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Dairy Cattle Nutrition Workshop Continuing education for feed industry professionals and nutritional consultants 2011 PROCEEDINGS November 8-10, 2011 Holiday Inn, Grantville, PA Presented by the Penn State Extension Dairy Team

Nutrition Workshop Proceedings 2011

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Page 1: Nutrition Workshop Proceedings 2011

Dairy CattleNutrition WorkshopContinuing education for feed industry professionals and nutritional consultants

2011 PROCEEDINGS

November 8-10, 2011Holiday Inn, Grantville, PA

Presented by the Penn State Extension Dairy Team

Page 2: Nutrition Workshop Proceedings 2011
Page 3: Nutrition Workshop Proceedings 2011

2011 Dairy Cattle Nutrition Workshop

Agenda

November 8, 2011

12:00 – 1:00 Registration

1:00 – 5:00 Feed Management Certifi cation Workshop

7:00 – 9:00 Everything You Need to Know about Farm Ammonia Emissions (RSVP required)

November 9, 2011

7:00 – 8:15 Registration

Balchem Corporation Preconference Symposium

8:00 – 8:25 Coff ee and Danish

8:25 – 8:30 Introduction

8:30 – 9:10 Environmental Impact of Feeding Cows from a Nitrogen Effi ciency Perspective, Dr. Gert van Duinkerken, Wageningen UR Livestock Research, the Netherlands

9:10 – 9:50 Practical Nutrition: Amino Acid Supply in Diets Containing Corn Silage and Byproducts, Dr. Paul Kononoff , University of Nebraska-Lincoln

9:50 – 10:20 Break

10:20 – 11:00 Lysine in Dairy Cows: From Digestion to Milk Protein, Dr. Hélène Lapierre, Agriculture and Agri-Food Canada

11:00 – 11:20 Precision Release Nutrition - What You Need to Know, Dr. Ryan Ordway, Balchem Corporation

11:20 – 12:00 Measuring Feed Effi ciency on the Back of a Napkin, Dr. Robert Fry, Atlantic Dairy Management Services

12:00 – 12:15 Questions and Wrap Up

12:15 – 2:00 Lunch

12:15 – 1:30 ARPAS Northeast Chapter annual meeting

12:00 – 8:00 Exhibit hall open

2:00 – 3:15 Afternoon workshop - session 1

3:15 – 3:30 Break Sponsored by Balchem Corporation

3:30 – 4:45 Afternoon workshop - session 2

5:00 ARPAS Exam off ered

5:00 – 7:00 Reception in exhibit area Sponsored by Alltech Inc.

7:15 – 9:30 Evening Session and Dinner Sponsored by Alltech Inc. (RSVP required)

Moldy Silage Syndrome and Climate Eff ects on Crops, Nick Adams, Alltech

YouTube-proofi ng Agriculture: Lessons Learned from H$U$ and Social Media AGvocacy, Mr. Andy Vance, Agricultural Journalist and Commentator

Page 4: Nutrition Workshop Proceedings 2011

Mark your calendars!Future Dates for the Penn State Dairy Cattle Nutrition Workshop

November 13 - 14, 2012 November 12 - 13, 2013

Grantville, PA

November 10, 2011

6:30 – 7:45 Breakfast (RSVP required)Sponsored by Lallemand Animal Nutrition

Ketosis in Dairy Herds: Clostridial Silages and Other Factors, Dr. Gary Oetzel, University of Wisconsin-Madison

6:45 – 7:45 RegistrationCoff ee and Danish

7:00 - 3:00 Exhibit hall open

8:00 – 8:45 Managing the Rumen Environment to Control Milk Fat Depression, Dr. Tom Jenkins, Clemson University

8:45 – 9:30 What’s Happening in Nutrient Management in the Chesapeake Bay?, Dr. Doug Beegle, Penn State

9:30 – 10:15 Starch Digestibility in Ruminants, Dr. Bill Mahanna, Pioneer Hi-Bred, A DuPont Business

10:15 – 10:45 Break

10:45 – 11:45 Morning workshop - session 1

11:45 – 12:00 Break

12:00 - 1:00 Morning workshop - session 2

1:00 – 2:00 Lunch, Sponsored in part by Novus International

2:00 – 3:00 Afternoon workshop session

2:00 ARPAS Exam off ered

Coff ee service and breaks sponsored by Prince Agri-Products, Varied Industries Corporation, and Virtus Nutrition

Post-conference Seminar Sponsored by Novus International Inc. (RSVP required)

3:15 – 5:30 Removing Limitations on Cow Performance through Improved Cow Comfort

Welcome, Dr. Robin Rastani, Novus

The Novus C.O.W.S. Benchmark and Assessment Program: An Introduction, Mr. Ed Galo, Novus

Removing Limitations on Cow Performance through Behavior, Management, and Housing Design, Dr. Cassandra Tucker, University of California, Davis

Novus C.O.W.S. Implementation and Key Learnings, Ms. Kiyomi Ito, Novus

Wrap-up, Mrs. Suzy Demeester, Novus

Page 5: Nutrition Workshop Proceedings 2011

2011 Dairy Cattle Nutrition Workshop

Contents of Proceedings

Improving Nitrogen Effi ciency of Dairy Cows and Its Environmental Impact

G. van Duinkerken, A. Bannink, C. J. A. M. de Koning, J. Dijkstra, L. B. J. Šebek, J. W. Spek, and A. M. van Vuuren, Wageningen University, the Netherlands ...................................................................... page 1

Practical Nutrition: Amino Acid Supply in Diets Containing Corn Silage and Byproducts

P. J. Kononoff and H. Paz, University of Nebraska-Lincoln ..................................................................................page 15

Lysine in Dairy Cows: From Digestion to Milk Protein

H. Lapierre and D. R. Ouellet, Agriculture and Agri-Food Canada; L. Doepel, University of Calgary; and G. E. Lobley, University of Aberdeen .................................................................................................................page 19

Precision Release Nutrition — What You Need to Know

Ryan Ordway, Balchem Corporation ..........................................................................................................................page 27

Measuring Feed Effi ciency: Why and How on the Back of a Napkin

Robert C. Fry, Atlantic Dairy Management Services .............................................................................................page 29

Managing the Rumen Environment to Control Milk Fat Depression

T. C. Jenkins, Clemson University ................................................................................................................................page 31

What’s Happening in Nutrient Management in the Chesapeake Bay?

Douglas B. Beegle, Penn State ......................................................................................................................................page 39

Starch Digestibility in Corn Grain and Silage

Bill Mahanna, Pioneer Hi-Bred, A DuPont Business ..............................................................................................page 49

Byproduct Feeds and Milk Fat Depression

H. A. Ramirez-Ramirez and P. J. Kononoff , University of Nebraska-Lincoln ..................................................page 75

TMR AuditsTM Improve TMR Consistency

Tom Oelberg, Diamond V ...............................................................................................................................................page 81

Meeting Calves’ Needs: Winter Feeding and Amino Acids

Mark Hill, Gale Bateman, Jim Aldrich, and Rick Schlotterbeck, Nurture Research Center ......................page 87

Energy and Protein Nutrition for Transition Cows

Ric R. Grummer and Ryan Ordway, Balchem Corporation .................................................................................page 93

Current Concepts in Time Budgeting for Dairy Cattle

Rick Grant, Miner Institute .......................................................................................................................................... page 101

Novus C.O.W.S. Program: On-farm Assessments to Improve Cow Comfort

K. Ito, Novus International .......................................................................................................................................... page 113

Exhibitor Directory .................................................................................................................................................................... page 119

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2011 Penn State Dairy Cattle Nutrition Workshop 1

Improving Nitrogen Effi ciency of Dairy Cows and Its Environmental Impact

G. van Duinkerken1*, A. Bannink1, C. J. A. M. de Koning1, J. Dijkstra2,

L. B. J. Šebek1, J. W. Spek1,2 and A. M. van Vuuren1

1Wageningen UR Livestock Research, Edelhertweg 15, 8219 PH Lelystad, The Netherlands2Animal Nutrition Group, Wageningen University, Marijkeweg 40, 6709 PG Wageningen, The Netherlands

*Email: [email protected]

SUMMARY

In lacta ng dairy ca le, the nitrogen use effi ciency may vary between 16 to 36%. Advances in dairy cow nutri on and innova ons in management tools enable a substan- al increase in the nitrogen use effi ciency of dairy ca le.

In many countries, laws and regula ons on environmen-tal protec on challenge the dairy produc on chain to increase the nitrogen effi ciency on farm level. Prac cal opportuni es and restraints in this area are discussed and improvement in nitrogen use effi ciency of dairy ca le is put into the perspec ve of other environmental sustain-ability objec ves, animal health and farm economics.

INTRODUCTION

Nitrogen use effi ciency (NUE) at whole-farm level var-ies between 8% and 64% and declines as stocking rates increase (Powell et al., 2010). The NUE at animal level is the ra o between nitrogen (N) in milk and N intake. The maximum NUE of lacta ng dairy cows (producing 25 kg milk/day) is 43% (van Vuuren and Meijs, 1987). Howev-er, this is a theore cal maximum, based on assump ons for inevitable nitrogen (N) losses for maintenance and milk produc on. Van Vuuren and Meijs (1987) assumed that for maintenance these losses accumulate to 67 g N/day including N in skin and hair, endogenous urinary N, and endogenous fecal N. Furthermore, there is a minimum loss of 4.1 g N/day inherent to the produc- on of 1 kg milk, including ineffi ciencies in amino acid

u liza on in the intermediary metabolism, inevitable losses due to addi onal ruminal synthesis of microbial nucleic acids and addi onal endogenous fecal N losses (van Vuuren and Meijs, 1987).

Achieving this theore cal maximum NUE of lacta ng dairy cows is not feasible in prac ce due to subop mal N diges bility of diets (van Vuuren and Meijs, 1987) and subop mal amino acid composi on of ileal digested protein (Misciatelli et al., 2003; No sger and St. Pierre, 2003; van Vuuren and Meijs, 1987). Various studies on NUE of dairy ca le in prac ce indicate that, in de-

veloped areas, the common range of NUE in lacta ng dairy herds is 20 to 36% (Van Duinkerken et al., 2011), or could even become as low as 16% (Powell et al., 2010). This indicates that there might be a high poten al to reduce N losses at both animal and farm level. This paper focuses on advances in nutri on and innova ons in management tools, and their ability to contribute to a further increase of NUE of dairy ca le. Prac cal op-portuni es to further increase NUE and restraints in this area are discussed and improvement in NUE is put into the perspec ve of other environmental sustainability objec ves, animal health and farm economics.

ADVANCES IN NUTRITION

Protein Evaluation Systems

Classical protein evalua on systems for dairy ca le have the objec ve to es mate the supply of protein (i.e. amino acids) to the post absorp ve system of the animal. Furthermore, they es mate the minimal protein require-ment of the animal for maintenance func ons, milk produc on, pregnancy, and juvenile growth. In various countries around the world, scien sts have developed such protein evalua on systems, some mes embedded in a more integral feed evalua on or diet formula on system. Some currently used and widely recognized systems are the CNCPS in the USA (Fox et al., 2004), the DVE/OEB system in the Netherlands (van Duinkerken et al., 2011), the FiM system in the UK (Thomas, 2004), the NorFor system in the Nordic countries (Volden, 2011), the NRC system in the USA (NRC, 2001), and the PDI system in France (Vérité and Peyraud, 1989). Although these systems may vary considerably at detail level the general set-up of these systems has a lot of commonality. In all models the chemical characteriza on of the feed is the fi rst step, although the characteriza on methods can diff er. Next, the processes in the rumen are being described, taking into account the solubility and deg-rada on of feed components in the rumen, the rate of passage of feed components through the rumen and the effi ciency of rumen micro-organisms to synthesize

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2 November 8-10 Grantville, PA

microbial protein from the rumen available substrate. Furthermore, the protein diges bility in the small in-tes ne is es mated. Finally, equa ons are derived to determine the protein requirement of the animal, tak-ing into account various animal factors (e.g. live weight, produc on level, pregnancy, and/or age).

The various protein evalua on systems seem to diff er in the es mates or assump ons that are made for each individual element in a specifi c system, e.g. the effi ciency of microbial protein synthesis (MPS), the frac onal deg-rada on rate, or the frac onal passage rate. Usually, such individual system elements have been developed on the basis of in-depth literature and experimental studies. Table 1 provides an example of diff erences between protein evalua on systems in their approach to quan fy MPS effi ciency. This comparison indicates the obvious diff erences between the various systems. However, such diff erences should not be assessed as such, but should always be evaluated in its context, viz. the integral protein evalua on system. For example, if one system es mates a higher microbial effi ciency, this does not necessarily mean that the es mated supply of microbial protein to the duodenum is higher. O en diff erences in the assumed frac onal passage rate or degrada on rate compensate for the diff erence in MPS effi ciency. Furthermore, a higher predicted supply of microbial protein to the duodenum, does not necessarily mean that the total amino acid

supply to the post absorp ve system is higher, because this supply is also aff ected by other factors, such as the assumed diges bility in the small intes ne.

Various valida on studies indicate that, in general, mod-ern protein evalua on systems have a reasonable ability to predict the protein value of diets (E le and Schwarz, 2001; van Straalen et al., 1994; van Duinkerken et al., 2011). Therefore, it might be concluded that further developments in protein evalua on systems can only result in minor advances. It should be noted here that current protein evalua on systems for dairy ca le aim to match protein requirements with protein intake at pre-defi ned milk produc on levels, but are not suitable to predict the responses to dietary changes in terms of produc on level and product composi on and excre on of nutrients to the environment (Dijkstra et al., 2007). In par cular, upon reduc on of dietary crude protein levels the micro-organisms rely to an increasing extent on N recycled to the rumen via saliva or through the rumen wall, whereas current protein evalua on systems do not, or only to a crude level, represent this N recycling. There are indica ons (Sauvant and Mar n, 2006; van Duinkerken, 2011) that further progress in nutrient use effi ciency is s ll opportune, if other types of models are being developed and applied in dairying prac ce, possibly in combina on with the above men oned empirical protein evalua on systems. Modeling eff orts

Table 1. Overview of concepts used in various protein evalua on systems for dairy ca le to es mate microbial protein synthesis (MPS)

System Concept used to es mate MPS Reference

CNCPS Related to dietary frac onal degrada on rate and type of fermentable carbohydrates Fox et al., 2004

DVE/OEBDepending on type of rumen bacteria (par cle vs. liquid associated), rumen frac onal ou low rates and ATP yield of various feed components (sugars, starch, NDF, crude protein)

van Duinkerken et al., 2011

FiMDepending on the rumen degradability, rumen frac onal ou low rates (on the basis of dry ma er intake), and ATP yield of various feed frac ons (i.e. soluble and very small par cles, concentrate par cles and forage par cles)

Thomas, 2004

NorFor Depending on dry ma er intake, animal body weight, and the starch and N-corrected rest frac on in the feedstuff s Volden, 2011

NRC Microbial CP (g) = 130 × kg TDN orMicrobial CP (g) = 1.18 × g RDP (if RDP < 1.18 × 130 × kg TDN) NRC, 2001

PDIMicrobial CP1 (g) = 145 × kg FOM orMicrobial CP2 (g) = 0.9 × g RDP; the lowest value of microbial CP1 and mi-crobial CP2 should be used

Vérité and Peyraud, 1989

ATP = Adenosine triphosphate; CP = crude protein; TDN = total diges ble nutrients; FOM = fermentable organic ma er; RDP = rumen degradable protein

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2011 Penn State Dairy Cattle Nutrition Workshop 3

in the area of nutrient based dynamic mechanistic models (Baldwin, 1995; Dumas et al., 2008; Kebreab et al., 2009) appear to be quite promising in this respect. Furthermore, response based adap ve models based on real me process data (André et al., 2010; André et al., 2011) could be modifi ed with the objec ve to maximize nutrient use effi ciency instead of gross economic margin.

Low Protein Diets

A meta-analysis by Huhtanen and Hristov (2009) dem-onstrated that dietary CP concentra on is the most important factor infl uencing NUE at animal level. Ban-nink (2007) hypothesized that a CP frac on of 0.125 of dietary DM may be possible without loss of rumen fermenta on capacity. Various studies examined the eff ects of low protein diets on NUE and animal perfor-mance. Law et al. (2009) studied varying levels of dietary protein in early lacta on (up to d 150) and concluded that high-protein diets (17.3% CP in DM) improved feed intake and animal performance. Therea er, protein concentra on could be reduced to 14.4% CP in DM with no detrimental eff ects on animal performance.

Wu and Sa er (2000) studied the milk produc on response of dairy cows during a complete lacta on to various combina ons of dietary protein supplementa- on: 15.4, 17.4, 17.4, and 19.3% CP in DM during the fi rst 16 wk of lacta on and 16.0, 16.0, 17.9, and 17.9% CP in DM for wk 17 to 44 of lacta on, respec vely. Cumula ve milk yield at 308-d lacta on for each of the treatment groups was 10,056, 10,831, 11,095, and 11,132 kg, respec vely. They concluded that cows of this produc on level fed diets similar to those used in this experiment benefi t from dietary protein of approxi-mately 17.5% during the fi rst 30 wk of lacta on and that a reduc on in dietary protein to 16% can be made around wk 30 of lacta on. Colmenero and Broderick (2006) studied the eff ect of dietary CP concentra on on ruminal N metabolism in lacta ng dairy cows. Bacterial effi ciency (g of total bacterial non-ammonia N fl ow/kg of organic ma er truly digested in the rumen) and omasal fl ows of dietary non-ammonia N and total non-ammonia N also showed posi ve linear responses to dietary CP. Total non-ammonia N fl ow increased from 574 g/d at 13.5% CP in DM to 688 g/d at 16.5% CP in DM but did not increase further with the feeding of more CP. They concluded that under the condi ons of their study, 16.5% of dietary CP appeared to be suffi cient for maximal ruminal ou low of total bacterial non-ammonia N and total non-ammonia N. Bannink and Dijkstra (2008) applied a dynamic, mechanis c model

to inves gate the eff ect diges on on rumen func on and cow performance. The model describes rumen fermenta on, intes nal diges on and milk produc on based on diff erent types of nutrients absorbed from the gastrointes nal tract. Four cases of organic dairy farms were evaluated. Diets off ered to lacta ng cows contained between 14% and 15% CP in DM (Bannink and Dijkstra, 2008) and milk yield was between 22 and 34 kg of milk/day. The simula on results did not indicate a poten al limita on of rumen func on by N availability and simulated cow and observed milk yield matched closely. Under these circumstances the avail-ability of metabolizable energy was predicted to limit milk yield, not metabolizable protein.

The actual, marginal response in milk protein output to varia on in supply of metabolizable protein varies sub-stan ally, although current protein evalua on systems tend to use a fi xed effi ciency value or effi ciency values that vary within narrow ranges to calculate milk protein produc on. Mechanis c models though aim to predict the varia on in effi ciency rather than use presumed ef-fi ciencies to predict protein output from it (Dijkstra et al., 2007). An example is presented in Figure 1 derived from Cant (2005). In various experiments (n = 23), dairy ca le were provided with graded levels of metabolizable protein calculated according to the French PDI system. Upon cropping the data, a slope of 0.64 g milk protein/g PDI was obtained, and the value of 0.64 is the actual ef-fi ciency factor used in the PDI system. However, using mixed-model analysis, the protein response slope was only 0.24 g milk protein/g PDI. Diff erences in the ra o between PDI and energy available for milk produc on may well have caused the low marginal efficiency. Clearly, milk protein effi ciency is not fi xed but depends on the supply of non-protein nutrients as well.

Nitrogen losses in the rumen also have been a ributed to the asynchronous availability of energy and N com-ponents for MPS (Sinclair et al., 1993). Cabrita et al. (2006) extensively reviewed concepts to synchronize energy and N availability in the rumen. In general such synchroniza on is targeted at increased effi ciency of MPS, increased milk protein yield, and reduc on of N surplus at rumen level. This will lead to decreased MUN content, reduced N excre on (Kebreab et al., 2002; Nou-siainen et al., 2004; Burgos et al., 2007) and reduced NH3 emission (Frank and Swensson, 2002; van Duinkerken et al., 2005). However, Cabrita et al. (2006) did not fi nd consistent eff ects of synchroniza on of carbohydrate fermenta on and N availability on ruminal MPS supply,

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NH3 concentra on, or milk yield. Refl ux of N to the ru-men from blood urea through saliva and rumen wall, seems an appropriate N source to match the availability of slowly degradable carbohydrates (e.g. NDF), and consequently synchroniza on seems more appropriate for rapidly degradable protein and carbohydrates. An addi onal eff ect of rumen synchroniza on might be the preven on of 1) low rumen pH or even sub-acute rumen acidosis, 2) reduced ac vity of celluloly c rumen bacteria, and 3) reduced dry ma er intake (Dijkstra et al., 2002; Russell and Strobel, 2005). When feeding low protein diets, diet op miza on and the use of special feed ingredients become increasingly relevant. Van Du-inkerken et al. (2011) recommend a rumen degradable protein balance (RDPB) > 0 g at all moments during the day, whereas others recommend to use a safety margin of at least 100 g RDPB/day (De Campeneere et al., 2009). In diets with a low rumen degradable protein balance or situa ons with rumen fl uid ammonia levels below 50 mg/L (assumed general benchmark for the lower limit to prevent subop mal MPS), urea or encapsulated urea might be considered as an ingredient to increase nitrogen availability in the rumen. Garre et al. (2005) demonstrated that encapsulated urea can improve MPS effi ciency in a study with ammonia N levels in rumen fl uid ranging from 4.7 to 9.2 mg/dL between treatments. Souza et al. (2010) tested protected urea as a replacer for true feed proteins at a high level of dietary CP (18.35% CP in DM) and concluded that the par al replacement of soybean meal by encapsulated urea (11.4% soybean meal versus 0.4% encapsulated urea + 9.0% soybean meal) did not decrease the performance of lacta ng cows, although milk fat and total solids contents were reduced by the diet including encapsulated urea.

Rumen bypass amino acids are another category of special feed ingredients that can be added to custom-ize low proteins diets to the requirements for specifi c amino acids. Methionine is considered as one of the fi rst limi ng amino acids (Broderick et al., 1974; Schwab et al., 1976), especially in diets with oilseed meals as a main protein source. Also lysine is considered as po-ten ally limi ng (Broderick et al., 1974; Schwab et al., 1976), especially in corn based diets, whereas his dine is a poten ally limi ng amino acid in grass silage-based, low protein diets (Huhtanen et al., 2002).

Grass from Systems with Low N Fertilizer Application

In intensive grassland management systems, high N ap-plica on increases grass growth and as a consequence grass can be harvested in earlier stages of maturity giv-ing high nutri ve values and maximum voluntary intake of grass (Minson, 1990), but also high N concentra ons (van Vuuren et al., 1990). Valk et al. (2000) examined the eff ects of three levels of N fer liser (150, 300, 450 kg N/ha/yr) at DM yields between 1,500 and 2,000 kg/ha. Reducing N fer lisa on clearly decreased the CP content, but also the net energy value of grass. In autumn, but not in spring, reducing N fer lisa on level from 450 or 300 to 150 kg/ha/yr signifi cantly reduced grass DM intake. Con-sequently, in most situa ons milk produc on declined upon a reduc on from 300 to 150 kg/ha/yr. Obviously, N effi ciency increased markedly. In other work, lowering N fer lisa on did not reduce intake and milk produc on as long as the grass is grazed or harvested at the same age of regrowth (Peyraud and As garraga, 1998).

Ellis et al. (2011) derived simple rela onships for the rumen degrada on of grass NDF and CP. Reduced CP or increased NDF levels tended to decrease the poten ally

0 500 1000 1500

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Figure 1. Protein output in milk of lacta ng cows fed graded levels of metabolizable protein according to the PDI system (n = 23 experiments). Le hand side, cropped data set resul ng in a slope of 0.64. Right hand side, full data set analyzed with mixed-model techniques resul ng in a slope of 0.24. Adapted from Cant (2005).

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2011 Penn State Dairy Cattle Nutrition Workshop 5

degradable frac on of CP and NDF, and also to decrease the frac onal degrada on rate of both frac ons. These rela onships were used in a mechanis c model of di-ges on ad metabolism. The greatest benefi t in terms of N effi ciency were seen when a reduced CP content coincided with an increased soluble sugar content. However, simulated milk yield decreased slightly when sugars increased at the expense of CP.

Applica on of a lower rate of N fer liza on on grassland is an important strategy to reduce N losses from grass based dairy farming. Using a dynamic, mechanis c sim-ula on model of rumen fermenta on, extended with equa ons for diges on in the small and large intes ne and for calcula on of excreted compounds in urine and feces, Reijs (2007) es mated the impact of nutri on on the amount and the type of N excreted. Recently, these simula on results were published in combina on with predic on of enteric methane (Dijkstra et al., 2011). Next to other strategies (related to supplementa on of the diet and exchange of grass silage for maize silage, level of concentrate feeding), both a reduced N fer l-iza on rate and (cu ng of) a heavier grass sward were tested. Both reduced the CP content of grass strongly, which resulted in a concomitant reduc on of simulated N excre on, and a decrease of the frac on of immedi-ately available N in excreta. Although a lower applica on of fer lizer N and a heavier grass sward normally have a major impact on feed intake and the net energy value of grass, there s ll is a strong decline in N excre on per unit of milk produced. A lower N fer liza on hence may strongly reduce N excre on and hence the environmen-tal impact per unit of milk produced. However, also the intensity of the dairy farm (milk produc on aimed for per unit of area) which determines the N fer liza on rate required to fulfi l the forage needs on a farm and the number of animals and total N excreted per unit of area determine to a large extent the environmental impact.

High Sugar Grass Cultivars

Perennial ryegrass cul vars with a high concentra on of water soluble carbohydrates (WSC) have been advocated to reduce N excre on. These high WSC grasses have been proposed to off er benefi ts through increased milk yields per cow, either by increased diges bility of the high WSC grass and/or by increased grass intake. Also, the increased WSC content might s mulate rumen MPS, reducing losses of degraded CP in the rumen as ammonia. Some controversy has arisen over the poten al benefi ts though. Miller et al. (2001) compared a control and high WSC cul var and observed an increased N effi ciency

(from 23 to 30%), mainly because of the increased DM diges bility and increased milk yield, although intake did not increase. Miller et al. (2000) evaluated control and high WSC grasses and did not fi nd eff ects on milk produc on, though N effi ciency was improved. However, the control grass was fer lized 3 wks before harves ng, whereas the high WSC grass was not, crea ng diff erences in WSC and N levels due to this diff erence in fer liza on. Similarly, Moorby et al. (2006) also compared a control and high WSC and although N effi ciency decreased, milk yield did not. However, in this study the diff erences in WSC were enlarged by diff erences in me of cu ng. The control was harvested in the morning and the high WSC cul var in the a ernoon, thus in itself crea ng diff er-ences in WSC that may impact on intake, milk yield and N effi ciency (Abrahamse et al., 2009).

Studies of Tas et al. (2005, 2006a) and Taweel et al. (2005a,b) to the eff ect of high WSC cul vars (6 to 8 cul- vars were compared) without changes in fer lisa on

level or me of cu ng, did not show improvements in in-take or milk produc on, and N effi ciency did not change between most of the cul vars in the various studies. In these studies, grass was cut and fed indoors. The WSC diff erences used in these studies was smaller than in the studies of Miller et al. (2000, 2001) and Moorby et al. (2006) though. In grazing situa ons using 4 cul vars diff ering in WSC content, Tas et al. (2006b) again did not fi nd an improvement in N u lisa on of dairy ca le. To further examine the highly variable responses observed upon feeding high WSC grasses compared with control, Ellis et al. (2011) used a dynamic, mechanis c model and exchanged WSC against CP, NDF, or a mixture of CP and NDF. When WSC replaced CP, a much greater benefi t in N u liza on effi ciency was simulated when compared with WSC replacing NDF. However, simulated milk yield decreased slightly when WSC replaced CP, but increased when WSC increased at the expense of NDF. These simu-la on results help to explain the varia on in response observed in various experiments. Although the high WSC grasses may not increase milk produc on, the prospects for improved N u lisa on effi ciency appear sound.

High WSC may also aff ect ruminal pH and consequently NDF degrada on and animal health. Ruminal pH in dairy ca le grazing high-quality grass is characterized by nadirs below 5.8 par cularly during late evening (van Vuuren et al., 1986; O’Grady et al., 2008), which can be classifi ed as sub-acute ruminal acidosis. However, Moorby et al. (2006) observed no eff ect of WSC content on faecal NDF diges bility.

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INNOVATIONS IN MANAGEMENT TOOLS

New Sensor Technologies

Developments in the area of on-farm sensor technolo-gies are quite promising and might be able to contrib-ute to further improvement of nutrient use effi ciency. A lot of these technologies are related to (in-line) milk sampling and analysis on farms. Modern milking systems, like the automa c milking (AM) systems, are equipped with various sensors ranging from sensors to control the milking process and to measure milk yield, up to sensors that analyze the milk quality in several ways, i.e. milk composi on, cell counts, blood detec on, conduc vity, progesterone, milk clo ng parameters and so on (de Koning, 2011; Leitner et al., 2011). Some of these milk parameters are related to energy and protein metabolism of the cow and provide management informa on at both the herd level and at the animal level to support decisions on diet op -miza on and feeding strategy. Furthermore, there are developments to develop sensors for live body weight and body fat measurements for use in common prac- ce. However, it should be taken into account that all

these various sensors collect enormous amounts of data, which have to be processed with appropriate so ware. The challenge for both manufacturers and

end users is to detect any abnormali es in this so called cloud of data, so ac ons (management by excep on) can be taken by the farmer (de Koning, 2011).

Milk Urea Nitrogen as an Indicator of NUE

Although MUN can successfully be used as an indicator for UE, N excre on, and NH3 emission from dairy barns (van Duinkerken, 2011), there is some evidence that a certain varia on in MUN occurs which is not related to varia on in urinary urea-N (UUN) excre on (Spek et al., 2011) and subsequent NH3 emission. It is illustrated in Figure 2 (derived from Schröder et al., 2006) that over the full range of N excre on rates there is a good correla on between MUN and N excre on. However, the range of interest on farms aiming at a high NUE is more narrow (between 15 and 25 mg urea/dL milk) and in this range the correla on between N excre on and milk urea is rather poor.

If factors affecting the relationship between MUN and UUN excre on would become more pronounced, then the feasibility to use MUN as an indicator for NH3 emission would decrease, unless these “disturbing” factors can be incorporated in predic on models for UUN excre on or NH3 emission. A major “disturbing”

Figure 2. Rela onship between urinary N excre on (g N/cow/day) and milk urea content (mg urea/dL) in 9 experiments (derived from Schröder et al., 2006).

050

100150200250300350400450500

0 20 40 60 80

N u

rine

(g N

/cow

/day

)

Milk urea (mg urea/dl milk)

Experiment 1-4

Experiment 5

Experiment 6

Experiment 7

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factor might be the eff ect of genotype. If a certain heritability for MUN would have a minor rela onship, or no rela onship, with UUN, then it would be pos-sible to breed animals with lower MUN without the desired decreasing eff ect on UUN excre on and NH3 emission. Such a trend would constrain the poten al of MUN as an indicator for N and UUN excre on, and NH3 emission. Wood et al. (2003) calculated that heri-tability es mates for MUN in lacta ons one, two, and three were 0.44, 0.59, and 0.48, respec vely, and that heritability for yield traits (milk, fat, and protein) have a similar magnitude. A weak rela onship was observed between MUN and yield traits. Mitchell et al. (2005) and Stoop et al. (2007) found considerably lower heritabil-ity es mates for MUN, viz. 0.15 and 0.14, respec vely. However, in each of these three studies there were no data available to assess the rela onship between the heritability of MUN and UUN excre on. This rela on-ship was studied by Šebek et al. (2007) on the basis of a dataset consis ng of 15,720 week averaged data of 723 individual dairy cows from 26 diff erent feeding trials. The milk urea breeding values of these cows ranged from -5 to +6 mg milk urea per 100 g milk and average N effi ciency in the database was 29.9 ± 6.1%. Further sta s cal analysis showed that heritability for MUN was not related to N effi ciency (r2 = 0.03 and 0.08 at 28 and 105 days in milk, respec vely) and that this absence of a rela onship was independent of stage of lacta on, diff erences in the extent of mobiliza on of body protein during early lacta on, and of diff erences in reliability of the es mated breeding values for milk urea. As a consequence, Šebek et al. (2007) concluded that breeding dairy cows with low milk urea contents does not contribute to an improved N effi ciency, a re-duced N excre on, or a reduced NH3 emission. Other “disturbing” factors aff ec ng the rela onship between MUN and UUN excre on have recently been exten-sively reviewed by Spek et al. (2011). They concluded that varia on in milking and feeding frequency and varia on in the distribu on of feed intake over me aff ect the diurnal varia on in MUN. Hence, Spek et al. (2011) assumed that the ra o between MUN and UUN is probably aff ected as well by these factors. In the same review, they showed that body weight is posi- vely correlated with the ra o between UUN and MUN.

Furthermore, factors aff ec ng water intake and urine produc on (such as sodium, potassium, and N content of the diet (Bannink et al., 1999) and water restric on) were shown to aff ect MUN and its rela onship with UUN (Spek et al., 2011).

PRACTICAL RESTRAINTS

This paper focuses on the NUE of dairy ca le. Low N effi ciencies observed in animal produc on systems and more par cular in dairy ca le produc on have been highlighted by the Food and Agriculture Organiza on (FAO) of the United Na ons (Steinfeld et al., 2006). It is per nent to note here that part of the low N effi ciency is related to the low quality of diets for ruminants compared with monogastrics, and when expressed on a human-edible protein basis, a diff erent picture emerges (Gill et al., 2010). European legisla on and policies to reduce environmental pollu on by animal produc on do not include specifi c requirements or targets to directly improve animal N effi ciency (Oenema et al., 2011). Nevertheless, one would expect that an increase in animal N effi ciency clearly contributes to a more sustainable dairy produc on system. It should however be verifi ed if other sustainability objec ves are not compromised by a feeding management aimed at increased N effi ciency. Therefore, it is worthwhile to assess such a feeding management for its possible trade-off s concerning the integral ecological footprint, farm-economics, and animal health. Each of these three possible trade-off s are briefl y addressed herea er.

Integral Ecological Footprint

The FAO considers greenhouse gas (GHG) emission and N losses as major environmental concerns of animal produc on. The major GHG emi ed from livestock produc on are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), with the la er being the most potent (Steinfeld et al., 2006). The trade-off s between N losses and GHG emissions can be assessed at various levels, i.e. at the whole chain level (e.g. from “cradle to farm gate”), at a farm level, or at the level of the animal.

At the whole chain level, Life Cycle Assessment (LCA) is considered as a proper tool to assess the integral envi-ronmental impact of dairy produc on systems (Thomas-sen and de Boer, 2005). Such LCA studies o en exclude a er-farm emissions and focus on cradle-to-farm-gate emissions because globally, the la er contributes on average approximately 93% of the total dairy GHG emis-sions (FAO, 2010). Based on a cradle-to-farm-gate LCA, Thomassen et al. (2008) recommended three major routes to improve the integral environmental perfor-mance of milk produc on in the Netherlands. The fi rst is a reduced use of concentrate feed ingredients with a high environmental impact; the second a lower use of concentrates per unit of milk; and the third a reduc on of nutrient surpluses by improving farm nutrient fl ows.

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It can be hypothesized that either of these three routes can interact in a posi ve or nega ve way with farm-man-agement aimed at increased N effi ciency of dairy ca le.

At farm level, modeling studies are the most common tool to assess trade-off s between N losses and GHG emis-sions. Schils et al. (2006) used three modeling approaches to study the eff ects of improved N management on GHG emissions from Dutch dairy farms. They concluded that a more strict policy on the N management of dairy farms (as applied in the Netherlands since 1985) reduces the N surplus at a farm level, with reduced fer liza on and reduced grazing hours as the main explanatory factors. Furthermore, their calcula ons indicated that a reduc- on of the N surplus with 1 g N/kg milk reduced the GHG

emissions by approximately 29 g CO2-equivalents/kg milk. However, as discussed by Dijkstra et al. (2011), the results of Schils et al. (2006) are debatable because they are based on fi xed CH4 emission factors for three categories of feed (viz., concentrate, grass, and maize silage) and thereby ignore the large varia on between individual feedstuff s and their eff ects on enteric CH4 emission. This rebu al is also applicable to the results of Šebek and Schils (2006). They performed a study with Dutch dairy farmers using modeling approaches and group discus-sions on the feasibility of specifi c adjustments in farm-management. Šebek and Schils (2006) concluded that a mineral management aimed to reduce NH3 emissions and nitrate leaching also reduces enteric CH4 emission. This conclusion might not be reversible, meaning that a farm management aimed to reduce enteric CH4 emission does not necessarily also reduce NH3 emissions and nitrate leaching. Tamminga et al. (2007) discussed the side ef-fects of feeding strategies aiming to reduce CH4 loss and concluded that mi ga on strategies to reduce CH4 loss from cows might be at the expense of increased losses of CO2 and N2O elsewhere in the chain. Furthermore, they emphasized that it remains to be inves gated to what extent CH4 mi ga on op ons aff ect NH3 emissions.

At the animal level, experiments in respira on cham-bers are a useful means to study the relationship between N emissions and GHG emissions. Aguerre et al. (2010; 2011) performed a study with lacta ng dairy cows in such chambers to monitor CO2, CH4, and NH3 emission under diff erent feeding regimes. In their study the propor on of forage in the diet was set at four levels varying from 47 to 68% at a dietary DM basis. Forage consisted of alfalfa silage and corn silage (mixed at a 1:1 ra o on DM basis). Dietary crude protein was maintained at the same level for all four forage to

concentrate ra os. Increasing the forage propor on in the diet did not aff ect milk yield, DM intake, N effi ciency of the animals, NH3 emission, and CO2 emission. How-ever, there was a linear increase in CH4 emission rate, CH4 emission per unit of DM intake, and CH4 emission per unit of milk yield with increasing levels of dietary forage:concentrate. CH4 emission (expressed in g/kg energy-corrected milk) was increased by 26% upon in-creasing the propor on of forage in the diet from 47 to 68%. This study clearly shows that dietary adjustments can have a diff erent eff ect on NH3 and CH4 emissions. However, the primary feeding strategy to decrease NH3 emission is to reduce the dietary N content (Monteny and Erisman, 1998; Frank et al., 2002), and the study of Aguerre et al. (2010; 2011) does not resolve whether or not a reduc on in the level of dietary N has an eff ect on GHG emissions by dairy cows. A modeling study by Dijkstra et al. (2011) addressed this specifi c ques on. In this study, various nutri onal strategies were evaluated using a mechanis c model to simulate fermenta on and diges ve processes in the gastro-intes nal tract and intermediate metabolism of dairy cows. There was a posi ve, although small, correla on (r2 = 0.15) between es mated N and CH4 excre on. A reduced N excre on (g N/kg Fat and Protein Corrected Milk; FPCM) was associated with increased CH4 emission (g CH4/kg FPCM), although this rela onship varied be-tween diff erent treatments. For example, when corn silage was included in the diet, both N excre on and CH4 emission per kg FPCM were reduced, whereas a lowered N fer liza on level of the grassland reduced N excre on per kg FPCM, but increased CH4 emission per kg FPCM. The overall simula on results demonstrated a nega ve correla on (r2 = 0.54) between 1) the ra o of urinary urea-N to total N excre on and 2) CH4 emis-sion per kg FPCM. This generally indicates that diets with a low poten al for NH3 emission might have a high poten al to increase CH4 emission.

Animal Health

There are no clear observa ons described in literature on the eff ects of a low dietary N content on the health of dairy ca le. This might be due to the fact that “health” as such is rather diffi cult to defi ne and that there is not an obvious read-out parameter for health. Animal health might be defi ned as a certain state of func onal-ity of the animal’s body and relates to the ability of the animal to adapt to changes in its environment. By using such a defi ni on for dairy cows, some poten al read-out parameters can be iden fi ed, such as prevalence of diseases (e.g. mas s, ketosis, abomasal displacement),

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behavioral disturbances, or even mortality, furthermore reproduc ve and fer lity parameters (i.e. concep on rate, days open), metabolic parameters (i.e. glucose sta-tus, blood plasma non-esterifi ed fa y acids; NEFA), and parameters concerning the animal’s immune func on.

During the transi on period fl uctua ons in plasma amino acids have been observed (Meijer et al., 1995), which could also aff ect the immune response of dairy ca le. However, this study also revealed that amino acid mobiliza on from muscle ssue occurs to prevent disturbances from amino acid defi ciencies. No evi-dence has been found in literature demonstra ng that low-N diets as such directly cause disease, behavioral disturbances or death in dairy ca le, although it can be hypothesized that low N-diets might decrease diet degradability and feed intake. This would reduce net energy intake, thereby indirectly increasing the chance of animal health problems associated with a nega ve energy balance (van Knegsel et al., 2005; 2007) unless milk energy output is reduced to a similar extent.

There are some indica ons that during the close-up phase (last 3 weeks prepartum) of the transi on period, the supply of addi onal feed protein (above require-ments) reduces the prevalence of retained placenta (Cur s et al., 1985), improves glucose status (Putnam and Varga, 1998), and decreases concentra ons of NEFA in blood serum (Holtenius and Hjort, 1990). However, results are not consistent as Putnam and Varga (1998) did not observe a change in blood plasma NEFA when dietary CP was increased from 10.6 to 12.7 or 14.5% of DM in isocaloric diets with similar CP degradability. Further indica ons for benefi cial eff ects of dietary pro-tein supplementa on during the close-up period are lower fa y liver scores (Holtenius and Hjort, 1990) and reduced ketosis incidence (Cur s et al., 1985). Grummer (1995) concluded that an enhanced level of absorbed amino acids in prepartum cows (feeding above recom-menda ons of NRC (1988) may improve health, but that mechanisms of ac on have not been clearly iden fi ed. The benefi cial health eff ects of addi onal dietary pro-tein supply during the close-up period suggest that low N-diets during this par cular period might compromise some specifi c health elements.

