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the global dataset of historical
yields for major crops 1981–2016
toshichika Iizumi
1
✉
& toru Sakai
2
Knowing the historical yield patterns of major commodity crops, including the trends and interannual
variability, is crucial for understanding the current status, potential and risks in food production in the
face of the growing demand for food and climate change. We updated the global dataset of historical
yields for major crops (GDHY), which is a hybrid of agricultural census statistics and satellite remote
sensing, to cover the 36-year period from 1981 to 2016, with a spatial resolution of 0.5°. Four major
crops were considered: maize, rice, wheat and soybean. The updated version 1.3 was developed and
then aligned with the earlier version 1.2 to ensure the continuity of the yield time series. Comparisons
with different global yield datasets and published results demonstrate that the GDHY-aligned version
v1.2
+
v1.3 dataset is a valuable source of information on global yields. The aligned version dataset
enables users to employ an increased number of yield samples for their analyses, which ultimately
increases the confidence in their findings.
Background & Summary
Crop yield (production per unit harvested area) is an essential variable in many disciplines. Global yield data-
sets for the historical past have increasingly been used to analyze climate-crop relationships, food production
potential, food supply and demand, carbon and nitrogen cycling, greenhouse gas emissions from agriculture
and land-use change. Recently, food production losses caused by weather and climate extremes under changing
climate and improved stakeholder preparedness are concerns for many societies as the world experiences popu-
lation growth and subsequent increases in the demand for agricultural products.
An analysis of climate-crop relationships, in particular, the consequences of weather and climate extremes
on food production, requires a spatially explicit yield dataset spanning several decades. At the global scale, such
a dataset has only recently been developed. The global dataset of historical yield for major crops (GDHY)
1
is an
example of such a dataset. The GDHY is a hybrid of agricultural census statistics and satellite remote sensing.
Crop harvested area maps, crop calendar and share of production amount in different growing seasons for a crop
are also used as inputs for the GDHY dataset. Therefore, the grid-cell yield values recorded in the GDHY dataset
are model estimates rather than observations. Since its development and initial release in November 2013, efforts
have focused on improving the data quality, assessing uncertainties and extending the time coverage to include
more recent years.
The previous version 1.2 of the GDHY
2
,
3
covers the period of 1981–2011. Here, we updated the GDHY dataset
to include 2016 (that is, version 1.3) to meet the increasing demand for yield data from the scientific community,
food agencies and agrobusinesses. However, the satellite products and reanalysis data used as the inputs for the
development of version 1.3 are different from those used in earlier versions, as elaborated later in this article.
This difference requires an alignment of version 1.3 with version 1.2 to ensure the continuity of the annual yield
time series in the GDHY. Such alignment is essential for many applications in which time series analysis is often
utilized, e.g., to depict historical yield patterns and linkages to climate variability and change.
The aligned version v1.2
+
v1.3 of the GDHY described in this article
4
offers yield data for maize, rice, wheat
and soybean for the period of 1981–2016, with a spatial resolution of 0.5° and an explicit separation of cropping
seasons for some crops (major and second cropping seasons for maize and rice and winter and spring seasons for
wheat). The GDHY offers spatially explicit global analyses on crop yields and is especially useful for addressing
recent patterns in crop yields and the impacts of recent climate variability and change on global food production;
additionally, the GDHY can be used to evaluate global gridded crop model simulations and provide a basis for
global and seasonal crop forecasting systems.
1
national Agriculture and food Research Organization (nARO), tsukuba, Japan.
2
Japan international Research
center for Agricultural Sciences (JiRcAS), tsukuba, Japan.
✉
e-mail:
iizumit@affrc.go.jp
DaTa DesCripTor
opeN
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Methods
Updating the GDHY.
