Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Hua, W.; Chen, Z.; Luo, L.
The Effect of the Major-Grain-
Producing-Areas Oriented Policy on
Crop Production: Evidence from
China. Land 2022,11, 1375. https://
doi.org/10.3390/land11091375
Academic Editors: Sanzidur Rahman
and Uttam Khanal
Received: 7 July 2022
Accepted: 14 August 2022
Published: 23 August 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 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/).
land
Article
The Effect of the Major-Grain-Producing-Areas Oriented Policy
on Crop Production: Evidence from China
Wenyuan Hua 1,2, Zhihan Chen 1and Liangguo Luo 1,*
1Institute of Environment and Sustainable Development in Agriculture, China Academy of Agricultural
Science, Beijing 100089, China
2Environmental Economics and Natural Resources Group, Wageningen University,
6708 PB Wageningen, The Netherlands
*Correspondence: luoliangguo@caas.cn
Abstract:
As a powerful actor in the global food system, China experienced a significant drop in
crop production from 1998 to 2003, which posed a substantial threat to national food security and
led to the establishment of 13 major grain-producing areas (MGPA). Although some qualitative
research has found that the MGPA policy plays an important role in ensuring the national food
security, quantitative evidence on the effect of the MGPA policy and its potential mechanism remains
scarce. Based on China’s interprovincial panel data from 1998 to 2018, this study used a difference-in-
differences (DD) estimation strategy to analyze the treatment effect of the MGPA policy by taking the
assignment of 13 MGPA as a quasi-experiment. The results showed that the enforcement of the MGPA
policy significantly increased crop production, especially in terms of grain, rice and wheat yields. The
average grain yields were raised by 27.5%. The results of the event study analysis showed that the
treatment effects were sustainable in the following years of the policy implementation. This study
also explored alternative causal channels and found that the MGPA policy raised crop yields mainly
by expanding planting areas, improving the level of mechanization and increasing transfer payments.
These findings demonstrate the effectiveness of the MGPA policy in increasing crop production
in a developing country setting, which could enlighten policymakers in some less well-developed
countries on boosting crop production and maintaining food security.
Keywords: major grain-producing areas; crop production; food security; China
1. Introduction
“Food security” literally translates as “grain security” in Chinese, which is not only re-
lated to the security of a country, but also to world peace and social stability [
1
]. The United
Nations post-2015 sustainable development agenda has set the eradication of hunger as one
of important targets of the 17 Sustainable Development Goals (SDGs) in 2030. However,
nearly 750 million people were exposed to severe levels of food security globally in 2019,
and the number of people with food insecurity has been slowly increasing since 2014 (FAO,
2020). It was estimated that between 720 and 811 million people went hungry in 2020
according to the State of Food Security and Nutrition in the World 2021 report. Meanwhile,
in addition to the climate change [
2
] and economic inequality [
3
], the widespread of COVID-
19 pandemic also triggered a crisis on the global food security [
4
]. The COVID-induced
economic shock has threatened food security by reducing incomes and disrupting supply
chains, resulting in people’s reduced availability and affordability of food in both higher
and lower-income countries [
5
]. Therefore, transformations to increase the productive
capacity and stability of agricultural production are urgently needed, which requires the
building of a knowledge base to support.
As a powerful actor in the global food system, China has traditionally struggled to
feed its large population. China feeds approximately 18% of the world population with
only 8% of the global cultivated land (FAO, 2020). It is evident that China’s food security
Land 2022,11, 1375. https://doi.org/10.3390/land11091375 https://www.mdpi.com/journal/land
Land 2022,11, 1375 2 of 28
is closely related to the stability of the global food system. To ensure its food security,
the Chinese government has long put it high priority on the national political agenda [
6
].
However, from 1998 to 2003, China experienced a significant drop in crop production with
the production of rice, wheat and corn in 2003 falling down almost 18 percent from the
harvest in 1998. It posed a substantial threat to the national food security. After this crop
production crisis, 13 major grain-producing areas (MGPA) were established by Ministry
of Finance China, and a package of MGPA-oriented policies was implemented in the
13 regions. Although some qualitative research has found that the MGPA regions play
an important role in ensuring the national food security and improving the production
capacity [
7
,
8
], quantitative evidence on the effect of the MGPA policy and its potential
mechanism remains scarce. Quantitatively verifying the efficacy of the implementation of
the MGPA policy is important for China, as the ineffectiveness of such agricultural policy
may deepen China’s food crisis and threaten its food security. Furthermore, if the MGPA
policy failed to increase China’s crop production sustainably, the international crop price
would increase as a result of increased crop imports from China. This would threaten the
food security of low-income countries. Thus, the effectiveness of China’s MGPA policy is
not only a concern nationally but globally too.
A line of literature closely related to our work studies a certain set of factors affecting
agricultural production. The theoretical and empirical literature acknowledges that the
determinants of agricultural production can be categorized into mainly four types: physical
factors (e.g., terrain, topography and climate), infrastructural factors (e.g., irrigation, roads
and crop insurance), technological factors (e.g., farm machinery, pesticides and chemical fer-
tilizer) and institutional factors (e.g., land tenure, land tenancy and land reforms). In terms
of physical factors, they are defined as some natural resources including biophysical frame-
work of soils, water, temperature, flora and fauna. It is worth mentioning that these factors
do not work in isolation but the agricultural activity of a place is the product of combi-
nations of different physical factors [
9
–
11
]. In terms of infrastructural factors, following
the World Bank Report (1994), the definition of agricultural infrastructure was narrowed
down to comprise long-lived engineered facilities and other services which include roads,
electricity supplies and telecommunication. As illustrated by empirical studies, roads,
electricity supplies, telecommunication and other infrastructure are important stimulants to
agricultural output [
12
–
15
]. In terms of technological factors, empirical studies have found
the adoption of improved agricultural technologies remains to be a promising strategy to
achieve food security in many developing countries [
16
–
18
]. Institutional factors, which
refers to the particular system under which land is owned and managed, have a direct
bearing on agricultural production [
19
]. In practice, many developing countries have
implemented many institutional reforms in agriculture sector, including providing security
of tenure, computerization of land records and ceilings on agricultural holdings. Many
of these institutional reforms have been empirically evaluated their effectiveness [
20
,
21
].
Despite the growing interests and enthusiasm for analyzing agricultural production from
the perspectives of physical, infrastructural and technological dimensions, many studies
note that these practices are still finite and farmers in developing countries are faced with
a challenging environment [
22
], hence more focus should be on the role of institutional
factors in agriculture. In particular, for China’s MGPA policy, an institutional reform of
redistributing regions for agricultural production, the research on its effectiveness still
remains scarce. Given these, there is dire need to investigate the impact of the institutional
factors on agricultural production.
Another strand of literature related to our analysis is research on the agricultural
production in the MGPA regions. So far, many studies have been done to understand
the agricultural production in the MGPA regions. Zhang et al. [
23
] estimated the grain
production efficiency of the 13 MGPA regions between 2008 and 2017 and found that the
overall level of total factor productivity of grain production in China’s MGPA regions was
relatively high and fluctuated, with an average annual rise rate of 1.85%. Yang et al. [
24
]
identified the efficiency of the crop insurance in increasing crop production of the MGPA
Land 2022,11, 1375 3 of 28
regions which exhibit a higher level of spatial farming risk accumulation and larger natural
disaster pressures on farmers. Zhang et al. [
25
] empirically estimated the impact of the
farmland protection on the security of grain supply in the MGPA areas using the panel
data from 2010 to 2019 and found that the protection of cultivated land resources positively
impacted the security of grain supply. Although all results in these studies have shown
a significant increase in crop production in the MGPA regions, none of them empirically
examined the causality between the establishment of the MGPA policy and crop production.
The MGPA policy pertains to land management practices as it involves the allocation of
land resources for agricultural growth. Theoretically, the exchange of inputs may avoid
resource misallocation, which achieves higher marginal products and therefore improves
input elasticities in agriculture [
26
].The effective land and resource governance systems
that provide improved access, control, and rights to land and other natural resources is a
necessary condition for achieving stable crop production [
27
]. Besides, land management
practices are often with some production-oriented policies, which can be categorized
as input support [
28
,
29
] (e.g., subsidies for fertilizers and seeds and farm equipment),
output support [
30
,
31
] (e.g., countercyclical payments and price incentives), technical
support [
32
,
33
] (e.g., extension services and investment in structural development) and
financial support [
34
,
35
] (e.g., cash subsidies, loan aid and insurance aid). Although the
four types of agricultural policies often interact with each other, previous studies have
rarely treated them as a whole to investigate their impact on crop production.
Although China’s food security is now guaranteed [
6
], in the long run, it is still faced
with great challenges such as the rapid urbanization and spatial mismatch in agriculture
resources. The rapid urbanization coincides with a large-scale transfer of China’s cropland
to “marginal land”, which substantially imperiled food security and environmental sus-
tainability [
36
]. This urbanization trend has led a large number of people to migrate from
rural areas to cities. This rural-to-urban migration pattern intensifies the abandonment
of cultivated land, while increasing its non-agricultural use [
37
]. Furthermore, a serious
spatial mismatch exists between grain production and farmland resources in China, which
also poses a threat to China’s food security [
38
]. Thus, identifying the impact of the MGPA
policy on crop production and clarifying its mechanism could provide insightful policy
implications for alleviating food crisis in the future.
Given the above practical and theoretical background, to our knowledge, systematic
empirical evidence on the effectiveness of the implementation of the MGPA policy remains
scarce. Therefore, the objectives of this study are twofold: (i) The first is to investigate the
effects of the MGPA policy on crop production by carrying out a difference-in-differences
(DD) estimation and taking the assignment of 13 MGPA as a quasi-experiment based on
China’s interprovincial panel data from 1998 to 2018. To consolidate the reliability of the
baseline results, several robustness checks are also performed. (ii) The second objective is
to identify alternative causal channels of the treatment effects of the MGPA policy in terms
of agricultural planting areas, mechanization level and transfer payments using a causal
steps approach. This analysis could shed new light on maintaining food security from a
perspective of land management practice.
2. Major-Grain-Producing-Areas Oriented Policy in China
China’s production of rice, wheat and corn fell to around 400 million tons in 2003,
down almost 18 percent from the record harvest of 486 million tons in 1998, according
to statistics from the US Department of Agriculture. Meanwhile, this food crisis was
accompanied by a growing population and shrinking arable land area. To eliminate the
threat to food security, 13 major grain-producing areas were established at the end of 2003
by the Ministry of Finance China. These areas were Heilongjiang, Liaoning, Jilin, Inner
Mongolia, Hebei, Jiangsu, Anhui, Jiangxi, Shandong, Henan, Hubei, Hunan, and Sichuan.
Before 2003, the MGPA regions had been unofficially identified and were slightly different
from those announced in 2003. Specifically, Guangdong, Guangxi, Zhejiang, Gansu and
Shanxi had been classified as MGPA pre-2003, however, they were not included in the list
Land 2022,11, 1375 4 of 28
of 2003. Figure 1illustrates the geographic distribution of the MGPA regions in China.
Dark grey areas indicate the 13 MGPA regions, and white areas the non-MGPA regions.
The 13 MGPA regions cover 64% of the geographical area, are home to more than 50% of
the population, and produce 75.4% of China’s grain output [
7
]. Geographically, seven of the
13 MGPA regions are located in northern China, which is to the north of the Qinling-Huai
line
1
. This is consistent with the current observation that China’s agricultural center is
shifting from the south to the north, especially to the northeast of China.
