[2605.08113] Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa
[Submitted on 27 Apr 2026]
Title:Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa
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Abstract:Accurate predictions of smallholder maize yields across national boundaries are critical for food security planning in sub-Saharan Africa, yet most published benchmarks report within-country performance that overstates true generalisability. This paper evaluates whether geospatial foundation model embeddings, specifically Prithvi-EO-1.0-100M and ViT-Base, outperform traditional Sentinel-2 spectral features under a Leave-One-Country-Out cross-validation scheme on 6,404 maize field observations from five African countries. The results show a clear generalisability gap: within-country random cross-validation yields moderate R^2 values, but all feature sets perform poorly under cross-country testing, with universally negative R^2. Frozen Prithvi-EO embeddings provide no meaningful advantage over engineered spectral features for cross-country prediction in this setting. The paper argues that the main limitation is a shift in yield distribution between countries rather than representation quality and releases a reproducible negative benchmark for future work.
Comments: 9 pages, 10 figures, appendix, code and processed results released publicly
Subjects:
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.2.6
Cite as: arXiv:2605.08113 [cs.LG]
(or arXiv:2605.08113v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.08113
arXiv-issued DOI via DataCite
Submission history
From: Yaw Osei Adjei [view email] [v1] Mon, 27 Apr 2026 22:03:20 UTC (181 KB)
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