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Understanding Nitrogen Utilization in Dairy Cattle

Contents


Introduction

This fact sheet has been developed to support the implementation of the Natural Resources Conservation Service Feed Management 592 Practice Standard. The Feed Management 592 Practice Standard was adopted by NRCS in 2003 as another tool to assist with addressing resource concerns on livestock and poultry operations. Feed management can assist with reducing the import of nutrients to the farm and reduce the excretion of nutrients in manure.

The Natural Resources Conservation Service has adopted a practice standard called Feed Management (592) and is defined as “managing the quantity of available nutrients fed to livestock and poultry for their intended purpose”. The national version of the practice standard can be found in a companion fact sheet entitled “An Introduction to Natural Resources Feed Management Practice Standard 592”. Please check in your own state for a state-specific version of the standard.

Nitrogen (N) is the building block of proteins in feeds and forages. Protein is typically the most expensive component of the purchased feeds used in dairy rations. Nitrogen is also receiving more attention as a component of nutrient management plans on dairy farms and potential ammonia emissions.

Understanding how N is used in dairy cattle is important in improving both profitability and decreasing excretion from the cow into the environment. It is important to remember that dairy cows do not have a protein requirement. They really need amino acids available in the small intestine to support tissue growth and milk production. Basically, N utilization in dairy cattle is composed of two components. The first is providing an adequate supply of N and carbohydrates in the rumen to support the growth of rumen microorganisms and the production of microbial crude protein (MCP). The second part of the system is the utilization of amino acids in the small intestine to provide for the needs of the cow.

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Definitions

N = nitrogen; CP = crude protein; NPN =nonprotein nitrogen; TDN = total digestible nutrients; MP = metabolizable protein; MCP = microbial crude protein; SP = soluble protein; RDP = rumen degradable protein; RUP = rumen undegradable protein; NRC = National Research Council

Feed Nitrogen Fractions

Even though all feeds contain N, there is variation in the quantity of N in each feed and it’s availability and utilization in the dairy cow. Forage testing laboratories determine the quantity of N in the sample and multiply this value by 6.25 to obtain the crude protein (CP) value printed on the analysis report. This calculation assumes that feeds contain 16% N on a dry matter (DM) basis. An example calculation is:

Alfalfa silage = 3% N * 6.25 = 18.75% CP (both on a DM basis)

The challenge is that feeds could have the same CP value, but have a different feeding value to the dairy cow. Consider the following examples:

Alfalfa hay, alfalfa silage and alfalfa pasture – All 20% CP.

Raw and roasted soybeans – Both with 40% CP

Even though these feeds have the same CP level, we would not expect the same level of N utilization and milk production. If we are feeding 4 pounds/cow/day of raw soybeans to a dairy cow producing 80 pounds of milk, replacing these with 4 pounds of roasted soybeans would increase predicted milk production on a protein basis by 2-3 lbs. What is the reason for this?

One reason is that there are a number of N compounds found in feeds. This means that we need to better define the types of N compounds present in feeds. A simple to start is to classify feed N as either true protein or NPN. These can be defined as:

True protein = The N in feeds found in complex and linked structures as amino acid combinations. Examples are: albumins, globulins and amino acids. These feeds will vary in both the rate and extent of degradation that occurs in the rumen.

NPN = This is the N in simple compounds such as ammonia or urea (not as amino acids). These are considered to be rapidly available in the rumen.

The above breakdown is a start, but the true protein component needs to be better defined for use in ration formulation or evaluation programs. This is most commonly done in the following manner:

RDP = that portion of the total N intake that is degraded in the rumen. The NPN fraction is included in RDP.

RUP = that portion of the total N that is not degraded in the rumen and passes intact to the small intestine. There is a portion of the RUP fraction that is not available or digested in the small intestine and passes out in the feces. This is fraction C in the system described by Van Soest (1994).

Ruminal N Metabolism

A portion of the feed N that enters the rumen will be degraded to compounds such as peptides, amino acids or ammonia. The primary mechanism for this breakdown in the rumen is microbial proteolysis. The solubility, structure, and particle size of the feed will all influence the amount of degradation that takes place. There will always be a portion of the feed N that enters the rumen that is not degraded (RUP).

All RDP does not breakdown and be converted to ammonia at the same rate. Van Soest (1994) provided an overview of a system to define N sub-fractions that would permit better characterization of feed N availability and use in the dairy cow. This system includes the following fractions:

A – This is mainly NPN, amino acids, and peptides that are “instantly” available in the rumen.

B1 – This fraction has a fast rate of degradation in the rumen.

B2 – This fraction has a variable rate of degradation in the rumen.

B3 – This fraction has a slow rate of degradation in the rumen.

The use of this approach assists in doing a better job of describing N utilization in the rumen and improving the efficiency of feed N use. The use of this approach does require additional feed analysis data and computer formulation programs designed to utilize this information.

Microbial Protein

Microbial protein (MCP) is produced in the rumen by the rumen microorganisms. The key factors that determine the quantity of MCP synthesized is the quantity of ammonia available in the rumen and the supply of fermentable carbohydrates to provide an energy source. The availability of peptides may also stimulate the production of MCP by some rumen microorganisms. The NRC (2001) predicts MCP production as 13% of the discounted TDN (total digestible nutrients) available in the rumen.

Microbial protein can provide 50 – 80% of the amino acids required in the intestine by the dairy cow. Optimizing MCP production helps in increasing the efficiency of N use in the cow and controlling feed costs.
The benefits of MCP are related to:

  • MCP averages about 10% N (60-65% CP).
  • MCP is a good source of RUP.
  • MCP has a high digestibility in the intestine.
  • The amino acid profile of MCP is fairly constant.
  • MCP has an excellent ratio of lysine to methionine.

Protein Systems

There are 2 systems used to evaluate and balance rations for dairy cows on a protein basis. These are the CP (crude protein) and MP (metabolizable) protein systems. The CP system has been the most commonly used system.

The CP system is easy to use and has tabular feed composition and animal requirement information. This system assumes that all N in different feeds is similar in use and value to the cow. The Dairy NRC (2001) indicated that CP was a poor predictor of milk production. Nutritionists have modified the CP system to better meet their needs. They have added SP, RDP and RUP as additional factors to consider when using CP as the base for formulating dairy rations on a protein basis.

