Understanding, and then modelling, the effects of sowing date and cultivar on maize yield is essential to develop appropriate climate change adaptation strategies. Here we test the WOFOST model and a hybrid model, based on physiological crop conditions around flowering, against observed data collected during 4 years of field experiments in a Mediterranean environment under fully irrigated conditions. We simulate sowing date and cultivar responses by using 45-year historical meteorological records from the experimental weather station and future climate conditions till 2060 as projected by a set of regional climate models.

Both WOFOST and the hybrid approach reveal good performance in simulating average maize yield. However, the hybrid one outperforms WOFOST with respect to its responsiveness to changes in sowing date and cultivar.

These findings, besides stressing the importance of crop conditions around flowering in determining maize yield, point to lower yields (14 %–17 %, average reduction) under future climate conditions. The estimated losses may only be partially offset by changes in phenology and sowing dates.

了解并建模播种日期和品种对玉米产量的影响对于制定适当的气候变化适应策略至关重要。在这里，我们根据在完全灌溉条件下的地中海环境中 4 年的田间试验期间收集的观察数据，测试了 WOFOST 模型和基于开花前后生理作物条件的混合模型。我们通过使用来自实验气象站的 45 年历史气象记录和一组区域气候模型预测到 2060 年的未来气候条件来模拟播种日期和品种响应。

WOFOST 和混合方法在模拟平均玉米产量方面表现良好。然而，杂交种在对播种日期和品种变化的响应方面优于 WOFOST。

这些发现，除了强调开花周围作物条件在决定玉米产量方面的重要性之外，还指出未来气候条件下的产量会降低（14%–17%，平均减少）。估计的损失可能只能被物候和播种日期的变化部分抵消。