Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein DOI Creative Commons
Francesco Tiezzi, A. Fleming, F. Malchiodi

и другие.

Animals, Год журнала: 2022, Номер 12(9), С. 1189 - 1189

Опубликована: Май 6, 2022

The purpose of this study was to provide a procedure for the inclusion milk spectral information into genomic prediction models. Spectral data were considered set covariates, in addition covariates. Milk yield and somatic cell score used as traits investigate. A cross-validation employed, making distinction predicting new individuals’ performance under known environments, environments. We found an advantage including environmental covariates when predictions had be extrapolated This valid both observed and, even more, unobserved families (genotypes). Overall, accuracy larger than score. Fourier-transformed infrared can source calculation ‘environmental coordinates’ given farm time, extrapolating could serve example integration phenomic data. help using that present poor predictability at phenotypic level, such disease incidence behavior traits. strength model is ability couple with high-throughput information.

Язык: Английский

Progress and opportunities through use of genomics in animal production DOI

Huw E. Jones,

Philippe B. Wilson

Trends in Genetics, Год журнала: 2022, Номер 38(12), С. 1228 - 1252

Опубликована: Авг. 6, 2022

Язык: Английский

Процитировано

16
Automated phenotyping empowered by deep learning for genomic prediction of body size in the tiger pufferfish, Takifugu rubripes DOI Creative Commons
Zijie Lin,

Sota Yoshikawa,

Masaomi Hamasaki

и другие.

Aquaculture, Год журнала: 2024, Номер 595, С. 741491 - 741491

Опубликована: Авг. 15, 2024

Язык: Английский

Процитировано

3
The flight of chicken genomics and allied omics-a mini review DOI
Nidhi Sukhija,

K. K. Kanaka,

Rangasai Chandra Goli

и другие.

Ecological Genetics and Genomics, Год журнала: 2023, Номер 29, С. 100201 - 100201

Опубликована: Сен. 25, 2023

Язык: Английский

Процитировано

8
Estimating genetics of body dimensions and activity levels in pigs using automated pose estimation DOI Creative Commons
Wim Gorssen, Carmen Winters, Roel Meyermans

и другие.

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Сен. 13, 2022

Pig breeding is changing rapidly due to technological progress and socio-ecological factors. New precision livestock farming technologies such as computer vision systems are crucial for automated phenotyping on a large scale novel traits, pigs' robustness behavior gaining importance in goals. However, individual identification, data processing the availability of adequate (open source) software currently pose main hurdles. The overall goal this study was expand pig weighing with measurements body dimensions activity levels using an video-analytic system: DeepLabCut. Furthermore, these were coupled pedigree information estimate genetic parameters programs. We analyzed 7428 recordings over fattening period 1556 finishing pigs (Piétrain sire x crossbred dam) two-week intervals between same pig. able accurately relevant parts average tracking error 3.3 cm. Body metrics extracted from video images highly heritable (61-74%) significantly genetically correlated daily gain (rg = 0.81-0.92). Activity traits low moderately (22-35%) showed correlations production physical abnormalities. demonstrated simple cost-efficient method extract dimension traits. These estimated be heritable, hence, can selected on. findings valuable (pig) organizations, they offer automatically phenotype new behavioral level.

Язык: Английский

Процитировано

13
Editorial: Increasing sustainability in livestock production systems through high-throughput phenotyping approaches DOI Creative Commons
Amanda Marchi Maiorano, Michela Ablondi, Yongliang Qiao

и другие.

Frontiers in Genetics, Год журнала: 2024, Номер 15

Опубликована: Апрель 5, 2024

Editorial: Increasing sustainability in livestock production systems through high-throughput phenotyping approaches

Язык: Английский

Процитировано

2
Clustering of countries based on dairy productivity characteristics of Holstein cattle for breeding material selection DOI Creative Commons
A. F. Petrov, O. V. Bogdanova, К. Н. Нарожных

и другие.

Veterinary World, Год журнала: 2024, Номер unknown, С. 1108 - 1118

Опубликована: Май 1, 2024

Background and Aim: The aim of any breeding process is to create a herd based on certain parameters that reflect an ideal animal vision. Targeted herding involves selecting the source material be imported from another country. Therefore, there problem in importer rapidly form uterine canopy with required properties. purpose this study was evaluate set predictive milk productivity traits Holstein cattle across countries. Materials Methods: This research records 819,358 recorded animals 28 countries born after January 1, 2018, open databases. We used Euclidean metric construct dendrograms characterizing similarity according complex daughters bulls. Ward method minimize intracluster variance when forming clusters constructing corresponding diagrams. Principal component analysis reduce dimensionality eliminate effect multicollinearity. principal components were selected using Kaiser–Harris criteria. Results: A ranking multidimensional different over past 5 years performed. group leading led by USA established studied indicators, possible reasons for such division into groups described. Conclusion: pressure purposeful artificial selection prevails comparison natural concerning countries, which allows specialists choose suppliers buying materials. findings are solely data animals, may not represent entire breed population within each country, especially regions where record-keeping inconsistent. It expected further studies will include regional large enterprises part Interbull, mandatory verification validation. An important element work seen as ability compare populations scale, well studying differentiation other dairy. Keywords: material, productivity, dairy traits, cattle.

