Information on the grass growth over a year is essential for some models simulating the use of this grassland resource for the production of fodder or for feeding animals on pasture. Unfortunately, this information is rarely available. The challenge is to reconstruct grass growth from two sources of information: daily climate data (rainfall, radiation, etc.) and cumulative growth over the year. In this paper, we formulate this challenge as a problem of disaggregating the cumulative sum of growth into a time series. To address this problem, our method applies time series forecasting using climate information. Several alternatives of the method are proposed and compared experimentally using a database generated from a grassland simulator. The results show that our method can accurately reconstruct the time series, independently of the use of the cumulative sum of growth.