Animal nutrition and management in the 21st century: dairy cattle

ANIMAL FEED SCIENCE AND TECHNOLOGY

Animal Feed Science Technology 58 ( 1996) I - 18

Animal nutrition and management in the 2 1st century: dairy cattle William Chalupa *, David T. Galligan, James D. Ferguson Center jix Animul Health and Productivity, New Bolton Center, School oj’Veterinary Medicine, University of Pennsylvaniu, Kennett Squure, PA 19348. USA

Abstract Dairy producers strive to increase production and efficiency. Consumers want products that contain less fat and more protein. Negative impacts of pollutants from animal agriculture on the environment must be controlled. The foregoing can be accomplished by regulating metabolic processes of the dairy cow through nutrition and biotechnology. The application of genetic engineering techniques can increase production and its efficiency, change the composition of milk and improve prevention, diagnosis and treatment of disease. Competent nutrition, reproduction and health programs and improved information systems for managing and utilizing information will be required.

1. Introduction The overall goal of dairy production systems is the economic yield of dairy products of high nutritional quality for man with minimum negative impacts on animal reproduction and health and on the environment. Currently, only about 30% of energy and protein consumed by dairy cattle are captured in milk. Approximate energy losses are 30% in feces, 3% in urine, 5% in gases and 25% as heat. Fecal and urinary protein losses are each about one third of feed protein consumed. Thus production and its efficiency can be improved by minimizing losses incurred during digestion and metabolism. Consumers want animal products that contain low levels of fat. Proportions of fat and protein in milk are determined primarily by genetics but can be changed by nutrition and by methods that adjust digestive and metabolic processes.

* Corresponding

author

0377-8401/96/$15.00 0 1996 Elsevier Science B.V. All rights reserved SSDl 0377-840 1(95)008691

W. Chalupa

2

2. Strategies

et al./Animal

for feeding ruminant

Feed Science Technology 58 (1996) 1-18

animals

Ruminant animals have two metabolic systems: ruminal microbes and ruminant tissues. Optimizing productivity requires providing proper amounts and balances of nutrients to these two systems. The problem is that nutrients required by ruminal microbes and ruminant tissues are different. 2.1. Ruminal microbes The mixed microbial population of the rumen can use ammonia, amino acids and peptides as nitrogenous nutrients. However, functional groups of bacteria have different nitrogenous requirements. Bacteria that ferment cellulose and hemicellulose (structural carbohydrate fermenters) grow slowly and use ammonia as their primary nitrogenous nutrient. Bacteria that ferment sugars, starch and pectin (non-structural carbohydrate fermenters) can use ammonia as their nitrogenous nutrient but their growth is enhanced if peptides (and amino acids) are available. To avoid deficiencies of ammonia in the rumen, 50% of the degraded protein should be soluble protein. This guideline provides a basis for effective use of urea. Only carbohydrates or products of carbohydrate fermentation provide energy (ATP) at rates sufficient for growth of most ruminal microbes (Nocek and Russell, 1988). Thus replacing carbohydrate with fat may lead to decreased production of microbial protein. In these situations, diets should contain more undegraded (bypass) protein. 2.2. Ruminant tissues Amino acids are the only nitrogenous nutrients that are used for synthesis of tissue proteins (growth) and synthesis of milk protein (milk yield). Amino acids are provided by ruminal microbes and by dietary protein that escapes fermentative digestion in the rumen and, to a small extent, by body reserves of labile proteins. Energy is supplied primarily by volatile fatty acids arising from fermentative digestion of carbohydrates in the rumen. A secondary source of energy is fatty acids mobilized from adipose tissue. Energy derived from adipose tissue is valuable during the first 30 d of the lactation cycle when feed consumption is low but these body reserves must be replenished during the last 200 days of lactation. A third source of energy is dietary fat supplied by feeding high energy ingredients such as oil seeds, tallows and rumen inert fats. 2.3. Formulating

diets

Diets for ruminant animals should first be formulated to optimize the supply of nutrients required by ruminant tissues. The rumen system, however, cannot provide sufficient nutrients for high levels of growth or milk production. Thus, rumen inert (bypass) nutrients (fat, protein, amino acids and perhaps some vitamins) are needed to supplement nutrients from the rumen so that productivity of growing and lactating cattle can be optimized.

W. Chulupu et d/Animal

Fred Science Technology 58 (1996) I-18

3

2.3.1. Fat Because fat can be detrimental to ruminal microbes and affect intestinal absorption, the fatty acid composition (palmitic acid, stearic acid, unsaturated fatty acids) and the form of fat (triglycerides, free fatty acids, calcium salts) must be considered (Chalupa and Sniffen, 1992). Palmitic and stearic acids are relatively inert in the rumen while unsaturated fatty acids are detrimental to ruminal fermentation (either by coating feed particles or by being toxic to ruminal microbes). However, unsaturated fatty acids are inert in the rumen if they are in the form of calcium salts. In terms of fatty acid profile, there appears to be a negative correlation between absorption from the small intestine and the amount of stearic acid. The problem is more critical with products in the form of triglycerides vs. free fatty acids. Intestinal absorption of products that contain high amounts of stearic acid can be high if the they contain some oleic acid which probably enhances emulsification in the intestine. We divide fat into three fractions (Chalupa and Sniffen, 1992). Fraction I is fat in base feed ingredients (grains, forages and protein supplements). Base feed ingredients will give diets with about 3% fat. Fraction 2 fats are those from tallow and oil seeds (cotton seed, soybeans, etc.). Fraction 2 fat can provide an additional 2% fat bringing the total to 5% in dietary dry matter. Further increases in dietary fat should be with rumen inert fats at the rate of 2% fat which brings the total fat to 7% in dietary dry matter. We do not recommend above 7% fat in dietary dry matter because there seems to be an upper limit to the amount of fat (1.4- 1.6 kg day- ’) that can be absorbed from the small intestine. With dry matter intake of 22 kg day- ’, diets with 7% fat will fall within the range of the amount of fat that can be absorbed efficiently from the small intestine. 2.3.2. Protein Proteins vary in the extent of degradability in the rumen. For the last decade, diets have been formulated on the basis of crude protein and rumen escape (bypass) protein. In recent years, blends of plant, animal and marine proteins have gained wide acceptance. This acknowledges that the tissue requirement is for amino acids and that feed ingredients vary in amino acids in the rumen escape (bypass) fraction. We need to look at amino acids in the rumen bypass fraction of feed ingredients. Soy proteins are low in methionine, corn proteins are low in lysine, blood meal is the best source of lysine, fish meal is the best source of methionine and corn gluten meal, while deficient in lysine, is an excellent source of isoleucine and a good source of methionine (Chalupa and Sniffen, 1993). 2.3.3. Amino acids With many diets, the combination of microbial protein from the rumen plus dietary protein which escapes fermentative digestion may not provide optimum amounts and balances of amino acids. The primary methods developed to prevent fermentative digestion of amino acids are structural manipulation to produce amino acid analogs and coating with resistant materials. The main amino acid analogs evaluated are methionine hydroxy analog, N-(hydroxymethyl) DL-methionine calcium, and mono plus di-N-(hydroxymethylj-L-lysine cal-

