Assessment of milk yield and nursing calf feed intake equations in predicting calf feed intake and weaning weight among breeds

Evaluation of Strategies to Improve the Environmental and Economic Sustainability of Cow–Calf Production Systems

Simple Summary

Beef cattle have a significant contribution to greenhouse gas emissions globally, but they have a unique ability to digest plant material that is inedible for humans, thus producing human food from grasslands and rangelands. Additionally, many people around the world depend upon cattle ranching of grasslands and rangelands for their livelihoods. Identifying the strategies likely to have the largest impact on greenhouse gas emissions while improving or maintaining economic returns is necessary to guide future research. The goal of the current study was to evaluate four potential strategies for improving the environmental and economic sustainability of cow–calf production. The four strategies included (1) decreasing the feed required for maintenance, thus increasing the feed available for growth, (2) decreasing the time for cows to rebreed after calving, (3) increasing the digestibility of pasture grass, and (4) increasing the yield of pasture grass. A computer simulation model of a cow herd in Kansas, U.S.A., was modified to create variation in the four strategies. Decreasing the feed required for maintenance improved both environmental and economic sustainability, and increasing the yield of pasture grass improved economic sustainability, implying that these strategies should be primary targets to enhance the sustainability of cow–calf production systems.

Abstract

Grazing cow–calf production systems account for 60 to 70% of the greenhouse gas emissions of U.S. beef production. The objective of this analysis was to evaluate the importance of management strategies (cow maintenance energy requirements, reproductive efficiency, forage nutritive value, and forage yield) on the sustainability of cow–calf production systems using a sensitivity analysis in a production systems model. The Beef Cattle Systems Model was used to simulate a cow–calf production system in the Kansas Flint Hills using Angus genetics over a 24 year time period. The model was modified to create variation among cow herds in the base net energy for the maintenance requirement (NEm_Req), postpartum interval (PPI), grazed forage digestibility (Forage_TDN), and forage yield per hectare (Forage_Yield). The model was run for 1000 iterations/herds of a 100-cow herd. A stepwise regression analysis in conjunction with standardized regression analysis was used to identify important predictors of an indicator of greenhouse gas (GHG) emission intensity, dry matter intake per kilogram weaned, and two indicators of economic sustainability, winter feed use and returns over variable costs, using R statistical software. The most important predictor of DMI per kilogram weaned was calf weaning weight followed by NEm_Req, whereas returns over variable costs were primarily influenced by kilograms weaned per cow exposed and total purchased feed (supplement + winter feed), which were strongly influenced by NEm_Req and Forage_Yield, respectively. In conclusion, decreasing the net energy required for maintenance improved both economic and environmental sustainability, and increasing forage yield and length of the grazing season improved economic sustainability, implying that these strategies should be primary targets to enhance the sustainability of cow–calf production systems.

A mathematical nutrition model adequately predicts beef and dairy cow intake and biological efficiency

The beef cow-calf sector accounts for 70% of feed consumed and greenhouse gases emitted for the beef industry, but there is no straightforward method to measure biological efficiency in grazing conditions. The objective of this study was to evaluate a mathematical nutrition model to estimate the feed intake and biological efficiency of mature beef cows. Data from dams (N = 160) and their second and third progeny (312 pairs) were collected from 1953 through 1980. Individual feed intake was measured at 28-d intervals year-round for dams and during 240-d lactation for progeny. Body weights of progeny were measured at 28-d intervals from birth to weaning, and of dams at parturition and weaning each production cycle. The milk yield of dams was measured at 14-d intervals. Dam metabolizable energy intake (DMEI) and milk energy yield (MEL) of each cow were predicted using the Cattle Value Discovery System beef cow (CVDSbc) model for each parity. Biological efficiency (Mcal/kg) was computed as the ratio of observed or predicted DMEI to observed calf weaning weight (PWW). Pearson correlation coefficients were computed using corr.test function and model evaluation was performed using the epiR function in R software. Average (SD) dam weight, PWW, DMEI, and observed MEL were 527 (86) kg, 291 (47) kg, 9584 (2701) Mcal/production cycle, and 1029 (529) Mcal, respectively. Observed and predicted DMEI (r = 0.93 and 0.91), and observed and predicted MEL (r = 0.58 and 0.59) were positively correlated for progeny 2 and 3, respectively. The CVDSbc model under-predicted DMEI (mean bias [MB] = 1,120 ± 76 Mcal, 11.7% of observed value) and MEL (MB = 30 ± 25 Mcal, 2.9% of observed value). Observed and predicted progeny feed intake were not correlated (r = 0.01, P-value = 0.79). Observed and predicted biological efficiency were positively correlated (r = 0.80 and 0.80, P-value ≤ 0.05) for parity 2 and 3, respectively, and the CVDSbc model under-predicted biological efficiency by 11% (MB = 3.59 ± 0.25 Mcal/kg). The CVDSbc provides reasonable predictions of feed intake and biological efficiency of mature beef cows, but further refinement of the relationship between calf feed intake and milk yield is recommended to improve predictions. Mathematical nutrition models can assist in the discovery of the biological efficiency of mature beef cows.

