How Does Global Economic Volatility Shape Agricultural Resilience? Institutional, Structural, and Technological Evidence from China's Provincial Panel Data (2000–2022)

Research on World Agricultural Economy | Volume 07 | Issue 04 | December 2026
Research on World Agricultural Economy
https://journals.nasspublishing.com/index.php/rwae
ARTICLE
How Does Global Economic Volatility Shape Agricultural
Resilience? Institutional, Structural, and Technological Evidence
from China’s Provincial Panel Data (2000–2022)
Yuanhan Yang
School of Economics, The University of Sydney, Sydney 2006, Australia
ABSTRACT
Intensifying global economic volatility—encompassing financial crises, trade conflicts, and pandemic shocks—
poses growing threats to agricultural stability in major food‑producing nations. We construct a composite Agricul‑
tural Economic Resilience Index (AERI) across three theoretically grounded dimensions—resistance (engineering
resilience), recovery (ecological resilience), and adaptability (evolutionary resilience)—using an entropy weight–
TOPSIS procedure applied to provincial panel data from China’s 31 administrative units over 2000–2022. China’s
agricultural economy demonstrated consistent resilience across three representative shock episodes (the 2008 fi‑
nancial crisis, the 2018 China–US trade friction, and the 2020 COVID‑19 pandemic). The mean composite AERI
reached 0.681, ranking second among four major agricultural producers (United States: 0.742; Brazil: 0.623; India:
0.589). The mean supply‑chain recovery period of 1.8 years outperformed Brazil (3.2 years) and India (2.9 years).
Panel VAR variance decomposition attributes resilience to institutional factors (elasticity coefficient = 0.312), struc‑
tural factors (0.231), and technological factors (0.186), establishing institutional arrangements as the dominant
driver . This study advances the agricultural resilience literature by integrating all three resilience traditions into
a unified composite index, providing systematic cross‑national evidence for large developing‑country agricultural
economies, and identifying a compound generative logic—institutional primacy, structural support, and technology‑
enabled adaptation—that offers an empirical reference for resilience‑oriented policy design in emerging economies.
*CORRESPONDING AUTHOR:
Yuanhan Yang, School of Economics, The University of Sydney, Sydney 2006, Australia; Email: 0501gczyws@gmail.com
ARTICLE INFO
Received: 1 April 2026 | Revised: 1 May 2026 | Accepted: 6 May 2026 | Published Online: 13 November 2026
DOI: https://doi.org/10.36956/rwae.v7i4.105728
CITATION
Yang, Y ., 2026. How Does Global Economic Volatility Shape Agricultural Resilience? Institutional, Structural, and Technological Evidence
from China’s Provincial Panel Data (2000–2022). Research on World Agricultural Economy. 7(4): 413–436.
DOI: https://doi.org/10.36956/rwae.v7i4.105728
COPYRIGHT
Copyright © 2026 by the author(s). Published by Nan Yang Academy of Sciences Pte. Ltd. This is an open access article under the Creative
Commons Attribution‑NonCommercial 4.0 International (CC BY ‑NC 4.0) License (https://creativecommons.org/licenses/by‑nc/4.0/).
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Research on World Agricultural Economy | Volume 07 | Issue 04 | December 2026
Keywords: Global Economic Volatility; Agricultural Economic Resilience; Institutional Factors; Industrial Diversifi‑
cation; Digital Agriculture; Food Security

1. Introduction
   The world economy appears to be undergoing pro‑
   found structural transformation that could indicate a
   new era of instability. Moreover , the significant evidence
   shows that since the 2008 global financial crisis, eco‑
   nomic volatility has risen in both frequency and severity.
   In light of these key findings, the COVID‑19 pandemic,
   the food and energy disruptions triggered by the Russia‑
   Ukraine conflict, diverging monetary policies among ma‑
   jor economies, and the spread of de‑globalization sen‑
   timent may together demonstrate that an exception‑
   ally complex landscape of global economic instability
   has emerged. Furthermore, agricultural economies—as
   the foundational sector of national economic systems—
   indicate that the results of external shocks carry grow‑
   ing importance for both researchers and policymakers
   regarding stability and recovery capacity. The economy
   shows volatility rising sharply [1,2]. The global economy
   shows that structural instability has become its defining
   condition. However , the findings indicate that since the
   2008 financial crisis, the frequency and severity of eco‑
   nomic shocks have increased markedly. Furthermore,
   the COVID‑19 pandemic, the food and energy disrup‑
   tions triggered by the Russia–Ukraine conflict, diverg‑
   ing monetary policies among major central banks, and
   the spread of de‑globalization sentiment could demon‑
   strate that an exceptionally volatile external environ‑
   ment now appears to characterize global conditions.
   In light of the evidence, agricultural economies show
   that, as the foundational sector of national economic
   systems, growing pressure to maintain stability and re‑
   covery capacity could indicate significant structural vul‑
   nerability. The sector faces mounting strain. China
   shows that it occupies a unique position in this land‑
   scape. Moreover , the significant evidence indicates that
   as the world’s largest agricultural producer and con‑
   sumer , the stability of its agricultural economy might
   demonstrate consequences well beyond domestic food
   security for 1.4 billion people. Additionally, the key find‑
   ings show that China’s rural revitalization strategy has
   been accompanied by sustained expansion of fiscal sup‑
   port for agriculture, including subsidies, policy‑based
   credit, and rural financial innovation. Given that the re‑
   sults demonstrate that these institutional inputs form
   the bedrock of ongoing resilience enhancement, the ev‑
   idence indicates that the convergence of green finance
   and rural digitalization appears to reshape agricultural
   development pathways and might provide new mecha‑
   nisms for self‑repair under external pressure [3,4]. Digi‑
   talization provides new repair mechanisms. This study
   applies the three‑tradition framework to the Chinese
   case, with the aim of both testing its transferability and
   identifying country‑specific resilience drivers. Whether
   the resilience logic of China’s agricultural economy dif‑
   fers meaningfully from that of other major agricultural
   producers remains unclear . The institutional arrange‑
   ments, industrial structures, and technological trajecto‑
   ries that characterize China’s agricultural system do not
   map cleanly onto frameworks derived from market‑led
   economies. Deeper investigation from the perspectives
   of comparative and development economics is therefore
   necessary[5–7].
   Given that the evidence demonstrates that re‑
   silience assessments cannot overlook this relationship,
   the key findings indicate that these diverse perspec‑
   tives could converge on a shared proposition. Evidence
   shows cross‑domain frameworks are needed. Therefore,
   the significant results show that studying agricultural
   economic resilience under intensifying global volatility
   could demonstrate the value of moving beyond disci‑
   plinary boundaries. In light of the findings, this study
   indicates that the resilience performance of China’s agri‑
   cultural economy under global economic volatility, and
   the economic logic underpinning it, could establish a
   meaningful focus for further analysis.
   The study shows that three objectives could guide
   the research: (i) systematically measuring China’s agri‑
   cultural resilience across different types of global shocks;
   (ii) decomposing the internal economic mechanisms—
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defined here as the “economic logic” of resilience forma‑
tion, referring specifically to the relative contributions
of institutional arrangements, industrial structure, and
technological progress to observed resilience outcomes—
and (iii) identifying the distinctive and potentially gen‑
eralizable features of China’s resilience pattern through
cross‑national comparison with major agricultural pro‑
ducers. However , the significant findings indicate that
the term “economic logic” appears throughout this pa‑
per in this precise, bounded sense. Furthermore, the key
evidence could demonstrate that this term refers to the
causal mechanisms and factor contributions that explain
why China’s AERI performs as it does. In light of these re‑
sults, the important data show that this framing makes no
normative claim about optimality or universal applicabil‑
ity. The term “economic logic” carries a bounded mean‑
ing only. Research shows the third contribution offers
empirical reference for agricultural resilience policy de‑
sign. However , the important study shows that this re‑
search both enriches the theoretical literature on agricul‑
tural economic resilience and provides an academic re‑
sponse to China’s agricultural modernization practice.
2. Literature Review
Theoretical research on agricultural economic re‑
silience has its origins in broader inquiries into the
shock‑absorbing capacity of economic systems. Cru‑
cially, the resilience concept has generated substantial
scholarly attention in European and North American
scholarship over the past five decades—attention that
the existing agricultural economics literature has not
fully incorporated. Three distinct intellectual traditions
are relevant. The engineering resilience tradition, for‑
malized by Pimm (1984), defines resilience as the speed
at which a system returns to equilibrium after a per‑
turbation[8]. The ecological resilience tradition, devel‑
oped by Holling (1973) reframes resilience as the mag‑
nitude of disturbance a system can absorb before re‑
organizing around a different stability domain [9]. The
evolutionary resilience tradition, synthesized by Martin
(2012) and Pike et al. (2010), treats resilience as the
capacity to adapt structure and development paths un‑
der sustained stress [10,11]. Walker et al. (2004) show
that resilience, adaptability, and transformability rep‑
resent interrelated but conceptually distinct properties
of complex adaptive systems [12]. Moreover , the signif‑
icant findings of Briguglio et al. (2009) indicate that
economic resilience should be understood as a con‑
structed policy outcome layered on top of inherent struc‑
tural vulnerability—a framing that might indicate direct
applicability to agricultural economies exposed to in‑
ternational commodity markets [13]. Furthermore, the
three dimensions of the AERI constructed in this study—
resistance, recovery, and adaptability—show that they
correspond directly to these three traditions, provid‑
ing important theoretical justification for the choice of
dimensions. In light of these results, the selection of
specific indicators within each dimension appears to
demonstrate that the theoretical grounding remains ro‑
bust across varied analytical contexts. Study shows di‑
mensions align with traditions. Global economic volatil‑
ity indicates that scholarly focus on resilience has inten‑
sified, gradually making it a key concern in agricultural
economics. However , the significant evidence shows
that the rise and decline of livestock markets appear to
demonstrate how agricultural economies achieve struc‑
tural adjustment under external pressure. Research link‑
ing historical exchange networks to agricultural organi‑
zation demonstrates that trade connectivity shaped the
structure and resilience of agricultural production even
in pre‑modern societies. The institutional development
of applied economics research provides the human capi‑
tal needed for resilience analysis [14].
Digital technologies contribute to agricultural re‑
silience by reducing transaction costs, optimizing re‑
source allocation, and strengthening market connectiv‑
ity [15]. Systematic reviews of data‑linkage approaches
confirm that multi‑source data integration improves an‑
alytical precision and policy relevance [16]. The coordi‑
nated deployment of digital infrastructure alongside in‑
stitutional risk‑sharing mechanisms represents an effec‑
tive policy combination for resilience enhancement [17].
Information technology enhances farmers’ risk toler‑
ance and adaptive capacity[18]. Historical studies of agri‑
cultural economics education reveal that knowledge sys‑
tem construction shapes long‑term agricultural develop‑
ment trajectories [19,20]. Studies on poverty show that
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agricultural vulnerability becomes embedded in struc‑
tural traps when institutional support is absent [21,22].
