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Populating the ingredient database with nutrient values

After selecting the feed formulation software that we are comfortable working with, the next step is to “populate” it with feed ingredients

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Feed formulation software requires a database of ingredients, each with a host of nutrient values, in order to calculate the final mix of ingredients according to our specifications.

After selecting the feed formulation software that we are comfortable working with, the next step is to “populate” it with feed ingredients. After all, all animal feeds start as a mix of individual feed ingredients.

Nevertheless, feed ingredient names are not alone sufficient because feeds must be made to meet specific dietary nutrient specifications. For example, a complete broiler feed may be required to contain 1.5% total lysine, and this lysine will be provided by corn, soybean and feed-grade lysine, such as L-Lysine HCl. Thus, these three ingredients must be entered into the database along with their total lysine concentration. As it will be quickly realized, we will need about 25 ingredients and another 25 nutrients for each ingredient to be entered into the database to start making some basic feed formulas.

As our experience and needs or opportunities increase, more ingredients will need to be entered into the database, creating the need to allow for a large capacity during software selection. The same applies to nutrients, especially if we formulate for many species, each requiring their own nutrient definitions.

Some software packages limit the size of their databases first to make their programs run faster, and secondly to keep cost down. With modern computing power and software coding advances, such limitations appear more as a marketing effort to create price differentiation among one’s own products rather than as an actual effort to keep things simple and focused to customer needs.

In populating a feed formulation system’s database with feed ingredients, there are two problems that are closely associated.

Nutrient definitions

First, we must decide what nutrient definitions we require on which to formulate our feeds. Total lysine was used above as an example, but scientific advances have enabled the use of digestible lysine instead – something that closes the gap between the needs of the target animal and the nutritive value of the formulated feed.

There is digestible lysine – and all other amino acids – for pigs, but also for poultry, and the two are not the same. And, even within the term digestibility, we have different variations, although most nutritionists have agreed to a great measure on the finer details. And, in the case of ruminants, we have a host of totally different values such as rumen degradable protein (or amino acids), and so on.

The same plethora of choices applies to most other nutrients and, of course, to the energy of any and all feed ingredients. We could not possibly use all available terms and remain practical, so one has to make an early selection of what is best. In general, the simpler and more common the feed ingredients to be used, the less sophisticated need be the ways we describe their nutritive value.

But, as we depart from the common and venture into more exotic ingredients, we need to increase our accuracy, hence the need for a more advanced database. In brief, when selecting which ingredient database to use, one has first to eliminate those that do not include the nutrient definitions that will be used during feed formulation.

Ingredients and sampling

The second issue in selecting an appropriate database is its size and depth. With that, we need to ensure the database includes a great number of ingredients, and it is better to have more than we think necessary.

But, having a large number of ingredients is not enough. Each ingredient should be represented there as the culmination of a great number of samples being tested. Thus, if a database presents us with corn based on two samples, while another used the average of 2,000 samples, the selection is easy.

Some advanced databases even include the standard deviation of sample analyses, something that comes handy in advanced feed formulation and in other feed business-related decisions.

So, in short, a large and deep database based on extensive sampling is always preferred, especially if one plans on making feeds for a greater geographic region or for more generic purposes.

Here, it should be reminded that databases are not always easy to combine. A recent study using the digestible amino acid values from two major sources indicated that the two sets were not interchangeable, leading to miscalculations if combined to formulate feeds.

Sources of ingredient nutrient values

The easiest source is book values, such as those published by universities and other public institutions. Apart from the cost of the book, there is no further expense and there is no annual maintenance fee. Such values, as prepared by reputable scientists, are of tremendous value, but they tend to become extinct as commercial interests prevail.

The second source is to buy a database, which is often based on book values, enhanced by proprietary data, often the result of a lifetime of feed ingredient analyses. In my opinion, these databases are good enough for the region in which they were developed, but too expensive for their value.

Additionally, there are databases offered for free from commercial entities with an interest in selling feed ingredients. Such is the case of the two large databases described above, which are available from two major amino acid suppliers.

And, of course, some feed formulation programs come with an already established, albeit limited, database of ingredients. In my view, these values are good enough as an exercise material, but unless they originated from a named entity I trust, I would not want to use them commercially.

Finally, experienced users will begin with any of the above databases and amend it gradually with their own data, coming from laboratory analyses, manufacturer data sheets, or any other source they deem trustworthy.

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