The Dairy NRC (2001) has suggested moving to a MP system to better define and refine protein formulation and utilization. This system fits with the biology of the cow. The challenge is that this system is not tabular and requires the use of computer programs to calculate both MP requirements and the MP supplied by feeds and MCP. The industry is changing to an MP approach. This system should provide an opportunity to improve the efficiency of protein use in dairy cattle. The use of this system will also decrease N excretion to the environment and lower potential ammonia emissions.

Total N Use in Dairy Cows

It is important to realize that the dairy cow is a dynamic rather than static system. This means that the actual value of a feed N source will vary depending on a number of factors. These include:

  • The proportion of the total N intake used in the rumen versus the small intestine.
  • The length of time the feed remains in the rumen (rate of passage).
  • The rate at which the feed is degraded in the rumen (rate of digestion).
  • The amino acid profile of the RUP fraction.
  • The digestibility of the RUP and MCP fractions in the small intestine.

This situation is similar to the energy value of feeds that occurs due to differences in dry matter intake (DMI) and rate of passage. Dairy cows with higher levels of DMI have a higher rate of passage and lower feed energy values. This is the reason for discounting feed energy values based on level of DMI and milk production (NRC, 2001).

The NRC (2001) computer model was used to determine the RDP and RUP for soybean meal in a ration for dairy cows. The base ration was for a cow producing 80 pounds of milk and contained 5 pounds of DM from soybean meal. This ration was then evaluated for cows producing 60, 100 or 120 lbs. of milk. The ration ingredients were all kept in the same proportion, but total ration DMI was adjusted using the NRC program predicted intakes. This would be similar to cows fed a 1-group TMR. The RDP and RUP values for soybean meal in this ration were:

Milk, lbs/day RDP, % of CP RUP, % of CP
60 60 40
80 59 41
100 56 44
120 54 46

The reason for the higher RUP value in higher producing cows is the decreased amount of time the soybean meal stays in the rumen. Thus, there is less time for N degradation and proteolysis to take place. This example also indicates the challenge with using tabular values to describe the RDP and RUP fractions in feeds. This is the reason that computer programs that can integrate DMI, rate of passage and rate of digestion are needed as we continue to refine formulation and evaluation approaches.

Summary

Nitrogen is the most expensive component of purchased feed costs on most dairy farms. Ration programs that incorporate the concepts of feed fractions and variable feed contributions to the animal provide an opportunity to fine tune nutrition and improve the efficiency of nutrient use. This will also lower nutrient excretion to the environment and usually improves income over feed cost.

References

NRC, 2001. National Research Council. Nutrient Requirements of Dairy Cattle. 7th rev. ed. National Academy of Science, Washington, DC.

Van Soest, P.J. 1994. Nutritional ecology of the ruminant. Cornell University Press, Ithaca, NY.

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Disclaimer

This fact sheet reflects the best available information on the topic as of the publication date. Date 4-12-2007

This Feed Management Education Project was funded by the USDA NRCS CIG program. Additional information can be found at Feed Management Publications.

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This project is affiliated with the Livestock and Poultry Environmental Learning Center.

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Project Information

Detailed information about training and certification in Feed Management can be obtained from Joe Harrison, Project Leader, jhharrison@wsu.edu, or Becca White, Project Manager, rawhite@wsu.edu.

Author Information

L.E. Chase
Cornell University
lec7@cornell.edu

Reviewer Information

Mike Hutjens – University of Illinois

Floyd Hoisington – Consulting Nutritionist

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Strategies to Reduce the Crude Protein (Nitrogen) Intake of Dairy Cows for Economic and Environmental Goals

Contents


Introduction

This fact sheet has been developed to support the implementation of the Natural Resources Conservation Service Feed Management 592 Practice Standard. The Feed Management 592 Practice Standard was adopted by NRCS in 2003 as another tool to assist with addressing resource concerns on livestock and poultry operations. Feed management can assist with reducing the import of nutrients to the farm and reduce the excretion of nutrients in manure.

Of the nitrogen (N) fed to dairy cows, only 21 to 38% actually is exported as milk or meat. That means 62 to 79% of the N fed to cows is for the most part excreted via urine and feces of cows. Most N voided in urine is quickly emitted as ammonia whereas the percent of fecal N converted to ammonia is quite variable depending upon storage management and land application method. Because most N consumed in excess of requirement is excreted in urine, to improve efficiency of N use, urinary N needs to be reduced. Changes in diet formulation can improve efficiency of N use on dairies.

Please check this link first if you are interested in organic or specialty dairy production

Definitions

N = nitrogen; CP = crude protein = N X 6.25; NPN = nonprotein N; TDN = total digestible nutrients; RDP = ruminally degraded protein; LYS = lysine; MUN = milk urea N; MCP = microbial crude protein; RUP = ruminally undegraded protein; MET = methionine; FCM = fat-corrected milk; IOFC = income over feed costs

Historical Diet Formulations For Cows

Protein nutrition of dairy cows has evolved over the decades. Initially, the approach was to determine the % or amount of dietary CP that cows needed for milk production. However, any CP consumed in excess of the cows’ requirements is excreted via urine. Thus, by feeding the cow to meet, and not exceed, her CP requirement, N excretion is reduced. The major weakness of formulating diets on a CP basis is that it ignores the type of CP consumed. For example, under this system all N in NPN sources (urea for example) is treated the same as the N in soybean meal, and clearly they are much different. The N in NPN does not include long chains of amino acids in the form of “true” protein whereas most of the N in soybean is amino-acid bound. Therefore, soybean meal contributes amino acids to both the ruminal micro-organisms and to intestinal amino acid absorption whereas NPN contributes only to the latter (Figure 1).

The goal of feeding protein to lactating cows is to support milk protein synthesis while meeting the needs for maintenance, growth and replacement of lost body protein. All proteins synthesized in the body have set amino acid patterns, so if a particular amino acid is lacking during protein synthesis, formation of that protein stops. Thus, we are not really trying to supply dietary protein to cows, we are trying to supply enough of each amino acid such that no single amino acid limits protein synthesis.

Another approach to improve upon balancing diets on a CP basis was the Burrough’s metabolizable protein system (Burroughs et al., 1975). This system considered the amount of dietary N that was solubilized in the rumen and could be used for microbial protein synthesis, a calculation that also involved the amount of TDN available to support microbial growth. To the calculated microbial protein was added the amount of dietary protein that escaped ruminal degradation. Use of ammonia and fermentable carbohydrate for microbial protein synthesis is illustrated in Figure 1. Finally, adjustment factors for digestibility and unavoidable fecal losses were applied to yield a Metabolizable Protein value. This system required knowing the amount of protein in feeds that was converted to ammonia in the rumen, the amount of feed protein that escaped ruminal breakdown, and the TDN value for the feeds. The concept was excellent but the system needed refinement.

