries the
variability explained by climate was even higher. In the most
productive Australian wheat belt (among the top 50% of global
wheat producers) climate variability explained
B
43% of the total
yield variability and in parts of Western Australia it was
4
60%.
In Western Europe in the United Kingdom, France, Germany,
Spain and Italy, climate variability explained
B
31–51% of the
wheat yield variability. In Eastern Europe and the former Soviet
Republics such as the Russian Federation, Ukraine, Kazakhstan
and in Hungary, 23–66% of the wheat yield variability was
explained by climate variability and normal and extremes of
temperature variability was important (Fig. 3c). Although
temperature variability in Western Europe was in general more
important, precipitation variability also explained part of the
wheat yield variability with the exception of Spain where
precipitation variability was the dominant factor (Fig. 3c and
Supplementary Figs 2 and 3). In India and China, the top two
global wheat producers, we detected statistically significant
relationships in 71 and 62% of their wheat harvested lands and
on average 32 and 31% of yield variability, respectively, was
explained by climate variability. In China precipitation variability
explained most of the variability; in India temperature and
precipitation variability were equally important (Fig. 3c). Aver-
aged globally, climate variability explained
B
35% of the wheat
yield variability.
Regional variations in Soybean
. Approximately 50% of the
world’s total soybean production was harvested from
B
42% of
the land concentrated in only three countries: the United States,
Brazil and Argentina. Adding soybean lands in India and China
made up 75% of the top soybean production global areas.
In general, the variability in soybean yield related to climate
variability was higher in Argentina (
B
43% of yield variability of
0.5 tons/ha/year averaged over all areas with statistically sig-
nificant climate influence, but
B
47% when averaged over the
most productive areas), followed by
B
36% of the yield variability
explained over all the statistically significant U.S. soybean areas.
Approximately 26–34% of the yield variability in Brazilian, Indian
and Chinese soybean yields was explained by climate variability
with locations of substantially higher variability explained present
in all countries (Fig. 2d).
Discussion
We show how much of the year-to-year variability in crop yields
was associated with climate variability within and across regions.
As the demand for crops increases globally
19
and productivity
gains fail to keep pace with projected demands
12
, ensuring the
stability of national food supplies and farmer livelihoods to
variable production will be even more important. Low global food
stocks in conjunction with fluctuation in agricultural production
can, in particular, contribute to food price spikes
20,21
. Regions
with high crop yield variability would disproportionately
contribute to this effect especially if they are also the
major breadbaskets of the world
20,21
. Even in regions with
comparatively lower yields, fluctuations in crop production may
impact the local food security. Our study is unique in giving a
global spatially detailed account of where and by how much crop
yields have varied and how much of this was driven by climate
variability.
We found that there were numerous regions where climate
variability explained more than 60% of the yield variability in
maize, rice, wheat and soybean (Fig. 2). Many of these regions
were in the most productive global areas such as Midwestern U.S.
and the Chinese Corn Belt for maize, and Western Europe and
Australia for wheat.
Our study identifies unique spatial patterns in the effects of
temperature and/or precipitation variability on yields—for
example, rice and wheat in India (Fig. 3b,c) as well as maize
and soybean in the United States (Fig. 3a,d). Our simple
classification of the prevailing relationships between climate and
crop yields enables digging deeper into trends for particular
regions. While relatively high resolution compared with past
research our results are constrained by the resolution of the data,
which is at the political unit and monthly climate data. Within
political units, at specific field/subnational locations, the climate
variability impact could be higher or lower.
The 32–39% of the yield variability explained by climate
variability translates into large fluctuations in global crop
production. For example,
B
39% of the maize yield variability
of 0.6 tons/ha/year explained by climate variability over 94
million ha translates into an annual fluctuation of
B
22 million
tons in global maize production over the study period. Similar
climate variability driven average annual rice, wheat and soybean
production variability is
B
3, 9 and 2 million tons, respectively.
These average fluctuations are similar to the total maize
production of many Latin American and African countries or
the total rice production of some Asian countries or total wheat
production of some Eastern European countries. In some cases
the impact of climate variability is higher in poorer regions such
as in northeastern Brazil for maize, and Central India for rice.
However, even in the most productive global areas such as wheat
in Western Europe and maize in the United States Midwest the
influence of climate variability on yield variability is very high and
in specific political units
4
75%. The following section discusses
our regional and continental findings in the context of previous
smaller-scale research, which we use to help validate/corroborate
our results and explore possible drivers.
