Evidence map›Paper›PMID 42362573›Full record

ArticleNPJ science of food2026

Underlying export characteristics and projected country positions in the agri-food trade: A global system analysis.

Amirhosein Ghozatfar, Tina Sardashti, Deniz Berfin Karakoc

Abstract read
In one paragraph

Article in NPJ science of food, 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

3 authors.

Amirhosein GhozatfarDepartment of Management Science, University of Strathclyde, Glasgow, UK.
Tina SardashtiSTOR-i Centre for Doctoral Training, Lancaster University, Lancaster, UK.
Deniz Berfin KarakocSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA. dkarakoc@asu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global agriculture and food trade plays a critical role in ensuring food security and economic stability. A country's agri-food export share is contingent upon various social, economic, and physical factors, and it defines its position within the global trade system. In this study, we propose a machine learning framework to systematically identify the most informative characteristics associated with export shares of countries and explore their projected positions within the agri-food trade. Our study focuses on 25 countries with varying export shares spanning from 1988 to 2022. The results show that the performance of logistics systems, population, reliability of government, and value of agricultural production have the greatest impact on shaping the export share of a country. Further, by 2035, the United States, the Netherlands, Brazil, Germany, China, and Australia are projected to be the leading exporters of global agriculture and food trade, following current trends closely. Our findings can offer insights to policymakers and researchers, enabling more strategic decision-making.

Identifiers

PMID42362573
PMCPMC13385785

What OpenQuestion holds

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LicenceCC BY
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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.