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Solving complex livestock production problems is a pressing issue for achieving long-term sustainability and profitability and often requires modeling techniques. The use of mathematical models is undoubtedly a daily practice in the livestock industry, especially nutrition, and has improved animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Although many students and professionals use spreadsheets and existing empirical models for nutrition and management [NASEM (2016)], there is still a need to understand the complexity of livestock systems and the utility of flight simulator models. At the same time, more complex models (although robust) may fail to provide new insights for experienced nutritionists due to poor userfriendliness. A systems understanding goes beyond simply obtaining a desired output, such as optimizing a total mixed ration, but instead leads to identifying high-leverage solutions and gaining insight. Further, parameterizing and calibrating variables and equations and testing management scenarios is straightforward. However, developing causal feedback linkages (A to B and B to A) and identifying time delays is less intuitive and more challenging for novices. Model flight simulators grounded in fully documented, calibrated models provide a means to introduce practitioners to a methodology of insight generation because the user designs and runs the model scenarios for themselves, challenging their mental models. Such approaches are generally more impactful (compared with someone telling them) because they have gained insight into the system themselves, and, in the case of open source (white box models), they can explore equations and parameters. Therefore, understanding how to utilize dynamic models in scenario-based simulations is critical in training current and future modelers in animal science. This hands-on model training will cover the basics of System Dynamics modeling and allow participants to run real-time animal production simulations. Finally, participants will be “debriefed” to unpack “ah ha” moments that were unexpected. The debrief will include using models to develop accurate guidelines and recommendations for those who cannot use computer models and developing experimental designs to test hypotheses and “validate” model recommendations (proof in the pudding). Participants will gain knowledge of System Dynamics applications for animal production systems, experience using flight simulators, and their utility in teaching, informing, and guiding their livestock production or that of a client. The “flight simulator” will focus on production and nutrition with species-agnostic principles. Participants will also have a new tool to identify areas for improvement in model development (i.e., what is missing?), research questions, or industry needs. The need for modelers who can turn big data into insight, knowledge, and wisdom using a systems approach is becoming even more critical due to the increasing use of precision livestock farming. Thus, providing the livestock industry with trained systems modelers will help achieve current and future sustainability challenges. [ABSTRACT FROM AUTHOR]

The addition of natural plant secondary compounds to ruminant feed has been extensively studied because of their ability to modify digestive and metabolic functions, resulting in a potential reduction in greenhouse gas emissions, among other benefits. Condensed tannin (CT) supplementation may alter ruminal fermentation and mitigate methane (CH4 ) emissions. This study’s objective was to determine the effect of quebracho CT extract [QT; Schinopsis quebracho-colorado (Schltdl.) F.A. Barkley & T. Meyer] within a roughage-based diet on ruminal digestibility and kinetic parameters by using the in situ and in vitro gas production techniques, in addition to blood urea nitrogen (BUN) and ruminal (volatile fatty acid [VFA], NH3 -N, and protozoa count) parameters. Twenty rumen-cannulated steers were randomly assigned to four dietary treatments: QT at 0%, 1%, 2%, and 3% of dry matter (DM; QT0: 0% CT, QT1: 0.70% CT, QT2: 1.41% CT, and QT3: 2.13% CT). The in situ DM digestibility increased linearly (P = 0.048) as QT inclusion increased, whereas in situ neutral detergent fiber digestibility (NDFD) was not altered among treatments (P = 0.980). Neither total VFA concentration nor acetate-to-propionate ratio differed among dietary treatments (P = 0.470 and P = 0.873, respectively). However, QT3 had lower isovalerate and isobutyrate concentrations compared with QT0 (P ≤ 0.025). Ruminal NH3 and BUN tended to decline (P ≤ 0.075) in a linear fashion as QT inclusion increased, suggesting decreased deamination of feed protein. Ruminal protozoa count was reduced in quadratic fashion (P = 0.005) as QT inclusion increased, where QT1 and QT2 were lower compared with QT0 and QT3. Urinary N excretion tended to reduce in a linear fashion (P = 0.080) as QT increased. There was a treatment (TRT) × Day interaction for in vitro total gas production and fractional rate of gas production (P = 0.013 and P = 0.007, respectively), and in vitro NDFD tended to be greater for QT treatments compared with no QT inclusion (P = 0.077). There was a TRT × Day interaction (P = 0.001) on CH4 production, with QT3 having less CH4 production relative to QT0 on day 0 and QT2 on days 7 and 28. Feeding QT up to 3% of the dietary DM in a roughage-based diet did not sacrifice the overall DM digestibility and ruminal parameters over time. Still, it is unclear why QT2 did not follow the same pattern as in vitro gas parameters. Detailed evaluations of amino acid degradation might be required to fully define CT influences on ruminal fermentation parameters and CH4 production. [ABSTRACT FROM AUTHOR]