Figure 1. MGPA provinces.
The establishment of the MGPA is supplemented by some MGPA-specific policies.
After carefully sorting out the MGPA oriented policies during our research period from 1998
to 2018, as Table 1shows, we classify these policies into three types: production support,
market management and natural resources management. Such classification is based on
the food and agriculture policy classification of FAO. Among these MGPA oriented policies,
several MGPA-specific policies are widely recognized. In terms of the production support
policy, rewarding counties that produced large harvests, was added to the MGPA policy
package in 2005. Specifically, a county was deemed to have produced a large harvest
when its average yearly crop yields for the past five years were above 200 thousand tons
and commodity crops above 5000 tons. Also, when a county’s yields were ranked in the
top 100 of all areas in the MGPA, it received some extra bonus subsidy from the central
government. This MGPA-specific policy not only increased the willingness of farmers
to plant crops, but also reduced the financial pressure on local government. In terms
of the agricultural risk management policy, subsidies for the disaster prevention and
mitigation in agriculture were implemented in 2012. The central government implemented
for the first time the subsidy policy for agricultural disaster prevention and mitigation
by subsidizing six key technologies for winter wheat, northern corn and southern early
rice production. Abdur and Wang [
39
] found that these policies played an important
role in helping farmers restore production and living. For the value-chain-development
oriented policy, China formulated a plan for the construction of a high-quality grain
industry (2004–2010) immediately after identifying the 13 MGPA regions. This policy
was designed to accelerate the upgrading of the grain industry for the 13 MGPA regions
by cultivating superior crop breeds, promoting the construction of standard farmland,
improving agricultural mechanization and advancing disease and pest control techniques.
Meanwhile, it also incorporated technology advancement into the existing agricultural
value chain through better training, financing and fertilizer. As for the conservation and
management of resources policy, the central government of China officially launched the
Action Plan for the Zero Increase of Fertilizer Use in 2015. The goal of this plan was
to reduce the fertilizer use without reducing food production especially in the MGPA
Land 2022,11, 1375 5 of 28
regions. Lastly, the establishment of grain production functional area in 2017 scientifically
demarcated the grain production functional areas of rice, wheat and corn, the production
and protection areas of soybean, cotton and rapeseed, and management of groundwater
overexploitation funnel areas in North China.
In general, the MGPA policy can be summarized as following features: (i) The spatial
agglomeration of the MGPA regions. China’s agricultural center had been in the south for
a quite long time and the traditional pattern of grain transportation was from the southern
regions to the northern regions during the long-term historical accumulation of agricultural
production [
40
]. However, with the establishment of MGPA policy, seven of the 13 MGPA
regions are located in northern China and the spatial pattern of the food production has
shifted from transporting grain to the north to relying mainly on the northern regions as a
result of the conversion of farmland in the southern regions, the expansion of cultivated
land in the northern regions [
41
], the transfer of agricultural labor to non-agricultural
industries [
42
] and the adjustment of the planting structure [
40
]. The regions with high grain
output per capita are now concentrated in northern and eastern China, while regions with
low grain output per capita are mainly in southern and western China [
43
]. The proportion
of grain output in 15 northern provinces in the national grain output has increased from
45.65% in 2000 to 59.22% in 2020, while for the southern areas, it declined from 54.35% in
2000 to 40.78% in 2020. In addition, the MGPA regions agglomerate to the relatively less
developed areas when compared with the non-MGPA regions. Existing studies indicate
that the MGPA regions sacrifice their economic development for China’s food security [
44
].
In contrast, the food supply of the non-MGPA regions is largely supported by the MGPA
regions’ grain production, and the development of the non-MGPA regions is more economic-
oriented. For example, Zhejiang was not included in the 2003 list, despite being one of
the unofficial major grain-producing provinces and having better agricultural resources.
Zhejiang was not included on the list because it may take more economic responsibility
with its well-equipped industry and intensified city groups. (ii) The comprehensiveness
of the MGPA policy. The MGPA policy is not merely a land management practice for
reallocating land resources for food production. It is also followed by a comprehensive
MGPA-regions-specific agricultural policies. The MGPA policy has multiple areas of
action, mainly including financial subsidies (e.g., rewarding the county for large harvests ),
technical support (e.g., upgrading local agricultural infrastructure) and input support (e.g.,
promoting the adoption of superior crop breeds). These integrated sub-policies support
and complement each other to increase grain output. In terms of the source of funds,
formal financial institutions are less interested in financing the agricultural sector because
it is a high-risk business with high transaction costs, asymmetric information and low
profits [
45
]. However, the funds for implementing the MGPA policy are provided by
both the central financial budget and local supporting funds, which can safeguard the
stability and sustainability of the MGPA policy from financial constraints. In addition,
the MGPA policy is also a dynamic policy which, through successive reforms, has adapted
to new challenges faced by China’s agriculture. The Chinese government has so far
created and implemented a series of MGPA sub-policies to meet new challenges such as
addressing national market fluctuations and price volatility, using natural resources in a
more sustainable manner and contributing to climate change mitigation.
Land 2022,11, 1375 6 of 28
Table 1. The brief summary of the MGPA oriented policy.
Policy Classification Policy Sub-Classification Policy Time Policy Specification
MGPA oriented
agricultural policy
Production support Production subsidy
Rewarding counties that
produce large harvests. 2005 When a county’s yields were ranked in the top 100 of all areas in the MGPA, it will receive
some extra bonus subsidy from the central government.
The subsidy policy for soil
testing and fertilizer
recommendation.
2005
Focusing on five segments of “testing, formulating, producing, supplying and fertilizing”,
agricultural agencies launched soil testing, formulated scientific fertilization scheme and
generalized the scientific technique of fertilization.
Supporting policies for
agricultural standardized
production.
2006
The subsidy funds were mainly used for the integration of grain production standards,
the publicity of standards, the construction of core demonstration areas, the establishment
of leading enterprises and and the brand cultivation.
The construction of large grain
commodity bases. 2007
More than 60 large grain commodity bases have been built in several areas of the MGPA to
upgrade local agricultural infrastructure and strengthen scientific and technological support
for grain production.
The construction of high
standard farmland. 2010
It is a key measure to consolidate and improve grain production capacity and ensure
national food security, which mainly focuses on arable land protection, soil fertility
improvement, and efficient water-saving irrigation.
Market management
Agricultural risk management
Subsidies for the disaster
prevention and mitigation in
agriculture.
2012
Special funds were allocated to provide subsidies for the implementation of drought
resistant technique in the northeast region and the implementation of "one spraying and
three prevention" technique in the winter wheat producing areas.
Value chain developments
The construction of high-quality
grain industry. 2004
The plan was designed to improve the quality of grain production by cultivating superior
crop breeds, promoting the construction of standard farmland, improving agricultural
mechanization and advancing disease and pest control techniques.
The construction of modern
agriculture demonstration zone. 2010
Taking green and recycling agriculture as the leading industry, it strived to build a pilot area
with efficient grain production and quality improvement, a model area for sustainable
development in agriculture.
Natural resources management
Conservation and
management of resources
A pilot scheme for agricultural
resources recuperation. 2014
Returning farmland to forests and grasslands for steep slopes, seriously desertified
farmland and important water sources areas. Carrying out comprehensive management of
groundwater overexploitation funnel areas in North China.
Policy of reducing fertilizer
application and increasing
efficiency.
2015 It was designed to reduce the amount of fertilizers and increase the efficiency on the
premise of stable food production growth and adequate protection of food security.
Land policy The establishment of grain
production functional area. 2017
It was aimed to scientifically demarcate the grain production functional areas of rice, wheat
and corn, and the production and protection areas of soybean, cotton, rapeseed, sugar cane
and natural rubber.
Land 2022,11, 1375 7 of 28
3. Methodology and Data
3.1. Regression Model
3.1.1. Difference-in-Differences Model
In order to identify the effect of the MGPA policy, a difference-in-differences model
(DD) is widely used as an effective method for separating the time trend effect and the
policy effect [
46
]. DD is a quasi-experimental design that makes use of longitudinal data
from treatment and control groups to obtain an appropriate counterfactual to estimate a
causal effect. It is typically used to estimate the effect of a specific intervention or treatment
by comparing the changes in outcomes over time between the intervention group and the
control group. In our analysis, the provincial variations in the adoption of MGPA policy
enables us to carry out DD analysis. Specifically, there are two groups of provinces: those
designated as MGPA (treated provinces) and those not (control provinces). There are two
sample periods, pre-MGPA and post-MGPA, with the pre-MGPA period ranging from 1998
to 2004 and the post-MGPA period ranging from 2005 to 2018. The grain yield of MGPA
provinces was compared to that of non-MGPA provinces (the first difference) before and
after the implementation of the MGPA policy (the second difference).
The DD estimation specification is as follows:
Yit =α+β(Di×Tt) + γZit +λi+δt+εit (1)
where
Yit
, our measure of grain yield from province
i
in year
t
, is proxied by grain yield,
rice yield and wheat yield;
Di
indicates whether the province has been designated as
MGPA i.e.,
Di=
1 if province
i
is a MGPA province and
Di=
0 if province
i
is a non-
MGPA province;
Tt=
1 indicates the post-treatment period and
Tt=
0 indicates the
pre-treatment period
2
;
Zit
are other independent variables;
λi
are province fixed effects, cap-
turing province
i
’s time-invariant characteristics, such as natural, climate and geographic
features;
δt
are year fixed effects, capturing all yearly shocks common to all provinces, such
as monetary policy and business cycles; εit is the error term.
One concern with the above specification is that province-specific annual variations
may bias the estimation. One of these potential variations is natural disaster. Specifically,
if grain yield was affected by some specific disasters, the estimates could be mistakenly
attributed to the implementation of the MGPA policy. For example, during China’s 2008
snow storms, the excessive snowfall and ice in February paralyzed the southern provinces
and badly damaged their crops. To address such province-specific annual variations, we
followed the approach of Li et al. [
47
] and included the interaction of province
i
and year
t
(
λi×δt
) into Equation (1). We therefore used the following equation for DD estimation to
account for province-fixed, year-fixed and province-specific annual effects:
Yit =α+β(Di×Tt) + γZit +λi+δt+µ(λi×δt) + εit (2)
3.1.2. Event-Study Difference-in-Differences Model
DD relies on the parallel trends assumption which requires that in the absence of
treatment, the difference between the treatment and control group is constant over time [
46
].
Despite the estimated coefficients of treatment effects being statistically significant in the DD
estimation, the parallel trends assumption might still be a cause of concern. One estimation
strategy widely used is to implement an “event-study difference-in-differences” estimator
(ET-DD) [
48
]. The ET-DD estimation can also show the dynamic effects of the MGPA policy
on crop production if the parallel trends assumption is satisfied. The specification of the
ET-DD estimation model is:
Yit =α+
14
∑
k=−6
βk(Di×Tt) + γZit +λi+δt+µ(λi×δt) + εit (3)
where
k
describes the year before or after the enactment of the MGPA policy and
k=
0 is
normalized to 2004. In the regressions, k=−1 is left out as the reference year of 2003.