The amount of dietary protein that is degraded in the rumen is primarily determined by characteristics of the N-containing compound (e.g., solubility and linkages) and how long it resides in the rumen (Figure 1). Residence time in the rumen, i.e. ruminal passage rate, is determined by total feed intake (the more the cow eats the faster the feed tends to move through the rumen), particle size and specific gravity (smaller, heavier particles move faster than large, light particles through the rumen), and other factors such as how quickly the microbes ferment the feed. Certain feeds are fermented faster than others (barley is fermented faster than sorghum), and feeds can be treated to reduce their rate of protein breakdown (most treatments involve lowering the protein solubility). Obviously the calculations to determine the amount of dietary protein that is degraded, or conversely undegraded, in the rumen get complicated, hence computer models tremendously speed the calculations.

Current approaches to meeting the amino acid needs of cows

First, we try to maximize microbial protein synthesis in the rumen. Microbial protein is high in LYS, and LYS is often the most limiting amino acid for milk production in feeding situations commonly found in the confined operations. Maximizing microbial protein synthesis involves supplying fermentable carbohydrates and soluble N sources to enable rapid bacterial growth. We need ammonia and other forms of soluble N to be available to the bacteria simultaneously with the fermentation of the carbohydrates so the bacteria have everything they need for growth, which includes protein synthesis (Figure 1). If the N is solubilized and degraded too quickly, much is absorbed as ammonia and subsequently excreted as urea N in urine or MUN. If too little N is solubilized in the rumen, the ammonia concentration in the ruminal fluid is too low to maximize microbial protein synthesis (Stokes et al., 1991; Clark et al., 1992). Generally, the amount of fermentable carbohydrate in the rumen is most limiting to microbial protein synthesis. Hence, the NRC (2001) predicts the yield of MCP as 0.13 x TDN (discounted), i.e., 130g MCP/kg of TDN (discounted) when RDP exceeds 1.18 x MCP yield. If the RDP intake is < 1.18 x TDN predicted MCP, then MCP is 0.85 of RDP intake. Thus, if a cow consumes 15 kg of TDN (discounted), MCP flowing to the intestine is estimated as: 15 x 0.13 = 1.95 kg.

Secondly, diets are formulated to supply amino acids for milk production by including dietary proteins that will not be completely degraded in the rumen and have a high content of the amino acids believed to be most limiting for milk production. Some feed proteins have relatively high LYS concentrations (porcine blood meal), some have relatively high MET concentrations (corn gluten meal), and some have a good balance of LYS and MET (fish meal). Thus, diets contain multiple proteins, all of which degrade at different rates in the rumen. In addition, the ruminal degradation rates for the 20 amino acids found in proteins vary substantially. Fortunately, all these data are contained in software programs so the estimated flow of feed amino acids into the small intestine is quickly calculated. Use of computer models allows us to take advantage of complementary protein and other N sources to achieve lower CP diets to achieve comparable milk yields.

Case I.

Using RDP/RUP feed data to achieve diets with a lower % CP.
Reynal and Broderick (2005) fed four diets that varied in RDP. Their diet description and results are given in Table 1. Urinary N excretion decreased about 60 g/cow/day as the % CP and % RDP decreased in the diet, however, the % milk protein also decreased. Their data suggest 11.7% RDP in ration DM as the best compromise between profitability and environmental quality.

 

Table 1. Effect of Percent Ruminal Degradable Protein on Dietary Components and Cow Responses
Dietary Treatments
A B C D
CP, % 18.8 18.3 17.7 17.2
RDP, % DM1 13.2 12.3 11.7 10.6
RDP, % of CP 70.2 67.2 66.1 61.6
RDP, % DM2 12.5 10.9 9.2 7.7
RUP, % DM1 5.8 6.2 6.0 6.6
RUP, % of CP 30.8 33.9 33.9 38.4
RUP, % DM2 6.3 7.4 8.5 9.5
NEL2, Mcal/lb DM 0.709 0.704 0.704 0.704
3.5% FCM, lb/d 93.1 94.2 93.3 91.3
Milk true protein, % 3.14a 3.14a 3.07b 3.04b
MUN, mg/dL 15.9a 15.6a 13.6b 12.8b
BUN, mg/dL 13.8a 14.0a 11.8b 12.4b
Ruminal NH3-N, mg/dL 12.33a 11.76a 8.68b 5.71c
Urinary N excretion, g/d 295a 293a 237b 239b
Fecal N excretion, g/d 222 220 219 197
N Efficiency
Milk N, % of N intake 29.6 29.5 30.4 30.4
lb of milk/lb of N excreted 84.5a 87.2a 94.3b 99.8b
1Measured in vivo.
2Predicted by NRC (2001) model.
abcMeans within the same row without a common superscript differ P < 0.05.

 

Case II.

Formulations using RUP/RDP and specific amino acids to reduce CP intake.
This concept applies RDP/RUP in predicting amino acid flows to the small intestinal tract, then adding specific amino acids to meet the cow’s requirements. The advantage is to reduce total N intake and hence, N excretion, while reducing total feed costs. Examples of using amino acid formulation to reduce CP and maintain milk yield are given below.

Example 1. VonKeyserlingk et al. (1999) formulated two diets for cows that were primarily in early lactation. The control diet was formulated according to the 1989 NRC recommendations. A second diet was formulated with the CNCPS system and included a commercial protein source and intestinally available MET source. Using a commercial protein source and “rumen by-pass” MET allowed the CP level in the grain mix to be reduced by 2.9% units and total TMR by 1% unit (Table 2).

 

Table 2. Diets formulated using NRC (1989) guidelines or CNCPS program.
Item NRC (1989) CNCPS
CP, % DM 18.7 17.7
ADF1, % DM 21.1 21.8
NEL, Mcal/lb 0.82 0.86
1Acid detergent fiber.

No difference was observed in DM intake or milk production between cows fed the diets formulated by the two methods (Table 3). The authors concluded that the CNCPS afforded the opportunity balance rations for reduced CP level without loss in milk production.