Maintaining optimal body condition is an important concern for horse owners and managers as it can affect reproductive efficiency, athletic ability, and overall health of the horse; however, information regarding dietary requirements to maintain or alter BCS in the horse is limited. A recently developed model had high accuracy in predicting the energy required to alter BCS in the horse. However, the model was restricted to sedentary mares, while many horses are subject to physical work. The objective of this study was to expand the scope of that model to include exercising horses by incorporating previously published estimates of exercise energy expenditure and then testing the expanded model. Stock type horses (n = 24) were grouped by initial BCS (3.0 to 6.5) and assigned to treatments of light (L), heavy (H), or no-exercise control (C). Horses were fed according to the model recommendations to increase (I) or decrease (D) two BCS within 60 d. Thus, six treatments were obtained: HD, HI, LD, LI, CD, CI. Mean DE intake Mcal/d for each group was HD = 19.3 ± 0.90, HI = 29 ± 0.84, LD = 13.2 ± 0.54, LI = 23.1 ± 1.39, CD = 12.1 ± 0.79, and CI = 21.9 ± 0.94. BCSs were evaluated by three independent appraisers, days 0 and 60 values were used to calculate the average BCS change for HD = −0.88 ± 0.24, HI = 1.13 ± 0.24, LD = −1.5 ± 0.29, LI = 0.88 ± 0.38, CD = −1.38 ± 0.13, and CI = 1.35 ± 0.14. Statistical comparison of final observed and model predicted values revealed acceptable precision when predicting BCS and BW respectively in control horses (r 2 = 0.91, 0.98) but less precision when predicting body fat (BF) (r 2 = 0.51). Model precision for BCS, BW, and BF respectively in lightly (r 2 = 0.29, 0.85, 0.57) and heavily (r 2 = 0.04, 0.84, 0.13) exercised horses was low. Model accuracy was acceptable across all treatments when predicting BW (C b = 0.97, 0.96, 0.98). However, accuracy varied when predicting BCS (C b = 0.82, 0.89, 0.41) and BF (C b = 0.80, 0.55, 0.87) for the control, light, and heavy exercise groups, respectively. These results indicate that the revised model is acceptable for sedentary horses but the predictability of the model was insensitive to the exercising horse, therefore the exercise energy expenditure formulas incorporated into the model require revision. Packaging this model in a format that facilitates industry application could lead to more efficient feeding practices of sedentary horses, generating health, and economic benefit. Further investigation into energy expenditure of exercising horses could yield a model with broader applications. [ABSTRACT FROM AUTHOR]

The contemporary concern about anthropogenic release of greenhouse gas (GHG) into the environment and the contribution of livestock to this phenomenon have sparked animal scientists' interest in predicting methane (CH[sub 4]) emissions by ruminants. We contend that improving the adequacy of mathematical nutrition model estimates of production of meat and milk is a sine qua non condition to reliably determine ruminants' worldwide contribution to GHG. Focusing on milk production, we address six basic nutrition models or feeding standards (mostly empirical systems) and five complex nutrition models (mostly mechanistic systems), describe their key characteristics, and highlight their similarities and differences. We also present derivative systems. We compiled a database of milk production information from 37 published studies from six regions of the world, totalling 173 data points: 19 for Africa, 45 for Asia, 16 for Europe, 12 for Latin America, 44 for North America and 37 for Oceania. Four models were used to predict milk production in lactating dairy cows, and the adequacy of their predictions was measured against the observed milk production from our database. Even though these mathematical nutrition models shared similar assumptions and calculations, they have different conceptual and structural foundations inherent to their intended purposes. A direct comparison among these models was further complicated by the different models requiring unique inputs that are very often not available, and the low reliability of the inputs prevents an unbiased assessment of the model predictions. Very few studies have collected the necessary information to run more mechanistic systems, and users have to rely on standard information to populate many model inputs. Study effect was a critical source of variation that limited our ability to conclusively evaluate the models' applicability under different scenarios of production around the world. Only after study variation was removed from the database did the adequacy of the model predictions of milk production improved, but deficiencies still existed. On the basis of these analyses, we conclude that not all models were suitable for predicting milk production and that simpler systems might be more resilient to variations in studies and production conditions around the world. Improving the predictability of milk production by mathematical nutrition models is a prerequisite to further development of systems that can effectively and correctly estimate the contribution of ruminants to GHG emissions and their true share of the global warming event. A database of milk production information from 37 published studies from six regions of the world, totalling 173 data points, was used to test four ruminant nutrition models. We conclude that not all models were suitable for predicting milk production and that simpler systems might be more resilient to variations in studies and production conditions around the world. [ABSTRACT FROM AUTHOR]