Land 2022,11, 1375 8 of 28
3.1.3. Propensity Score Matching Method
China exhibits appreciable regional differences across its huge territory and some of
these differences are closely associated with the enactment of the MGPA policy. It suggests
that the MGPA policy is more easily implemented in provinces with developed agricultural
resources. If so, this reduces the comparability between the treatment and control provinces
and confounds our estimation. To provide a good counterfactual for the treatment provinces
in the period studied, the propensity score matching (PSM) method was used to mitigate
selection bias by matching observations of treatment provinces with control provinces.
Besides, PSM can also serve as a robustness check of our baseline estimation using DD
method. Following Rosenbaum and Rubin [49], the PSM is modeled as:
p(X) = Pr(D=1|X) = E(D|X)(4)
where D = 0, 1 is an indicator of whether the province has been assigned as a MGPA
province; X is a vector of pre-treatment characteristics.
Following Heckman et al. [
50
], we let
Y1
be the grain yield if the province i is a MGPA
province (D = 1) and
Y0
if the province i is a non-MGPA province (D = 0). Thus, the average
treatment effect on the treated (ATT) is specified as:
ATT =E(Y1−Y0|D=1) = E(Y1|D=1)−E(Y0|D=1)(5)
Then the treatment effects based on the propensity score is estimated as follows:
ATT =E(Y1|D=1, p(X)) −E(Y0|D=0, p(X)) (6)
3.2. Indicators and Variable Selection
3.2.1. Explained Variables
As discussed in Section 2, the major grain-producing areas were established as a result
of China’s falling grain production. The MGPA policy is aimed at increasing grain-oriented
production. Hence we select the yearly provincial grain yield as the outcome of interest,
which is defined as the output of grain, wheat, maize, sorghum, tubers, soybean and several
other crops by China Agricultural Statistical Yearbooks. Besides, rice and wheat yield are
also incorporate as two supplementary dependent variables because they are the two most
important crops [
51
]. In 2021, the outputs of rice and wheat were respectively 21.29 million
tons and 13.70 million tons, both ranking the highest in the world and accounting for about
55% of China’s total food production. The increase of grain yield is expected to be mainly
illustrated by the increase of the rice and wheat yield. Therefore, the estimation of the
MGPA policy’s effect on the two supplementary dependent variables can also serve as a
robustness check for our baseline regression which employs the yearly provincial grain
yield as the explained variable .
3.2.2. Key Explanatory Variable
According to the DD model setting, the core explanatory variable of this paper is the
implementation of the MGPA policy, which is a dummy variable. Specifically, the core
explanatory variable equals to 1 when the province has been designated as a MGPA
province and 0 if province is a non-MGPA province. In addition, a dummy variable is often
used in regression analysis to distinguish different treatment groups [
52
]. In our paper,
whether the province has been assigned as a MGPA province is our interest. In other words,
we just focus on whether the MGPA policy has been implemented, which does not involve
building an explicit index system for the implementation of the MGPA policy. Therefore,
a dummy variable can represent the implementation status with two distinct categories in
our analysis.
Land 2022,11, 1375 9 of 28
3.2.3. Control Variables
A number of control variables relating to crop production have been included. Fer-
tilizer consumption and pesticide consumption per mu
3
are two important inputs for
agricultural production [
53
]. As China’s agricultural growth has mainly been attributed to
the improvement of productivity, especially the improvement of mechanization level in
agriculture [
26
], the fixed asset which is closely related to the investment of technical equip-
ments in agriculture is also included. Research shows that participation in rural non-farm
activities exerts a pronounced impact on agriculture, household farm decisions and house-
hold food security [
54
]. Hence, non-agricultural income earned from non-farm activities has
been included as a control variable. Following Janvry et al. [
55
], non-agricultural income
was measured as the farmer’s wage income per capita
4
. Due to the fact that the agricultural
production system can benefit from participation in trade through the introduction of new
skills and techniques [
56
], trade openness which is defined as the ratio of a province’s
sum of exports and imports to that province’s GDP is also incorporated. The rate of ur-
banization is also included because a person’s diet and demand for agricultural products
will be transformed by urban expansion [
57
]. Zhong et al. [
58
] suggest that the frequent
use of modern technologies resulting from the industrial revolution has increased crop
yields, thus we included industrialization which is measured as the ratio of the output in
the secondary industry to GDP. Lastly, rural financial efficiency also affects agricultural
yields by extending agriculture-oriented financial services to farmers. Following Wang and
Sun [
59
], we use the ratio of yearly rural loans to deposits as an indicator of rural financial
efficiency, with data obtained from Chinese Rural Credit Cooperatives (1998–2018).
A threat to the identification is that the treatment effects would be confounded when
there were other policies being enacted around the same time as implementation of the
MGPA policy. After studying Chinese government documents, we found two agricultural
policies that may have biased the estimation. One was the enactment of Law of Rural Land
Contract (LRLS) in 2002
5
, which enabled farmers to legally transfer, re-contract, enter into
share-holding ventures and exchange the rights of land use. The other was the abolition of
China’s agricultural tax in 2006
6
, which had been in existence over 2600 years. Existing
studies have found that these two policies can affect farmers’ grain production [
60
,
61
].
To relieve any confounding effects of these policies, two dummy variables were included.
LRLS
indicated the enactment of Law of Rural Land Contract in 2002 and
Tax
indicated
the abolition of agricultural tax in 2006.
3.3. Data Sourcing
Our list of provinces designated as MGPA was derived from an official Ministry of
Finance of the People’s Republic of China document from December 2013, “The Plan for the
Reform and Improvement of Agricultural Development.”. The 13 MGPA were Heilongjiang,
Liaoning, Jilin, Inner Mongolia, Hebei, Jiangsu, Anhui, Jiangxi, Shandong, Henan, Hubei,
Hunan, and Sichuan. During the sample period, this list remained unchanged.
In order to estimate the treatment effects, a balanced panel of provincial data was
constructed to estimate the effects of interest. The sample periods covered 1998 to 2018,
as Chongqing was separated from Sichuan and designated a provincial-level municipality
in 1997. In almost all cases, data were collected from various sources, including China
Rural Statistical Yearbooks (Ministry of Agriculture, 1998–2018), National Agricultural
Product Cost and Revenue Survey Data books (Ministry of Agriculture, 1998–2018), China
Agricultural Statistical Yearbooks (Ministry of Agriculture, 1998–2018), China Statistical
Yearbooks on Environment (Ministry of Environment, 1998–2018), and China Statistical
Yearbooks (NBSC, 1998–2018). In addition, all economic variables were deflated using
1997’s CPI. Detailed descriptive statistics are presented in Table 2.
Land 2022,11, 1375 10 of 28
Table 2. Descriptive statistics of variables.
Variables Definition of Variables Mean S.D. Min Max
Grain Annual grain yields (log) 16.16 1.23 12.74 18.15
Rice Annual rice yields (log) 5.11 2.45 −2.30 7.94
Wheat Annual wheat yields (log) 4.10 2.40 −3.22 8.22
Pesticide Pesticide use per 10,000 yuan of the primary industry output (log) −7.81 1.15 −
11.68
−5.73
Fertilizer Fertilizer use per 10,000 yuan of the primary industry output (log) −1.18 0.62 −6.14 3.59
Fixed-asset investment Fixed-asset investment per capita (log) 5.91 0.69 3.48 7.62
Non-agricultural income Non-agricultural income per capita (log) 6.76 0.97 3.49 9.07
Trade openness The ratio of a province’s sum of exports and imports to that province’s GDP 0.29 0.37 0.02 1.70
Urbanization The ratio of urban population to rural’s 0.43 0.18 0.10 0.90
Industrialization The ratio of the secondary industry output to GDP 0.44 0.08 0.19 0.60
Rural financial level The ratio of annual rural loans to deposits 0.68 0.14 0.33 1.97
Grain planting areas Grain planting areas per capita (log) 0.09 0.06 0.00 0.38
Wheat planting areas Wheat planting areas per capita (log) 0.02 0.02 0.00 0.06
Rice planting areas Rice planting areas per capita (log) 0.02 0.02 0.00 0.10
Transfer payment Transfer payment per capita (log) 5.26 1.25 2.58 8.13
Mechanization Mechanization level per capita (log) −0.04 0.81 −1.57 9.49
4. Results
4.1. Tests for Some Statistical Problems
To check for the variable collinearity, we perform a variance inflation factor (VIF)
analysis, which has been widely used to test collinearity. The VIF test results of the
explanatory variables in this study are summarized in Table 3. Among all variables,
the largest VIF value is 2.75. Generally, a VIF above 4 indicates that multicollinearity might
exist, therefore multicollinearity is free from concern in our analysis.
Table 3. The result of VIF test.
Variables Pesticide Fertilizer Fixed-Asset
Investment
Non-
Agricultural
Income
Trade
Openness Urbanization Industrialization
Rural
Financial
Level
VIF 2.03 1.28 2.50 2.75 1.73 2.05 1.30 1.12
1
VI F 0.49 0.78 0.40 0.36 0.58 0.49 0.77 0.89
To verify whether the regression model contains a heteroskedastic error, White’s test
proposed by White [
62
], has been widely used. White’s test, which compares the esti-
mated variances of regression coefficients under homoskedasticity with the ones under
heteroskedasticity, has an asymptotic chi-squared distribution and works well in large
samples [
63
]. We perform a White’s test and the
p
-value is 0.492, suggesting that the null
hypothesis of homoscedasticity or no heteroscedasticity should be accepted. For the possi-
ble autocorrelation, a test proposed by Wooldridge [
64
] is very attractive because it requires
relatively few assumptions and is easy to implement [
65
]. The result of the Wooldridge
test shows that
p
-value is 0.0751, indicating that there is no first-order autocorrelation at
a 5% confidence level in our linear panel-data model. Besides, following most empirical
studies using panel data, all our empirical estimation is built on a robust-standard-errors
technique for panel regression which is invented by Hoechle [
66
]. The code program pre-
sented by Hoechle not only could enable the estimates to be heteroskedasticity consistent
but also make the standard error estimates be robust to general forms of cross-sectional
and temporal dependence, i.e., autocorrelation. Therefore, the statistical problems of the
heteroscedasticity and autocorrelation are relieved by using the estimation program.
In terms of the linearity and adequacy of the model setting, on the one hand, our
select of control variables are based on the literature review (Section 3.2) and hence these
control variables’ linear relationship with the dependent variable has been examined by
previous studies. Besides, the adequacy of the model can be partly illustrated by
R2
,
and the
R2
of our baseline regression model is above 92%, as reported in the baseline
regression, suggesting that at least 92% of variance in the dependent variable that can be
explained by the independentvariables. Therefore, the performance of our regression model
Land 2022,11, 1375 11 of 28
is good. On the other hand, linearity in parameters within linear regression requires that
model equation has correct functional form specification. This can be evaluated through
Ramsey RESET test [
67
] which evaluates whether linear regression fitted values non-linear
combinations explain dependent variable. If linear regression fitted values non-linear
combinations explain dependent variable, then model equation has incorrect functional
form specification. The result of Ramsey RESET test reported in Table 4shows that the
linearity is valid and model specification is correct.
Table 4. The results of White’s test, Wooldridge test and Ramsey RESET test.
Test Null Hypothesis For χ2-Statistic p-Value
White’s test There is no heteroscedasticity. 555.000 0.492
Wooldridge test There is no first-order autocorrelation. 3.385 0.076
Ramsey RESET test Model has no omitted variable. 1.660 0.175
For the normality, the assumption requires a normal distribution that applies only to
the residuals, not to the independent variables as is often believed [
68
]. We have tested the
residuals’ normality of the model and the result below (Table 5) shows that the residuals of
our model are normally distributed.
