Welcome to the Spring 2000 Forager

The Wisconsin Grazing Dairy Analysis (WGDPA) confirms that Grazing (Management Intensive Rotational Grazing- MIRG) is an economically viable alternative for many Wisconsin Dairy farm families probably at all herd sizes.

In contrast to large, modern confinement dairy systems, management intensive rotational grazing (MIRG) systems can provide a satisfactory income level on a farm that one family can operate with their own labor and management. This is good news for many farm families.

What makes the news even better still for these families is that a traditional, Wisconsin dairy farm with average or better management has a good chance of improving its financial performance by judicious adoption of MIRG, especially if they are facing some important investment decisions. But while many Wisconsin dairy farm families have switched from a traditional Wisconsin confinement dairy system to MIRG without experiencing a traumatic transition, don't expect a switch to MIRG to produce overnight results.

All Wisconsin farm families who are thinking about ways to become more economically competitive in the face of the current low milk prices, or families starting a dairy operation or who are facing a major investment decisions with their existing dairy operation, owe it to themselves to consider grazing as an alternative before making their final decision. Grazing isn’t for everyone, but it could be the best choice for many families. Here are some characteristics that make an existing dairy farm a good candidate to try or switch to grazing:

1) A desire to achieve economic viability on a farm small enough to be handled by one family’s labor. The large modern confinement system, by definition, is not an option for someone who wants to stay small. However, recognize that dairy grazing should also be economically competitive at any larger herd size too.

2) Average or better management ability. Grazing is a different management system instead of a reduced management system. And because most Upper Midwest dairy farmers “grew up” with conventional systems, and because so much of our research and dairy industry in the Upper Midwest has focused on confinement systems, it might be argued that it’s more difficult to manage a grazing system.

3) A willingness to change and try new things. Switching from a conventional to a grazing system does require a considerable shift in thinking. For example, a well-managed pasture looks quite different form a well-managed “hay” field.

4) The absence of much unproductive debt. The data in the WGDPA indicates that of the three factors of profitability, the one that most separates graziers from conventional farms is investment/debt control. The debt level is the component of investment control that is most critical. When people discuss the advantages of a grazing system, they often say that it reduces costs. When comparisons of the three factors of profitability are made between graziers in the WGDPA and conventional dairy farms, the graziers have a noticeable advantage in fixed costs, but not necessarily in variable costs per cwt. of milk equivalent sold. Regardless of one’s biases, most people would agree that for a given number of cows, a state of the art grazing operation should require less investment/cow (and therefore less debt) than a modern confinement system. Consequently, a switch to grazing is not the salvation for a traditional dairy farm burdened with too much unproductive fixed debt.

5) The desire to avoid a new major investment. Can be happy with a farming operation that minimizes the use of equipment. A Wisconsin grazing system has less need for ownership and use of equipment than is typically found on traditional or large modern confinement dairy farms in Wisconsin. Consequently, dairy farmers who enjoy the equipment party of farming much more than the other parts would not be happy with a grazing system.

Nutritionists have become concerned about the accuracy of current forage analyses. They are having considerable difficulty balancing rations and having the herd respond as predicted. This problem has become more acute as higher herd production has increased the need for greater precision in testing ration components.

Lack of animal response to forage analysis relates to the chemical estimates of energy and protein availability that we have used for the last 30 years. While these tests have served us well at lower levels of animal production, it is important to recognize their limitations. The limitations consist of poor relationship between fiber or protein estimate and digestibility, lack of uniform chemical tests and variation in energy prediction equations from fiber content.

We have become so accustomed to predicting energy from acid detergent fiber that we seldom go back and look at the original relationship to consider its accuracy. The digestibility and TDN relationships were developed by simply regressing fiber vs digestibility as shown in the figure at the top of the next column. The original relationships were reported to explain about 70% of the variation in forage digestibility (note that 30% of the variation was not explained). However, when we went to laboratories and collected legume/grass samples that farmers had submitted, we found that ADF explained only 55% of the variation in digestibility. The much lower relationship between ADF and digestibility is likely due to the fact that the original data was based largely on research samples from University Experiment Stations that did not contain the full range of effects seen on farms. I think all will agree that this relationship is too poor to use in today’s high producing herds. Note also that each set of data produces a different regression, so we have multiple equations for predicting TDN and NE_L from ADF.

Such regression data is really only appropriate to apply to the same kinds of samples as were used to develop the relationship. The equation most commonly used to predict TDN was based heavily alfalfa with some alfalfa/grass samples included. Thus, it is not appropriate to use the ADF to TDN or digestibility relationship from other regions or, especially, for other forages. However, this is commonly done.

A second method is estimate energy of forages is to use summative equations proposed originally by Goering and Van Soest (1970) and, more recently, by Conrad (1984) or Weiss et al. (1992). These do a much better job of predicting energy from multiple chemical analysis rather than analysis of a single component. However, these have additional time and cost in doing the multitude of analyses required for such equations. Beware of laboratories claiming to use these equations and then estimating (or using book values) for components. When estimates rather than actual analysis are used, the output of the equation will be no better than from using a single component as above.