The existing literature shows that three gaps re‑
main unaddressed, each representing a distinct theo‑
retical or methodological advance beyond prior AERI‑
type indices. Moreover , the evidence shows that this
study departs from that tradition by anchoring each
of the three AERI dimensions—resistance, recovery,
and adaptability—directly in a specific resilience the‑
ory (engineering, ecological, and evolutionary, respec‑
tively), providing transparent theoretical justification
for every indicator included. Study shows framework
extends to state‑led developing‑country context where
land tenure, price stabilization, and technology diffu‑
sion are centrally administered. Furthermore, the im‑
portant findings show that this extension tests whether
the framework’s explanatory logic transfers across in‑
stitutional environments. Additionally, the evidence
indicates that most prior studies measure a single re‑
silience dimension—typically resistance or recovery—
rather than integrating all three into a unified compos‑
ite that captures both short‑run buffering and long‑run
structural adaptation. In light of these significant re‑
sults, the key contribution demonstrates that this paper
lies precisely in this integration: a system can exhibit
strong resistance and rapid recovery while remaining
structurally rigid. Notwithstanding this important ev‑
idence, a single‑dimensional index indicates that such
risk remains undetected. The composite AERI revealed
asymmetries.
3. Research Methods and Data
Sources
3.1. Measurement Framework and Indica‑
tor System
The AERI shows that its construction across three
dimensions could correspond to the three resilience tra‑
ditions reviewed in Section 2. Moreover , each indicator
appears to be selected on the basis of its theoretical rel‑
evance to the dimension it represents. Furthermore, the
significant evidence indicates that empirical availabil‑
ity across the full provincial panel remains a key selec‑
tion criterion. In light of these findings, the framework
demonstrates that theoretical grounding and data avail‑
ability together shape indicator choice. However , the re‑
sistance dimension shows that it measures the capacity
of the agricultural system to maintain basic functioning
during a shock—consistent with engineering resilience.
Additionally, the composite food production capacity in‑
dex indicates that output buffering relative to minimum
subsistence requirements demonstrates greater initial
defensive strength. Given that the coefficient of vari‑
ation in agriculture’s share of GDP reflects macroeco‑
nomic stability, the significant evidence shows that a
lower value indicates less sensitivity to output fluctua‑
tions. Thus, the stability of agricultural product price in‑
dices could demonstrate that market‑level shock absorp‑
tion appears measurable across the full provincial panel.
Agricultural employment absorption rate shows that
the sector sustains rural livelihoods during disruptions.
Notwithstanding this result, the recovery dimension in‑
dicates that it measures the speed and completeness of
return to the pre‑shock trajectory—consistent with eco‑
logical resilience. Moreover , the mean‑reversion speed
of annual agricultural output growth could demonstrate
that the sector may regain its growth path at measur‑
able rates. Furthermore, the restoration rate of disaster‑
affected arable land shows that physical recovery of pro‑
ductive capacity appears quantifiable. Therefore, the in‑
come recovery elasticity of rural residents’ disposable in‑
come indicates that household‑level financial recupera‑
tion demonstrates multi‑layered post‑shock adjustment
dynamics.
The repair cycle of agricultural trade volumes shows
restoration of external market linkages. In light of these
findings, the adaptability dimension shows that it mea‑
sures structural adjustment capacity under sustained
pressure—consistent with evolutionary resilience. How‑
ever , agricultural R&D investment intensity indicates
that innovation inputs available for structural transfor‑
mation appear significant as a proxy measure. Addi‑
tionally, the significant mechanization and digital tech‑
nology coverage rates demonstrate that process‑level
technological adaptation could reflect broader structural
change. Given that the agricultural industrial diversifica‑
tion index—measured as the inverse of the Herfindahl–
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Hirschman Index—reflects the breadth of the productive
base, the key evidence shows that a higher value indi‑
cates lower concentration risk. Market participation rate
shows organizational modernization shift from small‑
holder structures[23]. Furthermore, the significant indica‑
tor weights could demonstrate that they are determined
using the entropy weight– TOPSIS method. Thus, the key
evidence shows that this approach derives weights from
the observed variation in each indicator across provinces.
Notwithstanding the results, the important findings in‑
dicate that avoiding the subjectivity inherent in expert‑
assigned weights appears to be a central methodological
advantage. Moreover , the coefficient of variation proce‑
dure could demonstrate that standardizing indicators be‑
fore aggregation may eliminate dimensional distortions.
Three dimensions combine through weighted summation
to produce composite AERI ranging from 0 to 1[24].
The choice of entropy weight– TOPSIS as the aggre‑
gation procedure warrants explicit justification, since al‑
ternative weighting approaches—including analytic hi‑
erarchy process (AHP) and principal component anal‑
ysis (PCA)—each carry distinct assumptions that bear
on the validity of the resulting composite. AHP assigns
weights through pairwise expert judgment, which intro‑
duces well‑documented subjectivity and sensitivity to
the composition of the expert panel; PCA derives weights
from the covariance structure of the data, but this struc‑
ture reflects statistical association rather than theoreti‑
cal significance, and the resulting components often re‑
sist clear substantive interpretation, as shown in Table

1. Entropy weighting, by contrast, derives each indi‑
   cator’s weight from the informational content embed‑
   ded in its observed cross‑provincial variation: an indi‑
   cator that is uniformly high or low across all provinces
   contributes little discriminatory information and accord‑
   ingly receives a lower weight, while an indicator that
   varies substantially across units carries greater diagnos‑
   tic value and is weighted more heavily. This property
   renders entropy weighting particularly appropriate for
   provincial panel data in which spatial heterogeneity—
   rather than expert opinion or latent statistical factors—
   is the primary source of analytical leverage. TOPSIS then
   ranks each province‑year observation by its simultane‑
   ous proximity to the ideal solution and distance from
   the negative‑ideal solution, producing a composite score
   that is bounded between zero and one and that pre‑
   serves the ordinal and cardinal structure of the under‑
   lying indicator space.
   Table 1. AERI Indicator System: Dimensions, Indicators, Measurement Methods, Units, and Data Sources.
   Dimension Indicator Measurement Method Unit Direction Data Source
   Resistance
   Composite food
   production capacity
   index
   Ratio of actual grain output to
   minimum subsistence threshold,
   averaged over provinces
   Index (0–1) + NBS China StatisticalYearbook; MARA
   Resistance
   Coefficient of variation
   in agriculture’s share
   of GDP
   Standard deviation divided by
   mean of agriculture‑to‑GDP ratio
   over rolling 5‑year windows
   Dimensionless − NBS ProvincialStatistical Yearbooks
   Resistance Stability of agriculturalproduct price index Inverse of the coefficient ofvariation of the provincialagricultural price index Index + NBS; MARA PriceMonitoring Reports
   Resistance
   Agricultural
   employment
   absorption rate
   Share of agricultural
   employment in total rural
   employment during shock years
   % + NBS China LabourStatistical Yearbook
   Recovery
   Mean‑reversion speed
   of agricultural output
   growth
   AR(1) coefficient estimated on
   annual agricultural output
   growth rate series
   Coefficient + NBS ProvincialStatistical Yearbooks
   Recovery
   Restoration rate of
   disaster‑affected
   arable land
   Ratio of restored arable land area
   to total disaster‑affected arable
   land within two years
   % +
   MARA Disaster
   Statistics; Ministry of
   Natural Resources
   Recovery
   Income recovery
   elasticity of rural
   disposable income
   Ratio of income growth rate in
   the two years post‑shock to the
   pre‑shock trend growth rate
   Elasticity + NBS Rural HouseholdSurvey
   Recovery
   Repair cycle of
   agricultural trade
   volumes
   Number of years for agricultural
   trade to return to pre‑shock level Years −
   General
   Administration of
   Customs of China; FAO
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Table 1. Cont.
Dimension Indicator Measurement Method Unit Direction Data Source
Adaptability Agricultural R&Dinvestment intensity Agricultural R&D expenditure asa share of agricultural GDP % + Ministry of Scienceand Technology; NBS
Adaptability Mechanizationcoverage rate Share of major crop cultivationand harvesting operations usingmechanical equipment % + MARA AgriculturalMechanizationStatistics
Adaptability Digital technologycoverage rate Share of agricultural operatingarea covered by IoT devices anddigital platforms % + MARA DigitalAgriculture Reports
Adaptability Agricultural industrialdiversification index
Inverse of the
Herfindahl–Hirschman Index
computed on sub‑sector output
shares
Index + NBS China StatisticalYearbook
Adaptability
Market participation
rate of new‑type
agricultural business
entities
Share of family farms,
cooperatives, and agribusinesses
in total agricultural sales value
% + MARA; NBSAgricultural Census
3.2. Econometric Model Design: Panel Data
Models and Shock‑Response Analysis
The econometric strategy shows that it unfolds
across two sequential layers. However , the first layer indi‑
cates that it estimates the overall effect of global economic
volatility on China’s agricultural resilience. Moreover , the
second layer appears to demonstrate that it traces the
dynamic transmission pathways through which shocks
propagate over time. Furthermore, the significant evi‑
dence shows that these two layers provide the key ana‑
lytical structure for the study . Strategy spans two layers.
Given that the first layer establishes the framework,
we specify a two‑way fixed‑effects panel model with the
AERI as the dependent variable. Additionally, the core
explanatory variable indicates that it represents a com‑
posite global economic volatility index constructed from
three components: the CBOE VIX fear index, the Baker et
al. Global Economic Policy Uncertainty (EPU) index, and
FAO agricultural commodity price volatility. In light of
these significant findings, the results show that control
variables include agricultural fiscal subsidy intensity, ru‑
ral credit availability, agricultural labor transfer rates,
changes in arable land area, the contribution rate of agri‑
cultural TFP , and provincial per capita GDP . Notwith‑
standing these important results, the evidence demon‑
strates that the baseline model takes the following form.
AERIit = αi + λt + β1Shockit +
n∑
k=2
βkXkit + εit
Here, αiand λt represent province fixed effects and
time fixed effects, respectively, controlling for time‑
invariant individual heterogeneity and common tempo‑
ral trends. To address potential reverse causality be‑
tween the core explanatory variables and the resilience
index, the System Generalized Method of Moments (SYS‑
GMM) estimator is applied as a robustness check.
Moreover , the significant Impulse Response Func‑
tions (IRFs) appear to demonstrate that the response
paths of each variable across different time windows
following a shock might indicate important analytical
value[25]. However , the study shows that the sample
could demonstrate that eastern, central, and western
economic zones provide significant sub‑group regres‑
sion values. Additionally, the significant threshold panel
model appears to examine that the resilience response to
global shocks might indicate important nonlinear thresh‑
old effects. In light of the key findings, the evidence
shows that this approach demonstrates that both aggre‑
gate patterns and regional differences appear to emerge
within a unified econometric framework. Notwithstand‑
ing these results, the study indicates that the critical
framework could establish that the approach demon‑
strates significant analytical scope. The study shows uni‑
fied framework identifies patterns and differences.