Table 5. The results of skewness/kurtosis tests for normality.
Variable Observations Pr(skewness) Pr(kurtosis) χ2p-Value
Residuals 535 0.359 0.944 0.870 0.647
H0: the variable is normally distributed.
Lastly, in terms of the endogeneity which may result from the omission of variables,
errors-in-variables, and simultaneous causality [
64
], we have employed an instrumental
variables (IV) estimation in the section of robustness check to relieve potential endogeneity
and our baseline results remain significant after using an IV estimation. IV estimation is
a widely used approach to relieve potential endogeneity in many empirical studies [
69
].
Besides, to avoid the omission of variables, we also include some topographic and mete-
orological factors that may affect crop production in the section of robustness check and
our baseline results remain significant after controlling for other variables. Such treatment
could relieve the potential estimation bias resulted from the endogeneity.
4.2. DD Estimation
Figure 2shows the annual yield of grain, rice and wheat in the treated and control
groups, namely MGPA and non-MGPA provinces. These graphs show that the yields for the
three crops approximately perform some similarity in the years before the enactment of the
MGPA policy, which agrees with the parallel historic paths assumption of DD estimation.
The trends tentatively suggest that the MGPA provinces saw a higher output growth after
2003 than the non-MGPA provinces, and this will be examined in more detail next.
Table 6shows the results for grain, rice and wheat. Columns (1), (3) and (5) include
the controls of province-specific annual effects except year and province effects to reduce
the estimation bias caused by potential province-specific annual variations. To control for
other ongoing policies that may bias the estimation, columns (2), (4) and (6) also include
the dummy variables for the two agricultural policies discussed above. The coefficients for
the treatment effects,
Di×Tt
, are all positively significant suggesting that across the three
crops, the implementation of MGPA policy increased crop yields, with an average increase
of 27.5% for grain yields, 47.8% for rice yields and 35.5% for wheat yields. These treatment
effects seem to be greater than expected, but are better explained after controlling other
independent variables (planting areas per capita, mechanization and transfer payment per
capita) in the following mechanism analysis which explores the potential causal channels
of the treatment effects.
Land 2022,11, 1375 12 of 28
01000 2000 3000 4000
Average annual yield of grain (10,000 tons)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Yea r
MGPA provinces Non-MGPA provinces
(a) Grain
200 400 600 800 1000 1200
Average annual yields of rice (10,000 tons)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Yea r
MGPA provinces Non-MGPA provinces
(b) Rice
0200 400 600 800 1000
Average annual yields of wheat (10,000 tons)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Yea r
MGPA provinces Non-MGPA provinces
(c) Wheat
Figure 2. Yields of grain, rice and wheat.
In addition to the baseline result, there are also several interesting findings concerning
factors affecting crop production. First, the positive relationship between crop production
and pesticide use is valid for all three crops. However, only wheat production is positively
associated with fertilizer use. The non-significant estimates for grain and rice yields may
be attributed to the overuse of fertilizer. Chemical fertilizer overuse is common and
serious in China with fertilizer use already severely exceeding international standards [
70
].
Second, the statistically significant coefficients for urbanization agree with previous findings
showing that crop production increases with urban expansion because people’s diets and
demand for agricultural products are changed and diversified food consumption needs
greater crop production [
57
]. Third, improving rural finances is beneficial to the increase
in crop production. China has many smallholder farmers who are extremely vulnerable
to unexpected events, so rural financial services, such as agricultural insurance, could
protect farmers when these events occur and therefore encourage farmers to increase their
investment in crops.
Land 2022,11, 1375 13 of 28
Table 6. The baseline DD estimation.
Dep. Var.: Yields Grain Rice Wheat
(1) (2) (3) (4) (5) (6)
Di×Tt0.271 *** 0.275 *** 0.481 *** 0.478 *** 0.350 *** 0.355 ***
(0.027) (0.027) (0.055) (0.055) (0.106) (0.107)
Pesticide 0.275 *** 0.280 *** 0.178 *** 0.174 *** 0.535 *** 0.524 ***
(0.029) (0.029) (0.056) (0.054) (0.163) (0.160)
Fertilizer 0.016 0.022 0.032 0.017 0.214 * 0.231 *
(0.020) (0.022) (0.032) (0.029) (0.124) (0.126)
Fixed-asset investment 0.099 *** 0.095 *** −0.040 −0.042 0.372 *** 0.362 ***
(0.030) (0.030) (0.064) (0.066) (0.112) (0.111)
Non-agricultural income −0.008 −0.010 0.243 *** 0.249 *** 1.071 *** 1.083 ***
(0.063) (0.065) (0.073) (0.069) (0.195) (0.195)
Trade openness −0.116 −0.115 −0.172 −0.168 1.025 *** 1.030 ***
(0.090) (0.090) (0.105) (0.105) (0.318) (0.318)
Urbanization −0.429 *** −0.374 *** −0.221 ** −0.339 ** −0.862 ** −0.750 **
(0.093) (0.087) (0.153) (0.149) (0.417) (0.372)
Industrialization −0.023 0.082 0.727 * 0.372 −2.007 *** −1.812 **
(0.231) (0.227) (0.387) (0.387) (0.762) (0.811)
Financial level 0.166 *** 0.164 *** 0.156 * 0.167 * 0.242 * 0.237 *
(0.054) (0.053) (0.087) (0.091) (0.101) (0.100)
LRLS Yes Yes Yes
Tax Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes
Province ×Year Yes Yes Yes Yes Yes Yes
Observations 555 555 535 535 535 535
R20.929 0.927 0.986 0.986 0.962 0.962
Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.
4.3. ET-DD Estimation
Figure 3shows the estimated coefficients along with the 95% confidence intervals for
the dynamic treatment effects. The coefficients for the pre-MGPA years (k =
−
2
∼
k =
−
6) are
all statistically distinguishable from zero, suggesting that the parallel trends assumption
holds. After the implementation of MGPA policies, there is an immediate and lasting
increase in grain and rice yields implying that the treatment effects of the MGPA policy
are sustainable. For wheat yields, the treatment effect becomes significant six years after
the policy’s implementation. Such a delayed treatment effect may be attributed to farmers
being less motivated to plant wheat as a result of its decreasing profitability7.
-0.2
0.0
0.2
0.4
0.6
0.8
Dynamic effects
-6 -4 -2 0 2 4 6 8 10 12 14
Years relative to enactment of MGPA-oriented policies
(a) Grain
-0.5
0.0
0.5
1.0
Dynamic effects
-6 -4 -2 0 2 4 6 8 10 12 14
Years relative to enactment of MGPA-oriented policies
(b) Rice
Figure 3. Cont.
Land 2022,11, 1375 14 of 28
-0.5
0.0
0.5
1.0
1.5
Dynamic effects
-6 -4 -2 0 2 4 6 8 10 12 14
Years relative to enactment of MGPA-oriented policies
(c) Wheat
Figure 3. Tests for parallel trends.
4.4. PSM-DD Estimation
When selecting matching covariates, one rule for selection is that the covariates are
meant to be predictors of post-intervention outcomes, which are not themselves affected
by the event [
50
]. To this end, our matching covariates include rural family size, sex ratio,
educational attainment and agricultural land per capita. To improve the sample efficiency
of the estimates [
71
], we removed treated observations whose propensity scores were out
of the range of those of the control groups. The PSM-DD estimates based on the matched
sample are shown in Table 7. The coefficients of the treatment effect (
Di×Tt
) for grain, rice
and wheat are all positively significant whether controlling for province-specific annual
effects or two other ongoing policies. The magnitudes of the coefficients are quite similar
to the results of the DD estimation. Thus, our baseline findings from the DD estimation
remain valid after using the PSM-DD for mitigating selection bias.
Table 7. The PSM-DD estimation.
Dep. Var.: Yields Grain Rice Wheat
(1) (2) (3) (4) (5) (6)
Di×Tt0.246 *** 0.253 *** 0.448 *** 0.442 *** 0.382 *** 0.383 ***
(0.026) (0.025) (0.054) (0.054) (0.107) (0.108)
Control Variables Yes Yes Yes Yes Yes Yes
LRLS Yes Yes Yes
Tax Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes
Province ×Year Yes Yes Yes Yes Yes Yes
Observations 546 546 526 526 526 526
R20.928 0.926 0.987 0.987 0.963 0.963
Standard errors in parentheses. *** p<0.01.
4.5. Robustness Checks
In this section, we perform three further robustness checks on our baseline findings.
They are placebo tests using alternative treatment provinces, an instrumental variables
estimation using local annual production of raw coal as the instrumental variable for the
enactment of the MGPA policy, and case studies using synthetic control methods.
Placebo tests: To verify DD estimation, Chetty et al. [
72
] recommended using “fake”
treatment groups, namely, randomly assigning policy shocks to sample areas. Specifically,
for our estimation sample, 13 fake MGPA provinces were randomly selected from the
31 provinces and the remaining 18 provinces become fake control groups. Then, a series of
fake treatment variables i.e.,
Df ake
i×Tt
, were constructed based on that random assignment.
Given that these randomly constructed treatment provinces were not necessarily imple-
Land 2022,11, 1375 15 of 28
mented with real MGPA oriented policies, the outcome of interest should be insignificant.
In other words, any significant coefficients for fake treatment effects,
βf ake
, would suggest the
invalidity of our baseline DD estimation. Following the method of Cai et al. [
73
], to rule out
bias from any rare events, we carried out this random data generating procedure for 500 times.
Figure 4shows the kernel density of 500 random estimates and associated
p
-values for
grain, rice and wheat yields. The mean values of the fake treatment effect for three crops
are all around zero, specifically, the mean coefficient is −0.001 for grain, 0.003 for rice and
0.001 for wheat. The distribution center of 500 random estimates for three crops are all
close to zero and their associated p-values are mostly larger than 0.1. Our real coefficients
for treatment effects, represented by the red line, clearly differ from that of the placebo tests.
Thus, these results again lend support to our baseline DD estimation.
0
1
2
3
4
Density/P-value
-0.4 -0.2 0.0 0.2 0.4
Coefficients
kernel = epanechnikov, bandwidth = 0.0252
Coefficients' density of placebo tests
(a) Grain
0.0
0.5
1.0
1.5
2.0
Density/P-value
-0.5 0.0 0.5
Coefficients
kernel = epanechnikov, bandwidth = 0.0456
Coefficients' density of placebo tests
(b) Rice
0.0
0.5
1.0
1.5
Density/P-value
-1.0 -0.5 0.0 0.5 1.0
Coefficients
kernel = epanechnikov, bandwidth = 0.0837
Coefficients' density of placebo tests
(c) Wheat
Figure 4. Placebo tests.
Instrumental variables (IV) estimation: Using IV estimation can help remove potential
bias arising from the pre-existing differences between the treatment and control groups [
74
].
Specifically, instrumental variables can rule out the pre-trends caused by confounders
between the treatment and control groups [
47
]. In this study, we selected local annual
production of raw coal as the instrumental variable for the enactment of the MGPA policy.
There are two reasons that display the validity of using this instrumental variable. First,
the origin of most coal is plant debris in wetlands from hundreds of millions of years ago
in swampy forests. Hence, regions that have a large production capacity of raw coal are
often agriculture-friendly places with rich natural resources, and MGPA policy is more
likely to be implemented in such provinces. Second, to our knowledge, there is no direct
relationship between the production of raw coal and crop yields.
