Enzymes are now widely used in animal feed for various reasons, such as inhibiting antinutritional factors of ingredients, improving access to nutrients, compensating the lack of enzymes either for breaking down chemical bonds or the absence because of an undeveloped gastrointestinal tract in young animals. With more information becoming available software developers are now challenged to value enzymes in feed formulation programmes.
By Bruno Duranthon, CEO, A-systems, Versailles, France
There are several types of enzymes that have a distinct action on feed digestibility. Carbohydrases break down the polysaccharides in the raw materials used in animal feed, part of which has poor or low digestibility. Arabinoxylans represent a significant proportion of these low-digestible components. They play an important anti-nutritional role and considerably reduce the nutritional values of the raw materials in which they are present. Enzymes will break down the xylan and arabinose chains and will therefore reduce the anti-nutritional effect of the plant cell wall components when used in animal feed. As far as phytases are concerned, the action of enzymes will release the phosphorus stored in the phytates, thereby making the phosphorus available to animals, but at the same time reducing the amount of phosphorus excreted, since it has not been absorbed.
As a result, a raw material used in a feedstuff containing enzymes will have higher nutritional values than without the enzymes. Depending on the species, the nutritional value will be different.
Incorporating into formulation
Enzymes are difficult to factor into feed formulation software. Although various methods are commonly used today by formulators, each method has its drawbacks, either in terms of managing the data or accurately taking account of the effects. The questions raised by formulators often go unanswered, due to the difficulty in implementation. For example, how do you select an enzyme? How do you choose the right quantity? What is the enzyme's nutritional contribution? Which solution offers the best value for money?
We identified three commonly-used methods:
a) Create as many nutrients as there are enzyme-enhanced nutrients: for example, a "standard poultry ME" and "an enhanced poultry ME". The difficulty to overcome is the increase in the number of nutrients, maintaining the level of nutrients and the difficulty in choosing the right enzyme. If we assume that every single enzyme has a specific enhancement, just as many nutrients as there are pairs (species + enzyme) will need to be created. This management approach would soon incur errors.
b) Another solution involves assigning "magic values" to the enzyme to reflect the enzyme's contribution during formulation. The main drawback with this method is the obvious lack of precision. To take into account the effect of raw materials, users would need to create just as many enzymes as there are raw materials. Once again, data management would prove difficult.
c) Finally, the third method involves scaling down the formula's nutritional requirements, for example by considering that the formulation with enzymes reduces the ME requirement by 50 kcal. This method, though relatively simple, is highly inaccurate, because it fails to take account of the factors associated with the raw materials.
However, none of these methods can be routinely used for choosing the enzyme, and all struggle to provide an answer to the questions raised by the formulator. A-Systems (publisher of Allix² formulation software) has developed a model enabling users to factor in the different effects associated with the use of enzymes using three steps:
- Description of the enzymes' effects on the raw materials in a set of equations for describing the enzymes' effects on the raw materials. These equations are grouped within "enzymatic profiles" that describe all the effects of a specific enzyme on the raw materials for each species.
- The enzyme used could have consequences on the formula's nutritional constraints.
- A mathematical modelling tool allows the formulator to automatically and easily evaluate the different enzyme choices available. If the impact associated with the dose is known, it can be taken into consideration in the optimisation.
The user will have all the necessary information to assess the enzyme's impact on the formula, and also compare the costs of each solution.
[Source: Enzyme special]