Application of the 1996 NRC to Protein and Energy Nutrition of Range Cattle

Patterson, Hubert H.; Adams, Don C.; Klopfenstein, Terry J.; Lardy, G. P.

doi:10.15232/s1080-7446(15)31113-x

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…Accomplishment of this goal should lead to improved animal production by informing livestock producers and land managers when strategic, least cost supplements or feeding protocols are needed to achieve economic and environmental production goals. It is anticipated that this effort will compliment previous reviews of the 1996 Beef NRC (Lardy et al, 2004;Patterson et al, 2006) and provide useful information for the NRC Subcommittee on Nutrient Requirements for Beef Cattle.…”

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confidence: 82%

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BEEF SPECIES SYMPOSIUM: An assessment of the 1996 Beef NRC: Metabolizable protein supply and demand and effectiveness of model performance prediction of beef females within extensive grazing systems1,2,3

Waterman

Caton

Löest

et al. 2014

Journal of Animal Science

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Interannual variation of forage quantity and quality driven by precipitation events influence beef livestock production systems within the Southern and Northern Plains and Pacific West, which combined represent 60% (approximately 17.5 million) of the total beef cows in the United States. The beef cattle requirements published by the NRC are an important tool and excellent resource for both professionals and producers to use when implementing feeding practices and nutritional programs within the various production systems. The objectives of this paper include evaluation of the 1996 Beef NRC model in terms of effectiveness in predicting extensive range beef cow performance within arid and semiarid environments using available data sets, identifying model inefficiencies that could be refined to improve the precision of predicting protein supply and demand for range beef cows, and last, providing recommendations for future areas of research. An important addition to the current Beef NRC model would be to allow users to provide region-specific forage characteristics and the ability to describe supplement composition, amount, and delivery frequency. Beef NRC models would then need to be modified to account for the N recycling that occurs throughout a supplementation interval and the impact that this would have on microbial efficiency and microbial protein supply. The Beef NRC should also consider the role of ruminal and postruminal supply and demand of specific limiting AA. Additional considerations should include the partitioning effects of nitrogenous compounds under different physiological production stages (e.g., lactation, pregnancy, and periods of BW loss). The intent of information provided is to aid revision of the Beef NRC by providing supporting material for changes and identifying gaps in existing scientific literature where future research is needed to enhance the predictive precision and application of the Beef NRC models.

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confidence: 82%

“…Native forages exhibit wide variations in nutrient quality and quantity (Patterson et al, 2006;Lardy and Caton, 2012). Consequently, a need for supplementation is often required to offset nutrient deficiencies and improve or sustain production during periods when forage quantity or quality becomes inadequate to meet animal requirements.…”

Section: Environmentmentioning

confidence: 99%

BEEF SPECIES SYMPOSIUM: An assessment of the 1996 Beef NRC: Metabolizable protein supply and demand and effectiveness of model performance prediction of beef females within extensive grazing systems1,2,3

Waterman

Caton

Löest

et al. 2014

Journal of Animal Science

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“…Lardy et al (2004) indicated that additional research and data compilation are needed in regard to microbial efficiency (which impacts both energy and MP predictions), diet chemistry of multiple cool-and warm-season grasses and forbs (within and across seasons), and effects of environment and topography on cow performance. Patterson et al (2006) evaluated the application of the 1996NRC protein and energy models using cow data from 8 previously published data sets representing Nebraska and Montana. When comparing 1996NRC BCS predictions with actual BCS changes, they determined the 1996NRC performed satisfactorily as long as 1) predicted TDN based on in vitro OM disappearance (iVomD) was converted to DE (Rittenhouse et al, 1971), 2) model adjustments for ACTIVITY were not used, and 3) model adjustments for environmental conditions were adjusted to indicate that cattle were acclimated to the temperature.…”

Section: Background and Previous Assessments Of The 1996 Beef Nrcmentioning

confidence: 99%

“…This would indicate the ability to quantify DM or forage intake is critical in developing energy models, and periods of active physiological change (either animal or plant) are more dynamic than current model structures can predict. Patterson et al (2006) also indicated additional research to more precisely define microbial efficiency, especially on low-quality forage diets, is critical to both the energy and protein models used in the 1996NRC. Block et al (2010) evaluated the 1996NRC energy and DMI models using middle and late gestation Angus beef cows wintered in western Canada.…”

Section: Background and Previous Assessments Of The 1996 Beef Nrcmentioning

confidence: 99%

BEEF SPECIES SYMPOSIUM: Potential limitations of NRC in predicting energetic requirements of beef females within western U.S. grazing systems1,2

