Using real-time data at silage making to drive milk from forage
Using real-time data at silage making to drive milk from forage
Ifan Ifans runs a 390 autumn calving dairy herd aiming to produce the most milk from forage as he can through a multi cut 5 cut silage system and extended grazing season. The silage harvester used at Tyddyn Cae collects data on all silage cuts however, Ifan is yet to make use of the data to further improve milk from forage at Tyddyn Cae.
The use of NIRS analysis of forages is becoming increasingly widespread from routine use in laboratories under the control of laboratory scientists through to hand-held mobile instrumentation used on- farm with static samples and on machinery such as self-propelled TMR mixer wagons and forage harvesters. All NIRS methods rely on prediction models to estimate silage quality. For those prediction models to be accurate they need large datasets of relevant forages to be analysed by chemical methods alongside NIRS scanning under the conditions which they are to be used in practice. From these datasets accurate prediction models can be derived and ultimately used in practice.
The harvester that will be used in this project contains NIRS instrumentation in the chute that allows for the estimation of Dry Matter, Crude Protein, Water Soluble Carbohydrate, Neutral Detergent Fibre, Acid Detergent Fibre and Crude Fibre. In addition to this analysis the forage harvester can also measure fresh matter yield. The above information enables forage yield maps to be determined alongside quality parameters, all of which are available on a field-by-field basis.
This project aims to analyse the data available for the silage quality throughout the season on a trailer by trailer and field by field basis, comparing the harvester data with a hand held NIRS and samples sent to laboratories for NIRS or Wet Chemistry analysis and how using data can further drive milk from forage in dairy herds.
Through driving further improvement in efficiency in these key business areas, the project will also contribute to the Sustainable Land Management outcomes including:
- reduce the farms greenhouse gas emissions
- contribute to high herd health and welfare
- resource efficient