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Citation: Chou, J.; Jin, H.; Xu, Y.;
Zhao, W.; Li, Y.; Hao, Y. Impacts and
Risk Assessments of Climate Change
for the Yields of the Major Grain
Crops in China, Japan, and Korea.
Foods 2024,13, 966. https://doi.org/
10.3390/foods13060966
Academic Editor: Adrián
Rodríguez-Burruezo
Received: 9 January 2024
Revised: 11 March 2024
Accepted: 16 March 2024
Published: 21 March 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
foods
Article
Impacts and Risk Assessments of Climate Change for the Yields
of the Major Grain Crops in China, Japan, and Korea
Jieming Chou 1,2,3,*,† , Haofeng Jin 1 ,2 ,† , Yuan Xu 1,2 , Weixing Zhao 1,2, Yuanmeng Li 1,2 and Yidan Hao 1,2
1Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University,
Beijing 100875, China; 202221051189@mail.bnu.edu.cn (H.J.); xuyuan01@mail.bnu.edu.cn (Y.X.);
201921051146@mail.bnu.edu.cn (W.Z.); 202231051098@mail.bnu.edu.cn (Y.L.);
202121051159@mail.bnu.edu.cn (Y.H.)
2Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University,
Beijing 100875, China
3Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
*Correspondence: choujm@bnu.edu.cn
†These authors contributed equally to this work.
Abstract: Climate change poses a high risk to grain yields. Maize, rice, and wheat are the three major
grain crops in China, Japan, and Korea. Assessing the impacts and risks of climate on the yields of
these grain cops is crucial. An economy–climate model (C-D-C model) was established to assess the
impacts of climate factors on the grain yields in different crop areas. The peaks over threshold model
based on the generalized Pareto distribution was used to calculate the value at risk and the expected
shortfall, which can evaluate the yield risk of different crops. The impact ratio of climate change
was employed to estimate the impacts of climate change under different climate scenarios. The main
conclusions can be summarized as follows: the impacts of climate factors on grain yields and the risk
vary widely across the different regions and crops. Compared to 1991–2020, climate change from
2021 to 2050 exerts positive impacts on rice and wheat, while the negative impacts on maize in the
crop areas are significantly affected by climate factors. The impact ratios of climate change are larger
in the SSP1-2.6 and the SSP5-8.5 scenarios than under the SSP2-4.5 scenario. These findings are useful
for targeting grain yields in smaller study areas.
Keywords: China, Japan and Korea; climate change; crop yields; risk
1. Introduction
The increased surface temperatures and the increased frequency and intensity of ex-
treme events due to climate change have impacted grain crop growth, with predominantly
negative effects [
1
]. The Sixth Assessment Report (AR6) of the Intergovernmental Panel on
Climate Change (IPCC) promoted eight representative key risks (RKRs), including food
security [
1
]. Grain security is an important component of food security and is related to
the basic lives and livelihoods of people. Grain production is the basis for grain security.
Climate change affects the yields of various types of crops positively or negatively in
different regions in multiple ways [
1
,
2
]. From the perspective of the results of agricultural
production activities, the risk of grain production means the uncertainty in the per unit
yield or total production reduction [
1
,
2
]. From the viewpoint of the process mechanism,
the risk of grain production originates from the dynamic interaction among climate-related
hazards (i.e., risk-causing factors), the exposure of grain crops to hazards and the vulnera-
bility of grain crops to hazards [
1
]. Within the context of climate change, the risks to grain
production are becoming increasingly prominent. Volatility in grain production can lead to
an insufficient food supply, trigger grain price volatility, affect trade flows, and impact the
livelihoods of people, resulting in systemic risks [3].
Foods 2024,13, 966. https://doi.org/10.3390/foods13060966 https://www.mdpi.com/journal/foods
Foods 2024,13, 966 2 of 22
The assessment of grain production risks is important for adjusting agricultural activ-
ities, developing preventive measures, and mitigating loss damage. Hazards have been
categorized as emergent and gradual hazards [
4
]. Currently, risk assessment of emergent
hazards receives widespread attention. Emergent hazards are often extreme weather and
climate events. The event process is of short duration and can produce loss or damage
in a short period of time, e.g., floods [
5
,
6
] and droughts [
7
]. There are two main types of
risk assessment methodologies for emergent hazard generation. One is to consider risk
as a function of the danger of climate hazards, as well as the vulnerability and exposure
of the crops [
4
,
8
,
9
]; the other one is to model yields or losses using a probability distribu-
tion [
6
,
10
–
13
]. The assessment methods based on hazards, vulnerability, and exposure are
based on the connotation of risk in the construction of indicator systems and assessment
models [
14
,
15
]. Indicators are first selected or constructed to assess the above three factors,
after which a yield risk function for these three factors is established [
6
,
16
–
18
]. This type of
method can capture the mechanisms of grain production losses but tends to focus only on
single consistent risk factors. Few studies have focused on systemic compound risks. This
may be due to the complexity of the formation mechanisms of systemic compound risks.
However, the methods based on the probability distribution of yields focus on directly
expressing the risk magnitude from the perspective of the final impact, without considering
the loss mechanism, overlooking the complex mechanisms of systemic compound risks.
This class of methods can be further divided into three main categories [
19
]. The first
category aims to fit a probability density distribution function of the yield per unit area
and to calculate the probability of occurrence in both good and bad years. The second
category aims to select indicators to represent the level of interannual fluctuations in the
yield per unit area. Holst et al. considered the variance in the yield per unit area as a risk
factor and established a flexible nonlinear fixed-effects panel data model that can be used
to separately analyze the marginal contributions of climate factors to the average yield
and yield risk [
10
]. They found that climate change affects grain production differently in
northern and southern China [
10
]. Tigchelaar and Finger et al. analyzed the effect of climate
change on crop yield variability by calculating the coefficient of variation (CV). Tigchelaar
et al. examined warming-induced changes in the variability of the maize yield per unit area
and concluded that future warming increases the likelihood of globally synchronized maize
production shocks [
13
]. Finger et al. explored the impacts of climate change scenarios
on the variability of maize and winter wheat yields on the Swiss Plateau [
20
]. The third
category involves the exceedance probability of yield reduction. The exceedance proba-
bility refers to the probability that the yield reduction (loss) exceeds a certain threshold.
Stojanovski et al. simulated the weather crop index (WCI) and used burn yield analysis
to calculate the distribution of the rice yield loss in Hunan Province, and they obtained
an aggregate exceeding probability function (AEP) curve [
12
]. Based on grain yield data,
the method of probability statistics is used in the above research to evaluate the risk to
the grain yield. However, the distribution of grain losses often exhibits non-normal and
thick-tail characteristics, and the tail contains much information. There is little research
on modelling the tail data of production reduction. In addition, most large-scale impact
and risk assessment studies do not distinguish between various grain crop types. In fact,
different grain crop yields exhibit different dimensions and should not be simply summed.
China, Japan, and Korea impose an enormous influence in northeast Asia, and grain
production exerts a notable influence on society and the economy in northeast Asia. The
aim of this paper is to evaluate the impacts of climate factors and future climate change
on the grain yields of these three countries and the risk of grain production reduction in
the current climate state. Rice, wheat, and maize were selected in this paper. In impact
evaluation, the model constructed considered both socioeconomic factors and climate
factors. In risk assessment, this paper focused on the data tail characteristics to ensure
more accurate assessment results. The conclusions could provide a reference for the risk
management of grain production in these three countries.
Foods 2024,13, 966 3 of 22
2. Materials and Methods
2.1. Overview of the Study Area
China, Japan, and Korea are the three most influential countries in northeast Asia (
Figure 1
).
In 2021, the GDPs of China, Japan, and Korea were USD 17,734,062.65, USD 4,940,877.78,
and USD 1,810,955.87, respectively, which were at the forefront of northeast Asia (data
source: The World Bank, https://data.worldbank.org/indicator/NY.GDP.MKTP.CD, ac-
cessed on 5 July 2023). The populations of China, Japan, and Korea were
1412.36 million
,
125.68 million
, and 51.74 million people, respectively. The above three countries are greatly
affected by the East Asian monsoon. Instability of the East Asian monsoon results in an
unstable climate environment and large climate variability in these three countries.
Foods 2024, 13, x FOR PEER REVIEW 3 of 26
2. Materials and Methods
2.1. Overview of the Study Area
China, Japan, and Korea are the three most influential countries in northeast Asia
(Figure 1). In 2021, the GDPs of China, Japan, and Korea were USD 17,734,062.65, USD
4,940,877.78, and USD 1,810,955.87, respectively, which were at the forefront of northeast
Asia (data source: The World Bank, hps://data.worldbank.org/indica-
tor/NY.GDP.MKTP.CD, accessed on 5 July 2023). The populations of China, Japan, and
Korea were 1412.36 million, 125.68 million, and 51.74 million people, respectively. The
above three countries are greatly affected by the East Asian monsoon. Instability of the
East Asian monsoon results in an unstable climate environment and large climate varia-
bility in these three countries.
Figure 1. Scope and division for rice (A), wheat (B), and maize (C) of the study area. The numbers
on the map indicate the crop areas listed in Table 1.
Table 1. Division of crop areas.