3.3. Data Sources, Sample Selection and
Variable Description
The study draws on data from multiple authorita‑
tive statistical sources to ensure the reliability and repli‑
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cability of the findings. Moreover , the domestic data
show that primary sources such as the China Statisti‑
cal Yearbook published by the National Bureau of Statis‑
tics, the National Agricultural Statistics of the Ministry
of Agriculture and Rural Affairs, and provincial statisti‑
cal yearbooks could provide significant support for the
analytical framework. Furthermore, the evidence indi‑
cates that these sources provide key variables includ‑
ing agricultural output value, grain production, arable
land area, agricultural labor , rural disposable income,
fiscal subsidies to agriculture, and mechanization lev‑
els. In light of these findings, the international data
demonstrate that sources such as the World Bank’s
World Development Indicators (WDI), the IMF World
Economic Outlook database, the FAO statistical database,
and the CBOE VIX volatility index appear to establish
the global economic volatility proxies and international
agricultural commodity price series [26]. International
data constructs global proxies and price series. How‑
ever , the trade uncertainty data show that the Global
Economic Policy Uncertainty Index (EPU Index) devel‑
oped by Baker and colleagues could provide important
results for the analysis. Additionally, the agricultural
technological progress indicates that province‑level To‑
tal Factor Productivity (TFP) estimates appear to demon‑
strate that quantification could establish significant find‑
ings across the sample. Given that the evidence demon‑
strates that all raw data were verified and cleaned, the re‑
sults show that the analytical sample could reflect impor‑
tant data integrity. Nevertheless, the sample indicates
that the 31 provincial‑level administrative units in main‑
land China—including municipalities and autonomous
regions—could demonstrate key findings over the pe‑
riod 2000 to 2022, forming an unbalanced provincial
panel dataset. Sample covers 31 units over 2000–2022.
Therefore, the sample period shows that three major
global economic shock events—the 2008 international
financial crisis, the escalation of China–US trade fric‑
tions in 2018, and the COVID‑19 pandemic shock in
2020—demonstrate that the evidence appears to sup‑
port a reasonably comprehensive assessment of agricul‑
tural resilience across different shock types[27]. Notwith‑
standing these results, the significant findings indicate
that provinces such as Tibet and Qinghai, where statis‑
tical coverage and data continuity are limited, demon‑
strate that missing values appear to require treatment
through linear interpolation. Thus, the results show
that this important approach indicates that the evidence
demonstrates that data reliability appears to be pre‑
served across the analytical sample.
The findings show that provinces with compara‑
tively low data quality are excluded in robustness checks
to verify that the stability of the baseline results demon‑
strates reliability. Moreover , the significant evidence in‑
dicates that agricultural economic data for the United
States, Brazil, India, Russia and Australia are extracted
from the FAO and World Bank databases, forming an in‑
ternational sub‑sample for cross‑national comparison in
Section 4. Furthermore, the core dependent variable is
the AERI, constructed as described in Section 3.1, and
the results show that this measure appears to capture
resilience outcomes effectively. Given that the compos‑
ite index demonstrates meaningful aggregation, the core
independent variable is a global economic volatility in‑
dex formed by standardized weighted aggregation of the
VIX index, the EPU index and agricultural commodity
price volatility, where higher values may indicate greater
external shock intensity. Higher values show greater
shock intensity. However , the six categories of con‑
trol variables show that agricultural fiscal support could
demonstrate relevance, measured as agricultural fiscal
expenditure as a share of total local government expen‑
diture. Additionally, the significant evidence indicates
that rural financial development is proxied by the ratio
of agriculture‑related loans from rural financial institu‑
tions to agricultural GDP . Therefore, the key results show
that agricultural labor structure appears to be captured
by the share of agricultural workers in total employment.
In light of the findings demonstrating measurement va‑
lidity, agricultural openness indicates that total agricul‑
tural trade as a share of agricultural GDP could demon‑
strate the degree of external market integration. The
study shows that natural disaster intensity is linked to
cropland affected. Nevertheless, the significant findings
show that regional economic development level could be
proxied by the logarithm of per capita GDP , controlling
for the confounding effects of baseline economic condi‑
tions on resilience outcomes[28,29]. Moreover , the results
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indicate that all continuous variables appear to be log‑
transformed or standardized to support comparability
across the evidence. Thus, the key evidence shows that
outliers are winsorized at the 1st and 99th percentiles
to limit the influence of extreme observations that might
affect estimation results. Winsorizing limits influence of
extreme values on results.
4. Results Analysis
4.1. Shock Identification of Global Eco‑
nomic Volatility and Transmission
Mechanisms in the Agricultural Sector
4.1.1. Transmission Pathways of Interna‑
tional Agricultural Commodity Price
Volatility
Table 2 summarizes the key transmission indica‑
tors across the three shock episodes. Three findings
stand out. The findings show that three key patterns
emerge from Table 2 . Moreover , the significant evi‑
dence indicates that the 2008 financial crisis generated
the largest absolute price shock: the FAO food price
index rose 24.3%, domestic soybean import prices in‑
creased 18.6%, and the agricultural production cost in‑
dex climbed 12.4%. Furthermore, the results demon‑
strate that China’s agricultural GDP growth rate fell by
only 0.6 percentage points, from 3.7% to 3.1%, as shown
in Figure 1. In light of these findings, the data show that
this limited decline appears to reflect a meaningful insti‑
tutional buffer [30]. GDP decline shows crisis buffered in‑
stitutionally.
4.1.2. The Impact of Global Trade Frictions
on China’s Agricultural Trade Struc‑
ture
Trade friction escalation suggests that significant
structural disruption may have emerged across China’s
agricultural import and export patterns. Moreover ,
the evidence indicates that these effects operate across
three key dimensions: contraction in trade volume, re‑
configuration of trading partner diversity, and shifts in
commodity composition. Given that the data demon‑
strates sharp contraction, the findings show that China’s
agricultural purchases from the US fell dramatically—
from USD 24.18 billion in 2017 to USD 9.34 billion in
2018, a year‑on‑year drop of 61.4%. Furthermore, the
significant evidence appears to show that soybean im‑
ports alone fell from 32.85 million t to 16.64 million t,
a reduction of 49.4%. Trade shows supply gap is filled
via South American producers, particularly Brazil and
Argentina[31].
Table 2. Key Indicators of International Agricultural Price Transmission during Three Global Economic Shock Episodes.
Shock Type ShockPeriod FAO FoodPrice IndexIncrease DomesticImportPriceIncrease AgriculturalProductionCostIncrease Change in AgriculturalGDP Growth Rate PriceTransmissionElasticityCoefficient
International Financial Crisis 2007–2008 24.30% 18.60% 12.40% −0.6 percentage points 0.43
China–US Trade Friction 2018–2019 11.70% 15.20% 9.80% −0.4 percentage points 0.51
COVID‑19 Pandemic Shock 2020–2021 14.50% 10.30% 8.70% −0.3 percentage points 0.38
Figure 1. Transmission of International Agricultural Commodity Price Shocks to China’s Agricultural Economy (2007–2022).
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The reorientation of China’s agricultural exports to‑
ward ASEAN, the European Union, and Belt and Road
countries could indicate a significant strategic shift
in trade priorities. However , the findings show that
ASEAN’s share of China’s agricultural exports rose from
21.3% in 2017 to 26.8% in 2020, reflecting important
diversification efforts. Additionally, the key evidence
demonstrates that the market diversification strategy
appeared to yield measurable results[32]. Findings show
that the strategy links exports to new partners. Details
are presented in Table 3 and Figure 2.
Three interpretive observations deserve elabora‑
tion beyond the summary statistics in Table 3. First, the
declining price transmission elasticity from 0.43 in 2008
to 0.38 in 2020 does not reflect weakening global mar‑
ket integration; rather , it reflects the progressive deep‑
ening of China’s domestic price stabilization architecture.
The grain reserve system’s capacity to absorb and release
stocks countercyclically expanded substantially between
these two episodes—central reserve grain capacity rose
from approximately 50 million tonnes in 2008 to over
200 million tonnes by 2020—and this expanded buffer
physically intercepted a larger share of the import price
signal before it reached domestic producers. The elastic‑
ity decline is therefore most accurately read as an institu‑
tional achievement rather than a structural decoupling.
Table 3. Changes in China’s Major Agricultural Import and Export Structure Before and After China–US Trade Frictions (2017–
2022).
Indicator 2017 2018 2019 2020 2021 2022
Agricultural exports to the US (USD 100 million) 74.2 53.6 49.8 55.3 68.4 72.1
Agricultural imports from the US (USD 100 million) 241.8 93.4 107.2 261.3 324.6 358.2
Share of soybean imports from Brazil (%) 53.2 75.6 64.3 60.8 58.7 61.4
Agricultural trade deficit (USD 100 million) 488.2 312.7 401.5 512.6 578.3 621.4
Share of agricultural exports to ASEAN (%) 21.3 22.8 23.6 25.1 26.2 26.8
Source: Data sourced from the General Administration of Customs of China, the Ministry of Agriculture and Rural Affairs, and the FAO statistical database.
Figure 2. China’s Agricultural Trade Structure Changes under Trade Friction (2017–2022): (a) Import & Export Value with the
US; (b) Trade Deficit & ASEAN Export Share.
4.1.3. The Effects of International Capital
Flows and Exchange Rate Volatility
on Agricultural Input Costs
International capital flows and RMB exchange rate
fluctuations show that China’s agricultural production
costs are shaped through three distinct pathways: im‑
port prices of agricultural inputs, the scale of foreign di‑
rect investment in agriculture, and agricultural commod‑
ity futures markets. Moreover , the significant findings
indicate that specific episodes demonstrate that this re‑
lationship appears particularly clear during reform pe‑
riods. Given that the evidence demonstrates reform‑
era sensitivity, the key results show that the 2018–
2019 trade friction period could show a depreciation
of approximately 8.7%, with imported fertilizer average
prices rising 11.4% and agricultural machinery import
costs increasing 9.2%. In light of these significant find‑
ings, the data indicate that the combined effect appears
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to have compressed the cost‑profit margin from 6.8%
in 2017 to 4.3% in 2019 [33]. The combined effect com‑
pressed cost‑profit margin from 6.8% to 4.3%.
This procyclical pattern shows that supply‑side
price pressures on agricultural inputs are amplified in
significant ways. Furthermore, the findings indicate that
international commodity futures markets play an impor‑
tant role in the evidence. Moreover , speculative capital
activity on these markets indicates that domestic agricul‑
tural input pricing appears to be shaped through price
discovery mechanisms. Given that the correlation coeffi‑
cient between CBOT soybean futures prices and domes‑
tic soybean meal spot prices reached 0.83 over the pe‑
riod (2015 to 2022), the significant results demonstrate
that the indirect influence of global financial market
volatility on domestic agricultural input costs is clearly
non‑negligible[34]. Thus, the data show that Table 4 and
Figure 3 present the supporting evidence.
Table 4. RMB Exchange Rate Fluctuations and Agricultural Input Cost Changes in Key Periods (2015–2022).
