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Soyeurt H, Franceschini S, Bahadi M, Leblois J, Brostaux Y, Dehareng F, Frizzarin M, Tiplady K, Dale L, Nickmilder C. Rapid selection of milk mid-infrared spectra for creating a world representative spectral database of dairy cow population. J Dairy Sci 2024:S0022-0302(24)01020-8. [PMID: 39033920 DOI: 10.3168/jds.2024-24911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/27/2024] [Indexed: 07/23/2024]
Abstract
The advantage of employing mid-infrared spectrometry for milk analysis in breeding lies in its ability to quickly generate millions of records. However, these records may be biased if the calibration process does not account for their spectral variability when constructing the predictive model. So, this study introduces a novel method for developing a World Representative Spectral Database (WRSD) to reduce the risks of spectral extrapolation when predicting dairy traits in new samples. Utilizing a 2-phase selection procedure that is both efficient and minimizes memory usage, we first generate a decomposition matrix via Principal Component Analysis (PCA) on a data set of 2,324,443 records. The next phase iterates spectral selection based on a location index from PCA scores, calculating spectra occurrence frequency for refined barycenter estimations. The chosen spectra's barycenter closely aligns with the entire data set, proving the efficacy of using just 3 principal components (PCs). Applied to 4 varied data sets, totaling over 21 million records, we select 583,440 spectra to represent spectral diversity, with selection rates between 2.00% and 14.88%. This selection illustrates the spectral variability across different dairy populations and data providers. Demonstrated through a hypothetical calibration set of 71 samples, the WRSD's utility for algorithm developers becomes apparent. This calibration set covers between 91.42 to 98.50% of the WRSD variability, except for the Irish data set (3.50%), indicating a need for additional samples to accurately represent Irish variability and minimize spectral extrapolation. This study offers valuable insights into the representativeness of training sets for capturing spectral variability within targeted dairy populations. While the current WRSD does not fully encompass global milk spectral diversity, its development underscores the importance of gathering more data and standardizing spectral information across spectrometer brands. Ultimately, the WRSD proves crucial not just for trait prediction but also for identifying abnormal milk samples, also marking a significant relevance in dairy science technology.
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Affiliation(s)
- H Soyeurt
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium.
| | - S Franceschini
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - M Bahadi
- Lactanet, 555 Boul des Anciens-Combattants, H9X 3R4, Sainte Anne de Bellevue, QC, Canada
| | - J Leblois
- Walloon Breeders Association, Eléveo, Ciney, Belgium
| | - Y Brostaux
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - F Dehareng
- Agricultural Walloon Research Centre, Gembloux, Belgium
| | - M Frizzarin
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - K Tiplady
- Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand
| | - L Dale
- Regional association for performance testing in livestock breeding of Baden-Wuerttemberg (LKV - Baden-Wuerttemberg), Stuttgart, Germany
| | - C Nickmilder
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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2
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Chen Y, Atashi H, Qu J, Delhez P, Runcie D, Soyeurt H, Gengler N. Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding. J Dairy Sci 2024:S0022-0302(24)00975-5. [PMID: 38969006 DOI: 10.3168/jds.2023-24319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 06/10/2024] [Indexed: 07/07/2024]
Abstract
With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).
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Affiliation(s)
- Yansen Chen
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium.
