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Browsing by Author "Daley, Veridiana L."

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    Feed Intake of Growing Dairy Heifers Raised under Tropical Conditions: A Model Evaluation Using Meta-Analysis
    Busanello, Marcos; Sousa, Debora Gomes de; Mendonça, Filipe Araújo Canedo; Daley, Veridiana L.; de Almeida, Rodrigo; Bittar, Carla Maris Machado; Lanna, Dante Pazzanese Duarte (MDPI, 2021-11-07)
    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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    Improving nitrogen efficiency and profitability of dairy cattle in the United States
    Prestegaard-Wilson, Jacquelyn M. (Virginia Tech, 2022-09-08)
    The objectives of these studies were to assess U.S. dairy nutritionists' approaches toward balancing CP in lactating cow diets, and to leverage existing knowledge of postabsorptive AA metabolism through the application of a mathematical ration-balancing model to predict N efficiency through a more accurate postabsorptive amino acid (AA) delivery. In experiment 1, dairy nutritionists (n = 77) that fed a total of 521,000 lactating dairy cows responded to a questionnaire related to demographic information, feelings toward environmental nitrogen (N) excretion, and dietary CP balancing practices. Eighty-nine percent of nutritionists balanced diets based on one or more individual AA requirements of dairy cows. The primary concern with formulation of lower CP diets was the cost per unit of metabolizable protein (MP). In the second study, three treatments were fed to lactating Holstein cows (n = 48) to test proof of concept of NASEM 2021 and a nonlinear optimizer: a control balanced to fulfill all nutrient needs of lactating dairy cows producing 45 kg milk/d using the NRC (2001) dairy model (NRC01), and two diets balanced with a nonlinear optimizer that fulfilled requirements according to the updated NASEM (2021) dairy model and attempted to either: 1) maximize N efficiency through tailored post-ruminal AA supply (NEFF), or 2) maximize income over feed cost (IOFC). A simulation function was written in RStudio (version 2022.02.3) to predict daily animal performance with NASEM 2021. Dry matter intake, milk, and milk components from both the observed data and the simulation data matrices were analyzed as repeated measures (days) in a mixed model to test for both observed and predicted (simulated) differences in treatment means. Income over feed cost was $4.83, $4.77, and $5.12/cow/d for NRC01, IOFC, and NEFF, respectively (P = 0.96). Nitrogen efficiency (%) was greatest for NEFF (33.7), followed by NRC01 (28.9) and IOFC (23.4; P < 0.05 between all treatments). Based upon simulation data, NASEM 2021 predicted relative performance differences between animals that consumed treatments with differing absorbed EAA supplies, although residual analyses revealed that further progress could be made in milk protein (g/d), milk fat (g/d), milk yield (kg/d), and DMI (kg/d) predictions.
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    Modeling fatty acids for dairy cattle: Models to predict total fatty acid concentration and fatty acid digestion of feedstuffs
    Daley, Veridiana L.; Armentano, L. E.; Kononoff, P. J.; Hanigan, Mark D. (2020-08)
    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 by-product 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 Ca-salts 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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    Models to predict milk fat concentration and yield of lactating dairy cows: A meta-analysis
    Daley, Veridiana L.; Armentano, L. E.; Hanigan, Mark D. (Elsevier, 2022-10)
    Few models have attempted to predict total milk fat because of its high variation among and within herds. The objective of this meta-analysis was to develop models to predict milk fat concentration and yield of lactating dairy cows. Data from 158 studies consisting of 658 treatments from 2,843 animals were used. Data from several feed databases were used to calculate dietary nutrients when dietary nutrient composition was not reported. Digested intake (DI, g/d) of each fatty acid (FA; C12:0, C14:0, C16:0, C16:1, C18:0, C18:1 cis, C18:1 trans C18:2, C18:3) and absorbed amounts (g/d) of each AA (Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, Val) were calculated and used as candidate variables in the models. A multi-model inference method was used to fit a large set of mixed models with study as the random effect, and the best models were selected based on Akaike's information criterion corrected for sample size and evaluated further. Observed milk fat concentration (MFC) ranged from 2.26 to 4.78%, and milk fat yield (MFY) ranged from 0.488 to 1.787 kg/d among studies. Dietary levels of forage, starch, and total FA (dry matter basis) averaged 50.8 +/- 10.3% (mean +/- standard deviation), 27.5 +/- 7.0%, and 3.4 +/- 1.3%, respectively. The MFC was positively correlated with dietary forage (0.294) and negatively associated with dietary starch (-0.286). The DI of C18:2 (g/d) was more negatively correlated with MFC (-0.313) than that of the other FA. The best variables for predicting MFC were days in milk, FA-free dry matter intake, forage, starch, DI of C18:2, DI of C18:3, and absorbed Met, His, and Trp. The best predictor variables for MFY were FA-free dry matter intake, days in milk, absorbed Met and Ile, and intakes of digested C16:0 and C18:3. This model had a root mean square error of 14.1% and concordance correlation coefficient of 0.81. Surprisingly, DI of C18:3 was positively related to milk fat, and this relationship was consistently observed among models. The models developed can be used as a practical tool for predicting milk fat of dairy cows, while recognizing that additional factors are likely to also affect fat yield.
  • Models to predict milk fat concentration and yield of lactating dairy cows: A meta-analysis [Supplemental material]
    Daley, Veridiana L.; Armentano, Louis; Hanigan, Mark D. (American Dairy Science Association, 2022)
  • Supplemental material - Predicting ruminally undegraded and microbial protein flows from the rumen
    Hanigan, Mark D.; Carneiro de Souza, Vinícius; Martineau, Roger; Daley, Veridiana L.; Kononoff, Paul (Elsevier Inc. and Fass Inc., 2021-03-02)
    This is a supplemental material of the paper entitled "Predicting ruminally undegraded and microbial protein flows from the rumen".
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    Use of Mechanistic Nutrition Models to Identify Sustainable Food Animal Production
    Hanigan, Mark D.; Daley, Veridiana L. (2020-02)
    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.