Table 8shows the two-stage least squares (2SLS) regression of the instrumental vari-
ables estimation. The first-stage results are presented in columns (1), (3) and (5). The coeffi-
Land 2022,11, 1375 16 of 28
cients of the instrumental variable,
Rawcoali×Tt
, are all significantly positive suggesting
that the large production capacity of raw coal is a valid indicator for the enactment of
MGPA policies. Columns (2), (4) and (6) show the second-stage results for grain, rice
and wheat, respectively. The treatment effects remain statistically positive and significant,
with the magnitude of coefficients being even bigger. These 2SLS results imply that our
baseline findings in DD estimation are robust.
Table 8. Instrumental variables estimation.
Grain Rice Wheat
Dep. Var.: Di×TtGrain
Yields Di×TtRice Yields Di×TtWheat
Yields
(1) (2) (3) (4) (5) (6)
Rawcoali×Tt0.055 *** 0.052 *** 0.052 ***
(0.010) (0.002) (0.010)
Di×Tt1.332 *** 1.072 *** 3.116 ***
(0.231) (0.360) (0.962)
Year FE Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes
Province ×Year Yes Yes Yes Yes Yes Yes
Observations 571 571 550 550 563 563
R20.783 0.927 0.784 0.978 0.782 0.873
Standard errors in parentheses. *** p<0.01.
Synthetic control methods: The synthetic control method, proposed by Abadie et al.
(2007) [
75
], can effectively be used for comparative studies when exact matches are un-
available, which offers a sensible generalization of DD estimation [
76
]. We carried out
comparative case studies focusing on the grain yields of three MGPA provinces. The three
provinces were Shandong, Jiangxi and Liaoning, located in three traditional agricultural
zones, specifically the Yellow River, Huai River and Hai River, the middle reaches of the
Yangtze River, and the northeast of China.
We first constructed a synthetic Shandong, Jiangxi and Liaoning from the donor pool,
all the non-MGPA provinces. The synthetic Shandong, Jiangxi and Liaoning mirrored the
values of the predictors
8
of grain yields in real Shandong, Jiangxi and Liaoning before the
establishment of MGPA. We then estimated the treatment effect of the MGPA policy on
grain yields as the difference in grain yields between case provinces and their synthetic
versions in the years after the MGPA were established. Figure A1 shows that the post-
intervention growth paths of the three provinces significantly increased over the growth
paths of their synthetic versions.
We further carried out a series of placebo tests confirming that our estimated treatment
effects for the three case provinces were unusually larger relative to the distribution of
fake treatment effects obtained from applying the same synthetic control analysis to the
donor provinces. Figure 5shows the results of the placebo tests for Shandong, Jiangxi
and Liaoning. The dotted lines show the difference in grain yields between each province
in the donor pool and their synthetic versions. The superimposed solid lines indicate
the differences for case provinces. As the graphs show, the estimated difference for case
provinces during the 2004–2018 period was unusually larger relative to the distribution of
the differences for the donor provinces. These results illustrate the link between the MGPA
policy and grain yields, which further support our baseline findings.
Land 2022,11, 1375 17 of 28
the enactment of the MGPA policy
Shandong
control provinces
-1.0
-0.5
0.0
0.5
1.0
gap in grain yields (log)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
year
(a) Shandong
the enactment of the MGPA policy
Jiangxi
control provinces
-1.0
-0.5
0.0
0.5
gap in grain yields (log)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
year
(b) Jiangxi
the enactment of the MGPA policy
Liaoning
control provinces
-1.0
-0.5
0.0
0.5
gap in grain yields (log)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
year
(c) Liaoning
Figure 5. Synthetic control methods.
Controls for other variables: It is evident that the agricultural activity is closely related
to some topographic and meteorological factors. To test the validity of our baseline result,
several topographic and meteorological variables are incorporated into the regression anal-
ysis. Given the data accessibility at the provincial level, relief degree of land surface (RDLF),
surface water resources (SF), sunshine hours (SH), temperature (TEM) and precipitation
(Pre) are included as control variables. Following Feng et al. [
77
], RDLS is defined as the
topographic relief above the horizontal surface of average elevation in a certain area, and it
is an important index for evaluating environment conditions
9
. The dataset uses provinces
as the statistical unit and is based on 1 km
×
1 km raster data for extraction which serves as
a macro scale regional assessment [78]. The surface water resources data is collected from
China Water Statistical Yearbook (1998–2018). The data of sunshine hours, temperature and
precipitation are collected from China Meteorological Data Network.
Table 9reports the result of controlling for the topographic and meteorological factors.
The coefficients for the treatment effects,
Di×Tt
, are still positively significant and similar
to the baseline results, suggesting that the treatment effect of the MGPA policy is still sig-
nificant even after controlling the topographic and meteorological factors. One interesting
finding is that RDLS is negatively associated with the agricultural output, which shares the
similar conclusion of Krummel and Su [79].
Land 2022,11, 1375 18 of 28
Table 9. Controls for other variables.
Dep. Var.: Grain Rice Wheat
(1) (2) (3) (4) (5) (6)
Di×Tt0.277 *** 0.277 *** 0.496 *** 0.496 *** 0.346 *** 0.346 ***
(0.026) (0.026) (0.055) (0.055) (0.109) (0.109)
Control variables Yes Yes Yes Yes Yes Yes
RDLS −5.258 *** −5.258 *** −13.296 *** −13.296 *** −10.102 *** −10.102 ***
(1.785) (1.785) (3.741) (3.741) (9.205) (9.205)
SF −0.027 ** −0.027 ** −0.010 −0.010 −0.006 −0.006
(0.014) (0.014) (0.017) (0.017) (0.043) (0.043)
SH 0.002 0.002 0.024 0.024 −0.838 −0.838
(0.154) (0.154) (0.205) (0.205) (0.529) (0.529)
TEM 0.114 0.114 −0.123 −0.123 −0.224 −0.224
(0.177) (0.177) (0.235) (0.235) (0.542) (0.542)
Pre 0.139 0.139 −0.188 −0.188 −0.173 −0.173
(0.085) (0.085) (0.153) (0.153) (0.305) (0.305)
Year FE Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes
Province ×Year Yes Yes Yes Yes Yes Yes
Observations 571 571 550 550 563 563
R20.985 0.985 0.987 0.987 0.962 0.962
Standard errors in parentheses. ** p<0.05, *** p<0.01.
Controls for other cultivated land spatial planning schemes: A threat to the identifica-
tion is that the treatment effects would be confounded by some other cultivated land spatial
planning schemes. After studying Chinese government documents, we found two land
spatial planning schemes that may have biased the estimation. One is the cultivated land
balance program (CLB). In 1999, given the magnitude of the cultivated land loss in China,
the National Bureau of Land Management (the predecessor of the MLRC) adopted the CLB
of maintaining the existing amount of cultivated land nationally. CLB focused particularly
on the balance between cultivated land losses by construction occupation and cultivated
land supplement. According to this approach, if a plot of cultivated land was replaced by
construction, the land developer should create another plot of cultivated land [
80
]. Another
one is the main functional areas planning (MFAP) which incorporated national nature
reserves, world cultural and natural heritage sites, national scenic attractions and forest
parks into the national list of prohibited development areas. Specifically, it divided the
national land space into four main functional areas: optimized development areas, key
development areas, restricted development areas and prohibited development areas. It
was aimed to effectively improve the efficiency of space utilization and realize the goal of
sustainable development, which also incorporated the space utilization of arable lands [
81
].
Therefore, both land planning programs had the potential to involve the redistribution
of cultivated land and confound the treatment effect of the MGPA policy. To relieve any
confounding effects of the two land planning programs, two dummy variables for the im-
plementation of these land planning programs were included.
CLB
indicates the enactment
of the cultivated land balance program and
MFAP
indicates the implementation of the
main functional areas planning.
Table 10 shows the estimation result of controlling for the cultivated land balance
program and the main functional areas planning. The coefficients for the treatment effects,
Di×Tt
, are still positively significant, suggesting that the contribution of the MGPA
policy to the increase in grain production is still significant even after controlling the
potential confounding effect of other land spatial planning schemes. For the empirical
comparison between the MGPA policy and other land spatial planning schemes, it may
require systematic evaluation and is waiting for future research.
Land 2022,11, 1375 19 of 28
Table 10. Controls for other cultivated land spatial planning schemes.
Dep. Var.: Grain Rice Wheat
(1) (2) (3) (4) (5) (6)
Di×Tt0.296 *** 0.293 *** 0.415 *** 0.415 *** 0.285 ** 0.288 **
(0.025) (0.025) (0.042) (0.042) (0.105) (0.106)
Control variables Yes Yes Yes Yes Yes Yes
CLB 0.155 *** 0.158 * 0.256 *
(0.042) (0.084) (0.149)
MFAP 0.178 *** 0.093 ** 0.101
(0.033) (0.041) (0.104)
Year FE Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes
Province ×Year Yes Yes Yes Yes Yes Yes
Observations 571 571 550 550 563 563
R20.982 0.982 0.985 0.985 0.956 0.956
Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01.
4.6. Alternative Causal Channels
The analysis so far has provided evidence that the MGPA policy can significantly
increase local grain, rice and wheat yields. In this section, we will further explore the causal
channels of such positive treatment effects in terms of agricultural planting areas, mecha-
nization level and transfer payments using the causal steps approach built by Heerink et al.
(2006) [
82
]. The analysis of causal channels here only focuses on grain yields and the results
for rice and wheat can be found in the Appendix A.
Expanding planting areas: Column (1) of Table 11 shows the first-step results, suggesting
that the implement of the MGPA policy significantly increased local grain planting area per
capita. Specifically, the grain planting area increased by 2.7% on average, with the figures for
rice and wheat being 0.92% and 0.48%, respectively (in Appendix ATables A1 and A2).
Column (3) of Table 11 shows the second-step results. The coefficients of
Di×Tt
and planting area per capita are all positively significant at the 1% level indicating that,
combined with the first-step result, the causal channel of expanding planting areas is
statistically valid for grain. The coefficient of
Di×Tt
fell from 0.271 (column (2)) to
0.166 after controlling for planting area per capita. This consolidates the idea that the
implementation of the MGPA policy raised grain yields by increasing planting areas. This
is also the case for the increase in rice yields with the treatment effect decreasing from 0.48
to 0.39 when the rice planting area is included in regression. However, no causal link was
found between wheat yields and expanding planting areas.
Table 11. Channel 1—expanding planting areas.
Dep. Var.: Planting Area per Capita Grain Yields Grain Yields
(1) (2) (3)
Di×Tt0.027 *** 0.271 *** 0.166 ***
(0.003) (0.027) (0.048)
Planting area per capita 3.895 ***
(1.410)
Control Variables Yes Yes Yes
Year FE Yes Yes Yes
Province FE Yes Yes Yes
Province ×Year Yes Yes Yes
N555 555 555
R20.940 0.984 0.987
Standard errors in parentheses. *** p<0.01.
Improving mechanization level: Column (1) of Table 12 shows that the estimated
impact of the MGPA policy on local mechanization,
Di×Tt
, was significant and positive.
Specifically, the implementation of the MGPA policy improved local mechanization by
21.8%. This can be attributed to the MGPA-preferred agricultural machinery subsidies,
Land 2022,11, 1375 20 of 28
a sub-project of the MGPA policies. Many studies suggest that agricultural mechanization
in China, and especially in MGPA, has been accelerated by the government’s increase of
the subsidy for agricultural machinery purchases since 2004 [83].