Various studies have inves gated rela onships between N intake and fer lity. Laven et al. (2007) reviewed as-socia ons between dietary N and fer lity, but mainly focused on high-N diets. They concluded that high levels of dietary N do not rou nely reduce fer lity.

Furthermore, they hypothesized that cows may be able to adapt to high-N diets, sugges ng that diets that may reduce fer lity when introduced during cri cal peri-ods (e.g. shortly before insemina on), do not reduce fer lity when introduced at an earlier stage. Ferguson and Chalupa (1989) found that overfeeding of protein results in a reduced concep on rate. They furthermore summarized the most probable causes for rela onships between protein nutri on and reproduc on effi ciency: 1) toxic eff ects of NH3 and its metabolites on gametes and early embryos, 2) deficiencies of amino acids, 3) exacerba ons of nega ve energy balance, and 4) altera ons in the hypothalamic-hypophyseal-ovarian axis. Rajala-Schultz et al. (2001) studied the associa on between MUN and fer lity in Ohio dairy cows. Cows with MUN levels below 10.0 mg/dL were 2.4 mes more likely and cows with MUN levels between 10.0 and 12.7 were 1.4 mes more likely to be confi rmed pregnant than cows with MUN values above 15.4 mg/dL. These results indicate that decreasing the dietary N content of lacta ng dairy cows, which generally results in decreasing MUN levels, appears to posi vely relate to dairy cow fer lity with an expected higher detectable pregnancy rate during herd checks. In a comparable study involving 10 Iranian dairy herds, Nourozi et al. (2010) used days from calving to concep on or to the end of the study as read-out parameters for fer lity. They demonstrated that cows with MUN values of 12 to 14 and 14 to 16 mg/dL had higher fer lity (15 and 8%, respec vely) and cows with MUN values > 18 mg/dL had lower fer lity (10%) compared to cows with MUN value < 12 mg/dL. These results suggest that MUN con-centra ons adversely associated with fer lity might be > 18 mg/dL. Also Ferguson et al. (1988) and Gustafsson and Carlsson (1993) indicated that MUN, amongst other factors, can be applied as an indicator for the fer lity of lacta ng dairy ca le. Van Saun et al. (1993) performed an experiment with primigravid Holstein dairy cows and showed that dietary supplementa on with rumen un-degradable protein prepartum improved reproduc ve performance. They suggested that addi onal protein prepartum minimizes mobiliza on of maternal labile protein pools postpartum. This may directly affect oocyte development or have an indirect eff ect on the metabolic status of the animal.

There are no clear observa ons described in literature indica ng that low-N diets cause metabolic disturbances and metabolic diseases. Doepel et al. (2000) performed an experiment with transi on dairy cows and found that liver triglyceride concentra ons and plasma NEFA

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concentra ons, both prepartum and postpartum, were not diff erent between cows fed a high-protein (16% of DM) vs. a low-protein (11% of DM) prepartum diet. However, in early lacta on, a suffi cient dietary content of glucogenic nutrients is benefi cial for the preven on of health problems associated with a large nega ve energy balance and improved fer lity (van Knegsel et al., 2005; 2007). If the lacta ng dairy cow has a shortage of glu-cogenic nutrients, it has the ability to u lize glucogenic amino acids for gluconeogenesis (Young, 1977), although Doepel et al. (2009) indicated that in early lacta on, metabolic priority is given to direct amino acids toward milk protein produc on rather than gluconeogenesis. Gluconeogenesis reduces the amino acid pool available for milk protein synthesis and will increase MUN to some extent (Schepers and Meijer, 1998).

Farm Profi tability

Van Calker et al. (2004) used a specifi c dairy farm, linear programming model to assess the eff ects of environmen-tal policy and management measures on economic and ecological sustainability of Dutch dairy farms. Compared to a situa on without environmental policy (basis situa- on), a change in farm management to a situa on con-

forming to Dutch environmental policies for 2004 and compliance with the nitrate direc ve of the European Commission (EC, 1991), led to a decrease in net farm income of approximately € 2,500/farm/year. However, including this environmental policy improved six out of seven ecological indicators (except for ecotoxicity) result-ing in a reduced environmental impact of dairy farming.

Doornewaard and de Haan (2010) compared the aver-age economic performance of 17 dairy farms with a farm management aimed at reduced N and P losses to the environment, with maintenance of the average eco-nomic performance of Dutch dairy farms. The calculated net result in 2008 amounted to €32.79 and €32.77 per 100 kg milk for the 17 farms and the average Dutch farm, respec vely. However, diff erences in farm size between the two groups might have infl uenced the outcome of this study. The average farm size of the 17 farms was 60 ha and 945,000 kg milk in 2008, whereas the average Dutch farm size was 45 ha and 612,000 kg milk.

De Haan and ter Veer (2004) assessed the cost eff ec ve-ness of measures applied in the farm management of “De Marke” up to 1998 to reduce the N-surplus per ha. De Marke is a research dairy farm in the Netherlands aiming to reduce N and P losses to the environment. In 1998, the farm had 31 ha grassland, 24 ha land for cul-

va on of fodder crops and produced 658,500 kg milk. Cost eff ec veness was defi ned as the economic eff ect, in €/100 kg milk, a er reduc on of the N-surplus of 1 kg/ha. For this par cular farm, the cost eff ec veness was posi ve for the measures “reduced fer liza on at grassland” and “applica on of manure in plant rows in corn.” Cost eff ec veness was nega ve when grazing was reduced (both for reduced grazing hours per day and reduced length of the grazing season).

The above mentioned economic results of various studies indicate that an increased N effi ciency in dairy ca le does not necessarily compromise economic per-formance of dairy farms in the Netherlands. Similar con-clusions were drawn in the USA by Powell et al. (2010).

Depending on farm specifi c condi ons and measures taken, economic eff ects can vary considerably. Daat-selaar et al. (2010) used observa ons contained in the Farm Accountancy Network Database of the Nether-lands for the years 1991 to 2006 and studied the inter-rela onships between farm management, economic performance, and environmental quality. They em-phasized the large varia on in farm management, farm structure, and results in the Netherlands. Furthermore, they concluded that various combina ons of measures are possible in common prac ce which result in lower mineral surpluses in the soil, less nitrate leaching, and be er fi nancial farm results.

CONCLUSION

The NUE in lacta ng dairy herds ranges from 16 to 36% in prac ce, whereas the theore cal maximum N effi -ciency for lacta ng dairy cows is 43%. There is a large poten al to reduce N losses in prac ce and advances in nutri on and innova ons in management tools can clearly contribute to this objec ve.

ACKNOWLEDGEMENT

The authors thank Balchem Corpora on for their sup-port. Contribu ons of authors were possible by the fi nancial support from the Commission of the European Communi es, FP7, KBB-2007-1 (Rednex), and by Dairy-man, a project in the INTERREG IVB program, co-funded by the European Regional Development Fund. Part of the research described in this paper was supported by the Dutch Dairy Board (PZ), the Dutch Product Board Animal Feed (PDV), the Dutch Ministry of Economic Aff airs, Agriculture and Innova on (EL&I), Wageningen University, and Wageningen UR Livestock Research.

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INTRODUCTION

The most recent publica on of the Nutrient Require-ments of Dairy Ca le (7th Edi on) was released in Janu-ary of 2001. In that, year a total of 75.7 million acres of corn were planted and valued $2.12/bu. Addi onally, high protein soybean meal was priced $167.7/ton. In 2001 milk was also valued at $15.00/cwt and USDA es mated that the feed costs averaged $6.75/cwt. Obvi-ously, much has changed today but one concept which has grown in importance to the U.S. dairy industry is managing feeds and their costs related to produc on. Including feed byproducts is a widely prac ced method to reduce the cost of producing milk and as a result, the way byproducts are used in dairy diets has been intensively studied over many years. For example, in the text Feeds and Feeding (1898) W.A. Henry describes a Finish study published in 1893, which reported that compared to cows consuming oats, those consuming corn-whiskey dis llers grains produced 12% more milk and 9% more milk fat. A later version of this text (1911) es mated an annual produc on of merely 60, 000 tons. This is in stark contrast to today when it is es mated that the U.S. produces 42 million tons of dis llers grains and solubles (DDGS) from corn ethanol produc on each year. Henry’s texts illustrate that dairy ca le can eff ec vely u lize feed byproducts and that this fact is not new. However, what has changed is the type and chemical composi on of many of the byproducts avail-able and the growing supply that consistently presents new challenges to the dairy producer and feed industry.

METABOLIZABLE PROTEIN AND AA

RECOMMENDATIONS

The National Research Council’s (NRC) publication outlines the nutrient requirements for dairy cattle (NRC, 2001) and calculates the supply of metaboliz-able protein (MP) as the sum of rumen microbial crude protein (MCP) fl ow, protein not degraded in the rumen (RUP) and passing to the small intes ne, and lastly endogenous protein. Although specifi c ra on eff ects

on the supply of MP have been well studied and the NRC (2001) es mates have proven useful, the model is based on tradi onal ra ons that rely heavily on SBM, animal proteins, and other tradi onal feedstuff s. Over me the feed markets have changed, challenging many

nutri onists to rethink the inclusion of tradi onal feeds and causing many to rely more heavily on byproduct feeds. In doing so, although the nutrient requirements of the animal have not changed, the prac ce of balanc-ing ra ons has probably grown in complexity. Specifi -cally these changes have resulted in the need for more knowledge of the chemical characteriza ons of feed and predic ons of the models themselves.

The NRC (2001) publica on has proven to be a powerful and accurate tool for dairy nutri onists to evaluate the predicted supply of nutrients in a given ra on. More specifi cally this publica on notes that the op mal level of LYS and MET in MP is es mated to be 7.2% and 2.4%, resul ng in the 3.0:1.0 ra o. However because the lev-els are virtually impossible to reach in most prac cal se ngs, members of the commi ee have prac cally suggested 6.6% and 2.2% as targets of LYS and MET re-spec vely. The recommenda ons for LYS are based on a dataset in which MET is > 1.95% of the MP while MET recommenda ons are based on a dataset in which LYS > 6.50% of MP. Over the last several years there has been a drama c paradigm shi in feed markets. The cost of energy feeds has risen while plant proteins are no longer always the most expensive commodi es needed on a farm. As a consequence of this, it has become temp ng for nutri onists to rely heavily on high protein feeds as a contributor of energy. This reliance has resulted in changes in the nutrients ca le received but feeding excessive nitrogen may also occur. This prac ce results in excessive nitrogen excre on and is not sustainable. More importantly, there is a growing need to understand how we can formulate ra ons to contain minimal amounts of protein to ul mately minimize environmental impacts associated with dairy produc on. In this situa on the

Practical Nutrition: Amino Acid Supply in Diets

Containing Corn Silage and Byproducts

P. J. Kononoff and H. Paz

University of Nebraska-Lincoln

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importance of evalua ng the AA composi on supply of ra ons is of growing importance.

CORN GRAIN AND SILAGE

Corn grain and silage con nue to be major components of ra ons fed to dairy ca le in the U.S. Because of this, nutri onists o en u lize feed analysis data of corn silage as the star ng point for ra on balancing. More specifi -cally, the quality of corn silage usually dictates how a ra on is balanced and what ingredients are included. In 1993, Rulquin and Verite reported a series of literature summaries that were intended to evaluate the varia on in the composi on of amino acids in duodenal digesta of ca le. In that study the minimum and maximum level of LYS and MET was observed to be 4.82% to 8.42% and 1.27% to 2.99% for LYS and MET respec vely. Addi on-ally, as the propor on of corn-based protein increased, the concentra on of LYS, THR, VAL, and ARG also de-creased. More recently, Robinson (2010) summarized data from 22 diets used on CA dairy farms and observed that as the propor on of corn-based protein in the diet increased, the propor on of LYS in duodenal digesta decreased. The reason for these observa ons is simple: protein from corn contains a low concentra on of LYS; corn and feeds of corn origin are low in LYS as can be seen by es mates listed in Table 1. As a consequence of this, it is no surprise that LYS is o en considered to be the most limi ng when a large propor on of the protein is of corn origin (Schwab et al., 2004).

COMMON FEED BYPRODUCTS: CHEMICAL COM-

POSITION AND IMPACTS IN MILK PRODUCTION

DDGS

The dry milling process is rela vely simple. Specifi cally, corn (or possibly some other starch source) is ground, fermented, and the starch converted to ethanol and CO2. Approximately 1/3 of the DM remains as the feed product following starch fermenta on. As a result, all the nutrients are concentrated 3-fold because most grains contain approximately 2/3 starch. For example, if corn is 4% oil, the DDGS will contain approximately 12% oil. The rise of the corn-ethanol industry has resulted in a drama c increase in the availability of DDGS. Over the past several years a number of published studies have sought to evaluate the nutri onal value of this feed to the lacta ng dairy cow. Like corn, DDGS are low in LYS (2.06% of RUP), and as a result LYS is suggested to be a limi ng AA in diets that rely heavily on corn-based ingredients (Rulquin and Verite, 1993). In 1998 Nichols et al. evaluated the addi on of a protected AA supple-ment (lysine and methionine) in diets containing DDGS. To test this, the product was added to a diet which con-tained 14% of the diet DM as SBM without any DDGS as well as to a diet which contained no SBM and 20% DDGS. The result of the experiment demonstrated that the protected AA product improved the concentra on of milk protein for both experimental diets. The ani-mals’ response to supplemental lysine would suggest that the diet containing DDGS was defi cient in LYS. At the University of Nebraska-Lincoln, a series of experi-

Table 1. Lis ng of amino acids of common feeds including major byproducts used the Northeastern US

% CP1 RUP, % CP2

MET LYS ARG THR LEU ILE VAL HIS PHE TRP

% of RUP1

Alfalfa Silage 20.0 40.9 0.73 6.02 6.39 5.00 9.26 6.01 7.14 2.62 6.32 1.84Corn Silage 9.50 35.3 0.80 2.13 1.84 2.13 6.40 2.40 3.20 1.07 2.94 0.11SBM, 48% 55.0 42.6 1.30 6.49 7.74 6.49 8.66 3.99 4.39 2.69 5.22 1.41Corn 9.00 47.3 1.12 1.65 1.82 1.65 10.73 2.69 3.75 2.06 3.65 0.37Wet Brewers Grains 29.0 35.4 1.76 2.28 2.14 2.28 9.39 3.76 3.78 1.48 5.56 0.37Canola Meal 41.5 35.7 1.40 6.67 6.78 6.67 7.99 4.94 6.44 4.04 4.68 1.22DDGS 30.3 50.8 1.20 2.06 4.15 2.06 9.07 2.78 5.24 1.82 4.20 1.64Corn Gluten Feed 22.5 30.0 2.09 1.24 3.17 1.24 16.22 4.34 5.04 2.45 6.48 0.37Corn Gluten Meal 65.5 74.6 2.09 1.24 3.17 1.24 16.22 4.34 5.04 2.45 6.48 0.37Co onseed 23.5 22.9 0.63 3.85 10.4 3.85 6.33 3.77 5.27 3.14 5.85 1.74Blood Meal 93.0 77.5 1.07 9.34 5.01 9.34 13.40 0.88 9.08 6.45 7.86 1.88Fish Meal 67.9 65.8 2.84 7.13 7.19 7.13 7.01 4.53 4.81 2.30 4.33 0.521 Based on CPM Dairy book values (Boston et al., 2000). 2 Na onal Research Council (NRC). 2001. Nutrient Requirements of Dairy Ca le. 7th Revised Edi on. Natl. Acad. Sci. (Washington DC).

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ments evalua ng the impact of corn milling by-products from the dry milling industry have been published in the Journal of Dairy Science. In two of these studies, the inclusion of DDGS had a signifi cant nega ve eff ect on the concentra on of protein in milk (Janicek et al., 2008; Kelzer et al., 2009). Using the NRC (2001) we retrospec vely evaluated the predicted fl ow of LYS and MET for these studies. The evalua on for treatments tested in Janicek et al. (2008) demonstrated that as the propor on of DDGS increased from 0 to 30% of the diet DM, the propor on of LYS in MP decreased from 6.69 to 5.62% of the MP, and this may in part explain why milk protein also dropped from 3.18 to 3.14%. In the study of Kelzer et al. (2008) the model also predicted that as the propor on of DDGS increased from 0 to 15% the propor on of LYS in MP decreased from 6.40 to 5.88% of the MP while the propor on of milk protein also dropped from 3.23 to 3.15%. A third study (Gehman and Kononoff , 2010) also observing the inclusion of wet dis llers grains and solubles also resulted in a reduc on in the predicted fl ow of LYS, but no eff ect was observed in milk protein. Interes ngly, although the inclusion of the byproduct reduced the predicted fl ow of LYS, the predicted fl ow of MET for all diets was very low in MET, averaging 1.79% of the MP. Addi onally, a poten al reason for this lack of eff ect is that the concentra on of CP increased with the addi on of byproducts in the diet, thus the supply of LYS may have been maintained.

Corn Gluten Feed

Compared to dry milling which produces DDGS, the wet milling process is complex because the corn kernel is par oned into several components to facilitate high value marke ng. Specifi cally, the oil is extracted and sold and the corn gluten meal that remains contains a large amount of bypass protein, commonly marketed to the dairy, poultry, or pet industries. Wet milling is a process that requires use of high quality (No. 2 or bet-ter) corn that results in numerous products produced for primarily human use. During this process, corn is “steeped” and the kernel components are separated into corn bran, starch, corn gluten meal (protein), germ, and soluble components. Wet corn gluten feed (WCGF) usually consists of corn bran and steep, with germ meal added if the plant possesses the capabili es. Wet corn gluten feed can vary depending on the plant capabili- es. Steep liquor contains more energy than corn bran

or germ meal as well as protein (Sco et al., 1997). Therefore, plants that apply more steep to corn bran or germ meal will produce WCGF that is higher in CP and energy. Wet CGF usually contain 22.5% CP, with a rumen

undegradable protein (RUP) value of approximately 30% CP (NRC, 2001). Like other corn-based byproducts, WCGF is low in LYS (Table 1). During wet milling, corn gluten meal is removed and marketed in higher value markets. Corn gluten meal should not be confused with CGF, as corn gluten meal contains approximately 65% CP and a RUP value of approximately 75% CP (NRC, 2001).

Brewers Grains

Beer ranks among the top fi ve most consumed bever-ages in the world (Fillaudeau et al., 2006). The primary byproduct resul ng from the beer making process is brewers grains, which are the extracted residue result-ing from the manufacture of wort. As with other by-products from the food industry, brewers grains (BG) are a safe, nutrient-dense feed for dairy cows. Compared to SBM, the AA composi on of BG contains less LYS (6.49 vs. 2.28% of RUP; Table 1) but it contains similar concentra ons of MET (1.30 and 1.76% RUP; Table 1, for SBM and BG respec vely). The low LYS content of BG is indica ve of DDGS and may present many similar challenges when balancing for AA. In support of this are the observa ons of Davis et al (1982), who observed that increases in the propor on of BG in the diet from 0 to 40% of the diet DM result in reduc ons in the concentra on of milk protein.

Canola Meal

The concentra on of LYS in canola meal (CM) is rela- vely high (6.67% of RUP). Because the concentra on

of LYS is high, depending upon cost, it may be a good source of protein in place of DDGS. In support of this no on Mulrooney et al. (2009) evaluated the impact of increasing the inclusion of DDGS with CM. These in-ves gators observed that as the concentra on of DDGS increased from 0 to 10% of the diet DM, and as the concentra on of CM decreased from 7 to 0% of the diet DM, the yield of milk protein decreased from 1.08 to 1.03 kg/d. In this study, as expected, the concentra on of plasma LYS also increased with the inclusion of CM. This study is par cularly noteworthy because diets were formulated to contain less than 15% CP and s ll contain a high propor on of corn silage. The observed response was likely due to the low LYS contained in DDGS. Dairy nutri onists should be encouraged to evaluate the HIS content of diets if they are relying on CM as a major source of protein as it may also become limi ng. This has been shown to be of par cular importance as lower levels of corn silage and high levels of grass hay are fed.

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CONCLUSIONS

Feed byproducts con nue to be used as common and cost eff ec ve ingredients in ruminant diets. When in-cluding these feeds in dairy diets, nutri onists should ensure that the diet contains adequate levels of lysine but keep in mind that although corn milling byproducts contain low levels of lysine the supply of methionine should also be closely tracked. Future research should be directed towards understanding how diets may be formulated to contain higher amounts of many of these ingredients without aff ec ng milk produc on and composi on.

REFERENCES

Boston RC, Fox DG, Sniffi n CJ, Janczewski R, Munsen R, Chalupa W. 2000.0The conversion of a scien fi c model describing dairy cow nutri on and produc on to an industry tool: the CPM Dairy project. In: McNamara JP, France J, Beever D editor. Modeling Nutrient U liza on in Farm Animals. Oxford, UK: CABI Publishing. p. 361–377.

Davis, C.L., D.A. Grenwalt, and G.C. McCoy. 1983. Feeding value of pressed brewers grains for lacta ng dairy cows. J. Dairy Sci. 66:73.

Fillaudeau L, Blanpain-Avet P, Daufi n G. 2006. Water, waste-water and waste management in brewing industries. J. Cleaner Prod. 14: 463-471.

Gehman, A.M. and P.J. Kononoff . 2010. U liza on of N in cows consuming wet dis llers grains with solubles in alfalfa and corn silage based dairy ra ons. J. Dairy Sci. 93: 3166-3175.

Henry, W.A. 1898. Feeds and Feeding. M.J. Cantwell, Printer, Madison WI.

Henry, W.A. 1911. Feeds and Feeding. M.J. Cantwell, Printer, Madison WI.

Nichols, J. R., D. J. Schingoethe, H. A. Maiga, M. J. Brouk, and M. S. Piepenbrink. 1998. Evalua on of corn dis llers grains and ruminally protected lysine and methionine for lacta ng dairy cows. J. Dairy Sci. 81:482–491.

Na onal Research Council (NRC). 2001. Nutrient Require-ments of Dairy Ca le. 7th Revised Edi on. Natl. Acad. Sci. (Washington DC).

Janicek, B.N., P.J. Kononoff , A.M. Gehman, and P.H. Doane. 2008. The effect of feeding dried distillers grains plus solubles on milk produc on and excre on of urinary purine deriva ves. J. Dairy Sci. 91: 3544-3553.

Kelzer, J.M., P.J. Kononoff , A.M. Gehman, K. Karges, and M.L. Gibson. 2009. Eff ects of feeding three types of corn milling co-products on ruminal fermenta on and diges bility in lacta ng Holstein dairy ca le. J. Dairy Sci. 92: 5120-5132.

Mulrooney, C. N., D. J. Schingoethe, K. F. Kalscheur, and A. R. Hippen. 2009. Canola meal replacing dis llers grains with soluble for lacta ng dairy cows. J. Dairy Sci. 92:5669–5676.

Robinson, P.H. 2010. Impacts of manipula ng ra on metabo-lizable lysine and methionine levels on the performance of lacta ng dairy cows: A systema c review of the literature. Livestock Science 127: 115-126.

Rulquin, H. and R. Verite. 1993. Amino acid nutri on of dairy cows: produc on eff ects and animal requirements. Page 55 in Recent Advances in Animal Nutri on. P.C. Garnsworthy and D.J.A. Cole, eds. No ngham University Press.

Schwab, C. G., R. S. Ordway, and N. L. Whitehouse. 2004. Amino acid balancing in the context of MP and RUP re-quirements. In: Proc. 2004 Florida Ruminant Nutri on Symposium, Gainesville, FL, p. 10-25.

Sco , T., T. Klopfenstein, R. Stock, and M. Klemesrud. 1997. Evalua on of corn bran and corn steep liquor for fi nishing steers. Neb. Beef Rep. MP 67-A:72-74.

USDA Economic Research Service, h p://www.ers.usda.gov/Data/ includes 2001 and 2011 plan ng years.

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2011 Penn State Dairy Cattle Nutrition Workshop 19

INTRODUCTION

A lacta ng dairy cow synthesizes between 3 and 5 kg of protein per day (Beque e et al., 1996; Lapierre et al., 2002), a quan ty considerably in excess of the amount of protein either ingested daily or absorbed from the diges ve tract (e.g. Lapierre et al., 2002). In prac ce, the requirements for metabolizable protein (MP) or, more importantly the cons tuent amino acids (AA), are determined not by total protein synthesis but only by the frac on of the protein synthesized for net anabolic purposes (secreted milk protein, endogenous fecal secre on, scurf plus any ssue reten on) plus the AA that are catabolized. Therefore, adequate es ma- on of the requirement of an AA requires knowledge

of the u liza on of this AA for each anabolic func on, plus the effi ciency of use.

Already 25 to 30 years ago it was recognized that the individual essen al AA (EAA) behaved diff erently and could be broadly classifi ed into two groups (Clark, 1975; Mepham, 1982). This division was based on the rela on-ship between the mammary uptake of these AA and their output in milk protein. The Group 1-AA, including His, Met, Phe + Tyr and Trp, had a ra o of mammary up-take to output in milk protein approximately equivalent to unity, indica ng a stoichiometric transfer of the mam-mary uptake into milk. In contrast, Group 2-AA, that were comprised of Lys and the branched-chain AA (Ile, Leu and Val) had a mammary uptake greater than milk output. These observa ons led to the ini al conclusion that the AA limi ng milk protein output would be those from Group 1; this would exclude Lys. In prac ce and with increasing research on AA nutri on, Lys and Met appeared to be the two fi rst-limi ng AA in typical diets from North America and similar recommenda ons for these two AA were proposed in the mid-90s from the laboratories of Rulquin et al. (1993) and Schwab (1996). The most recent Nutrient Requirements of Dairy Ca le (NRC, 2001), using a similar methodological approach to Rulquin et al. (1993) but with an updated database,

determined that Lys and Met should represent respec- vely 7.08% and 2.35% of MP supply for maximal yield

of milk protein, close to the recommenda ons of 7.3% and 2.5% of protein diges ble in the intes ne (PDI) es mated by Rulquin et al. (1993). Recently, these val-ues have been adjusted slightly using the fi nal version of the NRC model to predict concentra ons of Lys and Met in MP (Schwab et al., 2009), with a further update with an expanded database to 6.95% and 2.38% MP for Lys and Met, respec vely (Whitehouse et al., 2010a,b). Es ma ons were also given when models other than NRC were used, CPM-Dairy yielding slightly higher rec-ommenda ons and AMTS.Ca le slightly lower values (Whitehouse et al., 2009; 2010a,b).

From these observa ons, two key ques ons imme-diately arise: 1) How best to express requirement for individual AA? The ini al recommenda ons used propor on of total supply (MP or PDI) due to limited knowledge on AA nutri on precluding a factorial ap-proach (NRC, 2001); however, with recent increased knowledge on AA metabolism, would the factorial approach be more appropriate? 2) Why is Lys so o en limi ng if the mammary uptake to milk output ra o usu-ally exceeds unity? This presenta on does not pretend to be an exhaus ve literature review on these issues but rather presents recent work from our laboratories that addresses elements of these specifi c ques ons.

EXPRESSION OF REQUIREMENTS OF

ESSENTIAL AMINO ACIDS & LYSINE

Although there is s ll uncertainty about the absolute AA requirements of dairy ca le (NRC, 2001), this must not prevent a empts to balance ra ons for individual AA rather than just ‘protein.’ The need to balance for individual AA is not just a theore cal concept because empirical evidence has shown that a good balance for individual AA is superior to an MP balance in terms of animal performance.

Lysine in Dairy Cows: From Digestion to Milk Protein

H. Lapierre1, D. R. Ouellet1, L. Doepel2, and G. E. Lobley3

1Dairy and Swine Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke,

QC, Canada, J1M 0C8; 2Faculty of Veterinary Medicine, University of Calgary, AB, Canada, T2N 4Z6; 3Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, AB21 9SB, UK

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There are two approaches used to defi ne AA require-ments in dairy cows. The factorial approach cumulates the requirements for individual func ons (mainte-nance, growth, pregnancy, and lacta on) each with specifi c AA composi on and a defi ned effi ciency of transfer of the digested AA for each func on. A good example is the Cornell Net Carbohydrate and Protein System (Fox et al., 2004), probably the most widely used model using this approach in North America. The other op on is to defi ne requirements based on an empirical dose-response rela onship between the AA propor ons in MP supply and either milk protein yield or concentra on. This is the approach adopted by NRC (2001), as well as by INRA (2007), with recom-menda ons proposed for Lys and Met.

Defi ning the experimental condi ons for the projects described below has raised the ques on: “How should the supply be best expressed to relate with changes in milk protein yield?” The expression of requirements as a propor on of MP has the advantage of simplicity and certainly has helped to convey the importance of balancing dairy ra ons for specifi c AA. The relevance of this approach may be ques onable however, especially if we want to further refi ne the es ma on of all EAA re-quirements. These issues will be discussed using Lys, but similar conclusions would be obtained for other EAA.

First, the propor on of total EAA in MP supply is not fi xed. For example, from the database used by Doepel et al. (2004), where all the diges ve fl ows of EAA had been es mated with NRC (2001), the propor on of total EAA rela ve to MP supply in control treatments varied from 42 to 48% (mean 45.4%). Within these studies, Lys supply averaged 104.9 g/d; assuming a fi xed diges ve fl ow of EAA (including Lys), but if a variable propor on of EAA rela ve to MP is used, Lys content would increase from 5.87 to 6.71% of MP as the EAA propor on increases from 42 to 48% of MP (Table 1). This is an important considera on as, for a similar MP supply, a decreased propor on of EAA is obviously associated with an increase of the non-EAA but the la er do not infl uence milk protein yield, based on an extreme comparison that involved abomasal infusion of water, EAA, non-EAA, and total AA (Doepel and Lapierre, 2010). That study also demonstrated clearly that the parameter that predicts milk protein yield is the absolute amount rather than the propor on of Lys rela ve to MP (Table 2). Addi on of 356 g of non-EAA did not improve milk protein yield while

maximum milk yield was observed when 359 g of EAA were added; there was no further increase when the non-EAA were infused in addi on to EAA. Importantly, across treatments the propor on of Lys rela ve to MP varied greatly and was not related to milk protein yield.

Nonetheless, we have to accept that our current knowl-edge on AA u liza on may limit the present develop-ment of the factorial approach to determine AA require-ments. For the factorial approach, in addi on to the requirements for MP (also necessary for the propor on approach), a good assessment of the AA composi on for the individual func ons, i.e. maintenance, growth, gesta on, and lacta on, is required. First, milk protein secre on cons tutes the largest requirement for AA and therefore, AA composi on of milk protein will have an impact on AA requirement. Using the average milk com-posi on of the diff erent protein frac ons (Lapierre et al., 2011) and AA composi on of these proteins calculated from AA sequencing and DNA base sequences (Farrell et al., 2004), AA composi on of secreted milk protein can be calculated (Table 3). Milk AA composi on should be based on true protein and not crude protein content as the non protein-N frac on of milk can change with the ra ons, so adop ng a constant AA composi on rela ve to crude protein is misleading when comparing ra ons that may alter non protein-N of milk. Second, the most important contributor to maintenance requirement is metabolic fecal protein (MFP), contribu ng on average

Table 1. Eff ect of the propor on of essen al amino acids (EAA) rela ve to metabolizable protein (MP) supply on the expres-sion of a constant lysine (Lys) supply rela ve to MP supply

Lys supply, g/d % EAA / MP Total MP % Lys / MP

104.9 42.0 1,787 5.87

104.9 45.4 1,654 6.34

104.9 48.0 1,564 6.71

Table 2. Eff ect of supply of amino acids on milk protein yield

Treatments1

Water EAA non-EAA TAA

Metabolizable protein (MP) supply2, g/d 1,431 1,790 1,787 2,146

Lys supply2, g/d 92 151 92 151

Lys, %MP 6.43 8.42 5.15 7.03

Milk protein yield, g/d 951 1,085 921 1,1441Abomasal infusion of essen al amino acids (EAA), non-EAA (non-EAA) or total AA (TAA).2Supply is es mated from diges ve fl ow of AA (es mated with NRC, 2001) plus the abomasal infusion.

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2011 Penn State Dairy Cattle Nutrition Workshop 21

to 64% of maintenance requirements in the studies used in Doepel et al. (2004), using NRC es ma ons. Most of the factorial models use a composi on based on the protein content of empty body but, in reality, these losses are proteins secreted into the gut and then excreted in the feces. This corresponds exactly to the defi ni on of endogenous protein losses, and obviously it would be more appropriate to use the AA composi on of endogenous protein secre ons. Unfortunately, this exact composi on is not yet well-defi ned in ruminants, but, as a fi rst step, the average of values obtained from the abomasum in ruminants and across the small in-tes ne in pigs has been proposed (Table 3; Lapierre et al., 2007). In addi on, it would be necessary to improve the accuracy of MFP es ma on as current es mates are only based on dry ma er intake (DMI; Fox et al, 2004; NRC, 2001) and surprisingly do not include any effi ciency of u liza on from the supply to the protein excreted. Recent work has determined rates of endogenous protein secre ons across the diges ve tract, including fecal endogenous losses, in dairy cows off ered diff erent types of diets (Ouellet et al., 2007). More studies are required to determine factors that aff ect endogenous protein losses and to refi ne the model used, but with the results obtained so far a value of 19.0 g MP/kg DMI has been proposed for lacta ng cows fed typical dairy ra ons (Lapierre et al., 2007). A second requirement for maintenance is scurf, which AA composi on has been proposed to be kera n (Table 3; Doepel et al., 2004). This is, however, a minor contributor (< 1%) to

MP requirement. Finally, urinary endogenous-N losses represent less than 10% of MP requirement, and cur-rently AA requirements are es mated from empty body AA composi on. These may alter based on a deeper analysis of the various frac ons of urinary-N, but the overall impact will probably be marginal.

One fi nal drawback of the propor on approach is that results cannot realis cally be a ained for all the EAA. For example, to a ain the recommenda ons of Doepel et al. (2004), we have to supply a diet that contains 50% of MP as EAA (Table 3). In that study, es ma ons of recommenda ons were fi rst obtained for individual EAA rela ve to total EAA, but then transferred to MP using an average propor on of EAA on MP of 48% (excluding Trp). With this approach, Lys and Met re-quirements were yielding similar recommenda ons to those of Rulquin et al. (1993) and NRC (2001; Table 4). As previously men oned, a supply of MP containing 50% of EAA is not realis c with prac cal diets, however. In contrast, the latest es ma ons from Rulquin et al. (2007) yield a sum of EAA closer to prac cal reality, approximately 45%. Unfortunately, by this approach the es mated requirement for certain AA, namely the branched-chain AA and Arg, are lower than usually provided by the ra ons normally fed, meaning that such low propor ons cannot be obtained ‘naturally’ for these AA. When more realis c propor ons of these AA are

Table 3. Essen al AA composi on of milk protein, metabolic fecal protein (MFP), kera n, and whole empty body (g/kg true protein)1

AA Milk2 MFP Kera n Whole empty body

Arg 36.8 56.4 50.6 84.0

His 28.6 32.2 13.8 31.1

Ile 60.4 47.2 66.7 35.7

Leu 102.1 58.7 133.4 85.1

Lys 86.3 67.9 42.6 80.5

Met 29.5 16.1 13.8 25.3

Phe 51.5 48.3 49.5 44.9

Thr 46.3 73.6 96.6 49.5

Trp 15.9 20.7 18.4 9.2

Val 68.3 64.4 80.5 50.61Adapted from Lapierre et al. (2007).2Composi on of milk protein secreted.

Table 4. Comparison of es ma on of requirement and supply of essen al amino acids (AA) rela ve to diges ble protein

AARequirements Supply

%AA/MP1 %AA/PDI2 %AA/MP

Arg 4.6 3.1 4.6

His 2.4 3.0 2.1

Ile 5.3 4.6 4.9

Leu 8.9 8.9 8.9

Lys 7.2 7.3 6.3

Met 2.5 2.5 1.9

Phe 5.5 4.6 5.0

Thr 5.0 4.0 4.9

Trp 1.7 1.7 1.2

Val 6.5 5.3 5.6

Total 49.6 44.9 45.41MP: metabolizable protein, from Doepel et al., 2004.2PDI: protein digested in the intes ne, from Rulquin et al. (2007).3AA diges ve fl ow and MP supply es mated with NRC (2001) from all the control treatments used in Doepel et al. (2004).

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22 November 8-10 Grantville, PA

included in the calcula ons, then the sum of EAA will also approximate to 50% of MP supply. One prac cal outcome is that with non-supplemented, conven onal ra ons, op mum effi ciency for all EAA is unlikely to be achieved. Indeed, this goal will probably only be met by use of rumen-protected supplements of the various EAA, but this will require more precise knowledge of the requirement for each individual AA.

A further crucial point involves the u liza on of a fi xed factor for conversion of absorbed MP or AA toward protein anabolism. A value of 0.67 is used by most of the models for MP, but it is clear that milk protein yield responds to AA supply in a curvilinear, and not linear, manner (Doepel et al., 2004; Lapierre et al., 2007). This implies that observed milk protein yield is higher than predicted at low MP supply and lower than predicted at high MP supply. Also, in models using the factorial approach, the effi ciency of u liza on of individual AA is diff erent for maintenance and lacta on (e.g. Fox et al., 2004). U liza on of a combined effi ciency for both maintenance and lacta on has been proposed (Lapierre et al., 2007).

In conclusion, although the perfect system is not yet available to determine requirements, there is compelling evidence that diets need to be balanced for AA. The pro-por on approach has the advantage of being simple to use and has certainly ini ated implementa on of AA bal-ance across diets. Nonetheless, as our basic knowledge on AA u liza on by the cow increases, we will be able to update the factorial approach and this will, in the long term, be a more soundly based and accurate scheme.

DEFINITION OF REQUIREMENTS OF

ESSENTIAL AMINO ACIDS & LYSINE

Metabolism of Essential Amino Acids

Over the last decade, research on AA metabolism has expanded from the mammary gland to also include AA u liza on between absorp on and mammary gland output, including metabolism by the splanchnic ssues. The la er comprise the portal-drained viscera (PDV: gut, spleen, pancreas, and associated mesenteric fat) plus the liver. In dairy cows, despite the fact that these ssues contribute less than 10% of body mass (Gibb et

al., 1992), they account for approximately 50% of both whole body oxygen consump on (Hun ngton, 1990) and protein synthesis (Lapierre et al., 2002). Interest-ingly, but unsurprisingly, the behaviour of EAA across the splanchnic ssues complements mammary metabolism

and, simplis cally, the EAA can be classifi ed in the same groups as those defi ned for mammary metabolism. The Group 1-AA (His, Met, and Phe+Tyr) are mainly catabo-lized within the liver so, consequently, post-liver supply is almost iden cal to both mammary uptake and milk output. In contrast, Group 2-AA undergo li le, if any, removal across the liver and thus have a post-liver sup-ply greater than mammary uptake which is also greater than milk output (Figure 1; Lapierre et al., 2011).

Lysine

Examina on of Figure 1 reveals 3 major sites of Lys u li-za on: across the PDV, in peripheral ssues, and within the mammary gland, where uptake is greater than milk protein output. Furthermore, Figure 2 illustrates how these diff erent u liza ons are altered by Lys supply.

Splanchnic ssues. First, in our example, Lys u liza on across the PDV has been es mated based solely on loss in endogenous secre ons, i.e. as MFP (from Lapierre et al., 2007). Although factors other than DMI certainly infl uence MFP, MFP was es mated from DMI in this calcula on. Therefore, the u liza on of Lys by the PDV was not diff erent when comparing Lys- and Lys+ which had similar DMI. Across the PDV, there was no report of oxida on of systemic Lys either in pigs, regardless of the level of protein in the diet (van Goudoever et al., 2000), or in sheep (Lobley et al., 2003). We therefore assumed no oxida on of Lys across the PDV. In addi on, predicted Lys duodenal fl ow and observed Lys PDV appearance support li le or no gut oxida on (Pacheco et al., 2006). Furthermore, on a net basis, removal by the liver is also very small and is barely aff ected by Lys supply (Figure 2).

0

0.25

0.5

0.75

1

Lys Met

Portal absorption

Liver removal

Post-liver supply

Mammary uptake

Milk

Figure 1. Net fl ux of two essen al amino acids representa ve of Group 1 (methionine) and Group 2 (lysine) in dairy cows, rela ve to net small intes nal diges ve fl ow. Adapted from Lapierre et al. (2011).

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2011 Penn State Dairy Cattle Nutrition Workshop 23

In consequence, most of absorbed Lys was available to peripheral ssues including the mammary gland.

Mammary gland. Mammary uptake of Lys exceeded post-splanchnic supply (Figure 2), and similar to the other AA of Group 2, the uptake of Lys by the mammary gland exceeded the output in milk protein (Figure 1). This raises a number of ques ons. Why would Lys be consid-ered limi ng when more is extracted by the gland than is secreted in milk protein? What is its role? Is the excess mammary uptake rela ve to milk output obligatory?

