Relationships of retained energy and retained protein that influence the determination of cattle requirements of energy and protein using the California Net Energy System

Fat deposition and partitioning for meat production in cattle and sheep

In markets for beef and sheep meat, an appropriate level of intramuscular fat (IMF) is highly desirable for meat-eating quality, but strategies to improve it usually lead to an undesirable excess in carcase fat, presenting a major challenge to livestock producers. To solve this problem, we need to understand the partitioning of fat among the major fat depots: IMF, subcutaneous fat (SCF) and visceral fat (VF). In most genotypes of cattle and sheep, the rate of accretion is lower for IMF than for SCF and VF, so genetic selection for a high level of IMF, or the use of an increased dietary energy supply to promote IMF deposition, will increase overall fatness and feed costs. On the other hand, feeding postnatal calves with excessive concentrates promotes IMF deposition, so a nutritional strategy is feasible. With genetic strategies, several problems arise: 1) positive genetic correlations between IMF, SCF and VF differ among genotypes in both cattle and sheep; 2) genotypes appear to have specific, characteristic rates of accretion of IMF during periods of growth and fattening; 3) most breeds of cattle and sheep naturally produce meat with relatively low levels of IMF, but IMF does vary substantially among individuals and breeds so progress is possible through accurate measurement of IMF. Therefore, an essential prerequisite for selection will be knowledge of the genetic correlations and fat accretion rates for each genotype. Currently, selection for IMF is based on existing technology that directly measures IMF in the progeny or siblings, or estimates IMF in live animals. New technology is needed to permit the simultaneous measurement of SCF and IMF in the field, thus opening up the possibility of accurate selection, particularly for fat partitioning in live animals. Specifically, there would be great value in detecting individuals with an IMF advantage at an early age so the generation interval could be shortened and genetic gain accelerated. Genetic gain would also be greatly aided if we could select for genes that control adipogenesis and lipogenesis and are also differentially expressed in the various depots.

A revised model of energy transactions and body composition in sheep

A mechanistic, dynamic model was developed to calculate body composition in growing lambs by calculating heat production (HP) internally from energy transactions within the body. The model has a fat pool (f) and three protein pools: visceral (v), nonvisceral (m), and wool (w). Heat production is calculated as the sum of fasting heat production, heat of product formation (HrE), and heat associated with feeding (HAF). Fasting heat production is represented as a function of visceral and nonvisceral protein mass. Heat associated with feeding (HAF) is calculated as ((1 - km) x MEI), where km is partial efficiency of ME use for maintenance, and MEI = metabolizable energy intake) applies at all levels above and below maintenance. The value of km derived from data where lambs were fed above maintenance was 0.7. Protein change (dp/dt) is the sum of change in the m, v, and w pools, and change in fat is equal to net energy available for gain minus dp/dt. Heat associated with a change in body composition (HrE) is calculated from the change in protein and fat with estimated partial efficiencies of energy use of 0.4 and 0.7 for protein and fat, respectively. The model allows for individuals to gain protein while losing fat or vice versa. When evaluated with independent data, the model performed better than the current Australian feeding standards (Freer et al., 2007) for predicting protein gain in the empty body but did not perform as well as for gain of fat and fleece-free empty body weight. Models performed similarly for predicting clean wool growth. By explicit representation of the major energy using processes in the body, and through simplification of the way body composition is computed in growing animals, the model is more transparent than current feeding systems while achieving similar performance. An advantage of this approach is that the model has the potential for wider applicability across different growth trajectories and can explicitly account for the effects of systematic changes on energy transactions, such as the effects of selective breeding, growth manipulation, or environmental changes.

Review: Harnessing extant energy and protein requirement modeling for sustainable beef production

