William Chalupa

Milk Fatty Acids II: Prediction of the Production of Individual Fatty Acids in Bovine Milk

Previously observed relationships between dietary composition and production of a small number of individual milk fatty acids were the motivation to examine whether equations could be developed to predict production of all the major individual milk fatty acids. Such equations could be incorporated into ration formulation programs and used to examine factors that influence milk fat composition. Data from 29 published experiments on Holstein cows that provided 120 dietary treatments were entered into CPM-Dairy to obtain estimates of amounts of individual long-chain fatty acids (LCFA) absorbed from the intestines. These derived data and other dietary and animal data including the reported fatty acid composition of milk fat were entered into a spreadsheet. Descriptors of diet included daily intake of dry matter, total fermentable carbohydrate, total fatty acids, and profile of dietary fatty acids, intake of neutral detergent fiber, supplemental fish-oil, buffer, and magnesium oxide. Cow data included body weight and days in milk (DIM). Multiple linear regression was used to develop equations to predict the production (g/d) of each of 26 major LCFA. The equations developed generally had R(2) values in excess of 0.5. Production (g/d) of total de novo fatty acids (C4:0 to C15:0) (PTdenovo) was found to be positively related to the intake of fermentable carbohydrate, and negatively related to the intake of fish oil fatty acids and the estimated total amount of unsaturated fatty acids absorbed from the intestines. The PTdenovo was greater in pasture-fed cows than total mixed ration-fed cows and was negatively related to the square root of DIM. Production of each individual de novo fatty acid was described by a fixed proportion of PTdenovo. These proportions were 0.12 +/- 0.006 (C4:0), 0.083 +/- 0.0039 (C6:0), 0.0516 +/- 0.0025 (C8:0), 0.111 +/- 0.003 (C10:0), 0.134 +/- 0.0037 (C12:0), 0.441 +/- 0.007 (C14:0), 0.046 +/- 0.0024 (C14:1), and 0.0432 +/- 0.0017 (C15:0). Separate independent equations were developed to describe the daily production of C16:0, C16:1, and the main individual preformed fatty acids (>C16). The productions of each of the main individual pre-formed fatty acids were generally strongly related to the corresponding estimated amount (g/d) of specific fatty acids absorbed from the intestines. Percentage estimates for the direct transfer of the major absorbed LCFA to their corresponding LCFA in milk were 42% (C16:0); 9.5% (C18:0); 47.5% (cis-9 C18:1); 16.1% (all isomers of trans-C18:1), 38% (cis-9, cis-12 C18:2); and 31% (cis-9, cis-12, cis15 C18:3). High dietary intake of fish oil fatty acids was negatively associated with the production of all of the major individual preformed fatty acids with the exception of C20:5 and C22:6. In some instances, particular dietary factors were found to have positive influences on production of one fatty acid and negative influences on another. For example, high levels of dietary magnesium oxide were positively associated with production of C17 fatty acids but negatively associated with production of C18:0 and cis-9, trans-11 C18:2 (conjugated linoleic acid). This analysis quantified effects of major dietary and cow factors on production of individual fatty acids in milk.

Protein and amino acid nutrition of lactating dairy cattle.

This article describes the National Research Council Model of protein metabolism and illustrates its use in meeting the protein requirements of lactating cows. Attention is then directed toward amino acid nutrition with emphasis on the need for models to estimate amino acid requirements. Finally, the potential to improve productivity with rumen-protected amino acids is considered.

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

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.

Protein and amino acid nutrition of lactating dairy cattle—today and tomorrow

Abstract The Cornell Net Carbohydrate and Protein System was used to (1) estimate absorbed amino acids provided by rumen escape protein and (2) formulate rations on the basis of amino acids. Forages, even those high in crude protein, only provide small amounts of rumen escape amino acids. Maize proteins are deficient in lysine. Oil seed proteins contain low levels of methionine. Animal and marine proteins, because of their high crude protein and low ruminal degradation, can be used to adjust the amino acid profile of absorbed protein, especially methionine (fish meal) and lysine (blood meal) Rations to support high levels of milk production need to contain protein sources with amino acid profiles that are complementary.