To examine these ques ons, two studies were con-ducted to determine the eff ect of Lys supply on mam-mary uptake in rela on to transfer of the excess-N to non-EAA (Lapierre et al., 2003; Lapierre et al., 2009). Bear in mind that although the EAA are extracted in equal or excess amount rela ve to their milk output, the inverse situa on exists for a number of non-EAA, where mammary uptake is less than milk output, indi-ca ng intra-mammary synthesis. For example, the N of Leu is transferred to non-EAA in milk protein in goats (Rubert-Alemán et al., 1999). Our fi rst study (Lapierre et al., 2003) showed that Lys-N was also transferred to non-EAA. What was unclear, however, was whether such a transfer was obligatory or simply refl ected how excess supply might be u lized by the dairy cow and, as such, infl uence the requirement for Lys.

This aspect was examined in the second study, where mul -catheterized cows were fed a protein-defi cient diet and infused via the abomasum with a mixture of AA, based on the profi le of casein and including, or not, Lys (Lapierre et al., 2009). For the Lys- and Lys+ treat-ments, Lys supply averaged 4.6% and 6.8% of MP. All

of the infused addi onal Lys was recovered as portal absorp on (Figure 2), in support of the conten on that there is no oxida on by the PDV, as previously men oned. Dele on of Lys from the infusate was ac-companied by a decreased mammary uptake and milk output (Figure 2). Nonetheless, the Lys- cows did exhibit some metabolic adjustment because periph-eral ssue removal of Lys also decreased and thus the propor onal reduc on in milk output was smaller than the decline in post-liver supply. Furthermore, although the uptake to output ra o decreased from 1.37 to 1.12 between the Lys+ and Lys- treatments, the lower value was s ll greater than unity, despite the severe defi ciency. This indicates that while the excess uptake of Lys by the mammary gland rela ve to output can be reduced, this is only par al and thus a major Lys dele on has a detrimental eff ect on milk protein pro-

duc on. To determine the mechanism for this nega ve impact, [2-15N]Lys was infused for 7 h on the last day of each experimental period. The enrichment of 15N-AA in both arterial supply and milk protein was then ana-lyzed to determine the contribu on of Lys to non-EAA synthesis (Figure 3). Under op mal condi ons there was a substan al contribu on of Lys-N to the synthesis of Asx (sum of Asn + Asp), Glx (sum of Gln + Glu), and Ser, the AA with the greatest defi ciency of mammary uptake rela ve to milk output. When Lys supply was reduced, transfer of N to these non-EAA s ll occurred but at much lower rates, and was the probable cause of the decline in milk protein output. We were unable to detect increased uptakes of another AA to replace the Lys contribu on. To summarize, although Lys is taken up in excess rela ve to milk output by the mammary gland, a fact that ini ally tempted people to assume that Lys could not be a limi ng AA, it seems that Lys plays an

0

20

40

Lys- Lys +

mm

ol/h

Portal absorptionLiver fluxPost-liverMammary uptakeMilk

Figure 2. Net fl ux of lysine in dairy cows infused with a mixture of amino acids excluding (Lys-) or including lysine (Lys+). Adapted from Lapierre et al. (2009).

0

2

4

6

Lys- Lys+

% o

f lab

elle

d A

A-N

Ala

Asx

Glx

Leu

Ser

Figure 3. Distribu on of 15N between AA (% of labelled AA-N) in milk collected a er 7 hours of a [15N]lysine infusion in dairy cows infused with a mixture of amino acids excluding (Lys-) or including lysine (Lys+). Adapted from Lapierre et al. (2009).

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important role in the udder for the synthesis of non-EAA. More detailed research is needed to determine if the mammary gland can replace part of the func on of Lys (from another EAA or excess non-EAA), but in normal feeding condi ons, the limita on of Lys is not solely due to its direct contribu on to milk protein synthesis, but also relates to a role in the synthesis of non-EAA.

CONCLUSION

In conclusion, a be er knowledge of the metabolism of proteins and AA will greatly help to defi ne requirements of the dairy cow. Globally, protein- or AA-derived secre-tory losses from the body (as metabolic fecal protein or urinary-N excre on) plus milk protein secre on (coupled with growth and gesta on, when present) represent MP and individual AA requirements of the lacta ng animal. As quan fi ca on of AA metabolism improves, this will enhance the accuracy of the factorial approach and permit be er AA balance of dairy ra ons. In addi on, for individual AA such as Lys, a greater understanding of their u liza on at the ssue level will allow determina- on of their roles and how this might limit milk protein

synthesis. Such informa on will help delineate with in-creased accuracy the requirement for each AA and allow predic on of poten al metabolic fl exibility in the face of any defi ciency. Indeed, an improved factorial approach will be a vital intermediate step in the progression of predic on systems towards the ul mate goal of realis c and func onal mechanis c models.

REFERENCES

Beque e, B. J., F. R. C. Backwell, J. C. MacRae, G. E. Lobley, L. A. Crompton, J. A. Metcalf, and J. D. Su on. 1996. Eff ect of intravenous amino acid infusion on leucine oxida on across the mammary gland of the lacta ng goat. J. Dairy Sci. 79:2217-2224.

Clark, J. H. 1975. Lactational responses to postruminal administra on of proteins and amino acids. J. Dairy Sci. 58:1178-1197.

Doepel, L. and H. Lapierre. 2010. Changes in produc on and mammary metabolism of dairy cows in response to es-sen al and nonessen al amino acid infusions. J. Dairy Sci. 93:3264-3274.

Doepel, L., D. Pacheco, J. J. Kennelly, M. D. Hanigan, I. F. López, and H. Lapierre. 2004. Milk protein synthesis as a func on of amino acid supply. J. Dairy Sci. 87:1279-1297.

Farrell Jr, H. M., R. Jimenez-Flores, G. T. Bleck, E. M. Brown, J. E. Butler, L. K. Creamer, C. L. Hicks, C. M. Hollar, K. F. Ng-Kwai-Hang, and H. E. Swaisgood. 2004. Nomenclature of the pro-teins of cows’ milk - Sixth revision. J. Dairy Sci. 87:1641-1674.

Fox, D. G., L. O. Tedeschi, T. P. Tylutki, J. B. Russell, M. E. Van

Amburgh, L. E. Chase, A. N. Pell, and T. R. Overton. 2004. The Cornell Net Carbohydrate and Protein System model for evalua ng herd nutri on and nutrient excre on. Anim. Feed Sci. Tech. 112:29-78.

Gibb, M. J., W. E. Ivings, M. S. Dhanoa, and J. D. Su on. 1992. Changes in body components of autumn-calving Holstein-Friesian cows over the fi rst 29 weeks of lacta on. Anim. Prod. 55:339-360.

Hun ngton, G. B. 1990. Energy metabolism in the diges ve tract and liver of ca le: Infl uence of physiological state and nutri on. Reprod. Nutr. Dev. 30:35-47.

INRA. 2007. Alimenta on des bovins, ovins et caprins. Besoins des animaux – Valeurs des aliments. Tables INRA Ed. Quæ. Versailles, France. 307p.

Lapierre, H., J. P. Blouin, J. F. Bernier, C. K. Reynolds, P. Dubreuil, and G. E. Lobley. 2002. Eff ect of supply of metabolizable protein on whole body and splanchnic leucine metabolism in lacta ng dairy cows. J. Dairy Sci. 85:2631-2641.

Lapierre, H., E. Milne, J. Renaud, and G. E. Lobley. 2003. Lysine u liza on by the mammary gland. In Progress in research on energy and protein metabolism. EAAP publica on No.109. Ed. W.B. Souff rant and C.C. Metges:777-780.

Lapierre, H., G.E. Lobley, D.R. Ouellet, L. Doepel and D. Pa-checo. 2007. Amino acid requirements for lacta ng dairy cows: reconciling predic ve models and biology. Proc. Cornell Nutri on Conference for feed manufacturers. Dpt. Anim. Science, Cornell University, NY: 39-59.

Lapierre, H., L. Doepel, E. Milne, and G. E. Lobley. 2009. Re-sponses in mammary and splanchnic metabolism to altered lysine supply in dairy cows. Animal 3:360-371.

Lapierre, H. G. E. Lobley, L. Doepel, G. Raggio, H. Rulquin, and S. Lemosquet. 2011. Mammary metabolism of amino acids in dairy cows. J. Anim. Sci. 89: Submi ed.

Lobley, G. E., X. Shen, G. Le, D. M. Bremner, E. Milne, A. G. Calder, S. E. Anderson, and N. Dennison. 2003. Oxida on of essen al amino acids by the ovine gastrointes nal tract. Br. J. Nutr. 89:617-629.

Mepham, T. B. 1982. Amino acid u liza on by lacta ng mam-mary gland. J. Dairy Sci. 65:287-298.

NRC. 2001. Nutrient Requirements of Dairy Ca le. 7th rev. Ed. Natl. Acad. Sci., Washington, DC.

Pacheco, D., C. G. Schwab, R. Berthlaume, G. Raggio, and H. Lapierre. 2006. Comparison of net portal absorp on with predicted fl ow of diges ble amino acids: Scope for improv-ing current models? J. Dairy Sci. 89:4747-4757.

Ouellet, D.R., D. Valkeners, G. Holtrop, G.E. Lobley, and H. Lapierre. 2007. Contribu on of endogenous secre ons and urea recycling to nitrogen metabolism. Proc. Cornell Nutri on Conference for feed manufacturers. Dpt. Anim. Science, Cornell University, NY: 1-24.

Rubert-Alemán J, G. Rychen, F. Casseron, F. Laurent, and G.J.

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Mar n.1999. 15N enrichment of casein amino acids in the milk from goats given a single intravenous dose of L-[15N]leucine. J. Dairy Res. 66:283-288.

Rulquin, H., P. M. Pisulewski, R. Vérité, and J. Guinard. 1993. Milk produc on and composi on as a func on of postru-minal lysine and methionine supply: a nutrient-response approach. Livest. Prod. Sci. 37:69-90.

Schwab, C. G. 1996. Rumen-protected amino acids for dairy ca le: Progress towards determining lysine and methionine requirements. Anim. Feed Sci. Technol. 59:87-101.

Schwab, C., D. Luchini, and B. Sloan. 2009. Reevalua on of the breakpoint es mates for the NRC (2001) required concen-tra ons of lysine and methionine in metabolizable protein for maximal content and yield of milk protein. J. Dairy Sci. 92, E-Suppl. 1:103.

van Goudoever, J. B., B. Stoll, J. F. Henry, D. G. Burrin & P. J. Reeds, 2000. Adap ve regula on of intes nal lysine me-tabolism. Proc. Nat. Acad. Sci. 97:11620-11625.

Whitehouse, N., C. Schwab, T. Tylutki, D. Luchini, and B. Sloan. 2009. Comparison of op mal lysine and methionine in metabolizable protein es mated by the NRC (2001), CPM-Dairy (v.3.0.10) and AMTS.Ca le (v.2.1.1) models. J. Dairy Sci. 92, E-Suppl. 1:103.

Whitehouse, N., C. Schwab, D. Luchini, and B. Sloan. 2010a. A cri que of dose-response plots that relate changes in content and yield of milk protein to predicted concentra- ons of lysine in metabolizable protein by the NRC (2001),

CPM-Dairy (v.3.0.10) and AMTS.Ca le (v.2.1.1) models. J. Dairy Sci. 93, E-Suppl. 1: 447.

Whitehouse, N., C. Schwab, D. Luchini, and B. Sloan. 2010b. A cri que of dose-response plots that relate changes in con-tent and yield of milk protein to predicted concentra ons of methionine in metabolizable protein by the NRC (2001), CPM-Dairy (v.3.0.10) and AMTS.Ca le (v.2.1.1) models. J. Dairy Sci. 93, E-Suppl. 1: 447.

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Notes

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2011 Penn State Dairy Cattle Nutrition Workshop 27

There are a number of approaches to producing a microencapsulated nutrient and the resul ng micro-encapsulate can have a wide range of performance characteris cs. The product’s func onality is highly dependent on the microencapsula on process as well as the coa ng materials used. For high performance microencapsulates, in addi on to consistent product proper es, the coa ng needs to provide three impor-tant func ons: stability, protec on, and bioavailability. The microencapsulate needs to provide stability of the nutrient during storage, for example in a moist feed and at adverse temperature ranges. When consumed by the ruminant, it needs to protect the nutrient from degrada on by rumen microbes, and thirdly, the coa ng needs to release the nutrient for absorp on in the small intes ne, which combined all result in providing high level of bioavailability. Assuming all microencapsulated products are created equal is incorrect and analogous to saying that every car on the market is built the same and contains the same technologies as all other cars.

IS MICROENCAPSULATION NECESSARY

AND WHY DO PRODUCTS NEED TO BE

“RUMEN PROTECTED”?

Microencapsula on allows for nutrients to be deliv-ered to targeted loca ons consistently and effi ciently. In the case of ruminants, microencapsula on allows nutrients to bypass the rumen for delivery to the small intes ne. Because the rumen microbes degrade almost everything they encounter, protec ng cri cal nutrients through microencapsula on is essen al to meet the needs of today’s high producing dairy cows. An example of the benefi t of microencapsula on to protect nutri-ents from rumen degrada on was demonstrated in an in vitro study conducted at the West Virginia Rumen Profi ling Lab, which compared unprotected raw niacin to a microencapsulated niacin product. A er 24 hours of ruminal incuba on, 94% of unprotected niacin was broken down in the rumen resul ng in only a 6% rumen bypass value. In contrast, only 11% of the microencap-sulated product was broken down in the rumen result-

ing in an 89% rumen bypass value. Although rumen microbes can synthesize many nutrients in amounts necessary to meet maintenance and modest milk pro-duc on demands, these amounts are generally not suffi cient to meet the needs of today’s high producing dairy cows. Simply feeding large amounts of a nutrient does not ensure a signifi cant increase in the delivery of that nutrient to the small intes ne, and this approach is generally expensive and not environmentally conscien- ous. Encapsula ng ingredients allows for the nutrient

to be eff ec vely delivered to the small intes ne for absorp on at a lower cost.

WHAT MAKES A HIGH PERFORMANCE

MICROENCAPSULATE?

Stability

The microencapsulate needs to provide nutrient stability during transporta on and storage to remain eff ec ve. When a poor quality microencapsulate is included in a mineral mix or a fi nished feed for several weeks, mois-ture in the feed can slowly migrate through the coa ng of the microencapsulate and begin to dissolve the nutri-ent. This can lead to coa ng instability and result in poor ruminal protec on. A high quality encapsulate should provide proven stability under typical storage condi ons.

Par cularly for lipid-coated microencapsulates, another important feature related to product stability is being tolerant to cool (< 40 °F) and freezing temperatures. Even when a product is handled and stored properly, in many parts of the world products will be subjected to envi-ronments where freezing and thawing of products will occur. Under these condi ons, some lipid-based coat-ings can undergo detrimental changes resul ng in the development of cracks in the coa ng and subsequently will result in poor ruminal stability and protec on. To ensure delivery of the nutrient to the small intes ne, a microencapsulate should be designed to withstand freeze/thaw cycles so that the feed and rumen stability characteris cs of the products are maintained.

Precision Release Nutrition - What You Need to Know

Ryan Ordway, PhD

Balchem Corporation

New Hampton, NY

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Protection

When consumed, the coa ng needs to protect the nutrient for 12 hours, and some mes longer, in the ru-men. As with feed stability, a microencapsulate should provide excellent protec on in the rumen which trans-lates to a product with a very high rumen bypass value.

Bioavailability

Once the microencapsulate bypasses the rumen, the coa ng needs to be designed to release the nutrient for absorp on in the small intes ne. Simply demonstra ng rumen stability does not ensure intes nal release and absorp on. Unfortunately, many people confuse rumen bypass with bioavailability when in fact, bioavailability is a combina on of rumen bypass and intes nal absorp- on. When evalua ng microencapsulated products,

cau on must be used when examining research data to ensure that the research results are repor ng actual nu-trient bioavailability and not just a rumen bypass value. For example, a product may have a high rumen bypass value of say 90%, but only have an intes nal diges bility of say 40%. The resul ng bioavailability value would then be 36% (90% x 40% = 36%). Conversely, another product may have a rumen bypass value of 60%, but also have an intes nal diges bility of 60%. In this example, the resul ng bioavailability value would also be 36%.

Another important concept to understand when de-termining bioavailability is to know the actual content of ac ve nutrient in the encapsulated product and to account for any other compounds that may be bound to the nutrient. For example, lysine and choline exist in the form of choline hydrochloride or lysine hydrochloride and as a result, the chloride content of the molecule must be accounted for. Lysine hydrochloride is 20% chloride and 80% lysine, which means that an encap-sulated product may contain 50% lysine hydrochloride but the actual lysine content is only 40%. This is the value that must then be used to calculate the amount of bioavailable nutrient. For example, let’s assume an encapsulated product contains 52% actual lysine (65% lysine hydrochloride), has an 80% rumen bypass value and has an intes nal diges bility of 80%. To determine the actual content of metabolizable lysine (actual lysine absorbed in the small intes ne and available for use by the cow) you must mul ply the lysine content (52%) x the rumen bypass value (80%) x the intes nal digest-ibility value (80%) which equates to a 33.3% metaboliz-able lysine content. As a result, for every 10 grams of product you are feeding, you are actually providing 3.33 grams of metabolizable lysine. The importance of this

process cannot be underes mated because there are large price varia ons between products and to fairly and eff ec vely compare one product to another, compari-sons must be made on a cost per gram of metabolizable nutrient basis. Otherwise, one product may appear to be signifi cantly more expensive on a cost per ton of product basis when in fact, it is actually less expensive when taking into account the amount of metabolizable nutrient it is providing.

CONCLUSIONS

There are several diff erent microencapsula on tech-nologies available in the marketplace designed to pro-tect nutrients from ruminal degrada on. As a result, nutri onists and dairy producers must be educated as to the diff erences between these technologies and understand the research data available for each of these products. Designing a microencapsula on that is stable under normal storage and feeding condi ons, provides a high level of rumen bypass and is highly diges ble in the small intes ne requires extensive research and development of the coa ng as well as research that ac-curately determines the bioavailability of the product in the animal. It is important to remember that not all microencapsulated products are created equal.

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2011 Penn State Dairy Cattle Nutrition Workshop 29

Feeding effi ciency has been described in many ways in an eff ort to measure the produc on effi ciency of con-ver ng dietary inputs into milk. Frequently it referred to as the Dry Ma er Effi ciency (DME) and is simply calculated as the pounds of milk produced divided by the dry ma er of feed consumed. Dependant on the stage of lacta on the DME for a herd will be between 1.30 and 1.70. If the DME is calculated for individual produc on groups within a herd the values may range from 2.0 to 1.0 as a group of cows move along the lac-ta on curve. Addi onal accuracy has been provided to the DME calcula on by adjus ng the milk produc on to a standardized milk fat and protein percentage. This calcula on is a more accurate determina on of feeding effi ciency and is referred to as Energy Corrected Dry Ma er Effi ciency (EC-DME).

Historically in non-dairy livestock produc on feeding effi ciency has most o en been described as the Feed Conversion Ra o (FCR), and is a measure of the animal’s effi ciency of conver ng feed to produc ve body weight mass. Lower FCRs indicate more effi cient conversion. For example, in the beef industry the typical FCR is 8:1 (also commonly referred to as the Feed:Gain Ra o) and can be lowered when new technology or improved man-agement systems are implemented. Ruminants on high forage diets will have higher FCR than the same species where forage and grain are used. Single stomach animals on an all grain diet will have the most effi cient FCRs.

Each species has their specifi c bench marks and goals for FCRs. Feeder pigs have a FCR of 3.4 to 3.6. Atlan c Salmon have a very good FCR of 1.2:1 while broilers 1.8:1, and layers 2:1. It is important to understand that industries using the FCR are measuring the dry weight of feed against the live or wet weight gain. For this reason at mes in the fi sh industry FCRs of less than 1:1 are possible with commercial diets, as the pellet being fed is a “dry” diet, and a high percentage of weight gained by the fi sh is water trapped in the ssues and cells.

In the dairy industry conven on has adopted the inverse calcula on of FCR to describe Feed Effi ciency. Because the end product (milk) is 87% water the FCR calcula on will be very low and usually less than 1.0. For example, if feed intake was 50 pounds and milk output was 80 pounds the FCR would be 0.625 (50 ÷ 80). The inverse of the feed conversion ra o becomes the feed effi ciency of 1.60 (80 ÷ 50). Feed effi ciency, contrary to FCR, is improved as the ra o gets higher.

Dairy feed effi ciency monitoring is rela vely new com-pared to other livestock species but never the less is quite an important indicator of profi tability. Feed ex-penses are o en at least 50% of the total revenue on a dairy farm. Minor changes in the conversion of feed to milk will impact whole farm profi tability. The gold stan-dard for measuring feeding profi t is the calcula on for Income Over Feed Cost (IOFC). However this calcula on is o en cumbersome on a day to day or week to week basis because milk pricing is unknown un l some me in the future and changes in feed prices are o en out of the control of management.

Feeding effi ciency on the other hand is quite easy to calculate and is directly propor onal to IOFC. In its most simplifi ed form FE is calculated knowing two metrics, milk produc on and dry ma er intake (FE = Milk ÷ DMI). Because of breed, stage of lacta on, season, and ge-ne cs a more correct measure of FE is calculated using energy corrected milk produc on. A er adjus ng milk volume for its bu erfat and protein content the output of milk volume is more accurately associated with dry feed intake. The Energy Corrected Dry Ma er Effi ciency (EC-DME) formula adjusts milk to standardized bu erfat and protein. The formula for calcula ng ECM is ((12.82 x fat lbs)+(7.13 x protein lbs)+(0.323 x milk lbs)).

A er correc ng milk volume for bu erfat and protein composi on, the EC-DME is a simple calcula on dividing the DMI into the ECM. Key points to remember when

Measuring Feed Effi ciency:

Why and How On the Back of a Napkin

Robert C. Fry, DVM

Atlantic Dairy Management Services

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evalua ng EC-DME are “higher is be er,” early lacta on will be higher than late lacta on, diet formula on and feed addi ves will infl uence the ra o, and higher digest-ible forages will drive the ra o higher. Most important is that EC-DME has a direct and posi ve infl uence on IOFC within the parameters under management control.

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2011 Penn State Dairy Cattle Nutrition Workshop 31

Managing the Rumen Environment to Control Milk Fat Depression

T. C. Jenkins

Department of Animal & Veterinary Sciences

Clemson University

FAT NOMENCLATURE

Fa y acids are chains of carbons that end in an acid group, or carboxyl group as it is referred to in biochem-istry. An example of a common fa y acid is stearic acid with 18 carbons and no double bonds (Figure 1).

Fa y acids, such as stearic acid, are referred to as saturated because all the carbons are holding the maximum number of hydrogens possible, or the fa y acid is “saturated” with hydrogen. Stearic acid is low in plant oils, but present in higher amounts in animal fats, par cularly in fats obtained from ruminant species such as beef tallow (Table 1).

Oleic acid and linoleic acid are examples of unsaturated fa y acids containing one or more double bonds (Figure 2). Oleic acid has a single double bond between carbons 9 and 10, and is referred to as a monounsaturated fa y acid. Linoleic acid is a polyunsaturated fa y acid containing two double bonds between carbons 9 and 10, and between carbons 12 and 13. Oleic acid is the predominant fa y acid in animal fats and some plant oils, such as canola oil (Table 1). Linoleic acid is the predominant fa y acid in many plant oils, including co onseed oil, soybean oil, and corn oil. Linolenic acid, with three double bonds, is the primary fa y acid in most pasture species and in linseed oil from fl ax.

Biohydrogena on of linoleic acid in the rumen begins with its conversion to conjugated linoleic acid (CLA). In this ini al step, the number of double bonds remains the same but one of the double bonds is shi ed to a new

posi on by microbial enzymes. Normally, the double bonds in linoleic acid are separated by two single bonds, but in CLA, the double bonds are only separated by one single bond. Many types of CLA are produced in the ru-men of dairy cows, but a common CLA produced from biohydrogena on of linoleic acid is cis-9, trans-11 C18:2.

As biohydrogena on progresses, double bonds in the CLA intermediates are then hydrogenated further to trans fa y acids having only one double bond. A fi nal hydrogenation step by the ruminal microbes elimi-

Figure 1. The structure of stearic acid; a saturated long-chain fa y acid.

Figure 2. Structures of the three primary unsaturated fa y acids consumed by ca le, oleic acid (top), linoleic acid (middle), and linolenic acid (bo om).

Table 1. Fa y acid composi on of several fat supplements used in livestock ra ons

Abbr.a Common Tallow Co onseed Canola Poultry

C14:0 Myris c 3.0 1.0 1.0C16:0 Palmi c 25.0 23.0 4.0 21.0C18:0 Stearic 21.5 3.0 2.0 8.0C18:1 Oleic 42.0 18.5 60.0 41.0C18:2 Linoleic 3.0 52.5 20.0 19.0C18:3 Linolenic 10.0 1.0aNumber of carbons:number of double bonds.From Rouse (2003).

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nates the last double bond yielding stearic acid as the final end product. Trans double bonds only diff er from cis double bonds in the placement of the hy-drogens (Figure 3). The hydrogens are shown on opposite sides of the double bond for trans fa y acids, but on the same side of the double bond for cis fa y acids. Although the diff erence

in structure between trans and cis fa y acids appears small, it causes signifi cant diff erences in their physical and metabolic proper es.

In cows on a typical forage diet, the major trans C18:1 present in ruminal contents is trans-11 C18:1. Most of the remaining isomers have double bonds distributed equally among carbons 12 through 16. The exact path-ways for the produc on of these posi onal isomers are not known. Linoleic and linolenic acids are converted to several trans C18:1 and C18:2 intermediates dur-ing biohydrogena on. Mosley et al. (2002) recently showed that the biohydrogena on of oleic acid by mixed ruminal microorganisms involves the forma on of several posi onal isomers of trans C18:1 rather than only direct biohydrogena on to form stearic acid as previously described.

THE ‘BIOHYDROGENATION THEORY’

OF MILK FAT DEPRESSION

The biohydrogena on (BH) theory repre-sents a unifying concept to explain the basis for diet-induced milk fat depression (MFD) where unusual intermediates of ruminal fa y acid BH accumulate in the rumen and eventually reduce milk fat synthesis in the mammary gland. Under certain dietary situ-a ons the rumen environment is altered and a por on of BH occurs via a pathway that produces trans-10, cis-12 CLA and trans-10 18:1 (Figure 4). Bifi dobacterium, Propioni-bacterium, Lactococcus, Streptococcus, and Lactobacillus isolates from other habitats have been reported to produce trans-10, cis-12-CLA. As these genera occur in the rumen, although generally at rather low numbers,

they may contribute to BH and specifi cally to trans-10, cis-12-CLA forma on in the rumen. Propionibacterium, Streptococcus, and Lactobacillus are also more numer-ous in the rumen with concentrate diets (Jenkins et al., 2008), which would again be consistent with greater trans-10, cis-12 CLA produc on with concentrate di-ets. Therefore, dietary situa ons causing MFD alter the pathways of rumen BH resul ng in changes in the specifi c trans-18:1 and CLA isomers available for uptake by the mammary gland and incorpora on into milk fat.

In vivo studies have revealed a vast array of trans-18:1 and CLA isomers present in digesta contents of ca le and sheep (Bauman and Lock, 2006). These in vivo studies were important in showing that many more intermedi-ates exist in ruminal contents than can be accounted for by most accepted pathways of BH. As many as 17 CLA isomers have been iden fi ed in ruminal contents taken from ca le (Jenkins et al., 2008). Yet, most published pathways of BH account for the synthesis of only one or two CLA isomers. Much of the a en on has been directed at intermediates of linoleic acid BH because it is believed to be the parent compound for most of the CLA isomers found in digesta contents of ca le. A recent study (Lee et al., 2011) completed at Clemson University incubated a stable isotope of linolenic acid with ruminal contents for 48 h. The results revealed the forma on of a mul tude of CLA isomers arising from linolenic acid, including cis-9, trans-11 and trans-10, cis-12 CLA.

As shown in Figure 4, this ‘trans-10 shi ’ in BH path-ways, and the associated increase in the trans-10 18:1 content of milk fat, is indica ve of the complex changes

Figure 3. Structural diff erences between cis and trans fa y acids.

Figure 4. The shi in intermediates produced from biohydrogena on of linoleic acid in ruminal contents as a result of a diet-induced microbial shi .

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2011 Penn State Dairy Cattle Nutrition Workshop 33

in ruminal BH pathways characteris c of MFD. Although trans-10 18:1 does not directly inhibit mammary syn-thesis of milk fat (Lock et al., 2007), it is rela vely easy to analyze compared to trans-10, cis-12 CLA and other CLA isomers. Therefore, in general, this fa y acid can serve as a surrogate marker for the type of altera ons in rumen BH that characterize diet-induced MFD.

RISK FACTORS FOR A MICROBIAL SHIFT

Rumen Fat Load

Total fa y acid concentra on in the rumen can be a key factor contribu ng to a microbial shi and the eleva on of the trans-10, cis-12 CLA isomer. Eleva ng fa y acid concentra on in ruminal contents may cause a number of changes in ruminal fermenta on characteris cs and microbial popula on distribu on. Ruminal changes are the result of the an microbial nature of unsaturated fa y acids, where fa y acids adsorb onto the cell mem-brane of selected microbial species, and then penetrate into the membrane causing disorganiza on of phospho-lipids and eventual cytological damage (Jenkins, 2002). Because some bacterial species are more suscep ble than others, the result is a microbial shi in the rumen. The fa y acid-induced microbial shi can disrupt fer-menta on of carbohydrate diges on causing a drop in the acetate to propionate ra o and possibly a reduc on in fi ber diges on (Jenkins, 2002). The microbial shi also can redirect the pathways of fa y acid BH causing accumula on of CLA isomers linked to milk fat depres-sion (Bauman and Lock, 2006).

Two factors that aff ect the an bacterial ac vity of lipids are fa y acid structure and concentra on. Free fa y acids generally disrupt fermenta on more than triglycerides, and an bacterial ac vity of free fa y acids can be enhanced by increas-ing the number of double bonds (Chalupa et al., 1984). Growth of some bacterial species is s mulated by low concentra ons of fa y acids, but inhibited at higher concentra- ons (Maczulak et al., 1981). In a emp ng

to predict ruminal fermenta on changes caused by dietary lipid, it is o en assumed that the fat load is contributed only by the fat supplement and that FFA concentra on is low. Both assumptions can be wrong. Fa y acids from the TMR and forage can sig-nifi cantly contribute to total rumen fat load, for example when animals are consuming immature pasture. Also, FFA concentra on may be elevated in some feed ingredients

such as whole cottonseed stored in warm, humid condi ons (Cooke et al., 2007), or in forages resul ng from hydroly c cleavage of esterifi ed lipids during hay-making (Yang and Fujita, 1997).

The term “rumen unsaturated fa y acid load” (RUFAL) is intended to refl ect the total unsaturated fa y acid supply entering the re culo-rumen each day from feed consumed. As defi ned, RUFAL accounts for intakes of unsaturated fa y acids from all feed ingredients rather than fa y acid intake coming only from fat supplements. It is hoped that RUFAL will be a be er indicator of fer-menta on disrup on in the re culo-rumen than relying just on the percentage of fat added to the diet. Past studies show that increasing RUFAL will eventually cause fermenta on disrup on, which can have nega ve eff ects on animal performance. Reduced feed intake and rumi-nal carbohydrate diges bility can lead to reduced milk produc on when RUFAL becomes excessive. Another nega ve consequence of excessive RUFAL is MFD.

RUFAL is calculated as the sum of the three primary un-saturated fa y acids consumed by dairy ca le, namely oleic (C18:1), linoleic (C18:2), and linolenic (C18:3) acids. Average RUFAL from fi ve published studies (Table 2) ranged from 473 g/d for dairy ca le fed no added fat to 696 g/d when ca le were fed fat supplements. The range of RUFAL within diet was 220 to 973 g/d for the control diets and 362 to 1,118 g/d for diets with added fat. Much of the RUFAL range could be accounted for by diff erences in DMI and fa y acid concentra ons in the diet DM.

From the data in Table 2, it is clear that the basal diet accounts for a considerable por on of RUFAL. The ra o of RUFAL for control diets to RUFAL for fat diets was 0.68

Table 2. Average intakes of major unsaturated fa y acids by dairy ca le fed a TMR without and with added fat averaged across fi ve published studies1

DMIkg/d

Diet FA g/kgDM

18:1g/d

18:2g/d

18:3g/d

RUFAL2

g/d

Control (n=5)Mean 19.4 37.3 139 299 44 473Min 12.0 18.6 53 133 26 220Max 27.3 55.4 242 690 74 973Fat Diets (n=21)Mean 19.4 59.5 280 371 56 696Min 12.3 28.2 111 164 26 362Max 25.7 83.5 571 710 88 1,1181 Taken from Jenkins and Bridges (2007).2 RUFAL = rumen unsaturated fa y acid load (g/day) = C18:1 + C18:2 + C18:3.

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in Table 2, showing that 68% of RUFAL was contributed by the basal diet ingredients. Fa y acid concentra ons in forages and grains typically range from 2 to 4% of DM, but their high intakes expose the rumen microbial popula on to considerable lipid. Lipid contribu ons from the basal diet are o en overlooked, with much of the focus directed at fa y acid contribu ons from the fat supplements only. As a result of ignoring basal lipid contribu ons, variability in animal responses o en do not line up with added fat levels.

Total RUFAL could be divided into two ruminal pools; esterifi ed and nonesterifi ed. The nonesterfi ed fa y acid component of RUFAL likely has more detrimental eff ect on the microbial popula on and MFD than the esterifi ed component of RUFAL. Unfortunately, very li le published data is available on distribu on of fa y acids in the rumen between the esterifi ed and nonesterifi ed pools. In one example (Table 3), the concentra on of nonesterifi ed fa y acids in ruminal contents increased from 2.03 to as high as 3.97 mg/g DM as soybean oil added to the diet increased from 0 to 8% (Bateman and Jenkins, 1998).

Low Rumen pH

Factors that can result in marked changes in ruminal pH through any 24-h period include: dietary carbohydrate profi le and rates of degrada on of the carbohydrate frac ons as aff ected by source, processing, and mois-ture; physically eff ec ve NDF (peNDF) supply as aff ected by source and par cle size; and produc on of salivary buffers as a function of peNDF supply and source (Shaver, 2005). Despite our general understanding of these factors, the degree and dura on of low ruminal pH required to cause suffi cient fl ux of PUFA through alternative pathways of ruminal BH is not known. Although data are limited, changes in rumen pH are

most likely associated with MFD because they cause a change in the bacterial popula on favoring those that have alterna ve BH pathways.

Mar n and Jenkins (2002) examined the con nuous culture incuba ons that were conducted at dilu on rate of 0.05 and 0.10/h with pH values of 5.5 and 6.5, and 0.5 and 1.0 g/L of mixed soluble carbohydrate. They found that the most infl uencial environmental factor on forma on of CLA and trans-C18:1 from linoleic acid was culture pH. At pH 5.5, the concentra on of trans-C18:1 and CLA were signifi cantly reduced. Similar eff ects were observed by Troegeler-Meynadier et al. (2003). They found that the amounts of BH products were always lower with pH 6.0 than with pH 7.0 in 24-h in vitro in-cuba ons. Low amounts of CLA at pH 6.0 could be due to low isomerase ac vity or to high reductase ac vity. Moreover, they found that low pH (pH 6.0) resulted in lower amount of trans-11 C18:1 at all incuba on mes compared with higher pH (pH 7.0), but concentra ons of trans-10 C18:1 were higher at 16 to 24 h of incuba on. Low pH inhibited ini al isomeriza on and the second reduc on (trans-11 C18:1 to stearic acid), leading to an accumula on of trans-11 C18:1 in ruminal cultures (Troegeler-Meynadier et al., 2006). Choi et al. (2005) reported that cis-9, trans-11 CLA are produced at pH higher than 6.2 by rumen bacteria, but trans-10, cis-12 CLA are produced more than cis-9, trans-11 CLA at lower pH. They concluded that trans-10, cis-12 CLA producing bacteria may be more aero and acid-tolerant than cis-9, trans-11 CLA producing bacteria.

Oleic acid BH was also aff ected by ruminal pH and dilu on rate. AbuGhazaleh et al. (2005) reported that low pH and dilu on rate restricted forma on of trans-C18:1 and increased the concentra on of stearic acid from oleic acid. They conducted the experiment using 13C-labeled oleic acid to determine BH intermediates in mixed ruminal microorganisms grown in con nuous cultures at diff erent pH and liquid dilu on rate. At pH 6.5 and 0.10/h dilu on rate, 13C enrichment was detected in trans-6 through trans-16 C18:1 isomers. However, at pH 5.5 and 0.05/h dilu on rate, 13C enrichment was not detected in any trans isomers with a double bond posi on over C10.

Qiu et al. (2004) reported that reduced ruminal pH can aff ect microbial popula ons, especially celluloly c bac-teria. Total celluloly c bacteria numbers are reduced, accompanied by reduced acetate to propionate ra o and altered BH when pH was low. The rumen pH also

Table 3. Distribu on of fa y acids (FA) in ruminal contents between the esterifi ed and nonesterifi ed pools as soybean oil in the diet increases from 0 to 8%1

mg/g DM 0 2 4 6 8

Esterifi ed FA, mg/g DM 0.64 0.77 1.36 1.14 0.75

Nonesterifi ed FA,mg/g DM 2.03 4.41 3.68 3.97 3.79

Total FA, mg/g DM2 8.11 20.42 24.96 24.79 32.38

1 From Bateman and Jenkins (1998). 2 Includes calcium salts of FA.

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2011 Penn State Dairy Cattle Nutrition Workshop 35

infl uenced fungal growth and metabolism. Culturing ruminal fungi at pH 6.0 and pH 7.0 slowed BH compared with pH 6.5. CLA produc on was increased by pH 7.0 compared to pH 6.0 and pH 6.5. Therefore, op mum pH was 6.5 and 7.0 for BH and CLA produc on, respec vely, by ruminal fungi (Nam and Garnsworthy, 2007).

Monensin

Ionophores disrupt ruminal BH similar to unsaturated fat supplements. Higher concentra ons of linoleic acid, trans C18:1, and CLA were maintained in con nuous cultures of ruminal bacteria following infusion of mo-nensin, nigericin, or tetronasin (Fellner et al., 1997). Feeding monensin had similar eff ects as fat supple-ments on enhancing linoleic acid and trans FA in milk of lacta ng cows and also caused a reduc on in milk fat percentage (Sauer et al., 1998).

Because of the an microbial nature of monensin and other ionophores, it is reasonable to expect that they func on according to the BH theory and cause a micro-bial shi in the rumen that alters the pathways of BH. Monensin increases maintenance requirements of gram posi ve bacteria in the rumen, which renders these bacteria less compe ve in the ruminal environment (Duffi eld and Bagg, 2000). Ruminal microorganisms capable of fa y acid hydrogena on are o en divided into Groups A and B based on their end-products and pa erns of isomeriza on during BH (Harfoot and Hazle-wood, 1997). Bacterial species in Group A hydrogenate linoleic acid to trans C18:1 but appear incapable of hydrogena ng monoenes. Group B bacteria can hydro-genate a wide range of monenes, including trans-11 C18:1, to stearic acid. The few bacterial species in Group B are gram-posi ve. Thus decreasing the number of bacteria that can carry out this process can poten ally lead to a ‘build-up’ of BH intermediates in the rumen. This was highlighted by Fellner et al. (1997) when they examined the eff ect of monensin following con nuous infusion of linoleic acid into rumen fermentors. With an unsupplemented diet the rate of 18:0 forma on was 7.5 mg/L/hr, whereas this decreased to only 2.7 mg/L/hr when monensin was supplemented (Fellner et al., 1997). It is important to remember, however, that an increased rumen ou low of BH intermediates will not be a problem if typical BH pathways are present. However, even if a small propor on of dietary PUFA are being hydrogenated through pathways that produce trans-10, cis-12 CLA and related intermediates, monen-sin can poten ally increase the risk of MFD.

INTERACTIONS AMONG RISK FACTORS

A single risk factor, such as starch source or feeding ionophores, might not contribute to MFD individually, but when combined, interac ons could suddenly trigger changes in BH that could lead to MFD. As an example, con nuous cultures of ruminal microorganisms were fed either a high corn or high barley diet along with two levels of soybean oil (0 and 5%) and two levels of monensin (0 and 25 ppm). Trans-10 18:1 was monitored as an indicator of a BH shi . The addi on of soybean oil increased trans-10 18:1 concentra ons in the cul-tures for both the corn and barley diets (Jenkins et al., 2003). To a lesser extent, monensin also increased trans-10 18:1 for both corn and barley. However, an interac on occurred when monensin and soybean oil were combined. Adding monensin with soybean oil did not further elevate trans-10 18:1 when the diet was corn-based. When the diet was barley-based, adding monensin with soybean oil elevated trans-10 18:1 more than either risk factor alone.

A similar grain by monensin by fat interac on was examined in lacta ng dairy cows (Van Amburgh et al., 2008). Eighty Holstein cows were assigned either a high (27.7%) or low (20.3%) starch diet for 21 d, followed by the addi on of Rumensin (13 ppm) or corn oil (1.25%) for an addi onal 21 d. Therea er, cows were switched to diets with opposite corn oil levels for a fi nal 21 d period giving eight treatments in a 2 x 2 x 2 factorial de-sign. Oil level was a higher risk factor for MFD compared to Rumensin, with a decrease from 3.32 to 2.99% for corn oil versus 3.20% to 3.11% for Rumensin. Feeding high-starch diets had borderline eff ects on MFD causing milk fat to decline from 3.25 to 3.06% (P = 0.10). Starch degradability might also have been a contribu ng factor to MFD in this study. The diets used in this study con-tained steam-fl aked corn which has an inherently fast rate of ruminal starch degrada on, therefore degrad-ability, compounded by high dry ma er intake, may be a more potent MFD risk factor than starch intake alone.

