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Got Grit? Data-Driven Detection and Genetic Analysis of Consistency and Resilience in Us Dairy Cattle.
Got Grit? Data-Driven Detection and Genetic Analysis of Consistency and Resilience in Us Dairy Cattle.
상세정보
- 자료유형
- 학위논문(국외)
- 기본표목-개인명
- 표제와 책임표시사항
- Got Grit? Data-Driven Detection and Genetic Analysis of Consistency and Resilience in Us Dairy Cattle.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 126 p.
- 일반주기
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 일반주기
- Advisor: Weigel, Kent A.;Penagaricano, Francisco.
- 학위논문주기
- Thesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
- 요약 등 주기
- 요약This dissertation explores various approaches to quantifying resilience in U.S. dairy cattle using high-frequency data, specifically daily milk weights. Chapter One provides an introduction and outlines the structure of the dissertation. Chapter Two presents a comprehensive literature review covering the use of high-frequency data in dairy production, the importance of improving dairy cow resilience, current definitions of resilience, and future directions for incorporating this novel trait into breeding programs. Chapter Three investigates the genetic basis of lactation consistency in U.S. Holsteins. Results indicate that consistency is a heritable trait that is favorably correlated with fertility, longevity, and health traits, offering producers the potential to select animals that perform reliably across a range of environmental and management challenges. Chapter Four introduces a novel approach for defining contemporary groups by leveraging pen-level information linked to daily milk yield data. Refining contemporary groups to more closely reflect the day-to-day management and environmental conditions to which individual cows are exposed can improve the accuracy of genetic evaluations, particularly the reliability of sire predicted transmitting abilities for milk yield. This method represents an important step toward more precise modeling of high-frequency phenotypes. Chapter Five describes the development and implementation of a data-driven method to detect pen-level perturbations and evaluates the heritability of a novel resilience phenotype, along with its genetic relationship to lactation consistency. Results reveal that heritability increases with the severity and duration of perturbations to which the animals are exposed, and that resilience and consistency capture distinct genetic traits. This distinction supports the selection of animals with generalized resilience or the ability to withstand diverse challenges. Chapter Six examines seven alternative resilience phenotypes derived from the perturbation detection framework, focusing on how cows respond to and recover from environmental and management disturbances. Phenotypes calculated over the full perturbation period demonstrated the highest heritabilities and lowest standard errors, while those based on single days or shorter windows were less reliable. These results underscore meaningful genetic differences among cows in their ability to sustain production during adverse events. Chapter Seven concludes the dissertation with a summary of key findings and their implications for future genetic selection and resilience phenotyping strategies in dairy cattle.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 원문정보보기
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a636
■1001 ▼aGuinan, Fiona Louise.
■24510▼aGot Grit? Data-Driven Detection and Genetic Analysis of Consistency and Resilience in Us Dairy Cattle.
■260 ▼a[S.l.]▼bThe University of Wisconsin - Madison. ▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a126 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Weigel, Kent A.;Penagaricano, Francisco.
■5021 ▼aThesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
■520 ▼aThis dissertation explores various approaches to quantifying resilience in U.S. dairy cattle using high-frequency data, specifically daily milk weights. Chapter One provides an introduction and outlines the structure of the dissertation. Chapter Two presents a comprehensive literature review covering the use of high-frequency data in dairy production, the importance of improving dairy cow resilience, current definitions of resilience, and future directions for incorporating this novel trait into breeding programs. Chapter Three investigates the genetic basis of lactation consistency in U.S. Holsteins. Results indicate that consistency is a heritable trait that is favorably correlated with fertility, longevity, and health traits, offering producers the potential to select animals that perform reliably across a range of environmental and management challenges. Chapter Four introduces a novel approach for defining contemporary groups by leveraging pen-level information linked to daily milk yield data. Refining contemporary groups to more closely reflect the day-to-day management and environmental conditions to which individual cows are exposed can improve the accuracy of genetic evaluations, particularly the reliability of sire predicted transmitting abilities for milk yield. This method represents an important step toward more precise modeling of high-frequency phenotypes. Chapter Five describes the development and implementation of a data-driven method to detect pen-level perturbations and evaluates the heritability of a novel resilience phenotype, along with its genetic relationship to lactation consistency. Results reveal that heritability increases with the severity and duration of perturbations to which the animals are exposed, and that resilience and consistency capture distinct genetic traits. This distinction supports the selection of animals with generalized resilience or the ability to withstand diverse challenges. Chapter Six examines seven alternative resilience phenotypes derived from the perturbation detection framework, focusing on how cows respond to and recover from environmental and management disturbances. Phenotypes calculated over the full perturbation period demonstrated the highest heritabilities and lowest standard errors, while those based on single days or shorter windows were less reliable. These results underscore meaningful genetic differences among cows in their ability to sustain production during adverse events. Chapter Seven concludes the dissertation with a summary of key findings and their implications for future genetic selection and resilience phenotyping strategies in dairy cattle.
■590 ▼aSchool code: 0262.
■650 4▼aAnimal sciences.
■650 4▼aAnimal diseases.
■650 4▼aBioinformatics.
■650 4▼aGenetics.
■653 ▼aConsistency
■653 ▼aContemporary groups
■653 ▼aDaily milk weights
■653 ▼aPerturbation detection
■653 ▼aResilience indicators
■653 ▼aU.S. Holsteins
■690 ▼a0475
■690 ▼a0476
■690 ▼a0369
■690 ▼a0715
■71020▼aThe University of Wisconsin - Madison▼bAnimal and Dairy Sciences.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0262
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359344▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