Язык: Английский

Процитировано

2
Monitoring mortality events in floor-raised broilers using machine learning algorithms trained with feeding behavior time-series data DOI
Anderson Antônio Carvalho Alves, Arthur Fernandes,

Vivian Breen

и другие.

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 224, С. 109124 - 109124

Опубликована: Июнь 9, 2024

Язык: Английский

Процитировано

2
Validating statistical properties of resilience indicators derived from simulated longitudinal performance measures of farmed animals DOI Creative Commons
Masoud Ghaderi Zefreh, Ricardo Pong‐Wong, Andrea Doeschl‐Wilson

и другие.

animal, Год журнала: 2024, Номер 18(8), С. 101248 - 101248

Опубликована: Июль 11, 2024

Resilience is commonly defined as the ability of an individual to be minimally affected or quickly recover from a challenge. Improvement animals' resilience vital component sustainable livestock production but has so far been hampered by lack established quantitative measures. Several studies proposed that summary statistics deviations animal's observed performance its target trajectory (i.e., in absence challenge) may constitute suitable indicators. However, these statistical indicators require further validation. The aim this study was obtain better understanding their discriminate between different response types and dependence on characteristics animals, data recording features. To purpose, milk-yield trajectories dairy cattle differing resilience, without when exposed short-term challenge, were simulated. Individuals categorised into three broad (with variation within each type): Fully Resilient which experience no systematic perturbation milk yield after Non-Resilient animals whose permanently deviates challenge Partially temporary perturbations recover. following previously suggested literature validated with respect sensitivity various features characteristics: logarithm mean squares (LMS), variance (LV), skewness (S), lag-1 autocorrelation (AC1), area under curve (AUC) deviations. Furthermore, methods for estimating unknown evaluated. All considered could distinguish type either other two known estimated using parametric method. When comparison Non-Resilient, only LMS, LV, AUC correctly rank types, provided observation period at least twice long period. Skewness general reliable indicator, although all showed correct dependency amplitude duration perturbations. In addition, except AC1 robust lower frequency measurements. general, (quantile repeated regression) combined (LMS, LV AUC) found most techniques ranking terms resilience.

Язык: Английский

Процитировано

2
Phenomics as an approach to Comparative Developmental Physiology DOI Creative Commons
Jamie C. S. McCoy, John I. Spicer, Ziad Ibbini

и другие.

Frontiers in Physiology, Год журнала: 2023, Номер 14

Опубликована: Авг. 14, 2023

The dynamic nature of developing organisms and how they function presents both opportunity challenge to researchers, with significant advances in understanding possible by adopting innovative approaches their empirical study. information content the phenotype during organismal development is arguably greater than at any other life stage, incorporating change a broad range temporal, spatial functional scales relevance plethora research questions. Yet, effectively measuring development, ontogeny physiological regulations functions, responses environment, remains challenge. "Phenomics", global approach acquisition phenotypic data scale whole organism, uniquely suited as an approach. In this perspective, we explore synergies between phenomics Comparative Developmental Physiology (CDP), discipline increasing sensitivity drivers change. We then identify itself provides excellent model for pushing boundaries phenomics, given its inherent complexity, comparably smaller size, relative adult stages, applicability embryonic suite questions using diversity species. Collection, analysis interpretation are largest obstacle capitalising on advancing our biological systems. suggest that within context form could provide effective scaffold addressing grand challenges CDP phenomics.

Язык: Английский

Процитировано

4
Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds DOI Creative Commons
Vittoria Asti, Michela Ablondi,

Alceu Dalle Molle

и другие.

Frontiers in Veterinary Science, Год журнала: 2024, Номер 11

Опубликована: Окт. 16, 2024

Introduction The shift of the horse breeding sector from agricultural to leisure and sports purposes led a decrease in local breeds’ population size due loss their original purposes. Most Italian breeds must adapt modern market demands, gait traits are suitable phenotypes help this process. Inertial measurement unit (IMU) technology can be used objectively assess them. This work aims investigate on IMU recorded data (i) influence environmental factors biometric measurements, (ii) repeatability, (iii) correlation with judge evaluations, (iv) predictive value. Material methods Equisense Motion S ® was collect 135 horses, Bardigiano (101) Murgese (34) analysis conducted using R (v.4.1.2). Analysis variance (ANOVA) employed effects measurements animal traits. Results discussion Variations several depending breed were identified, highlighting different abilities among horses. Repeatability performance assessed subset regularity elevation at walk being highest repeatability (0.63 0.72). positive between evaluations sensor indicates judges’ ability evaluate overall quality. Three algorithms predict judges score measurements: Support Vector Machine (SVM), Gradient Boosting (GBM), K-Nearest Neighbors (KNN). A high variability observed accuracy SVM model, ranging 55 100% while other two models showed higher consistency, 74 for GBM 64 88% KNN. Overall, model exhibits lowest error. In conclusion, integrating into evaluation offers valuable insights, implications training.

Язык: Английский

Процитировано

1