Numerous mathematical nutrition models have been developed in the last sixty years to predict the dietary supply and requirement of farm animals' energy and protein. Although these models, usually developed by different groups, share similar concepts and data, their calculation routines (i.e., submodels) have rarely been combined into generalized models. This lack of mixing submodels is partly because different models have different attributes, including paradigms, structural decisions, inputs/outputs, and parameterization processes that could render them incompatible for merging. Another reason is that predictability might increase due to offsetting errors that cannot be thoroughly studied. Alternatively, combining concepts might be more accessible and safer than combining models' calculation routines because concepts can be incorporated into existing models without changing the modeling structure and calculation logic, though additional inputs might be needed. Instead of developing new models, improving the merging of extant models' concepts might curtail the time and effort needed to develop models capable of evaluating aspects of sustainability. Two areas of beef production research that are needed to ensure adequate diet formulation include accurate energy requirements of grazing animals (decrease methane emissions) and efficiency of energy use (reduce carcass waste and resource use) by growing cattle. A revised model for energy expenditure of grazing animals was proposed to incorporate the energy needed for physical activity, as the British feeding system recommended, and eating and rumination (HjEer) into the total energy requirement. Unfortunately, the proposed equation can only be solved iteratively through optimization because HjEer requires metabolizable energy (ME) intake. The other revised model expanded an existing model to estimate the partial efficiency of using ME for growth (kg) from protein proportion in the retained energy by including an animal degree of maturity and average daily gain (ADG) as used in the Australian feeding system. The revised kg model uses carcass composition, and it is less dependent on dietary ME content, but still requires an accurate assessment of the degree of maturity and ADG, which in turn depends on the kg. Therefore, it needs to be solved iteratively or using one-step delayed continuous calculation (i.e., use the previous day's ADG to compute the current day's kg). We believe that generalized models developed by merging different models' concepts might improve our understanding of the relationships of existing variables that were known for their importance but not included in extant models because of the lack of proper information or confidence at that time.

Effect of live yeast supplementation on energy partitioning and ruminal fermentation characteristics of steers fed a grower-type diet in heat-stress conditions

The objective of this trial was to determine the influence of live yeast supplementation (LY), environmental condition (ENV), and their interaction (TRT) on energy partitioning, nitrogen metabolism, and ruminal fermentation dynamics of steers receiving a grower-type diet. The effects of LY and ENV were investigated using a 2 × 2 crossover design that spanned five periods. Eight Angus-crossbred steers were randomly split into pairs and housed in four outdoor pens outfitted with an individualized feeding system. Animals were limit-fed a grower diet (DIET) at 1.2% shrunk body weight (SBW) with no live yeast supplementation (NOY) or a grower diet top-dressed with 10 g LY/d for 14 d (1.2 × 1012 CFU/d). On days 13 and 14, animals were subjected to one of two ENV conditions, thermoneutral (TN; 18.4 ± 1.1 °C, 57.6 ± 2.8% relative humidity [RH]) or heat stress (HS; 33.8 ± 0.6 °C, 55.7 ± 2.7% RH), in two side-by-side, single-stall open-circuit, indirect respiration calorimetry chambers. Data were analyzed using a random coefficients model. Carryover effects were examined and removed from the model if not significant. Gross (GE), digestible, metabolizable, heat, and retained energies were not influenced by DIET, ENV, or TRT (P ≥ 0.202). Gaseous energy, as a percentage of GE, tended to increase during HS (P = 0.097). The only carryover effect in the study was for oxygen consumption (P = 0.031), which could be attributed to the tendency of NOY (P = 0.068) to have greater oxygen consumption. DIET, ENV, or TRT (P ≥ 0.154) had no effects on total animal methane or carbon dioxide emissions. Similarly, DIET, ENV, or TRT (P ≥ 0.157) did not affect ruminal pH, redox, protozoa enumeration, ruminal ammonia concentrations, and acetate-to-propionate ratio. Propionate concentrations were the greatest in animals in TN conditions receiving LY (P = 0.034) compared to the other TRT. This effect is mirrored by TN-LY tending to have greater acetate concentrations (P = 0.076) and total VFA concentrations (P = 0.065). Butyrate concentrations tended to be greater for animals fed LY (P = 0.09). There was a tendency for LY to have elevated numbers of Fusobacterium necrophorum (P = 0.053). Although this study lacked effects of LY on energy partitioning, nitrogen metabolism, and some ruminal parameters during HS, further research should be completed to understand if LY is a plausible mitigation technique to enhance beef animals' performance in tropical and sub-tropical regions of the world.

Estimation of Carcass Tissue Composition from the Neck and Shoulder Composition in Growing Blackbelly Male Lambs

This study was designed to develop predictive equations estimating carcass tissue composition in growing Blackbelly male lambs using as predictor variables for tissue composition of wholesale cuts of low economic value (i.e., neck and shoulder). For that, 40 lambs with 29.9 ± 3.18 kg of body weight were slaughtered and then the left half carcasses were weighed and divided in wholesale cuts, which were dissected to record weights of fat, muscle, and bone from leg, loin, neck, rib, and shoulder. Total weights of muscle (CM), bone (CB) and fat (CF) in carcass were recorded by adding the weights of each tissue from cuts. The CM, CF and CB positively correlated (p < 0.05; 0.36 ≤ r ≤ 0.86), from moderate to high, with most of the shoulder tissue components, but it was less evident (p ≤ 0.05; 0.32≤ r ≤0.63) with the neck tissue composition. In fact, CM did not correlate with neck fat and bone weights. Final models explained (p < 0.01) 94, 92 and 88% of the variation observed for CM, CF and CB, respectively. Overall, results showed that prediction of carcass composition from shoulder (shoulder) tissue composition is a viable option over the more accurate method of analyzing the whole carcass.

