ArticleScientific data2026
Global crop suitability datasets for 17 crops under present (2024) and future climate scenarios (2041-2100).
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Global crop suitability datasets for 17 crops under present (2024) and future climate scenarios (2041-2100).Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Accurately assessing crop suitability is essential for ensuring food security and guiding agricultural adaptation to climate change. However, existing suitability analyses are mainly based on empirical rules and coarse-resolution predictors, which limits their representativeness and prevents them from capturing contemporary global agricultural patterns with sufficient spatial detail. Here we propose a transferable novel framework that integrates crop samples from remote sensing-based crop layers and high-resolution environmental predictors to model the global suitability of 17 major crops, using the random forest model under present (2024) and future climate scenarios with a 1 km × 1 km resolution. The models achieve an overall accuracy exceeding 0.9 based on the independent test set. Compared with the Global Agro-edaphic suitability dataset from Global Agro-Ecological Zones-Model (GAEZ), our present suitability results show an improved accuracy, with a global pixel-level correlation coefficient (r) of 0.57, and strong agreement in classified suitability area comparisons (r = 0.99, R² = 0.97). This study offers valuable support for optimizing cropping patterns, enhancing agricultural resilience, and informing climate adaptation strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.