Grazing beef cows identified as efficient using a nutrition model partition more energy to lactation
B. R. dos Reis A , L. O. Tedeschi B , A. Saran Netto![https://orcid.org/0000-0003-0170-8134](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-2871-6065](/media/client/orcid_16x16.png)
A Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo, Pirassununga, SP 13635-900, Brazil.
B Department of Animal Sciences, Texas A&M University, College Station, TX 77843-2471, USA.
C Range Cattle Research and Education Center, University of Florida, Gainesville, FL 33865-9706, USA.
Animal Production Science 62(1) 40-54 https://doi.org/10.1071/AN20558
Submitted: 26 March 2020 Accepted: 5 July 2021 Published: 30 September 2021
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
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.
Keywords: beef cattle, biological efficiency, cow efficiency, cow size, energy efficiency index, energy metabolism, grazing systems, heat production.
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