The Energy Requirement for Maintenance of Nellore Crossbreds in Tropical Conditions during the Finishing Period

This study determined the energy requirement for maintenance of purebred Nellore cattle and its crossbreds using data from a comparative slaughter trial in which animals were raised under the same plane of nutrition from birth through slaughter and born from a single commercial Nellore cow herd. Seventy-nine castrated steers (361 ± 54 kg initial BW) were used in a completely randomized design by age (22 months ± 23 days of age) with four genetic groups (GG): Nellore (NL), ½ Angus x ½ Nellore (AN), ½ Canchim x ½ Nellore (CN), and ½ Simmental x ½ Nellore (SN). The experimental design provided ranges in ME intake, BW, and ADG needed to develop regression equations to predict NEm requirements. Four steers of each GG were slaughtered to determine the initial body composition. The remaining 63 steers were assigned to different nutritional treatments (NT) by GG; ad libitum or limit-fed treatments (receiving 70% of the daily feed of the ad libitum treatment of the same GG). Full BW was recorded at birth, weaning, 12, 18, and 22 months. In the feedlot, steers were fed for 101 days a diet containing (DM basis) 60% corn silage and 40% concentrate. No difference of age at weaning (P = 0.534) and slaughter (P = 0.179 and P = 0.896, for GG and NT, respectively) were observed. AN steers were heavier at weaning weight, yearling weight and had higher EBW (P = 0.007, P = 0.014 and P < 0.001, respectively) in comparison to NL, CN, and SN. There were no interactions (P > 0.05) between GG and NT for any variable evaluated. When fed ad libitum, AN steers had higher daily MEI (Mcal/d; P < 0.001) in comparison to NL, CN, and SN. On a constant age basis, differences were observed on body composition (P < 0.05) between GG. The slope (P = 0.600) and intercept (P = 0.702) of the regression of log HP on MEI were similar among GG. Evaluating at the same age and the same frame size, there were no differences in NEm requirement between Nellore and AN (P = 0.528), CN (P = 0.671), and SN (P = 0.706). The combined data indicated a NEm requirement of 86.8 kcal/d/kg 0.75 EBW and a MEm requirement had a common value of 137.53 kcal/d/kg 0.75 EBW. The km and kg values were similar among GG (P > 0.05 and P > 0.05, respectively) and were on average 63.2 and 26.0%, respectively. However, although not statistically different, the NEm values from NL showed a decrease in NEm of 5.76% compared to AN steers.

Characterization of body composition and liver epigenetic markers during periods of negative energy balance and subsequent compensatory growth in postpubertal beef bulls

This study aimed to characterize the effects of dietary restriction and subsequent re-alimentation on body composition and hepatic gene expression of epigenetic markers of DNA methylation, RNA m6A methylation, and histone acetylation in the liver of postpubertal beef bulls. Twelve Angus × Hereford crossbred bulls (n = 6, 23 ± 0.55 mo [young bulls], 558 ± 6.1 kg; and n = 6, 47 ± 1.2 mo [mature bulls], 740 ± 30.5 kg) were submitted to two dietary regimes per offering of the same hay: low plane of nutrition (90 d) and compensatory growth (90 d). Each animal acted as its own control and were fed Beardless wheat (Triticum aestivum) hay and mineral mix during the trial. Statistical analyses were performed using SAS 9.4 following a pre-post repeated measures design. Bulls in negative energy balance (NEB) decreased (P < 0.001) empty body weight (EBW; 23.1% [-139.1 kg]), empty body fat (EBF; 39.8% [-85.4 kg]), and empty body protein (EBP; 14.9% [-13.5 kg]) and fully recovered at the end of the trial. Body fat accounted for 77.1% of daily changes in body energy status, whereas body protein accounted for only 22.9% (P < 0.001). Relative abundance of epigenetic markers transcripts was analyzed via qPCR. Bulls at NEB tended (P ≤ 0.097) to increase gene expression of epigenetic markers of RNA m6A methylation (METTL14, VIRMA, and WTAP) and increased (P ≤ 0.050) the gene expression of epigenetic markers of DNA methylation (DNMT3A) and histone-acetylation (SIRT3 and SIRT7). Young bulls had a tendency (P ≤ 0.072) of higher RNA m6A methylation, VIRMA, and WTAP than mature bulls. Effect of diet × age interaction was not detected (P ≥ 0.137) for METTL14, VIRMA, WTAP, DNMT3A, SIRT3, or SIRT7. Younger bulls tended to have greater RNA m6A methylation levels than mature bulls, indicating that, while contemporaneously fed the same diet during periods of undernourishment followed by compensatory growth, age has an impact on this epigenetic mechanism. In conclusion, metabolic status seems to carry a greater impact on regulating bovine hepatic epigenetic mechanisms that modulate gene transcription, such as DNA methylation and histone acetylation, than on epigenetic mechanisms that regulate gene translation, such as RNA m6A methylation. During periods of undernourishment followed by compensatory growth, body fat pools appear to change more dynamically and are easily detected having a greater impact on epigenetic markers that modulate hepatic gene transcription rather than translation.