In the North China Plains (provinces of Hebei, Henan,
Shandong, Beijing, Tianjin and Shanxi) though crops are
irrigated
22
, water availability is a major problem
23
. Maize is a
summer crop in this region and monsoonal rainfall supplements
river
and
groundwater
irrigation.
High
growing
season
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temperature is common. Hence, both the temperature and
precipitation variability controls maize yield variability in the
North China plains. To the west of the North China Plains, in the
more arid Loess Plateau region, adaptation strategies to the arid
climate and the coincidence of rainfall during the later stages of
crop growth
24
lead to normal and extreme temperature variability
being a better explanation of maize yield variability in some areas
of Gansu and Ningxia and all of Shaanxi. Although it may appear
counter-intuitive that temperature variability would dominate for
rainfed maize, it is consistent with findings for rainfed maize
areas in the United States
25
where extreme temperature was
found to be a better predictor of maize grain yield due to its
control on soil water demand and transpiration rates. In contrast,
wheat is a winter crop and is highly dependent on irrigation in
the North China Plains. What our analysis shows is more
dependence on precipitation variability for wheat yields, which
may be due to the direct controlling influence on surface
irrigation water availability. In northeastern China (provinces of
Heilongjiang, Jilin, Liolin) maize and soybean are not widely
irrigated so precipitation variability was important, but rice is
irrigated so temperature variability became more important.
In Japan almost all the paddy rice crop is irrigated
22,26
and
hence temperature variability was more important compared with
precipitation variability. South Korean harvested rice is similarly
mostly irrigated and thus temperature variability was more
important for explaining rice yield variability. In Indonesia the
variability in rice yield explained by climate variability is often
low (in the 0 to 15% range only) and the explanation is on
account of temperature variability
27
except in some parts such as
Central Java where precipitation variability is also important
28
.
This is because rice is widely irrigated in Indonesia
22,29
.
In South Asia, especially northwest India, temperature
variability influences wheat yield variability widely, similar to
other findings
30
but further south in central and south India
precipitation variability in general is more important as between
half and three-fourths of wheat is rainfed winter wheat compared
with only a few percent in the northwest. Rice yield variability is
more influenced by precipitation variability in India indicating
the rainfed paddy growing conditions. In the more irrigated
parts
22
as in northwest India precipitation and temperature
variability or only temperature variability was important. In the
extreme southwestern parts of India similarly precipitation
and/or temperature variability was important as this region
receives very high rainfall. Temperature variability was the
important factor for rice yield variability in Bangladesh due to
high availability of water and intense irrigation controlling the
influence of precipitation variability. In some of the highly
irrigated rice areas in India such as areas of West Bengal state,
and the Mahanadi system in northern Orissa, climate variability
was not even statistically significant (Figs 2b and 3b).
In Australia wheat yield variability is largely explained by
precipitation variability as the wheat is grown under rainfed
conditions
22
and in agreement with previous findings
31,32
;
controlling for precipitation variability, however, temperature
variability was also an important factor in explaining wheat yield
variability
33
especially in parts of Western Australia, South
Australia and Queensland.
We found that maize yield variability is explained best by
normal and extreme precipitation variability related to ENSO in
many countries of Africa similar to previous findings as
in Zimbabwe
34
, which in turn is related to sea surface
temperature
35
. In South Africa maize is grown primarily in the
Highveld region with drier conditions in the west and wetter
conditions in the east
36
. Our analysis reflects these conditions
with precipitation variability being more important in the drier
west and temperature variability more important moving towards
the wetter eastern provinces of South Africa’s Highveld.
Moreover, high maize yield variability in South Africa has been
a concern
36
; indeed, we found that climate variability explained
4
60% of maize yield variability in the Highveld region especially
in the drier western parts of the Highveld of South Africa.