Crop Serial
Number Crop Area Geographical Areas Growing
Period
Rice
1 Double-cropping rice area in
South China
Guangdong, Guangxi, Hainan, Hong Kong, Macao, and
Taiw an 5–7, 8–10
2 Single- and double-cropping
rice areas in Central China
Jiangsu, Fujian, Shanghai, Zhejiang, Anhui, Jiangxi, Hu-
nan, Hubei, Sichuan, and Chongqing 5–7, 8–10
3
Single- and double-cropping
rice areas on the Southwest
Plateau of China
Guizhou, Yunnan, Tibet, and Qinghai 5–7, 8–10
4 Single-cropping rice area in
North China Beijing, Tianjin, Shandong, Hebei, and Henan 6–8
5
Early-maturing, single-crop-
ping rice area in Northeast
China
Heilongjiang, Jilin, and Liaoning 6–8
6 Single-cropping rice area in
dry area of Northwest China
Xinjiang, Ningxia, Gansu, Inner Mongolia, Shanxi, and
Shaanxi 6–8
7 Japan rice area 7–8
8 Korea rice area 7–8
Wheat
1 Winter wheat (autumn sow-
ing) area in northern China Shandong, Henan, Hebei, Shanxi, Beijing, and Tianjin (-) 1 11–5
2 Winter wheat (autumn sow-
ing) area in southern China
Fujian, Jiangxi, Guangdong, Hainan, Guangxi, Hunan,
Hubei, Guizhou, Yunnan, Sichuan, Chongqing, Jiangsu,
Anhui, Hong Kong, Macao, Taiwan, Zhejiang, and Shang-
hai
(-) 11–5
3 Spring wheat (spring sow-
ing) area of China
Heilongjiang, Jilin, Liaoning, Inner Mongolia, Ningxia,
Shaanxi, and Gansu 5–8
4 Winter and spring sowing
wheat areas of China Xinjiang, Tibet, and Qinghai (-) 11–5, 5–
8
150°E
150°E
140°E
140°E
130°E
130°
E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°
E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
0° 0°
020001000 km
1
2
3
4
5
67
8
150°E
150°E
140°E
140°E
130°E
130°E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
0° 0°
020001000 km
1
2
34
5
67
8
150°E
150°E
140°E
140°E
130°E
130°
E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°
E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
0° 0°
020001000 km
1
2
3
456
(A) (B) (C)
Figure 1. Scope and division for rice (A), wheat (B), and maize (C) of the study area. The numbers on
the map indicate the crop areas listed in Table 1.
Table 1. Division of crop areas.
Crop
Serial Number
Crop Area Geographical Areas Growing Period
Rice
1Double-cropping rice area in South China Guangdong, Guangxi, Hainan, Hong Kong,
Macao, and Taiwan 5–7, 8–10
2Single- and double-cropping rice areas in
Central China
Jiangsu, Fujian, Shanghai, Zhejiang, Anhui,
Jiangxi, Hunan, Hubei, Sichuan, and Chongqing 5–7, 8–10
3Single- and double-cropping rice areas on the
Southwest Plateau of China Guizhou, Yunnan, Tibet, and Qinghai 5–7, 8–10
4Single-cropping rice area in North China Beijing, Tianjin, Shandong, Hebei, and Henan 6–8
5Early-maturing, single-cropping rice area in
Northeast China Heilongjiang, Jilin, and Liaoning 6–8
6Single-cropping rice area in dry area of
Northwest China
Xinjiang, Ningxia, Gansu, Inner Mongolia, Shanxi,
and Shaanxi 6–8
7 Japan rice area 7–8
8 Korea rice area 7–8
Wheat
1Winter wheat (autumn sowing) area in
northern China
Shandong, Henan, Hebei, Shanxi, Beijing,
and Tianjin (-) 111–5
2Winter wheat (autumn sowing) area in
southern China
Fujian, Jiangxi, Guangdong, Hainan, Guangxi,
Hunan, Hubei, Guizhou, Yunnan, Sichuan,
Chongqing, Jiangsu, Anhui, Hong Kong, Macao,
Taiwan, Zhejiang, and Shanghai
(-) 11–5
3Spring wheat (spring sowing) area of China Heilongjiang, Jilin, Liaoning, Inner Mongolia,
Ningxia, Shaanxi, and Gansu 5–8
4Winter and spring sowing wheat areas
of China Xinjiang, Tibet, and Qinghai (-) 11–5, 5–8
5Japan wheat area (-) 12–5
6Korea wheat area (-) 11–5
Maize
1Spring maize area in northern China Heilongjiang, Jilin, Liaoning, Inner Mongolia,
Shanxi, Shaanxi, and Ningxia 5–9
2Summer maize area in the
Huang-Huai-Hai Plain Hebei, Tianjin, Beijing, Henan, and Shandong 7–9
3
Maize area in the Southwest China Mountains
Sichuan, Chongqing, Guizhou, and Yunnan 6–8
4Maize area in hilly southern China
Hubei, Anhui, Jiangsu, Shanghai, Zhejiang,
Hunan, Jiangxi, Fujian, Guangdong, Guangxi,
Hainan, Hong Kong, Macao, and Taiwan
6–7
5Irrigated maize area in Northwest China Xinjiang and Gansu 6–9
6Mazie area on the Qinghai–Tibetan Plateau
of China Qinghai and Tibet 6–9
7 Japan maize area 5–8
8 Korea maize area 4–8
1. (-) refers to the previous year.
Foods 2024,13, 966 4 of 22
2.2. Division of Crop Areas and Determination of the Growing Period
We do not further divide crop areas in Japan and Korea because the national territorial
area and internal differences in the natural environment of these two countries are relatively
small. We further divide the crop areas in China because the internal differences in the
natural environment are large. Since it is difficult to obtain statistical data at the municipal
and county levels, referring to existing research [
21
–
26
], the crop calendar of the U.S.
Department of Agriculture (USDA, https://ipad.fas.usda.gov/ogamaps/cropcalendar.
aspx, accessed on 15 March 2023), and the crop calendar of the Food and Agriculture
Organization of the United Nations (FAO, https://cropcalendar.apps.fao.org/, accessed on
15 March 2023), we divide the 34 provincial-level administrative units of China into different
crop areas. The crop areas are listed in Table 1.
The water and heat conditions in the different crop areas are different. The growing
periods of crops also differ. The growing period in each crop area is determined by referring
to existing research [
27
] and the crop calendar of the USDA (https://ipad.fas.usda.gov/
ogamaps/cropcalendar.aspx, accessed on 15 March 2023) (Table 1).
2.3. Data and Their Sources and Preprocessing Steps
The research data consist of three main categories: statistical data, historical meteoro-
logical data, and future meteorological data.
The statistical data include the year-by-year crop production, number of people em-
ployed in agriculture, area sown, and amount of fertilizer applied by crop area from
1991 to 2020. The data were obtained from the China Rural Statistical Yearbook, 60 Years of
Statistics on Agriculture in New China (https://zdscxx.moa.gov.cn:9443/misportal/public/
publicationRedStyle.jsp (accessed on 1 March 2023)), statistical yearbooks of China’s provin-
cial administrations, and the Food and Agriculture Organization of the United Nations
(FAO, https://www.fao.org/faostat/en/#data (accessed on 1 March 2023)). The Chinese
statistical data are based on provincial-level administrative districts. There are four vari-
ables collated: grain yields (i.e., grain production per unit area, kg/ha), fertilizer application
per unit area (kg/ha), sown area (thousands of hectares), and population employed in
agriculture (10,000 persons). Missing statistical data were interpolated. Regarding missing
records in the middle of the series, the k nearest-neighbor (KNN) method was used for
interpolation, with k = 1. Regarding missing grain data, the gaps were filled according to
the relationship between the yield, sown areas, and total yield. In regard to missing records
at the end of the series of the population employed in agriculture in each province of China,
different treatments were adopted according to the different conditions. The Augmented
Dickey–Fuller (ADF) unit root test was used to determine whether the data series of the
population employed in agriculture in all provinces exhibits smoothness after first-order
differencing. The LB statistic was then used to determine whether the differenced time
series was a white noise series. If the series was a nonwhite noise series, the ARIMA
model was employed to supplement any missing data (due to the reform of administrative
divisions, missing data in the tail of the data of Sichuan were estimated by subtracting the
tail data of Chongqing from the sum of the data of Sichuan and Chongqing.) If the series
was a white noise series, the curve-fitting method was used to fit the original series for
estimation purposes. The equations of the fitted curves are provided in Table A1.
The historical meteorological data include the average air temperature (
◦
C) and down-
ward shortwave radiation flux density received at the surface (W/m
2
) during the growing
period in each crop area from 1991 to 2020. Temperature data were obtained from the
CRU TS dataset version 4.06 (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.06/cruts.
2205201912.v4.06/ (accessed on 1 March 2023)). These data were obtained via interpo-
lation of the station data with an accuracy of 0.5
◦×
0.5
◦
. The downward shortwave
radiation flux density received at the surface was obtained from the variable “surface
solar radiation downwards (in J/m
2
)” in the ERA5-Land monthly averaged data from
1950 to present (https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e216
1bac?tab=overview (
accessed on
1 March 2023)). The accuracy of the data is 0.1
◦×
0.1
◦
,
Foods 2024,13, 966 5 of 22
and the data are reanalyzed data. The average of all gridded points within a given crop area
was used as the value for this crop area. The values of the above climate factors for each
month during the growing season were first calculated, and the average was then obtained.
The future meteorological data include the mean air temperature (
◦
C) and downward
shortwave radiation flux density received at the surface (W/m
2
) during the growing
period in each crop area from 2021 to 2050 under three scenarios (SSP1-2.6, SSP2-4.5, and
SSP5-8.5
). SSP1-2.6 represents a world with sustainable development and a low climate
change challenge. Under this scenario, the global CO
2
emissions significantly decrease,
reaching net zero levels after 2050. SSP2-4.5 represents the intermediate pathway, with
a medium climate change challenge. CO
2
emissions fluctuate at current levels before
beginning to decline at the middle of the century but do not reach net zero levels before
2100. SSP5-8.5 emphasizes the traditional economic orientation, with a significant increase
in CO
2
emissions. The NASA Earth Exchange Global Daily Downscaled Projections (NEX-
GDDP-CMIP6) dataset (https://doi.org/10.7917/OFSG3345 (accessed on 1 August 2023))
provides a set of global, high-resolution, bias-corrected climate change projections that
can be used to assess the impact of climate change on processes sensitive to small-scale
climate gradients and the influence of local topography on climatic conditions [
28
–
30
]. In
this paper, we calculated the equally weighted average of all models listed in Table A2 as
the projected data of the different future scenarios with a resolution of 0.5◦×0.5◦.