The method and procedure used to provide grid-cell yield estimates for version 1.3
of the GDHY are fully described in our related work
1
. In short, the procedure consists of four key steps: (1)
the country’s annual yield statistics were obtained from the Food and Agriculture Organization of the United
Nations statistical database (FAOSTAT
5
); (2) the grid-cell net primary production (NPP) was calculated using the
remotely sensed leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), reanalysis solar
radiation and reported crop-specific radiation-use efficiency to consider the spatial variations in yields within a
country; (3) the harvested area map (M3-Crops
6
) and crop calendars circa 2000 (SAGE
7
) were used to address
where and when a crop of interest was grown; and (4) when the crop calendars indicated that a crop of interest
was harvested twice in a year, the share of production amount by different cropping season of a crop available
in the US Department of Agriculture (USDA) report
8
was used to differentiate the yield estimates for different
cropping seasons. Production-weighted mean, instead of arithmetic mean, is utilized when average yield from
two cropping seasons with different production share is computed.
Some inputs used in the development of the version 1.3 dataset were different from those used in the version
1.2 dataset (Table
1
). The major differences were found in the satellite products and reanalysis data. The LAI
and FPAR inputs were changed from the GIMMS3g [Global Inventory Modeling and Mapping Studies third
generation products from the AVHRR (Advanced Very High Resolution Radiometer)] products
9
for the ver-
sion 1.2, to the more advanced MOD15A2 products
10
derived from the MODIS (Moderate Resolution Imaging
Spectroradiometer) for the version 1.3. The spatial and temporal resolutions of the MOD12A2 products (1-km
and 8-day, respectively) were finer than those of the GIMMS3g products (0.083° or 10-km and bi-monthly or
15-day), although the crop harvested area map with a spatial resolution of 10-km was commonly used for both
versions 1.2 and 1.3. The daily solar radiation data were also changed from the 1.125°-resolution JRA-25 reanaly-
sis
11
for the version 1.2 to the 0.563°-resolution JRA-55 reanalysis
12
,
13
for the version 1.3.
The GIMMS3g NDVI (normalized difference vegetation index) used in estimating the GIMMS3g LAI
and FPAR were calibrated against the MODIS LAI and FPAR products for the period of 2000–2009 (ref.
9
).
Thus, the continuity of the LAI and FPAR time series at 10-km and 15-day scales was expected. However, the
quality-checking of the GDHY version 1.3 dataset revealed persistent discontinuities in annual yield time series
between versions 1.2 and 1.3 for some locations, despite the use of the calibrated GIMMS3g LAI and FPAR prod-
ucts (Fig.
1
). Yields from the version 1.3 were almost always higher than those from the version 1.2. Addressing
the exact reasons for the discontinuities is beyond the scope of this article. However, the different reanalysis solar
radiation products between the two versions are one possible reason. And the different spatial resolutions of the
satellite products used in versions 1.2 and 1.3 is another possible reason. The version 1.2 dataset uses average NPP
over the 10-km grid cell, while the version 1.3 dataset uses the maximum NPP over the 1-km cropland grid cells
located within a 10-km grid cell. To solve this problem and supply users a version of the GDHY with continuity,
the two versions were aligned, as elaborated in the subsequent section.
alignment.
The two different versions of the GDHY described above were aligned according to the following
procedure. First, in the version 1.2 dataset, the annual yield time series for a given location, crop and cropping
season was decomposed into the linear combination of the yield trend component and the yield departure from
the trend component:
́
=
+
.
.
.
Y
Y
Y
,
(1)
t
t
t
v1 2,
v1 2,
v1 2,
where
.
Y
t
v1 2,
indicates the annual yield in harvesting year
t
(t ha
−
1
);
Y
v1 2
̄
.
indicates the yield trend component or
normal yield (t ha
−
1
); and
Y
v1 2
.
ˊ
indicates the yield anomaly that represents the yield departure from normal yield
GDHY
Version 1.2
Version 1.3
Aligned version
(v1.2
+
v1.3)
Reference
Iizumi
et al
.
2
,
3
This study
Time coverage
1981–2011
2000–2016
1981–2016
Spatial coverage
Global (grid-cell yield estimates for some locations are lacking when crop calendars are not
available)
Spatial resolution
0.5°
Crops
Maize (major/second), soybean, rice (major/second), wheat (winter/spring)
Country yield statistics
FAOSTAT
5
Satellite products
GIMMS3g 0.083° bi-monthly LAI and
FPAR
9
MOD15A2 1-km 8-day LAI
and FPAR
10
Hybrid of dataset
versions 1.2 and 1.3
Solar radiation
JRA-25 reanalysis
11
(1.125° and daily)
JRA-55 reanalysis
12
,
13
(0.563°
and daily)
Harvested area
M3-Crops
6
(0.083° and average around 2000)
Crop calendar
SAGE
7
[0.5° (unfilled version) and average around 2000]
Production share by cropping season USDA
8
(national and average in the 1990s)
Table 1.