Elsewhere, as in Kenya, we found that maize yield variability
was explained only by a complex relationship between both
precipitation and temperature variability consistent with previous
studies
37
. In Cameroon in West Africa and in northeastern
Nigeria precipitation variability alone does not explain maize
yield variability agreeing with previous findings
38,39
because,
while rain is beneficial for stable maize production, it also triggers
nitrogen leaching from nutrient poor soils, leading to a negative
feedback. In many of the other West African countries rainfall
variability explains maize yield variability but analyses show that
this was not the case everywhere and neither does climate
variability explain maize yield variability in all countries here as
farmers adopt various management strategies to overcome the
high rainfall variability
40
. However, other than Nigeria, our
analysis in West Africa was only at the country level and within-
country explanatory skill was lost on account of the scale of the
available yield statistics. Overall, precipitation variability is more
important in sub-Saharan Africa, pointing to the predominantly
rainfed system of maize cultivation
41
.
In most of the Eastern Europe and many regions of Western
European countries, the effect of temperature variability in
explaining wheat yield variability was more important as also
found in previous regional and global studies (refs 42,43, Fig. 3c).
This is because of the continental climate of Eastern Europe,
which causes a greater amplitude of temperature variability
44
.
Our study shows that normal, both normal and extreme, and
extreme temperature variability was important in explaining
wheat yield variability. In Southern Europe and in the
Mediterranean regions in addition to heat stress the water
limiting
conditions
that
are
common
44–46
resulted
in
precipitation variability also being important for wheat yield
variability. The influence of climate variability on wheat yield
variability was not statistically significant everywhere. Neither was
the explained variability in statistically significant areas high
everywhere. This was because farmers are already adapted, or
adapting, to climate change
47
, which has made them also more
adapted to variability. In the United Kingdom either precipitation
variability or both temperature and precipitation variability
explained
B
45% of wheat yield variability; the precipitation
variability is in turn related with the North Atlantic Oscillation
48
.
Maize is partly irrigated in France, but irrigation does not fully
mitigate dry conditions
49
; hence precipitation variability is
important and also because irrigated maize areas have only
recently increased in area and thus historically precipitation
variability could not be compensated as effectively as more
recently. The net result is that in many maize areas of France
historically both temperature and precipitation variability are
important
50
.
In the United States climate variability was important especially
in the Midwestern U.S. for maize yields. While in the upper
Midwest temperature variability was more important, in the
central Midwest precipitation variability was more important. In
Nebraska, a U.S. Great Plains state with a prevalence of irrigated
maize in its western part, temperature variability was more
important than in the eastern parts where precipitation variability
was more important. Many of the counties of the Great Plains
states with dryland maize meet their crop water demands partly
from irrigation
51
and we identify large number of counties where
both precipitation and temperature variability was important. In
other rainfed maize-producing countries normal and extreme
temperature conditions explained maize yield variability due to
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increased soil water demand that raised transpiration rates and
vapor pressure deficits
25,52
. Overall only temperature variability
explained maize and soybean yield variability in more harvested
regions (
B
37 and 38%, respectively) compared with precipitation
only explained regions (
B
31 and 36%, respectively); climate
variability explained part of the yield variability in
B
91% of the
U.S. maize harvested areas and 82% of soybean harvested areas.
Less adaptation of farmers to increasingly warmer temperatures
may explain why in larger areas temperature variability was
important
53
.
Only
B
46% of the maize harvested regions of Mexico have
crop yield variability influenced by climate variability (
B
27% of
the yield variability was explained). Precipitation variability was
more important overall, but pockets of regions where temperature
variability was more important exists such as in Sinaloa where
irrigated maize is important, and in Guerrero. Temperature
variability explained maize yield variability also in most Central
American countries. Further south in Brazil, precipitation
variability was more important overall; in specific regions
temperature variability is overall more important such as Mato
Grosso state due to its wetter climate, although in
B
23% of
Brazil’s maize harvested areas both temperature and precipitation
variabilities were important in explaining part of the maize crop
yield variability. In Argentina both temperature and precipitation
variabilities were equally important overall, though in specific
locations temperature variability was more important presumably
due to irrigated maize.
Although this is the most spatially detailed global assessment of
the links between historical climate variability and yield done to
date, our study has some limitations. For example, our estimation
of crop yield variability due to climate variability may under-
estimate the importance of climate variability impacts at specific
locations within political units. Future studies should investigate
this problem at an even finer resolution globally, but this is
challenging given historical yield data availability.