2.4. Methods
First, principal component scores were calculated using principal component analysis
to obtain the comprehensive climate factor (CCF) for each crop area [
31
]. The output
elasticity of the climate change factor was estimated by regarding the CCF as a climate
change factor input of the climate–economic model (C-D-C model) [
32
–
34
]. Based on the
climate yield loss data, the peaks over threshold (POT) model based on the generalized
Pareto distribution (GPD) was used to calculate the value at risk (VaR) and the expected
shortfall (ES) to assess the risk of the yields of the different crops in the current climatic
state. We adopted three scenarios, i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5, and calculated the
CCF from 2021 to 2050 under each scenario. The impacts on grain yields under the different
climate scenarios were estimated using the impact ratio of climate change (IRCC) [35–37].
2.4.1. Comprehensive Climate Factor
We obtained the CCF by extracting the main information of multiple climate factors
through principal component analysis (PCA) [
31
]. This index can be regarded as an
input factor of climate production in the C-D-C model. The specific calculation steps are
as follows:
Step 1. Choose the climate factors and the standardization approach.
When selecting climate factors, careful consideration should be given to the selection
of precipitation. Irrigation is a technical measure that has been used since ancient times
to resolve the lack of natural precipitation during agricultural activities. The amount
of precipitation often does not reflect the actual amount of water received by crops in
crop areas. For example, since the 1990s, China has achieved remarkable progress in
water-saving technology, gradually improved policy and institutional safeguards, and
significantly increased their investment in irrigation water conservancy projects [
38
]. Three
climate factors, namely, the mean air temperature, cumulative precipitation, and downward
shortwave radiation flux density received at the surface, were first selected to calculate
the CCF; then, only the mean air temperature and downward shortwave radiation flux
density received at the surface were selected to calculate the CCF. The CCF values obtained
in these two ways were substituted into the C-D-C model, and the effects of the two CCF
calculation methods were compared according to the adjusted R
2
and mean relative error
values. Finally, two climate factors, namely, the average air temperature (
◦
C) and the
downward shortwave radiation flux density received at the surface (W/m
2
), were selected
to calculate the CCF.
Foods 2024,13, 966 6 of 22
When calculating the value of a climate factor for a given year, we should select
appropriate months instead of all months of the year [
32
,
33
]. In this paper, the average
values of the climate factors during the growing period were calculated.
The method of calculating the CCF is described below using an example from one
crop area of a certain crop.
Step 2. Use a PCA on standardized variables with an 80% threshold to obtain the
principal component score zki, which is the score of the ith principal component in the kth
year (1991–2020).
Step 3. Calculate the CCF.
CCFk=
n
∑
i=1
(zki ×αi
n
∑
i=1
αi
) + 20 (1)
where αiis the variance contribution rate of the ith principal component.
The production factors in the C-D-C model must be positive [
32
]. To ensure that the
CCF is positive, it is shifted upwards by 20 units overall.
2.4.2. C-D-C Model
The C-D-C model is based on the C-D production function model proposed by Cherles
Cobb and Paul Dauglas to introduce climate production input factors [
33
]. The C-D-C
production function represents the relationship between production and inputs for a given
technology. The C-D-C model was used to estimate the effect of each production factor on
grain yields [32,33].
y=xβ1
1xβ2
2xβ3
3Cγµ(2)
The terms at the right of Equation (2) represent the production inputs. x
i
(i= 1, 2, 3)
is a socioeconomic input; Cis a climate input; and
µ
represents the other unconsidered
factors. The amount of the socioeconomic input factors is not necessarily equal to 3, but
each input needs to have a certain amount in regional farming. The terms at the left
of Equation (2) represent the economic output. Other parameters are used to show the
functional relationship between the input and the output. In order to estimate these
parameters, calculate the logarithm of both sides of Equation (2); the nonlinear function
can be transformed into a linear function, yielding Equation (3):
ln(y) = β1ln(x1) + β2ln(x2) + β3ln(x3) + γlnC+µ′(3)
where yis the grain yield (kg/ha); x
1
,x
2
, and x
3
are the population employed in agriculture
(10,000), the sown area (10
3
ha), and the fertilizer application amount per unit area (kg/ha),
respectively; Cis the climate production input factor—the CCF is used here;
β1
,
β2
,
β3
,
γ
,
and
µ′
are the coefficients to be estimated; and
β1
,
β2
,
β3
, and
γ
denote the output elasticity
of the factors, indicating the corresponding percent increase in the yields for a 1% increase
in the production factor.
The multiple linear regression model assumes that the dependent variable follows a
normal distribution. Therefore, the Kolmogorov–Smirnov test was used to perform the
normality test of ln(y). The partial regression coefficients of Equation (3) were estimated
using the ordinary least squares (OLS) method. The F-test was applied to the model to
determine the significance of the linear relationship between all independent variables and
the dependent variable. A t-test was performed using the partial regression coefficients
to determine whether the linear relationship between each independent variable and
the dependent variable was significant. The residual sum of squares (RSS) and adjusted
R
2
values were calculated to assess the fit of the model. A total of 6 leave-5-out cross-
validation steps were performed, and the mean of the relative error was calculated to
evaluate the extrapolated predictive ability of the model.
Foods 2024,13, 966 7 of 22
2.4.3. Impact Ratio of Climate Change
The impact ratio of climate change (IRCC) can be used to project the influence of future
climate change on grain yields.
In Equation (2), let N=xβ1
1xβ2
2xβ3
3µ, then y=NCCγ.
It is assumed that the average conditions from 1991 to 2020 are y
1
,N
C1
, and C
1
,
and the average conditions from 2021 to 2050 are y
2
,N
C2
, and C
2
, respectively. Let the
following apply:
y1=NC1Cγ
1, (4)
y2=NC2Cγ
2, (5)
y∗=NC2Cγ
1(6)
where y
∗
is the yield under a hypothetical scenario, which is the grain yield under the
socioeconomic level (N
C
) from 2021 to 2050 and the climate state (C) from 1991 to 2020.
Then, by subtracting Equation (6) from Equation (5), the part of grain yield only affected by
climate change was obtained, which is called the yield impact of climatic change (YICC)
∆
y.
∆y=y2−y∗=Cγ
2(NC2Cγ
2−NC2Cγ
1)
Cγ
2
=y2·Cγ
2−Cγ
1
Cγ
2
(7)
An algebraic transformation of Equation (7) was carried out to obtain Equation (8),
which can be defined as the impact ratio of climate change.
IRCC =∆y
y2
=Cγ
2−Cγ
1
Cγ
2
(8)
The IRCC denotes the proportion of the direct impact of climate change on grain yields
in the actual yields. It is a benefit index to measure the impact of climate change on the
economic output. Using this index, the sensitivity of output changes to climate change in
three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) can be analyzed.
2.4.4. Climate Yields and Climate Loss
Actual yields consist of trend yields, climate yields, and random yields [39]:
yactual =ytrend +yclimate +yrandom (9)
where y
actual
,y
trend
,y
climate
, and y
random
denote the actual yields, trend yields, climate
yields, and random yields, respectively.
Trend yields, also known as technical yields, respond to yields due to the level of
technology. Trend yields were calculated using four methods for the yield time series:
sliding 5-year average, sliding 3-year average, linear regression, and exponential regression.
These 4 climate yields are basically the same in each trend. Therefore, the average of
these trend yields calculated using the above four methods was chosen as the final trend
yield. The grain yield of a region in a given year is mainly determined using two factors:
technology and climate. The random yield can be ignored. Therefore, climate yields can be
calculated via Equation (10), which indicates the contribution of climate factors to the yield.
yclimate =yactual −ytrend (10)
Climate loss (y
loss
) is the opposite of climate yields. It is the yield amount lost due to
climate factors.
2.4.5. POT Model Based on the GPD
Climate loss data usually exhibit a thick tail that contains a wealth of information.
Therefore, tail modelling of climate loss is necessary. The peaks over threshold (POT) model
Foods 2024,13, 966 8 of 22
based on the generalized Pareto distribution (GPD) was used to calculate the value at risk
(VaR) and expected shortfall (ES) for estimating the risk of climate yield loss of the major
grain crops in China, Japan, and Korea in the climate state from 1991 to 2020.
Step1. Assess the thick tail.
Modelling data tails requires the data to exhibit thick tails. Thick-tailedness was
assessed by plotting a quantile–quantile plot (Q—Q plot) of the standard exponential
distribution [
40
,
41
]. If the scatter points were convex upwards overall, the data could be
considered to exhibit thick tails. Upon testing, we found that the data of all crop areas
exhibited thick tails except the climate loss data of the winter wheat (autumn sowing) area in
southern China. Therefore, the POT model based on the GPD could not be used to calculate
the VaR and ES for the winter wheat (autumn sowing) area in southern China. A normality
test of the data of this crop area was conducted. It passed the
Kolmogorov–Smirnov
test
with p
≈
0.9126. We accepted the original hypothesis that the data obeyed a normal
distribution.
Step 2. Fit the probability distribution and estimate the parameters.
Set a threshold
µ
. The POT model can only be modelled for the excess value of the
data beyond the threshold. It is assumed that N
µ
samples of the data are greater than the
threshold (N
µ
<n), where nis the number of sample data (n= 30). The excess value of the
data can be calculated as follows:
Yi=yloss,k−µ,i∈{1, 2, . . ., Nµ}. (11)
The conditional excess distribution function Fµ(Y) above this threshold is
Fµ(Y) = F(µ+Y)−F(µ)
1−F(µ). (12)
When
µ
is sufficiently large, it can be well approximated by the GPD regardless of
the form of the base distribution [
40
,
41
]. The parameters
ξ
and
β
were estimated using the
maximum likelihood estimation (MLE) method [42]:
G(Y) =
1−(1+Yξ
β)−1
ξ,ξ=0
1−e−Y
β,ξ=0
(13)
where ξis a shape parameter, βis a scale parameter, and β> 0.