Year RMB/USDChange (%) ImportedFertilizer PriceChange (%) ImportedPesticide A.I.Price Change (%) AgriculturalInput Price IndexChange (%) Agricultural FDIGrowth (%) AgriculturalCost‑ProfitRatio (%)
2015 −4.5 6.3 5.1 3.8 −9.6 6.2
2016 −6.8 4.2 3.8 2.6 −11.4 5.9
2017 0.7 −1.2 −0.8 −0.5 7.2 6.8
2018 −5.4 8.7 7.3 5.4 −13.8 5.6
2019 −3.3 11.4 9.2 6.1 −10.5 4.3
2020 6.5 −3.5 −2.9 −1.8 9.1 5.1
2021 2.3 18.6 14.2 11.3 6.4 4.8
2022 −9.1 22.4 17.8 14.6 −15.2 3.9
Note: Positive values for exchange rate changes indicate RMB appreciation; negative values indicate depreciation.
Source: Data sourced from the People’s Bank of China, the National Bureau of Statistics, the Ministry of Commerce, and the Ministry of Agriculture and Rural Affairs.
Figure 3. Impact of RMB Exchange Rate Fluctuations on Agricultural Input Costs (2015–2022): ( a) Exchange Rate Change vs.
Fertilizer & Pesticide Import Prices; (b) Agricultural Input Price Index vs. Cost‑Profit Ratio & FDI Growth.
4.2. Multi‑Dimensional Performance of
China’s Agricultural Economic Re‑
silience and International Comparison
4.2.1. Resilience Index Comparison: China,
the United States, Brazil and India
Cross‑national comparison of AERI scores over
2008–2022 shows that clear and consistent differences
exist across the four countries. Moreover , the findings
indicate that the United States leads with a mean com‑
posite AERI of 0.742, followed by China (0.681), Brazil
(0.623), and India (0.589). Given that this gradient ap‑
pears to correspond broadly to each country’s level of
agricultural modernization, the evidence shows that in‑
stitutional capacity plays a key role. Furthermore, disag‑
gregating by dimension could reveal that China scores
0.714 on recovery, slightly ahead of the United States
(0.698), reflecting that China’s institutional emergency
response mechanisms demonstrate significant effective‑
ness. Results show the United States leads adaptabil‑
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ity at 0.803, China at 0.712, indicating room for im‑
provement in technology‑driven long‑term adaptive ca‑
pacity [35,36]. Therefore, the key results show that this
profile links institutional capacity to observed outcomes.
Additionally, the results demonstrate that this contrasts
with the United States’ more balanced high‑resilience
pattern across all three dimensions. Notwithstanding
these results, the key evidence shows that the compar‑
ison provides a valuable reference point for understand‑
ing the institutional logic underlying China’s resilience
model. The study’s data are presented in Table 5 and
Figure 4.
Table 5. Composite AERI and Sub‑Dimensional Scores for Four Countries (2008–2022 Means).
Country
Mean
Composite
AERI
Resistance
Score
Recovery
Score
Adaptability
Score
AERI Decline:
Financial
Crisis
AERI Decline:
Trade
Friction
AERI
Decline:
COVID‑19
Mean
Recovery
Period
(Years)
China 0.681 0.718 0.714 0.712 0.038 0.061 0.029 1.8
United States 0.742 0.726 0.698 0.803 0.052 0.044 0.041 2.1
Brazil 0.623 0.601 0.598 0.672 0.071 0.068 0.053 3.2
India 0.589 0.574 0.561 0.632 0.083 0.079 0.067 2.9
Note: AERI values range from 0 to 1; higher values indicate stronger resilience. Decline figures represent the absolute difference between the shock year and the
preceding year .
Source: Data calculated by the author based on FAO, World Bank WDI, and national statistical yearbooks.
Figure 4. Comparison of Agricultural Economic Resilience Index (AERI) among China, the USA, Brazil and India (2008–2022):
(a) Annual AERI Trend; (b) Sub‑Dimensional Score Comparison (2008–2022 Mean).
4.2.2. The Food Security Dimension: Cross‑
National Comparison of Production
Stability and Self‑Sufficiency Rates
Food security performance shows that agricultural
resilience under external shocks is measurable through
core production indicators. Moreover , the findings from
cross‑national comparisons indicate that production sta‑
bility and self‑sufficiency rates provide a direct view of
how major agricultural producers differ in their capacity
to maintain domestic food supplies. However , the signifi‑
cant evidence appears to demonstrate that China’s grain
output coefficient of variation from 2000 to 2022 stands
at 0.042 [37,38].
However , the findings show that one structural vul‑
nerability stands out with particular clarity. Moreover ,
China’s soybean self‑sufficiency rate could indicate a per‑
sistent decline, falling from approximately 42.3% in 2000
to just 14.8% in 2022. In light of these results, this trend
may represent the most significant structural weakness
in China’s food security system, appearing to sit in stark
contrast to the high overall cereal self‑sufficiency rate.
Given that the production stability index—measured as
the ratio of the standard deviation to the mean over
rolling five‑year periods—could demonstrate a consis‑
tent pattern, the significant evidence shows that results
tell a coherent story. The stability index shows im‑
provement. For the periods 2008–2012, 2013–2017 and
2018–2022, the significant findings indicate that China’s
index values are 0.031, 0.028 and 0.024, showing contin‑
uous improvement. Furthermore, over the same three
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periods, the results show that India records 0.092, 0.081
and 0.074; Brazil records 0.118, 0.103 and 0.096; and the
United States records 0.061, 0.058 and 0.052. Notwith‑
standing these comparisons, the significant findings in‑
dicate that China’s relative stability advantage appears
clear and durable. Therefore, the evidence shows that
this advantage appears closely tied to China’s strict farm‑
land protection system, grain reserve management mech‑
anisms and agricultural subsidy policies. The data are
shown in full in Table 6 and Figure 5.
Table 6. Cross‑National Comparison of Core Food Security Indicators among Major Agricultural Producers (2000–2022).
Indicator China United States Brazil India
Mean total grain output (10,000 t) 57,842 48,631 21,453 26,784
Annual output coefficient of variation 0.042 0.068 0.112 0.087
Mean cereal self‑sufficiency rate (%) 96.8 128.6 142.3 91.2
Soybean self‑sufficiency rate (2022, %) 14.8 105.3 138.7 88.4
Production stability index (2018–2022) 0.024 0.052 0.096 0.074
Maximum output decline during shock periods (%) 0 −2.3 −5.8 −4.1
Mean annual output growth rate (%) 1.87 0.94 2.31 1.63
Note: A lower production stability index indicates greater stability. The maximum output decline is the largest single‑year negative growth rate recorded across the
three shock episodes.
Source: Data sourced from FAO, World Bank WDI and national statistical agencies.
Figure 5. Cross‑Country Comparison of Food Security Dimensions (2000–2022): ( a) Grain Production Index (2000 = 100)
Trends; (b) Cereal Self‑Sufficiency Rate and Soybean Self‑Sufficiency Rate (2022).
4.2.3. Agricultural Supply Chain Resilience:
International Assessment of Supply
Chain Completeness and Disruption
Resistance
Agricultural supply chain resilience appears to
demonstrate that it functions as a critical component of
overall agricultural economic resilience. Moreover , the
significant evidence shows that cross‑national assess‑
ment of supply chain completeness and disruption re‑
sistance reveals structural differences in how national
agricultural systems sustain stable operations under ex‑
ternal shocks. Furthermore, this study indicates that
the composite evaluation framework covers four dimen‑
sions: the Agricultural Supply Chain Completeness In‑
dex (ASCI), critical node concentration, disruption recov‑
ery speed, and supply chain localization rate. The study
suggests that China, the United States, Brazil, and In‑
dia may reveal important distinctions across these na‑
tional systems when examined systematically. Study
shows ASCI scores differ markedly [39,40]. Furthermore,
the significant findings indicate that China’s rapid re‑
bound could demonstrate that government‑led emer‑
gency logistics mechanisms, operating in coordination
with digital supply chain management platforms, appear
to show considerable resilience. Moreover , the key ev‑
idence shows that China’s core agricultural input local‑
ization rate stands at 82.6%, well above India’s 63.4%
and Brazil’s 58.7%. In light of these results, the data in‑
dicate that the localization rate for key agricultural tech‑
nology equipment reaches only 71.3%, suggesting that
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a 28.7% dependence on imports for high‑end agricul‑
tural machinery might represent a latent vulnerability
in the supply chain [41]. Notwithstanding these findings,
the results show that China records a mean disruption
intensity index of 0.183 across the three shock episodes
from 2008 to 2022. The disruption index shows China
outperforms the US (0.214), Brazil (0.312), and India
(0.287). However , the significant findings show that
China’s agricultural supply chain appears to maintain
relatively strong overall disruption resistance across dif‑
ferent shock types. Given that the evidence demon‑
strates these structural characteristics, the important re‑
sults indicate that a clear gap might remain between
China and the United States in supply chain digitaliza‑
tion and international diversification. Therefore, the key
findings show that these factors could represent critical
policy priorities for future resilience enhancement. The
study shows full details in Table 7 and Figure 6.
Table 7. International Assessment of Core Agricultural Supply Chain Resilience Indicators for Four Countries (2008–2022).
Assessment Indicator China United States Brazil India
Supply Chain Completeness Index (ASCI) 0.784 0.761 0.693 0.652
Critical node concentration (top‑five share, %) 68.3 71.4 62.8 74.6
Supply chain recovery time during COVID‑19 (days) 42 58 76 89
Core input localization rate (%) 82.6 91.3 58.7 63.4
Key technology equipment localization rate (%) 71.3 94.6 52.4 48.7
Mean disruption intensity index 0.183 0.214 0.312 0.287
Supply chain digitalization coverage rate (%) 63.4 84.2 41.6 38.9
Note: The Supply Chain Completeness Index and the disruption intensity index are both author‑calculated. Higher values indicate greater completeness; lower
values indicate weaker disruption intensity.
Source: Data sourced from the World Bank, FAO, national ministries of agriculture and the McKinsey Global Supply Chain Research Report.
Figure 6. International Assessment of Agricultural Supply Chain Resilience (2008–2022): (a) Supply Chain Completeness Index
& Disruption Intensity by Country; (b) Localization Rate & Digitalization Coverage Comparison.
4.3. Decomposing the Internal Economic
Logic of China’s Agricultural Economic
Resilience
4.3.1. Institutional Factors: The Contribu‑
tions of Land Policy, Agricultural Sub‑
sidies and Macroeconomic Regula‑
tion
Institutional factors show that China’s agricultural
economic resilience rests on foundational support from
key structural elements. However, the significant find‑
ings indicate that three elements work in concert: sta‑
ble land policy, a progressively strengthened agricul‑
tural subsidy system, and flexible macroeconomic reg‑
ulation. Moreover, the evidence demonstrates that to‑
gether they constitute an important institutional fire‑
wall against external shocks. Given that the data re‑
veal China’s strict farmland protection red line fixes
1.8 billion mu of arable land as a non‑negotiable con‑
straint, the results indicate that this policy appears crit‑
ical to long‑term stability. Land policy shows arable
area held firm. Furthermore, the findings show that be‑
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tween 2000 and 2022, the average annual net reduc‑
tion in farmland area was held to approximately 43,000
ha. Therefore, the significant evidence demonstrates
that this stands in sharp contrast to India’s average an‑
nual net loss of 187,000 ha and Brazil’s 214,000 ha over
the same period. Additionally, the results indicate that
the nationwide campaign to register and certify rural
land contractual management rights has also yielded
substantial findings. In light of these significant results,
the evidence shows that the land titling rate rose from
below 20% in 2013 to 96.8% in 2022. The titling rate
shows investment links. Notwithstanding the complex‑
ity of property rights, the important findings demon‑
strate that secure land tenure reduces the dampening
effect of property rights uncertainty on agricultural in‑
vestment. Thus, the evidence indicates that empirical es‑
timates suggest that each 10‑percentage‑point increase
in the titling rate raises farmers’ long‑term agricultural
investment intentions by approximately 3.2 percentage
points [42]. However, the key results show that agricul‑
tural subsidy policy has followed a sustained upward
trajectory across the study period. Furthermore, the sig‑
nificant data demonstrate that central government agri‑
cultural subsidies grew from RMB 14.55 billion in 2004
to RMB 186.83 billion in 2022, at a mean annual growth
rate of 13.6%.
