Mathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree (Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index (LAI) agreed well with measured values, showing root mean square error (RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha⁻¹ and 0.19, and R² values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs (initial buds and roots) in each growing season was introduced as the initial total dry weight (TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global (R²≥0.856, RMSE≤0.68 t ha⁻¹) and local accuracies (mean R²≥0.43, RMSE≤0.70 t ha⁻¹). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R² of 0.891 and RMSE of 0.591 t ha⁻¹, and local mean R² of 0.57 and RMSE of 0.66 t ha⁻¹, respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

数学模型已被广泛用于模拟一年生作物的生长动态，从而进行产量预测，但不适用于诸如枣树（Zizyphus jujuba）之类的果树。这项研究的目的是通过引入树龄作为关键参数，研究改进的WOFOST模型在预测枣产量中的潜在用途。该模型是使用从2016-2018年进行的专用现场实验收集的数据建立的。叶片，茎，果实的干重，总生物量和叶面积指数（LAI）的模拟生长动态与测量值非常吻合，显示均方根误差（RMSE）值为0.143、0.333、0.366、0.624 t ha ^-1和0.19和R ²值分别为0.947、0.976、0.985、0.986和0.95。出现，开花和成熟的模拟物候发育阶段分别比观察值早2、3和3天。此外，为了预测不同年龄树木的产量，引入了每个生长季节新器官（初始芽和根）的重量作为初始总干重（TDWI），将其平均，拟合和相同年龄树木的最佳值。结果显示了模拟的LAI的演变和响应TDWI变化的产量。当它认为TDWI集成树龄，表现出良好的全球（R建模性能显著提高² ≥0.856，RMSE≤0.68吨公顷^-1）和局部精度（平均值[R² ≥0.43，RMSE≤0.70吨公顷^-1）。此外，经优化的TDWI表现出最高的精度，全球验证的R ²为0.891，RMSE为0.591 t ha ^-1，局部平均R ²为0.57，RMSE为0.66 t ha ^-1。所提出的模型不仅可以准确地预测枣的产量，而且可以为模拟其他果树的生长提供基本策略。