Ther thresholds were determined by combining the mean exceedance function method
and the kurtosis coefficient method [
43
,
44
]. The mean exceedance function method deter-
mines the threshold based on the mean residual life plot of the sample data. The mean
exceedance function e(µ) can be expressed as
e(µ) = E(yloss −µ|yloss >µ) =
Nµ
∑
i=1
(yloss,i−µ)
Nµ
. (14)
The mean residual life plot was generated with
µ
as the horizontal axis and e(
µ
) as
the vertical axis. The appropriate
µ0
value was selected as the threshold so that e(
µ
) is
approximately linear for
µ≥µ0
. The kurtosis coefficient method is based on using the
kurtosis Kto determine the coefficient. First, the kurtosis of the sample data was calculated.
For K
≥
3, the maximum y
loss,k
of the data was removed from the samples. This process
was repeated until K< 3 was obtained. The maximum y
loss,k
from the remaining samples
was chosen as the threshold. Having combined the above two methods to determine the
threshold, the results are shown in Table 2.
Foods 2024,13, 966 9 of 22
Table 2. Threshold selection of each cropping area.
Cropping Area 112 3 4 5 6 7 8
Maize Threshold −31.62 −187.12 −128.88 −38.62 −324.12 −142.19 −27.97 −170.82
Sorting 16 5 5 19 4 10 5 6
Rice Threshold −182.32 −101.21 −275.18 −214.27 −87.64 −313.04 −185.19 −256.75
Sorting 5 4 3 5 10 5 5 5
Wheat Threshold −120.93 −120.78 55.76 −122.59 −237.00 −312.26 - -
Sorting 4 6 19 8 7 7 - -
1.
Sorting refers to the number of bits where the threshold is located in the ascending sequence of the data. The
crop areas in Table 1are labelled in order. These labels are used as Table 1serial numbers.
Step 3. Calculate the VaR and ES values.
VaR is the maximum possible climate loss in a specific period of time in the near
future at a certain confidence level. Compared with VaR, ES adds more information. ES is
defined as the conditional expectation value of VaR for which the loss data is greater than
confidence p. According to the previous derivation, VaR and ES were calculated according
to Equations (15) and (16) [45], respectively.
P(yloss >VaR) = 1−p,VaR =µ+β
ξ[n
Nµ
(1−p)]−ξ−1(15)
where p= 0.05.
ES =VaR +E(yloss −VaR|yloss >VaR) = E(yloss|yloss >VaR)
=VaR +β+ξ(VaR−µ)
1−ξ
(16)
3. Results
3.1. Impacts of the Climate Factors on Grain Yields
3.1.1. OLS Estimation Results
Output elasticities were estimated for each production factor using the C-D-C model
for each cropping area. The logarithms of the yield series for all crop areas passed the
Kolmogorov–Smirnov normality test, which indicates that the coefficients could be esti-
mated using the OLS method. All models for the rice crop areas demonstrated that the
original hypothesis of the F-test could be rejected at the 0.05 significance level, which
indicated that the model was significant. All models for the wheat crop areas except for
the Korean wheat crop areas suggested that the original hypothesis of the F-test could
be rejected at the 0.01 significance level, which suggested significance of the models. All
models for the maize crop areas demonstrated that the original hypothesis of the F-test
could be rejected at the 0.01 significance level, which denoted a significant model. The
residual sum of squares and adjusted R
2
were calculated, and the leave-5-out test was
performed. The results are listed in Table 3, revealing that the models were fitted and
extrapolated well.
The output elasticities of the production factors in each rice crop area are shown in
Figure 2. The climate output elasticities (
γ
) for Japan and Korea passed the significance
test, which demonstrated that the climate factors impose a significant effect on the yields
in these two crop areas. The climate output elasticity for Japan’s rice area is 1.88, which
indicates that a 1% increase in the CCF increases rice yields by 1.88%. The climate output
elasticity for Korea is 0.61, indicating that a 1% increase in the CCF causes an increase in rice
yields of 0.61%. Only
γ
is significant in the partial regression coefficients for the Japan rice
area and Korea rice area with large absolute values, while the partial regression coefficients
of the other economic and social factors are not significant. This demonstrates that the rice
yields in Japan and Korea are mainly influenced by climatic factors such as temperature
and solar radiation. Rice is a crop that is sensitive to climatic conditions. For example,
Foods 2024,13, 966 10 of 22
increased temperatures can lead to heat stress at critical growth stages, affecting pollen
vigor and leading to yield reductions. Japan and Korea are coastal countries with abundant
water vapor, high air humidity, and high cloudiness, which can reduce the amount of solar
radiation received at the surface and limit the photosynthesis of rice. Rising temperatures
combined with higher humidity may increase crop pests and diseases [
46
]. Japan and Korea
are located in monsoon climate zones with unstable climatic environments that are prone to
extreme weather events such as typhoons and persistent heavy precipitation. These types
of extreme weather events can cause flooding and waterlogging, which, in turn, can lead
to yield reductions [47]. In addition, typhoons and persistent precipitation can reduce the
solar radiation flux density received at the surface.
Table 3. C-D-C model test for each crop area.
Crop Area Residual Sum of Squares (RSS) Adjusted R2Mean of Relative Error
Double-cropping rice area in South China 0.0710 0.2803 −0.0077
Single- and double-cropping rice areas in Central China 0.0083 0.8972 −0.0012
Single- and double-cropping rice areas on the Southwest
Plateau of China 0.0710 0.4274 0.0191
Single-cropping rice area in North China 0.1189 0.6829 0.0341
Early-maturing single-cropping rice area in Northeast China 0.0315 0.7660 −0.0014
Single-cropping rice area in dry area of Northwest China 0.0721 0.8128 0.0114
Japan rice area 0.0661 0.6327 0.0046
Korea rice area 0.0397 0.5443 0.0063
Winter wheat (autumn sowing) area in northern China 0.0270 0.9593 −0.0064
Winter wheat (autumn sowing) area in southern China 0.0853 0.9056 0.0056
Spring wheat (spring sowing) area of China 0.1067 0.7978 0.0090
Winter and spring sowing wheat areas of China 0.0273 0.9445 0.0140
Japan wheat area 0.2909 0.3555 0.0111
Korea wheat area 0.5379 −0.0146 −0.0039
Spring maize area in northern China 0.0876 0.7334 0.0046
Summer maize area in the Huang-Huai-Hai Plain 0.0933 0.6751 0.0069
Maize area in the Southwest China Mountains 0.0578 0.8775 0.0114
Maize area in hilly southern China 0.0704 0.8291 0.0261
Irrigated maize area in Northwest China 0.0994 0.7696 −0.0071
Mazie area on the Qinghai–Tibetan Plateau of China 0.2295 0.4881 0.0150
Japan maize area 0.0027 0.9117 0.0034
Korea maize area 0.0904 0.7355 0.0014
In contrast, the output elasticities of the climatic factors were not significant in the
rice crop areas of China, while the output elasticities of the socioeconomic factors were
significant in most crop areas. This suggests that the rice yields in most Chinese crop
areas are influenced by socioeconomic factors, with less influence resulting from climate.
The output elasticities of the fertilizer application amount per unit area (
β3
) passed the
significance test at the 0.05 significance level, with positive values for all crop areas in
China, except for the double-cropping rice area in South China. This demonstrates that
an appropriate increase in the amount of fertilizer applied can help to improve the rice
yields in China. The increasing technology level reduces the sensitivity of rice production
to climate and improves its adaptability to different climate environments. The coefficients
of the sown area (
β2
) of the three crop areas—single- and double-cropping rice areas on
the Southwest Plateau of China, single-cropping rice area in North China, and single-
cropping rice areas in dry area of Northwest China—are significant. These three crop areas
exhibited small sown areas relative to the other crop areas from 1991 to 2020. In contrast,
the sown area of the crop areas with a statistically insignificant elasticity coefficient (
β2
)
was larger than the sown area of the other crop areas. The elasticity coefficients of the
population employed in agriculture (
β1
) were significantly negative in the single- and
double-cropping rice areas in Central China and the single-cropping rice areas in North
China, which suggests that there exists a relationship between the decrease in agricultural
labor and the increase in rice yields. This may occur because the improvement in planting
technology in these two crop areas released the agricultural labor force and increased the
level of yields at the same time. Moreover, the fact that the agricultural labor inputs in
these two crop areas were higher than those in the other crop areas from 1991 to 2020 and
generally showed a decreasing trend may support this explanation. In addition, although
the level of agricultural mechanization and technology was higher in the early-maturing,
Foods 2024,13, 966 11 of 22
single-cropping rice area in Northeast China, it still faced labor shortages, and
β1
was
significantly positive.
Foods 2024, 13, x FOR PEER REVIEW 11 of 26
test, which demonstrated that the climate factors impose a significant effect on the yields
in these two crop areas. The climate output elasticity for Japan’s rice area is 1.88, which
indicates that a 1% increase in the CCF increases rice yields by 1.88%. The climate output
elasticity for Korea is 0.61, indicating that a 1% increase in the CCF causes an increase in
rice yields of 0.61%. Only γ is significant in the partial regression coefficients for the Japan
rice area and Korea rice area with large absolute values, while the partial regression coef-
ficients of the other economic and social factors are not significant. This demonstrates that
the rice yields in Japan and Korea are mainly influenced by climatic factors such as tem-
perature and solar radiation. Rice is a crop that is sensitive to climatic conditions. For ex-
ample, increased temperatures can lead to heat stress at critical growth stages, affecting
pollen vigor and leading to yield reductions. Japan and Korea are coastal countries with
abundant water vapor, high air humidity, and high cloudiness, which can reduce the
amount of solar radiation received at the surface and limit the photosynthesis of rice. Ris-
ing temperatures combined with higher humidity may increase crop pests and diseases
[46]. Japan and Korea are located in monsoon climate zones with unstable climatic envi-
ronments that are prone to extreme weather events such as typhoons and persistent heavy
precipitation. These types of extreme weather events can cause flooding and waterlog-
ging, which, in turn, can lead to yield reductions [47]. In addition, typhoons and persistent
precipitation can reduce the solar radiation flux density received at the surface.
Figure 2. Partial regression coefficients for the rice crop areas. ***, **, and * indicate that the coeffi-
cient is significant at 1%, 5%, and 10%, respectively.
