2026-10-16T07:03:02Z https://data.mendeley.com/oai

oai:data.mendeley.com/nxz6bsd9vs.1 2026-10-08T10:21:47Z

Popkova, Elena Evidence gaps on digital agriculture and crop nutritional composition: Scopus records (2010–2026) Mendeley Data Digital agriculture technologies (IoT, remote sensing, AI-enabled decision-support systems) are widely promoted for improving production efficiency in terms of crop yields. 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. 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 Remote Sensing Decision Support System Internet of Things Precision Agriculture Crop Quality Nutritional Management Dataset doi:10.17632/nxz6bsd9vs.1 oai:data.mendeley.com/nxz6bsd9vs.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/nxz6bsd9vs 2026-10-08T10:21:47Z