Application of the gas production technique to feed evaluation systems for ruminants

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Abstract

A range of feed evaluation techniques is available to predict the nutritional value of ruminant feedstuffs. The aim of this paper was to critically evaluate use of gas production (GP) data as inputs to current feed evaluation systems, as well as to mechanistic rumen models. Topics discussed include prediction of rumen degradation of feed, efficiency of microbial synthesis, and the profile of volatile fatty acids (VFA) produced. Derivation of GP models to permit calculation of extent of degradation of organic matter (OM) has been of great value, as well as to reveal assumptions underlying models to analyse GP data. Gas production techniques (GPT) have good potential to predict rumen OM degradation, in particular through provision of kinetic information, but the potential to provide parameters of degradation of OM components seems limited. Optimal use of GP data to predict microbial efficiency and VFA formed is best achieved when the possibilities, and limitations, of batch culture GPT are recognised. The mechanisms governing microbial efficiency and VFA molar proportions in the GPT are not necessarily applicable to in vivo situations. A simple model based on classic microbial growth and substrate utilisation equations for batch cultures is used to illustrate the need to carefully interpret microbial efficiency in batch culture for use in in vivo situations. The profile of VFA in GPTs is only a reflection of the dynamic VFA profile in vivo. Of crucial importance for estimation of OM degradation, microbial efficiency and amount and type of VFA formed is knowledge of the fractional passage rate of ingesta from the rumen. The value of the GPT is greatly enhanced in combination with mechanistic modelling. However, the role of the technique is not in giving direct predictions of nutrient supply.

Introduction

Ruminants account for almost all of the milk and about one-third of the meat production worldwide (Food and Agriculture Organization, 2004). In view of this substantial contribution to the human food supply, it is not surprising that a great deal of research has been completed on the digestive system of ruminants, making it possible to develop quantitative approaches to increase understanding and integrate its various aspects (Dijkstra et al., 2005). Based on quantitative research, feed evaluation methods were developed for various purposes. Feed evaluation is the use of methods to describe feedstuffs with respect to their ability to sustain different types and levels of animal performance (France et al., 2000b). The practical importance of feed evaluation is obvious with respect to optimising efficiency of feed utilisation, ruminant output and financial return to the producer. Moreover, its importance to minimizing excretion of nutrients to the environment is gaining importance.

To evaluate feedstuffs or diets of ruminants, the feed or diet should ideally be fed to the ruminants of interest and production responses determined. Testing all possible feeds in all possible situations in vivo would clearly provide the most accurate ranking of feedstuffs in terms of nutritive quality, but it is neither practicable nor cost effective. Therefore, a range of feed evaluation techniques have been developed to predict feed value. In current feed evaluation systems worldwide, energy or protein available for absorption from the gastro-intestinal tract (GIT) is generally represented, followed by quantitative approaches to describe the required amounts of energy or protein for maintenance and production. The amount available for absorption from the GIT per unit feed consumed is largely determined by digestibility of the feed. A number of in vitro and in situ techniques have been developed to estimate degradability of feedstuffs in the rumen and their digestion in the whole GIT, including batch culture digestibility with rumen microbial inocula or added enzymes and in situ methods (e.g., López, 2005). Regression equations are applied to predict in vivo digestibility from these in vitro or in situ methods. More recently, rate of degradation of feeds has been studied using gas production (GP) profiles obtained from manual or automated systems of in vitro fermentation of feeds. The popularity of in vitro GP stems mainly from the ability to exercise experimental control, the capacity to non-destructively screen a large number of substrates, the kinetic information obtained and relatively low costs. Several equations have been proposed to describe GP profiles, and France et al. (2005) derived a general compartmental model to link the gas production technique (GPT) output to animal performance. Although a lot of GP data for several feedstuffs are available, these have generally not been incorporated to current feed evaluation systems.

Current feed evaluation systems have several drawbacks that are well recognised (e.g., Tamminga, 1992, Baldwin, 1995, Hanigan et al., 1997, Dijkstra et al., 2002). Single energy values for a feed quote static conditions, whereas the energy and/or protein supply depends on a number of interrelated factors, including site of digestion and level of feed intake. Energy and protein systems have been developed independently, despite vast evidence of the interactions between energy- and protein-yielding nutrients. A major criticism of all energy and protein systems is that they do not predict how performance, in terms of production level and product composition, will change in response to deliberate changes in feeding strategy. Because of these and other drawbacks, new feeding systems need to be based on mechanisms that govern the response of animals to nutrients, by quantitatively describing aspects of digestion and metabolism of individual nutrients in the ruminant. To describe the rumen fermentation processes, several mechanistic rumen models have been developed, e.g., Baldwin et al. (1987), Dijkstra et al., 1992, Dijkstra et al., 1996, Mills et al. (2001). However, none of these rumen models use GP data as an input to parameterise feed specific degradation rates or other parameters.

The objective of this paper is to evaluate critically the use of GP data as inputs to current feed evaluation systems as well as to mechanistic rumen models.

Section snippets

Current feed evaluation models

The most common approach to feed evaluation uses a metabolizable (ME) or net energy (NE) model for energy evaluation and a metabolizable protein (MP) model for protein evaluation, although there are several variants.

Energy systems, in essence, predict ME from digestibility of organic matter (OM) or from digestibility of individual feed components. The systems use equations that relate efficiency of utilisation of ME for various purposes (i.e., maintenance, growth, lactation) to the ratio of ME

Degradation rate and extent of OM degradation

As described above, digestibility of feeds is an important element of current feed evaluation systems. The GPT has been applied to provide such values, although most systems rely on prediction of in vivo degradability using other techniques. In Germany, the Hohenheim gas test (Menke and Steingass, 1988) is widely used to estimate in vivo digestibility and ME for ruminants. In this system, total gas volume at a fixed time point (i.e., 24 h) is combined with the feed's chemical composition to

Use of the GPT for microbial protein synthesis values

Protein of microbial origin usually accounts for a major part of the protein available for absorption from the GIT. Current protein evaluation systems share a common framework and predict microbial synthesis from estimates of rumen degradable OM or energy and rumen degradable N. Provided that degradable N is not limiting, microbial protein synthesis is estimated by multiplication of degradable OM or energy, and a fixed or variable microbial yield factor. Use of GPT in predicting OM degradation

Use of the GPT for VFA molar proportion values

Rumen VFA production is important for qualitative and quantitative reasons. In ruminants, VFA represent the major source of absorbed energy. Also, type of VFA formed and VFA profile determine partitioning of nutrients within the host animal. Of the principal VFA produced, acetate and butyrate are lipogenic, whilst propionate and valerate are glucogenic. Hence a shift in balance between lipogenic and glucogenic VFA production will impact nutrient availability and animal response. In most current

Conclusions

The GPT may be a powerful tool in feed evaluation. Optimal use of GP data for predicting animal responses is best achieved when limitations of the in vitro system that is utilised are recognised. Models that describe degradation and microbial synthesis processes in vitro are helpful in elucidating underlying assumptions. Such models indicate that mechanisms governing microbial efficiency and VFA molar proportions in vitro are not necessarily valid in vivo. Therefore, in vitro data cannot be

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