A recent on-farm epidemiological study was done in 2008 (Nydam et al., 2008) to establish risk factors that contrib-ute to MFD in commercial dairy herds feeding Rumensin. This was an extensive study involving 79 commercial dairy herds across 10 states. Cow numbers ranged from 30 to 2,800 across herds, with a mean herd size of 474 cows. Milk fat percentage ranged from 2.7 to 4.3 (mean 3.43%) and Rumensin dose ranged from 150 to 410 mg/head/day (mean 258 mg/head/day). No signifi cant as-socia ons with herd milk fat percentage were seen with

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stall types, cooling systems, or feeding design. Herds with higher formulated DMI, however, tended to have lower milk fat percentage. No rela onships were seen between Rumensin dose and herd milk fat percentage. Likewise TMR concentra ons of DM, ADF, NDF, NFC, and crude fat did not relate to milk fat percentage.

TAKE-HOME MESSAGE

The discovery of major events in rumen lipid metabo-lism occurred decades ago, with very li le new infor-ma on available on the details of its biochemistry and regula on un l the discovery of the an carcinogenic proper es of cis-9, trans-11 CLA. This discovery made it apparent that even minute quan es of BH inter-mediates in the rumen could have drama c eff ects on health and metabolism of the host animal and humans consuming animal-food products. Eventually it was dis-covered that a second CLA, namely the trans-10, cis-12 isomer, was closely associated with MFD. This led to the BH theory of MFD that suggested feeding manage-ment was linked to an abnormal ruminal fermenta on causing accumula on of the trans-10, cis-12 isomer. In general, no single dietary factor is responsible for MFD, and interac ons among various dietary components can increase the rumen ou low of BH intermediates associated with MFD. It is important to consider factors that alter rates of BH (e.g. monensin or starch source) as not being causa ve for MFD but rather they interact with a predisposing condi on (e.g. altered ruminal BH pathways) to accentuate the eff ects on milk fat. Further research is required to be er understand the ruminal condi ons that promote the forma on of BH intermedi-ates that may trigger MFD. An improved understanding of these events will provide the cri cal framework with which to be er troubleshoot MFD.

REFERENCES

AbuGhazaleh, A. A., M. B. Riley, E. J. Thies, and T. C. Jenkins. 2005. Dilu on rate and pH eff ects on the conversion of oleic acid to trans C18:1 posi onal isomers in con nuous culture. J. Dairy Sci. 88:4334-4341.

Bateman,II, H. G. and T. C. Jenkins. 1998. Infl uence of soybean oil in high fi ber diets fed to nonlacta ng cows on ruminal unsaturated fa y acids and nutrient diges bility. J. Dairy Sci. 81:2451-2458.

Bauman, D. E. and A. L. Lock. 2006. Concepts in lipid diges on and metabolism in dairy cows. Proc. Tri-State Dairy Nutr. Conf. pp. 1-14. Available at: h p://tristatedairy.osu.edu/.

Chalupa, W., B. Rickabaugh, D. S. Kronfeld, and D. Sklan. 1984. Rumen fermenta on in vitro as infl uenced by long chain fa y acids. J. Dairy Sci. 67:1439-1444.

Choi, N. J., J. Y. Imm, S. J. Oh, B. C. Kim, H. J. Hwang, and Y. J. Kim. 2005. Eff ect of pH and oxygen on conjugated linoleic acid (CLA) produc on by mixed rumen bacteria from cows fed high concentrate and high forage diets. Anim. Feed Sci. Tech. 123-124:643-653.

Cooke, K. M., J. K. Bernard, C. D. Wildman, J. W. West, and A. H. Parks. 2007. Performance and ruminal fermenta on of dairy cows fed whole co onseed with elevated concentra- ons of free fa y acids in the oil. J. Dairy Sci. 90:2329-2334.

Duffi eld, T. F. and R. Bagg. 2000. Use of ionophores in lacta ng dairy ca le: A review. Can Vet. J. 41:388-394.

Fellner, V., F. D. Sauer, and J. K. G. Kramer. 1997. Eff ect of nigericin, monensin, and tetronasin on biohydrogena on in con nuous fl ow-through ruminal fermenters. J. Dairy Sci. 80:921-928.

Harfoot, C. G., and G. P. Hazlewood. 1997. Lipid metabolism in the rumen. In: Hobson PN. Stewart CS (eds) The rumen mi-crobial ecosystem. London, Chapman and Hall, pp 382-426.

Jenkins, T. C. 2002. Lipid transforma ons by the rumen micro-bial ecosystem and their impact on fermenta ve capacity. pp 103-117 in Gastrointes nal Microbiology in Animals, S. A. Mar n (Ed.), Research Signpost, Kerala, India.

Jenkins, T. C., and W. C. Bridges, Jr. 2007. Protec on of fa y acids against ruminal biohydrogena on in ca le. Eur. J. Lipid Sci. Technol. 109:778-789.

Jenkins, T. C., V. Fellner, and R. K. McGuff ey. 2003. Monensin by fat interac ons on trans fa y acids in culutres of ruminal microroganisms grown in con nuous fermentors fed corn or barley. J. Dairy Sci. 86:324-330.

Jenkins, T. C., R. J. Wallace, P. J. Moate, and E. E. Mosley. 2008. BOARD-INVITED REVIEW: Recent advances in biohydroge-na on of unsaturated fa y acids within the rumen microbial ecosystem. J. Anim. Sci. 86:397-412.

Lee, Y. J. and T. C. Jenkins. Biohydrogena on of linolenic acid to stearic acid by the rumen microbial popula on yields mul ple intermediate conjugated diene isomers. 2011. J. Nutr. 141:1445-1450.

Lock, A. L., C. Tyburczy, D. A. Dwyer, K. J. Harva ne, F. Des-taillats, Z. Mouloungui, L. Candy, and D. E. Bauman. 2007. Trans-10 octadecenoic acid does not reduce milk fat syn-thesis in dairy cows. J. Nutr. 137:71-76.

Maczulak, A. E., B. A. Dehority, and D. L. Palmquist. 1981. Ef-fects of long chain fa y acids on growth of bacteria. Appl. Environ. Microbiol. 42:856-861.

Mar n, S. A., and T. C. Jenkins. 2002. Factors aff ec ng conju-gated linoleic acid and trans-C18:1 fa y acid produc on by mixed ruminal bacteria. J. Anim. Sci. 80:3347-3352.

Mosley, E. E., G. L. Powell, M. B. Riley, and T. C. Jenkins. 2002. Microbial biohydrogena on of oleic acid to trans isomers in vitro. J. Lipid Res. 43:290-296.

Nam, I. S., and P. C. Garnsworthy. 2007. Factors infl uencing

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biohydrogena on and conjugated linoleic acid produc on by mixed rumen fungi. J. Microbiol. 45(3):199-204.

Nydam, D. V., T. R. Overton, G. D. Mechor, D. E. Bauman, and T. C. Jenkins. 2008. Risk factors for bulk tank milk fat depression in northeast and Midwest US dairy herds feed-ing monensin. American Associa on of Bovine Prac oners, Charlo e, NC. Sept 25, 2008.

Qiu, X., M. L. Eastridge, K. E. Griswold, and J. L. Firkins. 2004. Eff ects of substrate, passage rate, and pH in con nuous culture on fl ows of conjugated linoleic acid and trans-C18:1. J. Dairy Sci. 87:3473-3479.

Rouse, R.H. 2003. Feed fats quality and handling characteris- cs. Mul -state Poultry Mee ng, May 20-22, 2003.

Saur, F. D., V. Fellner, R. Kinsman, J.K.G. Kramer, H. A. Jackson, A. J. Lee, and S. Chen. 1998. Methane output and lacta on response in Holstein ca le with monensin or unsaturated fat added to the diet. J. Anim. Sci. 76:906-914.

Shaver, R. D. 2005. Feeding to minimize acidosis and lamini s in dairy ca le. Proc. Cornell Nutr. Conf. pp. 49-60.

Troegeler-Meynadier, A., M. C. Nicot, C. Bayourthe, R. Moncoulon, and F. Enjalbert. 2003. Eff ects of pH and con-centra ons of linoleic and linolenic acids on extent and intermediates of ruminal biohydrogena on in vitro. J. Dairy Sci. 86:4054-4063.

Troegeler-Meynadier, A., L. Bret-Bennis, and F. Enjalbert. 2006. Rates and effi ciencies of reac ons of ruminal biohydrogena- on of linoleic acid according to pH and polyunsaturated

fa y acids concentra ons. Reprod. Nutr. Dev. 46:713-724.

Van Amburgh, M. E., J. L. Clapper, G. D. Mechor, and D. E. Bau-man. 2008. Rumensin and milk fat produc on. Proceedings of the 2008 Cornell Nutri on Conference.

Yang, U. M., and H. Fujita. 1997. Changes in grass lipid frac- ons and fa y acid composi on a ributed to hay making.

Grassl. Sci. 42:289-293.

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Notes

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2011 Penn State Dairy Cattle Nutrition Workshop 39

What’s Happening in Nutrient Management in the Chesapeake Bay?

Douglas B. Beegle

Distinguished Professor of Agronomy

Department of Crop and Soil Sciences, Penn State University

116 Ag Sciences and Industries Building

University Park, PA 16802

[email protected]

INTRODUCTION

Nutrient problems from agriculture in the Chesapeake Bay can be placed into two major categories. The fi rst are systemic problems related to the structure of ani-mal agriculture systems in the watershed. Much of the animal agriculture in the watershed is based on the signifi cant importa on of feed for the animals from off the farm, and o en from outside the watershed. The manure nutrients are typically not returned to where the feed crops are produced, but rather are spread on land near to the animal produc on facili es o en in excess of crop requirements. It is important to understand that these issues are beyond the control of the individual farmer trying to be profi table within this system. Fortu-nately, because of the larger land base need to provide the forage needs of dairy cows, this has been less of a problem in the dairy industry compared to swine and poultry. However, it has become more common for dairy farmers to maximize forage produc on and import most of the concentrate needs of the cows. In a typical corn silage and alfalfa/grass based forage system, if the cow numbers are maximized based on the forage produc on with concentrates coming from off the farm, signifi cantly more manure nutrients will be produced than the forage crops grown on the farm can use.

The other category of issues is on-farm nutrient manage-ment. Much progress has been made by research and extension in the Bay watershed toward developing and implemen ng improved nutrient management plans. Universi es, the USDA, and other public and private organiza ons have made major contribu ons to our un-derstanding of the processes that can result in nutrient pollu on and used that knowledge to develop manage-ment systems and technologies to help farmers achieve the maximum economic benefi t from nutrients with minimum environmental impact. Over the 30 plus years of the Chesapeake Bay Program, many major management changes have been adopted by farmers to address the concerns with the impact of agriculture on water quality.

Most public programs and policies intended to address the nutrient pollu on problems in the Bay have focused on on-farm nutrient management. Signifi cant progress has been made in reducing nutrient pollu on in the Bay, however, the progress has not met expecta ons (Figure 1). Most new ini a ves designed to accelerate progress toward mee ng environmental goals for the Bay con- nue to focus on encouraging or manda ng changes

in on-farm management, usually without addressing the systemic nutrient balance issues in the watershed. This approach has created a strategic confl ict between economic produc on and environmental protec on. Most of these eff orts to improve on-farm nutrient man-agement are external to the marketplace and o en in direct confl ict with the producer’s economic interests. It can be argued that while improved management will con nue to provide benefi ts, success will be limited unless the systemic problems that are beyond the famer’s control and are resul ng in a regional nutrient imbalance are also addressed. Following is a summary of what has been happening in the Bay watershed and what is coming in the near future.

STATE NUTRIENT MANAGEMENT PROGRAM

Since the 1990s Pennsylvania has had a nutrient man-agement law, fi rst Act 6 and more recently, Act 38. The Pennsylvania law requires high animal density farms, called Concentrated Animal Opera ons (CAO) to have an approved nutrient management plan (NMP). A CAO is any farm with an animal density greater than two animal equivalent units per acre of land available for manure applica on. At this animal density, most farms will have an excess of nitrogen above what can be u lized by the crops grown on the farm. The NMP includes an inventory of nutrients from all sources on the farm, a determina on of crop nutrient needs based on soil test recommenda- ons, and an alloca on of the nutrients to maximize the

agronomic benefi t of the nutrients while minimizing the environmental impact. Specifi cally, nitrogen (N) cannot be applied in excess of crop needs. Also, because excess

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40 November 8-10 Grantville, PA

phosphorus (P) is o en applied if manure is applied to meet crop N needs, an assessment of the risk of P impact on the environment is required using the Phosphorus Index (PI). These plans must be wri en by cer fi ed nutrient management specialists and approved either by the county Conserva on District Board or the State

Conserva on Commission. While this law only directly impacts a rela vely small percentage of farms in PA (ap-proximately 1,000 farms are CAOs), the plans wri en under this law cover the management of around half of the manure produced in the state. Also, because this law targets the high density farms, the manure nutrients

Figure 1. Progress toward mee ng the goals for agricultural nutrient reduc ons to the Chesapeake Bay. From: US EPA Chesapeake Bay Program (h p://www.chesapeakebay.net/status_agriculture.aspx?menuitem=19693).

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2011 Penn State Dairy Cattle Nutrition Workshop 41

on these farms are more likely to be an environmental threat, resul ng in poten ally greater environmental benefi t from improved nutrient management than might be expected based on the number of farms or even the amount of manure managed under this law.

FEDERAL NUTRIENT MANAGEMENT PROGRAM

The federal nutrient management program that impacts dairy farms is the Concentrated Animal Feeding Opera- ons program (CAFO). This regula on, under the Clean

Water Act, requires farms with a large number of animals confi ned in one loca on to have a Na onal Pollutant Discharge Elimina on System (NPDES) permit similar to a sewage treatment plant. In PA there are approximately 350 CAFOs. A dairy CAFO in Pennsylvania is defi ned as a farm with more than 1,000 animal equivalent units (AEUs) or a concentrated animal opera on (CAO) with 301 to 1,000 AEUs; or exceeds 700 dairy cows. DEP’s CAFO permit requirements include, but are not limited to:

• A Nutrient Management Plan that has been approved by the county conserva on district or the State Con-serva on Commission under the Act 38 program.

• An Erosion and Sediment Control Plan for acreage being plowed or lled.

• When required by regulation, a water quality management permit or engineer’s cer fi ca on for manure storage facili es.

• A water quality management permit when treat-ment facili es are proposed that include a treated wastewater discharge.

• A preparedness, preven on, and con ngency plan for pollutants related to the opera on.

• A plan to prevent discharges to surface waters from raw material storage areas.

Both of these state and federal programs have been in existence for a number of years and dairy producers are generally familiar with these programs. As noted earlier, while signifi cant progress has been made in reducing the contribu on of nutrients from agriculture to the Bay (Fig-ure 1), the goals have not been met and as a result legal ac on has required the federal government to take further ac on to achieve the environmental goals set for the Bay.

CHESAPEAKE BAY EXECUTIVE ORDER

In 2009 the Obama administra on issued an execu ve order for the Chesapeake Bay Protec on and Restora- on. This order “put the full weight of the federal gov-

ernment behind restoring the Bay” and established an

EPA and USDA ini a ve called: “Healthy Waters, Thriv-ing Agriculture.” The execu ve order was very heavy on regula on and enforcement of management prac ces. It would poten ally make all farms have CAFO permits. In 2010 the US EPA published very restric ve guidance for agricultural nutrient management for federally man-aged lands only, but indicated that the intent was that this guidance was a model for management of all agri-cultural lands. The guidance included: P-based manure rate restric ons, all manure must be incorporated, only conserva on llage systems could be used, no winter manure applica on, cover crops must be planted on all bare soil, and all manure must be treated to reduce the volume and nutrient loss.

CHESAPEAKE TOTAL MAXIMUM DAILY LOAD

(TMDL)

Under the Federal Clean Water Act, all waters that do not meet water quality standards are considered im-paired, and as a result a TMDL must be developed for that water body. Because the goals for nutrients in the Bay were not met by 2010, the US EPA was required to develop a TMDL for the Bay watershed. This TMDL represents the maximum amount of N, P, and sediment from all sources allowed to enter the Bay so that the Bay will meet and con nue to meet the water quality stan-dards. Table 1 shows the alloca ons for N, P, and sedi-ment for Pennsylvania. This table also shows where we were rela ve to these alloca ons in 2009 and also the total alloca ons for N, P, and sediment for the en re Bay watershed. The TMDL process required Pennsylvania to develop a Watershed Implementa on Plan (WIP) for the state and submit this plan to EPA for approval. The WIP included plans for: wastewater, agriculture, urban stormwater, forestry, resource extrac on, and mul ple sector sources. The core elements of the agriculture sec on of the WIP were: milestone implementa on and tracking, suppor ng implementa on of advanced technologies and nutrient trading, and enhancing com-mon sense compliance eff orts. This represents largely an intensifi ca on of the current programs that focus on requiring improved on-farm management of nutrients, rather than a major shi in programs. Also, it does not address the fundamental problem of excess nutrients in the watershed as a whole. For farmers who are CAOs or CAFOs they can expect more rigorous enforcement of compliance with their nutrient management plan and/or CAFO permit. For farmers who are not currently impacted by either of these state or federal laws, the plan calls for a major eff ort to bring these farms into baseline compliance with exis ng requirements under

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42 November 8-10 Grantville, PA

the state Clean Streams Law. The TMDL will have 2-year milestones that must be met, and the TMDL must be fully implemented by 2025.

PA CLEAN STREAMS LAW

Since the 1970s the PA Clean Streams Law has required all farmers with manure to develop and implement a manure management plan for their opera on. However, this provision of the law has rarely been enforced unless a pollu on event with manure occurs. As part of the WIP under the Bay TMDL, the plan is for all farms to be brought into full compliance with this requirement. This will most likely be the greatest impact of the TMDL on dairy farms in the state. This requirement will typically be met by using the DEP Manure Management Manual (MMM) to develop a farm Manure Management Plan (MMP). A farmer can also develop an Act 38 NMP. Fi-nally, any farmer who has a plan as a CAO or CAFO will already meet this requirement.

MANURE MANAGEMENT MANUAL – MANURE

MANAGEMENT PLAN

NOTE: At the me of this wri ng, the offi cial version of the MMM has not yet been released. Therefore, details presented here are dra and thus subject to change.

Manure Management Plans developed using the MMM will be farmer developed plans. It is not necessary to have a cer fi ed nutrient management specialist develop the plan. DEP through the county Conserva on Districts has a program underway to educate farmers about their obliga ons through farm visits. To assist with mee ng this requirement, extension is developing workshops, where most farmers who a end should be able to com-plete the plan at the workshop. Because this will likely have the greatest impact on more farmers, details of a MMP are summarized here.

Manure Application Rate, Method, Timing

There are several op ons for developing these plans for rate, method, and ming of manure applica ons. These range from very simple, but somewhat restric ve plans, to more complex but less restric ve plans. The farmer can choose the level of planning that is appropriate for his opera on. This is the fi rst decision that a farmer must make. The criteria for these diff erent levels of planning are as follows:

• Apply manure at Phosphorus removal rates (gener-ally low rates) with 150’ setback from water and winter restric ons, but no soil sampling required.

• Apply manure at Nitrogen rates (generally much higher rates) with 150’ setback from water with some winter restric ons, with soils test showing that soil test P is under 200 ppm.

• Nutrient Management Plan or Nutrient Balance Sheet with P Index done by a cer fi ed planner with a 100’ setback or a 35’ vegeta ve buff er from water.

To facilitate planning there are worksheets for calculat-ing the appropriate manure applica on rates, or lookup tables have been developed for common cropping situ-a ons that can be used to determine the appropriate manure applica on rates.

Manure Management Plans will typically be developed for each manure source for the diff erent crop groups on the farm. The MMP will not need to include specifi c fi eld by fi eld plans as in an NMP. An example might be three manure sources on the farm:

• Spring Cow from liquid storage• Fall Cow from liquid storage• Heifer Barn bedded pack

and fi ve crop scenarios that receive manure:

Table 1. Chesapeake Bay TMDL load alloca ons and 2009 loads (million lbs/year) for Pennsylvania watersheds and for the whole Bay watershed

Jurisdic on Basin Nitrogen Phosphorus Sediment

Alloca ons Calculated Load in 2009 Alloca ons Calculated

Load in 2009 Alloca ons

Pennsylvania

Susquehanna 71.74 101.57 2.31 3.41 1,660-1,826Potomac 4.72 6.19 0.42 0.53 221-243Eastern Shore 0.28 0.44 0.01 0.020 21-23Western Shore 0.02 0.030 0.001 0.0011 0.37-0.41PA Total 76.77 108.24 2.74 3.96 1,903-2,093

Bay Watershed All 203.39 12.62 6,135–6,749Source: Chesapeake Bay TMDL, US EPA, July 2010.

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2011 Penn State Dairy Cattle Nutrition Workshop 43

• Corn Silage with manure incorporated (25 ton/A expected yield)

• Corn Silage with manure unincorporated (25 ton/A expected yield)

• Corn Silage following Alfalfa (25 ton/A expected yield)

• Corn Grain following corn silage with a cover crop (150 bu/A expected yield)

• Grass Hay (4 ton/A expected yield)

In this example there would be, at most, 15 diff erent combina ons that would have to be planned regard-less of how many fi elds are on the farm. Some of these combina ons may not be relevant, such as bedded pack on the hay, so this management op on would not be necessary in the plan. For any given fi eld when the ma-nure is applied the farmer could go to the plan, fi nd the scenario that applies to the situa on for that fi eld, and lookup the planned manure rate, method, and ming.

The worksheet for determining manure applica on rates is the Nutrient Balance Sheet (NBS) that has been developed in Act 38 for determining applica on rates on impor ng farms for manure exported from a CAO or CAFO (Figure 2). Informa on required to complete the NBS includes: manure analysis, planned spreading season, and planned spreading management e.g. incor-pora on, manure history, previous legume history, any other sources of nutrients that will be applied. There is a spreadsheet version of the NBS available to facilitate using this approach (h p://panutrientmgmt.cas.psu.edu/pdf/NBS_Spreadsheet_2009-10.xls).

As an alterna ve to the calcula ons, tables of accept-able manure rates are included as part of the MMM. These tables have the common manure types including solid and liquid dairy manure. Rates are listed for a num-ber of common cropping scenarios. To use the tables the farmer fi nds the appropriate table for the type of manure (Figure 3 is part of the table for liquid dairy manure) and then selects the crop and management ( ming and method of applica on) that matches his situa on. The maximum allowable manure applica on rate is then read directly from the table. For example, (see Figure 3) if the manure is liquid dairy and the plan is to spread this in the spring with no incorpora on for corn silage with an expected yield of 25 ton/A, the maximum rate would be 16,000 gal/A. Next to the maximum rate is the amount of supplemental N that is required at this rate to meet the needs of the crop.

In this case if the 16,000 gal/A rate is applied, an ad-di onal 70 lb N/A will have to be applied as fer lizer to meet the needs of this corn silage crop. Note that in PA the maximum rate of liquid manure that is allowed in a single applica on is 9,000 gal/A, thus if the 16,000 gal/A rate is to be used it would have to be split into at least 2 separate applica ons. Any rate less than or equal to the maximum rate is acceptable in the MMP. If a rate less than the maximum is selected the supplemental N needs for the crop will have to be increased. The right hand column in the table provides the factor for making this adjustment. In this case the adjustment would be an addi onal 6 lb N/A more for each 1,000 gal/A diff erence in manure applied less than the maximum rate. In many cases if the farmer’s normal manure applica on rate is less than these maximums, the plan will simply be a documenta on that his normal management meets the requirements of the MMM. Figure 4 is an example of what a completed manure applica on rate and ming sec on of the MMP might look like.

In addi on to the manure rates for the crops grown on the farm, there are several other areas that must be addressed as part of a MMP. These include pasture management, management of animal concentra on areas, manure applica on setbacks from environmen-tally sensi ve areas, addi onal requirements for winter manure spreading, manure storage, and solid manure stockpiling and in-fi eld stacking.

Pasture Management

All pastures on the farm must be included in the plan. Similar to a crop fi eld, excess nutrients cannot be ap-plied to pastures by grazing animals. Farms with a graz-ing plan that meets the NRCS PA Tech Guide standards will sa sfy the pasture management requirements for an MMP. Otherwise, the pasture must be composed of dense vegeta on and managed to minimize bare spots, maintain 80% uniform vegeta ve cover with 3” high vegeta on all year. If these criteria are not met, the area may be considered an Animal Concentra on Area (ACA) and require addi onal measures to protect water quality (see next sec on).

Animal Concentration Areas Management

Animal Concentra on Areas (ACAs) (some mes called “Animal Heavy Use Areas”) are barnyards, feedlots, loaf-ing areas, exercise lots or other similar animal confi ne-ment areas that will not maintain the dense vegeta on of a pasture. The MMP must include management plans

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44 November 8-10 Grantville, PA

CMU/Field Iden fi ca on (Area must be clearly iden fi ed on a map)

Acres Crop Group Yield

Manure Plan Basis

(check plan-ning op on)

OPTION 1

P RemovalOPTION 2

N RequirementOPTION 3

P Index

• P removal rates• 150’ applica on setback from streams, lakes

or ponds

• N requirement rates• 150’ applica on setback from streams, lakes

or ponds• Soil test < 200 ppm

• P Index evalua on (must be a ached)

Will P banking be used? Soil Test P (ppm)

No

Yes, for years.

(Use the P2O5 column to determine acceptable rate) (Use the N column to determine acceptable rate) (Use appropriate column based on the P Index to

determine acceptable rate)

Manure TypeManure Analysis (lb/ton or 1000 gal)

Applica on Timing Applica on MethodTotal N P2O5 K2O

Notes

N 1 P2O5 1 K2O 1 Recommenda on Basis

A) Recommenda on or Removal (lb/A)N – Soil Test or Tables 1 & 2 (AG Table 1.2-6;1.2-8)P2O5 & K2O – Soil Test or Table 3 (AG Table 1.2-9)

Soil Tests

Crop Removal

B) Fer lizer Applied (lb/A)(Regardless of Manure e.g. Starter) Applica on Record & Notes

Record when the planned manure and fer lizer rates were applied or note changes.C) Other Organic Sources Applied (lb/A)

(e.g. Biosolids, Other Manure)

D) Residual Manure N (lb/A)Table 4 (AG Table 1.2-14B)

E) Previous Legume N (lb/A)Table 5 (AG Table 1.2-7) or Soil Test Report

F) Net Nutrient Requirement (lb/A)(A – B – C – D – E)

G) Manure Nutrient Content(lb/ton or lb/1000gal)

H) Nitrogen Availability FactorTable 6 (AG Table 1.2-14A)

I) Available Nitrogen(lb/ton or lb/1000gal) (G x H)

J) Balanced Manure Rate(tons/A or gallons/A)For N: (F ÷ I) For P: (F ÷ G)

K) Planned Manure Rate(tons/A or gallons/A)Must be less than or equal to the appropriate Balanced Rate based on the plan basis being used

L) Nutrients Applied at Planned Rate(lb/A) For N: (K x I) For P & K: (K x G)

Note: Nutrient balances for P2O5 and K2O based on crop removal (Row A) should not be used to determine ad-di onal fer lizer needs. Only recommenda ons based on soil tests should be used for this purpose.

M) Nutrient Balance at Planned Rate(lb/A) (F - L) (Indicate short or excess)

1 Comple on of N column required for all op ons; P2O5 column is op onal for N based rates; K2O is op onal for all rates.

Figure 2. Nutrient Balance Sheet Worksheet. This is used in the Pennsylvania Nutrient Management Program to determine appro-priate rates for applica on of imported manure from regulated farms. This same worksheet can be used to determine appropriate rate for manure applica on in a Manure Management Plan.

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2011 Penn State Dairy Cattle Nutrition Workshop 45

Figure 3. Example manure rate table from the Manure Management Manual. (DRAFT)

Liquid DairyNitrogen Based Manure Application Rates

Corn Grain Yield Groups (bu/A)

Manure Application MethodManure

gal/AFert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Spring Incorporation within 1 day 8000 0 10000 0 12000 0 14000 0 14Spring Incorporation within 1 week 11000 0 14000 0 16000 15 16000 45 10Spring No Incorporation 16000 20 16000 50 16000 80 16000 110 6Fall 16000 20 16000 50 16000 80 16000 110 6Winter with cover crop 5000 55 5000 85 5000 115 5000 145 11Winter No cover crop 5000 80 5000 110 5000 140 5000 170 6

Corn Grain after Alfalfa Yield Groups (bu/A)

Manure Application MethodManure

gal/AFert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Spring Incorporation within 1 day 4000 0 5000 0 6000 0 8000 0 14Spring Incorporation within 1 week 5000 0 7000 0 9000 0 11000 0 10Spring No Incorporation 9000 0 13000 0 16000 0 16000 20 6Fall 9000 0 13000 0 16000 0 16000 20 6Winter with cover crop 4000 0 5000 15 5000 35 5000 55 11Winter No cover crop 5000 20 5000 40 5000 60 5000 80 6

Corn Grain after Soybeans Yield Groups (bu/A)

Manure Application MethodManure

gal/AFert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Spring Incorporation within 1 day 5000 0 6000 0 8000 0 9000 0 14Spring Incorporation within 1 week 7000 0 9000 0 11000 0 13000 0 10Spring No Incorporation 13000 0 16000 0 16000 20 16000 40 6Fall 13000 0 16000 0 16000 20 16000 40 6Winter with cover crop 5000 15 5000 35 5000 55 5000 75 11Winter No cover crop 5000 40 5000 60 5000 80 5000 100 6

Corn Silage Yield Groups (ton/A)

Manure Application MethodManure

gal/AFert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Spring Incorporation within 1 day 9000 0 11000 0 14000 0 16000 0 14Spring Incorporation within 1 week 13000 0 16000 0 16000 35 16000 65 10

Spring No Incorporation 16000 40 16000 70 16000 100 16000 130 6Fall 16000 40 16000 70 16000 100 16000 130 6Winter with cover crop 5000 75 5000 105 5000 135 5000 165 11Winter No cover crop 5000 100 5000 130 5000 160 5000 190 6

Corn Silage after Alfalfa Yield Groups (ton/A)

Manure Application MethodManure

gal/AFert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Manuregal/A

Fert Nlb/A

Spring Incorporation within 1 day 5000 0 6000 0 8000 0 9000 0 14Spring Incorporation within 1 week 7000 0 9000 0 11000 0 13000 0 10Spring No Incorporation 13000 0 16000 0 16000 20 16000 40 6Fall 13000 0 16000 0 16000 20 16000 40 6Winter with cover crop 5000 15 5000 35 5000 55 5000 75 11Winter No cover crop 5000 40 5000 60 5000 80 5000 100 6

Manure Application RateAdjustment

For each 1000 gal/A less thanthe rate in the table, apply lbs. N

fertil izer listed below.

100 130 131 160 161 190 191 220

For each 1000 gal/A less thanthe rate in the table, apply

lbs. N fertilizer listed below.

100 130 131 160 161 190 191 220

For each 1000 gal/A less thanthe rate in the table, apply

lbs. N fertilizer listed below.

100 130 131 160 161 190 191 220

For each 1000 gal/A less thanthe rate in the table, apply

lbs. N fertilizer listed below.

17 21 22 25 26 29 30 33

For each 1000 gal/A less thanthe rate in the table, apply

lbs. N fertilizer listed below.

17 21 22 25 26 29 30 33

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46 November 8-10 Grantville, PA

for diver ng clean water from these areas; direc ng polluted water from these areas to a manure storage or treatment system, such as a vegeta ve treatment area; animal access to surface water must be limited to properly implemented livestock crossings; the size of denuded areas such as sacrifi ce lots must be minimized; areas where animals congregate, such as feed racks and shade, must be kept as far away from a water body as prac cal; movable structures crea ng ACAs, such as hay rings in pastures, should be relocated at least annually to minimize ACA development and manure concentra- on; and accumulated manure should be removed

rou nely from ACAs where prac cal to minimize the poten al for pollu on. Farms that have ACAs must address the ACA in the Manure Management Plan. The plan needs to iden fy Best Management Prac ces (BMPs) that are currently being implemented to prevent pollu on and, where necessary, include a schedule for obtaining assistance to develop and implement the necessary addi onal BMPs to address ACA problems.

Manure Application Setbacks from

Environmentally Sensitive Areas

The MMM contains setback requirements for mechani-cal manure applica on from environmentally sensi ve areas. Fields containing environmentally sensi ve areas must be iden fi ed and the required setbacks clearly indicated in the plan and on the farm map. Below are the proposed manure applica on setbacks in the MMM. Manure may not be mechanically applied:

• Within 100 feet of the top of the bank of a stream which generally fl ows during the season when manure is being applied and within 100 feet of a lake or a pond.

• A farmer can reduce this setback to 50 feet where a soil test done within the last 3 years shows P levels of less than 200 parts per million (ppm) and the farmer uses no ll prac ces with residue manage-ment or a cover crop.

• The setback can be further reduced to 35 feet where the farmer establishes a permanent vegetated buff er along the stream.

• Within 100 feet of an exis ng open sinkhole.• Within 100 feet of an ac ve private drinking water

source such as a well or a spring.• Within, at a minimum, 100 feet of an ac ve public

drinking water source. In some cases state and fed-eral laws may establish greater distances.

• Within the channel of a concentrated water fl ow areas in which vegeta on is not maintained such as a swale, gully, or ditch.

• For winter applica on, a setback of 100 feet from an above ground inlet to an agricultural drainage system (such as inlet pipes to piped outlet terraces) if surface water fl ow is toward the above ground inlet.

Winter Manure Application

There are addi onal manure applica on criteria that must be met for winter applica on of manure. For an

Figure 4. Example manure applica on management summary for a Manure Management Plan from the Manure Management Manual. (DRAFT)

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2011 Penn State Dairy Cattle Nutrition Workshop 47

MMP, winter is defi ned as: December 15 through Febru-ary 28; or any me the ground is frozen at least 4 inches; or any me that the ground is snow covered. Winter applica on of manure is discouraged in the MMM. Farmers that choose to apply manure in the winter will need to follow the proposed criteria listed below:

• The maximum applica on rate for the winter season is 5,000 gallons of liquid manure or 20 tons of dry non-poultry manure per acre and 3 tons of dry poul-try manure per acre. As an alterna ve maximum rate, a farmer can choose to calculate and apply manure to the phosphorus removal rate for the coming year’s crop.

• A setback of 100 feet from an above ground inlet to an agricultural drainage system (such as inlet pipes to piped outlet terraces) if surface water fl ow is toward the above ground inlet.

• All fi elds must have at least 25% crop residue at ap-plica on me or an established and growing cover crop. Hay fi elds, sod, and pasture fi elds may also be used for winter applica on. This would generally exclude winter manure applica on to corn silage fi elds that do not have an established cover crop, corn grain fi elds where a signifi cant por on of the fodder has been removed, and soybean fi elds.

• Manure may not be applied during winter on fi elds with slopes greater than 15%.

Farmers using a Cer fi ed Nutrient Management Planner to develop a nutrient management plan for the farm under the Nutrient Management Act, (Act 38), or ob-taining approval from the DEP or county conserva on district, may be provided some added fl exibility in the applica on of manure during the winter.

Manure Storage, Stockpiling, and In-fi eld Stacking

For manure storages, the plan requirements are mainly to document the details of the exis ng manure storages and any plans for expansion or addi ons. As of 2000, construc on of all new liquid or semi-solid manure storages has been regulated. Also, storages of a certain size and loca on need a DEP permit. As part of the MMP implementa on, for liquid or semi-solid manure storages the farmer must document that the storage is inspected monthly for evidence of overtopping or leaks, no visible cracking, rodent holes, tree or shrub growth on berms, and no visible slope failures or deteriora on or tears in any storage liner.

Some opera ons typically have one or more temporary stockpiling/stacking around the barn or in the fi eld to handle situa ons when direct manure applica on is unacceptable. Manure stacking in produc on areas must meet the NRCS constructed manure storage facil-ity standards. The requirements rela ng to stacking of manure in other areas are:

• Stockpile/stack manure on properly constructed improved stacking pads whenever possible.

• The manure must be dry enough to allow for stacking at least 5 feet in height. When stacked on the applica- on fi eld; the volume must be limited to the amount

that can be spread on fi elds nearby to the stack.

• Keeping all stockpiles/stacks at least 100 feet from sensi ve areas such as streams, lakes and ponds, 100 feet from any open sinkhole, 100 feet from any drinking water well (public or private).

• These stacks cannot be placed within an area of concen-trated water fl ow such as a swale, ditch, or waterway.

• Place stacks on areas with less than 8% slope.

• Where possible, place these areas at the top of a hill (within 100 feet from the top of a slope), diver ng upslope water away from stockpile/stacking areas.

• When stockpiling/stacking on unimproved areas in crop fi elds, the stockpiles/stacks should not be in the same loca on each year.

• Temporary stockpiled/stacked manure must be cov-ered if it will be in place for more than 120 days.

The MMP must include details for any planned stockpil-ing or stacking of manure in fi elds.

CONCLUSION

Manure management in the Chesapeake Bay watershed is changing and will present signifi cant challenges over the coming years. The implementa on of the Bay TMDL will require more formalized nutrient management and manure management plans for all farms with addi onal requirements for documenta on of plan implementa- on and enforcement for non-compliance. In addi on

to mee ng these regulatory requirements, we must also be aware of public percep on about how manure management is perceived and include these important considera ons in our management strategies.

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48 November 8-10 Grantville, PA

Notes

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2011 Penn State Dairy Cattle Nutrition Workshop 49

Starch Digestibility in Corn Grain and Silage

Bill Mahanna, Ph.D., Dipl. ACAN

Nutritional Sciences and Sales Support Manager

Pioneer, A DuPont Business

7100 NW 62nd Avenue

Johnston, Iowa 50131

515.229.3409 (cell) [email protected]

http://www.pioneer.com/home/site/us/livestock-feed-nutrition/

• Brief Review of Carbohydrate (CHO) Nutrition• Brief Review of Carbohydrate (CHO) Nutrition• Corn Kernel Physiology and Terminology• Commercial Laboratory Tests• Practical Feeding Implications

• Starch is not required in ruminant rations for either the animal or rumen microbes.• What rumen microbes require is a supply of rumen fermentable carbohydrates as

an energy source for growth and microbial protein synthesis– this can be supplied by a number of feed components including:

• StarchStarch• Sugar• Pectins• Digestible fiber

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50 November 8-10 Grantville, PA

NSC (laboriously measured enzymatically) = starch + sucrose + NDSF

• NFC and NSC are not interchangeable, especially forages, with much of difference being pectins & organic acids found in NFC but not NSC.

Feed NFC NSCAlfalfa silage 18.4 7.5CS 41.0 34.7Beet pulp 36.2 19.5HMSC 71.8 70.6Soyhulls 14.1 5.3Ground corn 67.5 68.7 Table source: 2001 NRC, p. 34

NFC (“NSC estimate” by difference) = 100-[CP+(NDF-NDFCP)+fat+ash] Plant Carbohydrates

CellContents

CellWall

HemicellulosesPecticSubstances

-glucans

FructansStarchesMono+Oligo-saccharides

OrganicAcids

Cellulose

ADF

NDSF

Galactans

N St h P l h id

NDF

Source: Dr. Mary Beth Hall. Source: G. Bethard at http://www.dasc.vt.edu/extension/nutritioncc/9718.html

The difficulty with Neutral Detergent Soluble Carbohydrate (NDSC) is that they have been represented as a single, calculated value which does not address the

nutritionally diverse nature of this pool. NDSC vary in potential to support microbial growth, rates of digestion, microbial fermentation characteristics and ability to be digested by mammalian enzymes. The lack of practical analytical

methods to separate the NDSC has been a major stumbling block in partitioning these components for use in the field

ADF and NDF are not “chemically pure”

measurements

NDSC

Non-Starch Polysaccharides

Rumen Small Intestine Large Intestine

Where Should Starch Be Digested

2. Bacterial protein Yes No No

5 E l 80% 100% 50%

3. Absorbed nutrients VFA Glucose VFA4. Methane loss Yes No Small

1. Prevent acidosis No Yes Yes

5. Energy value 80% 100% 50%

Optimal site depends on diet (fiber and undegraded protein levels), stage of production

and management (feeding frequency etc.)Source: Fred Owens, Pioneer Senior Research Scientist

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2011 Penn State Dairy Cattle Nutrition Workshop 51

When discussing starch digestion, we need to clarify if we are talking rumen, SI or total tract.

LargeIntestine

The form in which corn is fed (dry, fermented, flaked) influences the site of starch digestion in dairy cattle

SmallIntestine

Rumen

Why is Total Tract Digestion

In some feeding situations, it may be beneficial to bypass ruminal

digestion and increase intestinal digestion if microbial protein flow is already high and acidosis is a

concern…..

Source: Owens, F.A. and S. Soderlund. 2007. Getting the most out of your dry and high-moisture corn. Proceedings Four State Nutrition and Management Conference.

Why is Total Tract DigestionLower for Cows than Steers?

- Difference is primarily in the rumen- Cows have higher feed intakesand more forage (NDF) in diets

which increases ruminal outflow rates

• Primary starch digesting bacteria in the rumen:– Streptococcus bovis, ruminobacter amylophilus, prevotella

ruminicola, Butyrivibro fibrisolvens, Succinomonas amylolytica, Selenomonas ruminantium

– Each can digest starch but incapable of producing the variety of enzymes necessary to digest an entire grainkernel

• Starch digestion accomplished sequentially byg p q y ycomplementary bacterial species that associate to form a complex digestive “team” at the exposed surface of the grain.