| | - Hadi Atashi
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium; Department of Animal Science, Shiraz University, 71441-13131 Shiraz, Iran
| | - Jiayi Qu
- Department of Animal Science, University of California Davis, CA 95616 Davis, USA
| | - Pauline Delhez
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
| | - Daniel Runcie
- Department of Plant Sciences, University of California Davis, CA 95616 Davis, USA
| | - Hélène Soyeurt
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
| | - Nicolas Gengler
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
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3
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Gallo L, Berton M, Piazza M, Sturaro E, Schiavon S, Bittante G. Environmental impact of Holstein Friesian and 3-breed crossbred dairy cows using a life cycle assessment approach applied to individual animals. J Dairy Sci 2024; 107:4670-4684. [PMID: 38369114 DOI: 10.3168/jds.2023-24106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024]
Abstract
This study aimed to set up a life cycle assessment (LCA) approach at the level of individual animals to assess the effects of a 3-breed crossbreeding program on the environmental impact of cows. The study involved 564 cows, 279 purebred Holstein Friesian (HO) and 285 crossbred cows (CR), that originated from a 3-breed crossbreeding program based on the rotational use of Viking Red, Montebèliarde, and HO sires and kept in 2 dairy herds of northern Italy (224 and 340 cows/herd, respectively). The reference unit of the LCA model was the lifetime of cows, from the birth to culling or death. Data were collected at different levels: individual animal-based data referred to the whole life (birth, calving, dry, cull or death dates, and milk production); individual test-date collection of body measures and BCS, used to predict BW and to estimate energy requirements; common farm-based data concerning herd management (diets composition, and materials used). Data were used to compute DMI, milk and milk components production, gross income (GI), and income over feed costs (IOFC) pertaining to the lifespan of cows. An individual LCA-derived approach was set up to compute global warming potential (GWP), acidification and eutrophication potential (AP and EP, respectively), and land occupation (LO), which have been associated with different functional units (cow in her whole life or per day of life; kilograms of milk fat plus protein, and GI and IOFC [in euros] produced in the herd life). Data were analyzed using a generalized linear model including the fixed effects of genetic group (CR vs. HO), farm, and their interaction (genetic group × farm). Compared with HO, CR cows completed more lactations (+12%), had earlier first calving (-2 wk), yielded more fat plus protein in milk both in the lifespan (+8%) and per day of life (+4%). Concerning the environmental impact, when compared with HO herd mates, CR cows had nominal greater emissions per cow in the whole life, similar emissions per day of life and ∼3% lower GWP, AP, and EP per kilogram of fat plus protein yielded in lifespan. Income over feed costs per unit of emission tended to be ∼4% greater in CR compared with HO cows. Also, the use of land tended to be lower in CR compared with HO in most indicators considered. In conclusion, LCA could be adapted to represent individual animals. Moreover, managing dairy cows according to a 3-breed rotational crossbreeding scheme may be regarded as a strategy that can contribute to mitigate the emissions and to improve the environmental impact of dairy operations.
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Affiliation(s)
- L Gallo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy
| | - M Berton
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy.
| | - M Piazza
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy
| | - E Sturaro
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy
| | - S Schiavon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro (PD), Italy
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Fresco S, Vanlierde A, Boichard D, Lefebvre R, Gaborit M, Bore R, Fritz S, Gengler N, Martin P. Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra. Animal 2024; 18:101200. [PMID: 38870588 DOI: 10.1016/j.animal.2024.101200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 05/11/2024] [Accepted: 05/14/2024] [Indexed: 06/15/2024] Open
Abstract
Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.
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Affiliation(s)
- S Fresco
- Eliance, 149 rue de Bercy, 75595 Paris cedex 12, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France.