4

W. Chalupa et al./Animal

Feed Science Technology 58 (1996) I-18

cium. Methionine hydroxy analog appears to be more resistant to fermentative digestion than methionine, but substantial amounts do not appear to bypass the rumen (Chalupa and Sniffen, 1991). Amino acids have been coated with polymeric compounds, formalized protein, fat, mixtures of fat and calcium, mixtures of fat and protein, and with calcium salts of long chain fatty acids (Chalupa and Sniffen, 1991; Ferguson et al., 1993). Recently, a new product (Megalac Plus@) was introduced (Church and Dwight Co., Inc. Princeton, NJ). Megalac Plus@ contains 3% methionine hydroxy analog added during the manufacture of Megalaca. The calcium salts of long chain fatty acids afford protection of the metbionine hydroxy analog (Ferguson et al., 1993; Chalupa and Sniffen, 1993). 2.3.4. Carbohydrates Type of carbohydrate (fiber and non fiber carbohydrate (NFC)) affects the balance of microbial species in the rumen, proportions of volatile fatty acids (and lactate) produced and the amount of energy provided to ruminant tissues. Diets that are high in fiber do not provide sufficient energy for high levels of milk production so that body weight losses are exacerbated and cows produce below their genetic potential. Energy density can be increased by providing more NFC but this can lead to altered ruminal fermentation and disorders such as acidosis, milk fat depression, increased feet and leg problems and poor production (Sniffen, 1988). Plant carbohydrates are partitioned into structural (cell wall) and non-structural (non cell wall). Structural carbohydrates (fiber) are slowly digested by ruminal microbes whereas non-structural carbohydrates (non-fiber) are digested rapidly (Sniffen, 1988). Extraction with neutral detergent provides an estimate of cell wall and non cell wall components (Table 1). The exception is pectin which is associated with the cell wall but is extracted with neutral detergent. Pectin, however is digested at rates like starch rather than at rates like cellulose and hemicellulose. The method proposed by Sniffen (1988) for estimating fiber and non-fiber carbohydrates is Total carbohydrate (TC) = 100 - (crude protein + ether extract + ash) Non - fiber carbohydrate (NFC) = total carbohydrate - NDF

Table 1 Plant carbohydrates

a

Total carbohydrate Non cell wall

Cell wall

Sugars Starches

PeCtinS Cellulose Hemicellulose Lignin

a Sniffen ( 1988).

W. Chalupu et d/Animal Table 2 Feed carbohydrate

fractions

Fred Science Technology 58 (1996) l-18

5

a

Feedstuff

NDF b

NFC ’

F#Rl@S Alfalfa hay, early vegetative Alfalfa hay, late vegetative Alfalfa hay, early bloom Alfalfa hay, mid bloom Grass hay, late vegetative Grass hay, pre bloom Grass hay, early bloom Grass hay, mid bloom Corn silage, well eared Corn silage, few ears

35.8 40.0 43.7 46.9 57.0 62.2 65.4 67.2 45.0 55.0

25.5 24.7 24. I 23.8 10.3 8.4 8.2 8.7 39.7 29.0

Concenrrures Barley Beet pulp Brewers grains Citrus pulp Corn and cob meal Corn distillers grains Corn gluten feed Corn gluten meal Corn grain Corn hominy Cottonseed meal Linseed meal Oats grain Peanut meal Rapeseed meal Sorghum grain Soybeans Soybean meal (44% CP) Soybean meal (48% CP) Soy bean hulls Sunflower meal Wheat grain Wheat bran Wheat middlings

28.3 54.0 46.0 21.1 26.0 42.5 41.3 14.0 9.0 21.4 34.0 25.0 32.2 14.0 0 8.7 0 14.0 10.0 69.9 40.0 14.0 51.0 31.2

55.4 33.3 13.5 60.7 59.4 12.3 27.1 14.3 74.3 55.9 13.3 28.7 47.1 26.3 50. I 73.8 32.9 27.3 27.4 14.3 26.6 I I .o 20.6 35.6

a Sniffen (1988). b Neutral detergent fiber. ’ Non-fiber carbohydrate.