Grazing beef cows identified as efficient using a nutrition model partition more energy to lactation

Context The efficiency of the cow–calf sector could be enhanced by matching cow biological type to the production environment; however, methods to estimate the biological efficiency of grazing beef cows are not available. Aims This study utilised a mathematical nutrition model for ranking beef cows for estimated biological efficiency, determining energetic efficiency and evaluate relationships with other production traits. Methods Cow live weight, calf birth and weaning weight, calf birth and weaning date, and forage nutritive value of hay and pasture were collected for 69 Brangus crossbred beef cows over a complete production cycle. The Cattle Value Discovery System for Beef Cow (CVDSbc) model was used to compute metabolisable energy required (MER) for the cow, and energy efficiency index (EEI) was computed as the ratio of MER to calf weaning weight. Pearson correlation coefficients were computed among performance traits. During late lactation and gestation, low (n=8) and high (n=8) EEI cows were individually fed ad libitum for 44 and 32 days, respectively, then fed 0.5× the estimated metabolisable energy required for maintenance for 7 days (gestation experiment only). Apparent nutrient digestibility, heat production, and milk yield were measured. Key results EEI was strongly negatively correlated (P<0.05) with model predicted peak milk (−0.62) and calf weaning weight (−0.65), but moderately correlated (P<0.05) with cow live weight (0.46). Dry matter intake was not different (P>0.75) between low and high EEI cows even though low EEI cows weighed less (P<0.05) during late lactation and gestation experiments. Low EEI cows tended to have greater efficiency of metabolisable energy use for maintenance and gain (P<0.10), and EEI was negatively correlated (P<0.05) with the efficiency of metabolisable energy use for maintenance (−0.56) and gain (−0.57). Conclusion The CVDSbc model identified cows that weaned heavier calves due to greater dry matter intake of cows relative to live weight allowing more energy apportioned towards lactation, and more efficient use of metabolisable energy for maintenance and gain. Implications Energy efficiency index might provide a logical assessment of biological efficiency of beef cows in grazing production systems.

Relationships among feed efficiency traits across production segments and production cycles in cattle

Understanding the relationships between feed efficiency traits measured in different stages of production is necessary to improve feed efficiency across the beef value chain. The objective of this study was to evaluate relationships among feed efficiency traits measured as growing heifers and breeding females and in their progeny in three full production cycles, and relationships of dam residual feed intake (RFI) with lifetime and lifecycle cow efficiency traits. Data were collected on 160 mixed-breed heifers from 240 d of age to weaning of their third progeny, and postweaning performance of progeny until harvest in experiments initiated in 1953, 1954, 1959, 1964, 1969, and 1974. Individual feed offered was recorded daily, and feed refusals measured every 28 d. Milk yield was measured at 14-d intervals throughout lactation by machine or hand milking. Females and progeny were weighed at 28-d intervals and progeny were harvested at a constant endpoint of live grade or age depending upon the experiment. Feed efficiency traits of RFI and residual BW gain (RG) were computed as the residual from linear regression for developing heifers, dams (RFI and residual energy-corrected milk [RECM]), and postweaning progeny. Feed:gain ratio (FCR) was computed for developing heifers and postweaning progeny, and feed:milk energy ratio (FME) was computed for dams. Various measures of cow efficiency were calculated on either a life cycle or lifetime basis using ratios of progeny and dam weight outputs to progeny and dam feed inputs. Pearson correlations were computed among traits adjusted for a random year-breed-diet group effect. Heifer RFI (0.74) and RG (-0.32) were correlated (P ≤ 0.05) with dam RFI in parity 1 only, but were not correlated (P > 0.05) with dam RECM in any parity. Heifer RFI was correlated (P ≤ 0.05) with progeny RFI (0.17) in parity 3 only. Heifer FCR was not correlated with dam FME or progeny FCR in any parity. Dam RFI was weakly correlated (r = 0.25 to 0.36; P ≤ 0.05) among parities, whereas dam FME and RECM were strongly correlated (r = 0.49 to 0.72; P ≤ 0.05) among parities. Dam RFI in parity 1 and 2 was weakly correlated (r = -0.20 to -0.33; P ≤ 0.05) with cow efficiency ratios that included dam weight as an output, whereas dam RFI in parity 3 was not correlated with any cow efficiency ratio. In conclusion, feed efficiency traits were poorly correlated across production segments, but moderately repeatable across production cycles.