TECHNICAL ARTICLES Dairy

Net Energy Lactation as a measure of feed energy in dairy cow nutrition (Part 2)

In the previous issue of To the Point (Volume 11, Number 2, page 7) we looked at basic concepts associated with different systems used to describe the energy content of dairy cow feeds. We came to the conclusion that the Net Energy (NE) system is the most accurate one in terms of describing the useful energy in ruminant feeds. This is largely because the NE system does not overrate the energy value of roughage feeds relative to concentrate feeds. Overrating the energy value of roughage feeds is the most glaring disadvantage of the Metabolisable Energy (ME) system, especially if we take into account that roughage feeds form a relatively large proportion of the diets of dairy cows, in pasture-based and zero-grazing production systems.

However, there remains the problem of finding a practical way around the costly, tedious method of determining the NE value of a feed with live animals. Briefly, this method involves placing the test animal in a chamber with absolute environmental control (an animal calorimeter). In this contraption, every variable associated with the consumption, metabolism, excretion and loss of energy can be measured. It is not practically feasible to extend the calorimetric measurement of NE to the myriad variants of a particular feed (e.g. differences relating to cultivar, agro-ecology and cultivation practices at source, and so forth) or all of the large number of different feeds available for use in ruminant diets.

However, by making use of available scientific data and information, and harnessing modern technology innovations in various domains, it is possible to estimate NE values of feeds quickly, consistently, and at relatively low cost compared to animal calorimetry. These approaches are applied in dairy cow nutrition in various parts of the world, including Europe and the United States of America. For example, established dairy nutrition models like that of the National Research Council (NRC) and the California Net Carbohydrate and Protein System (CNCPS) derive net energy from mathematical equations. Such estimates of NE value of feeds are not necessarily absolutely accurate. However, accepting small errors in estimating NE is certainly more likely to advance effectiveness and efficiency in the feeding of dairy cows, than accepting well known (and certainly larger) errors in assessment of feed energy with a system like ME, for example.

In this issue of To the Point we present the vision that we in Meadow Feeds have towards describing the useful energy of feeds for dairy cows, and the actions taken to achieve it. We aim to have a dynamic system, i.e. one that will account for changes in the chemical structure of feeds over time, rather than a static description of energy value based upon some tabulated average, in some cases years old and relevant to other countries. We want a system that yields quick results, i.e. with a minimum lag time between the availability of feeds, analysing them, estimating their NE values, and using these values to formulate rations and diets for dairy cows. We want a system that is affordable, that will not escalate the cost of feeding cows. Finally, we want a system that will give us consistent results, with predictable outcomes in terms of dairy cow performance.

The mathematical models (prediction equations) used by Meadow Feeds for dynamic estimation of NE Lactation (NEL) are those proposed by the Centraal Veevoederbureau (CVB), The Netherlands, as published by Benedictus (1977). Firstly, gross energy (GE) of the feed is estimated, from the input variables crude protein, crude fat, crude fibre, residual carbohydrates (nitrogen free extract), and sugar (the latter is used only when feeds contain > 80 g sugar/kg dry matter). Hence, we need to analyse feed for these chemical fractions first. At Meadow Feeds, our laboratories are doing all of these analyses on a routine basis throughout the year. Secondly, the ME value of the feed is estimated. For this we need to know the digestibility of the aforementioned chemical fractions, when the feed in question is fed to ruminants. Digestibility values are extensively published in the scientific literature (also for local (SA) feeds). Where digestibility values are lacking, it can be generated quite easily from metabolic experiments. Next, the metabolisability (q) of the feed is calculated, as the ratio ME to GE. In the final step, NEL is calculated from an equation that uses q and ME as input variables.

Table 1 shows the results of applying the NEL calculations to two lucerne samples with different chemical composition, demonstrating the dynamic nature of the NEL values so derived. Table 1 also demonstrates the difference in NEL between a concentrate (maize), and roughage feeds of variable quality (lucerne and veld hay). Note that the ME value of veld hay is equivalent to 57.7% of the ME value of maize (8.03/13.92 x 100). When we do this comparison on the basis of NEL, we see that the NEL value of veld hay is equivalent to 51.1% of the NEL value of maize (4.47/8.74 x 100). These two comparisons illustrate how the ME system overrates the energy value of roughage feeds.

TABLE

To achieve quick, affordable, and consistent estimates of the NEL value of feeds, Meadow Feeds have invested in Near Infrared (NIR) technology (just implemented – October 2003 – at Meadow Feeds Natal). With NIR we are able to analyse the necessary chemical fractions within a few minutes, with a high degree of precision and repeatability. In contrast, we need several days to complete these analyses with wet chemistry, especially when large numbers of samples have to be analysed. The NEL calculations are automated by means of linked computer software, from the laboratory, to the least cost feed formulation programmes, and to dairy models like NRC-Dairy 2000 and CNCPS (or CPM, a derivative of the CNCPS model). In this way, Meadow can determine NEL values for the feeds used in our rations, and the feeds available to our clients on farm, and integrate the results into a comprehensive information service about dairy cow nutrition, available to our client base. We trust that this vision and actions will translate into more precise, predictable and efficient dairy cow feeding, with a positive effect on the bottom line – more profit from dairy farming.

Bibliography (Full details are available from the author)

Benedictus (1977), Bondi (1987), CVB (1999), NRC (1989 & 2000), Tylutki et al. (2002).

Date published: 2005-05-16

Author:
Stephen Slippers

Publication:
N/A