In:
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 117, No. 41 ( 2020-10-13), p. 25434-25444
Abstract:
With rapid economic growth and urbanization, self-sufficiency in crop production has become central to China’s agriculture policy. Accurate crop production statistics are essential for research, monitoring, and planning. Although researchers agree that China’s statistical authority has considerably modernized over time, China’s economic statistics have still been viewed as unreliable and often overstated to meet growth targets at different administrative levels. Recent increases in crop production reported by national statistics have also come under increasing scrutiny. This paper investigates crop production data quality from a planetary boundary perspective—comparing net primary production (NPP) harvested obtained from national statistics with satellite-driven NPP estimates that are supported by detailed observation of land cover, combined with observations on physical factors that limit plant growth. This approach provides a powerful means to check the plausibility of China’s grain production statistics at different administrative levels that can generate insights about their discrepancies and can contribute to improved crop production measurements. We find some evidence of potential misreporting problems from the lower administration level where the risk of manipulation of statistics is higher. We also find problems from provincial-level major grain producers. These values can also affect the national totals. Although the numbers are affected by large uncertainties, we find that improving the spatial resolution of key agricultural parameters can greatly improve the reliability of the indicator that in turn can help improve data quality. More reliable production data will be vital for relevant research and provide better insights into food security problems, the carbon cycle, and sustainable development.
Type of Medium:
Online Resource
ISSN:
0027-8424
,
1091-6490
DOI:
10.1073/pnas.1919850117
Language:
English
Publisher:
Proceedings of the National Academy of Sciences
Publication Date:
2020
detail.hit.zdb_id:
209104-5
detail.hit.zdb_id:
1461794-8
SSG:
11
SSG:
12