– initiated when amylolytic bacteria are attracted and adhere to starch granule surfaces and multiply, producing digestive enzymes that release soluble nutrients forming digestive pits on the surface of starch granules

– This attracts secondary colonizers to the digestive site.I ti th f f th t h l b

Bacteria attack on a starch granule

– In time, the surface of the starch granules becomecompletely covered by a definitive multi-species microbial population.

• Factors which alter this sequential development, such as grain processing and particle size, can profoundly affect both rate and extend of cereal grain digestion in the rumen

Source: McAllister, T., A. Hristov and Y. Wang. 2001. Recent Advances/Current Understanding of Factors Impacting Barley Utilization by Ruminants. Proceedings of 36th Annual Pacific Northwest Animal Nutrition Conference, Boise, Idaho. October 9-11, 2001

Page 58: Nutrition Workshop Proceedings 2011

52 November 8-10 Grantville, PA

• Two groups of protozoa (Holotrichs and Entodiniomorphs) also degrade starch(comprise 20-45% of rumen amylotic activity)– Engulf starch granules at rates inversely related to size of starch granules– Help regulate the rate of starch digestion and moderate post-feeding pH drop

• Up to 36 hrs may be required for protozoa to completely metabolize engulfed granules. • Attached to particulate matter and pass rumen much slower than bacteria• Osmotic pressure in the lower gut “explodes” them so starch available for digestion in intestines • Predators of amylolytic bacteria (reducing their populations and thus acid production)• Predators of amylolytic bacteria (reducing their populations and thus acid production)

The role of the aerobically sensitive rumen protozoal and

fungal population play in starch digestion will be unaccounted for unless in vitro methods are extremely conscientious about

maintaining temperature, anaerobic conditions and the activity of rumen fluid as it is transferred from donor animal

Source: McAllister, T., A. Hristov and Y. Wang. 2001. Recent Advances/Current Understanding of Factors Impacting Barley Utilization by Ruminants. Proceedings of 36th Annual Pacific Northwest Animal Nutrition Conference, Boise, Idaho. October 9-11, 2001

• Certain fungi (e.g. Neocallimastix frontalis) also produce amolytic enzymes in addition to all fungi having hyphae which exert a physical force and penetrate recalcitrant plant structures such as the grain pericarp.

transferred from donor animalto the in vitro vessel. Source: D. Sapienza, 2009

• Once pericarp is breached, rates of access to the starch granules are governed by the protein matrix and endosperm cell walls.– Lipids on the surface of the starch granules also play a role by reducing contact

between enzyme and substrate and reduced extent of granule swelling due to increasing hydrophobicity.

• Many of the bacteria capable of digesting starch lack B-glucanases and are incapable of degrading endosperm cell walls– They depend on cellulolytic organisms to penetrate and enable access to starch

granules

• Protein (prolamin, zein) matrix in corn is resistant to proteolytic attack and restricts access of bacterial amylases to the encompassed starch gran lesencompassed starch granules– As opposed to barley, whose rapid digestion facilitated by the fact that the

protein matrix is readily penetrated by a variety of proteolytic bacteria– Unlike corn and sorghum, barley endosperm is homogeneous throughout

and starch granules are more loosely associated with the protein matrix.

Source: McAllister, T., A. Hristov and Y. Wang. 2001. Recent Advances/Current Understanding of Factors Impacting Barley Utilization by Ruminants. Proceedings of 36th Annual Pacific Northwest Animal Nutrition Conference, Boise, Idaho. October 9-11, 2001 and Svihus, B., A.K Uhlen and O.M. Harstad. 2005. Effect of starch granule structure, associated components and processing on nutritive value of cereal starch: a review. Animal Feed Science and Technology 122 (2005) 303-320

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2011 Penn State Dairy Cattle Nutrition Workshop 53

abomasumabomasum small intestinesmall intestine

Rate of passage = Kp

Rate of digestion = KdSlower Kpand hhigher Kd equals i d i l

Intestinal Picture Source: http://dspace.library.cornell.edu:8080/bitstream/1813/12788/2/Ellis%20ppt.pdf

increased ruminal degradation

• It should be noted that starch digestion in the small intestines is ignored with most in vitro methods, as is compensatory starch

digestion in the large intestines that would contribute to total tract starch disappearance, especially with dry, rolled corn.

• Starch escaping ruminal fermentation is attacked by intestinal and pancreatic amylases in the small intestine.

• Grain processing methods that increase ruminal degradation of starch generally increase the digestiblity of residual starch that enters the intestines, although there will be a reduced supply due to more , g pp yextensive ruminal digestion of starch.

• Shifting site of digestion downstream also will reduce the likelihood of ruminal acidosis. However, reducing the supply of fermentable energy for ruminal bacteria will reduce microbial yield from the rumen.

– If metabolizable protein supply is low or marginal, milk production could be depressed. – If metabolizable protein needs are being adequately met by the current diet, increasing the extent of ruminal starch

digestion in order to enhance supply of microbial protein is unlikely to prove beneficial. – In some studies, an increased flow of protein to the intestines has increased dry matter intake.

• Starch absorbed as glucose from the intestines can have over 20%• Starch absorbed as glucose from the intestines can have over 20%greater caloric value than starch fermented to VFA within the rumen, so some sacrifice of small intestinal digestibility can be tolerated. – Some claim that ruminal escape starch is used primarily for body fat rather

than milk production• Most of these studies have been with glucose infused into the small intestines with mature steers or lambs with limited production

requirements for energy.• Infused glucose increases insulin which stimulates glucose incorporation into fatty acids, however, there is debate whether glucose

would be used for production needs in animals having a high production demand for energy, such as early lactation dairy cows.

Source: Mahanna, B. 2009. Digestibility of corn starch revisited: Part 1. Feedstuffs Vol. 81, No. 6. (page 1)

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54 November 8-10 Grantville, PA

• The proportion of total starch digested or fermented post-ruminally is much greater for lactating cows than for finishing steers presumably due to higher feed and NDF intakes that decrease the time that starch particles will remain in the rumen for microbial fermentation.

• Poor processing of corn kernels, which hydrate and escape the rumen on the liquid phase, may predispose cows to jejunal hemorrhagic syndrome if the reduced starch surface area limits pancreatic amylase activity thereby allowing excessive amounts of starch to flow to the lower intestinal tract. This increases the substrate to support growth of toxin-producing organisms such as clostridia or aspergillus.

Source: Mahanna, B. 2009. Digestibility of corn starch revisited: Part 1. Feedstuffs Vol. 81, No. 6. (page 1)

Picture Source: http://dspace.library.cornell.edu:8080/bitstream/1813/12788/2/Ellis%20ppt.pdf

• Brief Review of Carbohydrate (CHO) NutritionBrief Review of Carbohydrate (CHO) Nutrition• Corn Kernel Physiology and Terminology• Commercial Laboratory Tests• Practical Feeding Implications

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2011 Penn State Dairy Cattle Nutrition Workshop 55

• The corn kernel evolved to protect the seed until conditions are suitable for germinationfor germination.

• Fibrous pericarp protecting both embryo and its developmental energy source, the starch-rich endosperm.

• Starch granules are surrounded by hydrophobic proteins that repel water to prevent premature starch hydration that could interfere withhydration that could interfere withgermination.

• Seeds were designed to facilitate seed distribution in the feces of plant consuming animals; they were never designed to facilitate digestion.

Flint Corn(more vitreous starch)

Dent Corn

(more vitreous starch)

Dent

Dent Corn(more floury starch)

Germ side faces the tip end of the ear

Yellow dent corn, common in North America, is the result of historic crossbreeding between flint and floury types. Shorter season hybrids tend to have more flint in their pedigree which

increases cold tolerance and early vigor of the seedling.

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• Part of the GIPSA Official United States Standards for grain grading criteria. – used as a gauge of endosperm hardness by dry millers and alkaline cookers (corn chips, corn tortillas)– higher TW grain tends to maintain better grain quality in drying, shipping and handling (e.g. less fines, molding)

• Measure of the quantity of grain to fill a specific volume (bulk density) (Winchester bushel, 2150.42 cu in); reported lb /b TW d t i di t ith l hi h i ld b t th it i l t

Field tool used to approximate test weight

as lbs/bu. TW does not indicate either low or high yield, but rather it is a volume measurement.• TW & true density are somewhat correlated with more floury genetics typically having lower TW and density

– however, only about 40% of the variation in grain density can be attributed to variation in TW due to influences of kernel size, shape, maturity, germ content and pericarp slickness on TW measurements

• High TW corn requires less space, which means that fewer truckloads and storage bins are needed to store the same weight of grain

• TW increases with kernel dry-down (moisture loss) because dry matter is concentrated• The official test involves filling a test cup of known volume through a funnel held at a

specific height above the test cup to the point where grain begins to pour over the sides of the test cup (grain is not “packed” into the cup). A strike-off stick is used to level the grain in the test cup, and the grain remaining in the cup is weighed.g p g g p g

– grain weight in the cup is then converted to weight per Winchester bushel and reported as lb/bu – due to TW increasing with grain dry-down, the moisture content of the sample is also reported. – it is recommended that the sample be between 12 and 16% moisture for analysis.

• Most food grade corn users desire TW higher than 60 lb/bu with some requesting a minimum of 62 lb/bu. Transporting and storing lower density grain is more costly (on a weight basis), so buyers discount grain if test weight is below the minimum standard set by the USDA.

• Typical values are 56-64 with a typical range of 48-67 lb/bu• 56 lb/bu is required for No. 2 Grade corn• Grain with <54 lb/bu are usually charged a dockage on delivery.

Sources: http://www.ilcrop.com/ipglab/corntest/corndesc.htm#TestWeight and . Szasz, J., C. Hunt and F. Owens. 2006. Impact of starch source and processing on feedlot production with emphasis on high moisture corn. 2006 Pacific Northwest Nutrition Conference Proceedings. Matt Antos – Pioneer Agronomist – State College, PA

Lower test weights are due to a number of causes that reduce kernel fill: High test weight grainHigh test weight grainHigh test weight grainHigh test weight grain

lower temperatures,

lower sunlight (cloudy).

premature death due to leaf diseases

drought

freeze damage to late-developing fields

ear rots

Hybrid genetics also has a large influence on test weight.

Source: Matt Antos – Pioneer Agronomist – State College, PA

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2011 Penn State Dairy Cattle Nutrition Workshop 57

Extent of vitreousness best determined by:- Physical dissection- Measuring absolute density

(not test weight)

Floury

- Stenvert mill grinding method

Common Terminologydent vs. flint grain

floury vs. vitreous starch soft, porous vs. hard, denselight vs. heavy test weight

floury vs. horny endospermopaque vs. translucent (glassy)

Vitreous

You have to be a critical thinker in this business

• Results from meta-analysis of feeding and digestion trials with cereal grains often prove difficult to interpret given the wide diversity among trials in:

grind size– grind size,– particle size distribution, – kernel moisture, – presence of stress cracks in heat dried kernels and – kernel density– Such factors usually are not reported in research

publications.

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58 November 8-10 Grantville, PA

Hoard’s Dairyman February 25, 2010 pg 139

These trials were conducted with “book-end” hybrids not typical of

commercially available corn. Don’t expect these differences withexpect these differences with

commercial hybrids.

– Popularly referenced studiesoften investigate starch digestibility using extremes in vitreousness ranging from 3% tovitreousness ranging from 3% to66% (Taylor and Allen, 2005a,b,c) orfrom 25 to 66% (Allen et al., 2008)of the starch being vitreous.

– This makes sense when investigating the mode of action of starch digestion.

– However, caution should be

Mestres et al., 1995

,exercised when applying these observations to field situations with diets containing high yielding, commercial hybrids with more moderate density and zein-prolamin content

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2011 Penn State Dairy Cattle Nutrition Workshop 59

• An in vivo study by Corona et al. (2006) used four hybrids within the more typical range init (55 61 63 d 65%)vitreousness (55, 61, 63, and 65%)

– increasing vitreousness of dry rolled corn failed to significantly impact ruminal disappearance of starchbut surprisingly, tended to reduce post-ruminal digestion.

– as noted from other studies, differences in starch digestion due to vitreousness were obliterated when hybrids were steam flaked.

• In some studies (Correa et al., 2002; Ngonyamo-Majee et al., 2008)kernels were assayed as unfermented grain so drawingconclusions about feeding grain as fermented high-moistureconclusions about feeding grain as fermented high moisturecorn or corn silage is not really valid.

Source: Correa, C.E.S., R.D. Shaver, M.N. Pereira, J.G. Lauer and K. Kohn. 2002. Relationship between corn vitreousness and ruminal in situ starch degradability. J. Dairy Sci. 85:3008-3012

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Look at the effect of fermentation in this European study

• 2005 paper out of Denmark looking at starch digestibility of a very flinty French hybrid harvested (and processed) as corn silage at 25, 35 and 40% DM….– the ruminal starch digestion did decrease from 93 to 91%…when the

fresh crop was analyzed.After the crop was fermented no difference in ruminal starch

p y

– After the crop was fermented, no difference in ruminal starchdigestion was detected due to maturity even in this flinty germplasm harvested as mature as 40%DM!

Source: Jensen et al. Animal Feed Science and Technology. 118:3-4, 279-294

Effect of grain hardness on in-situ degradation of corn and on milk production.I. Andrighetto1, P. Berzaghi1, G. Cozzi1, G. Magni2, and D.A. Sapienza3,

1University of Padova, Italy, 2Pioneer Hi-Bred, Italy 3Pioneer Hi-Bred, Des Moines, IA.Abstract #1250 at 1998 ADSA/ASAS Joint Meetings - Denver

H d 3245Ob ti H d S ft HMC H S HS HMC Hard = 3245(more vitreous with test wt score = 8)

Soft = Bianca(less vitreousness)

HMC 3245

Observation Hard Soft HMC H vs S HS vs HMCIn-situ DDM, % 59.2 65.8 81.4 0.01 0.01DMI, kg/d 19.2 18.7 17.0 ns 0.05Milk yield, kg/d 31.8 31.4 31.4 ns nsYield 4% FCM, kg/d 29.9 30.0 28.5 ns nsFCM / DMI 1.56 1.60 1.68 ns nsMilk fat, % 3.60 3.70 3.38 ns 0.10Milk protein, % 3.19 3.17 3.18 ns nsA t t P ti 2 8 2 8 2 5 0 10 HMC = 3245

The vitreous hybrid but harvested as HMC

Grain fed at 30% of TMR (17.6% CP, 30.6% NDF). Dry grain ground to 2mm, HMC coarsely ground (100% cracked)(closest treatment to starch in corn

silage). 12 primiparous Holsteins (42DIM) in a 3x3 Latin square design (21d periods) replicated four times. In-situ DMI calculated at a 5%/hr passage rate

Acetate:Prop ratio 2.8 2.8 2.5 ns 0.10Ruminal NH3, mg/dl 11.4 9.9 6.8 0.10 0.01PUN, mg/dl 15.0 13.9 11.8 ns 0.01

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2011 Penn State Dairy Cattle Nutrition Workshop 61

Observation Hard Soft HMC H vs S HS vs HMC“Harder” starch hybrid had less starch digested in rumen but when put up

In-situ DDM, % 59.2 65.8 81.4 0.01 0.01DMI, kg/d 19.2 18.7 17.0 ns 0.05Milk yield, kg/d 31.8 31.4 31.4 ns nsYield 4% FCM, kg/d 29.9 30.0 28.5 ns nsFCM / DMI 1.56 1.60 1.68 ns nsMilk fat, % 3.60 3.70 3.38 ns 0.10Milk protein, % 3.19 3.17 3.18 ns nsAcetate:Prop ratio 2.8 2.8 2.5 ns 0.10

as HMC, it had even more digestedin the rumen than the softer texturedhybrid

Acetate:Prop ratio 2.8 2.8 2.5 ns 0.10Ruminal NH3, mg/dl 11.4 9.9 6.8 0.10 0.01PUN, mg/dl 15.0 13.9 11.8 ns 0.01

Trial showing form (dry vs. HMC) of the grain has a greater effect on digestibility than level of vitreousness

Observation Hard Soft HMC H vs S HS vs HMCNo statistical difference in DMIIn-situ DDM, % 59.2 65.8 81.4 0.01 0.01

DMI, kg/d 19.2 18.7 17.0 ns 0.05Milk yield, kg/d 31.8 31.4 31.4 ns nsYield 4% FCM, kg/d 29.9 30.0 28.5 ns nsFCM / DMI 1.56 1.60 1.68 ns nsMilk fat, % 3.60 3.70 3.38 ns 0.10Milk protein, % 3.19 3.17 3.18 ns nsAcetate:Prop ratio 2.8 2.8 2.5 ns 0.10

No statistical difference in DMIbetween hard and soft, but reducedDMI with HMC as would be expectedwith more ruminal starch digestion

pRuminal NH3, mg/dl 11.4 9.9 6.8 0.10 0.01PUN, mg/dl 15.0 13.9 11.8 ns 0.01

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62 November 8-10 Grantville, PA

Observation Hard Soft HMC H vs S HS vs HMCIn situ DDM % 59 2 65 8 81 4 0 01 0 01In-situ DDM, % 59.2 65.8 81.4 0.01 0.01DMI, kg/d 19.2 18.7 17.0 ns 0.05Milk yield, kg/d 31.8 31.4 31.4 ns nsYield 4% FCM, kg/d 29.9 30.0 28.5 ns nsFCM / DMI 1.56 1.60 1.68 ns nsMilk fat, % 3.60 3.70 3.38 ns 0.10Milk protein, % 3.19 3.17 3.18 ns nsAcetate:Prop ratio 2.8 2.8 2.5 ns 0.10

No difference in either milk yield or 4%FCM between hard and softHybrids, but an improvement in milk efficiency when the more vitreous hybrid was fed as HMC because of reduced DMI with HMC

Results show that the form of the grain (dry vs. HMC) has a greater effect on starch digestibility than level of vitreousness

Ruminal NH3, mg/dl 11.4 9.9 6.8 0.10 0.01PUN, mg/dl 15.0 13.9 11.8 ns 0.01

Source: Dr. Fred Owens, Pioneer Senior Research Scientist

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2011 Penn State Dairy Cattle Nutrition Workshop 63

Source: Dr. Fred Owens, Pioneer Senior Research Scientist

Source: Dr. Fred Owens, Pioneer Senior Research Scientist

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64 November 8-10 Grantville, PA

Source: Dr. Fred Owens, Pioneer Senior Research Scientist

Source: Dr. Fred Owens, Pioneer Senior Research Scientist

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2011 Penn State Dairy Cattle Nutrition Workshop 65

When fed as high moisture high moisture corncorn or steam flakedsteam flaked, the

The biggest impact on ruminal starch digestion is the form in which it is being fed (e.g. dry rolled vs. high moisture), not hybrid genetics.

10 Pioneer hybrids selected for vitreousness diversityRuminal Starch Digestibility %Total tract STRD corncorn or steam flakedsteam flaked, the

ruminal starch digestibility differences are extremely

small (1- 2%)

When fed as dry rolled dry rolled grain,grain, ruminal starch 85

90

95

100

Star

ch d

iges

tion

, %

MC

Ruminal Starch Digestibility, %Total tract STRD

digestibility between hybrids did differ by 10%

Bottom line: moving from HMC to dry rolled corn will alter the amount of ruminal starch

availability much more than the relatively small differences between hybrid genetics for ruminal starch digestibility when fed as dry rolled corn

80

34N

43

3 1G 9

8

3 3P6

6

34M

94

31N

27

32R4

2

31A

12

33B 5

0

33R 7

7

34B9

7

Dry

Rol

led

Flak

ed HM

Test Wt 5 5 6 6 6 6 5 5 4 7Scores

Processing by Hybrid Interaction (P<.01)

Source: Dr. Fred Owens, Pioneer Senior Research Scientist

25

30

f DM

Fecal Starch The drawback of feeding dry rolled grain (~1200 microns)

is the amount that escapes into the feces

Fecal Starch

0

5

10

15

20

Feca

l Sta

rch,

% o

f

lled

ed

Processing by Hybrid Interaction (P<.01)

0

34N43

31G9

8

33P6

6

34M

94

31N27

32R4

2

31A12

33B5

0

33R7

7

34B9

7

Dry

Rol

Flak

e

HM

C

3.4b 2.1c22.7a

Dry Grain MaturityTest Weight Score 5 5 6 6 6 6 5 5 4 7

Pioneer 10 Hybrid STRD StudySource: Dr. Fred Owens, Pioneer Senior Research Scientist

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66 November 8-10 Grantville, PA

Presented at the Tri-State Nutrition Conference

April 25 25 2006April 25-25, 2006

• Brief Review of Carbohydrate (CHO) Nutrition• Brief Review of Carbohydrate (CHO) Nutrition• Corn Kernel Physiology and Terminology• Commercial Laboratory Tests• Practical Feeding Implications

Page 73: Nutrition Workshop Proceedings 2011

2011 Penn State Dairy Cattle Nutrition Workshop 67

– DSA (Degree of Starch Access)• Developed at UW-Marshfield Lab• Total tract, in vitro, enzymatic STRD on undried, unground samples• Not being used very much today…replaced by UW Grain

Evaluation System below

Lab Methods to Monitor Starch Digestibility

April ‘06 Dairy Herd Management Magazine

– “Cow Relevant Starch Digestibility”• 6mm grind• in vitro or in situ

– 12-hr ruminal STRD– 8-hr intestinal STRD– Total tract STRD

– Since the writing of this 2006 article, three more STRD tests are now available:

• 2-hr and 7-hr in vitro rumen fluid

• UW Grain Evaluation System– Particle size– Moisture– Prolamin content

• FermentricsSource: http://www.dairyherd.com/directories.asp?pgID=724&ed_id=5316

UW-Feed Grain Evaluation System

Source: http://www.uwex.edu/ces/dairynutrition/Source: http://www.uwex.edu/ces/dairynutrition/documents/WisconsinFGES.pdf

Sou ce p // u e edu/ces/da y u o /

Most important factor is particle sizeSecond is kernel moisture (drives fermentation)

Third is the effect of vitreousness (but primarily in dry corn so know how fine to grind)

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68 November 8-10 Grantville, PA

(Prolamins, Zein)• Proteins surrounding starch granules

consist of prolamins, like zeins, and other proteins (albumins, globulins, glutelins).

• Prolamins are the starch encapsulating• Prolamins are the starch encapsulatingstorage proteins of interest because they can interfere with starch digestion.

• They derive their name from a relatively high content of the amino acid proline (and glutamine).

• Corn prolamins tend to be in higher concentrations in the vitreous (glassy) endosperm than in floury endosperm.

• Prolamins for each cereal grain have specific and historic names (wheat-gliadins, oats-avenins, barley-hordeins) and small grains have lower prolamin content that corn (zeins) or sorghum (kafirins)

Source: http://www.uwex.edu/ces/dairynutrition/documents/FGES-ProlaminGuide.pdf

Source: Mahanna, B. 2009. Digestibility of corn starch revisited: Part 1. Feedstuffs Vol. 81, No. 6. (page 3)

• Larson and Hoffman published work in 2008 on an easier lab method to quantify prolamin (zein) proteins in corn starch

JDS 91:4834, 2008.

Source: Hoffman and Shaver. 2009. A Guide to Understanding Prolamins http://www.uwex.edu/ces/dairynutrition/

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2011 Penn State Dairy Cattle Nutrition Workshop 69

UW Grain Evaluation System Results from UW Marshfield Lab – Dry Corn

Source: http://uwlab.dyndns.org/marshfield/

Relatively Narrow Range

UW Grain Evaluation System Results from UW Marshfield Lab – HM Corn

Source: http://uwlab.dyndns.org/marshfield/

Relatively Narrow Range

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• Brief Review of Carbohydrate (CHO) Nutrition• Brief Review of Carbohydrate (CHO) Nutrition• Corn Kernel Physiology and Terminology• Commercial Laboratory Tests• Practical Feeding Implications

Key Factors Influencing Starch DigestionFirst - If fed fermented (and how long) versus fed as dry grain

Owens et al., 1986

Benton et al., 2004

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2011 Penn State Dairy Cattle Nutrition Workshop 71

Second - Particle sizeKey Factors Influencing Starch Digestion

Lab data suggests too little attention to level and consistency of grain particle size on some dairies

Corn Silage Processing also needs closer monitoring

Source: Dave Taysom, Dairyland Laboratory N =1682

Third - Kernel CharacteristicsAmount of hard (vitreous) starch and zein-protein content

Key Factors Influencing Starch Digestion

Negative effects of vitreous starch are overcome with processing explaining why we don’t feedexplaining why we don t feedcracked corn to dairy cows.

Ramos et al., 2009

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72 November 8-10 Grantville, PA

• Depends on the grain processing method they will undergo. – For corn fed as dry, rolled grain, starch from

less vitreous, dent hybrids likely will be more extensively digested in the rumen than starch from more vitreous dent (or flint) hybrids

Hybrid Selection Implications

starch from more vitreous, dent (or flint) hybrids.• this increase may or may not be of benefit depending upon other

ration ingredients,the need for microbial protein production and the possible desire to shift starch digestion to the intestines to reduce the potential for ruminal acidosis.

– For corn silage, high-moisture corn or steam flaking, more vitreous, dent hybrids appear preferable because the processes of ensiling (protein matrix solubilization and acid hydrolysis) or flaking (physical gelatinization) will minimizes most adverse effects associated with vitreousness (Szasz et al., 2006; Owens, 2009).

• Research across hybrids suggests that larger kernels may lead to improved total tract organic matter digestibility when corn grain is fed in either the dry rolled, flaked or high-moisture formmoisture form.

– This may be partly because larger kernels have a greater ratio of starch to pericarp (fiber).– Field experience indicates that when dry corn is rolled, larger kernels result in more uniform

and complete processing. – In a Nebraska study (Jaeger et al., 2004) seven hybrids were dry rolled and fed to steers

average daily gain did not differ between hybrids but feed efficiency was superior for hybrids with a larger mean kernel weight.

– Some nutritionists have developed grain purchase and discount guidelines, such as a minimum of 35 grams per 100 kernels, as a proxy physical measurement index for ensuring larger kernel size and density (Soderlund, 2009).

Source: Mahanna, B. 2009. Digestibility of corn starch revisited: Part 2. Feedstuffs Vol. 81, No. 10.

High-moisture corn:Ensile at > 26% kernel moistureRoll or grind into storageRuminal StarchD increases by ~2% units per month out to about 12 months

Practical Approaches to Increasing Starch Digestibility

Steam-rolled or steam-flaked corn:Low flake weight (< 30 lb/bushel)Vitreous hybrids tend to produce larger, more stable flakes

Dry corn:Grind finely to avoid large particlesFloury endosperm grain may result in more finesVitreousness – not a significant factor if finely ground (800-1000 microns)

Zein content narro range among commercial h bridsZein content – narrow range among commercial hybrids

Corn silage:Kernel process (monitor frequently during harvest and quantify with lab test post-harvest)Chopping slightly shorter (17mm) improves roller mill effectivenessFermentation greatly diminishes any effects of vitreousness StarchD increases with longer-term storage (plateaus after 5 or 6 months)

Source: Fred Owens, Pioneer Senior Research Scientist

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2011 Penn State Dairy Cattle Nutrition Workshop 73

• The economic and ruminal-health implications of starch digestibility are immense.

• Digestion of corn grain by dairy cattle appears to be limited primarily by large particle size and pericarp shielding the endosperm.

– To a lesser extent, starch fermentation rate can be reduced in mature dry corn depending on the level of

The Bottom LineThe Bottom Linereduced in mature, dry corn depending on the level ofprotein-shielding (presence of prolamin proteins, commonly called zeins), that surround and link the starch granules in the portion of the endosperm containing more dense, vitreous starch.

• Fine grinding will increase starch digestion by cattle but more extensive processing methods such as ensiling or flaking greatly minimize or obliterate the effect of vitreousness on starch digestion.

– In addition to increasing total tract digestion, extensive processing also can shift site of starch digestion.

• Studies investigating the extremes in vitreousness (and bl i l i ) h id d l blpresumably in prolamin content) have provided valuable

insight into the biochemical and physical mechanisms that limit the rate of starch digestion.

– Caution should be exercised when attempting to apply these observations to field situations that use rations containing productive, commercial hybrids with a more moderate level of density and prolamin content.

– Furthermore, research results based on studies with dry, unfermented kernels typically do not apply when grain is processed (fermented as high-moisture corn and corn silage or steam flaked) prior to feeding.

Source: Mahanna, B. 2009. Digestibility of corn starch revisited: Part 2. Feedstuffs Vol. 81, No. 10.

• Until there is a better understanding of the variability and magnitude of cereal characteristics needed to influence

i l f th

The Bottom LineThe Bottom Lineanimal performance, there appears room, on most dairies, for near-term focus on quantifying and managing thevariability routinely experienced in corn grain and corn silage related to:– kernel moisture/maturity, – starch content, – particle size/distribution and

the increase in starch digestibility– the increase in starch digestibilityassociated with time in fermented storage

Source: Mahanna, B. 2009. Digestibility of corn starch revisited: Part 2. Feedstuffs Vol. 81, No. 10.

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Notes

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2011 Penn State Dairy Cattle Nutrition Workshop 75

Byproduct Feeds and Milk Fat Depression

H. A. Ramirez-Ramirez and P. J. Kononoff

Department of Animal Science

University of Nebraska-Lincoln

INTRODUCTION

The con nued growth of the corn ethanol industry results in an abundant supply of co-products that may be u lized as feedstuff s for livestock. One of the major co-products derived from the ethanol industry is dis- llers grains and solubles, wet (WDGS) or dry (DDGS).

This feedstuff is characterized by having high content of crude protein (CP), neutral detergent fi ber (NDF) and fat or ether extract (EE). Namely, on a dry ma er basis CP typically ranges between 26 and 35% (mean 31%), NDF ranges 29 to 38% (mean 34%) and EE ranges 9 to 16% (mean 13%) (Dairy One, 2010). This composi on represents a valuable supply of nutrients that makes DDGS a feedstuff for dairy ra ons as it provides CP and energy (NDF and EE) which are key nutrients for milk synthesis. Nonetheless, in the prac cal fi eld there is a common percep on that feeding ethanol byproducts at levels above 15% of the ra on (DM basis) causes milk fat depression (MFD). Several studies have shown that DDGS may be eff ec vely included in dairy ra ons between 20% (Anderson et al., 2006) and 30% (Janicek et al., 2008); in addi on, inclusion of DDGS in dairy ra- ons has resulted in improved feed effi ciency (Anderson

et al., 2006; Kleinschmit et al., 2006) and milk yield (Janicek et al., 2008). Therefore, the objec ve of the present workshop is to discuss the various factors that aff ect some diges ve processes in the rumen that may result in MFD when feeding DDGS.

WHY ARE BYPRODUCTS BLAMED FOR

CAUSING MFD?

As men oned earlier, DDGS are high in fat. The fa y acid profi le of corn oil includes several polyunsaturated fa y acids (PUFA). A high ruminal load of PUFA may aff ect the extent of biohydrogena on and lead to ac-cumula on of trans fa y acids that may ul mately cause MFD. In addi on, when formula ng a ra on containing DDGS, nutri onists and producers must be careful to take into account not only the amount of NDF in the diet but also the source of NDF. Ethanol byproducts

have a high content of fi ber (from the bran frac on of the corn kernel); however, it may not be eff ec ve fi ber meaning that it does not elicit high rates of ruminal mo lity, mas ca on, and saliva produc on. The end result of these factors is that ruminal pH may drop, leading to ruminal acidosis which has the poten al to exacerbate the nega ve eff ects of a high load of PUFA in the rumen. Thus it is cri cal to fully understand the nutri onal composi on of DDGS as it can also replace corn which lowers the starch content of the diet and decreases the risk of developing low rumen pH. It is important to underscore that starch level should not be considered as a requirement per se but rather as a nutrient that contributes to mee ng the cow’s energy requirement. Thus, starch may be replaced with other energy sources such as DDGS; however, even in diets low in starch (16% starch, DM basis), it has been shown that low ruminal pH may develop as a consequence of highly fermentable fi ber from DDGS and cause MFD.

LIPID METABOLISM IN THE RUMEN

Current feeding prac ces in the dairy industry com-monly involve feeding grains, vegetable oils, or animal fats to increase the energy density of the diet. As with many other nutrients, lipids are extensively transformed by rumen bacteria and as a result the animal absorbs a diff erent profi le of fa y acids compared to the original profi le in the feed (Van Soest, 1994). Rumen bacteria have restricted u liza on of fa y acids for metabolism; nonetheless, they have adapted to deal with these compounds to such extent that the type of fa y acids present in ruminant products is greatly aff ected by microbial metabolism (Harfoot and Hazlewood, 1988).

The fi rst step in ruminal lipid metabolism is hydrolysis of triglycerides releasing the glycerol backbone and free fa y acids. The glycerol backbone is readily u lized for energy which leads to forma on of vola le fa y acids. In the second step, unsaturated fa y acids (UFA) become saturated by microbial ac on in a process known as bio-

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hydrogena on (BH). The posi on of the double bonds are also altered by rumen microbes and are generally transformed to the trans form which is more stable but also harder to hydrogenate, which may cause accumu-la on of trans forms rela ve to cis (Van Soest, 1994).

BIOHYDROGENATION

Banks and Hilditch (1931) made important observa- ons that led to further studies of fa y acid profi les

in ruminant products. They observed that lipids from ruminant ssues had higher concentra on of saturated fa y acids than those of non ruminants. Early work with ruminants showed that defaunated sheep were able to hydrogenate linoleic and oleic acid at the same rate as normal sheep; from this it was concluded that protozoa are not essen al in the BH process (Dawson and Kemp, 1969). Therefore, it appears that rumen bacteria are responsible for the major propor on of BH and this ac vity is primarily associated with bacteria a ached to the par culate ma er (Dehority, 2003). The major species involved in rumen biohydrogena on is believed to be Butyrivibrio fi brisolvens and some others in the genera Treponema, Micrococus, Ruminococus, Eubac-terium, and Fusicillus (Harfoot and Hazlewood, 1988).

Linolenic, linoleic, and oleic acid are the major unsatu-rated fa y acids in current ruminant diets which include grains and oils as supplemental sources of energy. Fig-ure 1 depicts the biohydrogena on pathways of these fa y acids, which through full hydrogena on are turned into stearic acid. The biohydrogena on process is com-plex and involves interac ons among several bacterial species. Pure cultures have shown par al hydrogena on whereas culture of mixed rumen contents yields stearic acid (Kemp and Lander, 1984).

MILK FAT DEPRESSION

Fat is the most variable component in milk and can be modifi ed by changes in the feeding regimen (Palmquist et al., 1993); low milk fat syndrome or milk fat depres-sion is one of the major changes that may occur in milk fat concentra on. It is characterized by abnormally low levels of milk fat (i.e. reduc on up to 50%) without any changes in milk yield or yield of other components (protein, lactose). Bauman et al. (2008) men oned that milk fat depression has been a common problem in dairy farms for more than a century. Shingfi eld and Griinari (2007) pointed out that when diets contain high propor- ons of rapidly fermentable carbohydrates (for example

starch > 25% of DM) or supplements of vegetable oil or

marine lipids (> 5% of DM) it is likely that these diets can induce MFD. Similarly, Palmquist et al. (1993) pointed out that ra ons high in grains (≥ 50% of feed DM) in-crease starch concentra on and result in MFD.

MILK FAT DEPRESSION AND RUMEN MICROBES:

THE BIOHYDROGENATION THEORY

Throughout the years there have been several theories that explain the causa ve factors that trigger MFD; some have a ributed the disorder to limited mammary de novo synthesis of fa y acids because of reduced produc on of β hydroxybutyrate and acetate in the rumen. Another mechanism that has been proposed is the par oning of fa y acids toward adipose ssue due to an insulin response lowering the amount of fa y acids available in the mammary gland; one more is the inhibi on of de novo synthesis in the mammary gland by methylmalonate arising from decreased vitamin B12 and increased propionate in the rumen; and more recently a direct inhibi on in the mammary gland by trans fa y acids derived from incomplete biohydrogena- on of dietary fat in the rumen (Shingfi eld and Griinari,

2007). The la er mechanism has provided be er sup-

A))

B)

C)

Figure 1. Biohydrogena on pathway of A) α linolenic acid, B) Linoleic acid, and C) Oleic acid to fully saturated stearic acid. (Adapted from Har oot and Hazlewood, 1988).

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port to explain MFD since experiments dealing with geometric isomers of conjugated linoleic acid (CLA) have resulted in MFD reducing the concentra on of milk fat drama cally whereas the other theories, also supported by data, only accounted for less than 10% of the fa y acids mobilized for milk synthesis (Griinari and Bauman, 2005). One key observa on that aroused thoughts about the biohydrogena on theory was made by Davis and Brown (1970) when they suggested that milk from cows under MFD has higher concentra on of trans 18:1 fa y acids. With this background, recent research has focused on biohydrogena on of unsatu-rated fa y acids and produc on of CLA isomers that inhibit milk fat synthesis (Figure 2).

Chouinard et al. (1999) demonstrated that CLA alters the fa y acid profi le of milk fat and inhibits milk fat secre- on in dairy cows. In their study they infused a mixture

of CLA isomers into the abomasum of dairy cows to bypass rumen biohydrogena on. Their results showed very clearly that the infusion of CLA isomers drama cally reduced (by more than 50%) the concentra on of milk fat at three levels of infusion 50, 100, and 150 g/d of the CLA. Observed mean milk fat concentra ons were 1.43, 1.38, and 1.23% for 50, 100, and 150 g/d of the CLA mix-ture whereas the control treatment resulted in 2.81%. As expected and consistent with previous observa ons (Davis and Brown, 1970) milk from cows with MFD had higher concentra on of trans isomers of CLA. Four iso-mers were reported to have increasing concentra ons during the infusion period; formerly, 8, 10; 9, 11; 10, 12; and 11, 13, all in a cis, trans confi gura on.

Since the observations of Chouinard et al. (1999) pointed out the relevance of CLA trans isomers during MFD, Baumgard et al. (2000) focused their research on two CLA isomers that are commonly found in ruminant fat (cis-9, trans-11 CLA) and also a trans-10, cis-12 CLA isomer which is the puta ve source of trans 18:1 fa y acids which the concentra on is typically higher in cows

experiencing MFD. The results of the infusion of these isomers revealed that trans-10, cis-12 is a potent inhibi-tor of milk fat synthesis as it took about 24 h to exert its eff ect and by days 3 and 4 milk fat concentra on dropped dras cally (≈ 40% reduc on) whereas cis-9, trans-11 did not aff ect milk fat secre on. The control treatment resulted in 3.04% and 1.068 kg/d milk fat whereas trans-10, cis-12 was 1.92% and 0.696 kg/d milk fat. Altough trans-10, cis-12 CLA resulted in a drama c reduc on in milk fat, Perfi eld et al. (2007) men ons that there is a curvilinear response in the reduc on of fat, sugges ng that there may be addi onal CLA isomers responsible for MFD as trans-10, cis-12 is inadequate to account for the total reduc on in milk fat secre on. Another isomer associated with this disorder is trans-9, cis-11 CLA; this isomer accounted for 15% of the reduc- on in milk fat when it was infused into the abomasum

of dairy cows (Perfi eld et al., 2007).

In is important to note that these experiments have not shown the results of dietary treatments per se but rather the response to abomasal infusions of CLA, therefore one ought to assume that under certain di-etary condi ons, some of these CLA isomers arise in the rumen and cause MFD. One experiment that clarifi ed and confi rmed the results from the trials with aboma-sally infused dairy cows was conducted by Peterson et al. (2003). Their approach was to induce MFD through dietary treatments. Briefl y, the treatment responsible for MFD was high in concentrate (i.e. cracked corn was fed at 64.4% of the dietary DM) and low in fi ber (14.9% in the MFD treatment, compared to 31.1% NDF in the control diet). Results from this work confi rmed that dur-ing diet-induced MFD, the fa y acid profi le of milk fat is changed with a remarkably increased concentra on of trans-10, cis-12 CLA in milk fat.

Another key fi nding by Peterson et al. (2003) pertains to a coordinate suppression of mRNA abundance for mammary enzymes involved in milk fat synthesis. This inves ga on linked gene expression with diet-induced MFD. In short, MFD coincided with lower expression of genes encoding for proteins responsible for uptake of fa y acids from the bloodstream and proteins as-sociated with de novo synthesis of fa y acids, which demonstrates that MFD is the result of both lower absorp on and lower synthesis of fa y acids in the mammary gland. More recent research has elucidated more about the molecular mechanism by which CLA is responsible for MFD. Harva ne et al. (2009) u lized molecular techniques to measure gene expression of

Figure 2. Biohydrogena on pathway of linoleic acid. The upper pathway represents the shi during MFD having trans-10, cis-12 CLA as the puta ve source of trans-10, 18:1. Adapted from Shingfi eld and Griinari, 2007.

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enzymes and key regulators of lipid synthesis in adipose ssue of cows under MFD. They reported that the infu-

sion of trans-10, cis-12 CLA in to the abomasum of dairy cows resulted in up regula on of lipoprotein lipase, fa y acid synthase, stearoyl CoA desaturase, and fa y acid binding protein 4 in adipose ssue. Taken together, observa ons of these last studies (Peterson et al., 2003; Harva ne et al., 2009) strongly support the biohydro-genta on theory and its rela onship to MFD. During MFD there is a lipid repar oning process that accounts for the reduc on in mammary uptake and synthesis of fa y acids while concomitantly there is lipid accre on in adipose ssue which was not perceived in previous studies because of the short dura on of the trials.

FEEDING DISTILLERS GRAINS TO DAIRY CATTLE:

HOW MUCH CAN WE FEED?

