Temporal Disaggregation of the Cumulative Grass Growth

TL;DR

This paper presents a method to reconstruct detailed grass growth time series over a year from cumulative data and climate variables, addressing the challenge of seasonal distortions.

Contribution

It introduces a novel time series forecasting approach that disaggregates cumulative grass growth into daily values using climate data, validated with a grassland model.

Findings

Accurately reconstructs grass growth time series from cumulative data.

Method is robust regardless of the use of cumulative growth information.

Experimental validation shows high reconstruction accuracy.

Abstract

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.