Feed carbohydrate fractions are in Table 2. Grasses contain more NDF and less NFC than legumes. Proportions of NDF and NFC in corn silage depend on amount of grain. Concentrates are high in NFC but proportions of NDF and NFC in concentrates are variable. Concentrate NDF generally is not effective in maintaining rumen function. When formulating diets, factors affecting ruminal fermentation should be considered. Factors which influence ruminal fermentation include plant species (i.e. legume NDF is

6

W. Chulupu et al./Animal

Feed Science Technology 58 (1996) 1-18

fermented faster than grass NDF); rate of starch fermentation (wheat > barley > oats > corn > sorghum); particle size (effective NDF), forage maturity, feeding frequency and feed intake (Sniffen, 1988). Recommendations for amounts of NFC are variable. NFC should be about 35% of dietary dry matter (Hoover and Stokes, 1991). Fiber is required for the formation of an adequate ruminal mat (limits loss of fine particle size fiber from the rumen), adequate rumination, salivation, and rumen buffering. The amount of fiber (NDF) a cow can consume is regulated by rumen volume which in turn is related to body weight (Mertens, 1988; Sniffen, 1988). For multiparous cows, NDF capacity appears to be 1.0 to 1.3% of body weight. Because of their body structure, NDF capacity of primiparous cows appears to be 0.85-1.1% of body weight. NRC (1989) suggests that diets for high producing cows contain a minimum of 19% ADF and 25% NDF with 75% of total dietary NDF from forage. However, effectiveness of fiber in maintaining normal rumen function depends on particle size of fiber. 2.3.5. Feed additives Dairymen are exposed to many feed additives. Only feed additives that have undergone intensive research should be considered. Decision analysis (discussed later) provides a mechanism for selecting feed additives on the basis of their cost and the probability of a positive production response. The benefits of sodium bicarbonate are well established and it is included in about 75% of dairy rations. Magnesium oxide is often included. Niacin has received attention as an additive that alleviates depressions in milk protein that often occur when diets contain supplemental fat. 2.3.6. Milk composition Digestion end-products determine the yield and composition of milk (Thomas and Martin, 1988; Table 3). Milk yield is increased by acetate, glucose, amino acids and long-chain fatty acids. Milk fat content is increased by acetate, butyrate and long-chain fatty acids but decreased by propionate and glucose. Milk protein content is increased by amino acids, propionate and glucose. It is decreased by long chain fatty acids. Through nutrition, by optimizing ruminal fermentation and providing rumen inert (bypass) nutrients, we can produce designer dairy products that contain desired amounts of amino acids and fatty acids. This developing technology will have major impacts on increasing the consumption of dairy products. It is likely that transgenic dairy cows (discussed later) that secrete more protein in milk will require diets that provide more aminogenic and glucogenic nutrients whereas transgenic animals that secrete less fat in milk may be fed diets that provide less lipogenic nutrients. 2.3.7. Dry matter intake

Dry matter intake determines the concentrations of nutrients in diets needed to meet requirements for maintenance and production. In general, dry matter intake is regulated by animal size and production. The amount of dry matter an animal can consume is determined by dietary characteristics and environment (temperature, humidity) and

W. Cholupu et al./Animal Table 3 Effects of infusion of nutrients

Feed Science Technology58

on milk yield and composition

Product of digestion

Site of absorption

Acetate Propionate Butyrate Glucose Amino acids Long chain fatty acids

Rumen b Rumen b Rumen b Sm. intestines ’ Sm. intestines ’ Sm. intestines d

(1996) I-18

7

a

Response (% of control) Milk (kg day+8 -2 -5 +6 +7 +2

’)

Fat (%)

Protein (%)

Lactose (%)

+9 -8 + 14 - 10 -3 + 13

-1 +7 t2 -I

+2 +1 +2

+6 _

fl

-t I _

a Thomas and Martin (1988). ’ Intra-ruminal infusion. ’ lntra-abomasal infusion. d Intravenous infusion.

whether cows are in a comfortable vs. uncomfortable conditions. Dry matter intake is negatively correlated with dietary NDF because the amount of NDF an animal can consume is regulated by rumen volume. Rumen volume is related to size of animal (frame size and body weight). Dry matter intake is less with diets that contain grasses vs. legumes because grasses contain more NDF and grass NDF is fermented at slower rates than legume NDF. Dry matter intake is lower when diets contain high amounts of fermented feeds. This may be the result of organic acids, amines and ammonia produced during the fermentation process. Dry matter intake also decreases when moisture content of the diet is greater than 50%. 2.4. Allocation

of feeds

The objective in feed allocation is to provide ruminal microbes and ruminant tissues with steady flows of nutrients. Ideally, dairy cows should be fed total mixed diets 24 times a day. Realistically, this is not possible. Feeding dairy cattle is an intermittent process that is governed by facility limitations such as housing and the feed delivery system, numbers and grouping of animals, and labor availability. Total mixed diets should be fed at least twice and preferably four times daily. If forage and grain are fed separately, some forage should be fed before offering grain. It is important to feed protein and energy concentrates together. 2.5. Grouping

animals

The number of cow groups is dictated by the physical plant and also by herd size. Ideally, there should be a minimum of five groups of animals: (1) high production, (2) mid production, (3) low production, (4) dry cows, and (5) close-up dry cows (3 weeks prior to calving). Nutrient densities of diets for lactating cow groups are determined by the level of milk production of the herd. For example, herds that average 10000 to 13 000 kg will need to be fed diets formulated to support 45, 35 and 27 kg milk day-‘, whereas herds

8

W. Chalupa et al./Animal Feed Science Technology 58 (1996) 1-18

that average 8000-9000 kg can be fed diets to support 35, 27 and 18 kg milk day-‘. Diets with high nutrient densities are more expensive but as long as production increases, income over feed cost also increases. Recent research from Cornell University (Van Saunt, 1991) and on-farm observations show that close up dry cows have fewer post-partum disorders and reach peak milk yield if fed higher levels of crude and bypass protein (i.e. 14-15% crude protein with 36-39% bypass). More emphasis needs to be directed to dry cow nutrition since nutrients provided during the 3 weeks prior to calving impacts animal health and milk yield. As discussed below, body condition needs to be considered when moving cows from higher to lower producing groups. Dry cows should not gain or lose body condition. The close up group of dry cows should receive additional grain and feed ingredients that are used to formulate the diet for the high production group. 2.6. Body condition Physical extremes of obesity and emaciation at calving predispose dairy cows to health, reproduction and production problems. Due to inequalities between energy intake and energy required for milk production, physical condition changes throughout the lactation cycle (Ferguson, 1990; Otto et al., 1991). Body condition is an index of degree of body fatness. Cows are ranked on a graduated scale encompassing physical states of emaciated-thin-average-fat-obese by evaluating the degree of tissue (primarily fat) covering the lumbar vertebrae, pelvis and tail head. The most common system employed in the US is that proposed by Wildman et al. (1982) which ranks cows on a five point scale. It is common to divide the scale into subclasses of l/4 to l/2 points in between the integer classifications. Cows that are over-conditioned at calving have reduced feed intake during the early stages of the lactation cycle. Cows that have scores less than 2.5 by 30 days in milk have reduced milk yields and lower reproductive efficiency (Ferguson and Otto, 1989). Score at calving should be 3.0-3.5. Feed consumption should increase so that cows are not losing body condition after 30 days in milk and their score should not go below 2.5. Loss of 0.5- 1.O body condition score units provides net energy for 600- 1200 lb of milk. By 100 days in milk, cows should begin to replete reserves of adipose tissue so that scores are 3.0-3.50 at dry-off. During the dry period, body condition should be maintained or increased only slightly. 2.7. Formula vs. commodity feeding systems

