The controlled experiments are increasingly used to examine the effect of chilling on the spring phenology of temperate woody plants. Previous studies demonstrated a delaying effect of lack of chilling on spring events due to a negative relationship between chilling accumulation (CA) and heat requirement (HR). However, most experiments only lasted for one year, and limited studies examined the consistency of the CA-HR relationship between years. Here, we conducted a twig experiment with 14 temperate woody species in two winter seasons (2018/2019 and 2019/2020) to assess the between-year difference in the impact of chilling on HR of flowering or leaf-out. The CA and HR for each species in each year were estimated based on the common-used chilling and forcing model. The results showed that 8 of 14 species exhibited a significantly different slope or intercept of the linear function of HR against CA between two winter seasons. Using the first-year CA-HR function to simulate the second-year spring events would result in large uncertainties with root-mean-square errors ranging from 1.3 to 9.9 days. In addition to the original chilling model, we further selected an optimal one for each species from another 8 chilling models. The optimal chilling models varied among species and could reduce the RMSE of phenological simulation of 10 species by 0.1–3.7 days. Our results suggest that using one-year experimental data to develop the phenological model or estimate the chilling and heat requirement of spring events is unsuitable. Further studies are still needed to better quantify the rate of chilling under different temperature conditions for various species.

受控实验越来越多地用于检查冷却对温带木本植物春季物候的影响。先前的研究表明，由于冷量积累 (CA) 和热量需求 (HR) 之间的负相关关系，缺乏冷量对春季事件有延迟影响。然而，大多数实验只持续了一年，有限的研究检查了 CA-HR 关系的一致性。在这里，我们在两个冬季（2018/2019 和 2019/2020）对 14 种温带木本树种进行了树枝实验，以评估冷却对开花或落叶 HR 影响的年间差异。每年各物种的 CA 和 HR 是根据常用的冷强迫模型估算的。结果表明，在两个冬季之间，14 种物种中有 8 种表现出显着不同的 HR 对 CA 的线性函数的斜率或截距。使用第一年的 CA-HR 函数来模拟第二年的春季事件会导致很大的不确定性，均方根误差范围为 1.3 到 9.9 天。除了原始的冷却模型外，我们进一步从另外 8 个冷却模型中为每个物种选择了一个最佳模型。最佳冷却模型因物种而异，可以减少 我们进一步从另外 8 个冷却模型中为每个物种选择了一个最佳模型。最佳冷却模型因物种而异，可以减少 我们进一步从另外 8 个冷却模型中为每个物种选择了一个最佳模型。最佳冷却模型因物种而异，可以减少0.1-3.7 天对 10 个物种的物候模拟的RMSE。我们的结果表明，使用一年的实验数据来开发物候模型或估计春季事件的冷热需求是不合适的。仍需要进一步研究以更好地量化不同物种在不同温度条件下的冷却速率。