Sökresultat:
3 Uppsatser om Foderkvalitet - Sida 1 av 1
Vad orsakar höga ammoniumvärden i ensilage? :
High levels of ammonia in grass silage have been up for discussion by Swedish advisers. One
reason is that it is more difficult for the rumen-microbes to utilise high levels of ammonia. A
low level of ammonia is also good because if the cattle can use more of the protein in the
silage, they don?t need so much protein concentrate. That is very good for the economy at the
farm, and it is also good for the environmental problems caused by high levels of ammonia.
In this project, 24 samples of silage have been taken at 20 different farms in the south west of
Sweden.
Spansk skogssnigel (Arion lusitanicus) i ensilerat vallfoder : betydelse för fodrets näringsinnehåll och hygieniska kvalitet
This work is about silage contaminated with slugs (Arion lusitanicus). The hypothesis was: Do slugs affect the nutritional value and hygiene quality of silage? During the winter season 2007/2008 silages were discarded in big amounts due to the contamination of slugs. Both the Swedish Farmer Association (LRF) and the National Veterinary Institute (SVA) received phone calls from farmers and animal owners about contaminated silage. The Swedish University of Agricultural Sciences (SLU) and SVA decided to initiate this project as a master thesis.
Grazemore DSS för att prediktera beteskvalitet för mjölkkor :
The aim of this study was to examine if the predictions of the herbage quality in the software Grazemore Decision Support System (DSS) gives a reliable ground for milk production in the north of Scandinavia.
Pasture samples from one research farm (Umeå) and one organic farm (Nordingrå) was analysed on crude protein and organic matter digestibility. The results were statistically compared to the predicted values. Measured and predicted herbage mass was compared and a control if the predictions of milk production improved if the predicted input were replaced by the values from the analysis, was made.
The concentration of crude protein was underestimated by the model on both farms and the relationship between actual and predicted values was poor. Mean Prediction Error (MPE) was 24% and 31% respectively.