Evaluation of Differing Genetic Potentials on Beef Cattle Resource Use in the Great Plains

Lakamp, Andrew D; Aherin, Dustin; Larson, Robert L.; Ciampitti, Ignacio A.; Kopsa, C. J.; Weaber, R. L.; Bormann, Jennifer M; Rolf, Megan M

doi:10.4148/2378-5977.8223

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“…With the addition of economic values, the model can concurrently assess biological and economic efficiencies over any user-defined time frame ( Lancaster and Larson, 2022 ). It is also feasible to integrate land use, water use, and methane emissions into the model to evaluate individual and cow–calf system-wide resource and emissions efficiency based on genetic traits and management schemes ( Lakamp et al, 2022 ). The ability to assess alternate genetic and management strategies with associated biological uncertainties in a system-wide manner should prove a valuable feature as cattle production faces continuous pressure to become more resource efficient in an everchanging physical and economic environment.…”

Section: Resultsmentioning

confidence: 99%

Stochastic, individual animal systems simulation model of beef cow–calf production: development and validation

Aherin

Weaber

Pendell

et al. 2022

Translational Animal Science

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A stochastic, individual animal systems simulation model describing U.S. beef cow-calf production was developed and parameterized to match typical U.S. Angus genetics under cow-calf production conditions in the Kansas Flint Hills. Model simulation results were compared to available actual, multi-variate U.S. cow-calf production data reported according to Beef Cow-calf Standardized Performance Analysis (SPA) methodology through North Dakota State University’s CHAPS program to assess model validity. Individual animal nutrition, reproduction, growth, and health characteristics, as well as production state are determined on a daily time step. Any number of days can be simulated. These capabilities allow for decision analysis and assessment of long-run outcomes of various genetic, management, and economic scenarios regarding multiple metrics simultaneously. Parameterizing the model to match Kansas Flint Hills production conditions for the years 1995 through 2018, 32 different genetic combinations for mature cow weight and peak lactation potential were simulated with 100 iterations each. Sire mature cow weight genetics ranged from 454 kg to 771 kg in 45 to 46 kg increments. Sire peak lactation genetics were considered at 6.8, 9, 11.3, and 13.6 kg/d for all eight mature cow weights. Utilizing model results for the years 2000-2018, raw model results were assessed against actual historical cow-calf production data. Exploratory factor analysis was applied to interpret the underlying factor scores of model output relative to actual cow-calf production data. Comparing modeled herd output with CHAPS herd data, median average calf weaning age, average cow age, percent pregnant per cow exposed, and percent calf mortality per calf born of model output was 3.4 d greater, 0.2 yr greater, 1 percentage point less, and 1.7 percentage points greater, respectively. Subtracting the median CHAPS pre-weaning average daily gain from the median modeled pre-weaning average daily gain for each of the eight respective mature cow weight genetics categories, and then calculating the median of the eight values, the median difference was -0.21 kg per d. Performing the same calculation for birth weight and adjusted 205 d weaning weight, the modeled data was 4.9 kg and 48.6 kg lighter than the CHAPS data, respectively. Management and genetic details underlying the CHAPS data were unknown.

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Section: Resultsmentioning

confidence: 99%