Increasing numbers of dairy producers are purchasing commodities rather than formula feeds. Economics primarily depend on investments in on-farm feed mixing equipment. As long as investments are minimal, feed costs with commodity feeding often are less than feed costs with formula feeds. Innovative feed manufacturers are providing dairy producers with the option of customized least-cost formulations based upon blends of commodities that complement nutrients provided by forages.

W. Chalupa ef al./Animal Feed Science Technology 58 (1996) 1-18

9

3. The role of biotechnology in dairy production systems Biotechnology is the ‘buzz-word’ of the last two decades of the 20th century. To the layman, biotechnology conjures use of recombinant DNA technology or genetic engineering techniques to rearrange genes in microorganisms and animals to favorably alter metabolic processes. However, Louis Pasteur practiced the principles of biotechnology with his experiments on how microorganisms cause and prevent disease. These studies led to development of a vaccine against anthrax and the development of antibiotics (Keyworth, 1990). Th us in a broader context, biotechnology is more than the application of ‘genetic engineering’. For dairy production systems, it includes all methods that adjust digestive physiology and metabolism to increase productivity, adjust composition of milk and improve reproduction and health. Biotechnology already is used to improve productivity and health of food-producing animals. Ionophores improve feed efficiency of growing and fattening cattle primarily by selecting for rumen microbial populations that capture energy in volatile fatty acids rather than in methane (Chalupa, 1988). Computerized diet formulation programs (i.e. Galligan et al., 1989a) provide proper balances of nitrogen and energy to ruminal microbes and ruminant tissues and thus improve the efficiency of microbial protein production and the transfer of energy in fiber and non-fiber carbohydrates to volatile fatty acids. Processing low quality forages and crop residues can increase their digestibility in the rumen (Males, 1987; Conner and Richardson, 1987; Klopfenstein et al., 1987). Advances in the 21st century can be made through the application of genetic engineering techniques to improve production and its efficiency, change the composition of milk, and prevention, diagnosis and treatment of disease. Much progress already has been made in (a) development of bacteria that can produce large quantities of proteins that are normally found in livestock, and are important regulators of metabolism, (b) acceleration in selection and breeding improvements in animals and plants by gene transfer (Baile and Krestel-Rickert, 1988), (c) development of DNA probes to diagnose infectious diseases, and (d) production of monoclonal antibodies to treat diseases (Keyworth, 1990; Sorensen, 1990). Unfortunately, most consumers do not understand the positive benefits of biotechnology. They only have been exposed to the remote negative aspects. Unbiased educational programs are needed for the following biotechnology programs to have a major impact on the production of food animal production in the 21st century. 3.1. Regulation of metabolism Animal genes that control the production of proteins that regulate metabolism have been isolated and inserted into plasmids. Plasmids carry the genes into bacteria and the bacteria produce large quantities of protein (Keyworth, 1990). Bovine and porcine somatotropin are the first genetically engineered metabolic modifiers to be researched extensively. In the future animal genes for production of other proteins will be isolated and used to develop bacteria that produce other regulators of metabolism.

10

W. Chalupa et al./Animal Feed Science Technology 58 (1996) 1-18

Bovine somatotropin (BST), improves productive efficiency of dairy cattle by partitioning more nutrients to milk. BST does not affect digestibility, maintenance requirements or the partial efficiency of milk synthesis nor does it act directly on the mammary gland. BST affects mammary tissue indirectly by its action on the liver and other tissues such as the kidney to stimulate production of insulin-like growth factors which act on the mammary gland to increase milk synthesis. Nutrients for increased milk yield are provided by increased feed intake and coordination of metabolism to increase supplies to the mammary gland of glucose, amino acids and fatty acids (Chalupa and Galligan, 1989). 3.2. Gene transfer The goals of traditional breeding and molecular biological methods using gene transfer are the same: to improve agricultural species of animals and plants. Genetic change traditionally has been made by using genetic variation within breeds or strains and between breeds or strains. These methods depend on the recombination of large numbers of genes which may mask expression of desirable traits or yield undesirable as well as desirable traits. Molecular biological methods allow processes to be manipulated one gene at a time so that new traits can be expressed quicker and with greater precision (NRC, 1987). Introduction into embryos of genes coding for proteins that regulate physiological processes is a potential method for improving productivity, adjusting the composition of milk and increasing disease resistance. Because of the generation interval and costs of working with food-producing animals, most research has been with mice but transgenic pigs, sheep and cattle have been produced (Bremel et al., 1989). Currently, only 0.5 and 2% of microinjected embryos result in positive transgenic animals (Bremel et al., 1989). For application to food-producing animals, this low efficiency must be improved. The feasibility of introducing foreign genes into the genome of mice, rabbits, sheep and pigs was demonstrated using mouse metallothionein I promoter/regulator fused to human growth hormone (Hammer et al., 1986). In the future, genes coding for other peptides that regulate metabolic processes may be introduced into food-producing animals to improve production and efficiency. Composition of milk is determined primarily by genetics and secondarily by management, especially nutrition. Molecular biological methods have, the potential to produce transgenic dairy cows that produce milk with different composition profiles (Bremel et al., 1989). The concentration of casein may be increased or the properties of casein may be changed to increase the value of milk for production of cheese. The protein P-lactoglobulin, present only in the milk of ruminants, has no known function in milk synthesis and is a problem in some manufacturing processes. It potentially could be eliminated through genetic engineering. Concentration of fat in milk could be decreased by reducing the amount of acetyl CoA carboxylase, the key enzyme that regulates the rate of de novo synthesis of fat from acetate in the mammary gland. Concentration of lactose in milk could be reduced by removal of cw-lactalbumin or introduction of an enzyme into milk to break lactose down to glucose and galactose. However, because lactose is the primary regulator of osmotic pressure in milk, its reduction could decrease