The coefficients in the first two rows of column (3) are significantly positive. The treat-
ment effect, the coefficient of
Di×Tt
, in regression (3) is slightly smaller than that in
regression (2). These results suggest that the treatment effect of the MGPA policy is partially
caused by boosting mechanization. For rice, such a causal channel exists, however, it is
statistically insignificant for wheat.
Table 12. Channel 2—improving mechanization.
Dep. Var.: Mechanization Grain Yields Grain Yields
(1) (2) (3)
Di×Tt0.218 *** 0.271 *** 0.263 ***
(0.040) (0.027) (0.027)
Mechanization 0.037 *
(0.021)
Control Variables Yes Yes Yes
Year FE Yes Yes Yes
Province FE Yes Yes Yes
Province ×Year Yes Yes Yes
N555 555 555
R20.783 0.984 0.985
Standard errors in parentheses. * p<0.1, *** p<0.01.
Increasing transfer payments: As column (1) in Table 13 shows, the transfer payments
for MGPA have increased by 30.2% since the enactment of the MGPA policy. The second-
stage results in columns (2) and (3) illustrate that the causal channel that the MGPA policy
boosts grain yields by increasing the transfer payments to MGPA farmers. Such direct
financial support can be realized in many ways, including rewarding counties for producing
a large harvest. This MGPA-specific policy not only motivates farmers to plant more crops,
but also reduces the financial pressure on local governments. Heilongjiang, one of the
MGPA, was offered a 21.13 billion yuan reward in total from 2005 to 2013. Meanwhile,
Heilongjiang has doubled its crop yields in under five years.
The causal channel for transfer payments is very obvious for wheat yields. The results,
reported in Appendix ATable A2, show that the treatment effect falls from 0.350 to 0.244
when including the transfer payments into the baseline DD regression. Combined with the
fact that the profit from planting wheat is shrinking, expanding direct transfer payments
for wheat-growing farmers has become one of the few effective ways of increasing their
motivation to plant the crop. However, for rice, there is no robust causal link between rice
yields and increased transfer payments.
Table 13. Channel 3—increasing transfer payments.
Dep. Var.: Transfer Payment Grain Yields Grain Yields
(1) (2) (3)
Di×Tt0.302 *** 0.271 *** 0.229 ***
(0.059) (0.027) (0.026)
Transfer payment per capita 0.140 ***
(0.023)
Control Variables Yes Yes Yes
Year FE Yes Yes Yes
Province FE Yes Yes Yes
Province ×Year Yes Yes Yes
N555 555 555
R20.942 0.984 0.986
Standard errors in parentheses. *** p<0.01.
Land 2022,11, 1375 21 of 28
5. Discussion
5.1. The Policy Recommendations
The global food security challenge is straightforward in the context of the climate
change and widespread of COVID-19 pandemic. As China feeds approximately 18% of
the world population, China’s food security is closely related to the stability of the global
food system. In the long run, China’s stable food production is still faced with great
challenges, e.g., the rapid urbanization and spatial mismatch in agriculture resources.
Looking back to history, China experienced a significant drop in crop production in 2003
which posed a substantial threat to national food security. After this crop production crisis,
13 MGPA regions were established by Ministry of Finance China. However, the empirical
evidence of such land management practice’s effect on crop production remains unclear.
Identification of the mechanism that how the MGPA policy affect crop production will
provide important policy implications for maintaining food security from a perspective of
land management practices.
This paper focuses on exploring the impact of the MGPA policy on food security from
the perspective of crop (grain, rice and wheat) production. The baseline results of this paper
demonstrated that the establishment of the MGPA regions provided favorable conditions
for increasing crop production. This result is consistent with the findings of the research on
the relationship between land management policy and food production, which finds that
the effective land and resource governance system that provides improved access, control,
and rights to land and other natural resources is a necessary condition for achieving stable
crop production [
27
]. Therefore, the implementation of the MGPA policy is without doubt
a successful land management policy for achieving food security. Given the uncertainty
in future trends of global food production due to a series of challenges, China should
continue to consolidate policy support in the MGPA regions. Meanwhile, the fact that
natural resources, especially land resources, is irreversible [
84
] reminds policymakers that
they should fundamentally recognize the value of natural resources in the MGPA regions.
Then the future policy preference in agriculture should be given more to the MGPA regions.
Besides, the features of the MGPA policy could provide some policy implications for
maintaining food security by some land management practice. First, as the MGPA policy
is followed by some sub-policies which aim at dealing with different issues in China’s
agriculture at different times, the land management policy should be a dynamic policy
which could be adaptive to new challenges faced by agricultural production. The reason is
that the global food problem concerns the dynamics of economic growth, trade policy and
even climate change [
53
]. The land management practice must be designed for continual
improvement and adjustment to meet the needs of a changeable environment. Second,
the MGPA policy is not merely for reallocating land resources for food production, but an
integrated policy which is combined with some monetary and technical support policies.
Similarly, such finding is acknowledged by Barry and Augustinus [
85
], who find that the
comprehensive land policies which utilize sub-policies with different domains could exert
a larger impact. Hence, the design of land management policy should incorporate other
policy packages. In this way, these integrated sub-policies support and complement each
other to realize the policy objective.
The findings in investigating alternative causal channels of the treatment effect found
that the MGPA policy raised crop yields mainly by expanding planting areas, improving the
level of mechanization and increasing transfer payments. It is evident that the irreversible
land resource is the most important factor for agricultural production. However, in the
context of China’s rapid urbanization, the expansion of large cities and regions that have
experienced rapid economic growth and urban development, causing the loss of cultivated
land [
86
,
87
]. Hence, the policies designed to protect cultivated land, especially in the MGPA
regions, are urgently needed. To preserve arable land, it is necessary not only to maintain
quantity but also to improve quality, and to keep the double red line of quantity and
quality [
88
]. It is also necessary to invest in agricultural research as agricultural technology
is considered the main driver in solving China’s shortage of arable land [
89
]. In terms of
Land 2022,11, 1375 22 of 28
the second causal channel of improving the level of mechanization, it is consistent with
many empirical research’s finding. For example, Gong finds that over the past 25 years,
China’s agricultural growth has mainly been attributed to the improvement of productivity,
especially the improvement of mechanization level in agriculture [26]. Therefore, the gov-
ernment should enhance the knowledge and skills of adult members, including household
head, to adopt the latest mechanization technologies for land management. Agricultural
policy should also focus on promoting agricultural mechanization technologies that are
economically viable and friendly to females and older people to increase the adoption of
agricultural mechanization. For the last causal channel of increasing transfer payments, it
is also in keeping with the conclusion of Hu et al. that the financial support significantly
improves agricultural TFP growth [
90
]. With the easy access to financial support, farmers
can use these financial resources to adopt and foster technology innovations, which are well
documented to improve agricultural production [
91
]. Local governments and banks should
continue to improve the financial support for farmers, particularly the usage of financial
services in rural areas and in agricultural production. In addition, paying attention to
the financial services usage and the availability of credit to individuals with real needs is
effective in promoting agricultural production.
5.2. The Methods’ Applicability and Results’ Reliability
In this paper, the DD model has been employed as a starting point for identifying
the treatment effect of the MGPA policy. The applicability of this method is illustrated
by other research on identifying treatment effects in policy analysis (see Cheng et al. [
92
];
Tan et al. [93]
and Wang [
94
]). In general, different from the case of randomized exper-
iments that allow for a simple comparison of treatment and control groups, DD is an
evaluation method used in non-experimental settings, which has been widely used in
economics, public policy, health research, management and other fields. The use of the DD
model is detailedly discussed by Fredriksson et al. [
95
]. Due to the DD model relies on the
parallel trends assumption which requires that in the absence of treatment, the difference
between the treatment and control group is constant over time [
48
], an ET-DD model was
employed to not only perform a parallel trends but also served as a robustness check for the
baseline DD estimation. Although the DD method is a common strategy for evaluating the
effects of policies or programs that are instituted at a particular point in time, sometimes the
cross-sectional difference may reduce the comparability between the treatment and control
group which eventually leads to a biased estimate. To relieve such concern and provide
a good counterfactual for the treatment group, the PSM method was used to mitigate
selection bias by matching observations of treatment provinces with control provinces.
Such treatment has gained popularity in many empirical studies [96,97].
Although the results in this study could be comparable with the previous findings
arguing that land management policy is one of the major driving forces for agricultural
development [
27
,
98
], however, this research has certain drawbacks. First, agricultural
production is a complicated process which is influenced by many factors. The results
will be more unbiased if these factors, especially some climatic and topographic factors,
are comprehensively considered. Second, this paper evaluated the effectiveness of the
MGPA policy merely from the perspective of crop production. However, the indicator
system for the MGPA policy can be improved and the results will be more reliable if future
research is built in other perspectives. Third, this study used provincial data and could only
provide insights into practice at the level of provincial areas and could not be refined at the
municipal level. One possible direction for future work is to use more detailed county data,
even micro-data to study the effectiveness of the MGPA policy.
6. Conclusions
Based on China’s interprovincial panel data from 1998 to 2018, this study used a
difference-in-differences (DD) estimation strategy to analyze the treatment effect of the
MGPA policy by taking the assignment of 13 MGPA as a quasi-experiment. It primarily
Land 2022,11, 1375 23 of 28
draws the following conclusions: (i) the MGPA policy did indeed increase crop production,
specifically, grain, rice and wheat yields, and such positive treatment has been sustainable
over the long term. Across the three kinds of crops, the MGPA policy led to an average rise
of 27.5% for grain yields, 47.8% for rice yields and 35.5% for wheat yields. (ii) After the
implementation of the MGPA policy, there is an immediate and lasting increase in grain
and rice yields, however, for wheat yields, the treatment effect became significant six years
after the policy’s implementation. Such a delayed treatment effect may be attributed to
farmers’ being less motivated to plant wheat as a result of its decreasing profitability in the
first few years after the policy implementation. (iii) The MGPA policy has increased grain
yields mainly by expanding planting areas, improving mechanization levels and increasing
transfer payments. Specifically, due to the establishment of the MGPA regions, the grain
planting area increased by 2.7% on average, with the figures for rice and wheat being
0.92% and 0.48%, respectively. The implementation of the MGPA policy improved local
mechanization by 21.8% and increased the transfer payments by 30.2%. These findings from
the evaluation of the MGPA policy greatly increase understanding of how land management
policies positively affect crop production in such a large developing country. Given the
great similarity to agriculture production in developing countries, these findings may
enlighten policymakers in some less well-developed countries on boosting crop production
and eradicating hunger.
Author Contributions:
Conceptualization, W.H.; methodology, W.H.; software, W.H.; validation,
W.H. and L.L.; data curation, W.H.; writing—original draft preparation, W.H.; writing—review and
editing, W.H., L.L. and Z.C.; visualization, W.H.; funding acquisition, L.L. All authors have read and
agreed to the published version of the manuscript.
Funding:
This work was supported by The National Key Research and Development Program of
China (Grant no. 2018YFE0107000) and Ministry of Science and Technology of the People’s Republic
of China (Grant no. 2016YFD0201306).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data were collected from various sources, including China Rural
Statistical Yearbooks (Ministry of Agriculture, 1998–2018), National Agricultural Product Cost and
Revenue Survey Data books (Ministry of Agriculture, 1998–2018), China Agricultural Statistical
Yearbooks (Ministry of Agriculture, 1998–2018), China Statistical Yearbooks on Environment (Ministry
of Environment, 1998–2018), and China Statistical Yearbooks (NBSC, 1998–2018).
