Utilising advancements in NIR technology to optimise the feed production process

Published Wednesday, 15th June 2016

Understanding the variability in feedstuffs could be the key to unlocking greater returns from feed formulation, according to Dr Hadden Graham, Global Technical Director at AB Vista.

Recent advances in NIR technology will allow us to better understand the variation within and between feedstuffs, helping support more accurate formulation decisions and enhancing the efficiency of the feed formulation process.

“At a time when feed production makes up to 70% of variable animal production costs, such developments have real potential to optimise rations – and feed efficiency,” Dr Graham says.

“As well as better laboratory-based NIR equipment, we now have in-line equipment as well as hand-held NIR units that can be used at any point in the production process; in the mill or on farm. There have also been advances in NIR software, such as the development of on-line calibrations.”

In a new video released by AB Vista, Dr Graham details how recent advancements in NIR technology can be used to help better understand feedstuff variability, and therefore maximise profitability in three key areas of feed formulation: phytic P, energy and amino acids.

“Phytic P content varies between different feedstuffs and within a single raw material – it’s vital to know the phytic P content of feeds because this helps to decide how much phytase to add. NIR technology can analyse that content.”

Studies in swine feeds have shown that the phytic P percentage varied from as low as 0.1% up to as high as 0.5%. When adding a phytase which can release up to 80% of phytic P, that can have a significant effect on the economy of producing feeds, Dr Graham says. 

“In addition, the considerable variability in the energy value of different cereals, coupled with changes in nutritional value following heat processing of soyabean and canola meals (in lysine, for example), should also be analysed.”

 “NIR technology can provide comprehensive data on the variation of feedstuffs, with which we can start to make economic decisions around optimising feed formulation.”

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