In some countries both crop yield and weather data may have
quality issues
13,18
. Our study is based on yield data at the county/
district/municipal/department or larger political unit level, so we
used crop harvested area weighted gridded weather data for the
political units. However, weather data from individual stations
could give a distinct climate-yield response signal due to its very
localized scale. To test this latter issue, we carried out a separate
analysis using daily station data from
B
100 U.S. counties
54
that
contributed to
B
25% of total U.S. maize production. We found
statistically significant correlation (
r
¼
0.54;
P
¼
0.001) between
analyses conducted by the two different data sets. A stronger
relationship is likely not present with station data analysis due to
the sheer size of some political units and lack of complete
coverage, which is present in gridded data (Supplementary Fig. 4).
As our yield was measured at the political unit, the use of the
harvested area weighted gridded weather data
18
for each political
unit is appropriate, similar to previous upscaling usage
1
, and the
likely reason that we typically found a stronger statistical
relationship with yields over time (Supplementary Fig. 5). In
contrast, the use of station data would be appropriate if crop
yields were measured at same sites or locally, and the direct use of
gridded data then less appropriate without downscaling.
While climate variability is a significant factor and responsible
for 32–39% of global crop yield variability, it is certainly not the
only controlling factor
55
. Our study only considered broad
precipitation and temperature effects though in an unprecedented
spatial detail; however, there are myriad other factors that could
influence climate-yield relationships, as informed by more local
scale research. Our study does not consider factors such as
changing cloud cover (and solar radiation), wind speed, surface
ozone exposure
56
, or decomposed the basic climate variables of
temperature and precipitation further into the timing of heat
stress
57
, the timing of dry and wet spells
58
, or soil moisture
59
.
We have also not considered the amplification or dampening of
climate variability impacts via other agronomic challenges such as
pest and pathogen infestation
60
and irrigation
61
. Climate change
may also have influenced how frequently crops are harvested
62,63
,
for example, now allowing double cropping in hitherto colder
single cropped regions, but we were unable to include such
precision as the only globally available crop calendar
64
was static,
even though we updated it using the most recent information
available (see Supplementary Fig. 6). Other factors to consider in
future studies are altitudinal effects
65
and the quality of crop
yields
48
. What we have investigated is the influence of the
variability of temperature and precipitation on crop yield
variability. The unexplained yield variability includes the
numerous agronomic challenges and decisions that farmers
make each year such as the availability and use of agronomic
inputs
57
, pest and pathogen infestations
60,66
, soil management
66,67
,
irrigation
61
, distribution of varied crop maturity types
68
, socio-
economic conditions
55,63,69
and political or social strife
13
.
Our study therefore is an initial assessment to identify
locations worldwide where historically climate variability has
been relatively important in explaining crop yield variability.
From the perspective of stabilizing farmer incomes and national
food supply and security, this new high-resolution information at
the global scale should help direct further research and policy
more effectively to those regions where climate variability poses
the greatest risk and provide leverage points
70
in the most critical
regions. If climate variability is predicted to increase in the same
regions where climate variability historically explained most of
the crop yield variability, strategies to stabilize crop production
should be prioritized to ensure stable future crop production and
prevention of future food price spikes. The high-resolution
models that we have built may be used to evaluate future climate-
related yield variability research, provide cross-comparison
against the results of crop simulation models and address
alternate factors contributing to the spatial heterogeneity in
climate-yield response.
Methods
Modelling set-up
.
Further details regarding the data used are given in
Supplementary Methods 1. To determine how much of the variability in crop yields
was explained by climate variability, we first detrended the crop yield and climate
variables—temperature and precipitation—following
1
(see the example in
Supplementary Fig. 7) over the period 1979–2008. Note that we use two forms of
temperature and precipitation—the seasonal or growing season average value and
the average conditions 12 months before harvest or the annual value to account for
antecedent conditions. This resulted in four different combinations of detrended
climate variables, and as we used both the linear and squared forms of seasonal and
annual temperature and precipitation there was a total of eight forms of climate
variables. We used these detrended variables in different combinations to linearly
regress with the detrended crop yields at each of the 13,500 political units. To avoid
over-fitting we limited our analysis to a total of 27 combinations of climate
variables, resulting in 27 regression equations, to capture the relationships between
climate variability and crop yield variability at each political unit and of the
basic form:
Y
c
¼
f T
c
;
P
c
ð
Þ
ð
1
Þ
where
Y
c
is the observed set of detrended crop yields for crop ‘c’ in units of tons/ha/
year at each political unit;
In equation 1,
T
c
can represent for crop ‘c’ at a given political unit the
temperature associated with the main growing season
64
or the temperature for 1
year before the crop’s harvest to capture antecedent conditions.
