Global gridded maps of yield potential of the Global Yield Gap Atlas (GYGA)

<p>A complete description of maps' methods, accuracy, strengths, and limitations is available in <a href="https://doi.org/10.1038/s43016-024-01029-3">Aramburu-Merlos et al. (Nat. Food, 2024)</a>.&nbsp;</p> <p>Briefly, we combined site-specific yield potential estimates of the <a href="https://www.yieldgap.org/">Global Yield Gap Atlas</a> with gridded environmental predictors in a machine-learning metamodel to generate global maps of yield potential at a 30-arc-second resolution for maize, wheat, and rice, separately for irrigated and rainfed conditions. Model predictions were restricted to their <a href="https://doi.org/10.1111/2041-210X.13650">area of applicability</a> and lands harvested with the given crop and water regime condition (harvested area &gt; 0.5% according to&nbsp;<a href="https://doi.org/10.7910/DVN/PRFF8V">SPAM v2.0</a>).</p> <p>&nbsp;</p>