【摘要】 作物生长模型是模拟和预测作物产量的重要工具，其中通过作物对温度和日长因子响应而建立物候模型会直接影响最终模拟结果，因此需要通过建模优化和参数校准来提高模拟精度。基于此，通过梳理当前温度和日长模型构建的理论基础,将其各自分为线性模型和非线性模型2种类型,对比分析了其中代表性函数的特性和呈现形态,描述了各个函数的优缺点和实际应用的现状,总结当前温度和日长模型发展存在的主要问题：模型的机理解释性不强、函数优化难度高、集成应用程度低、实际应用受限，提出针对性的解决方法：物候模型优化需要在坚持精细化田间试验的基础上从微观层面不断发展模型，综合多影响因子并挖掘最优混合模型来提高模拟精度。兼顾准确性与普适性以满足实际应用需求的模型函数构建是未来需要研究的方向。更多还原

【Abstract】 Crop growth models are important tools for simulating and predicting crop yields, and because the phenological model created by how crops react to factors like temperature and day length directly influences the simulation results, it is necessary to increase simulation accuracy through modeling optimization and parameter calibration. Based on this, this paper reviewed the current temperature and day length models, classified them into linear and non-linear models, compared and analyzed the characteristics and presentation formats of representative functions, described the advantages and disadvantages of each function as well as the current practical applications. Summarized the primary issues with the present temperature and day length model’s development: weak mechanistic interpretation, difficult function optimization, low level of integration and limited practical application. The following targeted solutions were suggested: to optimise the model at the micro level, based on refined field trials, and to improve the accuracy of the simulation by integrating multiple influencing factors and exploring optimal mixed models. Future research should focus on developing model functions that allow for accuracy and universality to satisfy the demands of real uses.更多还原