Title: FNIRS: prediction of energy utilization and nitrogen digestion at large scale in pigs
Funded by: Svineafgiftsfonden (Danish Pig Levy Foundation)
AU project manager: Associate professor Bart Buitenhuis (QGG)
Collaboration partners: ANIVET, QGG, Landbrug & Fødevarer
Project period: 1 January 2024 - 31 December 2025
Funding amount: 2024: 1,095,000 DKK. 2025: TBA
Project description:
Improving the digestibility of nutrients in pigs is an important trait in breeding due to global resource scarcity and greenhouse gas emissions from pork production. In addition, a better digestibility of nutrients will benefit the farmer's profitability. Feed efficiency, which is included in the Danish pig breeding goals today, is a general measure of feed utilization, rather than the utilization of a specific nutrient (e.g. nitrogen or phosphorus) or energy utilization. Determining nutrient and energy digestibility, however, requires extensive chemical analyzes of feed and faeces. For genetic improvement of nutrient and energy digestibility in pigs, a large number of animals will need to be tested on a weekly basis. Recently, pig faeces-based near-infrared spectroscopy (FNIRS) has been shown to be a promising cost-effective method for large-scale measurement of nutrient and energy digestibility and feed efficiency in pigs and has been investigated in practice in e.g. France.
In order to implement FNIRS for measuring nutrient digestibility in practice, it is necessary to develop an FNIR-based prediction model for nutrient and energy digestibility based on a reference population where feed composition, feed intake and faeces composition for the individual pig are known. At Aarhus University, there is a large biological dataset that has been collected over the years, and where feed and faeces from individual pigs have been analysed.
In this project, we propose that 1) the already collected samples will serve as an initial reference population to build a FNIR-based nutrient prediction model, i.e. nitrogen and energy digestibility, and 2) test and validate the developed prediction model in a Danish pig breeding population.
