Evidence map›Paper›PMID 39009735›Full record

ReviewNature food2024

Pathways to identify and reduce uncertainties in agricultural climate impact assessments.

Bin Wang, Jonas Jägermeyr, Garry J O'Leary, Daniel Wallach, Alex C Ruane, Puyu Feng, Linchao Li, De Li Liu, Cathy Waters, Qiang Yu and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature food, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. 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

12 authors.

Bin WangNew South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales, Australia. bin.a.wang@dpi.nsw.gov.au.ORCID 0000-0002-6422-5802
Jonas JägermeyrNASA Goddard Institute for Space Studies, New York, NY, USA.
Garry J O'LearyAgriculture Victoria, Department of Energy, Environment and Climate Action, Horsham, Victoria, Australia.
Daniel WallachInstitute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany.
Alex C RuaneNASA Goddard Institute for Space Studies, New York, NY, USA.ORCID 0000-0002-5582-9217
Puyu FengCollege of Land Science and Technology, China Agricultural University, Beijing, China.ORCID 0000-0003-4845-9876
Linchao LiNew South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales, Australia.ORCID 0000-0002-6615-1309
De Li LiuNew South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales, Australia.
Cathy WatersGreenCollar, The Rocks, Sydney, New South Wales, Australia.
Qiang YuState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, China.ORCID 0000-0001-6950-1821
Senthold AssengTechnical University of Munich, School of Life Sciences, Digital Agriculture, HEF World Agricultural Systems Center, Freising, Germany. senthold.asseng@tum.de.ORCID 0000-0002-7583-3811
Cynthia RosenzweigNASA Goddard Institute for Space Studies, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.

Indexed as

AgricultureClimate ChangeCrops, AgriculturalHumansModels, TheoreticalUncertainty

Identifiers

PMID39009735

What OpenQuestion holds

Textmetadata
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.