Acknowledgments: We would like to express our great gratitude to Xi Yang from Gansu State Grid
for her companion in drafting the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
the enactment of the MGPA policy
16 17 18 19
grain yields(log)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
year
real Shandong synthetic Shandong
(a) Shandong
the enactment of the MGPA policy
16 17 18
grain yields(log)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
year
real Jiangxi synthetic Jiangxi
(b) Jiangxi
Figure A1. Cont.
Land 2022,11, 1375 24 of 28
the enactment of the MGPA policy
16 17 18
grain yields(log)
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
year
real Liaoning synthetic Liaoning
(c) Liaoning
Figure A1. Synthetic control methods without placebo tests.
Table A1. The channels of rice.
Dep. Var.:
Channel 1 Channel 2 Channel 3
Planting
Area per
Capita
Rice Yields Rice Yields Mechanization Rice Yields Rice Yields Transfer
Payments Rice Yields Rice Yields
Di×Tt0.009 *** 0.481 *** 0.391 *** 0.218 *** 0.481 *** 0.475 *** 0.302 *** 0.481 *** 0.483 ***
(0.001) (0.055) (0.064) (0.040) (0.055) (0.055) (0.059) (0.055) (0.061)
Planting area per capita 9.752 ***
(1.953)
Mechanization 0.029 **
(0.043)
Transfer payment −0.006
(0.041)
Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Province ×Year Yes Yes Yes Yes Yes Yes Yes Yes Yes
N537 535 535 555 535 535 555 535 535
R20.942 0.986 0.987 0.783 0.986 0.987 0.942 0.986 0.986
Standard errors in parentheses. ** p<0.05, *** p<0.01.
Table A2. The channels of wheat.
Dep. Var.:
Channel 1 Channel 2 Channel 3
Planting
Area per
Capita
Wheat
Yields
Wheat
Yields Mechanization Wheat
Yields
Wheat
Yields
Transfer
Payments
Wheat
Yields
Wheat
Yields
Di×Tt0.005 *** 0.350 *** 0.339 *** 0.218 *** 0.350 *** 0.351 *** 0.302 *** 0.350 *** 0.244 **
(0.001) (0.106) (0.109) (0.040) (0.106) (0.106) (0.059) (0.106) (0.104)
Planting area per capita 2.297
(5.167)
Mechanization −0.003
(0.057)
Transfer payment 0.365 ***
(0.083)
Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Province FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Province ×Year Yes Yes Yes Yes Yes Yes Yes Yes Yes
N537 535 535 555 535 535 555 535 535
R20.942 0.986 0.987 0.783 0.986 0.987 0.942 0.986 0.986
Standard errors in parentheses. ** p<0.05, *** p<0.01.
Notes
1
The geographical dividing line of North-South China is formed by the Qinling Mountains and the Huai River, which are also
environmental features affecting climate regulation, soil conservation, water maintenance and biodiversity conservation.
2
In this paper, the enactment year of MGPA policy is set to 2004 because the official release of MGPA documents was on
3 December 2003 and the MGPA policy started in 2004.
3Mu is a Chinese unit of land measurement. It is commonly 806.65 square yards (0.165 acre, or 666.5 square meters).
Land 2022,11, 1375 25 of 28
4
The income is classified into four types: (i) income earned from agriculture, forestry, livestock, and fishery; (ii) income earned
from self-employment in non-farm activities such as industry, transportation, construction, and services, (iii) income earned
from formal or informal wage, including salary, allowance, bonus, dividend, and other kinds of remuneration, and (iv) other
non-productive incomes, such as pensions, transfers, grants/subsidies, rents, and financial income. (ii) and (iii) are normally
considered as non-farm household income.
5
This law was formulated in accordance with the Constitution for the purpose of stabilizing and improving the two-level
management system based on household contract management, giving the people long-term and guaranteed land use rights,
and protecting the legitimate rights and interests of the parties to the rural land contract.
6
For a long time, China’s industrialization and modernization have benefited from agricultural tax. However, agricultural tax was
cancelled due to the decline of the relative importance of agricultural tax in the whole fiscal revenue.
7
From 2008 to 2016, the profit from planting wheat decreased from 164.51 yuan per mu to 21.29 yuan per mu. This fall was mainly
a result of the slow upward trend of wheat price relative to the rapid rise in planting costs. Meanwhile, the profit from planting
rice is about 13 times higher than that of wheat.
8
The predictors of grain yields are rural household size, sex ratio, educational attainment, agricultural land per capita, and grain
yields in 1998, 2000 and 2002.
9
RDLS is defined as follows:
RLDS =ALT/
100
+{((Max(H)−Min(H)) ×(1−P(A)/A))}/
500, where RDLS is relief degrees
of land surface; ALT is the average elevation in a grid cell (m); Max(H) and Min(H) represent the highest and lowest altitudes in
this grid cell respectively (m); P(A) is the area of flat land (km2); and A is the total area of the extraction unit.
References
1.
Liu, Y.; Zhou, Y. Reflections on China’s food security and land use policy under rapid urbanization. Land Use Policy
2021,109, 105699. [CrossRef]
2.
Singh, R.K.; Joshi, P.K.; Sinha, V.S.P.; Kumar, M. Indicator based assessment of food security in SAARC nations under the
influence of climate change scenarios. Future Foods 2022,5, 100122. [CrossRef]
3.
Rashidi Chegini, K.; Pakravan-Charvadeh, M.R.; Rahimian, M.; Gholamrezaie, S. Is there a linkage between household welfare
and income inequality, and food security to achieve sustainable development goals? J. Clean. Prod.
2021
,326, 129390. [CrossRef]
4.
Ma, N.L.; Peng, W.; Soon, C.F.; Noor Hassim, M.F.; Misbah, S.; Rahmat, Z.; Yong, W.T.L.; Sonne, C. Covid-19 pandemic in the lens
of food safety and security. Environ. Res. 2021,193, 110405. [CrossRef] [PubMed]
5.
Dasgupta, S.; Robinson, E. Impact of COVID-19 on food insecurity using multiple waves of high frequency household surveys.
Sci. Rep. 2022,12, 1865. [CrossRef] [PubMed]
6.
Chen, L.; Chang, J.; Wang, Y.; Guo, A.; Liu, Y.; Wang, Q.; Zhu, Y.; Zhang, Y.; Xie, Z. Disclosing the future food security risk
of China based on crop production and water scarcity under diverse socioeconomic and climate scenarios. Sci. Total Environ.
2021,790, 148110. [CrossRef]
7.
Lu, S.; Liu, Y.; Long, H.; Guan, X. Agricultural Production Structure Optimization: A Case Study of Major Grain Producing Areas,
China. J. Integr. Agric. 2013,12, 184–197. [CrossRef]
8.
Zhang, L.; Pang, J.; Chen, X.; Lu, Z. Carbon emissions, energy consumption and economic growth: Evidence from the agricultural
sector of China’s main grain-producing areas. Sci. Total Environ. 2019,665, 1017–1025. [CrossRef]
9.
Dawadi, B.; Shrestha, A.; Acharya, R.H.; Dhital, Y.P.; Devkota, R. Impact of climate change on agricultural production: A case of
Rasuwa District, Nepal. Reg. Sustain. 2022,3, 122–132. [CrossRef]
10.
do Prado Tanure, T.M.; Miyajima, D.N.; Magalhães, A.S.; Domingues, E.P.; Carvalho, T.S. The Impacts of Climate Change on
Agricultural Production, Land Use and Economy of the Legal Amazon Region Between 2030 and 2049. EconomiA
2020
,21, 73–90.
[CrossRef]
11.
Yadav, P.; Jaiswal, D.K.; Sinha, R.K. 7—Climate change: Impact on agricultural production and sustainable mitigation. Glob. Clim.
Chang. 2021, 151–174. [CrossRef]
12.
Shamdasani, Y. Rural road infrastructure & agricultural production: Evidence from India. J. Dev. Econ.
2021
,152, 102686.
[CrossRef]
13.
Khan, M.T.I.; Ali, Q.; Ashfaq, M. The nexus between greenhouse gas emission, electricity production, renewable energy and
agriculture in Pakistan. Renew. Energy 2018,118, 437–451. [CrossRef]
14.
Okakwu, I.; Alayande, A.; Akinyele, D.; Olabode, O.; Akinyemi, J. Effects of total system head and solar radiation on the techno-
economics of PV groundwater pumping irrigation system for sustainable agricultural production. Sci. Afr.
2022
,16, e01118.
[CrossRef]
15.
Lasley, P.; Padgitt, S.; Hanson, M. Telecommunication technology and its implications for farmers and Extension Services. Technol.
Soc. 2001,23, 109–120. [CrossRef]
16.
Khanh Chi, N.T. Driving factors for green innovation in agricultural production: An empirical study in an emerging economy.
J. Clean. Prod. 2022,368, 132965. [CrossRef]
17.
da Silveira, F.; Lermen, F.H.; Amaral, F.G. An overview of agriculture 4.0 development: Systematic review of descriptions,
technologies, barriers, advantages, and disadvantages. Comput. Electron. Agric. 2021,189, 106405. [CrossRef]
Land 2022,11, 1375 26 of 28
18.
Forkuor, G.; Amponsah, W.; Oteng-Darko, P.; Osei, G. Safeguarding food security through large-scale adoption of agricultural
production technologies: The case of greenhouse farming in Ghana. Clean. Eng. Technol. 2022,6, 100384. [CrossRef]
19.
Rizov, M. Institutions, reform policies and productivity growth in agriculture: Evidence from former communist countries.
NJAS-Wagening. J. Life Sci. 2008,55, 307–323. [CrossRef]
20.
Lele, U.; Goswami, S. Agricultural policy reforms: Roles of markets and states in China and India. Glob. Food Secur.
2020,26, 100371. [CrossRef]
21.
Totin, E.; Segnon, A.; Roncoli, C.; Thompson-Hall, M.; Sidibé, A.; Carr, E.R. Property rights and wrongs: Land reforms for
sustainable food production in rural Mali. Land Use Policy 2021,109, 105610. [CrossRef]
22.
Mazhar, R.; Xuehao, B.; Wei, Z. Fostering sustainable agriculture: Do institutional factors impact the adoption of multiple
climate-smart agricultural practices among new entry organic farmers in Pakistan? J. Clean. Prod. 2020,283, 124620. [CrossRef]
23.
Zhang, D.; Wang, H.; Lou, S. Research on grain production efficiency in China’s main grain-producing areas from the perspective
of grain subsidy. Environ. Technol. Innov. 2021,22, 101530. [CrossRef]
24.
Yang, R.; Wang, L.; Xian, Z. Evaluation on the Efficiency of Crop Insurance in China’s Major Grain-Producing Area.
Hydrometallurgy 2010,1, 90–99. [CrossRef]
25.
Zhang, Z.; Meng, X.; Elahi, E. Protection of Cultivated Land Resources and Grain Supply Security in Main Grain-Producing
Areas of China. Sustainability 2022,14, 2808. [CrossRef]
26. Gong, B. New Growth Accounting. Am. J. Agric. Econ. 2020,102, 641–661. [CrossRef]
27.