A summary of the different GDHY versions considered in this article. More methodological details are
available in Iizumi
et al
.
1
.
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(t ha
−
1
). The normal yield was calculated by applying the 5-year (
t
-4 to
t
) moving average method to the annual
time series:
Y
Y
5
(2)
t
t
t
t
v1 2,
4 v1 2,
=
∑
.
.
−
.
The yield values in the version 1.3 dataset were also decomposed, as the version 1.2 dataset were processed
(
́
Y
Y
Y
t
t
t
v1 3,
v1 3,
v1 3,
=
+
.
.
.
; and
=
.
∑
−
.
Y
t
Y
v1 3,
5
t
t
t
4 v1 3,
).
Second, the two versions of the GDHY were combined into a single time series using the following rule:
=
=
…
+
′
+ ′
=
…
+
−
+ ′
=
…
.
. + .
.
.
.
.
.
.
.
.
̄
̄
̄
̄
Y
Y
t
Y
Y
Y
t
Y
Y
Y
Y
t
1981,
, 1999
2
2000,
, 2010
[
(
)]
2011,
, 2016
(3)
t
t
t
t
t
t
v1 2 v1 3,
v1 2,
v1 2,
v1 2,
v1 3,
v1 2,2010
v1 3,
v1 3,2010
v1 3
For the period of 1981–1999, in which only the version 1.2 dataset is available, the yield values in the aligned
version (
. + .
Y
v1 2 v1 3
) are equal to those of version 1.2. For the period of 2000–2010, both versions are available. The
normal yields were taken from version 1.2, and the average yield anomalies across the two versions were added to
the normal yields. For the remaining period (2011–2016), only version 1.3 is available. The yield anomalies were
taken from version 1.3. In contrast, the normal yields were computed by adding the changes in the normal yields
between 2010 and the target years (2011–2016), as computed based on version 1.3, to the normal yield in 2010 of
version 1.2. When the alignment led to a negative value, the yield value was replaced with zero. By using this
procedure, the two versions were harmonized into a single aligned version referred to as the GDHY version
v1.2
+
v1.3 dataset (Fig.
1
).
Data records
The GDHY aligned version v1.2
+
v1.3 dataset files include the annual crop yield time series in tonnes per hectare
(t ha
−
1
) for each grid cell. The files are in NetCDF4 format and were generated by using library version 4.6.1.0;
they are available at XXXX/yield_YYYY.nc4, where XXXX indicates the crop and cropping seasons (i.e., maize_
major, maize_second, rice_major, rice_second, wheat_winter, wheat_spring and soybean); and YYYY indicates
the year (i.e., 1981,
…
, 2016). Only a single cropping season is considered for soybean. The dataset is freely avail-
able at PANGAEA
4
.
0
1
2
3
4
5
6
7
8
Yield (t ha
−1
)
1980 1985 1990 1995 2000 2005 2010 2015
0
1
2
3
4
5
Yield (t ha
−1
)
1980 1985 1990 1995 2000 2005 2010 2015
0
1
2
3
4
5
6
7
8
9
Yield (t ha
−1
)
1980 1985 1990 1995 2000 2005 2010 2015
Year
0
1
2
3
4
5
6
Yield (t ha
−1
)
1980 1985 1990 1995 2000 2005 2010 2015
Year
Maize (long=104.750, lati=13.250)
Soybean (long=303.750, lati=−30.250)
Rice (long=116.750, lati=29.250)
Wheat (long=254.750, lati=44.750)
GDHY_v1.2
GDHY_v1.3
GDHY_v1.2+v1.3
EarthStat
Fig. 1
Yield time series in the selected locations for different versions of the GDHY. Yield data obtained from
version 1.2, version 1.3 and aligned version v1.2
+
v1.3 are presented. Locations indicated by longitude and
latitude were arbitrarily selected for explanatory purposes. Five-year average yields at three time points centered
on 1995, 2000 and 2005 were obtained from the EarthStat dataset
13
and are also shown for reference purposes.