 

Table 3. Performance of dairy cows fed rations formulated by NRC (1989) guidelines or CNCPS formulation program.
Item NRC (1989) CNCPS
All cows
DMI, lb 47.4 46.6
Milk, lb 82.8 81.5
Multiparous Cows
Milk, lb 96.5 94.3
Milk fat, % 2.88 3.12
Milk protein, % 3.12 3.11
Primiparous Cows
Milk, lb 69.0 68.8
Milk fat, % 3.17 3.31
Milk protein, % 3.22 3.20

Example 2. Harrison et al. (2000) used the CPM (Cornell, Penn State, and Miner Institute) model to formulate two diets containing undegraded protein sources in the form of canola derivative or animal-marine blend. Each of these diets was estimated to be slightly deficient in LYS and MET. Two additional diets were formulated that were supplemented with a MET source and free LYS-HCl to improve the dietary supply of MET and LYS. The postpartum levels of MET and LYS in the non-supplemented diets were targeted to be at ~ 100% of the requirements (1.9% MET/MP and 6.4% LYS/MP) and 116% of MET (2.2% MET/MP) and 106% of LYS (6.6% LYS/MP) for supplemented diets. When formulating the diets, it was considered that 20 g of the commercial MET source provided 7 g of ruminal escape MET (Koenig et al.,1998) and 40 g of free LYS-HCl provided 8 g of ruminal escape LYS (Velle et al., 1998). Cows were fed the experimental diets from ~28 days before calving through week 17 postpartum. At 9 weeks post-partum, cows received rBST per label.

There tended to be increased yield of 3.5 FCM for cows fed the diet containing animal-marine bypass protein (Table 4). In early lactation, and at 14 to 17 weeks of lactation, there was an improvement in milk that appeared to be related to supplemental MET and LYS-HCl. In the early weeks of lactation (weeks 1 to 4) the MET supplemented cows fed the animal-marine blend protein source diet produced the most milk. After the beginning of rBST use (week 5), cows fed both un-supplemented diets (canola derivative and the animal-marine blend) produced more milk when supplemented with MET and LYS-HCl. A trend (P<0.14) was observed for increased milk fat percentage when the diets were supplemented with MET and LYS-HCl. These observations support the use of supplemental MET and LYS particularly during the critical need periods of early lactation and post rBST administration.

 

Table 4. Performance of cows fed diets containing supplemental sources of rumen undegraded amino acids.
P <
Item Treated canola protein Treated canola protein + Lys & Met Animal-marine blend protein Animal-marine blend protein + Lys & Met Pro-
tein		 Suppl-
ement		 Pro-
tein x Suppl
DMI, lb 48.2 48.0 48.8 47.7 NS NS NS
Milk, lb 85.4 85.6 87.1 87.3 NS NS NS
3.5 FCM, lb 86.9 87.6 89.3 91.1 0.08 NS NS
Milk fat, lb 3.08 3.12 3.19 3.28 0.03 NS NS
Milk fat, % 3.65 3.71 3.68 3.80 NS 0.14 NS
Milk protein, % 3.09 3.13 3.12 3.36 NS NS NS
Milk protein, lb 2.62 2.62 2.68 2.86 0.22 NS NS

Case III.

The importance of formulating for desired ratios of MET to LYS.
In another study (Harrison et al., 2003), researchers employed the CPM formulation model to reduce dietary CP from 18% to 16% by replacing alfalfa silage with corn silage and undegraded protein sources (Tables 5 & 6). Diet #3 was predicted to have the best ratio and supply of MET and LYS, and resulted in the highest milk yield, and ratio of milk true protein to diet protein (Table 7). The reduced milk yield of cows fed diet #2 emphasizes the need to ensure the ratio of LYS to MET is ~ 3.2 to 1. Total N import (as feed N) onto the dairy was reduced by nearly 9% and IOFC was increased 6.5% by diet #3.

 

Table 5. Chemical Analysis of Total Mixed Rations (% DM).
Item Diet 1 Diet 2 Diet 3 SE
CP 18.6 16.0 16.0 0.35
NDF1 38.9 41.2 44.7 1.88
Soluble CP 7.53 5.1 5.4 0.38
Soluble CP, % of CP 40.5 31.9 33.8
NFC2 31.9 34.4 30.7 2.16
1Neutral detergent fiber
2Nonfiber carbohydrate

 

Table 6. Diet Formulation Results from CPM
Item Diet 1 Diet 2 Diet 3
Lysine, % required 89 99 116
Methionine, % required 91 116 109
Ratio of Lys/Met 3.32 2.89 3.16
MP1 balance, g -477 -104 -117
1Metabolizable protein

 

Table 7. Response of cows to diets that differ in crude protein and ratio of lysine to methionine
Item Diet 1 Diet 2 Diet 3 SE P <
DMI, lb 44.9 45.1 45.1 2.97 NS
Milk, lb 78.8 77.9 82.5 5.10 NS
Milk Fat, % 3.80a 3.24b 3.79a 0.151 0.01
Milk Protein, % 3.08 3.08 3.07 0.071 NS
MUN, mg/dL 18.8a 13.0b 14.4b 0.92 0.01
CP Intake, lb/d 8.34 7.22 7.22
Milk True Protein/Feed CP 0.29 0.33 0.34
Reduction in CP imports, % — 8.6 8.6
IOFC1, $/d/cow 5.49 4.64 5.85
1Income over feed costs

 

Case IV.

Impact of reduced dietary % CP on N excretion on a commercial dairy. A field study (Harrison et al., 2002) was conducted with a high producing herd to compare the general herd diet formulated at ~18% CP to a diet that was reformulated at ~17% (Table 8). Milk production was maintained while N imports to the farm (Tables 9 & 10) were decreased. In addition, the reformulated diet increased IOFC (Table 11). These results agree with those of Wattiaux and Karg (2004) who reported a 16% drop in urinary N when a diet with 18% CP was reformulated to 16.5% CP.

