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

Abstract

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.

Evaluation and Development of a Nutrition Model to Predict Intake and Growth of Suckling Calves

“…Forages with lesser digestibility ferment at slower rates, resulting in longer rumen residence times, and forages with greater digestibility ferment at faster rates, resulting in shorter rumen residence times, whereas in vitro methods use a constant rumen fluid incubation time, which does not account for the differing rumen residence times. Bryan [62] used serial in situ incubation times (12,24,36,48, and 72 h) for forage samples collected from pastures grazed by suckling calves: in vivo forage digestibility throughout the grazing season was best estimated by differing incubation times. The average intercept and slope of the published equations is 8.075 and 0.861, respectively, which is similar to the intercept and slope of the newly developed equation; thus, the new equation may be robust across forage species and maturities.…”

“…Process-based soil and plant computer models have been used to evaluate effects of animal grazing on soil parameters [5][6][7][8][9], and to predict the performance of growing and finishing cattle from nutrition and environmental conditions [10,11]. A recent dynamic, stochastic cow herd model has been developed to evaluate the sustainability of management decisions over long time horizons [12,13], but the current model is inadequate in predicting the growth of suckling beef calves consuming forage.…”

Simulation Approaches Used for Management and Decision Making in the Beef Production Sector: A Systematic Review

“…The least common method of validation is face validity ( n = 5), which involves the use of an expert panel review to determine how well the model represents the production system in real life and/or determine whether the outputs are reasonable given the expert’s experiences and expectations. Face validation can be used if a robust dataset or similar studies are not available for a direct comparison [ 35 ] or can also be used to provide an extra level of validation [ 25 ].…”

The Adoption of Robotic Process Automation Considering Financial Aspects in Beef Supply Chains: An Approach towards Sustainability

“…Simulation-based testing is a powerful technique to assess and evaluate the operational and financial performance of supply chains and helps firms to increase profit margins and evaluate costs beforehand. Thus, a scenario analysis using a simulation allows business users and stakeholders to conduct sustainability assessments and adopt better approaches to create sustainable value [74].…”