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technical Validation
approaches for validation.
We used two different methods to validate the GDHY aligned version
v1.2
+
v1.3 dataset: (1) it was compared with another dataset developed by different research group with the
authors; and (2) an analysis conducted in earlier peer-reviewed literature was reproduced using the aligned data-
set to confirm whether the reproduced results resemble the earlier ones when datasets with different spatial res-
olutions were analyzed.
Comparison with another dataset.
Another global, spatially explicit, historical yield dataset described
in Ray
et al
.
14
is available at the EarthStat website (
http://www.earthstat.org/
). We downloaded the dataset labeled
“Harvested Area and Yield for 4 Crops (1995–2005)”. In this dataset, the average yield and average harvested area
of the four crops at three time points [1995 (1993–1997), 2000 (1998–2002) and 2005 (2003–2007)] are available.
Because the original dataset has a grid size of 5 minutes by 5 minutes in longitude and latitude, we aggregated
the EarthStat yield data into a grid size of 0.5° by 0.5° in longitude and latitude for a consistent comparison. The
average harvested area map at the corresponding time point from the EarthStat was used as the weight when the
EarthStat average yield data at a given time point were spatially aggregated.
For the GDHY aligned version dataset, the average yields for the three time points were computed using the
harvested area in 2000 as the weight throughout the study period because no time-varying harvested area map
is available for any version of the GDHY. The changes in average yield between 1995 and 2005, relative to 2000,
were computed using the two different datasets and are shown in Fig.
2
. Annual time series data of the EarthStat
dataset are not publicly accessible. Therefore, the two datasets were compared in terms of changes in average yield
between the two time points. The calculated yield changes were color-coded according to the 10 categories (the
8 yield change categories from “Below
−
10%” to “Above 50%”, “No yield data are available” and “Non-cropland”
in Fig.
2
). Then, the inter-dataset agreement was measured by the kappa coefficient
15
using the categorical yield
change data. The kappa coefficient values ranged from 0.748 to 0.766, indicating good agreement between the
EarthStat dataset and the GDHY aligned version dataset. The fact that the EarthStat dataset is solely based on
national or subnational agricultural census statistics
14
underpins the reliability of the GDHY aligned version
dataset.
reproduction of earlier analysis results.
We repeated the analysis described in Iizumi
et al
.
16
that esti-
mated the impacts of the El Niño Southern Oscillation (ENSO) on global yields. The GDHY version 1.0 dataset
1
Fig. 2
Yield changes for the 1995–2005 period for different datasets. The EarthStat dataset (left) and the GDHY
aligned version v1.2
+
v1.3 dataset (right) were used to compute the average yield at three points: 1995 (1993–
1997), 2000 (1998–2002) and 2005 (2003–2007). Changes in the average yield between 1995 and 2005, relative
to 2000, are presented. The numbers shown in each panel indicate the kappa efficient value computed against
the EarthStat dataset using the 10 color-coded categorical yield change data over the land area.
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(1.125° and the time coverage of the 25-year period from 1982 to 2006; see Table A in Supporting Information of
Iizumi
et al
.
2
for more details) is used in Iizumi
et al
.
16
. We used the GDHY aligned version dataset for the period
of 1981–2016 for reproduction. The reason for the different time periods is that the validation of the aligned
version dataset is the main purpose of this reproduction, and the yield data for the period of 1982–2006 in the
aligned version are solely based on version 1.2 (see Alignment section in this article). For these reasons, we used
the time period of 1981–2016 for the aligned version dataset, with the assumption that the average ENSO impacts
on yield is less sensitive to the choice of time period studied. Because of the longer study period than that used in
the original work, we replaced the Extended Reconstructed Sea Surface Temperature version 4 (ERSSTv4) data-
set
17
with the ERSSTv5 dataset
18
. Therefore, the method used to address the yield impacts of ENSO is an expanded
version of description in our related work
16
.
The kappa coefficient values calculated against the original results (interpolated into 0.5° resolution for a
consistent comparison) ranged from 0.487 to 0.553 for the impacts of El Niño, which is a warmer phase of ENSO
(Fig.
