Quantification of forest Gross Primary Productivity (GPP) is important for understanding ecosystem function and designing appropriate carbon mitigation strategies. Coupling forest biometric data with canopy photosynthesis models can provide a means to simulate GPP across different stand ages. In this study we developed a simple framework to integrate biometric and leaf gas-exchange measurements, and to estimate GPP across four Mediterranean pine forests of different post-fire age. We used three different methods to estimate the Leaf Area Index (LAI) of the stands, and monthly gas exchange data to calibrate the photosynthetic light response of the leaves. Upscaling of carbon sequestration at the canopy level was made by implementing a Big Leaf and a Sun/Shade model, using both average and variant (monthly) photosynthetic capacity values. The Big Leaf model simulations systematically underestimated GPP compared to the Sun/Shade model simulations. Our simulations suggest an increasing GPP with age up to a stand maturity stage. The shape of the GPP trend with stand age was not affected by the method used to parameterise the model. At the scale of our study, variability in stand and canopy structure among the study sites seems to be the key determinant of GPP.

中文翻译：

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将光合测量与生物特征数据相结合，估算不同火灾后地中海松林的总初级生产力 (GPP)

森林总初级生产力 (GPP) 的量化对于了解生态系统功能和设计适当的碳减排策略非常重要。将森林生物特征数据与冠层光合作用模型相结合，可以提供一种方法来模拟不同林龄的 GPP。在这项研究中，我们开发了一个简单的框架来整合生物特征和叶片气体交换测量，并估计四个不同火灾后年龄的地中海松林的 GPP。我们使用三种不同的方法来估计林分的叶面积指数 (LAI)，并使用每月的气体交换数据来校准叶子的光合光响应。通过使用平均和变异（每月）光合容量值，通过实施大叶和太阳/阴影模型，在冠层水平上增加碳固存。与 Sun/Shade 模型模拟相比，Big Leaf 模型模拟系统地低估了 GPP。我们的模拟表明，随着林龄达到林分成熟阶段，GPP 会增加。林龄的 GPP 趋势的形状不受用于参数化模型的方法的影响。在我们的研究范围内，研究地点之间林分和冠层结构的可变性似乎是 GPP 的关键决定因素。