The American dairy industry consumes about 42 to 46% (Na onal Corn Growers Associa on, No date; Renew-able Fuel Associa on, 2008) of the total dis llers grains (DG) produced in the country. Several studies have shown the eff ects of u lizing DG in dairy ra ons, and it has generally been demonstrated to be an eff ec ve feed when incorporated into dairy feeding systems as it supports similar or higher milk yield compared to control diets (Schingoethe et al., 2009). In feedlot diets inclusion of 20% DDGS (DM) has resulted in greater economic returns (Buckner et al., 2008), it is likely that in dairy ra ons inclusion of DDGS results in a similar situa on as it can replace propor ons of highly priced feedstuff s such as corn and soybean meal.

Even though DDGS have a valuable nutri onal compo-si on, dairy nutri onists tend to limit the inclusion of DDGS to 10% of the dietary DM (Janicek et al., 2008; Schingoethe et al., 2009). One reason for this is that the fat content is high, generally ranging between 10 and 12% (Kleinschmit et al., 2006; Schingoethe et al., 2009). This may result in milk fat depression due to the high content of unsaturated fa y acids present in DDGS which can alter lipid metabolism in the rumen, thus altering the metabolism in the mammary gland so that milk fat synthesis is decreased as well as fa y acid uptake. Anderson et al. (2006) reported that when dairy cows were fed DG at 20% of the ra on DM, milk yield was observed to be about 2.5 kg/d higher for cows con-suming DG. In addi on, milk fat and milk protein yield were also greater for DG diets compared to a control diet. Likewise, Kleinschmit et al. (2006) reported that cows consuming a ra on with 20% DDGS increased milk yield, 4% FCM, and ECM compared to the control ra on.

In prac ce there is a common percep on that high inclusion of DDGS in the ra on reduces the concentra- on of milk fat (Kleinschmit et al., 2006; Janicek et al.,

2008; Schingoethe et al., 2009; Hippen et al., 2010). Leonardi et al. (2005) reported a linear decrease in milk fat percentage as the inclusion of DDGS increased in the diet. This reduc on was only signifi cantly diff erent between 10 and 15% DDGS when milk fat dropped from 3.33 to 3.24%. Similarly Hippen et al. (2010) reported that DDGS fed at 20% of the diet resulted in a reduc on in the concentra on of fat in milk. These changes were slight and not very drama c, as diets with no DDGS averaged 3.21% and 1.41 kg of milk fat whereas diets with DDGS averaged 3.03% and 1.27 kg. In contrast to the response observed by Leonardi et al. (2005), sev-eral other experiments reported no eff ect on milk fat percentage when DDGS were included at 20% in the ra on (DM) (Kleinschmit et al., 2006). Furthermore, Janicek et al. (2008) fed 30% DDGS with no diff erences in milk fat percentage. In both works, there was a sig-nifi cant increase in milk fat yield due to increased milk produc on. The results from these works demonstrate that DDGS can be included eff ec vely in dairy ra ons at levels between 20 and 30% of the dietary dry mat-ter without adverse eff ects on milk fat concentra on.

REFERENCES

Anderson, J. L., D. J. Schingoethe, K. F. Kalscheur, and A. R. Hippen. 2006. Evalua on of dried and wet dis llers grains included at two concentra ons in the diets of lacta ng dairy cows. J Dairy Sci. 89:3133-3142.

Banks, A., and T. P. Hilditch. 1931. The glyceride structure of beef tallows. Biochem J. 25:1168-82.

Bauman, D. E., J. W. Perfi eld, 2nd, K. J. Harva ne, and L. H. Baumgard. 2008. Regula on of fat synthesis by conjugated linoleic acid: lacta on and the ruminant model. J Nutr. 138:403-9.

Baumgard, L. H., B. A. Corl, D. A. Dwyer, A. Saebo, and D. E. Bauman. 2000. Iden fi ca on of the conjugated linoleic acid isomer that inhibits milk fat synthesis. Am J Physiol Regula-tory Integra ve Comp Physiol. 278:R179-R184.

Buckner, C. D., T. L. Mader, G. E. Erickson, S. L. Colgan, D. R. Mark, V. R. Bremer, K. K. Karges, and M. L. Gibson. 2008. Evalua on of dry dis llers grains plus solubles inclusion on performance and economics of fi nishing beef steers. Prof. Anim. Scient. 24:404-410.

Chouinard, P. Y., L. Corneau, D. M. Barbano, L. E. Metzger, and D. E. Bauman. 1999. Conjugated Linoleic acids alter milk fa y acid composi on and inhibit milk fat secre on in dairy cows. J Nutr. 129:1579-1584.

Dairy One. 2010. Forage Laboratory Services: Feed Composi-

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on Library. On Line: h p://www.dairyone.com/Forage/FeedComp/MainLibrary.asp. Accessed on January, 21 2011.

Davis, C. L., and R. E. Brown. 1970. Low-fat milk syndrome In: Physiology of diges on and metabolism in the ruminant. Ed. T. A. Phillipson, Oriel Press, Newcas le upon Tyne (UK). Pp. 545-565.

Dawson, R. M., and P. Kemp. 1969. The eff ect of defauna on on the phospholipids and on the hydrogena on of unsatu-rated fa y acids in the rumen. Biochem J. 115:351-352.

Dehority, B. A. 2003. Rumen Microbiology. Nottingham University Press.

Griinari, J. M., and D. E. Bauman. 2005. Milk fat depression: concepts, mechanisms and management applica ons. In: Ruminant physiology: diges on, metabolism and impact of nutri on on gene expression, immunology and stress. Eds. K. Serjsen, T. Hvelplund, M. O. Nielson. Wageningen Academic Publishers, Wageningen (The Netherlands) pp. 523-555.

Harfoot, C. G., and G. P. Hazlewood. 1988. Lipid Metabolism in the Rumen. In: The Rumen Microbial Ecosystem. Ed. P. N. Hobson. Elsevier Applied Science.

Harva ne, K. J., J. W. Perfi eld, 2nd, and D. E. Bauman. 2009. Expression of enzymes and key regulators of lipid synthesis is upregulated in adipose ssue during CLA-induced milk fat depression in dairy cows. J Nutr. 139:849-54.

Hippen, A. R., D. J. Schingoethe, K. F. Kalscheur, P. L. Linke, D. R. Rennich, M. M. Abdelqader, and I. Yoon. 2010. Sac-charomyces cerevisiae fermenta on product in dairy cow diets containing dried dis llers grains plus solubles. J Dairy Sci. 93:2661-9.

Janicek, B. N., P. J. Kononoff , A. M. Gehman, and P. H. Do-ane. 2008. The eff ect of feeding dried dis llers grains plus solubles on milk produc on and excre on of urinary purine deriva ves. J Dairy Sci. 91:3544-3553.

Kemp, P., and D. J. Lander. 1984. Hydrogena on in vitro of α-linolenic acid to stearic acid by mixed cultures of pure strains of rumen bacteria. J. Gen. Microbiol. 130:527-533.

Kleinschmit, D. H., D. J. Schingoethe, A. R. Hippen, and K. F. Kalscheur. 2007. Dried dis llers grains plus solubles with corn silage or alfalfa hay as the primary forage source in dairy cow diets. J. Dairy Sci. 90:5587-5599.

Kleinschmit, D. H., D. J. Schingoethe, K. F. Kalscheur, and A. R. Hippen. 2006. Evalua on of various sources of corn dried dis llers grains plus solubles for lacta ng dairy ca le. J Dairy Sci. 89:4784-94.

Leonardi, C., S. Ber cs, and L. E. Armentano. 2005. Eff ect of increasing oil from dis llers grains or corn oil on lacta on performance. J. Dairy Sci. 88:2820-2827.

Na onal Corn Growers Associa on. No date. Co-products. On line: h p://www.ncga.com/coproducts. Accessed on August 28, 2010.

Palmquist, D. L., A. D. Beaulieu, and D. M. Barbano. 1993. Feed and animal factors infl uencing milk fat composi on. J Dairy Sci. 76:1753-1771.

Perfi eld, J. W., II, A. L. Lock, J. M. Griinari, A. Saebo, P. Del-monte, D. A. Dwyer, and D. E. Bauman. 2007. Trans-9, cis-11 conjugated linoleic acid reduces milk fat synthesis in lacta ng dairy cows. J Dairy Sci. 90:2211-8.

Peterson, D. G., E. A. Ma tashvili, and D. E. Bauman. 2003. Diet-induced milk fat depression in dairy cows results in increased trans-10, cis-12 CLA in milk fat and coordinate suppression of mRNA abundance for mammary enzymes involved in milk fat synthesis. J Nutr. 133:3098-102.

Ramirez Ramirez, H. A., P. J. Kononoff , and K. Nestor. 2010. Eff ects of feeding brown midrib corn silage and dried dis ll-ers grains with solubles on performance of lacta ng dairy cows. J. Dairy Sci. 93 E-Suppl. 1:487.

Renewable Fuel Associa on. 2008. Changing the Climate. Ethanol Industry Outlook 2008. On line: h p://www.etha-nolrfa.org/page/-/objects/pdf/outlook/RFA_Outlook_2008.pdf?nocdn=1. Accessed on August 29, 2010.

Schingoethe, D. J., K. F. Kalscheur, A. R. Hippen, and A. D. Garcia. 2009. Invited review: The use of dis llers products in dairy ca le diets. J. Dairy Sci. 92:5802-5813.

Shingfi eld, K. J., and J. M. Griinari. 2007. Role of biohydrogena- on intermediates in milk fat depression. Eur. J. Lipid Sci.

Technol. 109:799-816.

Van Soest, P. J. 1994. Nutri onal Ecology of the Ruminant 2nd ed. Comstock Publishing Associated, a division of Cornell University Press, Ithaca, NY.

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Notes

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2011 Penn State Dairy Cattle Nutrition Workshop 81

INTRODUCTION

A consistent healthy rumen environment every day for every cow on a dairy is perhaps one of the greatest suc-cess factors to a dairy’s fi nancial well-being. However, can the dairy maintain a consistent healthy rumen environment every day for every cow? The answer is usually ‘no’ because there are so many factors that can aff ect the rumen environments of all of the cows on the dairy. The easiest way to understand this is to consider all of the factors that can aff ect dry ma er intake. These factors include weather, cow comfort, calving, pen moves, ra on changes, moisture changes in wet feeds, mold and wild yeast in wet feeds, sor ng, crowding, treatment of animals, and fi nally inconsistent TMRs.

Diamond V personnel started investigating factors aff ec ng TMR consistency in January 2008 on large dairies in the Upper Midwest and across the U.S. We soon called this inves ga ve process the TMR AuditTM. A TMR AuditTM is an on-farm evalua on of the feed stor-age and prepara on, mixing and delivery of the TMR, ingredient varia on and shrink, and u liza on of labor and resources. It is designed to reduce varia on in the TMR and improve the effi ciency of the feeding opera- on. Many of the key factors aff ec ng TMR consistency

will be discussed in this paper.

TMR AUDITTM PROCEDURE

TMR samples were obtained along the feed bunk im-mediately a er delivery before the ca le begin ea ng and sor ng. Ten spot samples per load were taken at equally spaced posi ons along the bunk. The sampling posi ons were determined by coun ng the number of posts suppor ng the free stalls and then dividing that number by ten. A one-cup measuring scoop was used to collect enough material to fi ll a quart-sized plas c bag that could be zipped shut to exclude air. Samples were placed in a 5-gallon pail un l par cle separa on analysis using the original Penn State shaker box (two screens and pan). TMR was scooped from the bot-

tom, middle, and top of the windrow of TMR at each sampling loca on. Backup samples were some mes taken and tested for moisture, crude protein, starch, ADF, NDF, and ash using NIR analysis.

Samples of weigh backs were obtained by taking 5 spot samples along the bunk associated with each pen and placing into a quart-sized plas c bag. Par cle size analysis using the Penn State shaker box was used to compare weigh backs to the original TMR and deter-mine the extent of sor ng.

Coeffi cients of varia on were determined on the top, middle, and bo om screens for each load of TMR or on the NIR nutrient analysis using the method of Herrman and Behnke (1994). Because of the large varia on seen in the top screen for lacta on ra ons, we used only coeffi cients of varia on for the middle and bo om pan of the Penn State shaker box to assess TMR consistency. The top screen, middle and bo om pan can be used to assess dry cow and replacement heifer ra ons because the percentage on the top screen is usually close to 30%.

Coeffi cients of varia on of 1 to 2% are o en seen when mixing condi ons of the wagon are excellent, and co-effi cients of varia on of 3 to 5% are good, indica ng no apparent problems with TMR mixing. Coeffi cients of varia on above 5% can indicate a variety of prob-lems such as overfi lling, under mixing the last added ingredient, worn mixer augers, improper loading of ingredients, inadequate loading order of ingredients, and poorly processed low-quality hay.

In addi on to the Penn State shaker box, other tools used during TMR audits are stop watches, grain sieves, digital cameras with video capability, and an infrared camera. The infrared camera can show hea ng of feeds and TMR.

The TMR audit basically follows the feeder’s steps in making a TMR, which include reading weigh back lev-

TMR AuditsTM Improve TMR Consistency

Tom Oelberg, Ph.D.

Diamond V

59562 414th Lane

New Ulm, MN 56073

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els, facing silage, ge ng mixing equipment ready, and fi nally loading, mixing and delivering the TMRs. Photos, videos, notes, and TMR samples are taken and all of the informa on is put into a PowerPoint presenta on that is shared with the feed management team at the end of the audit. Diamond V has conducted over 500 audits across the U.S. since January 2008.

SPOT SAMPLING VS. QUARTER SAMPLING

Diamond V personnel conducted a TMR mixing study on a large commercial dairy in the Upper Midwest in February 2010 and showed no diff erence in the coef-fi cients of varia on between samples taken from the same load of TMR using the spot sampling method vs. quarter sampling method. There were ten spot samples and ten quarter samples. Quarter samples were ob-tained by scooping the en re cross sec onal area of the windrow of TMR in the feed bunk with a scoop shovel. The material was piled on a 4-feet square piece of smooth plywood. The pile was turned several mes and quartered into 4 sec ons with the shovel. Two alternate quarters were discarded and the remaining two were mixed. This procedure was repeated un l the remain-ing material could fi t into a quart-sized plas c bag. A marker (Micro-Tracers, Inc. San Francisco, CA) was used to measure mixing performance, and the coeffi cients of varia on (20%) for the marker was similar between the two sampling methods. This varia on was higher than expected as it was part of a larger study looking at the eff ect of ingredient inclusion level on mixer perfor-mance. Spot sampling has been our preferred method of sampling because it is much faster and takes less labor to collect samples before the cows consume a signifi cant por on of the diet immediately a er delivery.

FACTORS AFFECTING TMR CONSISTENCY

Silage Face Management

The goal of silage face management is to remove just enough silage to feed the en re herd with-out excess loose silage remaining that can heat up and spoil. The amount removed from the silage face should be enough to avoid hea ng and spoilage on the face. This amount will vary according to number of ca le being fed, ambient temperature, and silage density. Generally we like to see a minimum of 6 inches removed dur-ing cold weather and 12 inches or more during warm weather. The silage face should be smooth and ver cal to minimize exposure to air and the growth of spoilage yeast, bacteria, and molds.

The plas c should be cut back each day with the leading edge weighted down to minimize penetra on of air into the silage from wind. Many dairies cut the plas c back every two to three days to once a week depending on the weather. If they see rain in the forecast, they only cut the plas c back enough for one day to minimize silage moisture changes due to rain.

A large issue with silage face management is the inher-ent varia on in moisture and nutrient levels across the silage face. Generally haylage is more variable than corn silage. The Diamond V TMR AuditTM recommends facing the en re face of the silage pile into a windrow and then pushing and li ing the ends of the windrow into a central pile as shown in Figure 1. This procedure makes the silage in the pile more consistent in moisture and nutrient levels as shown in Figure 2. Ten spot samples of haylage were taken from the face, windrow, and pile analyzed for pH, moisture, protein, ADF, NDF, soluble protein, phosphorus, and sulfur. The averages (avg) for each of these nutrients were similar with respect to sample loca on, but the

Figure 1. Recommended way to face silage piles to provide consistent TMRs.

Figure 2. Infl uence of haylage sampling loca on on varia on in nutrient levels.

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coeffi cients of varia on (CV) decreased consistently from face to windrow and from windrow to pile. The pile of haylage had the most consistent nutrient levels allowing for more consistent loads of TMR.

Under Mixing

Adequate mixing me is very important for obtain-ing consistent TMRs. Figure 3 shows the rela onship between CV of salt content in a meal type ra on and mixing me. A complete mix was obtained a er 5 min-utes of mixing when the CV in salt content of the ra on reached 10% or less (Harner et al., 1995). Under mixing the last ingredient added to a TMR is one of the most common mistakes we see on dairy farms. Three to fi ve minutes is usually needed to get a complete mix, but o en we only see 30 to 60 seconds of mix me. Also, as the augers wear inside the wagon mix me needs to be increased to get the TMR completely mixed. However, this is o en ignored and we see inconsistent TMRs with worn equipment.

Over Filling

Over fi lling the TMR wagon is another big reason why we see inconsistent TMRs. Figure 4 shows a horizontal wagon fi lled past the top of the metal box. When the load size was reduced from 23,000 to 18,000 lbs of a lacta on mix there was a reduc on in the CV in Penn State shaker box par cle separa on as shown in Figure 5. Figure 6 shows more varia on in dry ma er, crude protein, starch, and ash in a lacta on TMR from a TMR wagon that was over fi lled compared to the very next load that was not over fi lled.

Figure 3. Two-ton capacity drum mixer rota ng at 3 r.p.m.

Figure 4. Over fi lled Mono-Mixer Model 2090.

MIXING TIME (MINUTES)

10 21 3 4 5 6 7 8 8 10 11

10

20

30

40

50

60

70

80

90

Quantab

Salt Meter

Adequate Mixing Line

COEF

FICI

ENT

OF

VARI

ATIO

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Figure 5. Penn State shaker box levels and coeffi cients of varia on for over fi lled and normal fi lled Mono-Mixer wagon.

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Figure 6. Coeffi cients of varia on in nutrients with an over fi lled and normal fi lled auger-reel horizontal TMR mixer.

Figure 7. Hay clump in TMR.

Ingredient Load Order

There is no consistent recommenda on on ingredient load order, but it depends on the type of wagon (ver -cal vs. horizontal), level and type of hay (chopped vs. round bales or squares) and feeder experiences with mixing TMRs. Many mes we will see clumps of hay, haylage, or high-moisture corn in the TMR. O en mes moving these items up in the load order while the mixer is running will allow more processing me to reduce these clumps and make the TMR more consistent. Figure 7 shows a clump of hay in lacta on TMR, and Figure 8 shows a very inconsistent TMR as determined by shaking ten samples through the Penn State shaker box. Figure 9 shows an improvement in the consistency of the TMR a er the hay was added earlier in TMR mix and allowed to blend with the haylage in a 4-auger wagon. Also, under processing of low-quality hay very o en causes inconsistent par cle size distribu on of the TMR along the feed bunk.

Proper Loading of Liquids

Adding liquids to TMR is one of the most important ways to reduce sor ng. However, how the liquids are added and mixed into the TMR is a large factor in maintaining consistency. Figure 10 shows a drama c improvement in TMR consistency as measured by par cle size in a Penn State shaker box when the liquid was added properly. There was consistency in the TMR par cle size as shown by the do ed lines. The milk produc on of the herd went up about two pounds within 7 to 10 days a er the herd started adding and mixing the liquid correctly.

TYPES, BRANDS, AND MODELS OF MIXERS

We o en get the ques on, “Which mixer is the best mixer?” With the excep on of one or two models of mixers that are no longer manufactured, you can ex-pect to get very consistent loads of TMR with almost all ver cal and horizontal wagons that exist on the market today. The best TMR wagon is one that is well maintained and used with proper loading and mixing procedures. Ingredient loading sequence and degree of forage processing are the major diff erences between horizontal mixers and ver cal mixers. Long-stem hay is loaded fi rst in ver cal wagons, but not in horizontal wagons. Limited amounts of forage can be processed with reel-auger wagons, whereas larger amounts can be processed with 4-auger horizontal and ver cal wagons. The other key diff erences between ver cal and horizontal wagons are minimum and maximum load sizes. The big advantage horizontal wagons have over ver cal wagons is excellent mixing of small loads of feed typical for smaller pens of ca le such as close-up dry cow and fresh cow pens on many dairies in the Eastern U.S. Our experience indicates that the

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2011 Penn State Dairy Cattle Nutrition Workshop 85

Figure 8. A lacta on TMR with hay clumps.

Figure 9. Lacta on TMR shown in fi gure 7, but hay added earlier in the mix behind haylage in a 4-auger horizontal wagon.

Figure 10. Penn State shaker box results of a TMR with liquid added properly (do ed lines) and improperly (solid lines) to a twin-screw ver cal wagon.

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10

Perc

ent

on P

enn

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Bunk Sample Location

MiddleBottomMiddleBottom

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minimum load size in a ver cal wagon is the amount of TMR needed to cover the top of the ver cal screws. O en mes with under-fi lled ver cal wagons, you can have mineral, protein and grain hang up on the auger fl igh ng and not get mixed into the TMR. However, ver cal wagons can mix very large loads of very wet TMR containing high levels of ingredients such as wet dis llers grains. Experience has shown that auger-reel horizontal wagons can only be fi lled to about 50% of maximum volume with fi nishing beef diets containing rela vely high levels of wet dis llers grains.

The other key issues with selec ng a mixer wagon are the dealer service level and company technical support including support for the load cells and scale head.

KEY MESSAGES

• Maintaining a consistent healthy rumen every day for every cow on the dairy is one of the keys to fi nancial success.

• The TMR on paper o en is not the one all cows eat.• The Diamond V TMR AuditTM has iden fi ed key fac-

tors as to why the TMR on paper does not match the one consumed by the cows.■ Silage face management■ Under mixing of the last added ingredient■ Over fi lling■ Ingredient mix order■ Improper loading of liquids■ Under processing low-quality hay

• The best TMR mixer is one that is well maintained and loaded and operated properly to obtain a well-mixed, consistent ra on.

• As a dairy consultant you have the opportunity to help your clients fi nd solu ons to TMR inconsisten-cies and to guide them to use research proven prod-ucts that provide good returns on the investment.

REFERENCES

Eisenberg D. Micro-Tracers, Inc. San Francisco, CA 94124.

Harner JP, III, Behnke K, and Herrman T. 1995. Rota ng Drum Mixers. Kansas State University. MF2053.

Herrman T and Behnke, K. 1994. Tes ng Mixer Performance. Kansas State University. MF-1172.

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2011 Penn State Dairy Cattle Nutrition Workshop 87

Meeting Calves’ Needs: Winter Feeding and Amino Acids

Mark Hill, Gale Bateman, Jim Aldrich, and Rick Schlotterbeck

Nurture Research Center

Provimi North America

Lewisburg, OH

FEEDING CALVES IN THE WINTER

Introduction

For many years, one management approach for farmers has been to feed more milk or milk replacer (MR) or more fat to their calves in the winter as a source of fuel for hea ng their calves. Scibilia et al. (1987) demonstrat-ed that low fat MR powders fed at approximately 1.3 lb daily without feeding starter did not support adequate ADG under cold condi ons. When fat was added, ADG was increased and normal calf body temperatures were maintained under their cold stress treatment.

Another management approach has been to adjust housing and bedding to maintain a low stress environ-ment for calves. For example, the use of deep straw bedding supported more ADG of calves housed in an un-heated, dra -free nursery during cold weather than the same weight of hardwood shavings (Hill et al., 2007b).

We will discuss the integra on of housing and feeding programs for calves during winter condi ons. The em-phasis will be on feeding programs with proper housing, ini ated with brief comments on housing.

Housing and Insulation

Winter housing trumps feeding. You may agree or dis-agree with this, however, please consider this ques on. Would you rather hike 5 miles in a snowstorm 1) wearing a good pair of boots and a good pair of insulated cover-alls or 2) wearing a good pair of boots in your birthday suit with a dozen energy bars to eat on your hike?

In the study of Scibilia et al. (1987), their calves were housed in environmentally controlled rooms to create 25 and 50 °F temperatures. Calves were housed in el-evated stalls with no bedding on a grated fl oor. Other than their hair coat, they had no way to keep warm. Added dietary calories via fat helped the calves stay warm and maintain growth rate. However, many farm-

ers do not house calves this way. In the Western US (less so in the Eastern US), it is more common to see calves housed in elevated crates outside with no protec on from wind. In the Eastern and Midwestern US, calves are some mes housed in elevated crates within heated rooms (very common in the veal industry).

Bedding type (straw be er than shavings) was more eff ec ve than amount of calories fed in suppor ng ADG (Hill et al., 2007b) during the winter with both conven onal MR fed from 1 to 1.5 lb of powder daily and high protein MR fed at 1.5 to 2 lb of powder daily. Deep straw bedding impacts not only calf ADG but calf health in a posi ve way. Deep straw bedding has been shown to reduce the concentra on of airborne bacte-ria and incidence of respiratory disease and scouring in calves (Lago et al., 2006; Hill et al., 2007b, 2011b) compared to shallow straw, shavings, or sand bedding.

Bedding serves to insulate the calf from cold, provided that it is deep and dry, allowing the calves to ‘nest’. Under similar milk and MR feeding programs with good management and dra -free, unheated housing, with deep, dry bedding, calf ADG has been reported greater in winter vs. warmer seasons in Ontario, Canada (McK-night, 1978), Minnesota (Chester-Jones et al., 2008), and Ohio (Bateman et al., 2010). So, under good housing and management, cold stress can be mi gated.

Feeding Programs

The CP and energy intake from MR greatly impacts calf ADG, carcass protein, and carcass fat deposi on (Don-nelly and Hu on, 1976; Blome et al.; 2003, Bartle et al., 2005). When CP was restricted or limited rela ve to energy consumed, the extra energy was stored as fat. Tikofsky et al. (2001) reported that when under equal CP and energy intake (powder fed varied from approximately 1.9 to 2.3 lb/calf daily with the diff erent fat percentages of the MR and no starter was fed) that energy from fat was more likely to result in fat deposi-

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on than energy from lactose. Bascom et al. (2007) reported a trend for less ADG when a high protein MR with 33% fat was fed to calves compared to a high CP MR with 16% fat; however, fat deposi on was consid-erably greater in the calves fed the 33% fat MR. In the previous trials, calves were fed MR to greatly exceed their maintenance requirements (i.e. ‘accelerated’ MR programs) but were not fed starter. When calves were fed MR and starter to greatly exceed their maintenance requirements during cold temperatures in an unheated nursery, Hill et al. (2007b) reported that added energy from MR fat did not improve ADG but added energy from MR lactose and CP improved ADG. As fat was increased from 13 to 23% fat in a 26% CP MR powder fed at 1.5 lb daily (energy intake increasing with fat percentage), ADG responded quadra cally being the least at 13 and 23% fat (Hill et al. 2009c). Diges on of DM, fat, Ca, and P declined as fat percentage increased. Body composi on was not measured. Fat intake is a way to provide energy and reduce body fat loss during sub-thermoneutral temperatures; however, its applica- on in calves fed close to maintenance requirements

and housed in hutches during cold weather has yielded mixed results (Davis and Drackley, 1998).

A reason for the mixed results of increasing the nu-trients fed via the milk or MR is the balance between intake of nutrients from MR or milk and early life calf growth with the development of the rumen, starter intake, and growth later in the calf’s life. Numerous researchers have reported that feeding too much milk or MR will result in a post-weaning reduc on in ADG by depressing starter intake (Davis and Drackley, 1998; Hill et al., 2007b). Terre et al. (2006) and Hill et al. (2010) a ributed a por on of the lower ADG to a lower diges- on of starter in the calves fed the high vs. low amount

of MR. Calves fed high amounts of MR produced less amylase, likely because starter intake was delayed and low (Hill et al., 2010). Amylase produc on increased with age and intake of starter (Guilloteau et al., 1985). Rumen development was also less in calves fed high vs. low amounts of MR (Suarez-Mena et al., 2011).

In research with high protein MR (24 to 28% CP), it ap-pears that low starter intake and post-weaning slumps in ADG are a concern at feeding rates exceeding 1.5 lb DM from milk or MR. Taking 3 to 7 days to reduce the liquid diet (i.e. only feeding half the maximum or mul- ple step downs in liquid) has resulted in post-weaning

slumps in ADG (Hill et al., 2007b, 2010; Terre et al., 2007; Sweeney et al., 2010). When greater amounts of milk

or MR are fed, long step-down (weaning) periods must be implemented. Kahn et al. (2007) took 25 days took reduce the MR fed un l completely weaned and re-ported no slump in ADG or starter intake post-weaning. Sweeney et al. (2010) reported 10 days (starter intake) or 22 days (ADG) were needed to gradually decrease milk with an automated milk feeder to avoid slumps in intake or ADG post-weaning. Hill et al. (2007b) fed a sim-pler step-down program for 21 days to reduce the MR fed before complete weaning and reported no slump in ADG and intake. This program fed a 26% CP, 17% fat MR powder. For the fi rst 5 days, 1.8 lb of powder was fed. For days 6 to 21, 2 lb of powder was fed. For days 22 to 39, 1.5 lb of powder was fed. For days 40 to 42, 0.75 lb of powder was fed.

Hill et al. (2009a) fed 26% CP MR that contained either 17 or 31% fat and observed no change in starter intake but less ADG in calves fed the high fat MR. The fa y acid profi les of these diets were confounded and might have explained the diff erences in ADG since the fa y profi le of the 31% fat MR appeared defi cient in essen al fa y acids. However, all of the fat in 31% fat MR and most of the fat in the 17% fat was edible lard, a saturated fat and the predominate source of fat used in MR in the US.

Sabb et al. (1986) reported that pancrea c lipase ac- vity in rats substan ally declines when corn starch

replaces fat at dietary fat concentra on below 23%, especially if the fat was saturated. The fat concentra- on of the complete diet consumed by the calf fed MR

and starter is typically declining with age because of increasing starter intake that is low in fat combined with an o en fi xed MR intake. Thus, lipase produc on could drop off in the later pre-weaning period of the calf if lipase produc on in calves is as in rats.

Guilloteau et al. (1985) reported that lipase was greatest in the milk-fed calf and decreased as starter consump- on increased during weaning and post-weaning. Sabb

et al. (1986) reported that rat pancrea c lipase ac vity was not linearly related to lipid intake and lipase ac vity diff ers rela ve to the lipid content and type in the diet. They reported no diff erence in lipase ac vity from diets with 5 to 20% fat but large increases in ac vity with diets containing 23 and 29% fat. Addi onally, rats fed diets with saturated fat had much lower lipase ac vi es than unsaturated fat diets at 17% fat (Sabb et al., 1986). In Hill et al. (2009c), during the days pre-weaning diges- on measurements declined as fat increased from 13

to 23% of the MR, the total dietary fat (starter plus MR,

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predominately saturated fat from lard) ranged from 11 to 18%. So if it was the same as in rats, lipase produc- on would have been declining in the calves.

Hill et al. (2009c) also reported serum amylase concen-tra ons, starter intake, and diges on of non-structural carbohydrates to decrease with increasing fat intake. This is consistent with measures of amylase ac vi es in the intes ne of calves and rats. Amylase was lowest in the milk-fed calf and increased as starter consump on increased during weaning and post-weaning (Guillo-teau et al., 1985). In rats, amylase ac vity was directly related to carbohydrate intake (Thornburg et al., 1987; Sankaran et al., 1987) and declines with the intake of fat (Forman and Schneeman, 1980).

Increased fat intake has resulted in the reduc on in Ca and P diges bility in species other than calves (French and Elliot, 1943; Calverley and Kennedy, 1947) and calves (Toullec et al., 1980; Hill et al., 2009c), especially when saturated fats were fed (Yacowitz et al., 1967).

The op mum CP to energy ra os in MR-fed calves have ranged from 43 to 61 g CP/Mcal ME while the fat con-centra ons in the op mum MR treatments have ranged from 17 to 24% (Donnelly and Hu on, 1976; Blome et al.; 2003, Bartle et al., 2005; Hill et al., 2009bc). Of these trials, only Hill et al. (2009bc) fed starter, weaned the calves, and measured post-weaning performance. They fed 17% fat MR powders ranging from 22 to 28% CP at either 1.25 or 1.5 lb daily or 26% CP MR powder ranging from 13 to 23 % fat at 1.5 lb daily. Calf ADG was maximized with the 26% CP, 17% fat MR powder fed at 1.5 lb daily (55 g CP/Mcal ME). Increasing a 20% CP MR powder from 1 lb daily up to 1.5 lb daily has resulted in limited to no increases in ADG from birth to 8 wk of age (Hill et al., 2007b). This relates to inadequate protein rela ve to energy and MR subs tu ng for or reducing starter intake and diges on.

So in research where calves are fed MR and starter, increasing fat intake, a possible winter feeding strategy, is lowering the op mum CP to energy ra o of the diet. Addi onally, diges on of the diet is decreasing as fat is increased by possibly reduced concentra ons of lipase and amylase. However, increased fat intake may be resul ng in more fat deposi on in the body. Possibly fat is being mobilized from the body to maintain body weight when fat intake or feeding rate of the MR is not increased.

Summary and Take-Home Message

From this review of the literature, a ra onalized pro-gram would be to fi rst insure that the calf was bedded properly (deep, dry bedding allowing the calf to ‘nest’) and housed in a dra -free environment if cold housing is employed. Heated barns could be an op on. Adequate and proper housing minimizes the impact of feeding program on the calf. If high feeding rates over 1.5 lb of DM are to be fed, insure the calves have approximately 21 days to step-down in liquid intake prior to complete weaning. This allows the rumen to develop to avoid slumps in intake and ADG post-weaning. A successful program (Hill et al., 2007b) has been to feed 1.5 lb of a 26% CP, 17% fat MR throughout the pre-weaning period and take the last 3 days before weaning to feed the calves in the AM only (0.75 lb of powder for the last 3 days). For those wan ng to feed more powder, this program of feeding a 26% CP, 17% fat MR powder was successfully modifi ed (Hill et al., 2007b). For the fi rst 5 days, 1.8 lb of powder was fed. For days 6 to 21, 2 lb of powder was fed. For days 22 to 39, 1.5 lb of powder was fed. For days 40 to 42, 0.75 lb of powder was fed. Similarly, pasteurized milk could be fed in a similar manner using an equivalency of approximately 1 lb MR being equal to 1 gallon of pasteurized milk. A similar approach may be taken for those that rou nely feed 1 lb of a conven onal 20 or 22% CP MR powder. Feed 1.5 lb for the fi rst 21 days, then feed 1 lb un l day 39, and 0.5 lb un l day 42 and complete weaning.

AMINO ACIDS FOR CALF MILK REPLACERS

Introduction

Un l recently there has been li le research related to the op mum amino acid concentra ons in the diets of calves less than a month of age. There are op mum ra os published in older veal calves not fed starter. It is common prac ce in lacta ng dairy cow, poultry, and swine nutri on to balance diets for amino acids and ideal ra os to lysine (Lys) are well-researched. In lactat-ing dairy ca le, requirements for Lys and methionine (Met) are also established. However, the dairy ca le NRC (2001) does not include sugges ons or require-ments for amino acids for calves.

We will discuss relevant research related to op mum amino acid concentra ons and ra os in calf milk replacers.

Optimum Concentrations and Ratios

Prior to 2008, the most recent summaries of the amino acid requirements of calves were Williams and

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Hewitt (1979), van Weerden and Huisman (1985), Toullec (1989), and Gerrits et al. (1997) in 5-week to 5-month old calves. They were all conducted in calves fed milk and no starter. In 2007, in calves less than a month of age and fed starter, it was demonstrated that supplemen ng L-Lys and DL-Met to whey-based milk replacers (20% CP, 20% fat fed at 440 g DM/d) would increase ADG and lower serum urea N compared to unsupplemented milk replacer (Hill et al., 2007a). In 2008, op mal concentra ons of amino acids for milk replacers were es mated for calves less than a month of age, fed milk replacer and starter feed, and housed and managed under commercial condi ons. In these experiments, calves averaged 48 kg BW, consumed 204 g CP/d, 17 g Lys/d, and gained 0.46 kg BW/d (Hill et al., 2008a). Es mated requirements were 0.31 Met to Lys ra o, 0.54 Met+cysteine to Lys ra o, and a threonine to Lys ra o less than 0.62. Milk replacers balanced with these amino acid ra os supported over 15% greater ADG and feed effi ciency than those without. These ra o es mates were recently corroborated by Wang et al. (2011) in milk replacer with 50% milk protein and 50% soy protein from soy protein concentrate.

Gram es mates in the calves less than a month of age (Hill et al., 2008) appeared as great or greater to require-ments in older calves fed more milk replacer, housed in climate controlled rooms, and not fed starter. Gerrits et al. (1997) es mated requirements of 16.3 g Lys/d, 4.2 g Met/d, 10.8 g Thr/d, and 7.6 g of Met+Cys/d in 90 kg veal calves consuming 263 g CP/d and gaining 0.932 kg/d. The es mates of Gerrits et al. (1997) for these amino acids increased 11% for 90 kg calves consuming 352 g CP/d and gaining 1.056 kg/d. Williams and Hewi (1979) es mated that 6 to 14 wk old, 50 to 58 kg BW calves gaining 0.25 kg/d required 7.8 g Lys. Es mated ra os of amino acids to Lys for selected studies are shown in Table 1.

Fligger et al. (1997) reported that arginine (Arg) supple-mented to dairy calves increased ADG and altered some measurements of immunity. Recently, supplemental Arg was shown not to change ADG, feed effi ciency, starter intake, hip width, and serum pre-weaning concentra- ons of albumin, alkaline phosphatase, amylase, crea -

nine, glucose, total protein, and urea-N when added to 26% CP, 2.45% Lys, 0.75% Met, 17% fat MR fed at 1.5 lb powder daily (Hill et al., 2011a). In Trial 1, MR contained 0.28 or 0.44 Arg to Lys ra os. In Trial 2, MR contained 0.27 or 1.0 Arg to Lys ra os.

Addi onally, his dine (His) was evaluated in 26% CP, 2.45% Lys, 0.75% Met, 17% fat MR fed at 1.5 lb powder daily (Hill et al., 2011a) at 0.2 or 0.42 His to Lys ra os. It did not change performance or serum measurements.

Op mum ra os of protein, Lys, and Met to energy have been published (Hill et al., 2009b). Milk replacer pow-ders fed to provide 3.26 Mcal metabolizable energy per day (1.25 lb of powder) supported maximum calf body weight gain when they contained 24% CP, 2.16% Lys, 0.65% Met, and 1.4% Thr. Powders fed to provide 3.71 Mcal metabolizable energy per day (1.5 lb of powder) supported maximum calf body weight gain when they contained 26% CP, 2.34% Lys, 0.72 % Met, and 1.5% Thr.

Summary and Take-Home Message

Supplemen ng the correct amount of amino acids to all milk protein milk replacers has improved ADG and feed effi ciency by 15% or more. This further extends the knowledge for formula ng calf diets for op mum performance and economic return to the farmer.

REFERENCES

Bartle , K. S., F. K. McKeith, M. J. VandeHaar, G. E. Dahl, and J. K. Drackley. 2006. Growth and body composi on of dairy calves fed milk replacers containing diff erent amounts of protein at two feeding rates. J. Anim. Sci. 84:1454-1467.

Bascom, S. A., R. E. James, M. L. McGilliard, and M. Van Am-burgh. 2007. Infl uence of dietary fat and protein on body composi on of Jersey bull calves. J. Dairy Sci. 90:5600-5609.

Bateman, II, H. G., T. M. Hill , J. M. Aldrich, R. L. Schlo erbeck, and J. L. Firkins. 2011. Meta analysis of the impact of ini- al serum protein concentra on and empirical predic on

model for growth of neonatal Holstein calves through eight weeks of age. J. Dairy Sci. (accepted for publica on).

Blome, R. M., J. K. Drackley, F. K. McKeith, M. F. Hutjens, and G. C. McCoy. 2003. Growth, nutrient u liza on, and body composi on of dairy calves fed milk replacers containing diff erent amounts of protein. J. Anim. Sci. 81:1641-1655.

Calverley, C. E., and C. Kennedy. 1947. The eff ect of fat on calcium and phosphorus metabolism in normal growing rats under a normal dietary regime. J. Nutr. 33:165-175.

Chester-Jones, H., D. M. Ziegler, R. Larson, C. Soderholm, S. Hayes, J. G. Linn, M. Raeth-Knight, G. Golombeski, and N. Broadwater. 2008. Applied calf research from birth to six months. Pages 106-112 in Proc. 4 State Dairy Nut. Mgmt. Conf., Dubuque, IA, Iowa State University Press.

Davis, C. L., and J. K. Drackley. 1998. The Development, Nu-tri on, and Management of the Young Calf. pp. 221-222. Iowa State Univ. Press, Ames.

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Donnelly, P. E., and J. B. Hu on. 1976. Eff ects of dietary protein and energy on the growth of Friesian bull calves. I. Food intake, growth, and protein requirements. N. Z. J. Agric. Res. 19:289-297.

Fligger, J. M., C. A. Gibson, L. M. Sordillo, and C. R. Baum-rucker. 1997. Arginine supplementa on increases weight gain, depresses an body produc on, and alters circula ng leukocyte profi les in preruminant calves without aff ec ng plasma growth hormone concentra ons. J. Anim. Sci. 75:3019-3025.

Forman, L. P., and B. O. Schneeman. 1980. Eff ects of dietary pec n and fat on the small intes nal contents and exocrine pancreas of rats. J. Nutr. 110:1992-1999.

French, C. E., and R. F. Elliot. 1943. The interrela on of calcium and fat u liza on. J. Nutr. 25:17-21.

