Evaluating spatiotemporal variations in agricultural production efficiency and total factor productivity change index across China

【Objective】Water shortage is a pressing issue in many regions of China, where significant differences exist in agricultural production conditions and efficiency among provinces, municipalities, and autonomous regions. Assessing the agricultural production efficiency of these regions is essential for identifying developmental bottlenecks and devising strategies to enhance both productivity and water use efficiency.【Method】A Data Envelopment Analysis (DEA) model was used to evaluate the comprehensive efficiency, pure technical efficiency, and scale efficiency of agricultural production in 31 provinces, municipalities, and autonomous regions across China from 2004 to 2022. Additionally, the DEA-Malmquist productivity index was applied to analyze the components of efficiency change and the temporal variation characteristics.【Result】The results indicate that, from 2004 to 2022, eight provinces and municipalities achieved DEA efficiency in agricultural production in all years, 12 regions were DEA efficient in some years, and 11 provinces and autonomous regions were consistently non-DEA efficient. Despite total factor productivity indices exceeding 1.000 both nationally and regionally, indicating an overall increase in agricultural production efficiency, the technical efficiency, pure technical efficiency, and scale efficiency remained largely unchanged. Technological progress was identified as the primary driver of the observed increase in efficiency. In regions that are non-DEA efficient, such as Hebei and Shanxi, redundant inputs in agricultural water resources and effective irrigated areas suggest that reducing excessive input or reallocating underutilized inputs could enhance production efficiency and the effectiveness of farmland water conservancy investments. Regions with fluctuating pure technical and scale efficiency indices around 1.000 should focus on optimizing production scale and improving the efficiency of existing technologies. Conversely, regions with low technological progress indices, such as Xinjiang and Yunnan, should prioritize technology optimization, adopt water-saving innovations, and enhance farmland water infrastructure.【Conclusion】Our findings reveal significant regional variations in agricultural production efficiency and total factor productivity across China. Strategies for improving these efficiencies are outlined, providing a basis for targeted interventions to enhance agricultural productivity and water use efficiency in water-scarce regions.

Institutions

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-05-01
DOI
https://doi.org/10.13522/j.cnki.ggps.2024367
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating spatiotemporal variations in agricultural production efficiency and total factor productivity change index across China

SHILAP Revista de lepidopterología
Mycorrhizal Fungi and Plant Interactions
article

Evaluating spatiotemporal variations in agricultural production efficiency and total factor productivity change index across China

article en

Abstract

【Objective】Water shortage is a pressing issue in many regions of China, where significant differences exist in agricultural production conditions and efficiency among provinces, municipalities, and autonomous regions. Assessing the agricultural production efficiency of these regions is essential for identifying developmental bottlenecks and devising strategies to enhance both productivity and water use efficiency.【Method】A Data Envelopment Analysis (DEA) model was used to evaluate the comprehensive efficiency, pure technical efficiency, and scale efficiency of agricultural production in 31 provinces, municipalities, and autonomous regions across China from 2004 to 2022. Additionally, the DEA-Malmquist productivity index was applied to analyze the components of efficiency change and the temporal variation characteristics.【Result】The results indicate that, from 2004 to 2022, eight provinces and municipalities achieved DEA efficiency in agricultural production in all years, 12 regions were DEA efficient in some years, and 11 provinces and autonomous regions were consistently non-DEA efficient. Despite total factor productivity indices exceeding 1.000 both nationally and regionally, indicating an overall increase in agricultural production efficiency, the technical efficiency, pure technical efficiency, and scale efficiency remained largely unchanged. Technological progress was identified as the primary driver of the observed increase in efficiency. In regions that are non-DEA efficient, such as Hebei and Shanxi, redundant inputs in agricultural water resources and effective irrigated areas suggest that reducing excessive input or reallocating underutilized inputs could enhance production efficiency and the effectiveness of farmland water conservancy investments. Regions with fluctuating pure technical and scale efficiency indices around 1.000 should focus on optimizing production scale and improving the efficiency of existing technologies. Conversely, regions with low technological progress indices, such as Xinjiang and Yunnan, should prioritize technology optimization, adopt water-saving innovations, and enhance farmland water infrastructure.【Conclusion】Our findings reveal significant regional variations in agricultural production efficiency and total factor productivity across China. Strategies for improving these efficiencies are outlined, providing a basis for targeted interventions to enhance agricultural productivity and water use efficiency in water-scarce regions.

SHILAP Revista de lepidopterología
Tsinghua University (CN)
Openalex Percentile: Top 47%
Mycorrhizal Fungi and Plant Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.