Evidence map›Paper›PMID 42625758›Full record

ArticleFrontiers in artificial intelligence2026

Future-proofing agricultural research: FAIR principles for agriculture AI agents (FAIR4AG

Chenhao Qian, YeonJin Jung, Haowen Hu, Yijing Gong, Ariana Nicole Negreiro, Victor E Cabrera, Renata Ivanek, Martin Wiedmann

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chenhao QianDepartment of Food Science, Cornell University, Ithaca, NY, United States.
YeonJin JungDepartment of Food Science, Cornell University, Ithaca, NY, United States.
Haowen HuDepartment of Animal Science, Cornell University, Ithaca, NY, United States.
Yijing GongDepartment of Animal & Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States.
Ariana Nicole NegreiroDepartment of Animal & Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States.
Victor E CabreraDepartment of Animal & Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States.
Renata IvanekDepartment of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY, United States.
Martin WiedmannDepartment of Food Science, Cornell University, Ithaca, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid shift of large language models from conversational use to agentic reasoning is changing how scientific outputs must be structured for machine consumption. Agriculture stands to gain the most from this transition but currently has the least of the centralized, machine-ready infrastructure that biomedicine has built over decades. Agricultural knowledge remains dispersed across peer-reviewed journals, extension bulletins, technical reports, and multimedia field demonstrations, with associated code, data, and models often inaccessible to autonomous agents. We argue that the foundational FAIR principles and FAIR for Research Software (FAIR4RS) must be extended to a new standard of agent-actionability, and propose FAIR4AG

Indexed as

agentic AIagricultural researchFAIR (findable accessible interoperable and reusable) principleslarge language model (LLM)research data infrastructure

Identifiers

PMID42625758
PMCPMC13490078

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Registered trials

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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.