W. Cholupu ef ul./Animul

Feed Science Technology 58 (1996) I-18

II

milk volume. The production of monoclonal antibodies by the mammary gland could provide defenses against mammary pathogens. Intensive research programs are being directed towards producing new strains of rumen bacteria (Forsberg et al., 1986) that may lead to greater fermentation of cellulose, hemicellulose and perhaps lignin, bacteria that can survive under a wide range of environmental conditions in the rumen, bacteria that have lower maintenance requirements and thus improved efficiencies of growth, and bacteria that can metabolize compounds which are toxic at the tissue level of metabolism. Biotechnology can be used to genetically engineer plants so they have improved characteristics such as higher digestibility and better nutrient profiles while maintaining the traditional goals of increased yields at lower cost (NRC, 1987). 3.3. Diagnosis

and treatment of diseases

Animal diseases cost the American agricultural industry $17 billion annually (Office of Technology Assessment, 1986). Because each microorganism species has characteristic sequences of DNA, probes can be produced to diagnose an infection. Diseases can be treated more effectively using monoclonal antibodies produced using genetic engineering techniques (Kelley and Lewin, 1986; Keyworth, 1990; Sorensen, 1990).

4. Reproduction 4. I. Reproductive

management

Reproductive performance is a major determinant of profitability of dairy herds (Ferguson, 1989). Reproductive management programs should monitor and control (a) age at first calving, (b) calving interval, (c> pregnancy wastage (includes early embryonic death, visible abortions and calf mortality through 3 weeks of life), and (d) reproductive culling. Age at first calving and calving interval are especially important because they determine the number of lactations and, hence, the number of days a cow is at peak production during her lifetime. 4.1.1. Age atfirst calving Age at first calving should be as early as possible to initiate income from sale of milk. However, there is a lower age limit due to the high incidence of dystocia in immature heifers. The current recommendation is that Holstein heifers should calve at 24 months of age and weigh 550 kg (Ferguson, 1989). However, by increasing dietary protein and the proportion of bypass protein, it is possible to calve Holstein heifers at about 20 months of age (Van Amburgh et al., 1991). 4.1.2. Calving interval For the majority of cows, the most profitable calving interval is 1 l-13 months. This corresponds to a days open window of 55-l 15 days. Factors affecting calving interval are: pregnancy prevalence ( PP) (percentage of cows in the total population that become

12

W. Chulupa et al./Animal

Feed Science Technology 58 (1996) 1-18

pregnant, i.e. pregnant cows/total cows); conception rate (CR) (probability of a cow conceiving at a particular service, i.e. pregnant cows/number services); heat detection efficiency (HD) (proportion of cows in estrus that are detected, i.e. no. cows observed in estrus/number of cows actually in e&us); voluntary wait period (WVP) (days from calving until cows are eligible for insemination); pregnancy rate (PR) (number of cows that become pregnant during one estrus cycle. PR is a function of HD and CR (PR = HD X CR) and determines the VWP. For example, if HD is 70% and CR is 50%, PR is 35%. However, if HD is 50% and CR is 70%, PR is still 35%. With a PR of 35%, the VWP cannot exceed 60 days if the goal is 80% of cows pregnant at 120 days in milk. This corresponds to a calving interval of 13.2 months (Ferguson, 1989). 4.1.3. Pregnancy wastage Pregnancy wastage can have major impacts on reproductive performance. About 15-20% of conceptions fail to establish pregnancy because of embryonic mortality. Highest losses occur early, 6-15 days after conception. These cows return to estrus at normal or slightly longer intervals. however, even after pregnancy is confirmed, 5% of pregnancies will still be lost. Embryonic losses also can be increased by infectious disease (vibriosis, trichomoniasis, BVD, blue tongue) heat stress, chronic mastitis and other environmental insults. After the early embryonic period, abortion losses should be less than 2%. Age of cow, stage of lactation, fetal appearance, and diagnostic tests can be helpful in identifying the causes of abortion. Calves born dead should be less than 5-15% of calvings and calf mortality should be less than 10% for the first 3 weeks of life (Ferguson, 1989). 4.1.4. Reproductive culling

Reproductive culling represents involuntary culling as the decision to remove the cow from the herd is not based on milk production or genetic merit. This becomes a problem in herds with low PR. Nevertheless, cows not pregnant at the end of the breeding period should be culled and replaced with a heifer. Even though some revenue is derived from sale for beef, reproductive culls have a negative impact on profitability. Less than lo-15% of cows calving in a year should be culled for reproduction (Ferguson, 1989). 4.2. Technologies for improving reproductive pelformance Technological aids for improving reproductive performance primarily are directed towards inducing estrus and improving heat detection. 4.2.1. Inducing estrus

Prostaglandin to synchronize cows for breeding is a useful management tool (Ferguson, 1990). Reproductive performance, however, only shows marginal benefits from a random program. Prostaglandin is most efficient when used in a 14 day application program. Injected cows are monitored for estrus and inseminated if estrus is observed. If not inseminated, cows receive a second injection of prostaglandin 14 days later and are time bred (Ferguson and Galligan, 1993).