Bovine Somatotropin: Physiology, Lactational Responses and Implications for the Dairy Industry1

Summary Somatotropin is a polypeptide hormone of the anterior lobe of the pituitary gland. It consists of 191 amino acids and its molecular weight is 22,000. Blood concentrations of somatotropin reflect balances between release, degradation and binding to receptors. Somatotropin release is under the control of two hypothalamic polypeptides: somatotropin releasing factor and somatostatin. Administration of releasing factor and immunization against somatostatin may be alternatives to exogenous somatotropin for increasing production and efficiency. Somatotropin increases mild yield by 10 to 40% without affecting milk composition so long as the feeding program provides adequate energy and protein. Additional nutrients are derived from increased feed consumption and by diversion from body tissues to milk synthesis. Lactational efficiency is improved by diluting maintenance requirements and by directing absorbed nutrients to milk. Somatotropin causes a major redirection of nutrients from tissues to milk synthesis but mechanisms are not understood well. Somatotropin probably does not act upon the mammary gland directly. Some of the response may be mediated by somatomedins or other polypeptide hormones. Management and nutrition will determine whether improvements possible with somatotropin are realized. Diets normally fed during the early stages of the lactation cycle will be fed for longer periods. In order to gain acceptance, somatotropin must not compromise animal health, milk quality or the safety of milk for human consumption. Impacts upon DHIA records and sire-proofing will depend on how rapidly and widely somatotropin is used. Wide-spread adoption will mean that records will simply be raised to new levels. Sire-proofing will need to be monitored closely to detect abuses. Increased milk yields mean that fewer cows will be needed. Technological advances in the last 25 years have decreased the cow population and number of farms. This trend will continue even if somatotropin is not available commercially. Some dairymen may use somatotropin to maintain total production with fewer cows. In general, larger farms realise greater financial returns from favorable opportunities than smaller farms. However, the expected impact of not adopting somatotropin technology is decreased probability of survival and lower financial performance of both large and small farms.

Production of animal protein from nonprotein nitrogen chemicals.

Ruminants obtain amino acids (AA) from microbial protein synthesized in the rumen and from feed proteins that escape ruminal degradation. Synthesis of microbial protein provides a mechanism for obtaining AA from NPN. Effectiveness of NPN utilization depends upon production and utilization of ammonia by rumen microbes. Because ammonia is produced from protein and NPN, feeding proteins resistant to microbial degradation forces utilization of ammonia derived from NPN. The quantity of microbial cells formed in the anaerobic rumen fermentation system is primarily dependnt upon energy supply but can be modulated by types and supplies of other nutrients (i.e. amino-N, minerals growth factors) and by growth rate of rumen bacteria. Potential quantities of NPN that can be utilized with different feed ingredients can be estimated from amounts of feed protein degraded in the rumen, and requiring transformation into protein via growth of rumen microbes, and from amounts of energy provided by feed ingredients. High energy feed ingredients with low amounts of degradable protein are most favorable for NPN utilization, but NPN has also been used successfully with high-fibrous, low energy feed materials. Growth, lactation and reproduction have been obtained on diets containing more than 97% of the nitrogen from NPN, but microbial protein alone cannot provide quantities of AA needed for high levels of productivity. Regulating ruminal degradation of dietary protein and utilizing NPN for rumen protein production is a highly desirable strategy for producing human foods with ruminants.

Feeds, Ration Formulation | Models in Nutritional Management

Mathematical models of nutrition have been in use for over three decades and have stimulated improvements in feeding cattle. The field of animal nutrition has led the way in development and application of computer models in research and management. Now, more complete data sets and precise mathematical approaches have allowed us to improve and expand models of nutrient use. Models vary in complexity according to their objectives. Scientific models are usually developed downward from basic experimental data pertaining to metabolic processes. Production models are used to portray animal responses to different inputs. They are created from collections of data from animal- or herd-level experiments and are developed downward. These models are valid only within the domain of the data used in their development and should not be used to extrapolate beyond that. They are used in optimization of ration formulation and in whole-farm nutrient management. Various computer programs that are available for on-farm nutrient management planning are described. Research models are allowing us to explicitly include genetic information from genomic and transcriptomic work into nutrition models; in addition, these models are being expanded to the role of nutrition in reproduction and disease as well as environmental efficiency.