3
). This result indicated an intermediate level of agreement in the 6 color-coded categorical yield impact
data between the original and reproduced results. The comparison for the impacts of La Niña, a cooler phase of
ENSO, showed a similar level of agreement, as indicated by the kappa coefficient values of 0.486–0.550 (Fig.
4
).
These agreement levels are reasonable if one considers the difference in spatial resolution and the subsequent
difference in spatial coverage across the two versions. The original results have a larger spatial coverage than that
of the reproduced results because the larger grid cells (1.125°) used in the version 1.0 dataset often have effective
yield values even when yield data for most smaller grid cells (0.5°) located within a larger grid cell are missing.
Usage Notes
Any versions of the GDHY, including the aligned version v1.2
+
v1.3, are a valuable source of information on
global yields. However, caution is necessary when the goal is to make user findings derived by analyzing the
GDHY robust against the inherent uncertainties in the dataset. The following is a non-exclusive summary of
technical notes users should be aware of.
The yields available in the GDHY are model estimates and not free from error due to imperfect modeling,
inaccurate inputs, misreporting in agricultural census statistics, and use of time-constant information. Examining
the same working hypothesis using other yield datasets (preferably, observed yields) in addition to the GDHY is
a good practice to increase the confidence in the findings (e.g., refs.
2
,
19
,
20
).
Different conclusions could be made if different yield datasets were analyzed
2
,
21
. Practices to avoid leading to
conclusions sensitive to the choice of yield dataset are important. Such practice includes utilizing statistics of yield
Fig. 3
Yield impacts of El Niño for different versions of the GDHY. The original results of Iizumi
et al
.
16
are
based on the GDHY version 1.0 dataset for the period of 1982–2006 (left), while the reproduced results are
based on the GDHY aligned version v1.2
+
v1.3 dataset for 1981–2016 (right). The numbers shown in each
panel indicate the kappa efficient value computed against the original results using the 6 color-coded categorical
yield impact data over the land area.
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data (e.g., multi-year average yield, relative yield change) or categorical yield data for analysis, as was presented
in the Technical Validation section of this article, instead of analyzing raw yield values. Similarly, different spatial
resolutions of yield datasets could lead to different conclusions
19
,
22
,
23
, and therefore, an examination of a user’s
conclusions against uncertainty of this kind is encouraged.
Yields in some locations are lacking in the GDHY. A country or global production total aggregated from
grid-cell yields were underestimated if the yield dataset, of which the spatial coverage was incomplete, was ana-
lyzed. Calculating a country average yield and then multiplying it by a country’s harvested area are appropriate
methods to obtain reasonable estimates of total production for a given spatial unit using the GDHY. Note that the
beginning and ending years of the GDHY (i.e., 1981 and 2016, respectively, for the aligned version) have many
missing values in the Southern Hemisphere because crop durations in the region often span two calendar years
and yields cannot be estimated due to incomplete crop durations.
Code availability
The GDHY aligned version v1.2
+
v1.3 dataset is produced by combining versions 1.2 and 1.3 using a purpose-
build program written in Fortran90 with the standard mathematical library. The program code was compiled
on the MacOS platform but is potentially applicable to other platforms (e.g., Windows and UNIX). The code is
available from the corresponding author upon request.
Received: 4 December 2019; Accepted: 26 February 2020;
Published: xx xx xxxx
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88, 10–21 (2017).
acknowledgements
T.I. was partly supported by Grant-in-Aid for Scientific Research (No. 16KT0036, 17K07984 and 18H02317) of
the Japan Society for the Promotion of Science, The Environment Research and Technology Development Fund
(S-14) of the Environmental Restoration and Conservation Agency, Japan and the Joint Research Program of Arid
Land Research Center, Tottori University (30F2001).
author contributions
T.I. designed the study, developed the GDHY version 1.3 dataset, conducted the alignment and technical
validation, and drafted the manuscript. T.S. collected, processed and quality-checked the inputs used to provide
the GDHY version 1.3 dataset and helped write the manuscript.
Competing interests
The authors declare no competing interests.
additional information
Correspondence and requests for materials should be addressed to T.I.
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