 

Table 8. Chemical compositions of a control diet and a reformulated diet containing supplemental amino acids.
Item Control Reformulated
CP, % DM 17.8 17.0
Soluble Protein, % DM 6.4 6.0
Soluble Protein, % CP 35.7 37.0
NDF<suo>1</sup>, % DM 32.4 32.7
NFC2, % DM 39.0 39.8
1Neutral detergent fiber
2Nonfiber carbohydrate

 

Table 9. Treatment response to a diet reformulated on the basis of metabolizable methionine and lysine.
Item Control Reformulated SE P <
DMI, lb 56.7 55.2
CP Intake, lb 10.1 9.4
Milk, lb 99.9 101.9 0.53 0.007
3.5 FCM, lb 96.0 96.6 0.46 0.32
Milk fat, % 3.28 3.21 0.014 0.001
Milk protein, % 2.90 2.93 0.006 0.0009
MUN, mg/dL 17.5 14.5
Milk True Protein: Intake Protein Ratio 0.285 0.316

 

Table 10. Effect on nitrogen excretion when a diet was reformulated on the basis of metabolizable lysine and methionine
Item Control Reformulated % Change
N intake, g/d 734 680 -7.4
Milk total N, g/d1 240 246 +2.5
Predicted Urinary N, g/d2 289 239 -17.3
Calculated Fecal N, g/d3 205 195 -5.0
1(Milk True Protein/6.38) X 1.17
2Urinary N (g/d) = 0.026 x BW (kg) X MUN (mg/dL); J Dairy Sci. 85:227-233.
3Fecal N = Intake N – Milk N – Urine N

 

Table 11. Economic impact of reformulating a diet on the basis of metabolizable lysine and methionine
Item Control Reformulated
Feed Costs, $/d/cow 4.82 4.88
Milk Income, $/d/cow 11.92 12.10
IOFC1, $/d/cow 7.10 7.22
1IOFC = Income over feed costs

 

Summary

Reducing CP intake of high-producing cows can be achieved by strategic use of undegraded protein sources and amino acids (LYS and MET) under a variety of diet conditions. Diet reformulations can reduce N excretions by ~10% without negatively affecting milk yield or IOFC. These successes require the use of ration balancing software that estimate the amino acid (MET and LYS especially) needs of the lactating cow. Use of undegraded protein sources that have dependable concentrations of amino acids is critical to achieve consistent production responses.

 

RUP Fig 1.jpg

 

 

Selected References

Burroughs, W., D.K. Nelson, and D.R. Mertens. 1975. Evaluation of protein nutrition by metabolizable protein and urea fermentation potential. J. Dairy Sci. 58:611-619.

Clark, J.H., T.H. Klusmeyer, and M.R. Cameron. 1992. Symposium: Nitrogen metabolism and amino acid nutrition in dairy cattle: Microbial protein synthesis and flows of nitrogen fractions to the duodenum of dairy cows. J. Dairy Sci. 75:2304-2323.

Harrison, J.H., D. Davidson, L. Johnson, M.L. Swift, M. VonKeyserlingk, M. Vazquez-Anon, and W. Chalupa. 2000. Effect of source of bypass protein and supplemental Alimet and lysine-HCl on lactation performance. J. Dairy Sci. 83(suppl 1):268.

Harrison, J.H., D. Davidson, J. Werkhoven, A. Werkhoven, S. Werkhoven, M. Vazquez-Anon, G. Winter, N. Barney, and W. Chalupa. 2002. Effectiveness of strategic ration balancing on efficiency of milk protein production and environmental impact. J. Dairy Sci. 85(suppl.1):205.

Harrison, J.H., R.L. Kincaid, W. Schager, L. Johnson, D. Davidson, L.D. Bunting, and W. Chalupa. 2003. Strategic ration balancing by supplementing lysine, methionine, and Prolak on efficiency of milk protein production and potential environmental impact. J. Dairy Sci. 86(Suppl 1):60.

Koenig, K.M., L.M. Rode, C.D. Knight, and P.R. McCullough. 1999. Ruminal escape, gastrointestinal absorption, and response of serum methionine to supplementation of liquid methionine hydroxyl analog in dairy cows. J. Dairy Sci. 82:355.

NRC. 1989. National Research Council. Nutrient Requirements of Dairy Cattle. Vol. 6th rev. ed. Natl. Acad. Sci., Washington, DC.

NRC. 2001. National Research Council. Nutrient Requirements of Dairy Cattle. Vol. 7th rev. ed. Natl. Acad. Sci., Washington, DC.

Reynal, S.M. and G.A. Broderick. 2005. Effect of dietary level of rumen-degraded protein on production and nitrogen metabolism in lactating dairy cows. J. Dairy Sci. 88:4045-4064.

Stokes, S.R., W.H. Hoover, T.K. Miller, and R. Blauweikel. 1991. Ruminal digestion and microbial utilization of diets varying in type of carbohydrate and protein. J. Dairy Sci. 74:871-881.

Tamminga, S. 1992. Nutrition management of dairy cows as a contribution to pollution control. J. Dairy Sci. 75:345-357.

Velle W., T.I. Kanui, A. Aulie, and O.C. Sjaastad. 1998. Ruminal escape and apparent degradation of amino acids administered intraruminally in mixtures to cows. J. Dairy Sci. 81:3231-3238.

VonKeyserlingk, M.A.G., M.L. Swift, and J.A. Shelford. 1999. Use of the Cornell Net Carbohydrate and Protein System and rumen-protected methionine to maintain milk production in cows receiving reduced protein diets. Can. J. Anim. Sci. 79:397-400.

Wattiaux, M. A. 1998. Protein metabolism in dairy cows. In: Technical Dairy Guide—Nutrition, 2nd edition. The Babcock Institute for International Dairy Research and Development. The University of Wisconsin.

Wattiaux, M.A and K.L. Karg. 2004. Protein level for alfalfa and corn silage-based diets: II. Nitrogen balance and manure characteristics. J. Dairy Sci. 87:3492-3502.

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This fact sheet reflects the best available information on the topic as of the publication date. Date 6-20-2006

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J. H. Harrison
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Washington State University

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Michael Wattiaux – University of Wisconsin

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Evaluating Corn Silage Quality for Dairy Cattle

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Contents


Introduction

This fact sheet has been developed to support the implementation of the Natural Resources Conservation Service Feed Management 592 Practice Standard. The Feed Management 592 Practice Standard was adopted by NRCS in 2003 as another tool to assist with addressing resource concerns on livestock and poultry operations. Feed management can assist with reducing the import of nutrients to the farm and reduce the excretion of nutrients in manure.

The Natural Resources Conservation Service has adopted a practice standard called Feed Management (592) and is defined as “managing the quantity of available nutrients fed to livestock and poultry for their intended purpose”. The national version of the practice standard can be found in a companion fact sheet entitled “An Introduction to Natural Resources Feed Management Practice Standard 592”. Please check in your own state for a state-specific version of the standard.

