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A dynamic approach for BCS prediction in NDS Professional.

G. Esposito

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06-23-2020

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Abstract:

T111
A dynamic approach for BCS prediction in NDS Professional.
G. Esposito*1,2, M. Shipandeni3, E. Raffrenato1,2, E. Melli1. 1RUM&N Reggio Emilia (RE), Italy, 2Department of Animal Sciences, Faculty of AgriSciences, Stellenbosch University Stellenbosch (WC), South Africa, 3Department of Animal Science, University of Namibia Windhoek, Namibia.

Based on the body reserves equation (NRCS 2001), calculated as energy inputs minus energy for maintenance, pregnancy, milk yield (MY) and growth, the CNCPS is able to predict BCS. One limitation of the model is that the MY is a set value for the breed therefore, especially when evaluating medium to low producing cows, the accuracy of the model may be reduced. The aim of this study was to validate, and possibly improve, the BCS prediction of the CNCPS by including “cows' current potential” in the equation. The “cows' current potential” was calculated as predicted peak milk yield over breed-specific peak; in the equation, the fat catabolism equation proposed by Johnson et al. (2016) has also been taken into account. Data regarding lactation number, parity, age and BW, BCS, milk yield and composition at 5 DIM from 63 pluriparous Jersey cows were included in the alternative model (MBCS). The observed BCS taken at 35 DIM was used to validate the models. The observed values were regressed on the ones predicted by the CNCPS and MBCS models and RMSPE was calculated. The significance of the deviation of the intercept from 0 and the slope from 1 was analyzed by t-test. Both R2 and RMSPE indicated that the MBCS performed better than the CNCPS model. The CNCPS model had R2 lower than 0.60 with MBCS having R2 of 0.66. The CNCPS model had a slope of 0.2587 (P < 0.05) whereas the MBCS had a slope of 0.6134. The CNCPS model consistently overestimated loss of BCS at 35 DIM with low-producing cows, whereas it was more accurate with high-producing animals. Although the R2 is relatively low in both models, probably due to the limited size of the observed data set, the MBCS better predicts BCS at 35 DIM for both high-producing and medium to low-producing cows. The results show that although the BCS prediction still needs improvement, the dynamic model proposed by NDS Professional has the advantage to better predict fat catabolism and, therefore, BCS especially when working with medium to low producing dairy cows. Thus, providing the users with more accurate decision-making tools. Both the MBCS and the fat catabolism curve are now implemented in NDS Professional (RUM&N, Italy).

Keywords: modeling, CNCPS, BCS.