Gerrits, W. J. J., J. France, J. Dijkstra, M. W. Bosch, G. H. Tolman, and S. Tamminga. 1997. Evalua on of a model integra ng protein and energy metabolism in preruminant calves. J. Nutr. 127: 1243-1252.

Guilloteau, R., T. Corring, R. Toullec, and R. Guilhermet. 1985. Enzyme poten ali es of the abomasums and pan-creas of the calf. II. Eff ects of weaning and feeding a liquid supplement to ruminant animals. Reprod. Nutr. Develop. 25:481-493.

Hill, T. M., J. M. Aldrich, R. L. Schlo erbeck, and H. G. Bateman, II. 2007a. Amino acids, fa y acids, and fat sources for calf milk replacers. Prof. Anim. Sci. 23:401-408.

Hill, T. M., H. G. Bateman, II, J. M. Aldrich, and R. L. Schlo er-beck. 2007b. Eff ects of feeding rate of milk replacers and bedding material for calves in a cold naturally ven lated nursery. Prof. Anim. Sci. 23:656-664.

Hill, T. M., H. G. Bateman, II , J. M. Aldrich, R. L. Schlo erbeck, and K. G. Tanan. 2008. Op mal concentra ons of lysine, methionine, and threonine in milk replacers for calves less than fi ve weeks of age. J. Dairy Sci. 91:2433-2442.

Hill, T. M., H. G. Bateman, II , J. M. Aldrich, and R. L. Schlo er-beck. 2009a. Eff ect of consistency of nutrient intake from milk and milk replacer on dairy calf performance. Prof. Anim. Sci. 25:85-92.

Hill, T. M., H. G. Bateman, II , J. M. Aldrich, and R. L. Schlot-terbeck. 2009b. Op mizing nutrient ra os in milk replac-ers for calves less than fi ve weeks of age. J. Dairy Sci. 92:3281-3291.

Hill, T. M., H. G. Bateman, II , J. M. Aldrich, and R. L. Schlot-terbeck. 2009c. Effects of fat concentration of a high protein milk replacer on calf performance . J. Dairy Sci. 92:5147-5153.

Table 1. Es mated amino acid requirements from various trials (amino acids expressed in ra o to Lys)

Item Hill et al., 2008, whey1

Hill et al., 2008, skim1

Williams and Hewi , 1979

van Weerden and Huisman, 1985 Toullec, 1989 Gerrits et al.,

1997

Milk protein Whey Skim Skim Skim Skim SkimAge, wk 0 – 4 0 – 4 6 – 14 5 – 7 8 – 20 8 – 20ADG, kg 0.46 0.57 0.25 0.9 1.0 1.0Amino acidLys 1.00 1.00 1.00 1.00 1.00 1.00Met 0.31 0.31 0.27 -- -- 0.25Cys 0.24 0.12 0.20 -- -- 0.21Thr 0.79 0.62 0.63 0.50 0.60 0.66Arg 0.28 0.38 1.09 0.33 0.44 0.40Glu 1.88 1.52 -- -- -- --His 0.21 0.29 0.38 0.25 0.31 0.39Ile 0.64 0.60 0.44 0.62 0.67 0.43Leu 1.13 1.05 1.08 0.87 1.02 1.12Phe 0.35 0.51 -- -- -- 0.59Trp 0.19 0.17 0.13 0.11 0.09 0.12Tyr 0.31 0.50 -- -- -- 0.38Val 0.64 0.74 0.62 0.61 0.71 0.63Met+Cys 0.54 0.42 0.47 0.41 0.51 0.46Phe+Tyr 0.66 1.01 0.95 0.88 0.71 0.97

1In the trials using skim milk, Thr concentra ons ranged from 1.06 to 1.80% of the diet. There were no diff erences in ADG, however, serum measurements suggested some inadequacies at lower concentra ons below a ra o of 0.62 to Lys. Thr is naturally high in whey protein and does not allow one to test inadequacies.

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Hill, T. M., H. G. Bateman, II , J. M. Aldrich, and R. L. Schlot-terbeck. 2010. Eff ect of milk replacer program on diges on of nutrients in dairy calves. J. Dairy Sci. 93:1105-1115.

Hill, T. M., H. G. Bateman, II, J. M. Aldrich, and R. L. Schlot-terbeck. 2011a. CASE STUDY: Eff ects of adding arginine and his dine to dairy calf milk replacers. Prof. Anim. Sci. 27 (accepted for publica on).

Hill, T. M., H. G. Bateman, J. M. Aldrich, and R. L. Schlo erbeck. 2011b. Comparisons of housing, bedding, and cooling op- ons for dairy calves. J. Dairy Sci. 94:2138-2146.

Khan, M. A., H. J. Lee, W. S. Lee, H. S. Kim, S. B. Kim, K. S. Ki, J. K. Ha, H. G. Lee, and Y. J. Choi. 2007. Pre- and post-weaning performance of Holstein female calves fed milk through step-down and conven onal methods. J. Dairy Sci. 90:876-885.

Lago, A., S. M. McGuirk, T. B. Benne , N. B. Cook, and K. V. Nordlund. 2006. Calf respiratory disease and pen micro-environments in naturally ven lated calf barns in winter. J. Dairy Sci. 89:4014-4025.

McKnight, D. R. 1978. Performance of newborn dairy calves in hutch housing. Can. J. Anim. Sci. 58:517-520.

Michaylova, V., and P Ilkova. 1971. Photometric determina- ons of micro amounts of calcium with arsenazo III. Anal.

Chim. Acta. 53:194-198.

Na onal Research Council. 2001. Nutrient Requirements of Dairy Ca le. 7th rev. ed. Natl. Acad. Sci., Washington, DC.

Sabb, J. E., P. M Gadfrey, and P. M. Brannon. 1986. Adap ve response to rat pancrea c lipase to dietary fat: eff ects of amount and type of fat. J. Nutr. 116:892-899.

Sankaran, H., C. W. Deveney, E. C. Larkin, and G. A. Rao. 1992. Carhoydrate intake determines pancrea c acinar amylase ac vity and release despite chronic alcoholemia in rats. J. Nutr. 122:1884-1891.

Scibilia, L. S., L. D. Muller, R. S. Kensinger, T. F. Sweeney, and P. R. Shellenberger. 1987. Eff ect of environmental tempera-ture and dietary fat on growth and physiological responses of newborn calves J. Dairy Sci.70:1426-1433

Suarez-Mena, F. X., T. M. Hill, A. J. Heinrichs, H. G. Bateman, II, J. M. Aldrich, and R. L. Schlo erbeck. 2011. Eff ects of including corn dis llers dried grains with solubles in dairy calf feeds. J. Dairy Sci. 94:3037-3044.

Sweeney, B. C., J. Rushen , D. M. Weary , and A. M. de Pas-sillé. 2010. Dura on of weaning, starter intake, and weight gain of dairy calves fed large amounts of milk. J. Dairy Sci. 93:148-152.

Terre, M., M. Devant, and A. Bach. 2007. Eff ect of level of milk replacer fed to Holstein calves on performance during the preweaning period and starter diges bility at weaning. Livest. Sci. 110:82-88.

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disaccharides and pancrea c amylase in young and middle aged rats to a high carbohydrate diet. J. Nutr. 117:63-69.

Tikofsky, J. N., M. E. Van Amburgh, and D. A. Ross. 2001. Eff ect of varying carbohydrate and fat content of milk replacer on body composi on of Holstein bull calves. J. Anim. Sci. 79:2260-2267.

Toullec, R., M. Theriez, and P. Thivend. 1980. Milk replacers for calves and lambs. World Anim. Rev. 33:32-42.

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Wang, J. –H., Y. Tu*, N.-F. Zhang, X.-C. Xu, and Q.-Y. Diao. 2011. The limi ng sequence and proper ra o of lysine, methionine and threonine for calves fed milk replacers containing soy protein. J. Dairy Sci. 94 (abstract T305).

Williams, A. P., and D. Hewi . 1979. The amino acid require-ments of the preruminant calf. Br. J. Nutr. 41: 311-319.

Yacowitz, H., A. I. Fleischman, R. T. Amsden, and M. L. Bie-renbaum. 1967. Eff ect of dietary calcium upon lipid me-tabolism in rats fed staturated or unsaturated fat. J. Nutr. 92:389-392.

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FEEDING PREFRESH TRANSITION COWS

(Usually 3 Wk Prefresh to Calving)

Energy

For several decades, it was recommended to increase concentrate (grain) feeding during the fi nal 3 wk prior to calving. We o en say that we are “steaming up” the cow prior to calving or that we should be feeding a “steam up” diet prior to calving. The origin of the term is a ributed to Robert Bou lour who at the World Dairy Congress (1928) fi rst proposed the “steam up” ra on as a way to circumvent “the neglect of the prepara on of the cows for her lacta on period.” The term was meant to be an analogy to the prepara on of a steam thresher. Essen ally, the logic behind this feeding strat-egy was to adapt rumen microorganisms to higher grain diets that would be encountered by the cow following parturi on. By following this prac ce, it was believed that cows would be less likely to go off feed or experi-ence ruminal acidosis. Over the next decades, other reasons were put forth for steaming up cows prior to calving. These included: maximiza on of dry ma er intake (DMI), provision of more propionate to support gluconeogenesis and decrease fat mobiliza on from adipose ssue, and increasing rumen papillae length to

increase vola le fa y acid absorp on from the rumen. However, today, many nutri on consultants and scien- sts are sugges ng not to feed diets moderately high

in grain during the prefresh transi on period, because prac cal experience and research over the past 10 to 15 years has not supported the concept.

A summary of 10 studies that examined decreasing the forage-concentrate ra o (increasing non-fi ber carbohy-drate, NFC) of prefresh transi on diets is listed in Table 1. Cows went on to common diets postpartum (except Guo, 2007). In 6 of the 8 studies in which prepartum DMI was measured, there was a signifi cant increase when NFC was increased. Surprisingly, the increase in DMI occurred for sustained periods of me (i.e. 3 wk) even if cows were in posi ve energy balance at the me addi onal concentrate was introduced. In other

words, there does not seem to be a func onal feedback mechanism to maintain energy balance when increas-ing energy density in the diet during the pre-fresh transi on period. The obvious ques on is: does this increase in prepartum DMI provide some benefi t to the cow such that her postpartum health and produc vity is increased? Poten al benefi ts include: suppression

Energy and Protein Nutrition for Transition Cows

Ric R. Grummer, PhD

Ryan Ordway, PhD

Balchem Corp.

New Hampton, NY

Table 1. Eff ects of increasing prefresh transi on diet NFC on pre- and postpartum DMI and postpartum milk yield

Study Low/high NFC, % Change in prepartum DMI, kg/d

Change in postpartum DMI, kg/d

Change in milk yield, kg/d

Minor et al., 1998 35/44 +1.9 DNR DNRMashek and Beede, 2000 35/38 DNR DNR NSKeady et al., 2001 13/28 +1.7 NS NSHolcomb et al., 2001 25/30 +3.4 NS NSDoepel et al., 2001 24/30 NS NS NSRabelo et al., 2003&2005 38/45 +1.7 NS NSSmith et al., 2005 34/40 NS NS NSKamiya et al., 2006 28/33 +1.7 NS NSGuo et al., 2007 26/39 +2.6 NA NARoche et al., 2010 13/32 DNR DNR NSDNR = Did not report, NS = Non-signifi cant diff erence (P ≥ 0.05), NA = Non-applicable.

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of adipose lipid mobiliza on as feed intake decreases at calving, s mula on of acid produc on and rumen papillae growth, and acclima on of rumen microbial popula on to high starch diets. Data in Table 1 indicate that there were no carry over eff ects of treatment on postpartum DMI or milk yield (measurement dura on varies among studies). Some studies showed a transient increase in DMI immediately postcalving; however, this did not result in benefi cial eff ects on DMI or milk yield measured over a longer dura on (i.e., data shown in Ta-ble 1). In most of these studies, energy balance was not reported. However, if postpartum DMI and milk yields were not aff ected, it is unlikely that energy balance would have been aff ected. Another interes ng note is that these studies did not employ suffi cient numbers of animals to adequately determine treatment eff ects on health disorders. Nevertheless, it is unlikely animal health would have been aff ected without changes in DMI, milk yield, or both.

Nordlund et al. (Univ. Wisconsin, unpublished) devel-oped the Transi on Cow Index to monitor transi on cow programs on commercial dairy farms. The index uses 14 factors from historical DHIA records of individual cows to project milk yield the next lacta on. These projec ons are then compared to her expected milk yield deter-mined a er the fi rst milk test postpartum. Devia ons from expecta ons are calculated for a herd to determine if progress, presumably in the transi on cow program, is being made. They surveyed 32 commercial dairy herds and found no rela onship between fi ber in the prefresh transi on diet and herd Transi on Cow Index values (r2 = 6 x 10-5).

Why was steaming up cows regarded so important for success of transi on cows for 8 decades and now it is viewed by many as nonessen al? There may be several reasons, but as important as any is probably the advent of feed-ing totally mixed ra ons (TMR). Feed-ing TMR allows for small amounts of concentrate being consumed at any par cular me. This, in conjunc on with gradual increases in feed intake a er calving probably allow adequate adapta on to the higher concentrate diets being fed postpartum.

Studies employing other feeding strategies to infl uence energy intake

during the prefresh transi on period are shown in Table 2. In the fi rst three studies, forage neutral detergent fi ber (NDF) was replaced with nonforage NDF. In the fi nal three studies, feed was restricted to limit intake. With the excep on of a small change in blood beta-hydroxybutyrate (BHBA) in one study, there were no responses to treatment. These studies in combina on with the studies on altering forage to concentrate ra o provide a convincing picture that prefresh transi on diets have very li le eff ect on postpartum cow health and performance! Therefore, there appears to be considerable fl exibility in composi on of diets fed to prefresh transi on dry cows. The most important point is to avoid prolonged (> 2 to 3 d) periods of nega ve energy balance. Most cows can be fed low energy diets (0.6 Mcal NEl/lb) and s ll meet energy requirements un l a couple of days before calving. If any condi ons prevail that lead to poor feed intakes during the prefresh transi on period (heat stress, overcrowding, etc.), then steaming up diets may make sense.

Protein

The 2001 Dairy NRC es mated DMI and crude protein (CP) requirements (metabolizable protein [MP] require-ment/0.7) for prefresh transition heifers and cows (Figure1). Crude protein percentage needed in the diet to meet the cows’ requirement can be calculated ((CP requirement/predicted DMI) x 100) for any day prior to calving. The NRC commi ee did not have suffi cient data for determining the requirement for mammary growth, so es mates from VandeHaar et al. (1999) have been

Table 2. The eff ects of replacing forage NDF with nonforage NDF or limit feeding energy during the prefresh transi on period on postpartum dmi, lacta on, and metabolic parameters

Study Treatment Response

Chung et al., 2008 Forage v Nonforage NDF Increase BHBA 1.2 mg/dLNS-DMI, Milk, NEFA, Liver TG

Dann et al., 2007 Forage v Nonforage NDF NS-DMI, NEFA, BHBA, Liver TG

Smith et al., 2009 Forage v Nonforage NDF NS-DMI, Milk

Colazo et al., 2005 Ad libitum v Restricted (24% decrease in DMI) NS-DMI, Milk, NEFA

Dann et al., 2006 Ad libitum v Restricted (80% of NEl requirement)

NS-DMI, Milk, NEFA, BHBA, Liver TG

Holcomb et al., 2001 Ad libitum v Restricted (to 8.2 kg DM/d) NS-DMI, MILK, NEFA

NS = Nonsignifi cant, P ≥ 0.10. NEFA = nonesterifi ed fa y acids.

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included for calcula ng a more liberal es mate of CP needs. Several important points from Figure 1: 1. Heif-ers require more CP in the diet than cows. This is due to lower feed intakes and higher protein requirements to accommodate growth. Although it appears that cows could be fed diets less than 12% CP for the majority of the prefresh transi on period, this is not recommended since it is believed that 12% CP is the minimum needed to maximize fi ber diges on and microbial protein syn-thesis in the rumen. 2. Because the drop in feed intake accelerates as calving approaches, the need for a greater percentage CP in the diet also accelerates. 3. Crude protein requirements for mammary development are not trivial and increase CP needed in diets by 1 to 2 percentage units.

The large increase in percentage CP needed as calving approaches poses an interes ng ques on: what is the appropriate density when formula ng a diet for the en re three-week prefresh period? Should the diet be high in CP to minimize the likelihood of the cow or heifer ever experiencing a nega ve balance for MP? Or should you formulate for a lower level of CP that will meet the needs of the cow or heifer for the majority of the prefresh transi on period and minimize the period

of me in which she would be overfed protein. Most, but not all (NRC, 2001, Chapter 9) studies have indicated that milk and protein yield of cows is not infl uenced by prepartum protein content of the diet. In some studies, increasing the CP content of the diet above 12 to 13% has decreased postpartum feed intake (NRC, 2001, Chapter 9). The reason for this is not known, but it may be related to the reduced capacity of the liver to detoxify ammonia during the transi on period (Strang et al., 1998). Penn State researchers (Putnam and Varga, 1998) indicated that cows fed 10.5, 12.6, or 14.5% CP during the prefresh transi on period were all in posi ve nitrogen balance.

If cows and heifers are intermingled and fed the same diet prepartum, the diet should be formulated to meet the needs of the heifers. If they are housed separately, then separate diets could be formulated for each group. Considering additional requirements for mammary growth, heifers need approximately 1,000 g of MP per day (or 1,400 g of CP) while cows need approximately 860 g MP per day (or 1,230 g CP). It is best to make measurements of DMI on the farm so that percentage CP in the diet can be calculated, but remember never to go below 12% CP. Typically, cows will need 12% CP in the diet and heifers will need 14% CP. You may see

Figure 1. Es mates of the percentage CP needed in the diet to meet the protein requirements of cow and heifers at 21, 7 and 1 d prior to calving (NRC, 2001). Es mates are without or with considera on of poten al requirements for mammary growth.

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or hear recommenda ons for greater requirements for MP/CP and possibly the recommenda on for inclusion of protein sources that have high levels of rumen undegrad-able protein (RUP). These higher recommenda ons are usually made as “safety nets,” but research suppor ng them is lacking. Supplemen ng protein sources high in rumen RUP may come into play when prefresh diets are high in very poor quality forage, e.g., straw.

FEEDING POSTFRESH TRANSITION COWS

(Usually 0 to 3 Wk Postcalving)

Energy

Amazingly, an area of research that has received li le a en on is feeding of the immediate postpartum cow. Why? Researchers avoid doing studies on fresh cows be-cause tremendous variability amongst cows makes it dif-fi cult to design experiments with suffi cient replica on. Most fresh cow studies are ini ated at 3 wk postpartum or later when cow variability is reduced and there is less likelihood of losing a cow from the study! This is unfortunate because it easy to make an argument that nutri on of the cow during the fi rst 3 wk postpartum may be the most important. The most rapid decrease in energy balance and nega ve energy balance nadir usually occurs during the fi rst 3 wk postpartum. A er summarizing 26 studies, Brixy (2005) indicated that

posi ve energy balance was reached by approximately 50 d in milk and the minimum energy balance occurred at about 11 d in milk. We collected data from twenty studies published in peer reviewed journal ar cles since 1988 (Grummer and Rastani, 2003). The data indicated that there was a stronger rela onship between days to posi ve energy balance and energy density of the diet (r = 0.57, P < 0.0001) than peak milk yield. This data provides evidence that energy intake may be a more important factor aff ec ng return to posi ve energy bal-ance than milk yield, because energy intake is a func on of DMI and energy density of the diet.

Data from Rabelo et al. (2003, 2005) indicated that energy density of diets immediately postpartum are more cri cal than energy density of diets immediately prepartum. They u lized a 2 x 2 factorial arrangement of treatments. Cows were fed diets containing 1.55 (Dry Low - DL) or 1.65 Mcal NEl/kg DM (Dry High - DH) for the last 4 wk prior to calving. Following calving, one half the cows from each group were fed diets containing 1.67 (High - H) or 1.74 Mcal NEl/kg DM (Low - L) for the fi rst 3 wk a er calving. A er that, all cows were fed H. The experiment was designed to determine how best to transi on cows from far-off dry cow diets to a high energy lacta on diet.

Figure 2. Milk yield of cows fed diet containing 1.67 (High - H) or 1.74 Mcal NEl/kg DM (Low - L) for the fi rst 3 wk a er calving. A er that, all cows were fed H (Rabelo et al., 2003).

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Figure 2 shows the milk produc on results. There was no eff ect of prepartum treatment and there was no interac on between prepartum treatment and postpar-tum treatment. There was no main eff ect of postpartum treatment, but Figure 2 clearly shows the postpartum treatment by me interac on (P < 0.001). There was a divergence of curves un l 3 wk postpartum. At that me, treatments were terminated and the milk produc- on diff erence between treatments was maintained or

was narrowed. For the fi rst 35 d postpartum, cows on H were in a more favorable energy status as indicated by higher plasma glucose concentra ons (49.2 vs. 45.9 mg/dL; P < 0.001) and lower BHBA concentra ons (4.1 vs. 6.3 mg/dL; P < 0.001). There was no eff ect of prepartum diet on triglyceride (TG) accumula on in the liver at calv-ing; however, cows fed H postpartum had lower liver TG at the end of the 3 wk treatment period (11.1 vs. 15.6 ug TG/ug DNA; P = 0.07). By 35 d postpartum, liver TG was lower and there was no diff erence between treat-ments (4.2 vs. 4.7 ug TG/ug DNA; P = 0.84). However, it must be kept in mind that cows were on the same diet between 21 and 35 d postpartum. More importantly, energy balance should have been improving during this me; clearly TG was being cleared from the liver from

21 to 35 d postpartum.

Addi onal research is needed to determine the most appropriate feeding strategies of cows immediately postpartum. Emphasizing feeding NFC at the expense of NDF may reduce the likelihood of fa y liver and ketosis, but increase the likelihood of acidosis and dis-placed abomasum. Conversely, emphasizing feeding of NDF at the expense of NFC may decrease the likelihood of acidosis and displaced abomasum but increase the likelihood of fa y liver and ketosis. Unfortunately, it is too simplis c to recommend NFC and NDF levels in pos resh diets. NFC and NDF are heterogeneous frac- ons whose characteris cs vary greatly among feeds.

Rates of fermenta on of NFC/NDF are not consistent across feeds. Likewise, factors such as par cle length of NDF-rich feeds are variable and infl uence the rumen environment and rates of diges on. Unfortunately, inadequate informa on is available at this me to make precise feeding recommenda ons for cows dur-ing the fi rst 3 wk postpartum. This should be a major emphasis of research going forward, but inadequate herd sizes at most universi es make the appropriate studies nearly impossible to conduct. Research on commercial farms with large animal numbers should be considered, but conduc ng these types of studies can be problema c as well.

Protein

As is the case with energy, protein intake during the fi rst 3 wk postcalving may be insuffi cient to meet require-ments for milk produc on due to low feed intake. The cow responds to this by mobilizing reserves. However, in contrast to energy, the density of protein in the diet can be increased to reduce metabolic stress associated with mobilizing reserves. Unfortunately, there is li le research on which to base protein recommenda ons for pos resh transi on cows. Most studies examining protein or amino acid supplementa on of early lacta- on cows have started treatments beyond the post-

fresh transi on period. In several studies treatments started prepartum and con nued postpartum. Due to the poten al nega ve eff ects of overfeeding protein prepartum, interpreta on of results from these stud-ies is diffi cult because any posi ve eff ects of increasing protein feeding postpartum may have been negated by feeding addi onal protein prepartum. For example, So-cha and co-workers (2005) did not observe a response to increasing dietary CP from 16 to 18.5% immediately a er calving; however, the prepartum diet contained 15.6% CP. In contrast, Wu and co-workers (1997) observed a 10 lb/d increase in milk produc on, but only when cows came off a prepartum diet that was low in rumen unde-gradable protein (14% CP diets with 33.6 vs. 41.4% of CP as rumen undegradable protein). Despite the paucity of research data examining protein feeding during the fi rst 3 wk postpartum, a strong case can be made for not shortchanging cows on protein or amino acids during this period. Figure 3 shows the poten al for a nega ve MP balance for cows producing 60 or 100 lbs milk/d at 7 or 21 d postpartum. Two diff erent postpartum diets were evaluated using the NRC (2001), one with 15.7% CP and the other with 17% CP. Rumen degradable pro-tein (RDP) content of the diets was adequate. The NRC (2001) allowed es ma on of DMI and MP balance. A few important points: Assuming NRC predic ons are correct, diets will not provide suffi cient MP. As me postpartum increases, cows will gradually achieve MP balance due to increased feed and protein intake rela ve to protein requirements. But, prior to that, cows are likely to be in a nega ve MP balance and the likelihood is greater with higher levels of produc on. Consequently, the cow will either mobilize protein to support lacta on or milk produc on will be limited and below the inputs (60 or 100 lbs) used for this simula on using NRC (2001).

The concept of supplemen ng ruminally protected ami-no acids to improve MP balance and quality and reduce dietary CP should be as applicable to transi on cows as

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those later in lacta on. Ordway et al. (2009) fed heifers and mature cows a basal prepartum diet beginning 21-d prior to expected calving date containing 13.8% CP (diets averaged 1,200 g/d of MP with an average MP-balance of 313 g/d according to NRC (2001) predic ons) with ei-ther no addi onal rumen-protected methionine supple-menta on (Control) or with addi onal MP-Methionine supplied by MetaSmart or Smartamine M in amounts required to generate a 3 to 1 ra o of MP-Lysine to MP-Methionine. These same dietary treatments were con nued through 140-d postpartum with the basal diet containing 16.4% CP (diets averaged 2,400 g/d MP with an average MP-balance of -145 g/d according to NRC (2001) predic ons). The authors observed a linear response in milk protein concentra on with the addi- onal MP-Methionine sugges ng that cows did benefi t

from an improvement in amino acid supply as the ra o of MP-Lysine to MP-Methionine was improved to a 3:1 ra o, even on a rela vely low CP ra on.

Socha et al. (2005) observed that supplemen ng ru-men protected (RP)-Methionine and RP-Methionine + Lysine to cows receiving a basal diet containing 15.6%

CP beginning 14-d prepartum and con nuing on their respec ve amino acid treatments for 105-d postpar-tum when receiving either 16 and 18.5% CP diets was benefi cial. These authors concluded that there was no diff erence between RP amino acid supplemented cows receiving a 16 or 18.5% CP diet and numerically, the RP amino acid supplemented cows on the 16% CP diet consumed more DM, produced greater amounts of energy-corrected milk, and were more effi cient at conver ng dietary N into milk N than cows on the 18.5% CP diet which may indicate that the 16% CP diet was similar and perhaps superior in nutri ve content to the 18.5% CP diet. Interes ngly, these researchers in-creased the CP content from 16 to 18.5% by increasing the RDP frac on of the ra on rather than the RUP frac- on and concluded that this may have been the reason

for the lack of diff erence between the diets. Indeed, the authors of this paper have rou nely observed that these dietary diff erences are quite common on com-mercial dairy farms , i.e., diets containing higher levels of CP (e.g., > 17.5% CP) contain higher levels of RDP than lower CP diets (e.g., < 17.5% CP) probably because RDP sources have historically been less expensive than

Figure 3. Es mates (NRC, 2001) of poten al defi ciencies in MP when feeding a 15.7 or 17 percent CP diet to cows producing 60 or 100 lbs of milk. Dry ma er intakes of cows were es mated according to NRC (2001).

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2011 Penn State Dairy Cattle Nutrition Workshop 99

RUP sources. Given the current and, most likely, future high economical and environmental costs associated with all protein sources (both RDP and RUP), the re-sults of Ordway et al. (2009) and Socha et al. (2005) are suppor ve of the concept of supplemen ng both RP-Lysine and RP-Methionine in transi on cow diets to lower CP levels without sacrifi cing produc on or metabolic health.

Due to cost of protein supplements and environmental concerns with overfeeding protein, there is increasing pressure to scale back the percentage of CP in dairy diets. More research is needed, but nutri onists should carefully consider formula ng diets for the pos resh transi on pens that are of higher amino acid quality rela ve to other stage of lacta on diets. They should concentrate on providing suffi cient amounts of RDP and fermentable carbohydrates to s mulate micro-bial protein produc on and improve the quality of the RUP by providing highly diges ble sources of RUP and supplemen ng rumen-protected amino acids, such as lysine and methionine. The concept of providing limit-ing amino acids is probably most applicable to the cow immediately postpartum, par cularly if there is any tempta on to feed lower protein diets.

CONCLUSION

While much s ll remains to be uncovered regarding the most eff ec ve way to feed transi on dairy cows, research indicates that there is no one best approach for feeding prefresh transi on cows. In the fi eld, many dif-ferent nutri onal strategies for prefresh transi on cows have been demonstrated to be eff ec ve or ineff ec ve in maximizing produc on and health of the pos resh cow depending on environmental condi ons and feed man-agement on the farm. Minimizing extent and dura on of nutrient defi cits postcalving does appear to be cri cal to achieve op mal produc on, health, and reproduc on. Research to specifi cally iden fy feeding strategies for pos resh transi on cows (0 to 3 wk postpartum) that minimize nutrient defi cits is desperately needed.

REFERENCES

Brixy, J. D. 2005. Valida on of a predic on equa on for energy balance in Holstein cows and heifers. M. S. Thesis. University of Idaho, Moscow.

Doepel, L., H. Lapierre, and J. J. Kennelly. 2002. Peripartum performance and metabolism of dairy cows in response to prepartum energy and protein intake. J. Dairy Sci. 85:2315-2334.

Grummer, R. R., and R. R. Rastani. 2003. When should lactat-ing dairy cows reach posi ve balance? Prof. Anim. Scien st. 19:197-203.

Guo, J., R. R. Peters, and R. A. Kohn. 2007. Eff ect of a transi- on diet on produc on and performance and metabolism

of periparturient dairy cows. J. Dairy Sci. 90:5247-5258.

Holcomb, C. S., H. H. Van Horn, H. H. Head, M. B. Hall, and C. J. Wilcox. 2001. Eff ects of prepartum dry ma er intake and forage percentage on postpartum performance of lacta ng dairy cows. J. Dairy Sci. 84:2051-2058.

Kamiya, Y., M. Kamiya, and M. Tanaka. 2006. Eff ects of forage to concentrate ra o in prepartum diet on dry ma er intake and milk yield of periparturient cows during hot weather. An. Sci. J. 77:63-70

Keady, T. W. J., C. S. Mayne, D. A. Fitzpatrick, and M. A. McCoy. 2001. Eff ect of concentrate feed level in late gesta on on subsequent milk yield, milk composi on, and fer lity of dairy cows. J. Dairy Sci. 84:1468-1479.

Mashek, D. G. and D. K. Beede. 2000. Peripartum responses of dairy cows to par al subs tu on of corn silage with corn grain in diets fed during the late dry period. J. Dairy Sci. 83:2310-2318.

Minor, D. J., S. L. Trower, B. D. Strang, R. D. Shaver, and R. R. Grummer. 1998. Eff ects of nonfi ber carbohydrate and niacin on periparturient metabolic status and lacta on of dairy cows. J. Dairy Sci. 80:189-200.

Na onal Research Council. 2001. Nutrient Requirements of Dairy Ca le, 7th rev. ed. Washington, D. C.: Na onal Academy Press.

Ordway, R. S., S. E. Boucher, N. L. Whitehouse, C. G. Schwab, and B. K. Sloan. 2009. Eff ects of providing two forms of supplemental methionine to periparturient Holstein dairy cows on feed intake and lacta onal performance. J. Dairy Sci. 92:5154-5166.

Putnam, D. E., and G. A. Varga. 1998. Protein density and its infl uence on metabolite concentra on and nitrogen reten on by Holstein cows in late gesta on. J. Dairy Sci. 81:1608-1618.

Rabelo, E., R. L. Rezende, S. J. Ber cs, and R. R. Grummer. 2003. Eff ects of transi on diets varying in dietary energy density on lacta on performance and ruminal parameters of dairy cows. J. Dairy Sci. 86:916-925.

Rabelo, E., R. L. Rezende, S. J. Ber cs, and R. R. Grummer. 2005. Eff ects of pre- and pos resh transi on diets varying in dietary energy density on metabolic status of periparturi-ent dairy cows. J. Dairy Sci. 88:4375-4383.

Roche, J. R., J. K. Kay, C. V. C. Phyn, S. Meier, J. M. Lee, and C. R. Burke. 2010. Dietary structural to nonfi ber carbohydrate concentra on during the transi on period in grazing dairy cows. J. Dairy Sci. 93:3671-3683.

Smith, K. L., M. R. Waldron, J. K. Drackley, M. T. Socha, and T.

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R. Overton. 2005. Performance of dairy cows as aff ected by prepartum dietary carbohydrate source and supplemen-ta on with chromium throughout the transi on period. J. Dairy Sci. 88:255-263.

Socha, M. T., D. E. Putnam, B. D. Garthwaite, N. L. Whitehouse, N. A. Kierstead, C. G. Schwab, G. A. Ducharme, and J. C. Robert. 2005. Improving Intes nal amino acid supply of pre- and postpartum dairy cows with rumen-protected methionine and lysine. J. Dairy Sci. 88:1113-1126.

Strang, B. D., Ber cs, S. J., R. R. Grummer, and L. E. Armenatno. 1998. Eff ect of long-chain fa y acids on triglyceride ac-cumula on, gluconeogenesis, and ureagenesis in bovine hepatocytes. J. Dairy Sci. 81:728-739.

VandeHaar, M. J., and S. S. Donkin. 1999. Protein nutri on of dry cows. Proc. Tri-State Dairy Nutr. Conf. M. L. Eastridge, ed. April 20, Ft Wayne, IN, pages 113-130. The Ohio State University, Columbus.

Wu, Z., R.J. Fisher, C. E. Polan, and C. G. Schwab. 1997. Lac-ta onal performance of cows fed low or high ruminally undegradable protein prepartum and supplemental me-thionine and lysine postpartum. J. Dairy Sci. 80:722-729.

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SUMMARY

The interac on between facili es and management rou nes determine the physical and social environment within which a dairy cow must perform. Dairy cows have environmental requirements to support natural behaviors, well-being, op mal produc vity, fer lity, and health. Dairy cows have a behavioral me budget that must be sa sfi ed. The 24-hour me budget refl ects the net behavioral response of a cow to environmen-tal inputs. Devia ons from benchmarked behavioral rou nes represent departures from natural behavior and serve as a basis for es ma ng performance and economic losses due to poor facility or management environment. The housing environment must provide each cow with unhindered access to feed, water, and a comfortable res ng area. Major components of the cow’s environment that a dairy producer may control in-clude: ven la on and air quality, temperature-humidity index, photoperiod and light intensity, stall design and maintenance, space available per cow, feeding area de-sign and management, fl ooring trac on and compress-ibility, grouping strategies that build self-confi dence, stocking density of stalls, low-stress animal handling and movement, and me spent outside the pen and away from resources.

This paper will focus on the dairy cow’s res ng and feed-ing requirements, their rela onship with produc vity, and how the cow’s management environment aff ects her ability to prac ce these natural behaviors. Nega ve environmental condi ons such as improper grouping strategy and pen moves, overstocking stalls and manger space, and poor me budge ng compromise natural res ng, feeding, and rumina on behaviors. Spread-sheets are available to assess the impact of environment on cow behavior and milk produc on. A long-term goal is to incorporate environmental inputs into ra on formula on models to be er predict dynamic cow behavior, feed intake pa erns, ruminal fermenta on, and milk component output.

DAIRY COW BEHAVIORAL TIME BUDGETS

Essen ally, the 24-h me budget represents the net behavioral response of a cow to her environment. The func onal environment comprises the housing facility and how the cows are managed within the facility. Even a well-designed facility with poor management such as overcrowding will result in unnatural behaviors and poor produc vity and health. Devia ons in a dairy herd from benchmarked behavioral rou nes represent departures from desired natural behavior and serve as a basis for es- ma ng the performance and economic loss due to poor

management strategies. A spreadsheet is available at the Miner Ins tute web site to analyze the me budget of dairy cows housed in free-stall barns (www.whminer.org).

A simplifi ed daily me budget (Grant and Albright, 2000; 2001) for lacta ng Holstein dairy ca le housed in a free-stall environment is: 1) ea ng: 3 to 5 hours/day (9 to 14 meals/day), 2) res ng (lying): 12 to 14 hours/day, 3) social interac ons: 2 to 3 hours/day, 4) rumina ng: 7 to 10 hours/day (both standing and lying, 5) drinking: 0.5 hours/day, and 6) me spent outside the pen for travel to and from the parlor, milking, and other management prac ces: 2.5 to 3.5 hours/day. It is clear that, since there are only 24 hours in a day, a management envi-ronment that keeps a cow outside the pen for greater than 3.5 hours/day forces the cow to sacrifi ce some behavior – inevitably res ng or feeding me. Cows must perform these required behavioral ac vi es each day, and we cannot allow our management rou nes to interfere. Improper grouping strategies that result in overcrowding, commingling of primi- and mul pa-rous cows, excessive me in parlor holding pens, and greater than one hour daily in headlocks are common ways of disturbing the me budget and reducing herd produc vity, fer lity, and health.

NATURAL RESTING AND FEEDING BEHAVIORS

OF DAIRY CATTLE

An environment that allows natural res ng and feeding

Current Concepts in Time Budgeting for Dairy Cattle

Rick Grant

William H. Miner Agricultural Research Institute

1034 Miner Farm Road

Chazy, NY 12921

Phone: 518-846-7121 x116 Fax: 518-846-8445

Email: [email protected]

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102 November 8-10 Grantville, PA

behavior forms the founda on of dairy cow well-being and op mal performance.

Cows Have a Strong Behavioral

Requirement for Rest

Jensen et al. (2004) demonstrated that cows have a very strong mo va on to rest, and that this mo va-tion to rest increases as the length of deprivation becomes greater. In fact, lying behavior has a high priority for ca le a er even rela vely short periods of lying depriva on (Munksgaard et al., 2005). Cows will sacrifi ce feeding in an eff ort to recoup lost res ng me. Environmental factors that interfere with res ng o en reduce feeding behavior and vice versa. Metz (1985) evaluated cow response when access to either stalls or feed manger was prohibited. Cows a empted to maintain a fi xed amount of lying me, and their well-being was impaired when lying me was restricted for several hours daily. An addi onal 1.5 hours/day stand-ing me was associated with a 45-minute reduc on in feeding me. A similar rela onship was observed by Batchelder (2000) where cows experiencing a stocking density of 130% of stalls and headlocks preferred lying in free-stalls rather than ea ng post-milking and spent more me in the alley wai ng to lie down rather than ea ng. Benefi ts of res ng include: poten ally greater milk synthesis due to greater blood fl ow through the udder, greater blood fl ow to the gravid uterus, increased rumina on eff ec veness, less stress on the hoof and lameness, less fa gue stress, and greater feed intake. Grant (2004) has proposed that each addi onal one hour of res ng me translates into 0.9 to 1.5 kilograms more milk per cow daily. We need to manage the res ng environment to ensure access to comfortable stalls for 12 to 14 hours daily.

Cows Have a Naturally Aggressive Feeding Drive

Natural feeding behavior of lacta ng dairy cows was characterized by Dado and Allen (1994) as higher pro-ducing, older cows that eat more feed, eat larger meals more quickly, ruminate more effi ciently, and drink water more quickly than lower producing, younger cows. Some compe on for feed is inevitable with dairy cows; even with unlimited access to feed, cows interact in ways that give some cows an advantage over others (Oloff son, 1999). Hansen and Pallesen (1998) documented the cow’s naturally aggressive feeding drive by measuring the force applied to the feed barrier during ea ng. Cows willingly exert greater than 225 kg of force against the feed barrier in an a empt to reach as much feed in the

manger as possible. Pressure in excess of 100 kg is suf-fi cient to cause ssue damage. We need to manage the feeding environment such that the cow will not exert these amounts of force against the feed barrier while ea ng thereby resul ng in reduced feed consump on. Delivery of fresh total mixed ra on twice daily, feed avail-ability a er milking, ming feed push-ups to avoid ex-cessive pressure, lower stocking rate, separate grouping of primi- and mul parous cows, and any environmental factor that reduces compe on and displacements will enhance feeding me at the manger. Recent research from Europe showed that cows ruminate more when fed their diet three mes daily versus only once daily.

GROUPING STRATEGIES, SOCIAL

ENVIRONMENT, AND COW RESPONSES

Boe and Faerevik (2003) reviewed social and perfor-mance responses of calves, heifers, and mature cows to changes in their environment. Conven onally, we assume that 1) cows fi ght to establish social hierarchy, 2) fi gh ng stops once hierarchy is established, 3) domi-nant cows regulate access to the resources, 4) group size should not exceed number of cows an individual can recognize, 5) dominance hierarchy is rapidly estab-lished, and 6) the hierarchy is stable. Contrast this sta c depic on of group interac ons with a more dynamic and likely realis c scenario: 1) con nual and fl uctuat-ing levels of aggression, 2) forma on of subgroups within larger pens, 3) inability to recognize all peers when group size exceeds approximately 100 cows, 4) some individuals thrive, not by winning fi ghts, but by not par cipa ng, and 5) stable hierarchy formed within 2 days for cows with previous social experience and within 4 days for cows with no previous experience, and 6) posi on in the pen social structure is a func on of mo va on for the resource. These basic a ributes of group dynamics infl uence the response of cows to any set of environmental circumstances.

