Models to predict live weight from heart girth in crossbred beef heifers

Chico-Alcudia, Denis R; Portillo-Salgado, Rodrigo; Camacho-Pérez, E.; Peralta-Torres, Jorge Alonso; Ángeles-Hernández, Juan Carlos; Muñoz-Benítez, Alfonso Longinos; Lendechy, Víctor Hugo Severino; Gurgel, Antônio Leandro Chaves; Difante, Gelson dos Santos; Ítavo, Luís Carlos Vinhas; Chay-Canúl, Alfonso Juventino

doi:10.1007/s11250-022-03276-7

Cited by 6 publications

(1 citation statement)

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“…Milk cattle farming in tropical regions remains a traditional activity, and most producers rely on management practices that have been passed down for generations without much change. However, as noted by Chico-Alcudia et al [17], the lack of sophisticated infrastructure, such as measurement equipment to evaluate milk production and composition, and nutrient intake, is one of the primary limitations to increasing profitability in dairy farms. This lack of advanced technology hinders farmers' ability to accurately measure and monitor the various factors that affect milk production, thereby limiting their capacity to make informed decisions that can improve their operations.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%