| | - A Vanlierde
- Walloon Agricultural Research Centre, Animal Production Unit, 5030 Gembloux, Belgium
| | - D Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - R Lefebvre
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - M Gaborit
- INRAE UE326 Domaine Expérimental du Pin, 61310 Exmes, France
| | - R Bore
- Institut de l'Élevage, 149 Rue de Bercy, 75012 Paris, France
| | - S Fritz
- Eliance, 149 rue de Bercy, 75595 Paris cedex 12, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - N Gengler
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - P Martin
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
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Martínez-Marín G, Toledo-Alvarado H, Amalfitano N, Gallo L, Bittante G. Lactation modeling and the effects of rotational crossbreeding on milk production traits and milk-spectra-predicted enteric methane emissions. J Dairy Sci 2024; 107:1485-1499. [PMID: 37944799 DOI: 10.3168/jds.2023-23551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/19/2023] [Indexed: 11/12/2023]
Abstract
Rotational crossbreeding has not been widely studied in relation to the enteric methane emissions of dairy cows, nor has the variation in emissions during lactation been modeled. Milk infrared spectra could be used to predict proxies of methane emissions in dairy cows. Therefore, the objective of this work was to study the effects of crossbreeding on the predicted infrared proxies of methane emissions and the variation in the latter during lactation. Milk samples were taken once from 1,059 cows reared in 2 herds, and infrared spectra of the milk were used to predict milk fat (mean ± SD; 3.79 ± 0.81%) and protein (3.68 ± 0.36%) concentrations, yield (21.4 ± 1.5 g/kg dry matter intake), methane intensity (14.2 ± 2.0 g/kg corrected milk), and daily methane production (358 ± 108 g/d). Of these cows, 620 were obtained from a 3-breed (Holstein, Montbéliarde, and Viking Red) rotational mating system, and the rest were purebred Holsteins. Milk production data and methane traits were analyzed using a nonlinear model that included the fixed effects of herd, genetic group, and parity, and the 4 parameters (a, b, c, and k) of a lactation curve modeled using the Wilmink function. Milk infrared spectra were found to be useful for direct prediction of qualitative proxies, such as methane yield and intensity, but not quantitative traits, such as daily methane production, which appears to be better estimated (450 ± 125 g/d) by multiplying a measured daily milk yield by infrared-predicted methane intensity. Lactation modeling of methane traits showed daily methane production to have a zenith curve, similar to that of milk yield but with a delayed peak (53 vs. 37 d in milk), whereas methane intensity is characterized by an upward curve that increases rapidly during the first third of lactation and then slowly till the end of lactation (10.5 g/kg at 1 d in milk to 15.2 g/kg at 300 d in milk). However, lactation modeling was not useful in explaining methane yield, which is almost constant during lactation. Lastly, the methane yield and intensity of cows from 3-breed rotational crossbreeding are not greater, and their methane production is lower than that of purebred Holsteins (452 vs. 477 g/d). Given the greater longevity of crossbred cows, and their lower replacement rate, rotational crossbreeding could be a way of mitigating the environmental impact of milk production.
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Affiliation(s)
- Gustavo Martínez-Marín
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Hugo Toledo-Alvarado
- Department of Genetics and Biostatistics, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Nicolò Amalfitano
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.
| | - Luigi Gallo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
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6
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McParland S, Frizzarin M, Lahart B, Kennedy M, Shalloo L, Egan M, Starsmore K, Berry DP. Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples. J Dairy Sci 2024; 107:978-991. [PMID: 37709036 DOI: 10.3168/jds.2023-23577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.
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Affiliation(s)
- S McParland
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - M Frizzarin
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - B Lahart
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - M Kennedy
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - L Shalloo
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - M Egan
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - K Starsmore
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - D P Berry
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland.
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Ross S, Wang H, Zheng H, Yan T, Shirali M. Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning. J Anim Sci 2024; 102:skae219. [PMID: 39123286 PMCID: PMC11367560 DOI: 10.1093/jas/skae219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 08/07/2024] [Indexed: 08/12/2024] Open
Abstract
Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of "Bovine," exposure of "Statistical Analysis or Machine Learning," and outcome of "Methane Emissions". The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.