The core subsidy framework—comprising direct
grain subsidies, comprehensive agricultural input sub‑
sidies, improved seed subsidies, and agricultural ma‑
chinery purchase subsidies—shows that selective rein‑
forcement during each of the three shock episodes could
demonstrate meaningful stabilization capacity. More‑
over , the significant subsidy growth rates in the shock
years of 2008, 2018, and 2020 reached 18.4%, 16.2%,
and 22.7%, respectively, and the findings indicate that a
clear counter‑cyclical stabilization effect appears to have
emerged. Furthermore, the panel regression estimates
show that each one‑percentage‑point increase in sub‑
sidy intensity raises the current‑period AERI by approx‑
imately 0.018 units [43]. In light of these results, macroe‑
conomic regulation demonstrates that it has played an
equally important role in supporting the evidence for
systemic stability. Minimum price policy shows that
farmers’ incentives are stabilized. However , the min‑
imum grain purchase price policy shows that it has
been critical in stabilizing farmers’ production incen‑
tives, given that the key findings indicate cumulative in‑
creases of 72.4% for wheat and 61.8% for rice between
2008 and 2022. Additionally, the significant evidence
demonstrates that these price increases appear to have
effectively prevented price declines from eroding pro‑
duction motivation among farmers. Notwithstanding
the complexity of reserve operations, the national grain
reserve system’s periodic absorption and release opera‑
tions indicate that the domestic grain price volatility co‑
efficient might remain within 0.087—well below the in‑
ternational market level of 0.241. Thus, the important
results show that the estimated contribution elasticity
of the institutional price stabilization mechanism to agri‑
cultural resilience appears to be 0.312. The reserve sys‑
tem links volatility to resilience outcomes. In light of this
key evidence, the findings demonstrate that this elastic‑
ity figure might indicate that it is the single most signifi‑
cant variable among the three categories of institutional
factors examined. Full data are presented inTable 8and
Figure 7.
Table 8. Dynamic Changes in Core Indicators of China’s Agricultural Institutional Factors (2004–2022).
Year
Total Agricultural
Subsidies (RMB
100 million)
Arable Land
Area (100
Million mu)
Land Titling
Rate (%)
Minimum
Purchase Price for
Wheat (RMB/jin)
Grain Price
Volatility
Coefficient
Institutional Resilience
Contribution Elasticity
2004 145.5 18.37 — 0.72 0.134 0.187
2008 638.3 18.26 — 0.77 0.118 0.224
2012 1,078.20 18.25 18.4 0.95 0.103 0.258
2016 1,463.70 18.65 72.6 1.12 0.096 0.287
2018 1,604.80 18.65 88.3 1.15 0.091 0.301
2020 1,727.60 19.18 93.6 1.13 0.089 0.308
2022 1,868.30 19.14 96.8 1.29 0.087 0.312
Note: Land titling rate data are unavailable prior to 2012. Institutional resilience contribution elasticity is estimated via variance decomposition from the Panel VAR
model.
Source: Data sourced from the Ministry of Finance, the Ministry of Natural Resources, the Ministry of Agriculture and Rural Affairs, and the National Bureau of
Statistics.
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Figure 7. Contribution of Institutional Factors to China’s Agricultural Economic Resilience (2004–2022): ( a) Agricultural Sub‑
sidy, Land Tenure Rate & Grain Price Volatility; (b) Minimum Purchase Price & Institutional Resilience Elasticity.
4.3.2. Structural Factors: The Supporting
Role of Agricultural Industrial Diver‑
sification and Regional Coordination
Agricultural industrial diversification and regional
coordination show that these structural foundations
could demonstrate significant resilience in China’s agri‑
cultural economic system. However , the evidence ap‑
pears to indicate that these two mechanisms jointly sup‑
port the overall stability of the agricultural system un‑
der external pressure. Moreover , the findings show that
dispersing single‑industry risk could demonstrate im‑
portant stabilizing effects across regions. In light of
the data, China’s agricultural structure indicates that
sustained optimization has occurred across the indus‑
trial diversification dimension. Diversification shows
that crop share fell. Furthermore, the results show that
the share of crop farming in total agricultural output
fell from 55.2% in 2000 to 47.8% in 2022. Given that
the evidence demonstrates that animal husbandry rose
from 29.7% to 33.4%, the findings indicate that fish‑
eries rose from 9.1% to 11.3%, and agricultural sup‑
port services rose from 1.8% to 5.6%. Additionally,
the industrial diversification index—measured as the
inverse of the Herfindahl‑Hirschman Index—indicates
that values increased from 3.12 in 2000 to 4.87 in 2022,
reflecting a deepening diversification trend. Notwith‑
standing these results, significant panel regression esti‑
mates show that each one‑unit increase in the diversifi‑
cation index raises the composite AERI by approximately
0.043 units. Index links AERI significantly. Therefore,
the key findings indicate that this stabilizing effect ap‑
pears roughly 1.6 times larger during shock periods than
in non‑shock periods. Thus, the evidence shows that
these results could demonstrate a nonlinear resilience‑
enhancing mechanism [44]. Furthermore, the significant
results indicate that China’s functional division among
grain‑producing, grain‑consuming and balanced regions
appears to have become increasingly stable. However ,
the important findings show that the 13 major grain‑
producing provinces account for 78.6% of national grain
output.
Inter‑regional grain distribution mechanisms show
that approximately 120 million t per year flow to net‑
consuming regions, with the inter‑regional grain trans‑
fers growing at a mean annual rate of 4.3%. Moreover ,
the significant agricultural industry transfers from east‑
ern to central and western regions indicate that invest‑
ment expanded from approximately RMB 38.6 billion
in 2010 to RMB 214.3 billion in 2022. Furthermore,
the findings demonstrate that this gradient transfer has
helped relieve farmland pressure in the east while ac‑
tivating underutilized agricultural resources in the cen‑
ter and west. Given that the evidence appears to sup‑
port that regional coordination contributes measurably
to resilience, the results show that this contribution
continues to grow. Coordination links outcomes. The
inter‑regional AERI linkage coefficient across eastern,
central and western China rose from 0.412 in 2008 to
0.683 in 2022, and the key findings indicate that deeper
regional coordination has strengthened the aggregate
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shock‑absorbing capacity of the national agricultural
system. In light of the panel regression results, the data
demonstrate that each 0.1‑unit increase in the regional
coordination index raises the national agricultural re‑
silience index by an average of 0.031 units. However ,
the significant effect appears strongest under trade fric‑
tion shocks, with an elasticity coefficient of 0.047, and
the results show that this effect is somewhat lower un‑
der public health emergency shocks at 0.038. Notwith‑
standing these variations, the evidence indicates that the
findings highlight a distinctive buffering advantage of
structural regional coordination in managing externally
driven trade disruptions. Data shows patterns hold. Full
data are presented in Table 9 and Figure 8.
Table 9. Core Indicators of China’s Agricultural Industrial Diversification and Regional Coordination (2000–2022).
Year Crop FarmingShare (%) AnimalHusbandryShare (%) IndustrialDiversificationIndex Inter‑RegionalGrain Flow (100Million t) RegionalLinkageCoefficient Industry TransferScale (RMB 100Million)
2000 55.2 29.7 3.12 0.68 0.412 —
2004 53.8 31.2 3.34 0.74 0.438 —
2008 52.1 32.4 3.58 0.86 0.463 214.3
2012 50.6 32.8 3.82 0.98 0.521 628.4
2016 49.3 33.1 4.21 1.08 0.598 1,243.70
2018 48.6 33.3 4.48 1.14 0.634 1,687.20
2020 48.1 33.2 4.63 1.17 0.658 1,924.60
2022 47.8 33.4 4.87 1.2 0.683 2,143.00
Figure 8. Structural Factors Supporting China’s Agricultural Economic Resilience (2000–2022): ( a) Industrial Diversification
Index, Grain Inter‑Regional Flow & Regional Linkage Coefficient; (b) Agricultural Sector Composition Change.
4.3.3. Technological Factors: The Driving
Effects of Agricultural Technological
Progress and Digital Agriculture on
Resilience
Technological progress appears to be the endoge‑
nous driving force behind China’s sustained resilience
gains. Moreover, the maturation of agricultural innova‑
tion systems and the large‑scale rollout of digital agri‑
culture show that the agricultural system’s shock resis‑
tance and adaptive capacity have strengthened across
three layers: production efficiency, risk early warning,
and intelligent supply chain management. Furthermore,
the significant findings indicate that China’s agricultural
Total Factor Productivity (TFP) grew at a mean annual
rate of 2.34% from 2000 to 2022, which demonstrates
that the results outpace those of India (1.48%), Brazil
(1.87%), and the United States (1.96%) over the same
period. Given that the evidence demonstrates that the
contribution rate of technological progress to agricul‑
tural output growth rose from 38.6% in 2000 to 61.3%
in 2022, the key data show that technology may ap‑
pear as the dominant driver of agricultural expansion.
The study shows that TFP raises AERI. However, the
panel estimates show that each one‑percentage‑point
increase in agricultural TFP raises the composite AERI
by approximately 0.026 units. In light of the signifi‑
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cant findings, this effect indicates that during shock pe‑
riods, the results appear amplified by a factor of 1.84,
which demonstrates that accumulated technological ca‑
pacity provides a particularly powerful buffer under ex‑
ternal pressure [45]. Moreover, the important evidence
shows that agricultural R&D expenditure grew from
RMB 22.84 billion in 2005 to RMB 148.67 billion in
2022, at a mean annual growth rate of 11.2%. Thus, the
key findings indicate that the technology contribution
rate rose from 48.0% to 62.4% over the same period.
Evidence shows that the contribution rate rose signifi‑
cantly.