An index of forage quality, milk per ton of forage DM (Undersander et al., 1993), was developed using an energy value of forage predicted from ADF content and DMI potential of forage predicted from NDF content as its basis. The milk per ton quality index was later modified for corn silage (Schwab et al., 2003) using an energy value derived from summative equations (Schwab et al., 2003; NRC, 2001) and DMI predicted from both NDF content (Mertens, 1987) and in vitro NDF digestibility (IVNDFD, % of NDF; Oba and Allen, 1999b) as its basis. This milk per ton quality index (MILK2000; Schwab et al., 2003) has become a focal point for corn silage hybrid-performance trials and hybrid-breeding programs in academia and the seed-corn industry (Lauer et al., 2005). An update, MILK2006, will be discussed herein.

Please check this link first if you are interested in organic or specialty dairy production

Model NEL-3x and DMI

We (Schwab et al., 2003) modified the NRC (2001) TDNmaintenance summative energy equation for corn silage to include starch and non-starch NF C components with a variable predicted starch digestibility coefficient, and a direct laboratory measure of the NDF digestibility coefficient rather
energy value was derived from TDNmaintenance using the NRC (1989) empirical equation in MILK2000 (Schwab et al., 2003). In MILK2006, the NEL-3x energy value is derived using an adaptation of the TDN-DE-ME-NE conversion equations provided in NRC (2001).

Neutral detergent fiber content and IVNDFD are used to predict DMI (Schwab et al., 2003) in both MILK2000 and MILK2006. However, a one %-unit change in IVNDFD (% of NDF) from lab-average IVNDFD changes DMI 0.26 lb. per day (Oba and Allen, 2005; Jung et al., 2004) in MILK2006 versus the 0.37 lb. per day value (Oba and Allen, 1999b) that was used in MILK20

In MILK2000, variation in IVNDFD impacts NEL intake through effects on both NEL-3x content and DMI (Schwab et al., 2003). However, Tine et al. (2001) and Oba and Allen (1999a) reported that at production levels of intake, IVNDFD has minimal impact on NEL-3x content but impacts NEL intake primarily through effects on DMI. In MILK2006, the IVNDFD value used for calculating NEL-3x is adjusted for differences in DMI predicted from IVNDFD using an equation adapted from Oba and Allen (1999a). Thus, IVNDFD impacts NEL intake and hence the milk per ton quality index mainly through its impact on predicted DMI in MILK2006.

Non-fiber Carbohydrates and Their Digestibility

Dairy cattle nutritionists have long used non-fiber carbohydrate (NFC) as a quasi-nutrient rather than starch specifically. However, NFC is a calculated value (100-NDF-CP+NDFCP-Fat-Ash; NRC, 2001) comprised of varying proportions of starch, sugar, soluble fiber, and organic acids, and is subject to errors associated with analyzing the five nutrients used to calculate NFC. Although the NRC 2001 summative energy equation was based on NFC, starch rather than NFC is being used in summative energy equations (Schwab et al., 2003) by many commercial feed testing laboratories especially for corn silage which they have long been analyzing for starch content and have developed NIRS calibrations for starch determination. However, determining digestion coefficients for starch to use in summative energy equations has been difficult. The NRC 2001 model uses an NFC true digestibility coefficient of 98% and arbitrary processing adjustment factors. The MILK2000 model uses a non-starch NFC (NFC minus starch) true digestibility coefficient of 98% (NRC, 2001) and varies the starch true digestibility coefficient from a minimum of 76% (Firkins et al., 2001) to a maximum of 98% (NRC, 2001) using whole-plant DM and kernel processing as regression equation variables to predict apparent total tract starch digestibility (Schwab et al., 2003). Both approaches though are limited in their ability for detecting potential variation in starch digestibility across a wide array of samples, and novel lab assays are needed.

Starch, supplied in Midwestern and Northeastern diets primarily from dry or high-moisture corn grain and whole-plant corn silage, is an important source of energy for dairy cattle. However, the digestibility of corn starch can be highly variable (Nocek and Tamminga, 1991; Orskov, 1986; Owens et al., 1986; Rooney and Pflugfelder, 1986; Theuer, 1986). Various factors, particle size (fine vs. coarse grind), grain processing (steam flaked vs. dry rolled), storage method (dry vs. high-moisture corn), moisture content of high-moisture corn, type of corn endosperm, and corn silage maturity at harvest, chop length, and kernel processing, influence starch digestibility in lactating dairy cows. Because both physical and chemical properties of starch influence starch digestion, assessment of starch digestibility in the laboratory has been challenging.

In an attempt to address variation in starch digestibility, NRC (2001) suggested empirical processing adjustment factors (PAF) to adjust NFC digestion coefficients for high-starch feeds. However, since no system to measure variation in PAF for feedstuffs is available the PAF’s are subjective book values with minimal practical utility. For corn silage, U.S. Dairy Forage Research Center workers developed a kernel processing score (KPS; Ferreira and Mertens, 2005; Mertens, 2005) to assess adequacy of kernel processing in corn silage. But, the relationship between KPS values and in vivo starch digestibility measurements is not well defined. Ruminal in-vitro or in-situ degradation, either alone or in combination with in vitro post-ruminal enzymatic digestion of the ruminal residues, have been explored by some groups (Sapienza, 2002). Some commercial laboratories are attempting to employ in situ or in vitro systems to evaluate starch digestibility, but to date methods are highly variable between laboratories. Regardless of the method it is doubtful that samples can be fine ground as fine grinding of samples may mask differences among samples (Doggett et al., 1998). Relationships between in situ/in vitro measurements and in vivo starch digestibility are often not well defined. We recently developed an enzymatic lab assay, Degree of Starch Access (DSA), which is sensitive to differences in particle size, moisture content, and vitreousness of corn-based feeds (Blasel et al., 2006).

The DSA assay was found to be quite sensitive (Blasel et al., 2006) to particle size (R2 = 0.99) and moderately sensitive to DM content (R2 = 0.76) and endosperm type (R2 = 0.59), which are three primary factors that influence starch digestibility in corn grain. However, The DSA assay is a laboratory starch recovery procedure that does not result in a direct estimate of starch digestibility and only reveals differences in starch recoveries. For example, the DSA procedure would recover 95 percent of the starch in finely ground corn but only 5 percent of the starch in whole shelled corn. Thus, the DSA values provide an index of the variation in degree of starch access among feeds. We (Shaver and Hoffman, 2006) reviewed eight trials in the scientific literature (Taylor and Allen, 2005a; Remond et al., 2004; Oba and Allen, 2003; Crocker et al., 1998; Knowlton et al., 1998; Yu et al., 1998; Joy et al., 1997; Knowlton et al., 1996) with lactating dairy cows that reported total tract starch digestibility and particle size, moisture content, and endosperm type of the corns tested. From these data, we estimated their DSA values and evaluated the relationship between DSA and their measures of total tract starch digestibility. The resultant regression equation is applied to starch recovery values generated from the DSA assay to provide an estimate of total tract starch digestibility (termed Starch DigestibilityDSA; Shaver and Hoffman, 2006) which can be used in summative energy equations (Schwab et al., 2003; NRC, 2001) directly to calculate energy values for corn-based feeds on a standardized basis.

