Development of predictive models of fatty acid (FA) use by dairy cattle still faces challenges due to high variation in FA composition among feedstuffs and fat supplements. Two meta-analytical studies were carried out to develop empirical models for estimating (1) the total FA concentration of feedstuffs, and (2) the apparent total-tract digestibility of total FA (DCFA TTa ) in dairy cows fed different fat types. In study 1, individual feedstuff data for total crude fat (EE) and FA were taken from commercial laboratories (total of 203 feeds, 1,170,937 samples analyzed for total FA, 1,510,750 samples analyzed for total EE), and data for FA composition were collected from the Cornell Net Carbohydrate and Protein System feed library. All feedstuffs were grouped into 7 classes based on their nutritional components. To predict total FA concentration (% of dry matter) for groups of feeds, the total EE (% of dry matter) was used as an independent variable in the model, and all models were linear. For forages, data were weighted using the inverse of the standard error (SE). Regression coefficients for predicting total FA from EE (% of dry matter) were 0.73 (SE, 0.04), 0.98 (0.02), 0.80 (0.02), 0.61 (0.04), 0.92 (0.03), and 0.93 (0.03), for animal protein, plant protein, energy sources, grain crop forage, by-product feeds, and oilseeds, respectively. The intercepts for plant protein and byproduct groups were different from zero and included in the models. As expected, forages had the lowest total FA concentration (slope = 0.57, SE = 0.02). In study 2, data from 30 studies (130 treatment means) that reported DCFA TTa in dairy cows were used. Data for animal description, diet composition, intakes of total FA, and DCFA TTa , were collected. Dietary sources of fat were grouped into 11 categories based on their fat characteristic and FA profile. A mixed model including the random effect of study was used to regress digested FA on FA intake with studies weighted according to the inverse of their variance (SE). Dietary intake of extensively saturated triglycerides resulted in markedly lower total FA digestion (DCFA TTa = 44%) compared with animals consuming unsaturated FA, such as Casalts of palm (DCFA TTa = 76%) and oilseeds (DCFA TTa = 73%). Cows fed saturated fats had lower total FA digestion among groups, but it was dependent on the FA profile of each fat source. The derived models provide additional insight into FA digestion in ruminants. Predictions of total FA supply and its digestion can be used to adjust fat supplementation programs for dairy cows.

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To feed people in the coming decades, an increase in sustainable animal food production is required. The efficiency of the global food production system is dependent on the knowledge and improvement of its submodels, such as food animal production. Scientists use statistical models to interpret their data, but models are also used to understand systems and to integrate their components. However, empirical models cannot explain systems. Mechanistic models yield insight into the mechanism and provide guidance regarding the exploration of the system. This review offers an overview of models, from simple empirical to more mechanistic models. We demonstrate their applications to amino acid transport, mass balance, whole-tissue metabolism, digestion and absorption, growth curves, lactation, and nutrient excretion. These mechanistic models need to be integrated into a full model using big data from sensors, which represents a new challenge. Soon, training in quantitative and computer science skills will be required to develop, test, and maintain advanced food system models.

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Several models for predicting dry matter intake (DMI) of replacement dairy heifers have been developed; however, only a few have been evaluated using data from heifers of different breeds raised under tropical conditions. Thus, the objective of this study was to evaluate the DMI equations for dairy heifers managed under tropical conditions. A total of 230 treatment means from 61 studies using dairy heifers (n = 1513 heifers, average body weight = 246 kg) were used. The animals were grouped into two groups based on their genetics: (1) Bos taurus (Holstein, Jersey, Brown Swiss, and Holstein × Jersey) and (2) crossbred (Bos taurus × Bos indicus). Seven previously published DMI equations (HH, HHJ, QUI, STA, 2001 NRC, OFLin, and OFNLin) for heifers were evaluated using mean bias, slope bias, mean squared prediction errors (MSPE) and its decomposition, and other model evaluation statistics. For Bos taurus heifers, our results indicated that OFNLin and HHJ had lower mean bias (0.13 and 0.16 kg/d, respectively) than other models. There was no significant slope or mean bias for HHJ and OFNLin (p > 0.05), indicating agreement between the observed and predicted DMI values. All other models had a significant mean bias (p < 0.05), whereas the QUI model also presented a significant slope bias (p < 0.02). For crossbred heifers, the STA equation was the only one that did not present mean and slope bias significance (p > 0.05). All other DMI models had significant mean bias when evaluated using crossbred data (p < 0.04), and QUI, OFLin, and OFNLin also presented significant slope bias (p < 0.01). Based on our results, predictions from OFNLin and HHJ best represented the observed DMI of Bos taurus heifers (MSPE ≤ 1.25 kg2/d2, mean bias ≤ 0.16 kg/d), whereas STA was the best model for crossbred heifers (MSPE = 1.25 kg2/d2, mean bias = 0.09 kg/d). These findings indicate that not all available models are adequate for estimating the DMI of dairy heifers managed under a tropical climate, with HHJ and OFNLin for Bos taurus and STA for crossbreds being the most suitable models for DMI prediction. There is evidence that models from Bos taurus heifers could be used to estimate the DMI of heifers under tropical conditions. For heifer ration formulation is necessary to consider that DMI is influenced by breed, diet, management, and climate. Future work should also include animal genetic and environmental variables for the prediction of DMI in dairy heifers.

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The original article has been corrected.