In contrast, the output elasticities of the climatic factors were not significant in the
rice crop areas of China, while the output elasticities of the socioeconomic factors were
significant in most crop areas. This suggests that the rice yields in most Chinese crop areas
are influenced by socioeconomic factors, with less influence resulting from climate. The
output elasticities of the fertilizer application amount per unit area (β3) passed the signif-
icance test at the 0.05 significance level, with positive values for all crop areas in China,
μ'β1β2β3γ
Dou ble-cro pp ing rice area in Sou th China 10.88 0.31 −0.28 0.02 −0.51
Single- and double-cropping rice areas in
Cen tral Chin a 9.16 −0.2 0.11 0.15 −0.13
Single- and double-cropping rice areas on
the Southwest Plateau of China 11.89 0.60 −0.61 0.17 −1.08
Single-cropping rice area in North China 9.01 −0.38 0.47 0.17 −0.22
Early-maturing, single-cropping rice area in
Northeast Chin a 5.39 0.36 −0.14 0.35 −0.05
Single-cropping rice area in dry area of
Northwes t China 4.99 0.16 0.28 0.31 −0.19
Jap an rice are a 7.39 0.07 −0.37 −0.17 0.93
Korea rice area 7.44 −0.17 0.08 −0.01 0.35
coefficient
crop
area **
**
***
***
***
***
***
***
***
***
*
**
***
**
***
***
***
***
***
***
Figure 2. Partial regression coefficients for the rice crop areas. ***, **, and * indicate that the coefficient
is significant at 1%, 5%, and 10%, respectively.
The output elasticities of the wheat production factors for each crop area are shown
in Figure 3. Only the climate output elasticity (
γ
) of the Japan wheat region passed the
significance test at a significance level of 0.1, which indicates that the wheat yields are
mainly influenced by socioeconomic factors instead of climate factors. This probably
occurs because wheat is hardy and eurythermal. The elasticity coefficients of the fertilizer
application amount per unit area (
β3
) were significant at the 5% significance level for
all wheat cropping regions except Korea, and their values were greater than 0.3, which
is relatively large. This demonstrates that the wheat yields are significantly affected by
the amount of fertilizer applied. The elasticities of the number of people employed in
agriculture (
β1
) for the winter wheat (autumn sowing) area in northern China, the winter
wheat (autumn sowing) area in southern China, and the Japan wheat area were negative.
This may be due to the existence of a factor that is negatively correlated with agricultural
labor and positively correlated with the yields in these three regions, such as the level of
agricultural automation and the level of agricultural mechanization, which results in a
negative elasticity of labor. Owing to the improvement in the agricultural technology level,
the direct impact of agricultural labor itself on the wheat yield is limited. In addition, the
agricultural laborers in these three cropping regions exhibited higher inputs than those in
the other cropping regions during the study period and generally showed a decreasing
trend, which can also indirectly confirm this explanation.
The output elasticities of the maize production factors in each crop area are shown in
Figure 4. The climate output elasticities (
γ
) for the spring maize area in northern China
and the maize area in the Southwest China Mountains passed the significance test, which
indicated that the climate factors imposed a significant effect on the maize yields in these
two crop areas. Between them, the absolute value of the elasticity of the climate factor was
larger in the spring maize area in northern China, where a 1% increase in the CCF caused
a decrease in maize yields of 2.86%, followed by the maize area in the Southwest China
Mountains, where a 1% increase in the CCF cause a decrease in the maize yields of 0.54%.
Foods 2024,13, 966 12 of 22
The maize yields in the spring maize areas in northern China are more sensitive than those
in the maize areas in the Southwest China Mountains. Late spring frost is prone to occur in
the spring maize area in northern China, which is at the end of the maize sowing period
and the beginning of the growth period, affecting the growth of maize seedlings. There
are few light and heat resources in the maize area in the Southwest China Mountains with
high interannual variation, which exerts a notable impact on the growth of maize. The
socioeconomic output elasticities for the spring maize area in northern China were not
significant, which demonstrates that the maize yields in this crop area are mainly influenced
by climate factors. The output elasticity of the fertilizer application amount per unit area
(
β3
) for the maize area in the Southwest China Mountains passed the significance test at
a significance level of 0.01. Thus, the loss caused by climate factors could be reduced via
rational fertilization.
Foods 2024, 13, x FOR PEER REVIEW 13 of 26
Figure 3. Partial regression coefficients for the wheat crop areas. ***, **, and * indicate that the coef-
ficient is significant at 1%, 5%, and 10%, respectively.
The output elasticities of the maize production factors in each crop area are shown in
Figure 4. The climate output elasticities (γ) for the spring maize area in northern China
and the maize area in the Southwest China Mountains passed the significance test, which
indicated that the climate factors imposed a significant effect on the maize yields in these
two crop areas. Between them, the absolute value of the elasticity of the climate factor was
larger in the spring maize area in northern China, where a 1% increase in the CCF caused
a decrease in maize yields of 2.86%, followed by the maize area in the Southwest China
Mountains, where a 1% increase in the CCF cause a decrease in the maize yields of 0.54%.
The maize yields in the spring maize areas in northern China are more sensitive than those
in the maize areas in the Southwest China Mountains. Late spring frost is prone to occur
in the spring maize area in northern China, which is at the end of the maize sowing period
and the beginning of the growth period, affecting the growth of maize seedlings. There
are few light and heat resources in the maize area in the Southwest China Mountains with
high interannual variation, which exerts a notable impact on the growth of maize. The
socioeconomic output elasticities for the spring maize area in northern China were not
significant, which demonstrates that the maize yields in this crop area are mainly influ-
enced by climate factors. The output elasticity of the fertilizer application amount per unit
area (β3) for the maize area in the Southwest China Mountains passed the significance test
at a significance level of 0.01. Thus, the loss caused by climate factors could be reduced
via rational fertilization.
μ'β1β2β3γ
0.85
Winter wheat (autumn sowing) area
in northern China 6.65 −0.45 0.28 0.54 0.03
Winter wheat (autumn sowing) area
in s outhern China 5.97 −0.79 0.44 0.33 0.85
Spring wheat (spring sowing) area of
Chin a 14.56 −0.08 −0.17 0.31 −1.19 0.00
Winter and spring sowing wheat
areas of China 14.16 0.13 −0.28 0.39 −1.3
Jap an wh eat area 13.15 −0.73 0.14 0.63 −1.77
Kor ea wh eat area 9.90 −0.29 0.01 0.41 −0. 84
−1.77
coefficient
crop
area
***
**
***
***
***
**
***
**
***
***
***
**
**
**
***
**
*
*
*
Figure 3. Partial regression coefficients for the wheat crop areas. ***, **, and * indicate that the
coefficient is significant at 1%, 5%, and 10%, respectively.
Foods 2024, 13, x FOR PEER REVIEW 14 of 26
Figure 4. Partial regression coefficients for the maize crop areas. ***, **, and * indicate that the coef-
ficient is significant at 1%, 5%, and 10%, respectively.
3.1.2. Discussion of Multicollinearity
Economic data often suffer the problem of multicollinearity, which increases the var-
iance in the biased regression coefficients and reduces the validity of the coefficients [48].
The ridge regression (RR) estimation method proposed by Hoerl and Kennard could be
used to reduce the variance by sacrificing the unbiasedness of the estimation [49]. In this
paper, the existence of multicollinearity was assessed by calculating the variance inflation
factor (VIF) of the independent variables for each cropping area. If the VIF of some inde-
pendent variable is greater than 10, the independent variable will be considered to be lin-
early correlated with the other independent variables, and the partial regression coeffi-
cients must be re-estimated using the ridge regression method. As indicated in Table 4,
multicollinearity was found in the data of the early-maturing, single-cropping rice area in
Northeast China, the Japan rice area, the Korea rice area, the spring maize area in northern
China, the maize area in hilly southern China, and the irrigated maize area in Northwest
China. Ridge regression analysis was used for these six crop areas, and the variance infla-
tion factor method was used to determine the ridge parameter k automatically.
Table 4. VIF of each independent variable for each crop area.
Crop Area β1 β2 β3 γ
Double-cropping rice area in South China 1.1945 8.2247 8.6717 1.1898
Single- and double-cropping rice areas in Central China 2.4871 2.2701 2.9919 1.1801
Single- and double-cropping rice areas on the Southwest Plat-
eau of China 3.6948 2.0385 3.0412 1.4064
Single-cropping rice area in North China 1.8717 1.2607 1.6227 1.1130
Early-maturing, single-cropping rice area in Northeast China 2.5946 31.3800 25.0936 1.0502
Single-cropping rice area in dry area of Northwest China 1.9516 1.3012 1.8670 1.3341
μ'β
1
β
2
β
3
γ
0.45
Spring maize area in north ern
China 26.64 −0.61 0.15 0. 1 −2.86
Summer maize area in the
Huang-Huai-Hai Plain 10.67 −0.31 0.12 0.14 −0.26
Maize area in the Southwest
Chin a M ou nta ins 7.27 −0.08 0.26 0.45 −0.54
Ma ize area in hilly s ou th ern
China 3.99 0.06 0.31 0.45 −0.24 0.00
Irrigated maize area in
Northwes t China 8.71 −0.34 0.06 0.36 −0.04
Mazie area on the Qin ghai–
Tibetan Plateau of China 18.79 −0.58 0.06 0.12 −1.69
Japan maize area 9.27 −0.28 0.07 0.03 0.02
Kor ea maize are a 10.09 −0.38 0.01 −0.03 0.09
−0.45
coefficient
crop
area
***
***
***
***
***
***
***
***
***
*
*
**
***
***
***
**
***
**
*
Figure 4. Partial regression coefficients for the maize crop areas. ***, **, and * indicate that the
coefficient is significant at 1%, 5%, and 10%, respectively.
Foods 2024,13, 966 13 of 22
3.1.2. Discussion of Multicollinearity
Economic data often suffer the problem of multicollinearity, which increases the
variance in the biased regression coefficients and reduces the validity of the coefficients [
48
].
