Report on Agricultural Efficiency and Sustainable Development in China with Emphasis on Sustainable Development Goals (SDGs)

Abstract

China’s Ministry of Agriculture and Rural Development has launched the National Action Plan for Smart Agriculture (2024–2028) to modernize agriculture through technology and energy restructuring, aligning with several Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This report analyzes spatial geographic big data (2014–2022) at district and county levels to evaluate agricultural output value, influencing factors, and sustainable development potential. The Malmquist index is used to assess agricultural output efficiency across regions, identifying provinces with efficiency and inefficiency zones. A Spatiotemporal Geographically and Temporally Weighted Regression (GTWR) with interaction terms explores factors influencing agricultural output at national, efficient, and inefficient levels. The coupling coordination degree measures sustainable development capacity in five dimensions. Findings suggest targeted government policies based on agricultural efficiency disparities, recommending improved mechanization, optimized resource allocation, enhanced soil quality, and promotion of sustainable practices in efficient zones. For inefficient zones, strategies focus on technological advancement and resource management. Differentiated support is advised to balance economic, environmental, and population sustainability, fostering coordinated regional development consistent with SDGs.

Introduction

On October 25, 2024, China initiated a five-year Smart Agriculture Action Plan to enhance agricultural output through digital technology transformation, supporting SDG 2 (Zero Hunger) and SDG 9 (Industry, Innovation, and Infrastructure). Agriculture encompasses farming, animal husbandry, forestry, and fisheries, contributing significantly to economic development (SDG 8: Decent Work and Economic Growth). Agricultural output value measures the total value created by production activities within a year.

Climate change factors such as precipitation and temperature critically impact sustainable agricultural development (SDG 13: Climate Action). Economic and policy factors are also necessary for agricultural progress. Precision agriculture and technological innovation are essential to address regional disparities and resource coordination, aligning with SDG 12 (Responsible Consumption and Production).

Previous studies highlight the importance of mechanization, farmland multifunctionality, and resource management. This report emphasizes the use of Data Envelopment Analysis (DEA) and Malmquist index methods for efficiency analysis, providing a scientific basis for policy formulation to achieve sustainable agriculture (SDG 15: Life on Land).

Methods

Data Introduction

• Temperature data (2003–2022) from the National Tibetan Plateau Data Center.
• Soil texture data from the Chinese Academy of Sciences Resource and Environment Science and Data Center.
• Precipitation data (1901–2023) for 2891 counties and districts.
• Topographic data based on DEM elevation with 250m resolution.
• Socio-economic data from County Statistical Yearbooks and local government sources.

All data underwent quality control and consistency processing to ensure reliability.

Analysis of National Agricultural Output Efficiency

The Malmquist Index was applied to measure dynamic changes in total factor productivity (TFP), decomposed into efficiency change and technological progress. The study analyzed agricultural output value as the dependent variable, considering natural, technological, economic, and policy factors as inputs. Data standardization and imputation methods were used to prepare data from 2122 counties (2014–2022).

DEA models were employed to calculate comprehensive efficiency and TFP, with agricultural output value as the output variable and nine influencing factors as inputs. Spatial autocorrelation analysis revealed significant regional clustering of efficiency and technological changes, indicating the need for region-specific interventions.

Results

Spatial and Temporal Characteristics of Agricultural Inputs and Outputs

• Precipitation, temperature, silt, and clay content showed spatial patterns of “more in the east and south, less in the west and north.”
• Higher sand content was concentrated in northwest China.
• Machinery power was greater in eastern and northern regions.
• Government inputs were relatively balanced nationwide.
• Temporal trends showed fluctuating precipitation, increasing temperature, and rising machinery power and government investment.

Efficiency and Productivity Analysis

• Overall agricultural production efficiency improved, mainly due to management and scale optimization, supporting SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production).
• Technological progress showed slight regression, highlighting the need for innovation (SDG 9).
• Spatial autocorrelation indicated regional agglomeration of efficiency changes.
• Three categories of inefficient districts were identified: technologically deficient, comprehensively disadvantaged, and efficiency and socio-economic deficient.

Factors Impacting Agricultural Output Value

GTWR with interaction terms identified key factors influencing agricultural output, including:

• Natural factors: precipitation, temperature, soil texture, slope, elevation (SDG 15: Life on Land).
• Technological factors: total mechanical power (SDG 9: Industry, Innovation, and Infrastructure).
• Economic and policy factors: government input (SDG 17: Partnerships for the Goals).

Interactions such as slope and elevation negatively affected output, while government investment positively influenced agricultural output, emphasizing the importance of targeted policy support (SDG 2, SDG 13).

Analysis of Efficient and Inefficient Agricultural Production Areas

Efficient Areas (e.g., Shanxi Province)

• Strong positive effects from the interaction of mechanical power and government input.
• Negative effects from slope and elevation.
• Recommendations include increasing mechanization and government investment while managing environmental factors.

Inefficient Areas (e.g., Yunnan Province)

• Positive effects from the interaction of silt and government input.
• Negative effects from silt and elevation.
• Recommendations include enhancing soil management, increasing government support, and addressing elevation-related challenges.

Sustainable Agricultural Development

Challenges and Importance

The dual challenge of increasing productivity while ensuring sustainability aligns with SDG 2 (Zero Hunger), SDG 13 (Climate Action), and SDG 15 (Life on Land). Slowing productivity growth and environmental degradation necessitate integrated assessment systems combining productivity and sustainability metrics.

