论文标题
累积草生长的时间分解
Temporal Disaggregation of the Cumulative Grass Growth
论文作者
论文摘要
对于某些模型模拟了该资源在牧场上或用干草或草青贮饲料喂食动物的某些模型，有关草生长的信息至关重要。 不幸的是，此信息很少可用。面临的挑战是从两个信息来源重建草生长：通常的每日气候数据（降雨，辐射等）和一年中的累积增长。我们必须能够捕获季节性气候事件的影响，这些事件已知会在一年内扭曲生长曲线。在本文中，我们将这一挑战提出为将累积增长分为时间序列的问题。为了解决这个问题，我们的方法应用了时间序列预测，从以前的时间步骤使用气候信息和草增长。提出了该方法的几种替代方法，并使用基于草地过程模型产生的数据库进行了实验比较。结果表明，我们的方法可以准确地重建时间序列，而与累积增长信息的使用无关。
Information on the grass growth over a year is essential for some models simulating the use of this resource to feed animals on pasture or at barn with hay or grass silage. Unfortunately, this information is rarely available. The challenge is to reconstruct grass growth from two sources of information: usual daily climate data (rainfall, radiation, etc.) and cumulative growth over the year. We have to be able to capture the effect of seasonal climatic events which are known to distort the growth curve within the year. In this paper, we formulate this challenge as a problem of disaggregating the cumulative growth into a time series. To address this problem, our method applies time series forecasting using climate information and grass growth from previous time steps. Several alternatives of the method are proposed and compared experimentally using a database generated from a grassland process-based model. The results show that our method can accurately reconstruct the time series, independently of the use of the cumulative growth information.