Olarinre, A.; Oladeebo, O. Effects of Land Management Practices on Food Insecurity among Farming Households in Osun State,
Nigeria. Ph.D. Thesis, Ladoke Akintola University of Technology, Oyo, Nigeria, 2019. [CrossRef]
28.
Guo, L.; Li, H.; Cao, X.; Cao, A.; Huang, M. Effect of agricultural subsidies on the use of chemical fertilizer. J. Environ. Manag.
2021,299, 113621. [CrossRef]
29.
Theriault, V.; Smale, M. The unintended consequences of the fertilizer subsidy program on crop species diversity in Mali.
Food Policy 2021,102, 102121. [CrossRef]
30.
Lin, G.; Takahashi, Y.; Nomura, H.; Yabe, M. Policy incentives, ownership effects, and firm productivity—Evidence from China’s
Agricultural Leading Firms Program. Econ. Anal. Policy 2022,73, 845–859. [CrossRef]
31.
Hansen, H.O. Agricultural Policy Schemes: Price and Support Systems in Agricultural Policy. In Encyclopedia of Dairy Sciences
(Third Edition), 3rd ed.; McSweeney, P.L., McNamara, J.P., Eds.; Academic Press: Oxford, UK, 2022; pp. 703–713. [CrossRef]
32. Khafagy, A.; Vigani, M. Technical change and the Common Agricultural Policy. Food Policy 2022,109, 102267. [CrossRef]
33.
Carrer, M.J.; de Souza Filho, H.M.; de Mello Brandão Vinholis, M.; Mozambani, C.I. Precision agriculture adoption and technical
efficiency: An analysis of sugarcane farms in Brazil. Technol. Forecast. Soc. Chang. 2022,177, 121510. [CrossRef]
34.
Assa, H.; Sharifi, H.; Lyons, A. An examination of the role of price insurance products in stimulating investment in agriculture
supply chains for sustained productivity. Eur. J. Oper. Res. 2021,288, 918–934. [CrossRef]
35.
Alam, A.S.A.F.; Begum, H.; Masud, M.M.; Al-Amin, A.Q.; Filho, W.L. Agriculture insurance for disaster risk reduction: A case
study of Malaysia. Int. J. Disaster Risk Reduct. 2020,47, 101626. [CrossRef]
36.
Long, H.; Tu, S.; Ge, D.; Li, T.; Liu, Y. The allocation and management of critical resources in rural China under restructuring:
Problems and prospects. J. Rural Stud. 2016,47, 392–412. [CrossRef]
37.
Su, C.W.; Liu, T.Y.; Chang, H.L.; Jiang, X.Z. Is urbanization narrowing the urban-rural income gap? A cross-regional study of
China. Habitat Int. 2015,48, 79–86. [CrossRef]
38.
Li, T.; Long, H.; Zhang, Y.; Tu, S.; Ge, D.; Yurui, l.; Hu, B. Analysis of the spatial mismatch of grain production and farmland
resources in China based on the potential crop rotation system. Land Use Policy 2017,60, 26–36. [CrossRef]
39.
Rehman, A.; Jian, W. Agricultural Natural Disasters Mitigation in Hebei Province China. Adv. Mater. Res.
2014
,962–965, 1979–1988.
[CrossRef]
40.
Veeck, G. Delineating historical and contemporary agricultural production regions for China. Int. J. Cartogr.
2022
,8, 185–207.
[CrossRef]
41.
Xiao, P.; Xu, J.; Yu, Z.; Qian, P.; Lu, M.; Ma, C. Spatiotemporal Pattern Differentiation and Influencing Factors of Cultivated Land
Use Efficiency in Hubei Province under Carbon Emission Constraints. Sustainability 2022,14, 7042. [CrossRef]
42.
Li, Y.-l.; Ma, W.-q.; Jiang, G.-h.; Li, G.-y.; Zhou, D.-y. The degree of cultivated land abandonment and its influence on grain yield
in main grain producing areas of China. J. Nat. Resour. 2021,36, 1439. [CrossRef]
43.
Hu, T.; Ju, Z.; Zhou, W. Regional pattern of grain supply and demand in China. Dili Xuebao/Acta Geogr. Sin.
2016
,71, 1372–1383.
[CrossRef]
44.
Lu, C.; Liu, A.; Xiao, Y.; Liu, X.; Xie, G.; Cheng, S. Changes in China’s Grain Production Pattern and the Effects of Urbanization
and Dietary Structure. J. Resour. Ecol. 2020,11, 358–365. [CrossRef]
45.
Huirne, R.; Meuwissen, M.; Hardaker, J.; Anderson, J. Risk and risk management in agriculture: An overview and empirical
results. Int. J. Risk Assess. Manag. 2000,1, 125–136. [CrossRef]
46. Stuart, E.; Huskamp, H.; Duckworth, K.; Simmons, J.; Song, Z.; Chernew, M.; Barry, C. Using propensity scores in difference-in-
differences models to estimate the effects of a policy change. Health Serv. Outcomes Res. Methodol.
2014
,14, 166–182. [CrossRef]
[PubMed]
47.
Li, P.; Lu, Y.; Wang, J. The effects of fuel standards on air pollution: Evidence from China. J. Dev. Econ.
2020
,146, 102488.
[CrossRef]
Land 2022,11, 1375 27 of 28
48.
Lawler, E.C. Effectiveness of vaccination recommendations versus mandates: Evidence from the hepatitis A vaccine. J. Health
Econ. 2017,52, 45–62. [CrossRef] [PubMed]
49.
Rosenbaum, P.; Rubin, D. The Central Role of the Propensity Score in Observational Studies For Causal Effects. Biometrika
1983,70, 41–55. [CrossRef]
50.
Heckman, J.; Ichimura, H.; Todd, P. Matching As An Econometric Evaluation Estimator. Rev. Econ. Stud.
1998
,65, 261–294.
[CrossRef]
51.
Makino, A. Photosynthesis, Grain Yield, and Nitrogen Utilization in Rice and Wheat. Plant Physiol.
2010
,155, 125–129. [CrossRef]
52.
Canela, M.; Alegre, I.; Ibarra, A. Dummy Variables. In Quantitative Methods for Management; Springer: Cham, Switzerland, 2019;
pp. 57–63. [CrossRef]
53. Upton, M. The Economics of Food Production. Ciba Found. Symp. 1993,177, 61–71; discussion 71. [CrossRef]
54.
Osarfo, D.; Senadza, B.; Nketiah-Amponsah, E. The Impact of Nonfarm Activities on Rural Farm Household Income and Food
Security in the Upper East and Upper West Regions of Ghana. Theor. Econ. Lett. 2016,6, 388–400. [CrossRef]
55.
de Janvry, A.; Sadoulet, E.; Zhu, N. The Role of Non-Farm Incomes in Reducing Rural Poverty and Inequality in China; Working Paper
Series; Department of Agricultural & Resource Economics, UC Berkeley: Berkeley, CA, USA, 2005.
56.
Schneider, A.; Kernohan, D. The Effects of Trade Liberalisation on Agriculture in Smaller Developing Countries: Implications for the Doha
Round; CEPS Working Documents No. 244, 8 June 2006; CEPS: Brussels, Belgium, 2020.
57.
Satterthwaite, D.; Mcgranahan, G.; Tacoli, C. Urbanization and its implications for food and farming. Philos. Trans. R. Soc. Lond.
Ser. B Biol. Sci. 2010,365, 2809–2820. [CrossRef] [PubMed]
58.
Zhong, C.; Hu, R.; Wang, M.; Xue, W.; He, L. The impact of urbanization on urban agriculture: Evidence from China. J. Clean.
Prod. 2020,276, 122686. [CrossRef]
59.
Wang, Z.; Sun, G. An empirical analysis of the relationship between China’s economic growth efficiency, financial structure and
economic development. Manag. World 2003, 13–20. [CrossRef]
60.
Zhou, Y.; Li, X.; Liu, Y. Rural land system reforms in China: History, issues, measures and prospects. Land Use Policy
2020,91, 104330. [CrossRef]
61.
Heerink, N.; Kuiper, M.; Shi, X. China’s New Rural Income Support Policy: Impacts on Grain Production and Rural Income
Inequality. China World Econ. 2006,14, 58–69. [CrossRef]
62.
White, H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica
1980,48, 237–268. [CrossRef]
63.
Jeong, J.; Lee, K. Bootstrapped White’s test for heteroskedasticity in regression models. Econ. Lett.
1999
,63, 261–267. [CrossRef]
64.
Wooldridge, J. Econometric Analysis of Cross Section and Panel Data; MIT Press: Cambridge, MA, USA; London, UK, 2002;
Volume 58.
65. Drukker, D. Testing for Serial Correlation in Linear Panel Data Models. Stata J. 2003,3, 168–177. [CrossRef]
66.
Hoechle, D. Robust Standard Errors for Panel Regressions With Cross-Sectional Dependence. Stata J.
2007
,7, 281–312. [CrossRef]
67. Peters, S. On the Use of the RESET Test in Micro-econometric Models. Appl. Econ. Lett. 2000,7, 361–365. [CrossRef]
68. Meintanis, S. Testing for normality with panel data. J. Stat. Comput. Simul. 2011,81, 1745–1752. [CrossRef]
69.
Ullah, S.; Zaefarian, G.; Ullah, F. How to use instrumental variables in addressing endogeneity: A step-by-step procedure for
non-specialists. Ind. Mark. Manag. 2020,96, A1–A6. [CrossRef]
70.
Zhang, Y.; Long, H.; Wang, M.; Yurui, l.; Ma, L.; Chen, K.; Zheng, Y.; Jiang, T. The hidden mechanism of chemical fertiliser
overuse in rural China. Habitat Int. 2020,102, 102210. [CrossRef]
71.
Lechner, M.; Strittmatter, A. Practical Procedures to Deal with Common Support Problems in Matching Estimation. Econom. Rev.
2017. [CrossRef]
72. Chetty, R.; Looney, W.; Kroft, K. Salience and Taxation: Theory and Evidence. Am. Econ. Rev. 2009,99, 1145–1177. [CrossRef]
73.
Cai, X.; Lu, Y.; Wu, M.; Yu, L. Does environmental regulation drive away inbound foreign direct investment? Evidence from a
quasi-natural experiment in China. J. Dev. Econ. 2016,123, 73–85. [CrossRef]
74.
Freyaldenhoven, S.; Hansen, C.; Shapiro, J.M. Pre-event Trends in the Panel Event-Study Design. Am. Econ. Rev.
2019
,109, 3307–3338.
[CrossRef]
75.
Abadie, A.; Diamond, A.; Hainmueller, J. Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of
California’s Tobacco Control Program. J. Am. Stat. Assoc. 2007,105, 493–505. [CrossRef]
76. Hahn, J.; Shi, R. Synthetic Control and Inference. Econometrics 2017,5, 52. [CrossRef]
77.
Feng, Z.; Tang, Y.; Yang, Y.; Zhang, D. The relief degree of land surface in China and its correlation with population distribution.
Acta Geogr. Sin. 2007,62, 1073–1082.
78.
You, Z.; Feng, Z.M.; Yang, Y.Z. Relief Degree of Land Surface Dataset of China (1km). J. Glob. Chang. Data Discov.
2018
,2, 151–155.
[CrossRef]
79.
Robert, P.; Rust, R.; Larson, W.; Krummel, J.; Su, H. Topographic Effect and Its Relation to Crop Production in an Individual
Field. In Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, USA, 23–26 June 1996;
John Wiley &