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This paper evaluates whether geospatial foundation model embeddings like Prithvi-EO improve cross-country crop yield prediction in Sub-Saharan Africa compared to traditional Sentinel-2 features. The study finds that frozen embeddings do not significantly outperform spectral medians under rigorous Leave-One-Country-Out validation, suggesting country-level distribution shift is the primary bottleneck rather than feature representation quality.