W. Chalupa et al./Animal

Feed Science Technology 58 (1996) 1-18

13

4.2.2. Heat detection Methods suggested to improve efficiency of heat detection include pressure sensitive devices, milk progesterone assays and changes in electrical resistance of fluids in the vagina. 4.2.2.1. Pressure sensitive devices. Pressure sensitive devices used as heat detection aids include Hot Flash, KMAR patches, Estrumark (latex paint), Heat Mark (crayon marks) and Heat beeps (Ferguson, 1990). In order for pressure sensitive devices to be triggered, cows must be in standing estrus. For example, 87% of the devices were triggered by cows in estrus that were observed to stand to be mounted but less than 60% of the devices were triggered by cows in estrus not observed standing (Lee et al., 1990). 4.2.2.2. Milk progesterone. Milk progesterone kits have been available for several years. Milk progesterone can be used as a tool to predict the presence or absence of luteal tissue and as an aid in identifying cows with cystic ovaries and those experiencing early embryonic mortality. Progesterone tests alone will not improve reproductive performance; they should be used in conjunction with prostaglandin to synchronize es&us. Cows past the VWP and not yet inseminated can be identified as candidates for prostaglandin therapy based on milk progesterone levels. A key to successful use of milk progesterone kits is a person skilled in interpreting results obtained. 4.2.2.3. Electrical resistance of vaginal fluids. Electrical resistance of body tissues and fluids is related to water content. Increases in tissue water associated with the hormonal changes of estrus (low progesterone and high estrogen) decreases resistance. Lowest resistance occurs coincidental with ovulation, toward the end of standing estrus. Thus, measuring changes in vaginal resistance has the potential to aid in estrus detection and in determining the optimum time for insemination. Problems in using vaginal resistance to detect estrus are related to individual cow variation and metritis or vaginitis. With met&is and vaginitis there is an increase of vaginal mucus and tissue fluid which causes low resistance independent of hormonal changes. Canfield and Butler (1989) concluded that vaginal resistance should be measured every 12 h to be useful as an aid in estrus detection. Given the labor intensity, needed for disinfection of the probe between cows, frequency of measurements, individual cow variation and the high rate of false positives, vaginal resistance as routine method of estrus detection is not recommended (Ferguson, 1990). In the future, however, many of the problems may be minimized by surgical implantation of electrodes in vulvar tissue and the use of telemetry to record changes in vaginal resistance (Lewis et al., 1989).

5. Environmental

impact

Pollutants produced by cattle include nitrogen, minerals (P, K, Ca Na, S), carbon dioxide and methane. Carbon dioxide and methane may contribute to depletion of the ozone layer and the ‘greenhouse effect’. Excreted N, P, and K can be valuable when excreta are returned to the land that produced feed for the cattle enterprise. However

14

W. Chalupa et al./Animal

Feed Science Technology 58 (1996) I-18

when feeds are produced in several areas and excreta disposed of in only one area, soil fertility increases above needed levels and pollution of water sources occurs (Lanyon, 1990; Meyer, 1990). Increased productivity, whether achieved through biotechnology, management and nutrition or traditional selection and breeding methods, decreases relative losses arising from meeting the cows maintenance requirements and thus pollutants per unit of milk produced are lower. Nevertheless, feeding recommendations for optimum productivity may need to be modified to reduce pollution by livestock (Tamminga, 1990). Increased feed quality can reduce losses in methane and carbon dioxide. Fecal and urinary losses of N and P can be reduced by minimizing their intakes. Although productivity may be compromised, pollution can be reduced by implementing feeding programs that deviate from maximizing activities of the rumen microbial and ruminant tissue systems of metabolism.

6. Information technologies Historically, improvements in production efficiency have been through discovery and application of new biologic and management technologies. However, the dairy industry is undergoing dramatic changes in the use of information to improve economic efficiency. Computers and software systems have increased the magnitude of information collected and thus have affected many aspects of the dairy production system. In conjunction with abilities to collect, retrieve and manipulate large volumes of data, new methodologies have been developed to process information so it can be used to reach economically efficient decisions. (Madison et al., 1984; Fetrow et al., 1985; Galligan and Marsh, 1987). While many of these methodologies had their origin in other fields of science, they have been successfully modified to reflect the unique attributes of livestock production systems. 6.1. Herd and cow monitoring systems

Herd level monitoring systems have been developed to track disease occurrences along with production and reproduction (Fetrow et al., 1987) These programs allow for the identification of trends in health and production which can be linked to management changes. Cow level systems (i.e. DHIA programs) have also been expanded to track health, production and reproduction responses of individual cows. New types of reports are continually being developed to increase usefulness of data and improve interpretation. Expert systems are being integrated with these systems to allow for a more controlled and systematic approach to interpretation and problem solving (Kalter et al., 1990). 6.2. Decision sciences

Dairy producers and their consultants are continually faced with making decisions concerning health and productivity. As examples, should a disease (i.e. left displaced

W. Chalupa et d/Animal

r Intervention

Choose

Feed Science Technology 58 (1996)

Favorable

l-18

I5

V,, - C,

P 1 Uniavorable 1 - P,

V,, - C,

Favorable

V,, - C,

P1

LUnfavorable 1-P 1

V,. - C,

Fig. 1. Decision tree for choosing between two interventions. P,,, probability of success for intervention n; V,,,, favorable outcome value for intervention n; Vnu, unfavorable outcome value for intervention n; C,,, cost of intervention n.

abomasum) be treated or should the cow be sold for salvage value and replaced with another animal, should aids of detecting es&us and inducing estrus be used, and should technologies that increase milk yield or alter milk composition be adopted? Decision analysis is a logical and easy procedure for making choices. It provides a mechanism for describing complex problems in an explicit fashion and identifying available options, and allows for selection of optimal choices (Ngategize and Kaneene, 1985; Kaneene and Mather, 1982). Optimal choices are based on the probability of a favorable outcome (based on data from common knowledge or from published reports) and the cost of an intervention. As examples of the utility of decision analyses techniques, we present the use of decision trees and the economic cost of decisions. A generic decision tree to evaluate selection among two interventions is presented in Fig. 1 (Fetrow et al., 1987). Interventions could be treatment of disease vs. salvage value of the cow plus cost of a replacement animal, use of reproductive management aids or adoption of production enhancers. Each intervention has a probability of success (P,) and can yield favorable outcome values (V,,) and unfavorable outcome values (if,,,). By using the laws of Bayesian statistics and the cost (C,,) of the interventions, an expected value (Pn[Vnf - C,l+ 11 - Pnl[Vnu- CJ can be calculated for each decision. The expected value is the long term mean return realized if the decision is repeated on many animals. The intervention selected is the one that yields the greatest favorable expected value. Producers and consultants to the dairy industry face the potential of committing two types of error with every decision (Fig. 2) (Williamson, 1975; Galligan et al., 1989b; Galligan et al., 1991). Type 1 error is committed if a technology is used or an intervention is implemented when the actual economic benefits of a program is below the break-even value (break-even is defined as the necessary economic response to recover any cost associated with implementation of technology or an intervention). Type