The ridge regression (RR) estimation method proposed by Hoerl and Kennard could be
used to reduce the variance by sacrificing the unbiasedness of the estimation [
49
]. In
this paper, the existence of multicollinearity was assessed by calculating the variance
inflation factor (VIF) of the independent variables for each cropping area. If the VIF of
some independent variable is greater than 10, the independent variable will be considered
to be linearly correlated with the other independent variables, and the partial regression
coefficients must be re-estimated using the ridge regression method. As indicated in Table 4,
multicollinearity was found in the data of the early-maturing, single-cropping rice area in
Northeast China, the Japan rice area, the Korea rice area, the spring maize area in northern
China, the maize area in hilly southern China, and the irrigated maize area in Northwest
China. Ridge regression analysis was used for these six crop areas, and the variance
inflation factor method was used to determine the ridge parameter kautomatically.
Table 4. VIF of each independent variable for each crop area.
Crop Area β1β2β3γ
Double-cropping rice area in South China 1.1945 8.2247 8.6717 1.1898
Single- and double-cropping rice areas in Central China 2.4871 2.2701 2.9919 1.1801
Single- and double-cropping rice areas on the Southwest Plateau of China 3.6948 2.0385 3.0412 1.4064
Single-cropping rice area in North China 1.8717 1.2607 1.6227 1.1130
Early-maturing, single-cropping rice area in Northeast China 2.5946 31.3800 25.0936 1.0502
Single-cropping rice area in dry area of Northwest China 1.9516 1.3012 1.8670 1.3341
Japan rice area 11.4393 9.3500 6.6507 1.0939
Korea rice area 26.2476 17.9801 4.1156 1.0629
Winter wheat (autumn sowing) area in northern China 2.2262 1.4196 2.3437 1.1729
Winter wheat (autumn sowing) area in southern China 2.4213 2.7505 4.2112 1.6201
Spring wheat (spring sowing) area of China 1.3803 4.5462 4.6765 1.1909
Winter and spring sowing wheat areas of China 1.9761 1.0311 1.9356 1.0335
Japan wheat area 6.7006 1.4523 6.6055 1.1486
Korea wheat area 8.1970 4.6529 3.5710 1.0648
Spring maize area in northern China 2.4236 11.7103 9.0062 1.2765
Summer maize area in the Huang-Huai-Hai Plain 5.1883 8.7393 3.0180 1.0175
Maize area in the Southwest China Mountains 4.6703 6.2971 2.6591 1.1282
Maize area in hilly southern China 13.1194 7.6808 4.8837 1.3908
Irrigated maize area in Northwest China 1.6665 9.8974 14.1108 1.8945
Mazie area on the Qinghai–Tibetan Plateau of China 1.4767 2.1175 1.8110 1.3394
Japan maize area 9.0438 3.1719 6.3990 1.0870
Korea maize area 4.1217 2.2174 3.6954 1.1033
Compared to the coefficients estimated using the OLS, the absolute values of the
climate coefficients estimated via ridge regression analysis are lower, and the coefficients
of more socioeconomic factors passed the significance test (Table 5). The coefficient of the
population employed in agriculture (
β1
) for the Korea rice area passed the significance
test at the significance level of 0.05, but its absolute value is
−
0.07, which is very small.
The coefficient of the sown area (
β2
) is
−
0.24 for the Japan rice area and only passes the
significance test at the significance level of 0.10, which is weak. The ridge regression
estimation results still indicate that the yields in Japan and Korea are mainly influenced by
climate. The coefficient of the sown area (
β2
) is 0.05 for the early-maturing, single-cropping
rice region in northeast China and passed the significance test at a significance level of
0.01. Among the three maize crop areas, the elasticities of the fertilizer application amount
per unit area were lower in the ridge regression results than in the OLS estimates, and
the number of people employed in agriculture and the area under cultivation explained
more of the total variation in yields. In the three maize crop areas, the significance of the
climate coefficients did not change, while the socioeconomic factors could explain more of
the changes in the yields.
Foods 2024,13, 966 14 of 22
Table 5. Partial regression coefficients estimated using ridge regression analysis.
Crop Area Estimation Method µ′β1β2β3γ
Early-maturing, single-cropping rice area in Northeast China OLS 5.39 *** 10.36 ** −0.14 0.35 *** −0.05
RR (k= 0.19) 6.55 *** 0.18 0.05 *** 0.11 *** 0.02
Japan rice area OLS 7.39 *** 0.07 −0.37 −0.17 0.93 ***
RR (k= 0.08) 7.23 *** −0.03 −0.24 * −0.13 0.82 ***
Korea rice area OLS 7.44 *** −0.17 0.08 −0.01 0.35 ***
RR (k= 0.15) 8.19 *** −0.07 ** −0.06 −0.04 0.32 ***
Spring maize area in northern China OLS 26.64 *** −0.61 0.15 0.10 −2.86 ***
RR (k= 0.15) 25.62 *** −0.73 ** 0.11 *** 0.12 ** −2.42 ***
Maize area in hilly southern China OLS 3.99 0.06 0.31 ** 0.45 *** −0.24
RR (k= 0.12) 7.38 *** −0.18 ** 0.21 *** 0.32 *** −0.17
Irrigated maize area in Northwest China OLS 8.71 *** −0.34 0.06 0.36 ** −0.04
RR (k= 0.19) 8.20 *** −0.14 0.13 *** 0.19 *** −0.27
1. ***, **, and * indicate that the coefficient is significant at 1%, 5%, and 10%, respectively.
3.1.3. Discussion of Considering Technological Advancements
Agricultural technology develops over time within a crop area. Technological advance-
ments can be captured by adding dummy variables for a group of years into the model,
reflecting shifts in the C-D-C production function due to technological improvements.
The research period was divided into three stages: 1991–2000, 2001–2010, and 2011–2020.
It is considered that there is no difference in technical level in each stage and there are
differences between different stages.
ln(y) = β1ln(x1) + β2ln(x2) + β3ln(x3) + γlnC+b1d1+b2d2+µ′(17)
where
d1=1, the sample is during 2001–2010.
0, the sample is during 1991–2000 or 2011–2020. ,d2=1, the sample is during 2011–2020.
0, the sample is during 1991–2010.
b1and b2are the coefficients to be estimated.
The estimation results obtain using the OLS method are shown in Table 6. Having
fixed the influence of different levels of agricultural technology, the basic results did not
change. The yields in the Japan rice area, Korea rice area, Japan wheat area, Spring maize
area in northern China, and maize area in the Southwest China Mountains are mainly
affected by climate factors. Fertilizer application can play an important role in most Chinese
crop areas.
Table 6. Partial regression coefficients estimated considering the technological advancements.
Crop Area µ′β1β2β3γb1b2
Double-cropping rice area in South China 3.41 0.86 ** 1−0.18 0.13 −0.18 −0.11 *** −0.04
Single- and double-cropping rice areas in Central China 11.02 *** −0.19 *** −0.10 0.16 *** −0.07 −0.04 * −0.03
Single- and double-cropping rice areas on the Southwest Plateau
of China 16.40 *** −0.14 −0.42 0.29 ** −1.21 −0.01 −0.11
Single-cropping rice area in North China 9.75 *** −0.49 *** 0.17 0.41 *** −0.01 −0.13 * −0.13 *
Early-maturing, single-cropping rice area in Northeast China 5.47 *** 0.25 0.06 0.23 ** −0.05 −0.03 −0.01
Single-cropping rice area in dry area of Northwest China 5.66 0.13 0.53 *** 0.24 *** −0.46 0.10 ** 0.14 *
Japan rice area 8.14 *** 0.07 −0.48 ** −0.16 0.92 *** −0.04 −0.02
Korea rice area 7.91 *** −0.21 0.07 −0.03 0.33 *** −0.02 −0.03
Winter wheat (autumn sowing) area in northern China 7.26 ** −0.42 *** 0.18 0.53 *** 0.04 −0.01 0.02
Winter wheat (autumn sowing) area in southern China 6.15 −0.81 *** 0.44 0.33 ** 0.84 0.00 2−0.01
Spring wheat (spring sowing) area of China 14.00 *** 0.01 −0.24 * 0.28 * −1.07 −0.05 −0.04
Winter and spring sowing wheat areas of China 12.50 *** 0.12 * −0.16 *** 0.44 *** −1.18 0.03 −0.04
Japan wheat area 12.61 *** −0.48 0.04 0.60 * −1.87 * 0.08 0.14
Korea wheat area 12.02 ** −0.72 * −0.01 0.35 −0.65 −0.13 −0.28
Spring maize area in northern China 25.66 *** −0.52 0.11 0.09 −2.73 *** 0.01 0.04
Summer maize area in the Huang-Huai-Hai Plain 10.32 *** −0.32 * 0.17 0.13 −0.24 0.00 −0.02
Maize area in the Southwest China Mountains 7.56 *** −0.10 0.35 * 0.32 ** −0.59 ** 0.05 0.02
Maize area in hilly southern China 3.35 0.23 0.30 * 0.41 ** −0.38 0.06 0.08
Irrigated maize area in Northwest China 9.77 *** −0.40 * −0.05 0.37 ** −0.05 0.04 0.10
Mazie area on the Qinghai–Tibetan Plateau of China 19.46 *** −0.72 0.26 *** 0.02 −1.64 0.00 −0.32 **
Japan maize area 9.02 *** −0.24 *** 0.06 *** 0.03 0.03 0.00 0.01
Korea maize area 10.56 *** −0.45 *** 0.02 −0.03 0.07 −0.01 −0.05
1.
***, **, and * indicate that the coefficient is significant at 1%, 5%, and 10%, respectively.
2.
“0.00” means the value
is smaller than 0.01 and greater than 0.
Foods 2024,13, 966 15 of 22
3.2. Impacts of Future Climate Change on Grain Yields
The average value of the CCF from 1991 to 2020 was selected as the historical climate
state, and the average value of the CCF from 2021 to 2050 was selected as the future climate
state. The impact ratio of climate change (IRCC) was calculated for each crop area with a
significant climate coefficient. The results are shown in Figure 5. Overall, the contribution of
future climate change to future changes in grain yields is large in crop areas with significant
climate impacts. The climate affects grain yields to a lesser extent under scenario SSP2-4.5
and to a greater extent under scenarios SSP1-2.6 and SSP5-8.5.
