Accurate prediction of flowering time across diverse environments is crucial for effective crop management and breeding. While the accumulated temperature index (ATI) is widely used as an indicator for estimating flowering time, its traditional definition lacks systematic evaluation and genetic basis understanding. Here, using data from 422 rice hybrids across 47 locations, we identified the optimal ATI calculation window as 1 day after sowing to 26 days before flowering. Based on this redefined ATI, we developed a single-parameter model that outperforms the state-of-the-art reaction norm index model in both accuracy and stability, especially with limited training data. We identified 10 loci significantly associated with ATI variation, including two near known flowering time genes and four linked to ecotype differentiation. To enhance practical utility, we developed an efficient flowering time prediction kit using 28 functionally relevant markers, complemented by a user-friendly online tool (http://xielab.hzau.edu.cn/ATI). Our approach can be easily applied to other crops, as ATI is commonly used across various agricultural systems.

中文翻译：

---

重新定义累积温度指数，准确预测不同环境下水稻开花时间


准确预测不同环境中的开花时间对于有效的作物管理和育种至关重要。虽然累积温度指数 （ATI） 被广泛用作估计开花时间的指标，但其传统定义缺乏系统评价和遗传基础理解。在这里，使用来自 47 个地点的 422 个水稻杂交种的数据，我们确定了最佳 ATI 计算窗口为播种后 1 天至开花前 26 天。基于这种重新定义的 ATI，我们开发了一个单参数模型，该模型在准确性和稳定性方面都优于最先进的反应规范指数模型，尤其是在训练数据有限的情况下。我们确定了 10 个与 ATI 变异显著相关的基因座，包括 2 个近乎已知的开花时间基因和 4 个与生态型分化相关的基因。为了提高实用性，我们使用 28 个功能相关的标记开发了一种高效的开花时间预测试剂盒，并辅以用户友好的在线工具 （http://xielab.hzau.edu.cn/ATI）。我们的方法可以很容易地应用于其他作物，因为 ATI 通常用于各种农业系统。