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Conference Papers Year : 2022

Temporal Disaggregation of the Cumulative Grass Growth


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.
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Dates and versions

hal-03899264 , version 1 (14-12-2022)



Thomas Guyet, Laurent Spillemaecker, Simon Malinowski, Anne-Isabelle Graux. Temporal Disaggregation of the Cumulative Grass Growth. ICPRAI 2022 - 3rd International Conference on Pattern Recognition and Artificial Intelligence, Jun 2022, Paris, France. pp.383-394, ⟨10.1007/978-3-031-09282-4_32⟩. ⟨hal-03899264⟩
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