Social stability within a group of ca le is defi ned as the point when nonphysical agonis c interac ons among group members predominate, and the ra o of physical to nonphysical interac ons remains compara vely stable with me (Kondo and Hurnik, 1990). Social behaviors and locomotor ac vity return to baseline level within 5 to 15 d following a grouping change such as regrouping or commingling (Boe and Faerevik, 2003). For the fi rst 1 to 2 days following regrouping, displacements from the feed manger increase up to 2.5 mes, feeding rate increases up to 50%, lying bouts and me decrease by 15 to 20%, grooming decreases 5-fold, and milk yield

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2011 Penn State Dairy Cattle Nutrition Workshop 103

decreases up to 3 kg/day. We need to manage grouped cows such that rate of decline in physical interac ons occurs rapidly and the period of social stability is maxi-mized. Realis cally, animals move into and out of pens con nuously on many farms, and the challenge becomes managing the magnitude of increase in physical interac- ons that accompany regrouping and introduc on of

new animals into a pen. Cri cal examples are the close-up pen with weekly group changes and the fresh pen with a constantly changing social environment. Herds with greater than two pen moves during the transi on period experience twice as many abomasal displace-ments as herds with 2 or fewer pen moves.

Grouping of Primi- and Multiparous Cows

A common recommenda on is to group primi- and mul parous cows separately. Research confi rms that primiparous cows penned separately have 10 to 15% greater feed intake, 20% greater res ng me, and ap-proximately 10% higher milk produc on than those in mixed groups (summarized in Grant and Albright, 2000). Since primiparous cows are smaller and less experienced, they o en have diffi culty compe ng with mature cows for feed, water, and stalls. Important natural behavioral diff erences exist between primi- and mul parous cows. Younger cows take smaller bites and consume feed more slowly than mature cows. Domi-nant mature cows may displace younger cows from the manger, alter their me of feeding, or encourage faster feeding rates; separate grouping helps to ensure that heifers have enough me to feed throughout the day. Heifers housed separately also experience less body weight loss and greater effi ciency of fat-corrected milk produc on during the fi rst 30 days of lacta on (Bach et al., 2006), greater rumina on and drinking me, and higher milk fat percentage.

Res ng behavior is aff ected by grouping environment. Cows do not perceive all stalls equally; for instance domi-nant cows prefer stalls nearest the feed manger. Subordi-nate cows avoid lying in preferred stalls and will ruminate up to 40% less when forced to lie in stalls preferred by dominant cows (Grant, 2008, unpublished). It is possible that subordinate cows experience stocking densi es considerably higher than the simple ra o of cows to stalls.

STOCKING DENSITY, SOCIAL ENVIRONMENT,

AND COW RESPONSES

Overstocking reduces the cow’s ability to practice natural behavior, but it also improves the economic

return on facility investments and consequently is a common limita on in the cow’s environment. Research on stocking density (based on stalls primarily) indicates that at ~115 to 120% stocking density and beyond, rest-ing me is reduced by 12 to 27% and idle standing in alleys increases. The nega ve response in res ng is a func on of pen size with greater reduc ons in res ng observed for larger pens. We also need to understand more about the poten al diff erences in the responses to stocking rate based on type of housing such as 4- or 6-row free-stall barns. Ea ng rate is o en increased at higher stocking densi es and meal mes are redis-tributed later in the day, par cularly for subordinate cows. Rumina on may be reduced by as much as 25% at 130% stocking density (summarized in Grant, 2004). However, the goal is not elimina on of compe on, but management of compe on in the cow’s environment.

Stocking Density during the Transition Period

Crea ng the right environment for the transi on cow encourages natural feeding and resting behavior, greater feed intake and milk yield, and fewer metabolic disorders. Feeding, res ng, and rumina ng ac vity all decrease, and standing me increases sharply, right at parturi on. Management me typically increases from virtually none during the far-off dry period to as much as several hours per day soon a er calving. Elaborate fresh-cow protocols that entail extended me in headlocks will interfere with the cow’s me budget. To avoid disturbing the me budget, cows should not be locked-up for more than one hour con nuously. Cows should not be denied access to feed or stalls for greater than 3.5 hours/day.

Cook and Nordlund (2004) found that when stocking density was greater than 80% of stalls and manger in a pre-fresh group of mixed primi- and mul parous cows, milk yield was reduced for the primiparous cows during the fi rst 83 days in milk. For each 10% increase in pre-fresh stocking density above 80%, there was a 0.7 kg per day reduc on in milk yield for the primiparous cows. When headlock stocking density exceeds approximately 90% in the close-up pen, or manger space is less than 61 cm/cow, feed intake is markedly reduced. Addi onally, a strong posi ve rela onship exists between headlock stocking density greater than 90% in the close-up pen and higher incidence of abomasal displacements a er calving. Recent research at University of Bri sh Colum-bia and Miner Ins tute found that overstocking the feed bin increased the rate of ea ng for healthy, mul parous fresh cows. Stocking density greater than 80 to 90% of manger space in the pre-fresh and fresh pens results

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104 November 8-10 Grantville, PA

in lost milk produc on and greater fresh-cow health problems, especially for subordinate cows. The close-up and fresh cow is exquisitely sensi ve to environmental constraints such as overstocking, limited stall or manger availability, and excessive compe on.

Stocking Density for Cows beyond the Fresh Period

A study at Miner Ins tute evaluated the eff ect of 100, 113, 131, or 142% stocking density of stalls and manger space in a 4-row barn on behavior and produc on (Hill et al., 2006). The stocking densi es were obtained by chaining off stalls or closing headlocks. Consequently, alley space and social structure remained constant, which may have mitigated the negative effects of higher stocking densi es. Lying me was reduced by 1.1 hours/day when stocking density increased from 100 to 142%. At the same me, milk yield decreased from 43.0 to 41.5 kg/day. We have observed similar responses when stocking density was manipulated by adding cows to an exis ng pen in a recent study to be er mimic on-farm environments. As stocking rate increased, idle standing me in alleys increased and me spent rumina ng while lying decreased. In par- cular, idle standing increased 3-fold between 100 and

142% stocking density between midnight and 0400 h when cows ordinarily would be res ng in stalls. Ea ng rate increased with greater stocking density, and milk fat percentage was depressed while soma c cell count increased. A recently completed study at Miner Ins tute (Krawczel, 2008, unpublished) confi rmed the increase in mas s risk with higher stocking density even for cows of similar hygiene score.

A diff eren al response was observed between primi- and mul parous cows and lame versus sound cows. As stocking density increased, the diff erence in milk yield between younger and older cows grew from 2.7 kg/day at 100% stocking rate up to nearly 6.8 kg/day at stocking rates beyond 113%. Similarly, as stocking rate increased beyond 113%, the milk yield of lame cows was markedly reduced compared with sound cows. From 100 up to 131% stocking rate, the diff er-ence between sound and lame cows in milk yield rose to nearly 11.8 kg/day. The reduc ons observed in milk yield for primiparous and lame cows in mixed groups refl ected reduc ons in res ng and rumina on ac vity. Even though it is o en diffi cult to manage separate pens of primiparous cows and lame cows, we need to understand the poten al magnitude of compromised well-being and lost milk produc on when these cows are commingled with older and healthier cows.

In summary, when signifi cant overstocking is a com-ponent of the cow’s environment, we may observe: 1) altered feeding behavior and greater aggression and displacements at the manger, 2) reduced res ng me and greater idle standing in the alley, 3) decreased ru-mina on, 4) poten ally less milk produc on and lower milk fat percentage, 5) poten ally higher soma c cell count at similar hygiene score, 6) greater metabolic problems, and 7) reduced fer lity. Primiparous cows, lame cows, and other subordinate cows are most nega- vely aff ected by overstocking. The stocking density

that op mizes produc vity and economic returns will vary by farm, but it will likely be less than 120% for 4-row barns and closer to 100% for 6-row barns due to the added stocking rate of the feed manger. Express-ing stocking rate as a func on of stalls appears to be most appropriate for lacta ng and dry cows, except for transi on cows for which stocking density appears best expressed as a func on of manger space.

CONCLUSIONS

The cow’s social and physical environment comprises the housing facili es and management rou nes. The term “cow environment” o en refers solely to temperature-humidity index, ven la on and air movement, air quality, and photoperiod or light intensity issues. But, we cannot neglect the “social” environmental aspects that directly infl uence natural cow behavior such as me budge ng, grouping strategy, cow movement and handling, and stocking density. The res ng environment needs to allow approximately 12 hours/day of lying in a comfortable bed. The feeding environment needs to allow up to 5 hours/day of unhindered access to fresh ra on with minimal aggression, displacements, and poten al for rapid feed consump on or sor ng. Grouping and cow movement strategies are components of the environ-ment that substan ally impact natural behavior and performance. Stocking rate should not generally exceed ~120%, and should remain less than ~80% for transi on cow pens. Many management factors, in addi on to the ones discussed here, comprise the cow’s environment. The environment modulates predicted output (primarily feed intake and milk yield) based on physiological inputs in models of cow performance. Dynamic models in the future need to incorporate key components of the cow’s environment to most accurately predict feed intake, ru-minal condi ons, and milk output (Grant, 2004).

REFERENCES

Bach, A., C. Iglesias, and I. Busto. 2006. A computerized sys-tem for monitoring feeding behavior and individual feed

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2011 Penn State Dairy Cattle Nutrition Workshop 105

intake of dairy ca le in loose-housed condi ons. J. Dairy Sci. 87:358(Abstr.)

Batchelder, T.L. 2000. The impact of head gates and over-crowding on produc on and behavior pa erns of lacta ng dairy cows. In Dairy Housing and Equipment Systems. Man-aging and Planning for Profi tability. Natural Resource, Agri-culture, and Engineering Service Publ. 129. Camp Hill, PA.

Boe, K.E., and G. Faerevik. 2003. Grouping and social prefer-ences in calves, heifers, and cows. Appl. Anim. Behav. Sci. 80:175-190.

Cook, N.B., and K.V. Nordlund. 2004. Behavioral needs of the transi on cow and considera ons for special needs facility design. Vet Clin. Food Anim. 20:495-520.

Dado, R.G., and M.S. Allen. 1994. Varia on in and rela on-ships among feeding, chewing, and drinking variables for lacta ng dairy cows. J. Dairy Sci. 77:132-144.

Grant, R.J. 2004. Incorpora ng dairy cow behavior into man-agement tools. In Proc. 2004 Cornell Nutr. Conf. For Feed Manufac. October 21-23. Cornell University. Wyndham Syracuse Hotel. Syracuse, NY.

Grant, R.J., and J.L. Albright. 2000. Feeding behaviour. In Farm Animal Metabolism and Nutri on. J.P.F. D’Mello, ed. CABI Publishing. New York, NY.

Grant, R.J., and J.L. Albright. 2001. Eff ect of animal grouping on feeding behavior and intake of dairy ca le. J. Dairy Sci. 84:E156-E163.

Hansen, K., and C.N. Pallesen. 1998. Dairy cow pressure on self-locking feed barriers at diff erent posi ons. In Proc. Fourth Intl. Dairy Housing Conf. Jan 28-30, 1998. American Soc. Agric. Engin. St. Louis, MO.

Hill, C.T., R.J. Grant, H.M. Dann, C.S. Ballard, and R.C. Hovey. 2006. The eff ect of stocking rate, parity, and lameness on the short-term behavior of dairy ca le. J. Dairy Sci. 89 (Suppl. 1):304-305.

Jensen, M.B., L.J. Pedersen, and L. Munksgaard. 2005. The ef-fect of reward dura on on demand func ons for rest in dairy heifers and lying requirements as measured by demand func ons. Appl. Anim. Behav. Sci. 90:207-217.

Kondo, S., and J.F. Hurnik. 1990. Stabiliza on of social hier-archy in dairy cows. Appl. Anim. Behav. Sci. 27:287-297.

Metz, J.H.M. 1985. The reac on of cows to short-term depriva- on of lying. Appl. Anim. Behav. Sci. 13:310.

Munksgaard, L., M.B. Jensen, L.J. Pedersen, S.W. Hansen, and L. Ma hews. 2005. Quan fying behavioural priori es – ef-fects of me constraints on behaviour of dairy cows, Bos taurus. Appl. Anim. Behav. Sci. 92:3-14.

Olofsson, J. 1999. Compe on for total mixed diets fed for ad libitum intake using one or four cows per feeding sta on. J. Dairy Sci. 82:69-79.

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Where Does the Time Go?Time Budgets, Cow

Comfort, and Stocking Density

Rick GrantW. H. Miner Agricultural Research Institute

Chazy, NY

Cow Comfort and Management Environment: “The Big

Picture”

Physical Environment Social Environment

Resting

Ruminating

FeedingMeals

Meal lengthEating rate

Feed Intake, Productivity & Health

Gut Fill

ChemostaticMechanisms

Control

Modulation

Feeding Environment

Will this management environment affect response

to diet?

Management environment and herd performance (Bach et al., 2008)

47 herds with similar genetics were fed same TMRMilk yield varied by ±29 lb/d

Mean milk yield=65 lb/d

Non-dietary factors accounted for 56%of variation in milk yield

Feeding for refusals (64.1 vs 60.6 lb/d)Feed push-ups (63.7 vs 55 lb/d)Stalls per cow

Stalls per cow and milk production in 47 herds fed same TMR (Bach et al., 2008)

R2=0.32

Milk yield = 20.4 + (7.5 x stall/cow)

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2011 Penn State Dairy Cattle Nutrition Workshop 107

Typical time budget of dairy cow (free-stall environment)

5.0 h/d eating12-14 h/d lying (resting)2.0-3.0 h/d standing, walking, grooming, agonistic, idling0.5 h/d drinking20.5 to 21.5 h/d total needed

2.5 to 3.5 h “milking” = 24 h/d

Lame cows Healthy cows(Grant, 2004)

Time outside the pen, lying time, & lameness (Gomez and Cook, 2010)

11-12 h/d

11-12 h/d

Average of 53% of northeastern US dairy cows are clinically lame (NOVUS, 2010)

Common ways to disturb time budget (increase cost of production) on-farm …

Excessive time outside penMixing of primi- and multiparous cowsOvercrowding, excessive competition>1 h/d in headlocks, esp. fresh cowsShort pen stays during transition – social turmoilInadequate exercise – tie stallsUncomfortable stallsInadequate feed availability

Time away from pen and cow response: do time budgets really matter?

3 vs 6 h/d outside pen

Adjusted pen size versus parlor capacityMixed primi- and multiparous cows100% stocking density14-d periods

(Matzke, 2003)

Extra $1.00 to $1.50 per cow/d

Time Budget Behaviors:Primi- versus Multiparous Cows

Numerous natural behavioral differencesHeifers take smaller bites, eat more slowly, spend more time feedingHeifers typically less dominant, more easily displaced from manger, stalls, and waterHeifers avoid stalls previously occupied by dominant cows and ruminate up to 40% less

Rumination by primiparous cows in preferred/less preferred stalls(Krawczel, 2007)

PreferredLess

preferredP value

Ruminationtime, min/d

81.4 147.8 0.09

% resting time spentruminating

35.2 58.4 0.05

Possible long-term consequences?

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108 November 8-10 Grantville, PA

Effect of competition with older cows on first-calf heifers . . .

Environments of ~100% stocking density:

DMI reduced by 10%

Resting reduced by 20%

Milk reduced by 9% (Kongaard and Krohn, 1980)

Greater loss of BW by 30 DIM

Reduced FCM/DMI by 30 DIM (Bach et al., 2006)

Less drinking, rumination, and milk fat %(Bach et al., 2007)

Primi- versus multiparous cows(Hill et al., 2008)

100% 113% 131% 142%Multi - primiMilk, lb/d +5.9 +13.8 +21.1 +14.9

Milk losses reflect reductions in resting andrumination activityUp to $3.00 lost income per cow/d!$1.58 lost income at only 113% stocking rate

Cows have strong behavioral need to rest …

Cows spend less time eating both during & after periods of lying deprivation (Cooper et al., 2007)

Relationship between lost rest and eating time (Grant et al., 2010):

For every 3.5 minutes of lost rest, cows sacrifice 1 minute of eating

Resting: ~12 h/d“Vitamin R”

Relationship between resting and milk yield (Miner Institute data base)

(Grant, 2005)

Resting time (h)

Milk

yie

ld (l

b/d)

60

70

80

90

100

110

7 10 13 17

y = 49.2 + 3.7 xr2 = 0.31

~3.7 lb/dmore milk foreach extra hour

2 to 3.5 lb/cow

Increased resting time with greater DIM, milk yield (Bach et al., 2010)

Stall surface, resting, and milk yield (Calamari et al., 2009)

Reduction in milk during last 3 wk11.6 lb/d actual3.2 h/d less resting time predicts ~11.8 lb/d less milk

(3.2 h/d x 3.7 lb)

Stall Softness and 305-d Milk Production (lb/cow; Ruud et al., 2010)

ParityConcrete

(1)Rubber

(2)Soft

Mat (3)Multi-layer

mat (4)

Mattress(5)

1 13,338a 13,369a 13,572b 14,106d 13,746c

2 15,255b 15,048a 15,649c 16,139e 15,893d

3 16,086a 15,997a 16,498b 16,744c 16,788c

>3 15,767a 15,811a 16,221b 15,943a 16,500d

Mean 14,799b 14,749a 15,149c 15,464e 15,382d

Survey of 1,923 farms in Norway; related to greater lying times

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2011 Penn State Dairy Cattle Nutrition Workshop 109

Stall Width and Milk Yield (Cummins et al., 2005)

Compared: 48 in wide x 66 in curb to brisket locator x 45 in neck-rail height50 in x 70 in x 50 in

Total stall use (CCI): 50% vs 95%

Milk response: 3 lb/d more milkHow much renovation does this buy?Fewer stalls, more milk?

Response to stall renovation: four case studies (Cook, 2010)

Softer beds, larger stallsReasonable cost: farmers did some or all of laborPayback on investment: 0.5 to 3 years (average 1.9 years)Benefits:

Greater milk (3 to 14 lb/d)Lower turnover rates (-6 to -13%)Lower SCC (-37,000 to -102,000)Less lameness (-15 to 20%)

Prevalence of lameness in high producing cows (Espejo and Endres, 2007)

53 high-production pens on 50 dairy farmsGreater lameness prevalence most highly associated with

Greater time outside the pen

Time budgeting!

www.whminer.org

Time Budget Evaluator

What Naturally Stimulates Feeding Behavior?

Delivery of fresh feedFeed push-up

More important during the day rather than at night (DeVries et al., 2005)

Milking

Biggest driver of feeding is delivery of fresh feed

Feeding frequency of TMR

Reference FF/d

Eating time %

DMI%

Milk%

Rest%

DeVries et al. 1 vs 2x2 vs 4x

+3.5+4.6

-2.0-3.0

NRNR

-0.80*

Mantysaari et al. 1 vs 5x + 7.0 -4.8 -1.0 -12.1

Phillips and Rind 1 vs 4x +11.0 -6.3 -4.7 -8.6

Nikkhah et al. 1 vs 4x NS -5.2 -2.5 NS

*17% decrease in latency to lie down

Greater FF may improve rumen fermentation, rumination time, and eating time, but often it reduces lying time and DMI

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Stocking Density and Behavioral Responses

Feeding Behavior and Cow Comfort

Number of meals x Meal length x Rate of eating = Dry matter intake

Goal: increase number of meals

Limited access to feed, excessive competition leads to fewer meals, greater rate of eating

Chronic situation: poor rumen health, reduced feed efficiency

Stocking Density and Feeding Behavior

As stocking density increases:Greater aggression and displacementsTime of eating shiftedFewer mealsEating rate increasedGreater potential for sortingLargest effect on subordinate cows

Within limits, cows can adjust feeding behavior in response to variable SR

(Grant et al., 2010)

Stocking density and DMI by parity in mixed groups (Grant, 2010)

Interaction between parity and stocking densityComponent of future nutrition models?

MP

PP

Stocking density and relative resting response

(Winkler et al., 2003; Fregonesi et al., 2007; Wierenga and Hopster, 1990; Matzke and Grant, 2002; Hill et al., 2009; Krawczel, 2008; 2009; 2010)

y = -0.003x + 1.30R2 = 0.59

Overstocking reduces resting in stalls: what are cows doing?(activity from midnight to 4:00 am; Hill et al., 2009)

% of cows: 100% 113% 131% 142%Resting 71.1 70.0 63.7 58.7Feeding 11.8 12.6 14.6 15.4Standing in alley 3.9 5.4 8.7 12.6

Cows wasting timeat 142% SD; 1:00 am

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Ruminating Behavior and Management Environment

Physically effective NDF

Overcrowding

-10 to 20%

Mixed Parity Pens

-15%

Excessive Headlock Time

Heat Stress

-10 to 22%

-14%

8 to 9 h/d

Milk quality and stocking density(Hill et al., 2006)

100% 113% 131% 142%

Milk fat % 3.84 3.77 3.77 3.67

Overstocked cows eat faster (+25%), ruminate less (1 h/d less)

Clinical events per 305-d lactation (Krawczel et al., 2008)

overstocked cows experience greater pathogen load in the environment

greater teat end exposureexperience immune

suppression?

Stocking and Reproduction

Data from 153 farms used to identify factors affecting reproduction (Caraviello et al., 2006)

As bunk space in breeding pen decreased from 24 to 12 inches

% of cows pregnant by 150 DIM decreased from 70 to 35%Reduced conception rate (Schefers et al., 2010)

Value of a pregnancy ~$278(DeVries et al., 2007)

What is optimal stocking density?

Close-up and fresh: 80% of bunk space (30 in/cow)

Also a function of stall availability

Lactating cows 4-row barn: don’t exceed 115-120% of stalls

Mixed heifer & older cows: 100%

6-row barn: 100% of stalls?

Ensure access to feed, water, stalls

Adjust your attitude!

Are you a source of comfort or stress for your cows?

Empathy with cattle pain and milk production (Kielland et al., 2010)

Group 1=4.9; Group 2=6.7 on 1 to 10 scale(lower score=greater empathy with cattle pain)

HiEmpathy Low

Empathy

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Where do we go with cow behavior research?Can we improve nutrition models?

Here’s Bottom Line:Herds fed the same diet differ in

milk by ±29 lb/day = Management!

Improve cow environment (physical and social) to optimize time budget behaviors, health, performance, and herd profitability

Thank You . . .

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2011 Penn State Dairy Cattle Nutrition Workshop 113

INTRODUCTION

Over the past decade, the issue of cow comfort and the importance of non-dietary factors on produc ve performance of dairy cows have received increasing a en on. For instance, Bach et al. (2008) showed that 47 herds fed the same TMR and shared similar genet-ics, varied in average milk yield between 45 to 75 lb/cow/day. Among the environmental and management factors iden fi ed as explanatory of this varia on were stocking density and stall maintenance. Increasing pro-duc ve effi ciency is not only a nutri onal problem, but is a mul -factorial one. The cows’ environment must be designed and managed in such a way that allows them to maintain health, well-being, and produc vity.

Novus COWS (Comfort · Oxida ve Balance · Well-being · Sustainability) program brings these factors together and acts as a vehicle for engagement on topics of cow comfort. It is driven by the desire to provide a service to dairy producers, while contribu ng to op mizing animal well-being, produc ve effi ciency, and the sustainability of the industry. Our ul mate goal is to drive change. In order to do so, we must be able to 1) iden fy problems; 2) create mo va on for change; and 3) provide recom-menda ons with prac cal solu ons.

OVERVIEW: WHAT IS C.O.W.S. PROGRAM?

In 2010, Novus Interna onal Inc. partnered with The Uni-versity of Bri sh Columbia’s (UBC) Animal Welfare Program to undertake a na onwide cow comfort benchmarking study. UBC Animal Welfare Program is a globally recognized and respected research group contribu ng to the develop-ment of science-based solu ons to improving dairy ca le welfare. This collabora on resulted in the COWS program, expanding on a project ini ally developed by UBC and piloted on 43 dairies in Bri sh Columbia.

The COWS program assesses individual dairies on sev-eral cow comfort measures (e.g. lying me, lameness,

and hock injuries), and facility and management mea-sures (e.g. stall design, bedding quality, and stocking density). Ini al benchmarks for these measures have been created from data collected on 118 dairies in California, New York, Pennsylvania, Vermont, Texas, and New Mexico (Barrientos et al., 2011). Since then, Novus has commi ed to the implementa on of the program as a service to dairy producers in the United States.

Par cipa ng producers receive individualized reports (Appendix), comparing their data against the bench-marks from that region. This process brings aware-ness to the issues on the topic of cow comfort, while highligh ng opportuni es for improvement. This is done in a confi den al manner, and in terms of several objec ve measures, to encourage producers to par- cipate with no external judgment. The regional and

system-specifi c benchmarks show not only the industry averages but also the poten al for success. In this way, the COWS program off ers an alterna ve approach to on-farm assessment as a knowledge sharing vehicle, rather than a pass-or-fail audi ng scheme. Producers and their advisors are then encouraged to iden fy priority areas and plans of ac on that are specifi c to their dairy. If they decide to make certain changes, they have the opportunity to par cipate in a reassessment to evaluate the eff ec veness of the change. At this early stage of the program, we have already seen promising outcomes where par cipa ng producers have made real changes (some are small and inexpensive, while others are larger scale involving signifi cant investment) that translated into improved cow comfort as well as produc ve performance.

C.O.W.S. ASSESSMENT:

WHAT DO WE MEASURE AND WHY?

The on-farm assessment involves collec ng a number of animal-based measures and facility-based measures, previously developed and tested (Ito, 2009).

Novus C.O.W.S. Program:

On-Farm Assessments to Improve Cow Comfort

K. Ito

Dairy Business Unit

Novus International Inc.

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Animal-based Measures

Lying me. The measuring of lying me is a unique feature of the COWS assessment. Dairy cows are highly mo vated to lie down for up to 12 hours per day (Jen-sen et al., 2005), and lying is a high priority behavior compared with feeding and social contact when op-portuni es to perform these behaviors are restricted (Munksgaard et al., 2005). Lying me, along with the frequency of lying bouts and the dura on of individual lying bouts, has been iden fi ed as a sensi ve measure of stall comfort (Haley et al., 2001).

Tradi onally, stall comfort has been es mated by in-dices based on one- me observa on at a quick walk-through of the barn. For example, the Cow Comfort Index (CCI) is calculated as the propor on of the num-ber of cows lying in a stall out of the total number of cows ‘touching’ a stall (standing fully inside or perching in a stall). However, these indices do not refl ect actual lying me (Ito et al., 2009), and cannot be used as a replacement for this measure.

In the COWS assessment, lying me is measured using electronic data loggers (Ledgerwood et al., 2010). The loggers are a ached to 40 randomly selected cows from the assessment group, and record if the cows are lying or standing at 1-min intervals for 72 consecu ve hours (Ito et al., 2009). Farm average is calculated as the mean of individual daily lying mes (h/d), and is reported with the minimum and maximum lying mes from the group.

Prevalence of lameness. Lameness has been recognized as a serious produc on and welfare issue in the dairy industry for many years. Recent studies have es mated the lameness prevalence in North America to be 25 to 30%, but ranging widely from farm to farm (Cook, 2003; Espejo et al., 2006; Ito et al., 2010). The management strategy for lameness will depend on the extent and severity of the problem on each dairy. For example, a dairy that has 10% lameness and another dairy that has 50% lameness would need diff erent plans of ac- on; similarly, a dairy that has mostly mildly lame cows

would benefi t from a diff erent strategy than a dairy that has many severely lame cows. Therefore, the producer must know what the status of lameness is on their dairy specifi cally, and not act on the industry average.

Despite its importance, lameness detec on has been challenging for dairy producers; as a result lameness is of-ten underes mated (24.6% iden fi ed by trained observer vs. 8.3% es mated by producers; Espejo et al., 2006). As

lameness becomes increasingly more common, abnor-mal locomo on may become normalized, resul ng in the cows showing subtle signs of lameness being perceived as sound. By the me a cow is diagnosed as lame, the damage has already manifested in reduced performance (Green et al., 2002; Garbarino et al., 2004; Bicalho et al., 2008) and compromised welfare (Whay et al., 2003). Gait (or locomo on) scoring, a method that iden fi es subtle behaviors exhibited by lame cows, requires training and addi onal me commitment; however, it can be a valu-able tool for early detec on of lameness.

During the COWS assessment, all cows in the group are gait scored upon exit from the parlor, a er their rou ne milking. Gait scoring categorizes cows on a 5-point scale based on six gait a ributes: back arch, head bob (jerky head movement), tracking up (stride length), joint fl ex-ion (joint s ff ness), asymmetric steps, and reluctance to bear weight (Flower and Weary, 2006) as follows:

1. “Sound” – walks with a smooth and fl uid locomo- on, a fl at back and even steps.

2. “Imperfect gait” – walks with a slightly uneven gait and slight joint s ff ness but with no limp.

3. “Mildly lame” – walks with shortened strides, an arched back and a slight limp.

4. “Moderately lame” – walks with an obvious limp, a severely arched back and a jerky head bob.

5. “Severely lame” – not bearing weight on at least one limb and/or must be vigorously encouraged to stand or move; extremely arched back when standing and walking.

For the purpose of our assessment, cows scored as 1 or 2 are considered ‘not lame’, 3 are ‘mildly lame’, and 4 or 5 are ‘severely lame.’

Prevalence of hock lesions. Inappropriately designed and managed free-stalls o en cause injuries that com-promise cow comfort. Hock injuries are o en caused by rubbing of the leg on abrasive lying surface; in par cular, ma ress or rubber mats with minimal bedding are as-sociated with the highest risk of hock lesions (Weary and Taszkun, 2000; Lombard et al., 2010). Hock condi on of the same 40 cows selected for lying me assessment are scored, on a 3-point scale where 1 = healthy, 2 = hair loss, and 3 = swollen or injured (Lombard et al., 2010).

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Facility-based Measures

Facility design and management prac ces are recorded through an interview with the manager or herdsperson of each dairy, and by direct measurements in the barn where the assessment group of cows is housed. The measures include:

• Stall dimensions (free-stall herds) – length, width, neckrail and brisket board placement

• Bedding type and maintenance• Stocking density• Feedbunk design and management• Milking management – distance to parlor, me

away for milking

These measures, when reported to the producer, serve as guidelines for troubleshoo ng management. For example, if cows are not lying down, there may be several factors that are responsible. Factors that can aff ect lying me include: stall dimensions, type of lying surface, and the quan ty and quality of bedding mate-rial. Cows spend more me lying down on well-bedded stalls compared with poorly bedded ma resses (Tucker et al., 2003; Tucker and Weary, 2004) and on wider stalls with no brisket board (Tucker et al., 2004; 2006). Lying me decreases as the dryness of the bedding material

decreases (Fregonesi et al., 2007b; Reich et al., 2010) and as the stocking density increases (Fregonesi et al., 2007a). Producers can use these measures provided in the report (Appendix) to begin to iden fy risk factors for reduced lying me.

Management factors such as feeding and milking pro-cedures infl uence the me budget of the cow. Cows spend about half of their me lying down, and divide the rest for milking, feeding, and standing (in alleyway or inside stalls) (Gomez and Cook, 2010). The me the cows spend wai ng to get milked or to gain access to feed is the me taken away from what is available for lying down. Therefore, the management protocols must be considered together with the func onality of the stalls when interpre ng lying me.

All of these measures are mul -dimensional issues that require mul -dimensional approach to troubleshoot. For instance, lameness is a func on of the environ-ment, management, and physiology of the cow (Cook and Nordlund, 2009). Stall features that aff ect lying me may also aff ect lameness. Ma ress stalls are as-

sociated with lower lying me (Tucker et al., 2003) and also higher risk of lameness than deep-bedded stalls (Cook et al., 2004; Espejo et al., 2006; Ito et al., 2010). Prolonged standing me is a risk for lameness (Cook et al., 2004; Galindo and Broom, 2000), regardless of its cause: uncomfortable stalls, overstocking, or inap-propriate feeding and milking management. However, providing cows with a comfortable place to stand as an alterna ve to concrete can reduce the risk of lameness (Bernardi et al., 2009). Moreover, a complex rela onship exists between lameness and lying me, depending on the type of the stall surface as well as me available for rest (Gomez and Cook, 2010; Ito et

al., 2010). This complexity demonstrates that an eff ec- ve on-farm assessment must take a comprehensive

approach encompassing a mul tude of factors.

C.O.W.S. DISCUSSION:

WHERE DO WE GO FROM HERE?

COWS benchmarking project has revealed a number of opportuni es for improvement for the industry; however, many producers have already achieved considerable success in various areas of cow comfort (Barrientos et al., 2011). We aim to create a program where knowledge and experience can be shared, so that producers can learn from each others’ successes (or mistakes), and to collec vely develop ‘best man-agement prac ces.’ Novus con nues to collaborate with UBC on research and data analysis to iden fy risk factors, and to provide scien fi cally sound recom-menda ons for improved management. Future work is required in developing the most eff ec ve method for driving change and sustaining the eff ort.

TAKE HOME MESSAGES

• Novus COWS Program is a science-based, com-prehensive assessment aimed at op mizing cow comfort and well-being, while removing limita ons to produc on through improved management.

• The program brings awareness to cow comfort issues and facilitates discussion. It provides an ideal vehicle for engaging the producers, advisors, researchers, and the industry as a whole, with the common goal to develop prac cal solu ons.

• COWS benchmarking project has revealed a number of opportuni es for improvement, but many pro-ducers have already achieved considerable success in various areas of cow comfort. We aim to develop a program in which knowledge and experience can be shared for the collec ve progress of the industry.

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REFERENCES

Bach, A., N. Valls, A. Solans, T. Torrent. 2008. Associa ons between non-dietary factors and dairy herd performance. J. Dairy Sci. 91:3259-3267.

Barrientos, A. K., D. M. Weary, E. Galo, and M. A. G. von Keyserlingk. 2011. Lameness, leg injuries, and lying mes on 122 North American freestall farms. J. Dairy Sc. 94 E-Suppl. 1: 414.

Bernardi, F., J. Fregonesi, C. Winckler, D. M. Veira, M. A. G. von Keyserlingk and D. M. Weary. 2009. The stall-design paradox: Neck rails increase lameness but improve udder and stall hygiene. J. Dairy Sci. 92:3074-3080.

Bicalho, R. C., L. D. Warnick, and C. L. Guard. 2008. Strategies to analyze milk losses caused by diseases with poten al incidence throughout the lacta on: A lameness example. J. Dairy Sci. 91:2653–2661.

Cook, N. B. 2003. Prevalence of lameness among dairy ca le in Wisconsin as a func on of housing type and stall surface. J. Am. Vet. Med. Assoc. 223:1324–1328.

Cook, N. B., T. B. Benne , and K. V. Nordlund. 2004. Eff ect of free stall surface on daily activity patterns in dairy cows with relevance to lameness prevalence. J. Dairy Sci. 87:2912–2922.

Cook, N. B., and K. V. Nordlund. 2009. The infl uence of the environment on dairy cow behavior, claw health and herd lameness dynamics. Vet. J. 179:360–369.

Espejo, L. A., M. I. Endres, and J. A. Salfer. 2006. Prevalence of lameness in high-producing Holstein cows housed in freestall barns in Minnesota. J. Dairy Sci. 89:3052–3058.

Flower, F. C., and D. M. Weary. 2006. Eff ect of hoof patholo-gies on subjec ve assessments of dairy cow gait. J. Dairy Sci. 89:139–146.

Fregonesi, J. A., C. B. Tucker and D. M. Weary. 2007a. Over-stocking reduces lying me in dairy cows. J. Dairy Sci. 90:3349-3354.

Fregonesi, J. A., D. M. Veira, M. A. G. von Keyserlingk and D. M. Weary. 2007b. Eff ects of bedding quality on lying behavior of dairy cows. J. Dairy Sci. 90:5468-5472.

Galindo, F., and D. M. Broom. 2000. The rela onships between social behavior of dairy cows and the occurrence of lame-ness in three herds. Res. Vet. Sci. 69:75–79.

Garbarino, E. J., J. A. Hernandez, J. K. Shearer, C. A. Risco, and W. W. Thatcher. 2004. Eff ect of lameness on ovarian ac vity in postpartum Holstein cows. J. Dairy Sci. 87:4123–4131.

Gomez, A. and N. B. Cook. 2010. Time budgets of lacta ng dairy cattle in commercial freestall herds. J. Dairy Sci. 93:5772-5781.

Green, L. E., V. J. Hedges, Y. H. Schukken, R. W. Blowey, and A. J. Packington. 2002. The impact of clinical lameness on the milk yield of dairy cows. J. Dairy Sci. 85:2250–2256.

Haley, D. B., A. M. de Passillé, and J. Rushen. 2001. Assessing cow comfort: Eff ects of two fl oor types and two e stall designs on the behaviour of lacta ng dairy cows. Appl. Anim. Behav. Sci. 2001:105-117.

Ito, K. 2009. Assessing cow comfort using lying behaviour and lameness. MS Thesis. The University of Bri sh Columbia.

Ito, K., M. A. G. von Keyserlingk., S. J. LeBlanc, and D. M. Weary. 2010. Lying behavior as an indicator of lameness in dairy cows. J. Dairy Sci. 93:3553-3560.

Ito, K., D. M. Weary, and M. A. G. von Keyserlingk. 2009. Lying behavior: Assessing within- and between-herd varia on in freestall-housed dairy cows. J. Dairy Sci. 92:4412–4420.

Jensen, M. B., L. J. Pederson, and L. Munksgaard. 2005. The eff ect of reward dura on on demand func ons for rest in dairy heifers and lying requirements as measured by de-mand func ons. Appl. Anim. Behav. Sci. 90:207:217.

Ledgerwood, D. N., C. Winckler, C. B. Tucker. 2010. Evalua on of data loggers, sampling intervals, and edi ng techniques for measuring the lying behavior of dairy ca le. J. Dairy Sci. 93:5129-5139.

Lombard, J.E., C.B. Tucker, M.A.G. von Keyserlingk., C.A. Kopral, and D.M. Weary. 2010. Associa ons between cow hygiene, hock injuries, and free stall usage on US dairy farms. J. Dairy Sci. 90:1751-1760.

Munksgaard, L., M. B. Jensen, L. J. Pedersen, S. W. Hansen, and L. Ma hews. 2005. Quan fying behavioural priori es - eff ects of me constraints on behaviour of dairy cows, Bos taurus. Appl. Anim. Behav. Sci. 92:3-14.

Reich, L. J., D. M. Weary, D. M. Veira, M. A. G. von Keyserlingk. 2010. Eff ects of sawdust bedding dry ma er on lying be-havior of dairy cows. A dose-dependent response. J. Dairy Sci. 93:1561-1565.

Tucker, C. B. and D. M. Weary. 2004. Bedding on geotex le ma resses: How much is needed to improve cow comfort? J. Dairy Sci. 87:2895.

Tucker, C. B., D. M. Weary, and D. Fraser. 2003. Eff ects of three types of free-stall surfaces on preferences and stall usage by dairy cows. J. Dairy Sci. 86:521–529.

Tucker, C. B., D. M. Weary and D. Fraser. 2004. Free-stall dimensions: Eff ects on preference and stall usage. J. Dairy Sci. 87:1208-1216.

Tucker, C. B., G. Zdanowicz and D. M. Weary. 2006. Brisket boards reduce freestall use. J. Dairy Sci. 89:2603-2607.

Weary, D. M. and I. Taszkun. 2000. Hock lesions and free-stall design. J. Dairy Sci. 83:697-702.

Whay, H. R., D. C. J. Main, L. E. Green, and A. J. F. Webster. 2003. Assessment of the welfare of dairy ca le using animal-based measurements: Direct observa ons and inves ga on of farm records. Vet. Rec. 153:197–202.

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Appendix. Excerpt from COWS Report (Novus Interna onal Inc., 2010).

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We gratefully acknowledge the following organizations

for their generous support of this workshop.

2011 Dairy Cattle Nutrition Workshop Sponsors and Exhibitors

SILVER EXHIBITORS

Adisseo USA Inc.

Ag Processing Inc. / Amino Plus

American Farm Products

BASF Plant Science

BIOMIN USA Inc.

Dairy One Forage Lab

DSM Nutritional Products

ICC USA Inc.

Mercer Milling Company Inc.

MIN-AD Inc.

Papillon Agricultural Company

Tech Mix LLC

PRECONFERENCE SYMPOSIUM SPONSOR

Balchem Corporation

SPEAKER SPONSORS

Akey

Arm & Hammer Animal Nutrition

Dairy Records Management Systems

Diamond V Mills

Kemin Agrifoods

Pioneer Hi-Bred, A DuPont Business

EVENT SPONSORS

Alltech Inc.

Lallemand Animal Nutrition

Novus International Inc.

Prince Agri Products Inc.

Vi-COR

Virtus Nutrition

GOLD EXHIBITORS

ADM Alliance Nutrition Amlan International

APC Inc. / AgriLabsCentral Life Sciences

Cumberland Valley Analytical Services Inc. Digi-Star

Elanco Animal HealthJEFO Nutrition USA Inc.

Lancaster DHIA Milk Specialties Global Animal Nutrition

Multimin USA Inc. / TDL AgritechNittanyCow Software Services

NutriLinx LLCThe Old Mill-Troy Inc. Pfi zer Animal Health

QualiTech Inc.Red Dale Ag Service

RP Feed Components LLCSchnupp’s Grain Roasting Inc.

Venture MillingWenger Feeds

Westway Feed Products LLCZinpro Performance Minerals

BRONZE EXHIBITORS

Agri Analysis Inc.Bio-Vet Inc.

Chr. Hansen Inc.Kauff man’s Animal Health, Lira Animal Health Products

Northeast FeedNutriad Inc.

Phode InternationalSoyPLUS / SoyChlor

Triple -M- FarmsWeaver’s Toasted Grains

CONTRIBITUTORS

Co-operative Feed Dealers Inc.

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