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Affiliation(s)
- Stephen Ross
- School of Computing, Ulster University, Belfast BT15 1ED, UK
- Sustainable Livestock Systems Branch, Agri Food and Biosciences Institute, Hillsborough BT26 6DR, UK
| | - Haiying Wang
- School of Computing, Ulster University, Belfast BT15 1ED, UK
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast BT15 1ED, UK
| | - Tianhai Yan
- Sustainable Livestock Systems Branch, Agri Food and Biosciences Institute, Hillsborough BT26 6DR, UK
| | - Masoud Shirali
- Sustainable Livestock Systems Branch, Agri Food and Biosciences Institute, Hillsborough BT26 6DR, UK
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8
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Soyeurt H, Wu XL, Grelet C, van Pelt ML, Gengler N, Dehareng F, Bertozzi C, Burchard J. Imputation of missing milk Fourier transform mid-infrared spectra using existing milk spectral databases: A strategy to improve the reliability of breeding values and predictive models. J Dairy Sci 2023; 106:9095-9104. [PMID: 37678782 DOI: 10.3168/jds.2023-23458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/07/2023] [Indexed: 09/09/2023]
Abstract
The use of milk Fourier transform mid-infrared (FT-MIR) spectrometry to develop management and breeding tools for dairy farmers and industry is growing and supported by the availability of numerous new predicted phenotypes to assess the nutritional quality of milk and its technological properties, but also the animal health and welfare status and its environmental fingerprint. For genetic evaluations, having a long-term and representative spectral dairy herd improvement (DHI) database improves the reliabilities of estimated breeding values (EBV) from these phenotypes. Unfortunately, most of the time, the raw spectral data used to generate these estimations are not stored. Moreover, many reference measurements of those phenotypes, needed during the FT-MIR calibration step, are available from past research activities but lack spectra records. So, it is impossible to use them to improve the FT-MIR models. Consequently, there is a strong interest in imputing those missing spectra. The innovative objective of this study was to use the existing large spectral DHI database to estimate missing spectra by selecting probable spectra using, as the match criteria, common dairy traits recorded for a long time by DHI organizations. We tested 4 match criteria combinations. Combination 1 required to have equal fat and protein contents between the sample for which a spectrum was to be estimated and the reference samples in the DHI database. Combination 2 also required an equal urea content. Combination 3 requested equal fat, protein, and lactose contents. Finally, combination 4 included all criteria. When more than one spectrum was found during the search, their average was the estimated spectrum for the query sample. Concretely, this study estimated missing spectra for 1,700 samples using 2,000,000 spectral DHI records. For assessing the effect of this spectral estimation on the prediction quality, FT-MIR equations were used to predict 11 phenotypes, selected as their quantification used different FT-MIR regions. They were related to the milk fat and mineral composition, lactoferrin content, quantity of eructed methane, body weight (BW), and dry matter intake. The accuracy between predictions obtained from actual and estimated spectra was evaluated by calculating the mean absolute error (MAE). The criteria in the fourth and second combinations were too strict to estimate a spectrum for most samples. Indeed, for many samples, no spectra with the same values for those matching criteria was found. The third match criteria combination had a poorer prediction performance for all studied traits and spectral absorptions than the first combination due to fewer matched samples available to compute the missing spectrum. By allowing a range for matching lactose content (±0.1 g/dL milk), we showed that this new combination increased the number of selected samples to compute missing spectra and predict better the infrared absorption at different wavenumbers, especially those related to the lactose quantification. The prediction performance was further improved by performing queries on the entire Walloon DHI spectral database (6,625,570 spectra), and it varied among the studied phenotypes. Without considering the traits used for the matching, the best predictions were obtained for the content of saturated fatty acids (MAE = 0.15 g/dL milk) and BW (MAE = 12.80 kg). Yet, the predictions for the unsaturated fatty acids were less accurate (MAE = 0.13 and 0.018 g/dL milk for monounsaturated and polyunsaturated fatty acids), likely because of the poorer predictions of spectral regions related to long-chain fatty acids. Similarly, poorer predictions were observed for the amount of methane eructed by dairy cows (MAE = 47.02 g/d), likely because it is not directly related to fat content or composition. Prediction accuracies for the remaining traits were also low. In conclusion, we observed that increasing the number of relevant matching criteria helps improve the quality of FT-MIR predicted phenotypes and the number of spectra used during the search. So, it would be of great interest to test in the future the suitability of the developed methodology with large-scale international spectral databases to improve the reliability of EBV from these FT-MIR-based phenotypes and the robustness of FT-MIR predictive models.
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Affiliation(s)
- H Soyeurt
- Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium.
| | - X-L Wu
- Council of Dairy Cattle Breeding, Bowie, MD 20716; Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - C Grelet
- Walloon Agricultural Research Center, 5030 Gembloux, Belgium
| | - M L van Pelt
- Cooperation CRV, Animal Evaluation Unit, PO Box 454, 6800 AL Arnhem, the Netherlands
| | - N Gengler
- Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - F Dehareng
- Walloon Agricultural Research Center, 5030 Gembloux, Belgium
| | - C Bertozzi
- Walloon Breeders Association, 5590 Ciney, Belgium
| | - J Burchard
- Council of Dairy Cattle Breeding, Bowie, MD 20716
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9
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Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk. Foods 2023; 12:foods12061199. [PMID: 36981127 PMCID: PMC10048274 DOI: 10.3390/foods12061199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/28/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.
