Evidence map›Paper›PMID 40200021›Full record

ArticleNature food2025

Statistical approaches are inadequate for accurate estimation of yield potential and gaps at regional level.

Antoine Couëdel, Romulo P Lollato, Sotirios V Archontoulis, Fatima A Tenorio, Fernando Aramburu-Merlos, Juan I Rattalino Edreira, Patricio Grassini

Abstract read
PubMed Publisher
In one paragraph

Article in Nature food, 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

7 authors.

Antoine CouëdelDepartment of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.ORCID http://orcid.org/0000-0002-5786-0939
Romulo P LollatoDepartment of Agronomy, Kansas State University, Manhattan, KS, USA.ORCID http://orcid.org/0000-0001-8615-0074
Sotirios V ArchontoulisDepartment of Agronomy, Iowa State University, Ames, IA, USA.ORCID http://orcid.org/0000-0001-7595-8107
Fatima A TenorioDepartment of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
Fernando Aramburu-MerlosDepartment of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.ORCID http://orcid.org/0000-0003-0957-7482
Juan I Rattalino EdreiraDepartment of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
Patricio GrassiniDepartment of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA. pgrassini2@unl.edu.ORCID http://orcid.org/0000-0002-7501-842X

Funding

Foundation for Food and Agriculture Research (FFAR) 534264National Science Foundation (NSF) 2214604United States Department of Agriculture | National Institute of Food and Agriculture (NIFA) 12431808United States Department of Agriculture | National Institute of Food and Agriculture (NIFA) NEB-22-373
6 · The paper itself

Abstract

Accurate spatial information on yield potential and gaps is key to determine crop production potential. Although statistical methods are widely used to estimate these parameters at regional to global levels, a rigorous evaluation of their performance is lacking. Here we compared outcomes derived from four published statistical approaches based on highest average farmer yields over time and space against those derived from a 'bottom-up' approach based on crop modelling and local weather and soil data for major rain-fed crops in the United States. Statistical methods failed to capture spatial variation in water-limited yield potential, consistently under- or overestimating yield gaps across regions. Statistical methods led to conflicting results, with production potential almost doubling from one method to another. We emphasize the need for well-validated crop models coupled with local data, robust spatial frameworks and extrapolation methods to provide more reliable assessments of production potential from local to regional scales.

Indexed as

Crop ProductionCrops, AgriculturalModels, StatisticalRainSoilUnited StatesWeatherSoil

Identifiers

PMID40200021

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.