Abstract
Maize ( Zea mays L.) is Uganda's most widely grown staple crop, yet productivity remains below potential due to nitrogen (N) deficiency and rainfall variability. Crop models are widely used to analyse these constraints but are typically calibrated at few sites, raising concerns about their spatial transferability. This study evaluated whether a single well‐calibrated point‐based CERES‐Maize model within the Decision Support System for Agrotechnology Transfer (DSSAT) framework, spatially implemented using the Geographic Support System for Agrotechnology Transfer (GSSAT2), can be extrapolated nationally to simulate maize response to planting dates and N rates. Using 40 years (1985–2024) of NASA POWER weather data and SoilGrids v2.6 data, simulations were conducted at 5‐km resolution to estimate potential yield (YP), water‐limited yield (YW) and N‐limited yield (YNL). National mean YP was 7.7 t ha −1 , YW 6.1 t ha −1 and YNL 1.1 t ha −1 . N limitation was the dominant constraint, with yield responses plateauing beyond 100–150 kg N ha −1 . March and July were the most favourable planting windows. These results demonstrate that spatial application of a calibrated crop model can provide national‐scale insights, while highlighting the need for multilocation field validation before field‐level recommendations.