More field and in vivo evaluations of these laboratory assays related to starch digestibility (KPS, DSA, and in situ/in vitro) are needed. Therefore, the MILK2006 model continues to use the regression approach of MILK2000 (Schwab et al., 2003) as the default method for determining starch digestibility. But, user-defined options are available within the MILK2006 spreadsheet for determining starch digestibility from available KPS, DSA, or in situ/in vitro data. For hybrid performance trials where an objective is to assess true hybrid differences for kernel endosperm properties, the harvest maturity, whole-plant DM content, and sample particle size should be kept as similar as possible since these factors all influence the starch digestibility determinations.

Fiber and Its Digestibility

The NRC (2001) summative energy equation is based on fiber digestibility calculated using lignin. Whole-plant lignin content was found to have a strong negative relationship with IVNDFD within comparisons of brown midrib (bm3) hybrids to isogenic counterparts (Oba and Allen, 1999b). However, stover NDF and lignin contents increase while NDFD decreases with progressive maturity, but whole-plant NDF and lignin contents are constant or decline as grain proportion increases (Russell et al., 1992; Hunt et al., 1989). This may partially explain why for 534 corn silage samples, NDFD calculated using lignin according to NRC (2001) accounted for only 14% (P < 0.001) of IVNDFD variation (Schwab and Shaver, unpublished). Michigan State workers (Oba and Allen, 2005; Allen and Oba, 1996; M. S. Allen, personal communication, 2003 Tri-State Nutr. Conf. Pre-Symp.) reported that lignin (% of NDF) explained only half or less of the variation for corn silage IVNDFD. These observations coupled with the NRC (2001) suggestion that IVNDFD measurements could be used directly in the NRC model led us to implement IVNDFD rather than lignin-calculated NDF digestibility in the corn silage milk per ton models (Schwab et al., 2003). Use of NDF and IVNDFD in the corn silage milk per ton models has been discussed above.

Several commercial testing laboratories offer wet chemistry IVNDFD measurements. NIRS calibrations for predicting IVNDFD on corn silage samples are available at some commercial forage testing laboratories. However, Lundberg et al. (2004) found poor prediction by NIRS of corn silage IVNDFD. It is hoped that NIRS calibration equations can be improved upon in the future. The NRC (2001) recommended a 48-h IVNDFD for use in the NRC (2001) model, and for that reason we used 48-h IVNDFD measurements in MILK2000 (Schwab et al., 2003). However, debate continues within the industry about the appropriateness of 48-h vs. 30-h IVNDFD measurements. Some argue that the 30-h incubation better reflects ruminal retention time in dairy cows (Oba and Allen, 1999a) and that most of the in vivo trials that have evaluated effects of varying IVNDFD on animal performance also performed 30-h IVNDFD measurements (Oba and Allen, 2005). Labs and their customers also like the faster sample turn around that is afforded by the 30-h incubation time point. For that reason, and also for improved lab operation efficiency, a 24-h incubation time point is being employed by some labs. However, some argue that the 48-h incubation time-point is less influenced by lag time and rate of digestion, and thus is more repeatable in the laboratory (Hoffman et al., 2003). Hoffman et al. (2003) provided data on the relationship between 30- and 48-h IVNDFD measurements that showed a strong positive relationship (r-square = 0.84). But, the lab average at a specific incubation time point and the relationship between incubation time points within a lab can be highly variable among labs making the development of a universal incubation time point adjustment equation difficult. The average lignin-calculated corn silage NDF digestibility in the NRC (2001) is 59%. This reference point is important for adjustment of IVNDFD values from different labs and varying incubation time points so that the resultant TDN and NEL values are comparable to NRC (2001) values.

User-defined flexibility is available within the MILK2006 spreadsheet for entry of 48-, 30-, or 24-h IVNDFD incubation time point measurements. But, the labs incubation time point and average results for corn silage at that time point must also be entered into the spreadsheet along with the sample data. The 48-h IVNDFD incubation time point continues to serve as the default in the milk per ton spreadsheets. The Wisconsin Corn Silage Hybrid Performance Trials (Lauer et al., 2005) will continue to use the 48-h IVNDFD incubation time point because NIRS calibrations for this time point have been developed from corn silage samples obtained in this evaluation program over several years by locations and Justen (2004) did not find the earlier incubation time points to provide any benefit over the 48-h time point for hybrid selection.

Model Comparisons

Values for TDNmaintenance, NEL-3x, and milk per ton calculated using MILK2006 and MILK2000 across a wide range of whole-plant corn IVNDFD values and extreme quality differences are presented in Tables 1 and 2, respectively. The TDNmaintenance differences between MILK2006 and MILK2000 are minimal. The NEL-3x and milk per ton values are lower and the range in these values is compressed for MILK2006 relative to MILK2000 according to the equation differences between the two models that were described above.

Analysis of correlations between corn silage NDF, IVNDFD, starch, and starch digestibility and milk per ton estimates from MILK 2006, 2000, 1995, and 1991 models (n = 3727 treatment means; Shaver and Lauer, 2006) is presented in Table 3. Results show that the MILK2000 model was revolutionary relative to the earlier models (milk per ton hybrid rank correlation between MILK2000 and MILK1991 was only 0.68), because of its recognition of IVNDFD as an important quality parameter while the earlier models were influenced mostly by whole-plant starch and grain contents. The MILK2006 model relative to MILK2000 appears to be more evolutionary reflecting the relatively minor fine-tuning of equations (milk per ton hybrid rank correlation between MILK2006 and MILK2000 was 0.95), but the spreadsheet will allow for more user-defined flexibility. Future developments in laboratory methods for determining starch digestibility may influence its relationship to milk per ton estimates relative to the other quality measures.