16

W. Cholupa et aL/Animal

Feed Science Technology 58 (19%) I-18

Type I and II Errors Actual Results Decision Results

Use Product

AboveBreakeven

Bekw Breakeven

(PmltaMe)

Wnpmftable)

CORRECT

I

Not Use Product

II

I INCORRECT Type 2 Error

INCORRECT Type 1 Error

CORRECT I

I I I

Fig. 2. Errors associated with decision making.

2 error is committed when a producer fails to use a technology which has economic benefits above break-even. By applying the laws of Bayesian statistics and the cost of the intervention, we can calculate the economic cost associated with each error and thus make decisions which avoid the most costly error (Galligan et al., 1991). This methodology allows the decision maker the ability to account for variability seen in production responses for many technologies. As examples, the economic cost of type 1 and type 2 error were calculated for decisions regarding the use of an accepted technology, sodium bicarbonate, and an emerging technology, BST (Galligan et al., 1989a). For cows supplemented with sodium bicarbonate, the type 1 error cost was $0.02 day-’ while type 2 error cost was $0.32 day-‘. Hence, failing to add sodium bicarbonate to diets is potentially more costly then using it and having it yield a return below break-even. For BST, type 2 error cost ($0.66 day-’ ) was also more costly than type 1 error cost ($0.03 day-’ ) and thus the decision would be to use BST. With both technologies, type 1 error cost is less than type 2 error cost not only because of the magnitude of response and the cost of the technology but also because of consistent response of milk yield to sodium bicarbonate and BST. The methods describe above only considered the economics derived from using vs. not using an intervention. While this is a step forward in decision making, optimal economic returns may be obtained with combinations (portfolios) of interventions (Galligan and Marsh, 1988). For example, rather than treatment vs. replacement of all cows with a disease, financial risk may be minimized by using decision analysis to determine the proportion of animals that should be treated and should be replaced. Similar portfolios can be developed for treatment of other diseases, use of reproductive management aids and enhancers of milk yield.

7. Ration formulation NRC (19891, ARC (1980) and INRA (1989) presented frameworks based on the biology of nutrients, especially nitrogen metabolism, in ruminants that form the basis for calculating protein requirements of lactating dairy cattle. These systems detailed protein nutrition in terms of pools and transfer coefficients between pools. Because information needed for operation of detailed models of protein nutrition was not available, initial

W. Chalupa et al./Animal

Feed Science Technology 58 (1996)

I-18

17

recommendations of protein requirements were estimated from static-aggregated models that only considered overall ruminal degradation of dietary protein and synthesis of microbial protein driven only by rumen available energy. Although simplistic, these static-aggregated models were useful in educating dairy producers and their advisors on the dynamics of protein nutrition so that rations fed to high producing dairy cows usually now contain by-product feed ingredients that are resistant to ruminal degradation. The Cornell Net Carbohydrate and Protein System for evaluating cattle diets (Fox et al., 1990) is a mix of empirical and mechanistic approaches that describe feed intake, ruminal fermentation of protein and carbohydrate, intestinal digestion and absorption, excretion, heat production, and utilization of nutrients for maintenance, growth, lactation and pregnancy. The system can be applied at the farm level because diets are characterized according to fractions that are measured easily in most feed analyses laboratories. We have found the system to be especially valuable in estimating ruminal degradability of dietary protein and in determining whether ruminal microbes are provided with proper types and amounts of nitrogenous nutrients (i.e. ammonia, peptides). The system has also been useful in providing information on amino acid requirements and in identifying limiting amino acids (Fox et al., unpublished data, 1991). Ultimately, nutrient requirements of dairy cattle will be based on quantitative and dynamic mathematical descriptions of biochemical reactions. The efforts of Baldwin et al. (1987a), Baldwin et al. (1987b) and Baldwin et al. (1987~) demonstrate that biochemical data generated from tissue level experiments in vitro can be used to develop mechanistic whole-animal models that are useful in describing utilization of nutrients and nutrient requirements.

References ARC, 1980. The Nutrient Requirements of Ruminant Livestock. Commonwealth Agricultural Research Council, Slough, UK. Baile, C.A. and Krestel-Rickert, D.H., 1988. J. Anim. Sci., 66: 2125. Baldwin, R.L., France, J. and Gill, M., 1987a. J. Dairy Res., 54: 77. Baldwin, R.L., Thomley, J.H.M. and Beever, D.E., 1987b. J. Dairy Res., 54: 107. Baldwin, R.L., France, J., Beever, D.E., Gill, M. and Thomley, J.H.M., 1987~. J. Dairy Res., 54: 133. Bremel, R.D., Yom, H. and Bleck, G.T., 1989. J. Dairy Sci., 72: 2826. Cantield, R.W. and Butler, W.R., 1989. Theriogenology 198, 31: 835. Chalupa, W., 1988. In: W. Haresign and D.J.A. Cole (Editors), Recent Developments in Ruminant Nutrition. Butterworths, Boston, MA. Chalupa, W. and Galligan, D.T., 1989. J. Dairy Sci., 72: 767. Chalupa, W. and Sniffen, C.J., 1991. The Veterinary Clinics of North America-Food Animal Practice: Dairy Nutrition Management. W.B. Saunders Co., Philadelphia, PA, p. 353. Chalupa, W. and Sniffen, C.J., 1992. Proc. Michigan State Veterinary Conf., East Lansing, Michigan State University, East Lansing, MI. Chalupa, W. and Sniffen, C.J., 1993. hoc. Western Canadian Nutrition Conf., Calgary, University of Alberta, Edmonton, Alta. Conner, M.C. and Richardson, C.R., 1987. J. Anim. Sci., 65: 1131.

