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Maximising enzyme response in poultry diets

The use of exogenous enzymes to improve the nutritional value of cereal-based diets for poultry has grown tremendously. However, in -depth research in consistency of response, cost in use, convincing mode of action messages, effects of combinations of enzymes and interactions with bird age, species and nutrient requirements are often limited. In this article, Aaron Cowieson will give some insights into enzyme combinations and the use of multiple regression models to improve response consistency.

One of the major challenges in feed enzyme research is that in vivo data, be it performance or digestibility, is often equivocal. There are literally thousands of peer reviewed publications reporting a vast range of responses to feed enzymes from negative to highly positive (Rosen, 2002). A major reason for the variable responses that have been reported is that the response to exogenous enzymes is dependent on a large number of interacting factors such as bird age, health status, cereal type, cereal quality, nutrient balance, feed processing conditions and the environmental conditions under which the birds are reared (Bedford, 2002). With this background variance in animal performance, demonstrating a response to a feed enzyme is difficult unless the sources of variance can be minimised or the magnitude of the response is so great that the background 'noise' ceases to become a mitigating factor. In the case of xylanases and cellulases for the so-called 'viscous grains' and also for phytase the latter is true, with mean responses of a magnitude that improves consistency merely due to the very poor quality control diets that are fed. However, with non-phytase enzymes for diets that are based on the non-viscous cereals such as corn and sorghum the mean responses are generally of a lower magnitude and thus are at risk of being lost in the animal production 'noise' (Cowieson et al., 2006a). In a commercial production system it is impossible to reproduce the controlled conditions within a University research facility and thus demonstrating a significant response to an enzyme cannot be achieved by minimising variance. It is precisely for these situations that empirical and mechanistic models can be invaluable as they seek to predict the response to a feed enzyme based on key dietary, environmental and animal-related factors.
Predicting the response
Although the results of one experiment can be useful to market an enzyme product or to shed light on a particular mechanism they are not particularly informative regarding the mitigating factors in the measured response and may be an over or under-estimate of the efficacy of that product under a range of commercial systems. Variance associated with bird age and species, gender, feed type, ingredient quality, environmental conditions
and bird health status can be relatively easily controlled in a randomised block experiment under research conditions and may deliver a result that is not subsequently reflected in economic benefits to the end user. Thus, marketing of enzyme products based on a handful of strategically selected data sets that tell a convenient story do not greatly assist end users in predicting the response under their specific production systems (Bedford, 2002; Cowieson, 2005; Cowieson I., 2006a). It is to this end that empirical and mechanistic models can be invaluable in that they can highlight influential factors in the response to a feed additive that may not be apparent to a reviewer of one or more scientific publications. Rosen (2002; 2006) has adopted an empirical approach to animal modelling and has successfully emphasised the critical factors in enzyme response against a background of considerable performance variance. This empirical approach (termed 'holo-analysis') to the review of published feed additive effects is not only scientifically enlightening but can also be used to create nutritional tools to assist in the marketing of enzyme products. Tools such as AvicheckTM Corn and PhycheckTM (Danisco Animal Nutrition) are based on large databases of in vivo data and have been developed to give predictions to end users of Avizyme 1502 and Phyzyme XP based on key dietary and animal factors (Cowieson, 2005). The arbitrary addition of enzymes to poultry diets without the use of such tools to maximise economic benefits is crude and recommendations based on a handful of trial results cannot be expected to reflect response in a more complex production system. An example of this is that the magnitude of the response to phytase is determined to a large degree by the concentration of dietary phytate. This is intuitive as phytate is the primary substrate for phytase in poultry diets, especially for the bacterial 6-phytases which have a particularly high affinity for the fully phosphorylated myoinositol hexakisphosphate ester (Wyss et al., 1999). Although a handful of scientific publications on the scale of response to phytase in diets differing in phytate content may allow certain assumptions to be made, a full meta-analysis of the data is required across a large number of data points in order to establish the relationship between phytase bioefficacy and dietary phytate concentration. These kind of statistical approaches to large animal-derived datasets are extremely useful when generating matrix values for enzyme products as differences in response can be credited to a variety of important dietary, environmental or husbandry criteria and can subsequently allow the end user to ascribe robust matrix values to the product based on their own dietary constraints.
Enzyme combinations
Whilst the use of single enzyme products such as xylanase or phytase can be optimised using predictive models the situation is further complicated when both products are used simultaneously. In this situation, assumed additivity may lead to an under or overestimation of the scale of the response depending on overlap or synergy in the mode of action, and so lead to control diets that have been inappropriately formulated. As the global market for phytase becomes increasingly competitive and environmental legislation enforces its use, phytase addition to poultry diets by many nutritionists is becoming the norm. Thus the efficacy of non-phytase enzymes must be demonstrated in a diet that already contains phytase as a background activity, with proof of additivity of matrix values for all enzyme products in the diet. Recent work has attempted to elucidate the additivity of matrix values for phytase and carbohydrases in corn/
soy-based diets for broilers (Cowieson & Adeola, 2005; Cowieson et al. 2006b,c). In this work phytase and a carbohydrase/ protease enzyme cocktail (AvizymeTM 1502, Danisco Animal Nutrition) were added to a corn/soy diet that had been strategically formulated to allow for the predicted improvements in ME and the retention of Ca, P and amino acids. This removal of nutrients was based on predictive models (PhycheckTM and AvicheckTM Corn) that have been developed based on a large number of broiler performance and digestibility trials. The conclusion from this work was that combinations of enzymes are extremely effective in order to achieve a particular nutritional goal and both the scale and consistency of the response is improved through this strategy (Table 1). However, as the magnitude of response increases so does the opportunity to produce nutrient imbalance within the diet and so empirical tools are critical to allow for the strategic removal of oil, amino acids and inorganic phosphate sources, creating a control diet that maximises return on investment.
The observed response to exogenous enzymes is dependent on a large number of complex interactions between the animal, the diet and the environment. It is therefore inadequate to arbitrarily add an enzyme product to a diet and expect to achieve the same result that has been demonstrated under controlled research conditions. Holistic least square models are useful in this regard to shed light on key mitigating factors in the response to allow the end user to estimate the economic value under specific environmental, dietary or husbandry conditions. Exogenous enzymes are not a panacea but they are an extremely potent biotechnological tool when wielded with the necessary knowledge to maximise return on investment.
The response to exogenous enzymes depends on the animal, environment and the diet. 
Mechanistic and empirical models equip nutritionists with the knowledge they require in order to formulate a diet strategically to allow for the expected improvements in nutrient retention following addition of a supplemental enzyme. It is likely that the acceptance by the industry of novel enzyme technologies in the future will be determined to a significant degree by the ability of the feed additive company to demonstrate a holistic knowledge of the product to allow response to be predicted under a range of dietary conditions, precluding the arbitrary addition of enzymes based on minimum cost in use.
References are available on request.
Source: Feed Mix magazine. Issue Volume15 No.6

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