Foods 2024, 13, x FOR PEER REVIEW 17 of 26
1
***, **, and * indicate that the coefficient is significant at 1%, 5%, and 10%, respectively.
2.
“0.00”
means the value is smaller than 0.01 and greater than 0.
3.2. Impacts of Future Climate Change on Grain Yields
The average value of the CCF from 1991 to 2020 was selected as the historical climate
state, and the average value of the CCF from 2021 to 2050 was selected as the future climate
state. The impact ratio of climate change (IRCC) was calculated for each crop area with a
significant climate coefficient. The results are shown in
Figure 5
. Overall, the contribution
of future climate change to future changes in grain yields is large in crop areas with sig-
nificant climate impacts. The climate affects grain yields to a lesser extent under scenario
SSP2-4.5 and to a greater extent under scenarios SSP1-2.6 and SSP5-8.5.
Regarding the rice crop areas and wheat crop areas that are significantly affected by
climate, the impact of future climate change is mainly positive. In the maize crop areas
that are significantly affected by climate change, the impact of climate change is mainly
negative. Future climate change impacts on grain yields in actual yields are small in the
Japan rice region, Korea rice region, and maize area in the Southwest China Mountains,
whose
IRCC
is less than 10%. The weight of climate change impacts on the actual yields
is high in the Japan rice area and spring maize area in northern China, whose
IRCC
is
greater than 15%. This may be related to the unstable future climate environment in these
two crop areas [7,50].
Figure 5. IRCC in the crop areas with significant climate impacts under the different scenarios.
3.3. Risk Assessment of Grain Yields
3.3.1. Value at Risk (VaR) and Expected Shortfall (ES)
The annual climate yield loss of each crop area was calculated. A POT model based
on the GPD was constructed for the tail data, and the value at risk (VaR) and expected
shortfall (ES) were calculated. For rice (
Figure 6
A,B), the crop area with the highest risk of
climate yield loss is the single-cropping rice area in north China, followed by the Japan
rice area. The VaR of the single-cropping rice area in north China is 618.57 kg/ha, and the
ES is 1175.83 kg/ha. The VaR of the Japan rice crop area is 562.91 kg/ha, and the ES is 934.55
kg/ha. Both north China and Japan are located in the monsoon region of East Asia, where
the circulatory atmospheric circulation is unstable with high interannual variability in
precipitation [39]. Typhoons are frequent in Japan, and the growing period of maize over-
laps with the period of high typhoon frequency [47]. Climate warming has led to an in-
creasing frequency and intensity of droughts in north China [39]. The crop area with the
lowest risk is the single- and double-cropping rice area in central China, where the VaR
and ES do not exceed 200 kg/ha. The coefficients of the population employed in agriculture
Figure 5. IRCC in the crop areas with significant climate impacts under the different scenarios.
Regarding the rice crop areas and wheat crop areas that are significantly affected by
climate, the impact of future climate change is mainly positive. In the maize crop areas
that are significantly affected by climate change, the impact of climate change is mainly
negative. Future climate change impacts on grain yields in actual yields are small in the
Japan rice region, Korea rice region, and maize area in the Southwest China Mountains,
whose
|IRCC|
is less than 10%. The weight of climate change impacts on the actual yields
is high in the Japan rice area and spring maize area in northern China, whose
|IRCC|
is
greater than 15%. This may be related to the unstable future climate environment in these
two crop areas [7,50].
3.3. Risk Assessment of Grain Yields
3.3.1. Value at Risk (VaR) and Expected Shortfall (ES)
The annual climate yield loss of each crop area was calculated. A POT model based
on the GPD was constructed for the tail data, and the value at risk (VaR) and expected
shortfall (ES) were calculated. For rice (Figure 6A,B), the crop area with the highest risk
of climate yield loss is the single-cropping rice area in north China, followed by the Japan
rice area. The VaR of the single-cropping rice area in north China is 618.57 kg/ha, and the
ES is 1175.83 kg/ha. The VaR of the Japan rice crop area is 562.91 kg/ha, and the ES is
934.55 kg/ha. Both north China and Japan are located in the monsoon region of East Asia,
where the circulatory atmospheric circulation is unstable with high interannual variability
in precipitation [
39
]. Typhoons are frequent in Japan, and the growing period of maize
overlaps with the period of high typhoon frequency [
47
]. Climate warming has led to an
increasing frequency and intensity of droughts in north China [
39
]. The crop area with the
lowest risk is the single- and double-cropping rice area in central China, where the VaR and
ES do not exceed 200 kg/ha. The coefficients of the population employed in agriculture
and the fertilizer application amount per unit area in this crop area are significant, while
the climate elasticity of the output is not significant, which demonstrates that progress in
agricultural technology and an increase in the amount of fertilizer applied in this crop area
Foods 2024,13, 966 16 of 22
may be conducive to reducing climate-induced losses. Regarding wheat (Figure 6C,D),
most of the crop areas in China exhibit low risk, while those in Japan and Korea exhibit
high risk. However, Japan and Korea’s grain crops are dominated by rice, so a higher
risk of wheat yields would be less detrimental to local food security and their overall
socioeconomic situation. Regarding maize (Figure 6E,F), the yield risk is higher in the
spring maize area in northern China and the maize area on the Qinghai–Tibetan Plateau.
In regard to the spring maize area in northern China, the VaR is 627.94 kg/ha, and the
ES is 826.38 kg/ha. Regarding the maize area on the Qinghai–Tibetan Plateau, the VaR is
702.84 kg/ha, and the ES is 760.49 kg/ha. These findings are likely related to the unstable
climate environment during the growing season in these two areas. Late spring frost often
occurs in north China and northeast China. The Tibetan Plateau exhibits high altitude,
low temperatures, and long growing periods. However, the accumulated temperature is
usually not sufficient, resulting in unstable maturity and yields. Moreover, the large values
of the climate output elasticities for these two crop areas obtained through the C-D-C model
passed the significance test. This indicates that climate factors impose a greater influence
and risk on the maize yields in these two crop areas.
Foods 2024, 13, x FOR PEER REVIEW 18 of 26
and the fertilizer application amount per unit area in this crop area are significant, while
the climate elasticity of the output is not significant, which demonstrates that progress in
agricultural technology and an increase in the amount of fertilizer applied in this crop
area may be conducive to reducing climate-induced losses. Regarding wheat (Figure
6C,D), most of the crop areas in China exhibit low risk, while those in Japan and Korea
exhibit high risk. However, Japan and Korea’s grain crops are dominated by rice, so a
higher risk of wheat yields would be less detrimental to local food security and their over-
all socioeconomic situation. Regarding maize (Figure 6E,F), the yield risk is higher in the
spring maize area in northern China and the maize area on the Qinghai–Tibetan Plateau.
In regard to the spring maize area in northern China, the VaR is 627.94 kg/ha, and the ES
is 826.38 kg/ha. Regarding the maize area on the Qinghai–Tibetan Plateau, the VaR is
702.84 kg/ha, and the ES is 760.49 kg/ha. These findings are likely related to the unstable
climate environment during the growing season in these two areas. Late spring frost often
occurs in north China and northeast China. The Tibetan Plateau exhibits high altitude, low
temperatures, and long growing periods. However, the accumulated temperature is usu-
ally not sufficient, resulting in unstable maturity and yields. Moreover, the large values of
the climate output elasticities for these two crop areas obtained through the C-D-C model
passed the significance test. This indicates that climate factors impose a greater influence
and risk on the maize yields in these two crop areas.
160°E
160°E
150°E
150°E
140°E
140°E
130°E
130°E
120°E
120°E
110° E
110° E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
020001000 km
ES (kg/ha)
63.40
63.41 – 285.84
285.85 – 492.23
492.24 – 591.17
591.18 – 826.38
160°E
160°E
150°E
150°E
140°E
140°E
130°E
130°E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
020001000 km
VaR (kg / h a )
54.40
54.41– 244.86
244.87 – 363.00
363.01 – 497.45
497.46 –702.84
160°E
160°E
150°E
150°E
140°E
140°E
130°E
130°E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
020001000 km
ES (kg/ha)
198.34 – 225. 64
225.65 – 488. 43
488.44– 1175.83
160°E
160°E
150°E
150°E
140°E
140°E
130°E
130°E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
020001000 km
VaR (kg / h a )
171.21
171.22– 4 30.00
430.01– 6 81.57
160°E
160°E
150°E
150°E
140°E
140°E
130°E
130°E
120°E
120°E
110° E
110° E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
020001000 km
ES (kg/ha)
222.47
222.48– 236.90
236.91 – 316.7 1
316.72– 356.95
356.96– 766.60
160°E
160°E
150°E
150°E
140°E
140°E
130°E
130°E
120°E
120°E
110°E
110°E
100°E
100°E
90°E
90°E
80°E
80°E
70°E
70°E
50°N 50°N
40°N 40°N
30°N 30°N
20°N 20°N
10°N 10°N
020001000 km
VaR (kg / h a)
199.80
199.81– 225.64
225.65– 284.38
284.39– 285.92
285.93– 686.79
(A) VaR for rice crop areas (B) ES for rice crop areas
(C) VaR for wheat crop areas (D) ES for wheat crop areas
(F) ES for maize crop areas
(E) VaR for maize crop areas
Figure 6. VaR for rice (A), wheat (C), and maize (E) crop areas. ES for rice (B), wheat (D), and maize
(F) crop areas.
3.3.2. Sensitivity Analysis
The GPD-based POT model requires setting the confidence level p. The VaR and ES are
different at different confidence levels. The pvalue was changed from 0.95 to 0.99, and the
tail data were modelled again based on the same model. The results with the new pvalue
are shown in Figure 7. The VaR and ES at a confidence level of 0.99 were higher than those
Foods 2024,13, 966 17 of 22
at a confidence level of 0.95, which indicates that the confidence level must be carefully
determined. However, the rankings of the VaR and ES for each crop area of the same crop
remained essentially unchanged, indicating that the model used is relatively robust.