A third method of analysis is to estimate energy by measuring digestibility using in vitro or in situ digestibility as the methods. These are most closely related to animal performance. While these methods are good research tools, they are not for use as routine forage analysis due to cost, time consuming analysis, and significant run to run and laboratory to laboratory variation. This means the technique is useful for comparing samples run within a batch, but less so for results compared over time.

The potential exists to remedy most of these problems by judicious use of near infrared reflectance spectroscopy (NIR). NIR has an advantage of being a wavelength range where it can actually ‘see’ organic compounds, such as cellulose, starch and protein. Further, it can detect physical differences of the forage where chemical tests cannot. These two factors combine to make NIR a powerful tool for forage analysis. All of the discussion above indicates that we need to go to more direct animal estimates of performance rather than chemical estimates. Due to animal variability across animals and over time, the only economically feasible way we get closer to direct animal estimates is to use NIR to estimate digestion of compounds or fractions within the forage.

It is important to recognize that actual digestibility in the animal relates both to the forage and to rate of passage. Digestion of forage A shown in figure 3 would be about 58% for beef cattle or dairy heifers were rumen retention is about 48 hours, but only 40% for a dairy cow where forage stays in the rumen about 30 hours. Forage B is a second alfalfa hay with different digestion kinetics; it digests more slowly but to a greater extent. Forage A is best if fed to dairy cattle, and forage B is best if fed to beef cattle or dairy heifers. These differences can be described where rate of digestion is determined.

We at the University of Wisconsin have developed and released to forage testing laboratories, NIR equations for digestibility, involving digestion kinetics. Therefore, an estimate of rate of digestion would most accurately predict animal performance across a wide range of animal categories and feeding and performance levels because energy available for ruminant animals from a forage depends largely on the rate rumen microbes digest the forage (as well as rumen retention time). We believe that the best procedure for the long term is to estimate rate of digestion (k) plus the totally digestibility fraction (A fraction), partially digestible fraction (B fraction), and undigestible fraction (C fraction) (figure 4). These four factors will allow calculation of digestibility for the specific animal type and conditions under the ration being fed. Development of NIR in situ rate of digestion equations was a major undertaking that required two years of work and many fistulated animals. However, this change will far better characterize the diversity of forages fed to cattle than current analyses.

We have released the equations to commercial testing laboratories in Wisconsin and individuals can now get musc better energy evaluations for grass and legume hay and silage. The new values that can be determined are:

DNDF30 Degraded NDF (as % of DM) after 30 hours of in vitro incubation in rumen fluid (Goering and Van Soest, 1970). In CPM-Dairy, the 30 hour NDF digestibility value is used to adjust fiber digestion rates.

DNDF48 Degraded NDF (as % of DM) after 48 hours of in vitro incubation in rumen fluid (Goering and Van Soest, 1970). This value can be used to calculate TDN and NEl values that are compatible with values published in the feed composition tables (Table 7-1) of Nutrient Requirements of Dairy Cattle (NRC, 2001).

DNDF3M Degraded NDF (as % of DM) at 3 times maintenance intake calculated from in vitro NDF degradation kinetics. The NDF degradability is calculated using an NDF passage rate of.05/hr (ref). DNDF3M is an estimate of ruminal fiber degradation for a cow producing 30 kg of milk and consuming 20 kg of DM daily.

NEL3M Net Energy of Lactation (Mcal/kg) at three times maintenance intake. This value is derived by calculating TDN from DNDF3M (Goering and Van Soest, 1970) and converting TDN to NEl as in Moe and Tyrrell (1977). It is compatible with values published in the feed composition tables (Table 7-1) of Nutrient Requirements of Dairy Cattle (NRC, 1989).

% RUP Percent In situ Rumen Undegraded Protein expressed as a percent of Crude Protein.

2^nd Annual

Forage Foto Contest

2001 Wisconsin Forage Council Symposium

Eau Claire, Wisconsin

January 23 and 24, 2001

The Forage Foto Contest will again be held this year in conjunction with the WFC Forage Symposium on January 23 and 24. Wisconsin Forage Council members are eligible to submit forage related photos that will be displayed and voted on by attendees during the Symposium.

Participants can submit a Forage Foto in any of the following four categories:

· Hard at Work

· Future Foragers (Children)

· Scenic

· Have you ever seen one of these?

(Weeds, bugs, disease, tools, etc.)

Official rules are as follows:

§ Any size color picture is eligible.

§ Limit one picture per category per participant.

§ Pictures need not be from current year.

§ Pictures must be entered at the WFC Symposium registration table by 10 a.m. on Tuesday, January 23 to be eligible.

§ Each picture should be mounted to an index card or cardboard backing with a title. Your name can be placed on the back of the photo but must not be visible during the competition.

§ One first place prize worth $25 will be awarded for each of the four categories.

§ The pictures will be judged by attendees at the symposium.

§ Winners will be announced after the noon meal on Wednesday, January 24.

§ Pictures can be picked up after 1:30 p.m. on Wednesday, January 24.

§ Any pictures not picked by the end of the symposium will become the property of WFC.

§ Pictures from first-place winners are eligible for competition at the National Forage Council.

Volume 24, Number 4, December 2000

Come to The Annual Forage Council Symposium!