Evidence map›Paper›PMID 40645999›Full record

ArticleNature communications2025

Co-benefits for cropland yield, nitrogen emissions, and climate impact through multi-objective optimization agricultural manure solutions.

Yinghua Yin, Shufang Guo, Qiyu Xu, Jian Liu, Hongyuan Wang, Yanhua Zhuang, Zhen Wang, Lingling Hua, Hongbin Liu, Limei Zhai

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

10 authors.

Yinghua Yin *State Key Laboratory of Efficient Utilization of Arable Land in China, Key Laboratory of Nonpoint Source Pollution Control Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China.
Shufang Guo *Institute of Agricultural Environment and Resources, Yunnan Academy of Agricultural Sciences, Kunming, China.
Qiyu XuState Key Laboratory of Efficient Utilization of Arable Land in China, Key Laboratory of Nonpoint Source Pollution Control Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China.
Jian LiuDepartment of Soil and Land Use, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway.ORCID http://orcid.org/0000-0003-4199-1296
Hongyuan WangState Key Laboratory of Efficient Utilization of Arable Land in China, Key Laboratory of Nonpoint Source Pollution Control Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China.
Yanhua ZhuangInnovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China.ORCID http://orcid.org/0000-0002-7041-1118
Zhen WangCollege of Resources and Environment, Huazhong Agricultural University, Wuhan, China.ORCID http://orcid.org/0000-0002-7902-4093
Lingling HuaCollege of Bioscience and Resources Environment, Beijing University of Agriculture, Beijing, China.
Hongbin LiuState Key Laboratory of Efficient Utilization of Arable Land in China, Key Laboratory of Nonpoint Source Pollution Control Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China.
Limei ZhaiState Key Laboratory of Efficient Utilization of Arable Land in China, Key Laboratory of Nonpoint Source Pollution Control Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China. zhailimei@caas.cn.ORCID http://orcid.org/0000-0003-4929-3783

Funding

National Natural Science Foundation of China (National Science Foundation of China) U24A20635
6 · The paper itself

Abstract

Optimizing manure use and reducing chemical fertilizers are crucial for achieving the Sustainable Development Goals. However, optimal manure redistribution for cropland yield, nitrogen emissions, and greenhouse gas benefit remains unclear at large spatial scales. Here, we employed genetic algorithms and meta-analysis to obtain an optimal substitution rate for major crops in China. This could reduce synthetic nitrogen use and reactive nitrogen pollution by 13.3 and 2.0 Tg, cut ammonia and nitrous oxide emissions by 15.6% and 16.2%, and lower nitrogen runoff and leaching by 32.9%. The yield of eight major upland crops could be increased by 2.0-19.5%. The strategy involves relocating about 255 million pig equivalents, with 32.3% moved across provinces, mainly from central to northern and northeastern China. An estimated US$6.1 billion investment in livestock relocation could yield US$25.9 billion in benefits.

Identifiers

PMID40645999
PMCPMC12254328

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.