Accurate modelling of plant development is the basis for any assessment of climate change impact on crop yields. Most rice models simulate development (phenology) based on temperature and photoperiod, but often the reliability of these models is reduced beyond the environment they were calibrated for. In our study, we tested the effects of relative air humidity and solar radiation on leaf appearance rate in greenhouse experiments and analysed data sets from field studies conducted in two extremely different rice‐growing environments in Nepal and Senegal. We also analysed environmental effects on duration to flowering of one popular IRRI material (IR64) for eight different sites covering the entire temperature range where rice is widely cultivated. Both low relative air humidity and low solar radiation significantly decreased leaf appearance rate. Mean air temperature explained 81% of the variation in duration to flowering across sites, which was furthermore significantly influenced by relative air humidity. Across all sites, a simple linear regression approach including mean air temperature and mean relative humidity in the calculation of duration to flowering led to a root mean square error (RMSE) of 10 days, which was slightly lower than the RMSE of 11 days achieved with an automated calibration tool for parameter optimization of cardinal temperatures and photoperiod sensitivity. Parameter optimization for individual sites led to a much smaller prediction error, but also to large differences in cardinal temperatures between sites, mainly lower optimum temperatures for the cooler sites. To increase the predictive power of phenological models outside their calibration range and especially in climate change scenarios, a more mechanistic modelling approach is needed. A starting point could be including relative air humidity and radiation in the simulation procedure of crop development, and presumably, a closer link between growth and development procedures could help to increase the robustness of phenological models.

中文翻译：

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低地水稻发展的气候决定因素

精确的植物发育模型是评估气候变化对农作物产量影响的基础。大多数水稻模型基于温度和光周期来模拟发育（物候），但是这些模型的可靠性通常会降低到超出其校准环境之外的水平。在我们的研究中，我们在温室实验中测试了相对空气湿度和太阳辐射对叶片出现率的影响，并分析了在尼泊尔和塞内加尔两种截然不同的水稻种植环境中进行的田间研究的数据集。我们还分析了在一种广泛种植水稻的整个温度范围内的八个不同地点，对一种流行的IRRI材料（IR64）开花持续时间的环境影响。低的相对空气湿度和低的太阳辐射都显着降低了叶片的出现率。平均气温解释了整个站点开花持续时间变化的81％，此外，该温度还受到相对空气湿度的显着影响。在所有地点，计算开花期的简单线性回归方法（包括平均气温和平均相对湿度）可导致10天的均方根误差（RMSE），略低于采用方法获得的11天的均方根误差一个自动校准工具，用于优化基温和光周期敏感性的参数。单个站点的参数优化导致较小的预测误差，但也导致站点之间的主要温度差异较大，主要是较凉的站点的最佳温度较低。为了增加物候模型在其校准范围之外，特别是在气候变化场景中的预测能力，需要一种更加机械的建模方法。起点可以包括在作物发育的模拟过程中包括相对空气湿度和辐射，并且推测，生长与发育过程之间的紧密联系可以帮助提高物候模型的鲁棒性。