Estrus Prediction Models for Dairy Gyr Heifers
Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperat...
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/5e2d78633baf4a4b8fbfe4b044935ee7 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:5e2d78633baf4a4b8fbfe4b044935ee7 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:5e2d78633baf4a4b8fbfe4b044935ee72021-11-25T16:15:28ZEstrus Prediction Models for Dairy Gyr Heifers10.3390/ani111131032076-2615https://doaj.org/article/5e2d78633baf4a4b8fbfe4b044935ee72021-10-01T00:00:00Zhttps://www.mdpi.com/2076-2615/11/11/3103https://doaj.org/toc/2076-2615Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) and activity (ACT) in Dairy Gyr heifers provided by reticulo-rumen boluses and to test the ability of different models for estrus prediction. The RRT and ACT of 45 heifers submitted to estrus synchronization were recorded using reticulo-rumen boluses. The means of RRT and ACT at different time intervals were compared between the day before and the day of estrus manifestation. An analysis of variance of RRT and ACT was performed using mixed models. A second approach employed logistic regression, random forest, and linear discriminant analysis models using RRT, ACT, time of day, and the temperature-humidity index (THI) as predictors. There was an increase in RRT and ACT at estrus (<i>p</i> < 0.05) compared to the same period on the day before and on the day after estrus. The random forest model provided the best performance values with a sensitivity of 51.69% and specificity of 93.1%. The present results suggest that RRT and ACT contribute to the identification of estrus in Dairy Gyr heifers.Valesca Vilela AndradePriscila Arrigucci BernardesRogério Ribeiro VicentiniAndré Penido OliveiraRenata VeronezeAska UjitaJoão Alberto NegrãoLenira El FaroMDPI AGarticleactivitybody temperatureheatreproductionsensorsZebuVeterinary medicineSF600-1100ZoologyQL1-991ENAnimals, Vol 11, Iss 3103, p 3103 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
activity body temperature heat reproduction sensors Zebu Veterinary medicine SF600-1100 Zoology QL1-991 |
| spellingShingle |
activity body temperature heat reproduction sensors Zebu Veterinary medicine SF600-1100 Zoology QL1-991 Valesca Vilela Andrade Priscila Arrigucci Bernardes Rogério Ribeiro Vicentini André Penido Oliveira Renata Veroneze Aska Ujita João Alberto Negrão Lenira El Faro Estrus Prediction Models for Dairy Gyr Heifers |
| description |
Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) and activity (ACT) in Dairy Gyr heifers provided by reticulo-rumen boluses and to test the ability of different models for estrus prediction. The RRT and ACT of 45 heifers submitted to estrus synchronization were recorded using reticulo-rumen boluses. The means of RRT and ACT at different time intervals were compared between the day before and the day of estrus manifestation. An analysis of variance of RRT and ACT was performed using mixed models. A second approach employed logistic regression, random forest, and linear discriminant analysis models using RRT, ACT, time of day, and the temperature-humidity index (THI) as predictors. There was an increase in RRT and ACT at estrus (<i>p</i> < 0.05) compared to the same period on the day before and on the day after estrus. The random forest model provided the best performance values with a sensitivity of 51.69% and specificity of 93.1%. The present results suggest that RRT and ACT contribute to the identification of estrus in Dairy Gyr heifers. |
| format |
article |
| author |
Valesca Vilela Andrade Priscila Arrigucci Bernardes Rogério Ribeiro Vicentini André Penido Oliveira Renata Veroneze Aska Ujita João Alberto Negrão Lenira El Faro |
| author_facet |
Valesca Vilela Andrade Priscila Arrigucci Bernardes Rogério Ribeiro Vicentini André Penido Oliveira Renata Veroneze Aska Ujita João Alberto Negrão Lenira El Faro |
| author_sort |
Valesca Vilela Andrade |
| title |
Estrus Prediction Models for Dairy Gyr Heifers |
| title_short |
Estrus Prediction Models for Dairy Gyr Heifers |
| title_full |
Estrus Prediction Models for Dairy Gyr Heifers |
| title_fullStr |
Estrus Prediction Models for Dairy Gyr Heifers |
| title_full_unstemmed |
Estrus Prediction Models for Dairy Gyr Heifers |
| title_sort |
estrus prediction models for dairy gyr heifers |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/5e2d78633baf4a4b8fbfe4b044935ee7 |
| work_keys_str_mv |
AT valescavilelaandrade estruspredictionmodelsfordairygyrheifers AT priscilaarriguccibernardes estruspredictionmodelsfordairygyrheifers AT rogerioribeirovicentini estruspredictionmodelsfordairygyrheifers AT andrepenidooliveira estruspredictionmodelsfordairygyrheifers AT renataveroneze estruspredictionmodelsfordairygyrheifers AT askaujita estruspredictionmodelsfordairygyrheifers AT joaoalbertonegrao estruspredictionmodelsfordairygyrheifers AT leniraelfaro estruspredictionmodelsfordairygyrheifers |
| _version_ |
1718413258419863552 |