ArticleScientific data2025
High-resolution soybean trial data supporting the expansion of agriculture in Africa.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- Envirotyping-driven strategies to enhance soybean adaptation across Southern Africa.Scientific reports · 2026Article
- Leveraging probabilistic models to enhance soybean cultivar recommendation in Zimbabwe.BMC plant biology · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding how soybean cultivars respond to diverse environmental and management conditions is critical for guiding variety recommendations across African agroecosystems. In this work, we describe a large-scale dataset from multi-environment trials (METs) conducted in Africa over a ten-year period (2015-2024/25). It includes 292 trials carried out in 138 locations across 21 countries, 26,280.00 plots, evaluating 366 soybean varieties. The dataset contains data from morphologic, agronomic and quality traits, as well as detailed environmental information, such as soil features, weather variables, and management practices. We demonstrate the potential of this resource for decision-making in an emerging African soybean production chain by analysing the seed yield across all trials and within specific countries, highlighting cultivar performance and stability. We also applied envirotyping methods to investigate how environmental covariates influence seed yield. This dataset offers a valuable foundation for advancing research on genotype × environment × management (G × E × M) interactions and adaptation of soybean germplasm in Africa, supporting genetic evaluation, environmental modeling, and informed decision-making for soybean expansion across the African continent.
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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.