Mathematical Models to Predict Dry Matter Intake and Milk Production by Dairy Cows Managed under Tropical Conditions

This study aimed to create an equation to predict dry matter intake (DMI) and milk production and N-ureic in the milk of dairy cows managed in tropical conditions in Brazil. We used 113 observations from three experiments using lactating Jersey, Girolando, and Holstein cows. The goodness of fit of t...

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Veröffentlicht in:Agriculture (Basel) 2023-07, Vol.13 (7), p.1446
Hauptverfasser: Gurgel, Antonio Leandro Chaves, Santos, Geraldo Tadeu dos, Ítavo, Luís Carlos Vinhas, Ítavo, Camila Celeste Brandão Ferreira, Difante, Gelson dos Santos, Dias, Alexandre Menezes, Longhini, Vanessa Zirondi, Dias-Silva, Tairon Pannunzio, de Araújo, Marcos Jácome, Neto, João Virgínio Emerenciano, Fernandes, Patrick Bezerra, Chay-Canul, Alfonso Juventino
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Sprache:eng
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Zusammenfassung:This study aimed to create an equation to predict dry matter intake (DMI) and milk production and N-ureic in the milk of dairy cows managed in tropical conditions in Brazil. We used 113 observations from three experiments using lactating Jersey, Girolando, and Holstein cows. The goodness of fit of the developed equations was evaluated using the coefficients of determination (r2) and root mean square error (RMSE). There was a positive correlation between body weight and milk yield (MY) of r = 0.73. The equation considered DMI to be the most important variable to estimate the MY (r2 = 0.65). Four equations were adjusted to estimate the DMI, where, by a stepwise procedure, the first variable included in the equation was the neutral detergent fibre intake, which explained 92% of the DMI of the cows. However, when the variables BW, MY, and milk fat were included in the equation, there was a reduction of 0.06 in RMSE and an increase in precision (r2 = 0.94). The nutrient intake, milk production, and characteristics prediction equations present satisfactory precision and accuracy for dairy cows managed in tropical conditions in Brazil.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture13071446