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Development of a database on the nutrient content of Australian feed ingredients
AgriFutures Chicken Meat for funding this project and acknowledge support from the follow- ing companies in provid- ing their data for inclusion and publication within the database – Adisseo, Ajinomoto, Cootamundra Oil Seeds, DuPont, Evon- ik, Poultry Hub, Novus,
Dr Amy F Moss.
Figure 4. Composition (g/kg) of total NSP (non-starch polysaccharide – Total), soluble NSP (Soluble), insoluble NSP (Insoluble) and oligosaccharides (Oligo.) of three common Australian feed ingredients.
* from P12 feed- ingredients/
Thus, despite being a rela- tively small industry on the world stage, the Australian poultry industry must exert particular effort to enhance its sample numbers to en- sure accuracy of our recent Australian data.
In recent years, more ef- forts have been expended to develop our knowledge of NSP content such as the Poultry Hub NSP data, which has been incorpo- rated into the Australian Feed Ingredient Database.
content of wheat to 95 per- cent accuracy.
Australian data, as Austral- ian feed ingredients appear to experience almost as much variation as global data.
Premier Nutrition and RCI – and the following open access sources that were also included – Feed Grain Partnership, Bra- zilian Tables, Feedipedia and INRA.
And in Excel format at extensionaus.com.au/ chickenmeatrde/feed- database/
Variability within the database
Dietary fibre or true fi- bre is gaining attention, as crude fibre and other defi- nitions can mean that as much as 24 percent of the dietary components in in- gredients such as soybean meal are unaccounted for during formulation.
The need to characterise NSP is quite evident, as the composition of NSP differs greatly between ingredi- ents – see Figure 4.
When calculated for glob- al data, it was determined that 2177 samples were re- quired, which is well below the actual sample number within the database (n = 37,874) and thus we can be confident that this figure is reliable.
However, many sources do not present the number of samples or standard de- viation.
Dr Amy F Moss and Anna Nguyen University of New Eng- land Armidale NSW
Unsurprisingly, within the database there are substantially more global samples than Australian samples, and the number of samples for some Austral- ian ingredients is quite low.
The sample size required to predict the mean value for each nutrient specifica- tion to 90 and 95 percent accuracy was calculated and presented within the database.
Overall, only 13 percent of the Australian data compiled meets the sam- ple number required to ac- curately predict the mean value within 90 percent accuracy, compared to 40 percent of global data with- in 90 percent accuracy of predicting the true mean. Recommendations
Thus, the quality of data could be vastly improved by simply reinforcing the importance of this infor- mation for industry prac- tice.
For example, Figures 1 and 2 show the mean crude protein and mean number of samples reported within the database for each ingre- dient in the database con- taining crude protein.
Additionally, the survey of integrated poultry nutri- tionists revealed that non- starch polysaccharide data was rated as important be- cause there is a lack of data available.
Thus, there are two key recommendations from this project:
However, the standard de- viation of Australian sam- ples in remarkably high in comparison to global sam- ples – see Figure 3.
From these calculations, it is evident that many of the feed ingredients lack enough data to predict the mean with a high level of confidence.
The importance of a thorough understanding of the mean and variation or ‘spread’ of data for industry practice has been demon- strated.
• To increase the amount of recent Australian nutri- ent content data – includ- ing the standard deviation, not only the mean and number of samples
This brings attention to the challenge our vast con- tinent brings, with a wide variation of environments, climates, growing methods, cultivars, and such, all at- tributing to this variation.
Thus, developing a da- tabase of NSP content of feed ingredients is of im- portance.
For example, looking at the crude protein content of wheat, it is evident that the number of Australian samples (n = 370) was in- adequate, as 706 Austral- ian samples are required to determine the mean protein
Greater focus on deter- mining the nutritive value of feed ingredients is re- quired to have more reli- able estimates for the mean nutrient content of recent
• To improve our sam- pling methodology prac- tices in industry and re- search to ensure an accu- rate representation of the nutrient content and vari- ability within Australian feed ingredients. Acknowledgements
However, NSPs are noto- riously laborious and time consuming to analyse via wet chemistry, owing to their lack of characterisa- tion.
We would like to thank
Figure 3. Mean standard deviation reported in source data (g/kg) of 35 feed ingredients for Australian (dark grey) and global (light grey) data.
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National Poultry Newspaper, July 2022 – Page 13