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PSVIII-39 Late-Breaking Abstract: Equations to predict the effects of dietary deoxynivalenol on the performance of pigs

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The objectives of this study were to assess the accuracy of previously published equations for predicting effects of deoxynivalenol (DON) on the growth performance of pigs and to update the… Click to show full abstract

The objectives of this study were to assess the accuracy of previously published equations for predicting effects of deoxynivalenol (DON) on the growth performance of pigs and to update the equations based on the recently published data. A total of 55 data from 22 experiments published between 2013 and 2020 reporting the effects of DON on the performance of pigs were employed for the validation of the previously published equations and these data were used to update the equations. The REG procedure of SAS was used to validate and develop equations and the CORR procedure was used to determine correlation coefficients among variables. The validation of the previously published equations showed that the slope (0.33 ± 0.08, P < 0.05) representing the linear bias and intercept (-3.55 ± 1.60, P < 0.05) representing the mean bias of weight gain changes (ΔWG, %) were different from 0. The slope (0.55 ± 0.07, P < 0.05) and intercept (-6.94 ± 1.20, P < 0.05) of feed intake changes (ΔFI, %) were also different from 0. Dietary DON concentrations had a negative correlation with ΔWG (r = -0.80; P < 0.001) and ΔFI (r = -0.74; P < 0.001). Updated prediction equations for estimating ΔWG and ΔFI based on a total of 152 data from 41 experiments were: ΔWG = -5.76 × DON with r2 = 0.77 and ΔFI = -4.65 × DON with r2 = 0.71, respectively, where dietary DON concentrations ranged from 0 to 14.6 mg/kg. In conclusion, the novel equations developed based on the large number of observations including recent ones may accurately predict effects of dietary DON on the performance of pigs.

Keywords: predict effects; published equations; performance; effects dietary; previously published; performance pigs

Journal Title: Journal of Animal Science
Year Published: 2020

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