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10
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Nan L, Du C, Fan Y, Liu W, Luo X, Wang H, Ding L, Zhang Y, Chu C, Li C, Ren X, Yu H, Lu S, Zhang S. Association between Days Open and Parity, Calving Season or Milk Spectral Data. Animals (Basel) 2023; 13:ani13030509. [PMID: 36766398 PMCID: PMC9913365 DOI: 10.3390/ani13030509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/15/2023] [Accepted: 01/18/2023] [Indexed: 02/04/2023] Open
Abstract
Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.
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Affiliation(s)
- Liangkang Nan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chao Du
- Henan Institute of Science and Technology, College of Animal Science and Veterinary Medicine, Xinxiang 453003, China
| | - Yikai Fan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Wenju Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xuelu Luo
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Haitong Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Lei Ding
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yi Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chu Chu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chunfang Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoli Ren
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Yu
- Hebei Livestock Breeding Station, Shijiazhuang 050000, China
| | - Shiyu Lu
- Hebei Livestock Breeding Station, Shijiazhuang 050000, China
| | - Shujun Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Correspondence:
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11
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Coppa M, Vanlierde A, Bouchon M, Jurquet J, Musati M, Dehareng F, Martin C. Methodological guidelines: Cow milk mid-infrared spectra to predict GreenFeed enteric methane emissions. J Dairy Sci 2022; 105:9271-9285. [PMID: 36175234 DOI: 10.3168/jds.2022-21890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/29/2022] [Indexed: 11/19/2022]
Abstract
Various methodological protocols were tested on milk samples from cows fed diets affecting both methanogenesis and milk synthesis to identify the best approach for the prediction of GreenFeed system (GF) measured methane (CH4) emissions by milk mid-infrared (MIR) spectroscopy. The models developed were also tested on a data set from cows fed chemical inhibitors of CH4 emission [3-nitrooxypropanol (3NOP)] that just marginally affect milk composition. A total of 129 primiparous and multiparous Holstein cows fed diets with different methanogenic potential were considered. Individual milk yield (MY) and dry matter intake were recorded daily, whereas fat- and protein-corrected milk (FPCM) was recorded twice a week. The MIR spectra from 2 consecutive milkings were collected twice a week. Twenty CH4 spot measurements with GF were taken as the basic measurement unit (BMU) of CH4. The equations were built using partial least squares regression by splitting the database into calibration and validation data sets (excluding 3NOP samples). Models were developed for milk MIR spectra by milking and on day spectra obtained by averaging spectra from 2 consecutive milkings. Models based on day spectra were calibrated by using CH4 reference data for a measurement duration of 1, 2, 3, or 4 BMU. Models built from the average of the day spectra collected during the corresponding CH4 measurement periods were developed. Corrections of spectra by days in milk (DIM) and the inclusion of parity, MY, and FPCM as explanatory variables were tested as tools to improve model performance. Models built on day milk MIR spectra gave slightly better performances that those developed using spectra from a single milking. Long duration of CH4 measurement by GF performed better than short duration: the coefficient of determination of validation (R2V) for CH4 emissions expressed in grams per day were 0.60 vs. 0.52 for 4 and 1 BMU, respectively. When CH4 emissions were expressed as grams per kilogram of dry of matter intake, grams per kilogram of MY, or grams per kilogram of FPCM, performance with a long duration also improved. Coupling GF reference data with the average of milk MIR spectra collected throughout the corresponding CH4 measurement period gave better predictions than using day spectra (R2V = 0.70 vs. 0.60 for CH4 as g/d on 4 BMU). Correcting the day spectra by DIM improved R2V compared with the equivalent DIM-uncorrected models (R2V = 0.67 vs. 0.60 for CH4 as g/d on 4 BMU). Adding other phenotypic information as explanatory variables did not further improve the performance of models built on single day DIM-corrected spectra, whereas including MY (or FPCM) improved the performance of models built on the average of spectra (uncorrected by DIM) recorded during the CH4 measurement period (R2V = 0.73 vs. 0.70 for CH4 as g/d on 4 BMU). When validating the models on the 3NOP data set, predictions were poor without (R2V = 0.13 for CH4 as g/d on 1 BMU) or with (R2V = 0.31 for CH4 as g/d on 1 BMU) integration of 3NOP data in the models. Thus, specific models would be required for CH4 prediction when cows receive chemical inhibitors of CH4 emissions not affecting milk composition.
