Matches in SemOpenAlex for { <https://semopenalex.org/work/W4324257977> ?p ?o ?g. }
- W4324257977 endingPage "107752" @default.
- W4324257977 startingPage "107752" @default.
- W4324257977 abstract "Heat stress is increasingly affecting the production, health, and reproduction of dairy cows. Previous studies used limited variables as predictors of physiological responses, and the developed models poorly predict animal responses in evaporatively cooled environments. The aim of this study was to build machine learning models using comprehensive variables to predict physiological responses of dairy cows raised on an actual dairy farm equipped with sprinklers. Four algorithms including random forests, gradient boosting machines, artificial neural networks (ANN), and regularized linear regression were used to predict respiration rate (RR), vaginal temperature (VT), and eye temperature (ET) with 13 predictor variables from three dimensions: production, cow-related, and environmental factors. The classification performance of the predicted values in recognizing individual heat stress states was compared with commonly used thermal indices. The performance on the testing sets shows that the ANN models yielded the lowest root mean squared error for predicting RR (13.24 breaths/min), VT (0.30 °C), and ET (0.29 °C). The results interpreted with partial dependence plots and Local Interpretable Model-agnostic Explanations show that P.M. measurements and winter calving contributed most to high RR and VT predictions, whereas lying posture, high ambient temperature, and low wind speed contributed most to high ET predictions. When determining the ground-truth heat stress state by the actual RR, the best classification performance was yielded by the predicted RR with an accuracy of 77.7%; when determining the ground-truth heat stress state by the actual VT, the best classification performance was yielded by the predicted VT with an accuracy of 75.3%. This study demonstrates the ability of ANN in predicting physiological responses of dairy cows raised on actual farms with access to sprinklers. Adding more predictors other than meteorological parameters into training could increase predictive performance. Recognizing the heat stress state of individual animals, especially those at the highest risk, based on the predicted physiological responses and their interpretations can inform better heat abatement decisions." @default.
- W4324257977 created "2023-03-15" @default.
- W4324257977 creator A5001065920 @default.
- W4324257977 creator A5003748150 @default.
- W4324257977 creator A5033201032 @default.
- W4324257977 creator A5049655905 @default.
- W4324257977 creator A5050575971 @default.
- W4324257977 creator A5059394465 @default.
- W4324257977 creator A5073713049 @default.
- W4324257977 creator A5084403604 @default.
- W4324257977 date "2023-04-01" @default.
- W4324257977 modified "2023-09-26" @default.
- W4324257977 title "Predicting physiological responses of dairy cows using comprehensive variables" @default.
- W4324257977 cites W1279384 @default.
- W4324257977 cites W1970344927 @default.
- W4324257977 cites W1983332439 @default.
- W4324257977 cites W1995341919 @default.
- W4324257977 cites W1996111351 @default.
- W4324257977 cites W2015923526 @default.
- W4324257977 cites W2028145976 @default.
- W4324257977 cites W2045292752 @default.
- W4324257977 cites W2054889028 @default.
- W4324257977 cites W2057681397 @default.
- W4324257977 cites W2088519406 @default.
- W4324257977 cites W2122825543 @default.
- W4324257977 cites W2129064705 @default.
- W4324257977 cites W2132393620 @default.
- W4324257977 cites W2136182844 @default.
- W4324257977 cites W2471180850 @default.
- W4324257977 cites W2521168280 @default.
- W4324257977 cites W2602696356 @default.
- W4324257977 cites W2771237110 @default.
- W4324257977 cites W2794684032 @default.
- W4324257977 cites W2803432171 @default.
- W4324257977 cites W2808164093 @default.
- W4324257977 cites W2883900135 @default.
- W4324257977 cites W2898210550 @default.
- W4324257977 cites W2898713424 @default.
- W4324257977 cites W2904621083 @default.
- W4324257977 cites W2911498282 @default.
- W4324257977 cites W2911964244 @default.
- W4324257977 cites W2923665351 @default.
- W4324257977 cites W2942186133 @default.
- W4324257977 cites W2944431598 @default.
- W4324257977 cites W2952621897 @default.
- W4324257977 cites W2965457002 @default.
- W4324257977 cites W2967980546 @default.
- W4324257977 cites W2994632868 @default.
- W4324257977 cites W3001574971 @default.
- W4324257977 cites W3008074878 @default.
- W4324257977 cites W3014479918 @default.
- W4324257977 cites W3015352644 @default.
- W4324257977 cites W3024104723 @default.
- W4324257977 cites W3024511949 @default.
- W4324257977 cites W3027579608 @default.
- W4324257977 cites W3028445508 @default.
- W4324257977 cites W3037858752 @default.
- W4324257977 cites W3038407708 @default.
- W4324257977 cites W3046441667 @default.
- W4324257977 cites W3048388384 @default.
- W4324257977 cites W3048899190 @default.
- W4324257977 cites W3083236008 @default.
- W4324257977 cites W3087313836 @default.
- W4324257977 cites W3092141225 @default.
- W4324257977 cites W3094563936 @default.
- W4324257977 cites W3095048791 @default.
- W4324257977 cites W3097458508 @default.
- W4324257977 cites W3097789322 @default.
- W4324257977 cites W3143822502 @default.
- W4324257977 cites W3157179107 @default.
- W4324257977 cites W3173173113 @default.
- W4324257977 cites W3191198889 @default.
- W4324257977 cites W4220738122 @default.
- W4324257977 cites W4220743029 @default.
- W4324257977 cites W69806578 @default.
- W4324257977 doi "https://doi.org/10.1016/j.compag.2023.107752" @default.
- W4324257977 hasPublicationYear "2023" @default.
- W4324257977 type Work @default.
- W4324257977 citedByCount "1" @default.
- W4324257977 countsByYear W43242579772023 @default.
- W4324257977 crossrefType "journal-article" @default.
- W4324257977 hasAuthorship W4324257977A5001065920 @default.
- W4324257977 hasAuthorship W4324257977A5003748150 @default.
- W4324257977 hasAuthorship W4324257977A5033201032 @default.
- W4324257977 hasAuthorship W4324257977A5049655905 @default.
- W4324257977 hasAuthorship W4324257977A5050575971 @default.
- W4324257977 hasAuthorship W4324257977A5059394465 @default.
- W4324257977 hasAuthorship W4324257977A5073713049 @default.
- W4324257977 hasAuthorship W4324257977A5084403604 @default.
- W4324257977 hasBestOaLocation W43242579772 @default.
- W4324257977 hasConcept C105795698 @default.
- W4324257977 hasConcept C119857082 @default.
- W4324257977 hasConcept C139945424 @default.
- W4324257977 hasConcept C146849305 @default.
- W4324257977 hasConcept C152877465 @default.
- W4324257977 hasConcept C154945302 @default.
- W4324257977 hasConcept C169258074 @default.
- W4324257977 hasConcept C33923547 @default.