2026-09-18T23:13:00Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/1130162026-05-15T19:40:26Zcom_1813_35col_1813_47
Granados, German
Giordano, Julio
Nydam, Daryl
2023-03-31T16:40:24Z
2023-03-31T16:40:24Z
2022-12
ProQuest Submission ID: 11635
ProQuest Publication ID: 30000875
https://hdl.handle.net/1813/113016
https://doi.org/10.7298/nh2q-9771
15644085
157 pages
The overarching objective of the research presented in this thesis was to characterize associations between cow, herd, and environmental data with insemination outcome, and develop machine learning algorithms (MLA) to predict the outcome of the first service (FS) after calving in lactating dairy cows. The specific objective of the study presented in Chapter I was to compare patterns of multiple cow behavioral, physiological, and performance parameters collected by automated sensors before insemination for cows that became pregnant or not at FS. A secondary objective was to explore associations between pregnancy outcome at FS with previous gestation and early lactation performance and events, and with environmental conditions before insemination. An observational retrospective cohort study was conducted using data collected at a commercial dairy farm. Daily values for milk yield, milk components percent and yield, rumination and eating activity, physical and walking activity, resting time and bouts, body temperature, milk conductivity, and body weight collected by wearable and non-wearable sensors from -14 to 56 d after calving for 932 primiparous and 2,070 multiparous cows with a FS pregnancy outcome were available for analysis. Daily data were summarized as the average of seven periods of 4 to 7 d long from -14 to 56 d after calving and from -27 to -11, -10 to -3, -2 to - 1 d relative to timed AI for FS. The most notable differences observed for primiparous cows were greater milk yield, milk components yield, and fewer lying bouts per day for pregnant than non-pregnant cows. For the multiparous cow group, non-pregnant cows produced more milk and milk fat, had greater body temperature, more activity, more resting time, and had greater body weight changes after calving than pregnant cows. Associations of different strength and direction between FS outcome with previous gestation and previous and current lactation features, events, and performance for primiparous and multiparous cows were observed. Substantial variability between parity groups for the direction and magnitude of differences between pregnant and nonpregnant cows warrants use of parity either as a model predictor, or the development of parityspecific models for predicting FS outcome of lactating dairy cows. Chapter II of this thesis presents the development and performance of multiple MLA for predicting FS outcome using data presented in Chapter I. Decision Trees, Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting models were built and evaluated for primiparous and multiparous only and for both parities combined. Overall, we observed that these MLA trained with a combination of automated sensor cow behavioral, physiological and performance data, as well as herd outcomes and environmental data presented a wide range of performance. The best performing algorithms (i.e., most performance metrics values in the 90 to 95% range) were those for primiparous cows using Support Vector Machine and Logistic Regression models. Overall, the performance of MLA for multiparous cows was poor (i.e., all performance metrics <70%) considering the implications of predictions for practical application. In conclusion, different supervised MLA trained with a combination of cow parameters collected by automated wearable and non-wearable sensors, herd outcomes, and farm environmental conditions, presented large variation in performance despite using the same input data and the same algorithms. Large variation in algorithm performance due to parity suggested that different models might have to be developed for predicting FS outcome for primiparous and multiparous cows. Further research is needed to identify a combination of predictors, methods to summarize input data from predictors, and develop procedures to train MLA that yield the level of performance required for practical use of algorithms in commercial farms.
en
Dairy cow
Machine Learning
Parameters
Prediction
Pregnancy
Sensor
CHARACTERIZATION OF SENSOR AND NON-SENSOR COW, HERD MANAGEMENT, AND ENVIRONMENTAL DATA AND USE OF MACHINE LEARNING ALGORITHMS FOR PREDICTION OF PREGNANCY IN DAIRY CATTLE
dissertation or thesis
https://newcatalog.library.cornell.edu/catalog/15644085
application/pdf
Animal Science
Cornell University
Master of Science
M.S., Animal Science
https://hdl.handle.net/1813/59810.2
Publication
Cornell Theses and Dissertations
Granados, German
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
open.access
open.access
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<Abstract>The overarching objective of the research presented in this thesis was to characterize associations between cow, herd, and environmental data with insemination outcome, and develop machine learning algorithms (MLA) to predict the outcome of the first service (FS) after calving in lactating dairy cows. The specific objective of the study presented in Chapter I was to compare patterns of multiple cow behavioral, physiological, and performance parameters collected by automated sensors before insemination for cows that became pregnant or not at FS. A secondary objective was to explore associations between pregnancy outcome at FS with previous gestation and early lactation performance and events, and with environmental conditions before insemination. An observational retrospective cohort study was conducted using data collected at a commercial dairy farm. Daily values for milk yield, milk components percent and yield, rumination and eating activity, physical and walking activity, resting time and bouts, body temperature, milk conductivity, and body weight collected by wearable and non-wearable sensors from -14 to 56 d after calving for 932 primiparous and 2,070 multiparous cows with a FS pregnancy outcome were available for analysis. Daily data were summarized as the average of seven periods of 4 to 7 d long from -14 to 56 d after calving and from -27 to -11, -10 to -3, -2 to - 1 d relative to timed AI for FS. The most notable differences observed for primiparous cows were greater milk yield, milk components yield, and fewer lying bouts per day for pregnant than non-pregnant cows. For the multiparous cow group, non-pregnant cows produced more milk and milk fat, had greater body temperature, more activity, more resting time, and had greater body weight changes after calving than pregnant cows. Associations of different strength and direction between FS outcome with previous gestation and previous and current lactation features, events, and performance for primiparous and multiparous cows were observed. Substantial variability between parity groups for the direction and magnitude of differences between pregnant and nonpregnant cows warrants use of parity either as a model predictor, or the development of parityspecific models for predicting FS outcome of lactating dairy cows. Chapter II of this thesis presents the development and performance of multiple MLA for predicting FS outcome using data presented in Chapter I. Decision Trees, Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting models were built and evaluated for primiparous and multiparous only and for both parities combined. Overall, we observed that these MLA trained with a combination of automated sensor cow behavioral, physiological and performance data, as well as herd outcomes and environmental data presented a wide range of performance. The best performing algorithms (i.e., most performance metrics values in the 90 to 95% range) were those for primiparous cows using Support Vector Machine and Logistic Regression models. Overall, the performance of MLA for multiparous cows was poor (i.e., all performance metrics <70%) considering the implications of predictions for practical application. In conclusion, different supervised MLA trained with a combination of cow parameters collected by automated wearable and non-wearable sensors, herd outcomes, and farm environmental conditions, presented large variation in performance despite using the same input data and the same algorithms. Large variation in algorithm performance due to parity suggested that different models might have to be developed for predicting FS outcome for primiparous and multiparous cows. Further research is needed to identify a combination of predictors, methods to summarize input data from predictors, and develop procedures to train MLA that yield the level of performance required for practical use of algorithms in commercial farms.</Abstract>
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