Petersen

Mueller

Mulliniks

et al. 2014

Journal of Animal Science

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Assessment of beef cow energy balance and efficiency in grazing-extensive rangelands has occurred on a nominal basis over short time intervals and has not accounted for the complexity of metabolic and digestive responses; behavioral adaptations to climatic, terrain, and vegetation variables; and documentation of the effects of nutrient form and supply to grazing cattle. Previous research using pen-fed cows demonstrated differences (P < 0.01) in efficiency of weight change ranging from 135 to 58 g/Mcal ME intake. Furthermore, variation in efficiency of ME use for tissue energy gain or loss ranged from 36% to 80%. In general, energy costs for maintenance, tissue accretion, and mobilization were greatest in Angus-based cows, intermediate in Brahmanand Hereford-based cows, and least in dairy-based cows. The most efficient cattle may reflect the types that are successful in semiarid grazing environments with low input management. Successful range cattle systems are likely the result of retention of animals that best adapted to the grazing environment and thus were potentially more efficient. Animals exposed to a variety of stressors may continually adapt, so energy expenditure is reduced and may tend to depart from the modeled beef cow in the 1996 NRC Beef Cattle Requirements. Critical factors comprising cow lifetime achievement, including reproductive success, disease resistance, and calf weaning weight, may be driven by cow total energy utilization in energy-limiting environments. Therefore, energy adjustments for adapted cattle within these landscapes and seasonal BW changes can alter seasonal NE m requirements. Evaluated studies indicate that in static grazing environments, NRC prediction fitness was improved compared with predictions from dynamic systems where cattle were influenced less by management and more by environmental conditions. Preliminary herd analyses cast doubt on the accuracy of NRC BCS descriptions representing NE m requirements of adapted females utilizing semiarid rangelands. Possible gaps are proposed that could be the basis for prediction inaccuracies. A more complete understanding of mechanisms contributing to productivity in the field than the current model predicts will improve future models to better simulate energetic accountability and subsequent female performance.

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“… Swartz et al (2016) and Brunsvig et al (2017) used UC output to estimate N excretion in grazing heifers and cows. Other researchers have used PD:creatinine ( PD:C ) ratio to estimate microbial crude protein ( MCP ) yield over a grazing season in heifers ( MacDonald et al, 2007 ) and cows ( Patterson et al, 2006 ). Dórea et al (2017) , demonstrated a strong correlation between PD:C and both dry matter intake ( DMI ; R 2 = 0.84) and digestible DMI ( R 2 = 0.85).…”

Section: Introductionmentioning

confidence: 99%

Factors affecting urinary creatinine in heifers and cows1

Whittet

Watson

Erickson

et al. 2019

Translational Animal Science

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A series of total urine collections were conducted to evaluate the effects of age, diet, gestation, and body condition score (BCS) on urinary creatinine (UC) and purine derivative (PD) excretion in heifers and cows. For each collection, urine was collected over a 5-d period and composited by animal within day. Daily samples were analyzed for UC and PD concentration and averaged over the 5-d period. All animals were fed in individual stanchions at 2.0% of body weight (BW). To evaluate the relationship between age and UC excretion, 21 animals ranging from 5 to 80 months of age were fed a forage-based diet supplemented with dried distillers grains (DDG). Creatinine excretion (mg/kg BW) was not correlated with age (P = 0.37). To determine if diet alters UC, 11 heifers were sampled for two urine collection periods. In period 1, heifers were fed a forage-based diet supplemented with DDG. In period 2, heifers were fed a finishing diet (90% concentrate, 10% forage). Creatinine excretion (mg/kg BW) and PD:creatinine (PD:C) was greater (P = 0.01) for heifers when fed the forage-based diet than when fed the concentrate-based diet. Eleven cows fed a forage-based diet supplemented with DDG were sampled to determine the effect of gestation on urinary metabolites. Gestation did not affect UC (P = 0.42) or PD:C (P = 0.30). To evaluate the relationship between 12th rib fat thickness and metabolite excretion, 40 heifers were fed a common finishing diet. There was no relationship between UC (mg/kg BW; P = 0.28) or PD:UC (P = 0.47) and 12th rib fat thickness. To evaluate the relationship between BCS and UC, 11 cows were fed a forage diet supplemented with DDG. There was no relationship between BCS and UC (mg/kg BW; P = 0.99) or PD:C (P = 0.84). To evaluate daily and diurnal variation in UC, nine heifers were fed a forage diet supplemented with DDG. Seven of the heifers were fed a finishing diet (90% concentrate, 10% forage) in a second period. Urine was collected every 2 h from 0600 to 1800 hours. When expressed as mg/kg BW, UC excretion was not different across animals fed the forage-based (P = 0.40) or concentrate-based diet (P = 0.18). Stepwise regression indicated that at least 3 d of collection were required to estimate UC. Time within day and day within period effects were observed (P < 0.01) for UC from 2-h interval samples. The UC varies with type of diet and diurnal variation is present. Variation among animals is relatively small.

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Application of the 1996 NRC to Protein and Energy Nutrition of Range Cattle

Cited by 11 publications

References 25 publications

BEEF SPECIES SYMPOSIUM: An assessment of the 1996 Beef NRC: Metabolizable protein supply and demand and effectiveness of model performance prediction of beef females within extensive grazing systems1,2,3

BEEF SPECIES SYMPOSIUM: An assessment of the 1996 Beef NRC: Metabolizable protein supply and demand and effectiveness of model performance prediction of beef females within extensive grazing systems1,2,3

BEEF SPECIES SYMPOSIUM: Potential limitations of NRC in predicting energetic requirements of beef females within western U.S. grazing systems1,2

Factors affecting urinary creatinine in heifers and cows1

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