Improved variety adoption coverage demonstrates
that the expansion from 54.3% to 91.6% may suggest
significant progress, with improved seeds now account‑
ing for a stable 45% or more of grain yield gains. More‑
over , the significant evidence indicates that digital agri‑
culture has added important technological momentum
to the results. Furthermore, the area under agricultural
IoT device application indicates that growth from ap‑
proximately 80 million mu in 2015 to 420 million mu
in 2022—an increase of 425%—could demonstrate sub‑
stantial expansion. Given that the findings show county‑
level agricultural big data platform deployments rose
from 218 in 2016 to 1,862 in 2022, the key results
show that digital management raised agricultural me‑
teorological disaster warning accuracy from 63.4% to
87.6%, directly reducing annual disaster‑related agricul‑
tural losses by an estimated RMB 31.2 billion [46,47]. Dig‑
ital tools show pesticide use fell 18.3%, fertilizer 11.7%.
However , the significant findings show that the large‑
scale adoption of smart farming and precision agricul‑
ture technologies could demonstrate reduced pesticide
and fertilizer application volumes from their respective
peaks. Additionally, the key evidence indicates that pro‑
duction cost elasticity has narrowed as a result, com‑
pressing the pass‑through space for international agri‑
cultural input price volatility. Therefore, the results in‑
dicate that panel regression findings demonstrate that
each 10‑percentage‑point increase in digital agriculture
coverage raises the regional agricultural resilience index
by an average of 0.034 units. In light of these signifi‑
cant findings, the evidence shows that the effect appears
stronger in eastern regions (0.041) than in central and
western regions (0.027), reflecting spatial heterogeneity
in the technology‑resilience nexus tied to underlying in‑
frastructure conditions. Data show full results in Table
10 and Figure 9.
The spatial heterogeneity in the technology‑
resilience elasticity—0.041 in eastern provinces versus
0.027 in central and western provinces—merits inter‑
pretation beyond its surface implication of uneven in‑
frastructure. Two reinforcing mechanisms appear to
drive this gap. The first is a complementarity effect: dig‑
ital agriculture technologies generate larger resilience
dividends where physical infrastructure, financial ser‑
vices, and human capital are already sufficiently devel‑
oped to allow full utilization of the information these
technologies produce. Eastern provinces, which have
substantially higher rural broadband penetration rates
and denser networks of agricultural extension services,
can convert IoT ‑generated data into actionable produc‑
tion adjustments more rapidly than central and west‑
ern counterparts, where the supporting ecosystem is
thinner . The second mechanism is a risk‑exposure ef‑
fect: eastern coastal provinces face greater exposure to
international price signals through their higher agricul‑
tural trade openness, meaning that early‑warning and
precision management capabilities—which digital tech‑
nologies directly strengthen—yield larger resilience re‑
turns precisely because the underlying shock exposure
is greater .
Table 10. Dynamic Evolution of Core Indicators for China’s Agricultural Technological Progress and Digital Agriculture (2005–
2022).
Year Agricultural TFPGrowth Rate (%) Agricultural R&DExpenditure (RMB100 Million) TechnologyContributionRate (%) ImprovedVariety AdoptionRate (%) IoT ApplicationArea (100Million mu) Disaster WarningAccuracy Rate (%)
2005 1.86 228.4 48 54.3 — 63.4
2008 2.04 386.2 51.2 62.8 — 67.2
2012 2.18 628.4 54.6 72.4 — 71.8
2015 2.26 864.3 56.8 80.6 0.8 74.3
2018 2.31 1,124.60 58.9 86.3 2.1 79.6
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Table 10. Cont.
Year Agricultural TFPGrowth Rate (%) Agricultural R&DExpenditure (RMB100 Million) TechnologyContributionRate (%) ImprovedVariety AdoptionRate (%) IoT ApplicationArea (100Million mu) Disaster WarningAccuracy Rate (%)
2020 2.38 1,286.40 60.7 89.1 3.2 83.4
2022 2.46 1,486.70 62.4 91.6 4.2 87.6
Note: Agricultural IoT application area data are unavailable prior to 2015. Agricultural TFP growth rates are estimated using the DEA‑Malmquist index method.
Source: Data sourced from the Ministry of Science and Technology, the Ministry of Agriculture and Rural Affairs, the National Bureau of Statistics, and the Chinese
Academy of Agricultural Sciences.
Figure 9. Driving Effects of Agricultural Technology Progress and Digital Agriculture on Resilience (2005–2022): (a) R&D Invest‑
ment, TFP Growth Rate & Technology Contribution Rate; (b) IoT Coverage, Improved Seed Rate & Disaster Warning Accuracy.
5. Discussion
5.1. Comparative Advantages and Potential
Limitations of China’s Agricultural Re‑
silience Model
The empirical analysis shows that China’s agricul‑
tural resilience model appears distinctly institution‑led.
Moreover , the significant comparative advantages and
structural limitations indicate that this model presents
a critical tension in any assessment of its long‑term
sustainability. However , the model’s most prominent
strength indicates that the speed of institutional re‑
sponse and the capacity for rapid policy mobilization
remain key results of this institutional design. Further‑
more, three interlocking institutional arrangements—
counter‑cyclical subsidy adjustment, grain reserve man‑
agement, and rigid farmland protection—demonstrate
that producer expectations have been consistently sta‑
bilized across successive global shock episodes. Model
shows buffer mechanism outperforms market‑led alter‑
natives. Given that the evidence demonstrates this struc‑
tural advantage, China’s vertically integrated agricul‑
tural supply chain and regionally coordinated division
of labor indicate that the system could support strong
endogenous circulation capacity. Moreover , the signifi‑
cant findings show that this capacity enables relatively
rapid self‑repair when external supply chains are dis‑
rupted. Additionally, the key results indicate that this
advantage appears clearly demonstrated in the supply
chain recovery indicators recorded during the COVID‑
19 shock. Nevertheless, the important limitations of
the model show that institution‑led resilience depends
heavily on a continuous flow of policy resources. Fis‑
cal pressure weakens resilience capacity. In light of
the significant evidence, the early signs of diminishing
marginal returns indicate that the gradual narrowing of
subsidy elasticity coefficients could demonstrate impor‑
tant structural challenges. Furthermore, the high im‑
port dependency for critical agricultural commodities—
soybean self‑sufficiency stood at just 14.8% in 2022—
shows that this represents the most significant struc‑
tural vulnerability in the resilience system. However , the
findings indicate that a systemic shock in a major sup‑
plying country might demonstrate severe strain on the
stability of China’s domestic agricultural value chain [48].
Thus, the significant structural gap between eastern and
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central‑western regions in technology resilience elastic‑
ity shows that important spatial spillover effects appear
constrained by uneven digital agriculture coverage. Gap
weakens national system balance. Given that the evi‑
dence demonstrates that these structural weaknesses re‑
quire targeted repair , the key findings indicate that deep‑
ening the sustainability of China’s agricultural resilience
model appears to require more than consolidating ex‑
isting institutional strengths. In light of the significant
results, the data show that a more geographically bal‑
anced deployment of technology‑based resilience capac‑
ity could demonstrate important benefits for the overall
system.
The finding that structural diversification elasticity
(0.231) exceeds technological elasticity (0.186) in the
short‑to‑medium run departs from the emphasis in re‑
cent digital agriculture literature. The present results
do not contradict this literature but refine it: technol‑
ogy contributes most powerfully when existing struc‑
tural buffers are already in place, and the amplification
factor of 1.84 during shock periods suggests that tech‑
nology functions as a resilience multiplier rather than a
standalone driver . This distinction has practical signif‑
icance for policy sequencing: investing in digital infras‑
tructure before structural diversification is sufficiently
developed risks yielding suboptimal resilience returns.
5.2. The Sustainability of Resilience amid
Global Food System Restructuring
The global food system shows that it is undergo‑
ing its most profound structural transformation since
the Second World War . Moreover , the significant conver‑
gence of climate change, recurring geopolitical conflicts,
the erosion of multilateral trade rules, and the disrup‑
tive spread of emerging technologies indicates that ex‑
isting food security governance frameworks face serious
instability. Furthermore, the evidence shows that this
convergence has generated important scholarly debate
about whether agricultural economic resilience can re‑
main effective under novel shock conditions. However ,
the findings demonstrate that the case for sustained re‑
silience rests on several key arguments that could sup‑
port continued stability. The food system shows China’s
resilience. Additionally, the significant institutional ex‑
perience that China has accumulated indicates that its
well‑developed policy toolkit across successive shock
episodes may support effective governance responses.
Given that strict farmland protection secures the mate‑
rial foundation of agricultural production, the results
show that this policy could demonstrate important sta‑
bility for the system. In light of these findings, the con‑
tinuous expansion of grain reserve capacity indicates
that the system’s ability to absorb short‑term supply dis‑
ruptions could show important improvement. Notwith‑
standing these results, the large‑scale rollout of digital
and precision agriculture shows that production system
dependence on external inputs might demonstrate fun‑
damental reduction over time. Reserve expansion shows
that supply absorption improved. Therefore, the tech‑
nology substitution effect indicates that external shock
transmission channels might demonstrate further com‑
pression over the medium and long term. Nevertheless,
the skeptical position shows that it deserves equal crit‑
ical attention to the evidence. Moreover , the significant
findings demonstrate that climate change has driven a
sustained rise in the frequency and severity of extreme
weather events, pushing beyond the historical shock
boundaries assumed in existing resilience measurement
frameworks. Furthermore, the important results indi‑
cate that losses from compound drought‑flood extreme
events in major grain‑producing provinces such as Hei‑
longjiang and Henan have trended upward in recent
years. Climate risk tests buffers. Additionally, the evi‑
dence shows that the effectiveness of traditional institu‑
tional buffers against systemic climate risk may demon‑
strate serious testing under novel conditions. Given that
the geopoliticization of global food trade presents an‑
other key concern, the findings indicate that more than
30 countries imposed food export restrictions in succes‑
sion during the 2022 global food crisis [49]. In light of
these significant results, import resilience strategies cen‑
tered on trade diversification show that they now face a
sharply deteriorating external policy environment. How‑
ever , the important evidence demonstrates that the per‑
sistent aging of the agricultural labor force and rural‑
to‑urban migration represent deep structural pressures
on production sustainability. Aging workforce pressures
production sustainability. Moreover , the findings indi‑
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Research on World Agricultural Economy | Volume 07 | Issue 04 | December 2026
cate that the mean age of China’s agricultural workforce
had reached 48.3 years by 2022. Furthermore, the sig‑
nificant evidence shows that if intergenerational labor
replacement cannot be adequately addressed through
technological substitution, this could demonstrate a la‑
tent bottleneck constraining long‑run resilience. Thus,
the key results indicate that these important debates
suggest that the foundation for China’s agricultural re‑
silience and sustainability appears broadly solid. In light
of these significant findings, the evidence shows that its
effective boundaries under novel compound shock sce‑
narios might demonstrate the need for systematic dy‑
namic evaluation and proactive policy planning.
5.3. Research Limitations and Directions
for Future Research
This study has several limitations that future re‑
search should address. First, the AERI construction re‑
lies on publicly available provincial‑level data, which
may not fully capture within‑province heterogeneity in
resilience outcomes. County‑level or farm‑level data,
where available, would allow more precise identification
of the mechanisms through which institutional, struc‑
tural, and technological factors operate.
