Evidence map›Paper›PMID 35931696›Full record

ArticleScientific data2022

Maps of cropping patterns in China during 2015-2021.

Bingwen Qiu, Xiang Hu, Chongcheng Chen, Zhenghong Tang, Peng Yang, Xiaolin Zhu, Chao Yan, Zeyu Jian

Abstract read
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  8. Host-Adaptive Divergence Shapes the Genetic Architecture ofJournal of fungi (Basel, Switzerland) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Bingwen QiuKey Laboratory of Spatial Data Mining &Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, Fujian, China. qiubingwen@fzu.edu.cn.
Xiang HuKey Laboratory of Spatial Data Mining &Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, Fujian, China.
Chongcheng ChenKey Laboratory of Spatial Data Mining &Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, Fujian, China.
Zhenghong TangCommunity and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, 68558, Nebraska, USA.
Peng YangKey Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing, China.
Xiaolin ZhuDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Chao YanKey Laboratory of Spatial Data Mining &Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, Fujian, China.
Zeyu JianKey Laboratory of Spatial Data Mining &Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, Fujian, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 41771468National Natural Science Foundation of China (National Science Foundation of China) 42171325
6 · The paper itself

Abstract

Multiple cropping is a widespread approach for intensifying crop production through rotations of diverse crops. Maps of cropping intensity with crop descriptions are important for supporting sustainable agricultural management. As the most populated country, China ranked first in global cereal production and the percentages of multiple-cropped land are twice of the global average. However, there are no reliable updated national-scale maps of cropping patterns in China. Here we present the first recent annual 500-m MODIS-based national maps of multiple cropping systems in China using phenology-based mapping algorithms with pixel purity-based thresholds, which provide information on cropping intensity with descriptions of three staple crops (maize, paddy rice, and wheat). The produced cropping patterns maps achieved an overall accuracy of 89% based on ground truth data, and a good agreement with the statistical data (R

Identifiers

PMID35931696
PMCPMC9356131

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.