Effect of inclusion levels of low-fat dried distillers grains in finishing diets on protein and energy intake and retention and estimation of protein and energy requirements of young Nellore bulls fed with high concentrate diets

The objective was to evaluate the effect of including low-fat dried distillers grains (DDG) in finishing diets on protein and energy intake and retention and to estimate the protein and energy requirement of young Nellore bulls. Thirty-five animals were used: baseline (n = 4), maintenance (n = 4), and ad libitum intake (n = 27). Ad libitum animals were divided into four groups: diets with the inclusion of DDG at the levels of 0, 150, 300, and 450 g/kg (dry matter basis). At the end of the experiment, all animals were slaughtered. There was a linear reduction with increasing DDG levels in the total digestible nutrients intake (p = 0.008), metabolizable energy (ME) intake (p < 0.010), in total retained energy (p = 0.065), and in heat production (p < 0.001). Metabolizable protein (MP) intake increased linearly (p < 0.010) but retained protein did not differ (p = 0.499). Daily net energy and ME requirement for maintenance were 75.9 and 122 kcal/kg^0.75 EBW, respectively. Daily MP for maintenance was 3.6 g/kg^0.75 shrunk body weight. DDG inclusion in finishing diets reduces energy intake and deposition, and we recommend the equations of this study to estimate the requirements of young Nellore bulls.

Energy: Protein Ratio in Ruminants: Insights from the Intragastric Infusion Technique

Simple Summary

One key question that has confounded nutritional scientists for years is how the ruminant responds metabolically with respect to energy and nitrogen utilisation when no exogenous energy is consumed. Fasting metabolism studies using the intragastric infusion technique (IIT) showed this to be a glucose-deficient state characterised by elevated nitrogen excretion and heat production. However, modern feeding systems continue to adopt fasting as the basis for measuring utilisation efficiency of nutritionally balanced diets, giving rise to the false concept of greater feed utilisation below than above energy maintenance. Another IIT finding was that given the animal’s genetic capacity for protein accretion and provided a rumen undegradable protein is fed, ruminants do not catabolise amino acids as an energy source but instead retain these to attain substantial gains in tissue protein deposition, fuelled by endogenous energy reserves. This suggests that endogenous fat reserves could be used to retain body protein when feed supplies are scarce or of poor nutritive value and questions the need to use high-energy diets in the finishing pre-slaughter period. Moreover, body protein and body fat deposition were also shown to be negatively correlated, contradicting current feeding systems which assume that nitrogen retention is always negative in an underfeeding situation.

Abstract

Studies on energy:protein ratio in ruminants are constrained by rumen fermentation since it governs nutrient metabolism and the ratio of energy:protein yielding nutrients available for absorption. By circumventing rumen fermentation, the total intragastric infusion technique (IIT) allowed objective quantification of maintenance energy and protein requirements, volatile fatty acid utilisation efficiency, efficiency of energy utilisation for maintenance (K_m) and growth (K_f) and the origin of N retention responses to independent variation of energy and protein intake. This review outlines the key IIT findings and whether they are reflected in current feeding systems with implications for different production systems worldwide. Maintenance energy requirements are similar to those derived from comparative slaughter but maintenance N requirements are significantly lower. No differences in utilisation efficiency exist between acetic, propionic and butyric acids. At low energy intakes, endogenous energy reserves are utilised to retain amino acids and fuel substantial tissue protein gains. The use of fasting metabolism to measure the utilisation of nutritionally balanced diets is questioned since it is a glucose-deficient state. Inter-species differences in glucose metabolism appear to exist, suggesting that glucose requirements may be higher in cattle than sheep. The difficulty in predicting nutrient requirements, particularly protein, with any one technique is highlighted.

