Browsing by Author "Kononoff, P. J."
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- Modeling fatty acids for dairy cattle: Models to predict total fatty acid concentration and fatty acid digestion of feedstuffsDaley, 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.
- Predictions of ruminal outflow of essential amino acids in dairy cattleFleming, A. J.; Lapierre, H.; White, Robin R.; Tran, H.; Kononoff, P. J.; Martineau, R.; Weiss, W. P.; Hanigan, Mark D. (2019-12)The objective of this work was to update and evaluate predictions of essential AA (EAA) outflows from the rumen. The model was constructed based on previously derived equations for rumen-undegradable (RUP), microbial (MiCP), and endogenous (EndCP) protein outflows from the rumen, and revised estimates of ingredient composition and EAA composition of the protein fractions. Corrections were adopted to account for incomplete recovery of EAA during 24-h acid hydrolysis. The predicted ruminal protein and EAA outflows were evaluated against a data set of observed values from the literature. Initial evaluations indicated a minor mean bias for non-ammonia, non-microbial nitrogen flow ([RUP EndCP]/6.25) of 16 g of N per day. Root mean squared errors (RMSE) of EA.A predictions ranged from 26.8 to 40.6% of observed mean values. Concordance correlation coefficients (CCC) of EAA predictions ranged from 0.34 to 0.55. Except for Leu, all ruminal EA.A outflows were overpredicted by 3.0 to 32 g/d. In addition, small but significant slope biases were present for Arg [2.2% mean squared error (MSE)] and Lys (3.2% MSE). The overpredictions may suggest that the mean recovery of AA from acid hydrolysis across laboratories was less than estimates encompassed in the recovery factors. To test this hypothesis, several regression approaches were undertaken to identify potential causes of the bias. These included regressions of (1) residual errors for predicted EAA flows on each of the 3 protein-driven EA flows, (2) observed EAA flows on each protein-driven EAA flow, including an intercept, (3) observed EAA. flows on the protein-driven EAA flows, excluding an intercept term, and (4) observed EAA. flows on IMP and MiCP. However, these equations were deemed unsatisfactory for bias adjustment, as they generated biologically unfeasible predictions for some entities. Future work should focus on identifying the cause of the observed prediction bias.