P
c
similarly is the
precipitation for the main growing season for the crop ‘c’ for the political unit and
for 1 year before the crop’s harvest. The function f is limited to linear and quadratic
forms of these two detrended meteorological parameters, as is common practice in
studies correlating climate and agricultural production
1,5,16
. The terms included in
each of the 27 regression equations and their classification are provided in the
Supplementary Table and further details are given in Supplementary Methods 2.
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Statistical tests
.
The generated regression equations at each political unit, for
example,
B
13,500 sets of 27 equations per crop, were statistically tested next. We
first identified which functional form of
Y
c
¼
f
(
T
c
,
P
c
) from the set of 27 equations
at each political unit fit the data best using the Akaike Information Criterion (AIC),
which penalizes equations with more terms. However, because the model that best
fits the data may be no better than a random climate (null model), we conducted
F
tests at the
P
¼
0.10 level to determine whether the chosen model was sig-
nificantly better than the null model. In 21–47% of the global crop-harvested areas,
we found that the chosen model was no better than the null model at the
P
¼
0.10
significance level. Thus, in the remainder 53–79% of global crop harvested areas
yield variability is significantly influenced by climate variability over the study
period and our reported numbers are averages over these areas.
Using the statistically significant model with the best functional representation,
we next determined the coefficient of determination (
r
2
) or explanatory power of
the complete model, and the reduced models containing only temperature and only
precipitation terms. The residual is the unexplained yield variations.
The 30-year study period average harvested area and yield information at each
subnational location was used together with the observed coefficient of
determination for computing national and global harvested areas weighted
averages. Global and country-specific numbers are averaged only over those
53–79% of global crop harvested areas where the statistical models were significant.
Model bias and sensitivity
.
As a simple assessment of model bias, we performed
a bootstrapping exercise to assess the influence of including specific combinations
of years (80% of the years selected at each iteration) in our data on the overall yield
predictions (using a test set of 20% of the years) for each political unit, which we
standardized as the ratio of the average bias from the 99 repetitions to the average
of the crop yields for the study period in each political unit (Supplementary Fig. 8).
This is analogous to a leave-group-out cross validation approach used to examine
uncertainty in model selection. Locations of models with more restrictive
P
cutoff
values (
F
-tests) at
P
¼
0.01 and
P
¼
0.05 are shown in Supplementary Fig. 9. In
general, even though we used a less-restrictive
P
value of 0.1, the models selected
generally were significant at
P
¼
0.05 or less.
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Acknowledgements
We thank George Allez, Peder Engstrom, Maryan Abdi, Lewei He and Madison Rogers
for help in collecting and digitizing a portion of the data. We thank Peder Engstrom for
help with plotting Supplementary Fig. 4. This work greatly benefitted from discussions
with Jonathan Foley. We also benefitted from the comments of Kimberly Carlson, Lijun
Zuo and other colleagues. Research support was primarily provided by the Gordon and
Betty Moore Foundation and the Institute on Environment. Additional research support
was from the McKnight Foundation,the Grantham Foundation for the Protection of the
Environment, the World Wildlife Fund, The Nature Conservancy, Stanford University,
the National Science Foundation (NSF), the Department of Energy (DOE) and the
National Aeronautics and Space Administration’s (NASA) Interdisciplinary Earth
Science program. Contributions by General Mills, Mosaic, Cargill, Pentair, Google.org,
Kellogg’s, Mars and PepsiCo supported stakeholder outreach and public engagement.
G.K.M. was also supported by the Natural Sciences and Engineering Research Council
of Canada.
Author contributions
D.K.R. led the effort to design, conduct, analyze and write this report. J.S.G., G.K.M.,
P.C.W. contributed to statistical designing and writing. All authors analyzed and
commented on the results.
Additional information
Supplementary Information
accompanies this paper at http://www.nature.com/
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Competing financial interests:
The authors declare no competing financial interests.
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How to cite this article
: Ray, D. K.
et al.
Climate variation explains a third
of global crop yield variability.
Nat. Commun.
6:5989 doi: 10.1038/ncomms6989
(2015).
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NATURE COMMUNICATIONS | DOI: 10.1038/ncomms6989
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NATURE COMMUNICATIONS
| 6:5989 | DOI: 10.1038/ncomms6989 | www.nature.com/naturecommunications
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