An analysis of spatiotemporal patterns in Chinese agricultural productivity between 2004 and 2014. (October 2019)

• Record Type: Journal Article
• Authors: Jin, Gui; Li, Zhaohua; Deng, Xiangzheng; Yang, Jun; Chen, Dongdong; Li, Wenqiang
• Is Part Of: Ecological indicators. Volume 105(2019)
• Page Start: 591
• Page End: 600
• Publication Date: 2019-10
• Subjects: Agricultural productivity -- ESAP -- Spatiotemporal patterns -- China; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.05.073
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: The ESAP model was used to calculate land and agriculture productivity across China. The agricultural productivity of municipal areas across China was updated for the period between 2010 and 2014. Patterns in agricultural productivity spatiotemporal differentiation across China were obvious. Data showed a strong positive spatial correlation between the agricultural productivity of municipal administrative areas. Abstract: Understanding spatiotemporal patterns in Chinese agriculture is extremely important for national sustainable development. We apply the Estimation System of Agricultural Productivity (ESAP) model in this study to calculate this variable across China between 2004 and 2014 and further analyze spatiotemporal patterns at the municipal level using Moran's I and Local Indicators of Spatial Association (LISA) indices. The results of this study reveal that input factors have been dynamically embedded within this system and can therefore be utilized to avoid overestimates of agricultural productivity via the ESAP model, while the dependence of productivity on climatic factors also determines inter-annual variability and spatial patterns. The results of this analysis show that average agricultural productivity has tended to decrease spatially from south-to-north and from east-to-west across China, while generally increasing temporally between 2004 and 2014. The data presented in this study reveal a strong positive spatial correlation between values for agricultural productivity in municipal administrative areas, to the extent that identified LISA agglomeration types can mostly be categorized as either "high-high" (HH) or "low-low" (LL).