Evidence map›Paper›PMID 40057543›Full record

ArticleScientific data2025

Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning.

Min Zhang, Xinlei Xu, Junji Ou, Zengguang Zhang, Fangzheng Chen, Lijie Shi, Bin Wang, Meiqin Zhang, Liang He, Xueliang Zhang and 3 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

13 authors.

Min ZhangCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Xinlei XuCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Junji OuCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Zengguang ZhangCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Fangzheng ChenCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Lijie ShiCollege of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, Jiangsu, 225009, China.
Bin WangNew South Wales Department of Primary Industries, Wagga Wagga Agriculture Institute, Wagga Wagga, New South Wales, 2650, Australia.ORCID http://orcid.org/0000-0002-6422-5802
Meiqin ZhangCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Liang HeNational Meteorological Center, Beijing, 100081, China.ORCID http://orcid.org/0000-0002-4234-5782
Xueliang ZhangCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Yong ChenCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Kelin HuCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China.
Puyu FengCollege of Land Science and Technology, State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing, 100193, China. fengpuyu@cau.edu.cn.ORCID http://orcid.org/0000-0003-4845-9876

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soybean is a critical agricultural product in China, with domestic production unable to satisfy the substantial demand, leading to a huge reliance on imports. To support the scientific formulation of agricultural policies and the optimization of domestic planting structures, we developed a high-resolution annual soybean yield dataset for China (2001-2020), ChinaSoyYield1km. This dataset was generated by applying ensemble learning algorithms and spatial decomposition to a comprehensive set of multi-source data, including climate variables, remote sensing imagery, soil properties, agricultural management practices, and official yield records. The integration of these diverse datasets allows for a nuanced understanding of the factors influencing soybean yield at a 1-km resolution. The resulting dataset captures over 50% of the yield variability at the county scale, demonstrating superior accuracy compared to publicly available datasets with reductions in Root Mean Square Error (RMSE) ranging from 0.18 to 0.60 t/ha. It is anticipated that our dataset will enhance agricultural studies, planning, and policy-making related to soybean cultivation, providing a valuable resource for both the scientific community and government.

Indexed as

Glycine maxAgricultureChinaEnsemble LearningMachine LearningRemote Sensing Technology

Identifiers

PMID40057543
PMCPMC11890572

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.