  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.
