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Data · dataset · 2026

Evidence gaps on digital agriculture and crop nutritional composition: Scopus records (2010–2026)

Listed in Teesside University Research Data Repository

Digital agriculture technologies (IoT, remote sensing, AI-enabled decision-support systems) are widely promoted for improving production efficiency in terms of crop yields.

Description

But whether they help to improve crop contents, such as macro- and micronutrients, vitamins and other bioactive compounds, is much less clear considering the scientific output. This dataset supports an overview of studies linking digital technologies to a measured nutritional outcome in agricultural food products as a result of improved management practices.

It was built to highlight the following evidence gaps: 1. Published evidence comes from a small number of countries and does not cover the regions where nutrition-sensitive agriculture is most needed. A research country could be determined for 175 records (65.1%), covering 38 countries.

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China accounts for 47 of these (26.9%), and China, Spain and the United States together for 42.6%; 2. Crop nutritional composition is rarely studied as an outcome of digitally guided management. It is mostly treated as a quantity to predict or monitor, not as the result of a management change.

Protein appears in about 40% of records. Zn, Fe or Se appear in only 12 records (4.5%). Dataset contains 269 records from Scopus (260 journal articles, 9 conference papers; 2010–2026) depicting relation of digital africulture technology to nutritional content of crops, one row per study.

Every record passed AI-assisted title and abstract screening and was then annotated, and its research location was extracted from the full text where available. Records were retained only if they report a crop composition outcome and its location was verified. Columns include Scopus metadata such as title-abstract-keywords and DOI, inclusion decision rationale, evidence annota- tions, research location details and more.

Use cases include systematic review or meta-analysis with geomapping of the evidence base

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Where it is published

Catalogue records · 1

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Inferred from text
Text 75%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
Teesside University Research Data Repositoryoai:data.mendeley.com/nxz6bsd9vs.120 h agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[field].anzsrc:field:401304mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Remote Sensing']
concepts[field].anzsrc:field:460603mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Internet of Things']
concepts[field].local:field:earth-environmentalmapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[field].local:field:life-sciencesmapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[modality].local:modality:remote-sensingmapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Remote Sensing']
concepts[modality].local:modality:textenrichment · researchdata tees ac ukkeyword-concept-rules@1.0.0title+description (75%)
descriptionsource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/description
licensesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/rights
publication_datesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
titlesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/title