Second, the cross‑national comparison is con‑
strained by the availability of comparable indicators
across the four countries. Differences in data collection
methodologies across national statistical systems show
that measurement inconsistencies are difficult to fully
eliminate through standardization procedures. More‑
over , the significant findings indicate that future re‑
search should engage more directly with cross‑national
harmonized datasets, such as those produced by the FAO
or the World Bank’s LSMS program. Furthermore, the
evidence demonstrates that the three shock episodes ex‑
amined in this study represent distinct shock types but
share the key feature of being relatively short‑term in du‑
ration. Given that the AERI framework is grounded in en‑
gineering, ecological, and evolutionary resilience theory,
the results indicate that the framework is designed to
capture system responses across different time horizons.
Research shows application to long‑run pressures re‑
quires further development. However , the significant ev‑
idence shows that application to genuinely long‑run, sus‑
tained pressures—such as climate change‑driven pro‑
ductivity decline or permanent labor force contraction—
requires further methodological development, includ‑
ing the incorporation of non‑stationary time series ap‑
proaches and dynamic threshold models. Additionally,
the study could demonstrate that its focus exclusively
on the national and provincial levels may suggest impor‑
tant gaps in the results. In light of these findings, agricul‑
tural resilience indicates that the evidence is also shaped
by dynamics at the supply chain, enterprise, and house‑
hold levels. Moreover , the key findings show that fu‑
ture research should examine how macro‑level institu‑
tional arrangements transmit to micro‑level behavioral
responses. Findings show household adaptive capacity
links to structural drivers. Nevertheless, the significant
evidence could demonstrate that household‑level adap‑
tive capacity—including education, financial inclusion,
and social networks—appears to interact with struc‑
tural and technological resilience drivers.
6. Conclusion
This study shows that provincial panel data from
31 administrative units in China, spanning from 2000 to
2022, could demonstrate that a composite Agricultural
Economic Resilience Index across three dimensions—
resistance, recovery, and adaptability—provides impor‑
tant analytical structure. Moreover , the significant find‑
ings indicate that a two‑way fixed‑effects model appears
to reveal how China’s agricultural resilience operates
under global economic volatility. Furthermore, the ev‑
idence shows that a Panel VAR model provides key re‑
sults across three analytical layers: shock identification,
international comparison, and economic logic decom‑
position. In light of the significant results, a threshold
panel model indicates that these analytical layers could
demonstrate the critical mechanisms underlying agricul‑
tural resilience. The study shows that models reveal re‑
silience patterns across dimensions. Furthermore, the
evidence appears to indicate that the study examines
these layers systematically, provided that the analytical
framework can reveal important patterns across shock
episodes. Given that the results demonstrate consis‑
tency across all three major shock episodes, the find‑
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Research on World Agricultural Economy | Volume 07 | Issue 04 | December 2026
ings show that China’s agricultural economy could show
strong overall resilience in each case. Results show re‑
silience consistent across episodes. However , the mean
composite AERI of 0.681 indicates that China ranks sec‑
ond among the four‑country comparison sample, sug‑
gesting that the results could demonstrate notable com‑
parative performance. Additionally, the significant ev‑
idence shows that the mean supply chain recovery pe‑
riod of 1.8 years indicates that China outperforms both
Brazil and India on this key measure. In light of the find‑
ings on explanatory factors, the results show that institu‑
tional, structural, and technological contributions carry
resilience elasticity coefficients of 0.312, 0.231, and
0.186, respectively, and the evidence indicates that insti‑
tutional factors account for the largest share. Notwith‑
standing these strengths, the significant findings indi‑
cate that two structural weaknesses stand out: the
persistent decline in soybean self‑sufficiency—reaching
just 14.8% in 2022—and the uneven geographic distri‑
bution of digital agriculture coverage. Findings show
structural gaps persist.
China’s agricultural economic resilience shows that
a compound generative logic characterizes institutional
primacy, structural support, and technology‑enabled
adaptation. Moreover , the significant evidence indi‑
cates that this logic appears deeply embedded in China’s
specific institutional context—centralized land protec‑
tion policies, state‑administered price stabilization, and
nationally coordinated digital agriculture rollout—and
cannot be straightforwardly transferred to other set‑
tings. Furthermore, the findings show that these pol‑
icy implications appear , therefore, bounded by context.
Given that the evidence demonstrates that other large
developing economies consider resilience‑oriented agri‑
cultural policies, the key lesson indicates that repli‑
cation of China’s specific institutional instruments ap‑
pears less important than recognizing coherent align‑
ment. Findings show institutional, structural, and tech‑
nological factors determine outcomes. However , the sig‑
nificant results indicate that where state capacity dif‑
fers substantially from China’s, alternative institutional
configurations—such as cooperative‑based risk pooling
or market‑led diversification—may achieve compara‑
ble resilience outcomes through different mechanisms.
Additionally, the important evidence shows that fu‑
ture cross‑national research could examine whether this
compound generative logic holds under different state‑
market configurations. Notwithstanding these results,
the key findings indicate that such research would more
rigorously establish that boundary conditions for gener‑
alizability appear critical to consider . Research shows
cross‑national configurations affect generalizability out‑
comes. Nevertheless, the significant evidence shows
that this compound logic appears to demonstrate the
critical importance of coherent institutional alignment
across different development contexts.
Funding
This work received no external funding.
Institutional Review Board State‑
ment
This study is based entirely on publicly available
secondary data drawn from official statistical sources,
including the China Statistical Yearbook, provincial sta‑
tistical yearbooks, the FAO database, the World Bank
World Development Indicators, and the IMF World Eco‑
nomic Outlook database. No human participants, per‑
sonal data, or primary survey instruments were involved
in this research. Accordingly, formal ethics committee
review and approval were not required for this study.
Informed Consent Statement
Not applicable. This study relies exclusively on
publicly available aggregate statistical data published
by national and international statistical agencies. No
individual‑level data were collected, and no human sub‑
jects participated directly in the research. Therefore, in‑
formed consent procedures were not applicable to this
study.
Data Availability Statement
The data used in this study are derived from pub‑
licly accessible sources, including the NBS China Statis‑
tical Yearbook, provincial statistical yearbooks, the Min‑
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Research on World Agricultural Economy | Volume 07 | Issue 04 | December 2026
istry of Agriculture and Rural Affairs database, the FAO
statistical database, the World Bank World Development
Indicators, and the CBOE VIX index. Processed datasets
and computed index values used in the empirical anal‑
ysis are available from the corresponding author upon
reasonable request.
Conflicts of Interest
The author declares no conflict of interest regard‑
ing the publication of this paper . The author has no
financial, personal, institutional, or other relationships
that could inappropriately influence or bias the work re‑
ported in this manuscript.
AI Use Statement
The author used AI writing assistance tools solely
for grammar checking, sentence structure refinement,
and improving the readability of the English text in this
manuscript. AI tools were not used for data collection,
econometric analysis, index construction, interpretation
of empirical results, or generation of any scientific con‑
tent. All outputs produced with AI assistance were care‑
fully reviewed, verified, and revised by the author . The
author takes full responsibility for the integrity and ac‑
curacy of all academic content, including all research
ideas, data, analyses, model specifications, and conclu‑
sions presented herein.
References
[1] Iizumi, T ., Luo, J.J., Challinor , A.J., et al., 2014.
Impacts of El Nin ̃ o Southern Oscillation on the
global yields of major crops. Nature Communica‑
tions. 5(1), 3712. DOI: https://doi.org/10.1038/
ncomms4712
[2] Zheng, R., Liu, X., Gao, Z., et al., 2025. Level measure‑
ment and influencing factor analysis of agricultural
sustainable development in Shandong Province,
China. Environment, Development and Sustainabil‑
ity. In press. DOI: https://doi.org/10.1007/s106
68‑025‑07161‑9
[3] Qi, F ., Wang, Y ., 2026. Green finance, rural digi‑
tization, and high‑quality development of the ru‑
ral economy. Finance Research Letters. 89, 109242.
DOI: https://doi.org/10.1016/j.frl.2025.109242
[4] Abson, D.J., Fraser , E.D.G., Benton, T .G., 2013. Land‑
scape diversity and the resilience of agricultural re‑
turns: A portfolio analysis of land‑use patterns and
economic returns from lowland agriculture. Agri‑
culture & Food Security. 2(1), 2. DOI: https://doi.
org/10.1186/2048‑7010‑2‑2
[5] Wang, G., Luo, R., Li, M., et al., 2025. Agricul‑
tural industrial agglomeration and agricultural eco‑
nomic resilience: Evidence from China. Agriculture.
15(23), 2480. DOI: https://doi.org/10.3390/agri
culture15232480
[6] Aiesheva, G., Kazambayeva, A., Yessengaliyeva, S.,
et al., 2025. Assessment of the economic impact
of the saiga population on pasture ecosystems and
agriculture. Earth. 6(4), 155. DOI: https://doi.org/
10.3390/earth6040155
[7] Liao, K., Li, C., Jiang, L., 2025. How geographical in‑
dication products impact agricultural economic re‑
silience: Evidence from China. China Economic Re‑
view. 94, 102527. DOI: https://doi.org/10.1016/j.
chieco.2025.102527
[8] Pimm, S.L., 1984. The complexity and stability of
ecosystems. Nature. 307, 321–326. DOI: https://
doi.org/10.1038/307321a0
[9] Holling, C.S., 1973. Resilience and stability of eco‑
logical systems. Annual Review of Ecology and Sys‑
tematics. 4, 1–23. DOI: https://doi.org/10.1146/
annurev.es.04.110173.000245
[10] Martin, R., 2012. Regional economic resilience, hys‑
teresis and recessionary shocks. Journal of Eco‑
nomic Geography. 12(1), 1–32. DOI: https://doi.
org/10.1093/jeg/lbr019
[11] Pike, A., Dawley, S., Tomaney, J., 2010. Resilience,
adaptation and adaptability. Cambridge Journal of
Regions, Economy and Society. 3(1), 59–70. DOI:
https://doi.org/10.1093/cjres/rsq001
[12] Walker , B., Holling, C.S., Carpenter , S.R., et al.,
2004. Resilience, adaptability and transformability
in social‑ecological systems. Ecology and Society.
9(2), 5. DOI: https://doi.org/10.5751/ES‑ 006
50‑090205
[13] Briguglio, L., Cordina, G., Farrugia, N., et al., 2009.
Economic vulnerability and resilience: Concepts
and measurements. Oxford Development Studies.
37(3), 229–247. DOI: https://doi.org/10.1080/
13600810903089893
[14] Schroeder , C.T ., 2025. Agricultural economics re‑
search as cattle and beef markets ascended from
demise. Journal of Agricultural and Resource Eco‑
nomics. 50(4), 866–882. DOI: https://doi.org/10
.22004/AG.ECON.364785
[15] Headey, D., Fan, S., 2008. Anatomy of a crisis: The
causes and consequences of surging food prices.
