&lt;b&gt;Evaluation of body weight prediction Equations in growing heifers

Franco, Márcia de Oliveira; Marcondes, Marcos Inácio; Campos, José Maurício de Souza; Freitas, Denise Ribeiro de; Detmann, Edênio; Filho, Sebastião de Campos Valadares

doi:10.4025/actascianimsci.v39i2.33118

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Para la predicción del PV en vacas y novillas cruzadas (Holstein × Cebú) se han utilizado diferentes MB como el perímetro torácico (PT), ancho de cadera (AC), largo del cuerpo (LC), y altura a la cruz (ALTC) y a la cadera (ALTCAD) (Reis et al, 2008;Oliveira et al, 2013;Mota et al, 2013;Franco et al 2017). Reis et al (2008) reportan que la precisión de la estimación del PV utilizando MB, pueden ser afectadas por la raza, edad, tamaño corporal, condición corporal y estado fisiológico.…”

Section: Discussionunclassified

“…Reis et al (2008) reportan que la precisión de la estimación del PV utilizando MB, pueden ser afectadas por la raza, edad, tamaño corporal, condición corporal y estado fisiológico. Franco et al (2017) reportaron un r=0,88 y una r 2 =0,83 entre el PV y AC en novillas Holstein cruzadas; estos autores con-cluyeron que a pesar de que AC fue altamente correlacionadas con PV, mostró un bajo r 2 con un alto coeficiente de variación en relación a otras variables de MB, tales como LC, ALTC y ALTCAD. No obstante, el presente estudio demostró que la relación entre PV y AC fue alta, con un valor superior (r = 0,98 vs. 0,83) al reportado previamente para vacas Holstein (Franco et al, 2007).…”

Section: Discussionunclassified

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Desarrollo y evaluación de una ecuación para predecir el peso vivo en novillas cruzadas usando el ancho de cadera.

Chay-Canúl¹

2018

ITEA

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El objetivo del presente estudio fue desarrollar y evaluar una ecuación para predecir el peso vivo (PV) a partir del ancho de cadera (AC) en novillas cruzadas, criadas bajo condiciones de trópico húmedo en México. Se utilizó la información de 500 novillas (PV = 340,2 ± 105 kg, AC = 42,5 ± 9,48 cm y edad = 24 meses) producto del cruce entre Sardo negro, Suizo Americano, Simmental y Brahman para construir el modelo de predicción usando regresión no lineal. El coeficiente de correlación entre el peso vivo y ancho de cadera fue alto (r = 0,98; P < 0,001), y la ecuación final desarrollada fue: PV (kg) = 0,2615 (± 0,0167)* AC 1,902(± 0,0164) (P < 0,0001, r 2 = 0,97; Cuadrado Medio del Error = 554,52; Desviación Estándar Residual = 23,55 y n = 500). La evaluación de la ecuación se realizó a través del Model Evaluation System utilizando datos de 70 novillas de cruces similares (PV = 282,4 ± 114 kg, AC = 37,94 ± 7,94 cm y edad = 24 meses). La correlación entre los valores de peso vivo observado y predicho fue alta (r = 0,98) y se observó una alta coincidencia ente ambos tipos de valores (Ho: β = 0 y Ha: β = 1; P > 0,05). Los parámetros de precisión y exactitud mostraron que la ecuación presentó una alta precisión (r 2 = 0,97), exactitud (Cb = 0,99) y reproducibilidad (CCC = 0,97); asimismo, tuvo una alta eficiencia predicción (MEF = 096). La raíz cuadrada del cuadrado medio del error de precisión (RMSEP) representó el 8,11% del peso vivo observado. Se concluye que el ancho de cadera se relaciona altamente con el peso vivo, y consecuentemente, la ecuación desarrollada mostró predecir el peso vivo de novillas cruzadas mantenidas en condiciones de trópico con buena precisión y exactitud.

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Section: Discussionunclassified

Desarrollo y evaluación de una ecuación para predecir el peso vivo en novillas cruzadas usando el ancho de cadera.

Chay-Canúl¹

2018

ITEA

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“…Some studies have attempted to predict live weight in growing dairy cattle (Heinrichs et al, 1992;Franco et al, 2017); however, many of these relate to cattle that are months rather than days old (Heinrichs et al, 1992;Franco et al, 2017). Furthermore, much of the available research is based on heifers (Heinrichs et al, 1992;Franco et al, 2017) as opposed to dairy bulls, which were investigated in the present study. Some predictions of live weight in young dairy bull calves from morphological characteristics do, nonetheless, exist (Wilson et al, 1997).…”

Section: Short Communication Geneticsmentioning

confidence: 99%

Predicting male dairy calf live weight for use in calf management decision support

Dunne

Kelleher²,

Horan

et al. 2021

JDS Communications

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As the public scrutiny of dairy calf management and welfare intensifies, support tools to aid in farm workload and infrastructure management can be invaluable. Many purchasers of male dairy calves have minimum calf live weight thresholds. Being able to predict the age, and therefore date, that the male surplus dairy calves achieve this weight can be useful in planning; this is especially true if these predictions are available before the calf is born. Using estimates of genetic merit for a series of readily available traits, prediction of calf live weight was possible. A live weight predictive model was developed, trained, and tested using a 10-fold crossvalidation technique in order to quantify the accuracy and robustness of the predictions. The regression coefficients remained relatively consistent across each of the folds. With simple algebra, this prediction can be translated into the expected age when a calf will reach a target live weight. Highlights• A robust yet simple statistical model for predicting young male dairy calf live weight was developed.• The predictive model can be adapted to estimate the age when a calf reaches a target live weight.• The model can be integrated into a decision support tool to inform calf labor and infrastructure management.

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“…Some of the most popular morphometric measurements include heart girth circumference, wither height, hip width/height, and body length. These measurements are manually selected and used as features for traditional regression models, which result in predictive equations with one or more variables based on the number of selected measurements in various species, such as cattle ( Heinrichs et al, 1992 ; Franco et al, 2017 ; Goopy et al, 2018 ), pigs ( Groesbeck et al, 2002 ; Mutua et al, 2011 ; Sungirai et al, 2014 ; Al Ard Khanji et al, 2018 ), sheep ( Sowande and Sobola, 2008 ; Kunene et al, 2009 ; Chay-Canul et al, 2019 ; Canul-Solis et al, 2020 ), goats ( Sebolai et al, 2012 ), camels ( Fadlelmoula et al, 2020 ; Meghelli et al, 2020 ), and yaks ( Yan et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Wang

Shadpour

Chan

et al. 2021

Journal of Animal Science

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Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

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<b>Evaluation of body weight prediction Equations in growing heifers

Cited by 16 publications

References 8 publications

Desarrollo y evaluación de una ecuación para predecir el peso vivo en novillas cruzadas usando el ancho de cadera.

Desarrollo y evaluación de una ecuación para predecir el peso vivo en novillas cruzadas usando el ancho de cadera.

Predicting male dairy calf live weight for use in calf management decision support

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

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