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Affiliation(s)
- M Coppa
- Independent researcher, Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France
| | - A Vanlierde
- Walloon Agricultural Research Centre, B-5030 Gembloux, Belgium
| | - M Bouchon
- INRAE, UE1414 Herbipôle, 63122 Saint-Genès-Champanelle, France
| | - J Jurquet
- Institut de l'Elevage, 42 rue Georges Morel CS 60057, 49071 Beaucouzé cedex, France
| | - M Musati
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France; Department Di3A, University of Catania, via Valdisavoia 5, 95123 Catania, Italy
| | - F Dehareng
- Walloon Agricultural Research Centre, B-5030 Gembloux, Belgium
| | - C Martin
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France.
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12
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Shadpour S, Chud TC, Hailemariam D, Plastow G, Oliveira HR, Stothard P, Lassen J, Miglior F, Baes CF, Tulpan D, Schenkel FS. Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. J Dairy Sci 2022; 105:8272-8285. [DOI: 10.3168/jds.2021-21176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 06/09/2022] [Indexed: 11/19/2022]
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13
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Franceschini S, Grelet C, Leblois J, Gengler N, Soyeurt H. Can unsupervised learning methods applied to milk recording big data provide new insights into dairy cow health? J Dairy Sci 2022; 105:6760-6772. [DOI: 10.3168/jds.2022-21975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/13/2022] [Indexed: 11/19/2022]
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14
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Liu R, Hailemariam D, Yang T, Miglior F, Schenkel F, Wang Z, Stothard P, Zhang S, Plastow G. Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system. Animal 2022; 16:100469. [PMID: 35190321 DOI: 10.1016/j.animal.2022.100469] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 12/01/2022] Open
Abstract
Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R2val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R2val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R2val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R2val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.
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Affiliation(s)
- R Liu
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada; Key Laboratory of Animal Breeding and Reproduction of Ministry of Education, Hauzhong Agricultural University, Wuhan 430070, China
| | - D Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada.
| | - T Yang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
| | - F Miglior
- Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada
| | - F Schenkel
- Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada
| | - Z Wang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
| | - P Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
| | - S Zhang
- Key Laboratory of Animal Breeding and Reproduction of Ministry of Education, Hauzhong Agricultural University, Wuhan 430070, China
| | - G Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
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15
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Tedde A, Grelet C, Ho PN, Pryce JE, Hailemariam D, Wang Z, Plastow G, Gengler N, Froidmont E, Dehareng F, Bertozzi C, Crowe MA, Soyeurt H. Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows' Dry Matter Intake. Animals (Basel) 2021; 11:ani11051316. [PMID: 34064417 PMCID: PMC8147833 DOI: 10.3390/ani11051316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/30/2021] [Accepted: 05/01/2021] [Indexed: 01/19/2023] Open
Abstract
Simple Summary Dry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values. Abstract We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.
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Affiliation(s)
- Anthony Tedde
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (H.S.)
- National Funds for Scientific Research, 1000 Brussels, Belgium
- Correspondence:
| | - Clément Grelet
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | - Phuong N. Ho
- Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; (P.N.H.); (J.E.P.)
| | - Jennie E. Pryce
- Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; (P.N.H.); (J.E.P.)
- School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, VIC 3083, Australia
| | - Dagnachew Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Zhiquan Wang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Nicolas Gengler
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (H.S.)
| | - Eric Froidmont
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | - Frédéric Dehareng
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | | | - Mark A. Crowe
- UCD School of Veterinary Medicine, University College Dublin, Dublin 4, Ireland;
| | - Hélène Soyeurt
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (H.S.)
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