Agricultural Economics. 39(s1), 375–391. DOI:http
s://doi.org/10.1111/j.1574‑0862.2008.00345.x
[16] Suberi, H.K., 2025. Innovation ecosystem in agri‑
434

Research on World Agricultural Economy | Volume 07 | Issue 04 | December 2026
culture economy. Energy Nexus. 20, 100603. DOI:
https://doi.org/10.1016/j.nexus.2025.100603
[17] Ramsay, J., Forste, K., 2025. Byzantine plants: An ar‑
chaeobotanical study of the agricultural economy
and land use history of Caesarea Maritima. Vegeta‑
tion History and Archaeobotany. 35, 549–566. DOI:
https://doi.org/10.1007/s00334‑025‑01078‑z
[18] Deckers, K., Gates, H.M., 2026. Exchange networks
and agro‑economy at Kinet Ho ̈ yu ̈ k (Tu ̈ rkiye) from
the 3rd–1st millennium BC traced through prized
wood, olive, and grape charcoal remains. Journal of
Archaeological Science: Reports. 69, 105472. DOI:
https://doi.org/10.1016/j.jasrep.2025.105472
[19] Li, C.D., Li, N.J., 2025. The development of applied
economics at China Agricultural University: A re‑
view and comparative analysis. Journal of China
Agricultural University (Social Sciences Edition).
42(5), 32–50. DOI: https://doi.org/10.13240/j
.cnki.caujsse.20251013.005 (in Chinese)
[20] Tang, Y ., 2025. Theoretical mechanisms linking
the digital economy and agricultural economic re‑
silience: Construction and analysis based on a
quantitative spatial equilibrium model. Asia Pacific
Economic and Management Review. 2(6). DOI:http
s://doi.org/10.62177/apemr.v2i6.775
[21] Chenarides, L., Hanks, S.A., Berard, J., et al., 2025.
A review of data linkages for policy‑informing re‑
search in food and agricultural economics. Food
Policy. 137, 102996. DOI: https://doi.org/10.101
6/j.foodpol.2025.102996
[22] Liu, N., Yang, C., Cheng, Y ., 2025. Digital rural de‑
velopment, insurance investment, and agricultural
economic resilience. Finance Research Letters. 86,
108712. DOI: https://doi.org/10.1016/j.frl.2025
.108712
[23] Wan, Y ., Lu, W ., Wang, R., et al., 2025. How does in‑
formation technology enhance the agricultural re‑
silience: The practice of digital inclusive finance
in China. Frontiers in Sustainable Food Systems. 9,
1695589. DOI: https://doi.org/10.3389/fsufs.20
25.1695589
[24] Tefera, T ., Emana, B., Magnar , G., 2025. Uncover‑
ing rural household multidimensional poverty: Ev‑
idence from rain‑fed agricultural economy in East
Wallaga, Ethiopia. Quality & Quantity. 60, 4199–
4223. DOI: https://doi.org/10.1007/s11135‑0
25‑02432‑z
[25] Dionizi, B., Dhora, R., 2025. Reinventing rural
economies through sustainable business models:
A triple‑layered analysis. Frontiers in Sustainabil‑
ity. 6, 1665635. DOI: https://doi.org/10.3389/fr
sus.2025.1665635
[26] Akuma, G.O., 2025. Climate change, land use shifts,
and rural economies in Ethiopia. Sustainable Fu‑
tures. 10, 101318. DOI: https://doi.org/10.101
6/j.sftr.2025.101318
[27] Spence, C., 2025. Agriculture, Economy and Soci‑
ety in Early Modern Scotland: Edited by Harriet
Cornell, Julian Goodare, and Alan R. MacDonald.
Woodbridge: Boydell and Brewer , 2024. 308 pp.,
$130.00, hardback, ISBN 9781837650484. Agricul‑
tural History. 99(3), 501–503. DOI: https://doi.or
g/10.1215/00021482‑11778192
[28] Singh‑Peterson, L., De Daunton, F ., Drysdale, A., et
al., 2025. Diversifying rural economies: Identifying
factors that discourage primary producers from en‑
gaging in emerging carbon and environmental off‑
setting markets in Queensland, Australia. Sustain‑
ability. 17(15), 6847. DOI: https://doi.org/10.339
0/su17156847
[29] Chen, T ., Zhang, L., Wen, M., et al., 2025. Can the de‑
velopment of agricultural insurance promote the
resilience of agricultural economy? The dynamic
mechanisms of the digital economy development.
International Review of Economics and Finance.
103, 104386. DOI: https://doi.org/10.1016/j.ir
ef.2025.104386
[30] Heena, Dedha, P ., Reddy, B., et al., 2025. The eco‑
nomics of climate smart agriculture: Role of exten‑
sion education in adaptation strategies. Journal of
Scientific Research and Reports. 31(7), 660–667.
DOI: https://doi.org/10.9734/jsrr/2025/v31i
73285
[31] Qi, Z.Y ., Sun, B.J., Sun, G.B., 2025. The impact of
the digital economy on agricultural technological
innovation in China and its pathways. Science and
Technology Management Research. 45(10), 123–
135. Available from: https://kns.cnki.net/kcm
s2/article/abstract?v=olK‑MAP09vcYlJSBEuSUZ
xNL‑4SvK0vzByGnyvz18Cf13tUnabxNVtk6LvtpOj
kM70kdjx5cXwe‑Bd5QrEiT9XIav4VlkVHIYIFBO
Pw9kvP_Nh5nD55Lb25g5Hw1JyaowFMGpNuw8
iCjTqg5XBAjyQiipi6Vc44uc2W5dNgyIDW85TueH
ptLFQ==&uniplatform=NZKPT&language=CHS (in
Chinese)
[32] Sinha, N., Choudhary, V ., 2025. Transforming rural
economies through employment, livelihood, and
food security interventions: Evidence from Chhat‑
tisgarh. Indian Journal of Agricultural Economics.
80(3), 856.
[33] Sun, D., Wang, H., Ma, J., et al., 2025. The impact
of agricultural quasi‑integration organizations on
farmers’ income under the background of smart
agriculture. International Journal of High Speed
Electronics and Systems. 35(4), 2540635. DOI: ht
tps://doi.org/10.1142/S0129156425406357
[34] Mehdi, M., Li, B., Siddiqi, H.M., et al., 2025. Sustain‑
able energy development goals of an organic waste
biorefinery model for agriculture economies. En‑
ergy. 331, 136905. DOI: https://doi.org/10.101
435

Research on World Agricultural Economy | Volume 07 | Issue 04 | December 2026
6/j.energy.2025.136905
[35] Lamana, D.A., 2025. On the rural: Economy, So‑
ciology, Geography: by Henri Lefebvre, edited by
Stuart Elden and Adam David Morton, translated
by Robert Bononno, Minneapolis, University of
Minnesota Press, 2022, 304 pp., US$30 (paper‑
back), ISBN 9781517904692. The Journal of Peas‑
ant Studies. 52(4), 902–905. DOI: https://doi.org/
10.1080/03066150.2024.2445649
[36] Liu, C., Wang, Y ., Ma, J., et al., 2025. Constraining
factors and realization strategies for sustainable
development of the agricultural economy. Agricul‑
tural Industrialization. 3, 78–80, 98. (in Chinese)
[37] Gupta, A., Doan, K.M., Zhu, H., et al., 2025. Ru‑
ral economy‑wide impacts of Kenya’s home‑grown
school meals program. Food Policy. 134, 102887.
DOI: https://doi.org/10.1016/j.foodpol.2025.1
02887
[38] Medina Cruz, J.R., Enrique, Q.H.J., A Ƶ ngel, C.C.M., et
al., 2025. The sustainable development of rural
economies within the territorial scope of the geop‑
ark located between the Colca Valley and volcanoes
in the Arequipa region, Peru. Sustainable Futures.
9, 100716. DOI: https://doi.org/10.1016/j.sftr.2
025.100716
[39] Saputra, R., Havlı́ček, T ., Marhadi, M., 2025. Collab‑
orative rural governance strategy to enhance ru‑
ral economy through village‑owned enterprise us‑
ing soft system methodology. DANUBE. 16(2), 126–
153. DOI: https://doi.org/10.2478/danb‑2025‑0
007
[40] Panigrahi, M., Nayak, S.S., Rajawat, D., et al., 2025.
Genomic advancements in goat breeding: En‑
hancing productivity, disease resistance, and sus‑
tainability in India’s rural economy. Mammalian
Genome. 36, 761–786. DOI: https://doi.org/10.1
007/s00335‑025‑10138‑8
[41] Ganjali, N., Guney, C., 2025. Balancing economic
and environmental objectives in agriculture: A
game theory analysis. Process Integration and Op‑
timization for Sustainability. 9, 1445–1461. DOI:
https://doi.org/10.1007/s41660‑025‑00517‑8
[42] Qaim, M., Parlasca, M.C., 2025. Agricultural eco‑
nomics and the transformation toward sustainable
agri‑food systems. Agricultural Economics. 56(3),
327–335. DOI: https://doi.org/10.1111/agec.7
0023
[43] Beyene, B., Choramo, T .T ., Argaw, D., 2025. The ef‑
fect of women’s economic empowerment in agri‑
culture on household vulnerability to food insecu‑
rity in Dawuro zone, Ethiopia. Development and
Sustainability in Economics and Finance. 6, 100057.
DOI: https://doi.org/10.1016/j.dsef.2025.100057
[44] Njue, Z., Wangai, P ., Kioko, E., 2025. Economic bar‑
riers to the adoption of sustainable agriculture in
Tharaka‑Nithi County of Kenya. Asian Journal of
Advanced Research and Reports. 19(4), 289–300.
DOI: https://doi.org/10.9734/ajarr/2025/v19i4
984
[45] Shi, Y ., 2024. Measurement of agricultural eco‑
nomic resilience levels and regional disparity anal‑
ysis in China. Statistics and Decision. 40(21), 122–
126. DOI: https://doi.org/10.13546/j.cnki.tjyjc.20
24.21.021 (in Chinese)
[46] Mekonnen, Z.D., Olivera, R.H., Gomes, A., 2025. The
role of environmental changes in the development
of the agricultural economy during pre‑Aksumite
and Aksumite cultures. African Archaeological Re‑
view. 42, 333–356. DOI: https://doi.org/10.1007/
s10437‑025‑09618‑8
[47] Ito, F ., Higashiyama, K., 2025. Future missions of
Japanese agricultural economics. Japanese Journal
of Agricultural Economics. 27, 32. DOI: https://do
i.org/10.18480/jjae.27.0_32
[48] Ding, Y .L., Zhang, W .L., Zhao, Y .J., 2024. Empir‑
ical Test of the Impact of Digital Economy on
China’s Modernization of Agriculture and Rural Ar‑
eas. Statistics and Decision. 40(16), 16–21. Avail‑
able from: http://en.tjyjc.com/npublic/opdfjs/
web/viewer.html?file=https%3A%2F%2Fomo‑o
ss‑file110.thefastfile.com%2Fportal‑saas%2Fpg
2025042409115091700%2Fcms%2Ffile%2F%
E4%B8%81%E7%8E%89%E9%BE%99.pdf (in
Chinese)
[49] Shogenji, S., 2025. Half a century with agricultural
economics as a modest social science. Japanese
Journal of Agricultural Economics. 27, 25–27. DOI:
https://doi.org/10.18480/jjae.27.0_25
436