Ivan et al. (2005) evaluated “low-fiber” (26% starch, 49% NDF, 58% IVNDFD) versus “high-fiber” (22% starch, 53% NDF, 67% IVNDFD) corn silages in 30% NDF diets fed to lactating dairy cows. Reported per cow per day milk yields were converted to milk per ton of corn silage DM basis using their corn silage DMI data. Actual milk per ton was 168 lb. higher for high-fiber than low fiber corn silage. Model-predicted milk per ton estimates were 132 lb. and 297 lb. higher for high-fiber than low-fiber corn silage from MILK2006 and MILK2000 models, respectively. This suggests reasonable agreement with in vivo data for MILK2006 and better agreement with in vivo data for MILK2006 than MILK2000. Presented in Figure 1 is model-predicted milk per ton minus milk per ton calculated using in vivo data from 13 treatment comparisons in 10 JDS papers (Ballard et al., 2001; Ebling and Kung, 2004; Ivan et al., 2005; Neylon and Kung, 2003; Oba and Allen, 2000; Oba and Allen, 1999a; Qiu et al., 2003;Taylor and Allen, 2005b; Thomas et al., 2001; Weiss and Wyatt, 2002) for MILK2006 versus MILK2000. There was less model over-predictive bias for MILK2006 than MILK2000. The model-predicted milk per ton minus in vivo-calculated milk per ton difference exceeded 100 lb. (approximately 1 lb. per cow per day) for only 2 of 13 treatment comparisons with MILK2006 versus 8 of 13 treatment comparisons with MILK2000.

While these observations with MILK2006 are encouraging, more model validations relative to in vivo data are needed. The MILK2006 Excel Workbook can be downloaded at the University of Wisconsin’s Extension website.

Table 1. Impact of IVNDFD (average lab IVNDFD 58% of NDF) in whole-plant corn harvested at 35% DM content with kernel processing on TDN1x (%), NEL-3x (Mcal/lb.) and milk (lb.) per ton using MILK2006 or MILK2000 with nutrient composition adapted from NRC (2001) for “normal” corn silage (8.8% CP, 45% NDF, 27% starch, 4.3% ash, and 3.2% fat).
IVNDFD% MILK
2006
TDN1x 		MILK
2006
NEL-3x 		MILK
2006
Milk/ton 		MILK
2000
TDN1x 		MILK
2000
NEL-3x 		MILK
2000
Milk/ton
46 65.3 0.66 2936 66.4 0.69 3074
50 67.0 0.67 3037 68.2 0.71 3244
54 68.8 0.68 3138 70.0 0.73 3413
58 70.5 0.69 3237 71.8 0.75 3579
62 72.3 0.70 3336 73.6 0.77 3743
66 74.0 0.72 3434 75.4 0.79 3905
70 75.8 0.73 3530 77.2 0.81 4065

Table 2. Impact of “low” (45% DM, unprocessed, 8.8% CP, 54% NDF, 46% IVNDFD, 20% starch, 4.3% ash, and 3.2% fat) versus “high” (30% DM, processed, 8.8% CP, 36% NDF, 70% IVNDFD, 34% starch, 4.3% ash, and 3.2% fat) quality extremes in whole-plant corn on TDN1x (%), NEL-3x (Mcal/lb.) and milk (lb.) per ton using MILK2006 or MILK2000.
Quality MILK
2006
TDN1x 		MILK
2006
NEL-3x 		MILK
2006
Milk/ton 		MILK
2000
TDN1x 		MILK
2000
NEL-3x 		MILK
2000
Milk/ton
“Low” 56.2 0.55 2242 57.3 0.58 2418
“High” 76.3 0.74 3617 79.9 0.84 4256

Table 3. Analysis of correlations for selected corn silage nutrients and their digestibility coefficients with milk per ton estimates from MILK2006, 2000, 1995, and 1991 models (n = 3727 treatment means; Shaver and Lauer, 2006).
r-values MILK
2006
Milk/ton1 		MILK
2000
Milk/ton2 		MILK
1995
Milk/ton3 		MILK
1991
Milk/ton4
NDF% -0.46 -0.40 -0.94 -0.99
Starch% 0.48 0.44 0.75 0.74
IVNDFD, % of NDF 0.49 0.70 0.16 -0.10
StarchD, % of Starch 0.30 0.21 -0.25 -0.27
1Calculated as per Schwab et al. (2003) except for modifications discussed herein.
2Calculated as per Schwab et al. (2003).
3Calculated as per Undersander et al. (1993) except for in vitro DM digestibility adjustment.
4Calculated as per Undersander et al. (1993) using ADF and NDF.

Corn Silage Fig 1.jpg


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  • Thomas, E. D., P. Mandebvu, C. S. Ballard, C. J. Sniffen, M. P. Carter, and J. Beck. 2001. Comparison of corn silage hybrids for yield, nutrient composition, in vitro digestibility, and milk yield by dairy cows. J. Dairy Sci. 84:2217–2226.
  • Undersander, D.J., W.T. Howard, and R.D. Shaver. 1993. Milk per acre spreadsheet for combining yield and quality into a single term. J. Prod. Ag. 6:231 235.
  • Weiss, W. P., and D. J. Wyatt. 2002. Effects of feeding diets based on silage from corn hybrids that differed in concentration and in vitro digestibility of neutral detergent fiber to dairy cows. J. Dairy Sci. 85:3462–3469.
  • Yu, P., J. T. Huber, F.A.P. Santos, J. M. Simas, and C. B. Theurer. 1998. Effects of ground, steam-flaked, and steam-rolled corn grains on performance of lactating cows. J. Dairy Sci. 81: 777-783.

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Disclaimer

This fact sheet reflects the best available information on the topic as of the publication date. Date 5-25-2007

This Feed Management Education Project was funded by the USDA NRCS CIG program. Additional information can be found at Feed Management Publications.

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This project is affiliated with the Livestock and Poultry Environmental Learning Center.

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Project Information

Detailed information about training and certification in Feed Management can be obtained from Joe Harrison, Project Leader, jhharrison@wsu.edu, or Becca White, Project Manager, rawhite@wsu.edu.

Author Information

Randy Shaver
Professor and Extension Dairy Nutritionist
Department of Dairy Science
College of Agricultural and Life Sciences
University of Wisconsin – Madison
University of Wisconsin – Extension

Reviewer Information

Pat Hoffman – University of Wisconsin
Jim Barmore – Nutrition Consultant

Partners

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