Process-Based Modeling of Timothy Regrowth
Process-Based Modeling of Timothy Regrowth
复制标题
Timothy Regrowth 基于过程的建模
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
N. Caldwell
中科院分区：
文献类型：
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作者：
M. Oijen;M. Höglind;H. M. Hanslin;N. Caldwell
在此之前，有关草地早熟禾生长的文献较多。并开发了一个简单的基于过程的模型来解释和预测该物种对不同环境和管理制度的反应。该模型无法进行详细测试，因为当时只有生物量数据可用。然而，最近的实验工作已经产生了关于蒂莫西的生长和基本生理及其对不同生长阶段切割的反应的大量(n=633)和独特详细的数据集。本研究旨在利用该数据集对该模型进行检验，并利用该模型来确定决定Timothy采伐后再生长速率的关键生理和形态机制。模型测试包括对8个变量的模拟和测量进行比较：生物量、叶面积指数(LAI)、分蘖和叶密度、叶片出现和伸长率、碳水化合物浓度和比叶面积。虽然新的数据涉及不同的品种、不同的地点和不同的年份，但模型仍然能够解释数据集中近一半的变异[r2=0.468，归一化均方根误差=0.631]。这表明该模型的关键假设(即生长和分配依赖于植物的源库平衡，以及分蘖和叶面积动态之间的密切联系)是可信的。然而，原始模型不能解释这样的观察，即抽穗早期剪枝之后的滞后期往往比开花期剪枝更长。我们确定了六种机制，它们改善了模型的行为：(1，2)分蘖和出叶率依赖于碳水化合物浓度，(3，4)叶外观和叶片伸长率依赖于植物物候期，(5)从断头的生殖分蘖中萌发新的分蘖，以及(6)伸长的叶片数量与分蘖大小的比例。结合这些机制，然后使用Metropolis-Hastings蒙特卡罗方法进行重新参数化，改善了性能统计(r2=0.521，归一化均方根=0.415)，并解释了早期采伐后长期缓慢生长的原因。因此，这些机制可能是理解蒂莫西再生的关键。
Previously, the literature on the growth of timothy (Phleum pratense L.) in Scandinavia was reviewed and a simple process-based model was developed to explain and predict the response of this species to different environments and management regimes. The model could not be tested in detail, because only biomass data were available at the time. However, recent experimental work has generated a large (n = 633) and uniquely detailed dataset on the growth and underlying physiology of timothy and its response to cutting at different growth stages. The present study aimed to use this dataset to test the model, and to use the model to identify the key physiological and morphological mechanisms that determine the regrowth rate of timothy after cutting. Model testing consisted of comparing simulations and measurements for eight variables: biomass, leaf area index (LAI), tiller and leaf density, rates of leaf appearance and elongation, carbohydrate concentration, and specific leaf area. Although the new data referred to a different cultivar, a different site, and different years from those used in the original model parameterization, the model was still able to account for nearly half the variation in the dataset [r 2 = 0.468, normalized root mean squared error (RMSE) = 0.631]. This suggested that the key assumptions of the model (i.e., dependence of growth and allocation on the source-sink balance of the plants and a close link between tillering and leaf area dynamics) were plausible. However, the original model was not able to account for the observation that cutting at early heading tended to be followed by a longer lag phase than cutting at anthesis. We identified six mechanisms, not previously incorporated in the model, that improved its behavior: (1, 2) dependence of tillering and leaf appearance rate on carbohydrate concentration, (3, 4) dependence of leaf appearance and leaf elongation rate on plant phenological stage, (5) sprouting of new tillers from decapitated generative tillers, and (6) proportionality of the number of elongating leaves with tiller size. Incorporation of these mechanisms, followed by reparameterization using a Metropolis-Hastings Monte Carlo method, improved performance statistics (r 2 = 0.521, normalized RMSE = 0.415) and explained the long duration of slow growth after early cutting. These mechanisms may thus be keys to understanding timothy regrowth.