18

W. Chalupa et al./Animal

Feed Science Technology 58 (1996) l-18

Ferguson, J.D., 1989. In: Meeting the Challenges of New Technology. Monsanto Technical Symposium preceding the Cornell Nutrition Conf. Monsanto Agricultural Co., St. Louis, MO. Ferguson, J.D., 1990. In: Proc. Winter Management Schools, Cornell University, Ithaca, NY. Ferguson, J.D., Chalupa, W., Thomsen, N., Galligan, D.T. and Cummings, K., 1993. J. Dairy Sci., 76 (Suppl. I): 184. Ferguson, J.D. and Galligan, D.T., 1993. Compend. Contin. Educ., 15(4): 646. Fetrow, J., Madison, J.B. and Galligan, D.T., 1985. J. Am. Vet. Med. Assoc., 186: 792. Fetrow, J., Harrington, B., Henry, E.T. and Anderson, K.L., 1987. Compend. Contin. Educ., 10: 7.5. Forsberg, C.W., Crosby, B. and Thomas, D.Y., 1986. J. Anim. Sci., 63: 3 IO. Fox, D.G., Sniffen, C.J., O’Conner, J.D., Russell, J.B. and van Soest, P.J., 1990. The Search: Agriculture. Cornell University Agricultural Experiment Station No. 34, Cornell University, Ithaca, NY. Galligan, D.T. and Marsh, W., 1987. Prev. Vet. Med., 5: 79. Galligan, D.T. and Marsh, W., 1988. Prev. Vet. Med., 5: 251. Galligan, D.T., Chalupa, W. and Ramber,, m C.F., 1989a. J. Dairy Sci., 72 (Suppl. I): 445. Galligan, D.T., Chalupa, W. and Ramber,,,0 C.F., 1989b. J. Dairy Sci., (Suppl. I): 445. Galligan, D.T., Chahrpa, W. and Ramber,, D C.F., 1991. J. Dairy Sci., 74: 902. Hammer, R.E., Pursel, V.G., Rexroad, Jr., C.E., Eall, R.J., Bolt, D.J., Palmiter, R.D. and Brinster, R.L., 1986. J. Anim. Sci., 63: 278. Hoover, W.H. and Stokes, S.R., 1991. J. Dairy Sci., 74: 3630. INRA, 1989. Ruminant Nutrition. Recommended Allowances and Feed Tables. J. Janige (Editor). John Libbey Eurotext, London. Kalter, R.J., Skidmore, A.L. and Ferguson, J.D., 1990. J. Dairy Sci., 73 (Suppl. 1): 162. Kaneene, J.B. and Mather, E.C., 1982. In: Proc. of Cost Benefits of Food Animal Health. W.K. Kellog Foundation, Michigan State University, East Lansing, MI. Kelley, K.W. and Lewin, H.A., 1986. J. Anim. Sci., 63: 288. Keyworth, K., 1990. In: Biotechnology and Animal Health. Animal Health Institute, Alexandria, VA. Klopfenstein, T., Roth, L., Rivera, S.F. and Lewis, M., 1987. J. Anim. Sci., 65: 1139. Lanyon, L.E., 1990. J. Dairy Sci., 73 (Suppl. 1): 123. Lee, D.E., Ferguson, J.D. and Pilbeam, T., 1990. J. Dairy Sci., 73 (Suppl. 1): 284. Lewis, G.S., Aizinbud, E. and Lehrer, A.R., 1989. Anim. Reprod. Sci., 18: 183. Madison, J.B., Fetrow, J. and Galligan, D.T., 1984. J. Am. Vet. Med. Assoc., 185: 520. Males, J.J., 1987. Anim. Sci., 65: 1124. Mertens, D.R., 1988. Proc. Cornell Large Dairy Herd Management Conf., Cornell University, Ithaca, NY, p. 150. Meyer, J.L., 1990. J. Dairy Sci., 73 (Suppl. 1): 123. Ngategize, P.K. and Kaneene, J.B., J.B., 1985. Vet. Bull., 55: 153. Nocek, J.E. and Russell, J.B., 1988. J. Dairy Sci., 71: 2070. NRC, 1987. In: Agricultural Biotechnology. National Academy Press, Washington, DC. NRC, 1989. Nutrient Requirements of Dairy Cattle. National Research Council, Washington, DC. Office of Technology Assessment, 1986. In: Technology, Public Policy, and the Changing Structure of American Agriculture. US Government Printing Office, Washington, DC. Otto, K.L., Ferguson, J.D., Fox, D.G. and Sniffen, C.J., 1991. J. Dairy Sci., 74: 859. Sniffen, C.J., 1988. Proc. Application of Nutrition in Dairy Practice. American Cyanamid Co., Wayne, NJ. Sorensen, A.A., 1990. In: Biotechnology and Animal Health. Animal Health Institute, Alexandria, VA. Tamminga, S., 1990. J. Dairy Sci., 73 (Suppl. 1): 123. Thomas, P.C. and Martin, P.A., 1988. In: P.C. Gamsworthy (Editor), Nutrition and Lactation in the Dairy Cow. Butterworths, Boston, MA. Van Amburgh, M., Galton, D. and Fox, D., 1991. Cornell Heifer Management Symp. Anim. Sci. Mimeo. 149, Cornell Cooperative Extension Service, Ithaca, NY. Van Saunt, R., 1991. The Veterinary Clinics of North America-Food Animal Practice: Dairy Nutrition Management. W.B. Saunders Co., Philadelphia, PA, p. 353. Wildman, E.E., Jones, G.M., Wagner, P.E. and Bowman, R.L., 1982. J. Dairy Sci., 65: 495. Williamson, N.B., 1975. Aust. Vet. J., 41: 114.