EVALUATION OF CATTLE SUB-SPECIES ON GROWTH PERFORMANCE AND GAS FLUX AND THE COMPARISON OF PREDICTION MODEL ACCURACY AND AGGREGATION TO ESTIMATE ENTERIC METHANE EMISSIONS OF FINISHING BEEF STEERS

Abstract

Over the past several decades, extensive research has focused on quantifying the environmental impact of beef production and developing strategies to mitigate enteric methane (CH4) emissions. Two experiments were conducted to study growth performance, enteric CH4 production, and carcass characteristics of Bos indicus and Bos taurus steers managed with and without the use of growth-promoting technology. Following the completion of the field research outlined in experiments 1 and 2, data was compiled across numerous beef finishing experiments, including experiments 1 and 2, to generate a database to evaluate the accuracy of enteric CH4 prediction models.In the first experiment, the objectives of the study were to evaluate the comparative growth performance, carcass characteristics, and gas flux of yearling Bos indicus (BI; Brahman) and Bos taurus (BT; Angus) steers managed with (TRT) and without (CON) the use of growth-promoting technology (GPT). One hundred BI (initial body weight (IBW) = 342 ± 31 kg) and 100 BT (IBW = 341 ± 21 kg) steers were fed for 180d in 2 consecutive phases. In Phase 1, d 0-83, cattle of each sub-species were blocked by body weight and randomly assigned to a management treatment in 10-hd research pens (5 pens/treatment). In Phase 2, d 84-180, cattle were moved and randomly assigned to a 50-hd research pen (1 pen/treatment) equipped with 1 GreenFeed automated head chamber system (C-Lock, Rapid City, SD, USA) and 5 SmartFeed bunk systems (C-Lock, Rapid City, SD, USA ) for measuring individual gas flux of CH4, carbon dioxide (CO2), oxygen (O2), and hydrogen (H2) and feed intake, respectively. Data were analyzed with R (R Core Team, 2021, v. 4.4.1) software to assess the fixed effects of cattle sub-species, treatment, and their interaction for growth performance collected in Phase 1 and 2, and gas flux which was only measured in Phase 2. In phase 1, IBW did not differ (P > 0.75) by sub-species or treatment. Dry matter intake (DMI), average daily gain (ADG), and feed efficiency (G:F) were greater (P < 0.01) for BT, resulting in greater final body weight (FBW) for BT compared to BI. Within sub-species, DMI did not differ (P > 0.35) between treatments in Phase 1, but ADG, G:F, and FBW were greater (P < 0.01) for TRT compared to CON. In Phase 2, DMI increased with the use of GPT. However, there was a sub-species × treatment interaction (P ≤ 0.04) where greater increases in ADG and FBW were observed between TRT and CON for BT relative to BI. Furthermore, BI had a greater proportion of Standard and Select quality grades relative to BT. Daily CH4 production (g CH4/d), CH4 yield (g CH4/kg DMI), and yield of CH4 (% of gross energy intake) were less (P < 0.01) for BI than BT. The use of GPT decreased emissions intensity per unit ADG and carcass gain; furthermore, a sub-species × treatment interaction existed (P ≤ 0.05), where a greater decrease in EI was observed between CON and TRT for BT when compared to BI. Ultimately, BT had greater growth performance and carcass quality, but BI emitted less CH4, highlighting the complex tradeoffs resulting from sustainability-related research in beef production systems. In the second experiment, the study objectives were to evaluate the comparative growth performance, carcass characteristics, and gas flux of yearling BI (Brahman) and BT (Angus) steers managed with (GPT+) and without (GPT-) the use of GPT in winter conditions. One hundred BI (IBW = 364 ± 22 kg) and 100 BT (IBW = 323 ± 17 kg) steers were fed for 180 d in two consecutive phases. In Phase 1, d 0-83, steers of each sub-species were blocked by IBW and randomly assigned to a treatment (10 hd/pen, 5 pens/treatment). In Phase 2, d 84-180, steers were moved to Climate Smart Research Pens where each treatment was randomly assigned to a research pen equipped to measure individual feed intake and gas flux (50 hd/pen, 1 pen/treatment). Data were analyzed with R (R Core Team, 2021, v. 4.4.1) software to assess the fixed effects of cattle sub-species, treatment, and the sub-species × treatment interaction. In Phase 1, IBW differed (P < 0.01) by sub-species, but did not differ (P = 0.98) by treatment. The DMI, ADG, and G:F were greater (P < 0.01) for BT, resulting in greater FBW for Phase 1. Within sub-species, DMI, on a total daily basis and as a percentage of body weight, did not differ (P ≥ 0.09) between treatments in Phase 1, but ADG, G: F, and FBW were greater (P ≤ 0.04) for GPT+ than GPT-. In Phase 2, G:F and DMI when represented on a total daily and as a percentage of BW basis had a sub-species × treatment interaction (P ≤ 0.04) where greater increases in G:F and DMI were observed between GPT+ and GPT- for BI than for BT. Steers managed with GPT and BT steers had greater (P ≤ 0.01) ADG and FBW. Calculated yield grade and backfat thickness were greater (P < 0.01) for BT steers, and BT had more (P < 0.01) USDA Choice and Prime quality grades than BI. Longissimus muscle area was greater (P < 0.01) for BT and GPT+ steers. Daily CH4 emitted was less for BI steers; however, CH4 as a proportion of DMI and gross energy intake were less (P < 0.01) for BT steers. There was a sub-species × treatment interaction (P < 0.01) for CH4 per unit ADG, where a decrease was observed between GPT- and GPT+ for BI, while no difference was observed for BT. In conclusion, BT had greater growth performance and carcass quality, but observations differed by cattle sub-species when CH4 was reported on an absolute versus yield or intensity basis. Following the completion of the second study, data across 7 beef finishing experiments from 2023-2025 were compiled to create a database for analysis. The objective of this study was to determine the accuracy and precision of estimated enteric CH4 production generated by different aggregations of prediction models compared to observed CH4 emissions of finishing beef steers fed a concentrate-based diet. This study evaluated the accuracy and precision of 66 published enteric CH₄ prediction models and eight aggregations against observed CH₄ emissions from finishing steers fed a concentrate-based diet across seven independent experiments. Data were stratified into three databases: the full database (n = 888), Bos taurus database (n = 755), and Bos indicus database (n = 133). Prediction model performance was assessed using root mean square prediction error (RMSPE), concordance correlation coefficient (CCC), and decomposition of RMSPE into mean bias (MB), slope bias (SB), and residual bias (RB). Model aggregations were constructed based on weighted rankings derived from these metrics. Across all databases, aggregation approaches A1-A7 reduced RMSPE compared to most single model evaluations and produced CH₄ estimates within ±10% of the observed mean. However, the aggregation utilized by the Beef Cattle Nutrient Requirements Model (NASEM) consistently underpredicted CH₄. The top-performing aggregated models consistently incorporated DMI as a predictor, emphasizing the influence that feed intake has on CH₄ production. Despite improvements in overall prediction error, residual slope bias, especially for the Bos indicus database, and slight to moderate CCC values, according to criteria established by Proctor et al. (2024), indicate that further refinement is necessary to achieve high accuracy at the individual-animal level. These findings demonstrate that model aggregation offers a pragmatic and effective strategy for enhancing predictive performance of enteric CH₄ emissions in finishing beef steers, supporting improved GHG inventories, life cycle assessments, and mitigation strategies in beef production systems. However, continued development of robust prediction models will depend on sustained direct measurement of enteric CH₄ emissions, necessitating increased investment in research and methodological development.