Sustainable Development in Efficient Agricultural Areas

• Coupling coordination degree analysis in Shanxi Province showed primary coordination, with emphasis needed on population and environmental sustainability (SDG 11: Sustainable Cities and Communities, SDG 13).
• Social and resource systems had larger weights in sustainability assessment.
• Recommendations include improving sustainable agricultural practices and enhancing environmental protection.

Sustainable Development in Inefficient Agricultural Areas

• Yunnan Province’s assessment revealed better overall sustainability but significant regional disparities.
• Demographic and social systems contributed most to sustainability weights.
• Coordination between economy and environment improved, reflecting progress in green agricultural development (SDG 7: Affordable and Clean Energy, SDG 13).
• Challenges remain in population-environment and population-resource coordination, indicating pressure on ecological carrying capacity.
• Recommendations focus on balanced development and ecological restoration.

Conclusions and Policy Implications

1. Targeted Measures for Declining Efficiency: Strengthen agricultural technology research, development, and promotion; improve resource allocation and market mechanisms; and enhance scale efficiency to restore productivity, supporting SDG 2 and SDG 9.
2. Differentiated Support Policies: In high-efficiency areas like Shanxi, focus on improving population and environmental sustainability; in low-efficiency areas like Yunnan, emphasize coordinated economic and environmental development, aligning with SDG 11 and SDG 13.
3. Comprehensive Assessment Framework: Using county-level data, integrate demographic, social, resource, natural, technological, economic, and policy factors to guide precision support and differentiated subsidies, promoting SDG 1 (No Poverty), SDG 2, SDG 10 (Reduced Inequalities), and SDG 17.
4. Ecological Protection and Climate Goals: Promote water-saving irrigation, ecological restoration, and low-carbon technologies to contribute to China’s dual-carbon goals and global food security, supporting SDG 13 and SDG 15.

Alignment with Sustainable Development Goals (SDGs)

• SDG 2 (Zero Hunger): Enhancing agricultural productivity and output value to ensure food security.
• SDG 8 (Decent Work and Economic Growth): Promoting efficient and sustainable agricultural economic development.
• SDG 9 (Industry, Innovation, and Infrastructure): Encouraging technological innovation and mechanization in agriculture.
• SDG 11 (Sustainable Cities and Communities): Addressing population and environmental sustainability in agricultural regions.
• SDG 12 (Responsible Consumption and Production): Optimizing resource allocation and promoting sustainable agricultural practices.
• SDG 13 (Climate Action): Mitigating climate change impacts through sustainable agricultural development and low-carbon technologies.
• SDG 15 (Life on Land): Protecting soil quality and ecological functions in agricultural landscapes.
• SDG 17 (Partnerships for the Goals): Utilizing multi-source data and government collaboration for sustainable agriculture.

Data Availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Numbers 72371115 and 72261030, and by the Key Scientific Research Project of the Jilin Provincial Department of Education under Grant Number JJLH20250757KJ.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 2: Zero Hunger
   • The article focuses on improving agricultural output value, productivity, and sustainable agricultural development, which directly relate to ending hunger and achieving food security.
2. SDG 8: Decent Work and Economic Growth
   • Enhancing agricultural efficiency and mechanization supports economic growth and employment in rural areas.
3. SDG 9: Industry, Innovation and Infrastructure
   • Promotion of smart agriculture, technological advancement, and precision agriculture aligns with fostering innovation and infrastructure development.
4. SDG 12: Responsible Consumption and Production
   • The emphasis on sustainable agricultural practices and resource management supports sustainable consumption and production patterns.
5. SDG 13: Climate Action
   • Addressing climate change impacts on agriculture and promoting low-carbon technologies contribute to climate resilience and mitigation.
6. SDG 15: Life on Land
   • Focus on soil quality, ecological restoration, and sustainable land use supports the protection and sustainable use of terrestrial ecosystems.

2. Specific Targets Under Identified SDGs

1. SDG 2: Zero Hunger
   • Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers through secure and equal access to land, technology, and markets.
   • Target 2.4: Ensure sustainable food production systems and implement resilient agricultural practices.
2. SDG 8: Decent Work and Economic Growth
   • Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading, and innovation.
3. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade technological capabilities of industrial sectors, including agriculture.
4. SDG 12: Responsible Consumption and Production
   • Target 12.2: Achieve sustainable management and efficient use of natural resources.
5. SDG 13: Climate Action
   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters.
   • Target 13.2: Integrate climate change measures into national policies and strategies.
6. SDG 15: Life on Land
   • Target 15.3: Combat desertification, restore degraded land and soil, including land affected by desertification, drought, and floods.

3. Indicators Mentioned or Implied to Measure Progress

1. Agricultural Output Value
   • Used as an economic indicator to measure agricultural production results and productivity.
2. Total Factor Productivity (TFP) and Malmquist Index
   • Measures changes in agricultural efficiency, technological progress, and scale efficiency over time.
   • Indicators include efficiency change (effch), technological change (techch), pure technical efficiency change (pech), scale efficiency change (sech), and total factor productivity change (tfpch).
3. Coupling Coordination Degree
   • Quantifies the interaction and coordination between different systems such as population, economy, society, environment, and resources to assess sustainable development capacity.
4. Regression Coefficients from GTWR Model
   • Used to analyze the influence of natural, technological, economic, and policy factors on agricultural output value spatially and temporally.
5. Indicator Weights and Composite Scores
   • Entropy weighting method applied to indicators in population, economy, society, environment, and resources to measure sustainable agricultural development.
6. Spatial Autocorrelation (Moran’s I)
   • Used to assess spatial clustering and regional agglomeration of agricultural efficiency indicators.

4. Table of SDGs, Targets and Indicators

Source: nature.com