The El Niño Southern Oscillation (ENSO) significantly influences wheat yield variability in Australia. Tailoring crop agronomic managements to ENSO phases can effectively manage climate variability and reduce crop yield variability. Previous studies have mostly been conducted at a site scale, whose results are hardly extrapolated to a large heterogeneous area. Here, the CERES-Wheat model was calibrated at 5-minute grid scale and applied to simulate wheat yield and adaptation measures under different ENSO phases across wheat cultivation areas in Australia. The results show that the calibrated gridded model can capture wheat phenology and yield variations reasonably. Wheat yields are significantly affected by ENSO-induced changes in precipitation and maximum temperature. During El Niño phase, more dry days, hot days, frost days, water stress, and lower LAI (Leaf Area Index) and ET (Evapotranspiration) lead to an obvious yield decline, especially in southeastern Australia. Wheat sown in the mid-season (Day of Year, DOY of 144) increase yield by 433.5 ± 25.6 kg/ha (27.9 ± 1.6 %) during El Niño, while late-season sowing is more suitable for Neutral (DOY of 153.4) and La Niña phases (DOY of 147.5). A higher nitrogen rate is needed during La Niña (106 kg/ha) and Neutral (106.5 kg/ha) compared to El Niño (94.5 kg/ha) phase. Optimizing varieties is the most effective option and may increase yield by 624±20.9 kg/ha, 562.9 ± 21.5 kg/ha, and 614.3 ± 21 kg/ha for Neutral, El Niño, and La Niña phases, respectively, over traditional farming practices, respectively. The wheat varieties with a larger photoperiod sensitivity and shorter grain-filling duration are favorable during El Niño. Our findings demonstrate a powerful model-based decision support system in tailoring agronomic management practices to ENSO phases and local environments at a high resolution.

中文翻译：

---

根据 ENSO 阶段调整小麦农艺管理，以 5 分钟分辨率管理澳大利亚的气候变化


厄尔尼诺南方涛动（ENSO）显着影响澳大利亚小麦产量的变化。根据ENSO阶段调整作物农艺管理可以有效管理气候变化并减少作物产量变化。以前的研究大多是在场地规模上进行的，其结果很难外推到大的异质区域。在这里，CERES-Wheat模型在5分钟网格尺度上进行了校准，并应用于模拟澳大利亚小麦种植区不同ENSO阶段下的小麦产量和适应措施。结果表明，校准后的网格模型能够合理地捕捉小麦物候和产量变化。小麦产量受到 ENSO 引起的降水和最高温度变化的显着影响。厄尔尼诺阶段，干旱天数、炎热天数、霜冻天数、水分胁迫以及LAI（叶面积指数）和ET（蒸散量）较低导致产量明显下降，特别是在澳大利亚东南部。在厄尔尼诺现象期间，中季播种的小麦（一年中的某一天，DOY 144）可将产量增加 433.5 ± 25.6 千克/公顷（27.9 ± 1.6 %），而晚季播种更适合中性（DOY 153.4）和拉尼娜阶段（DOY 147.5）。与厄尔尼诺现象 (94.5 千克/公顷) 相比，拉尼娜 (106 千克/公顷) 和中性 (106.5 千克/公顷) 阶段需要更高的施氮量。优化品种是最有效的选择，在中性、厄尔尼诺和拉尼娜阶段，与传统耕作方法相比，产量可分别提高 624±20.9 公斤/公顷、562.9 ± 21.5 公斤/公顷和 614.3 ± 21 公斤/公顷， 分别。厄尔尼诺期间适宜选择光周期敏感性较大、灌浆期较短的小麦品种。 我们的研究结果展示了一个强大的基于模型的决策支持系统，可以以高分辨率根据 ENSO 阶段和当地环境定制农艺管理实践。