Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
Browse
3 results
Search Results
Item Agent-Based Modelling to Improve Beef Production from Dairy Cattle: Young Beef Production(MDPI (Basel, Switzerland), 2023-04-19) Addis AH; Blair HT; Kenyon PR; Morris ST; Schreurs NM; Garrick DJ; Zaborowicz M; Frankowski JApproximately 42% of the total calves born in New Zealand’s dairy industry are either euthanized on farms or commercially slaughtered as so-called bobby calves within 2 weeks of age. These practices have perceived ethical issues and are considered a waste of resources because these calves could be grown on and processed for beef. Young beef cattle harvested between 8 and 12 months of age would represent a new class of beef production for New Zealand and would allow for a greater number of calves to be utilized for beef production, reducing bobby calf numbers in New Zealand. However, the acceptance of such a system in competition with existing sheep and beef cattle production systems is unknown. Therefore, the current study employed an agent-based model (ABM) developed for dairy-origin beef cattle production systems to understand price levers that might influence the acceptance of young beef production systems on sheep and beef cattle farms in New Zealand. The agents of the model were the rearer, finisher, and processor. Rearers bought in 4-days old dairy-origin calves and weaned them at approximately 100 kg live weight before selling them to finishers. Finishers managed the young beef cattle until they were between 8 and 12 months of age in contrast to 20 to 30 months for traditional beef cattle. Processing young beef cattle in existing beef production systems without any price premium only led to an additional 5% of cattle being utilized compared to the traditional beef cattle production system in New Zealand. This increased another 2% when both weaner cattle and young beef were sold at a price premium of 10%. In this scenario, Holstein Friesian young bull contributed more than 65% of total young beef cattle. Further premium prices for young beef cattle production systems increased the proportion of young beef cattle (mainly as young bull beef), however, there was a decrease in the total number of dairy-origin cattle processed, for the given feed supply, compared to the 10% premium price. Further studies are required to identify price levers and other alternative young beef production systems to increase the number of young beef cattle as well the total number of dairy-origin beef cattle for beef on sheep and beef cattle farms. Some potential options for investigation are meat quality, retailer and consumer perspectives, and whether dairy farmers may have to pay calf rearers to utilize calves with lower growth potentialItem Pregnancy status predicted using milk mid-infrared spectra from dairy cattle(Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association, 2022-04) Tiplady KM; Trinh M-H; Davis SR; Sherlock RG; Spelman RJ; Garrick DJ; Harris BLAccurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.Item Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins.(Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association, 2022-12) Tiplady KM; Lopdell TJ; Sherlock RG; Johnson TJJ; Spelman RJ; Harris BL; Davis SR; Littlejohn MD; Garrick DJFourier-transform mid-infrared (FT-MIR) spectroscopy is a high-throughput and inexpensive methodology used to evaluate concentrations of fat and protein in dairy cattle milk samples. The objective of this study was to compare the genetic characteristics of FT-MIR predicted fatty acids and individual milk proteins with those that had been measured directly using gas and liquid chromatography methods. The data used in this study was based on 2,005 milk samples collected from 706 Holstein-Friesian × Jersey animals that were managed in a seasonal, pasture-based dairy system, with milk samples collected across 2 consecutive seasons. Concentrations of fatty acids and protein fractions in milk samples were directly determined by gas chromatography and high-performance liquid chromatography, respectively. Models to predict each directly measured trait based on FT-MIR spectra were developed using partial least squares regression, with spectra from a random selection of half the cows used to train the models, and predictions for the remaining cows used as validation. Variance parameters for each trait and genetic correlations for each pair of measured/predicted traits were estimated from pedigree-based bivariate models using REML procedures. A genome-wide association study was undertaken using imputed whole-genome sequence, and quantitative trait loci (QTL) from directly measured traits were compared with QTL from the corresponding FT-MIR predicted traits. Cross-validation prediction accuracies based on partial least squares for individual and grouped fatty acids ranged from 0.18 to 0.65. Trait prediction accuracies in cross-validation for protein fractions were 0.53, 0.19, and 0.48 for α-casein, β-casein, and κ-casein, 0.31 for α-lactalbumin, 0.68 for β-lactoglobulin, and 0.36 for lactoferrin. Heritability estimates for directly measured traits ranged from 0.07 to 0.55 for fatty acids; and from 0.14 to 0.63 for individual milk proteins. For FT-MIR predicted traits, heritability estimates were mostly higher than for the corresponding measured traits, ranging from 0.14 to 0.46 for fatty acids, and from 0.30 to 0.70 for individual proteins. Genetic correlations between directly measured and FT-MIR predicted protein fractions were consistently above 0.75, with the exceptions of C18:0 and C18:3 cis-3, which had genetic correlations of 0.72 and 0.74, respectively. The GWAS identified trait QTL for fatty acids with likely candidates in the DGAT1, CCDC57, SCD, and GPAT4 genes. Notably, QTL for SCD were largely absent in the FT-MIR predicted traits, and QTL for GPAT4 were absent in directly measured traits. Similarly, for directly measured individual proteins, we identified QTL with likely candidates in the CSN1S1, CSN3, PAEP, and LTF genes, but the QTL for CSN3 and LTF were absent in the FT-MIR predicted traits. Our study indicates that genetic correlations between directly measured and FT-MIR predicted fatty acid and protein fractions are typically high, but that phenotypic variation in these traits may be underpinned by differing genetic architecture.