The use of the in vitro gas production (IVGP) technique requires accurate determination of neutral detergent fiber (NDF) residue. However, the NDF determination using standard procedures are not always feasible for IVGP; thus requiring micro-NDF methods, which need autoclave (AC) and pressure cooker (PC) to boil the solution. A complete randomized design using a 3×3 factorial arrangement was implemented to investigate the effect of washing methods (WM: AC, PC, or ANKOM200) and solution ratios (WS: 100 mL neutral detergent solution (ND), 150 mL ND, or 100 mL H2O/g sample) to determine NDF residues, assuming ANKOM200 and 100 mL ND/g as the standard methodology. Each factor combination was performed in triplicate with a replicate being comprised of 12 bottles or bags (two blanks and five feedstuffs in duplicate). Feedstuffs were: alfalfa hay (AH), bermudagrass hay (BH), two high-forage rations (G1 and G2), and a high-concentrate ration (FR). Following each run, bottles were filtered to obtain the NDF. Data were analyzed by diet using a random coefficients model. An interaction of WM ′ WS was present for AH and G1 (P < 0.01), G2 and FR had tendencies (P = 0.08 and 0.06, respectively), whereas BH demonstrated no interaction (P = 0.37). The PC with 100 mL or 150 mL did not differ from the standard methodology for AH, G1, G2, and FR. The BH demonstrated differences between WM and WS (P < 0.01). The PC had lower NDF residue compared to the AC and ANKOM200, whereas H2O had substantially greater NDF residue relative to both ND ratios. We concluded that H2O is not a suitable substitute for ND solution regardless of the feedstuff. Both micro-NDF washing methods may be satisfactory depending on the type of feedstuff used but further investigation is required. [ABSTRACT FROM AUTHOR]

Beef production needs to increase from 60 million to 130 million tons by 2050 to feed a growing world population, and 70% of this production increase is expected from beef industries located in subtropical and tropical regions of the world. Bos indicus -influenced cattle predominate in these regions but are often managed using practices developed for Bos taurus breeds reared in temperate climates. Hence, a fundamental step to meet the increasing global demand for beef is to develop specific management for B. indicus -influenced cattle in tropical or subtropical environments. Bos taurus and B. indicus are different subspecies, and diverge in social and biological functions due to selection pressure caused by complex evolutionary and domestication processes. Bos indicus cattle display different social responses compared with B. taurus counterparts, which must be taken into account by management planning as these traits directly impact cattle performance and welfare. In tropical and subtropical regions, warm-season perennial C4 grasses are the dominant forages, and their availability has a significant influence on the productivity of beef cattle systems. The resilience of C4 grasses under adverse conditions is one of their most important characteristics, even though these forages have reduced nutritive value compared with forages from temperate climates. Accordingly, nutritional planning in tropical and subtropical conditions must include management to optimize the quantity and quality of C4 forages. Nutritional requirements of cattle raised within these conditions also require special attention, including inherent metabolic compromises to cope with environmental constraints and altered energy requirements due to body composition and heat tolerance. Nutritional interventions to enhance beef production need to be specifically tailored and validated in B. indicus- influenced cattle. As an example, supplementation programs during gestation or early life to elicit fetal programming or metabolic imprinting effects, respectively, yield discrepant outcomes between subspecies. Bos indicus- influenced cattle produce carcasses with less marbling than B. taurus cattle, despite recent genetic and management advances. This outcome is mostly related to reduced intramuscular adipocyte volume in B. indicus breeds, suggesting a lesser need for energy stored intramuscularly as a mechanism to improve thermotolerance in tropical and subtropical climates. [ABSTRACT FROM AUTHOR]