Foods 2024, 13, x FOR PEER REVIEW 20 of 26
Figure 7. Comparison of the VaR with different p values in rice (A), wheat (C), and maize € crop
areas. Comparison of the ES with different p values in rice (B), wheat (D), and maize (F) crop areas.
Figure 7. Comparison of the VaR with different pvalues in rice (A), wheat (C), and maize (E) crop
areas. Comparison of the ES with different pvalues in rice (B), wheat (D), and maize (F) crop areas.
Foods 2024,13, 966 18 of 22
4. Discussion
In this paper, three grain crops, namely, maize, rice, and wheat, were selected, and
different grain crop areas were delineated based on existing studies. A climate–economic
model (C-D-C model) was used to estimate the output elasticity of climate factors and
to assess the impact of climate factors on the grain yields in different crop areas. The
impacts of climate factors on the three major food crops in China, Japan, and Korea
varied according to the different crop types and crop areas [
10
,
38
,
46
,
51
,
52
]. Cropping
areas significantly affected by climatic factors tend to have unstable climate environments,
most of which occur in monsoon climate zones. Regarding rice, the yields in the Japan
rice area and the Korean rice area are mainly influenced by climate factors, while the
socioeconomic impacts are not significant. In China, the rice yields are mainly influenced
by socioeconomic factors and not significantly by climatic factors, which suggests that
China can use socioeconomic resources to better withstand the risk of regional climate
variability in rice production, including implementing measures such as investing more
capital and promoting technological progress to adapt to climate warming at the current
level of technology [
46
]. Japan and Korea exhibit lower ability to use socioeconomic
resources to withstand the risk of regional climate variability in rice production. Wheat
yields are not significantly affected by climate factors, which is generally consistent with
the results of existing studies [
46
,
52
]. Furthermore, this research revealed the extent of the
influence of major socioeconomic production factors in different wheat cropping regions by
dividing the crop regions and showed that the fertilizer application amount per unit area
is a common and significant influencing factor of the wheat yields in all cropping regions
except the Korea wheat area.
Although the C-D-C model constructed in this paper fits and hindcasts well without
considering crop moisture requirements, moisture remains an important ecological factor
for the growth of crops and must be emphasized in future studies. In addition, climate
change affects not only the yield but also the grain quality. The grain quality is no less
important for grain security than the yield and should be emphasized in future research.
Whether it is grain yield or quality issues, it always causes negative impacts on society that
are valuable to take into account.
IRCC is a new index to assess the impact of climate change on grain yields. Maize
yields will be reduced in future climate scenarios in northern China and southwest China.
Therefore, it is necessary for northern China and southwest China to choose an alternative
crop like rice or wheat that is not significantly influenced or positively influenced by climate
change to partially replace maize planting and also to develop advanced technology.
Based on the yield loss time series data, a GPD-based POT model was constructed to
calculate the VaR and ES to assess the yield risk in the different crop areas in the current
climate state. It has been shown that the grain yield risk is higher in northern China than in
southern China [
10
]. However, this finding varies from crop to crop. For rice and maize,
the risk of yield loss is higher in northern China than in southern China, which may be
related to the higher risk of drought in the north than in the south [
16
]. From 2000 to 2015,
the affected area and the direct economic loss due to agricultural drought in China showed
a distribution pattern of high values in the northeast and low values in the southwest [
50
].
Although drought-resistant varieties of rice and maize have also been cultivated, their
growth still requires sufficient water. For wheat, the risk is higher in southern China than
in northern China. This may occur because wheat itself is cold and drought tolerant, and a
large number of drought- and cold-tolerant varieties have been generated and cultivated
today. Moisture is not a very important limiting factor. Flooding could also cause a wheat
yield reduction or even crop failure. From 2000 to 2015, the flood-affected area and direct
economic losses in the middle and lower reaches of the Yangtze River were large, with
an upward trend [
50
]. From the perspective of the stability of food supply, the northern
provinces of China can expand the planting of wheat and reduce the planting of rice and
maize. China’s southern provinces could expand the cultivation of rice and maize and
Foods 2024,13, 966 19 of 22
reduce the cultivation of wheat. The north and south can achieve a balance of supply and
demand of different kinds of grain in each province through domestic grain trade.
5. Conclusions
Compared with most existing studies, this paper provides a more detailed delineation
of the crop areas in China for the different crops. The results of this study are more targeted
and regionalized. The following conclusions can be obtained:
(1)
The effects of climate factors on grain yields vary greatly from region to region and
from crop to crop, and the climate environments of regions significantly affected by
climate factors tend to be unstable. The rice yields in Japan and Korea are mainly
affected by climatic factors, while the rice yields in China are mainly affected by
socioeconomic production factors. The wheat yields in China, Japan, and Korea
are less significantly influenced by climate factors. Fertilizer application imposes
a significant positive effect on the wheat yields in most crop areas. The ability of
wheat to withstand the risk of climate change could be improved through rational
fertilization. The spring maize area in northern China and the maize area in the
Southwest China Mountainous are more affected by climate factors and less affected
by socioeconomic factors.
(2)
Under future climate scenarios, climate change from 2021 to 2050 exerts a positive
impact on the rice crop areas and wheat crop areas but a negative impact on the maize
crop areas relative to the climate state from 1991 to 2020. The impact of climate under
scenarios SSP1-2.6 or SSP5-8.5 on grain yields is greater than that under scenario
SSP2-4.5.
(3)
For rice and maize, the risk of yield loss is higher in northern China than in southern
China; regarding wheat, the risk of yield loss is higher in southern China than in
northern China. This may be related to crop growth habits and the regional climate
environment. The risks of yield losses in the Japan rice crop area, Japan wheat
crop area, and Korea wheat crop area are relatively high. The risks of yield losses
in the Japan maize crop area, Korea rice crop area, and Korea maize crop area are
relatively low.
Author Contributions: Conceptualization, J.C. and H.J.; methodology, J.C., H.J. and Y.X.; software,
H.J.; validation, J.C., H.J., Y.X., W.Z., Y.L. and Y.H.; formal analysis, J.C. and H.J.; investigation, H.J.;
resources, J.C. and H.J.; data curation, H.J.; writing—original draft preparation, H.J.;
writing—review
and editing, J.C., H.J., Y.X., W.Z., Y.L. and Y.H.; visualization, H.J.; supervision, J.C.; project adminis-
tration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of
the manuscript.
Funding: This research was funded by the International (Regional) Cooperation and Exchange
Program of National Natural Science Foundation of China, grant number 42261144687; the projects
of State Key Laboratory of Earth Surface Processes and Resource Ecology, grant number 2022-GS-01;
and the projects of the National Natural Science Foundation of China, grant number 42075167.
Data Availability Statement: The datasets used and analyzed during the current study are available
from the corresponding author upon reasonable request or corresponding websites and statistical
yearbooks.
Acknowledgments: The climate scenarios used were from the NEX-GDDP-CMIP6 dataset, prepared
by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange
and distributed by the NASA Center for Climate Simulation (NCCS). The elevation data used for
the graphs were obtained from the GEBCO (https://www.gebco.net/data_and_products/gridded_
bathymetry_data/, accessed on 5 July 2023).
Conflicts of Interest: The authors declare no conflicts of interest.
Foods 2024,13, 966 20 of 22
Appendix A
Table A1. Fitted curves for the population employed in agriculture.
Provincial-Level Administrative District Fitted Curve 1R2
Beijing y= 112e−0.024x0.7410
Tianjin y=−21.11ln(x) + 143.01 0.8356
Inner Mongolia y= 3.0086x+ 451.33 0.9054
Jilin y= 0.0412x3−2.6165x2+ 49.963x+ 239.59 0.9399
Heilongjiang y=−0.5616x2+ 30.485x+ 274.3 0.7244
Zhejiang y=−28.499x+ 1532.9 0.9405
Jiangxi y=−0.9503x2+ 25.921x+ 914.65 0.8269
Henan
y=
−
0.0496x
3
+ 0.5159x
2
+ 47.899x+ 2338.2
0.6863
Guangxi y= 0.0471x3−2.98x2+ 56.344x+ 1252.5 0.8693
1. x= year −1981 + 1; yis the rural population employed in agriculture (unit: 10,000 people).
Appendix B
Table A2. Model selection.
Model Institution and Description
ACCESS-CM2 CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS
(Australian Research Council Centre of Excellence for Climate System Science).
ACCESS-ESM1-5 CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS
(Australian Research Council Centre of Excellence for Climate System Science).
BCC-CSM2-MR Beijing Climate Center, Beijing 100081, China
CanESM5
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada
CMCC-CM2-SR5 Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy
CMCC-ESM2 Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy
IITM-ESM Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy
MIROC6
JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean
Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies,
Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan)
MPI-ESM1-2-HR
JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean
Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies,
Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan)
MPI-ESM1-2-LR
JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean
Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies,
Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan)
MRI-ESM2-0 Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan
NESM3 Nanjing University of Information Science and Technology, Nanjing, 210044, China
NorESM2-LM
NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research,
Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313, Norway), NERSC (Nansen Environmental and
Remote Sensing Center, Bergen 5006, Norway), NILU (Norwegian Institute for Air Research, Kjeller 2027, Norway), UiB
(University of Bergen, Bergen 5007, Norway), UiO (University of Oslo, Oslo 0313, Norway) and UNI (Uni Research,
Bergen 5008, Norway), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway
NorESM2-MM
NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research,
Oslo 0349, Norway), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and
Remote Sensing Center, Bergen 5006, Norway), NILU (Norwegian Institute for Air Research, Kjeller 2027, Norway), UiB
(University of Bergen, Bergen 5007, Norway), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research,
Bergen 5008, Norway), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway
TaiESM1 Research Center for Environmental Changes, Academia Sinica, Nankang, Taipei 11529, Taiwan, China
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