Development of a model to predict dietary metabolizable energy from digestible energy in beef cattle

Understanding the utilization of feed energy is essential for precision feeding in beef cattle production. We aimed to assess whether predicting the metabolizable energy (ME) to digestible energy (DE) ratio (MDR), rather than a prediction of ME with DE, is feasible and to develop a model equation to predict MDR in beef cattle. We constructed a literature database based on published data. A meta-analysis was conducted with 306 means from 69 studies containing both dietary DE and ME concentrations measured by calorimetry to test whether exclusion of the y-intercept is adequate in the linear relationship between DE and ME. A random coefficient model with study as the random variable was used to develop equations to predict MDR in growing and finishing beef cattle. Routinely measured or calculated variables in the field (body weight, age, daily gain, intake, and dietary nutrient components) were chosen as explanatory variables. The developed equations were evaluated with other published equations. The no-intercept linear equation was found to represent the relationship between DE and ME more appropriately than the equation with a y-intercept. The y-intercept (-0.025 ± 0.0525) was not different from 0 (P = 0.638), and Akaike and Bayesian information criteria of the no-intercept model were smaller than those with the y-intercept. Within our growing and finishing cattle data, the animal's physiological stage was not a significant variable affecting MDR after accounting for the study effect (P = 0.213). The mean (±SE) of MDR was 0.849 (±0.0063). The best equation for predicting MDR (n = 106 from 28 studies) was 0.9410 ( ± 0.02160) +0.0042 ( ± 0.00186) × DMI (kg) - 0.0017 ( ± 0.00024) × NDF(% DM) - 0.0022 ( ± 0.00084) × CP(% DM). We also presented a model with a positive coefficient for the ether extract (n = 80 from 22 studies). When using these equations, the observed ME was predicted with high precision (R2 = 0.92). The model accuracy was also high, as shown by the high concordance correlation coefficient (>0.95) and small root mean square error of prediction (RMSEP), <5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (> 93%), without mean or slope bias (P > 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, predicted by the dry matter intake and concentration of several dietary nutrients, using the 2 equations developed in this study.

Feeding behavior, water intake, and energy and protein requirements of young Nellore bulls with different residual feed intakes

This study aimed to determine feeding behavior, water intake (WI), and energy requirements of high- and low-residual feed intake (RFI) Nellore bulls. Data were collected from 42 weaned Nellore bulls (initial body weight [BW] 260 ± 8.1 kg; age 7 ± 1.0 mo) housed in a feedlot in group pens that contained electronic feeders, waterers, and a scale connected to the waterers. The individual dry matter intake (DMI), WI, and BW were recorded daily. The indexes of average daily gain (ADG), feed efficiency (gain to feed ratio), and RFI were calculated based on the data collected. The number of feeder and waterer visits and the time spent feeding or drinking water per animal per day were recorded as feeding behavior measures. Energy requirements for maintenance and gain were calculated according to the BR-CORTE system. Low-RFI bulls had lower DMI (P < 0.01) than high-RFI bulls, and no differences (P > 0.05) were observed between the two groups regarding WI, performance, and feeding behavior measurements. The net energy requirements for maintenance, metabolizable energy for maintenance, and efficiency of metabolizable energy utilization were 63.4, 98.6 kcal/metabolic empty body weight (EBW)0.75 daily, and 64.3%, respectively, for low-RFI bulls, and 78.1, 123.9 kcal/EBW0.75 daily, and 63.0%, respectively, for high-RFI bulls. The equations obtained for net energy for gain (NEg) were: NEg (Mcal/EBW0.75) daily = 0.0528 × EBW0.75 × EBG0.5459 for low-RFI and 0.054 × EBW0.75 × EBG0.8618 for high-RFI bulls, where EBG is the empty body gain. We did not observe any difference (P > 0.05) regarding the composition of gain in terms of protein or fat deposition between the two groups. Both groups also presented similar (P > 0.05) carcass and non-carcass traits. Therefore, our study shows that low-RFI Nellore bulls eat less, grow at a similar rate, and have lower maintenance energy requirements than high-RFI bulls. We also suggest that the lower feed intake did not compromise the carcass traits of more efficient animals, which would reduce production costs and increase the competitiveness of the Brazilian beef sector on the world market.

Cattle adapted to tropical and subtropical environments: social, nutritional, and carcass quality considerations

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.

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.