Fil: Vitantonio Mazzini, Lucas N. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Vitantonio Mazzini, Lucas N. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Borrás, Lucas. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Borrás, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Garibaldi, Lucas Alejandro. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina.Fil: Pérez, Diego H. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina.Fil: Gallo, Santiago. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina.Fil: Gambin, Brenda L. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Gambin, Brenda L. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.trueOptimizing grain production implies defining the best management practices for a set of particular environments. Argentinean farmers in the central temperate region are sowing maize at two contrasting sowing dates (September to October and December), exposing their crops to very different growing environments. We tested the influence of management and environmental variables affecting maize yield at early (ES) or late (LS) sowings. Our objectives were to (i) determine the most relevant management and environmental yield predictors for ES and LS, (ii) quantify the magnitude of their effects, and (iii) explore potential yield increments after optimizing crop management within each sowing. We conducted 91 on-farm multi-environment trials during six years (2010–2016) around the central temperate region, and analyzed 13 management and environmental variables. The most relevant environmental predictors (relative importance > 0.50) for both sowing dates included presence of a water table at sowing, rainfall during the crop cycle, and their interactions. Presence of a water table had a positive or negative effect for ES or LS, respectively. Management yield predictors varied depending on the sowing date. Stand density, N and S availability were important yield predictors at ES, while fungicide use, soil P, and N availability were the most relevant ones at LS. Farmers can increase yield at each sowing date by optimizing these management practices. Optimizing stand density and N availability in ES can have a ∼3,053 kg ha−1 effect, while fungicide use in LS can increase yield by ∼1040 kg ha−1. Determining the adequate sowing date based on the presence of a water table at sowing can have a ∼1000 kg ha−1 effect. Our results described specific management options for reducing yield gaps and optimize maize production across contrasting sowing dates.