Cranfield Environment Centre, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
Cranfield Environment Centre, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
Sci Total Environ. 2024 Dec 10;955:177181. doi: 10.1016/j.scitotenv.2024.177181. Epub 2024 Oct 22.
Inter-annual variations in crop production have significant implications for global food security, economic stability, and environmental sustainability. Existing crop yield prediction models primarily using meteorological variables may not adequately encapsulate the full breadth of weather influences on crop development processes, such as compound or extreme events. Incorporating weather patterns into crop models could provide a more comprehensive understanding of the environmental conditions affecting growth, enabling more accurate and earlier yield predictions. Our study examines 30 distinct UK Met Office weather patterns (MO30) based on mean sea level pressure. We investigate their association with weather conditions that limit winter wheat yield in the UK (1990-2020). Blocked, negative North Atlantic Oscillation (NAO) patterns create the highest risk of temperatures that are below optimal for crop yield. However, the connection between weather patterns and yield is complex, with differing effects at a regional scale and even at which point in the growth cycle they appear. It was found that anticyclonic weather patterns during sowing, emergence, vernalisation, anthesis, and grain filling exhibit a relationship with good crop yields with a Spearman correlation coefficient of up to 0.55 for a single weather pattern (WP3 during vernalisation in South East England), whilst cyclonic patterns can help during the terminal spikelet phenological phase. The strongest positive correlations were during sowing, emergence, and vernalisation, whilst the largest negatives were observed in anthesis and grain filling. The potential of combining weather patterns with existing crop simulation models to produce earlier and more accurate yield predictions is shown. This would enable effective crop management and climate mitigation strategies, critical to strengthening food security. Projected changes in weather pattern occurrences in the late 21st century will likely reduce crop yields. This is due to increased cyclonic weather patterns, which bring warmer, wetter conditions during the wheat's vernalisation stage, followed by warmer, drier conditions during the anthesis and grain-filling phases.
作物产量的年际变化对全球粮食安全、经济稳定和环境可持续性都有重大影响。现有的作物产量预测模型主要使用气象变量，可能无法充分涵盖天气对作物发育过程的全部影响，例如复合或极端事件。将天气模式纳入作物模型可以更全面地了解影响生长的环境条件，从而实现更准确和更早的产量预测。我们的研究基于平均海平面压力，检查了 30 种不同的英国气象局天气模式（MO30）与限制英国冬小麦产量的天气条件之间的关联（1990-2020 年）。阻塞型负北大西洋涛动（NAO）模式导致作物产量所需的最佳温度以下的温度风险最高。然而，天气模式和产量之间的联系很复杂，在区域尺度上甚至在生长周期的哪个阶段都有不同的影响。研究发现，播种、出苗、春化、开花和灌浆期间的反气旋天气模式与良好的作物产量有关，单一天气模式（英格兰东南部春化期间的 WP3）的斯皮尔曼相关系数高达 0.55，而气旋模式在终端小穗物候阶段可能会有所帮助。最强的正相关发生在播种、出苗和春化期，而最大的负相关发生在开花和灌浆期。结果表明，将天气模式与现有的作物模拟模型相结合，有可能产生更早、更准确的产量预测。这将有助于实现有效的作物管理和气候缓解策略，对加强粮食安全至关重要。预计在 21 世纪后期，天气模式出现的变化可能会降低作物产量。这是由于气旋天气模式的增加，在小麦春化阶段带来了更温暖、更湿润的条件，随后在开花和灌浆阶段带来了更温暖、更干燥的条件。
