Description
The nutritive value of forage changes during growth. For the protein content, a general evolution curve was found with the yield increase (Salette & Lemaire, 1984). The digestibility of the organic matter decreases during growth as cellulose and lignin content increase. Regrowth age is the main factor, which explains the digestibility decrease (Demarquilly & Jarrige, 1981). The crop age can be expressed in number of growth days but also in thermal age (cumulated temperature). We compared the digestibility change of three grass species during spring growth for two years as a function of yield increase, thermal age or number of days. Relationships were computed and compared to find the best one for predictive use.
Citation
Salamanca, M. E.; Lambert, R.; Gomez, M.; and Peeters, A., "Modelling the Digestibility Decrease of Three Grass Species During Spring Growth According to the Age of the Grass, the Thermal Age and the Yield" (2023). IGC Proceedings (1993-2023). 73.
https://uknowledge.uky.edu/igc/20/satellitesymposium4/73
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Plant Biology Commons, Plant Pathology Commons, Soil Science Commons, Weed Science Commons
Modelling the Digestibility Decrease of Three Grass Species During Spring Growth According to the Age of the Grass, the Thermal Age and the Yield
The nutritive value of forage changes during growth. For the protein content, a general evolution curve was found with the yield increase (Salette & Lemaire, 1984). The digestibility of the organic matter decreases during growth as cellulose and lignin content increase. Regrowth age is the main factor, which explains the digestibility decrease (Demarquilly & Jarrige, 1981). The crop age can be expressed in number of growth days but also in thermal age (cumulated temperature). We compared the digestibility change of three grass species during spring growth for two years as a function of yield increase, thermal age or number of days. Relationships were computed and compared to find the best one for predictive use.
