On some invariant ideals, and on extension of differentiations to seminormalization

AbstractLet A be a noetherian integral domain, D=(1,D1,…,Di…) be a differentation of A, and B be a ring such that A⊂B⊂Ā. In the paper we mainly prove (whenever Ā is finite over A): (a) if α is the conductor of A in B, then A√α is D-invariant. (b) D extends to the seminormalization +A of A in Ā

Strategies and tools for genetic selection in dairy cattle and their application to improving animal welfare

Genetic improvement of farm animals, especially selection within breeds focussed on high production and efficiency, is often cited as a potential threat to animal welfare. However, many animal welfare issues can be addressed, at least partially, by animal breeding and genetics. In this chapter, we explore the relationship between genetic selection and animal welfare, the strategies and tools for genetic improvement and how they can contribute to improved animal welfare. A growing public awareness of animal welfare and environmental issues has led to breeding goals being broadened beyond farmer profitability. As animal welfare and behaviour are complex and multi-factorial, so the emergence of selection indices that include a large number of traits to optimise animal welfare in a way that is consistent with enterprise sustainability for the farmer is necessary. This trend is likely to continue and will be aided by the advent of new technologies for measuring animal welfare in conjunction with DNA-based predictions of genetic merit (genomic selection). The dairy cattle industry has been exemplary for the application of genomic selection, in addition to enabling selection decisions to be made earlier in life, it can be used to select for traits where it was not possible to select for previously. These include important welfare-related traits, such as improved disease resistance and heat tolerance. Dairy cattle breeding is a very international activity with just a few breeding companies dominating the market in semen for the most numerous breeds, especially the Holstein. Consequently, genetic diversity within breeds is diminishing and although genetic gain has been significant, the rate of inbreeding now presents itself as a threat to the future success of breeding programmes. A greater emphasis on diversity in breeding programmes and the traits under selection is needed as major themes in research and application. Innovation in methods to measure these new traits, (e.g. molecular phenotyping, sensor development, digitalisation data science, etc.) could dramatically transform selection for animal welfare, as these technologies can enable large-scale objective measurements of animal behaviours. In addition to animal-based outcome measures, factors like housing, feeding, specific management practices pose other risks to welfare. Risk factors and their interactions have an impact on the development of diseases or other challenges to welfare. Collaborative efforts between animal behaviour scientists, geneticists, engineers, data scientists, and others will potentially provide solutions to these challenges

Participatory development of breeding goals in Austrian dairy cattle

Due to the possibilities of genomic selection and changing circumstances of production an evaluation of all steps in the breeding process is important. Optimisation involves the aspects of breeding goals, performance recording, genetic evaluation and breeding programmes. To achieve desired genetic gains whilst taking genomic selection into account, the inclusion of all relevant traits and their appropriate weighing within the total merit index is essential. To enquire the needs of the farmers a survey is carried out. The survey is designed as a pure online survey. From about 8.000 farmers across breeds, of whom the email address was available, 16% participated in the survey so far. Two peaks were observed close to the dates, when the internet link for the survey was distributed per email. Preliminary results show that the individual breeding goals of Fleckvieh and Brown Swiss breeders have shifted from dairy towards fitness and conformation traits during the last decade. High interest in novel traits like claw health, metabolism or inter- and crosssucking is observed as well. Selection response and monetary aspects from selection based on economic approaches will be compared with “desired gain” models based on the results of this survey. The survey is part of the Austrian project “OptiGene” with the main aim to optimise the different steps in the breeding process in order to achieve the long-term genetic gain desired by the farmers

Sensor data cleaning for applications in dairy herd management and breeding

Data cleaning is a core process when it comes to using data from dairy sensor technologies. This article presents guidelines for sensor data cleaning with a specific focus on dairy herd management and breeding applications. Prior to any data cleaning steps, context and purpose of the data use must be considered. Recommendations for data cleaning are provided in five distinct steps: 1) validate the data merging process, 2) get to know the data, 3) check completeness of the data, 4) evaluate the plausibility of sensor measures and detect outliers, and 5) check for technology related noise. Whenever necessary, the recommendations are supported by examples of different sensor types (bolus, accelerometer) collected in an international project (D4Dairy) or supported by relevant literature. To ensure quality and reproducibility, data users are required to document their approach throughout the process. The target group for these guidelines are professionals involved in the process of collecting, managing, and analyzing sensor data from dairy herds. Providing guidelines for data cleaning could help to ensure that the data used for analysis is accurate, consistent, and reliable, ultimately leading to more informed management decisions and better breeding outcomes for dairy herds.202

Mastitis has a cumulative and lasting effect on milk yield and lactose content in dairy cows

Milk lactose content (LC) physiologically decreases with parity order in dairy cows, but also after udder health inflammation(s) and in presence of elevated milk SCC in subclinical cases. Therefore, the progressive decrease in milk LC observed along cows' productive life can be attributed to a combination of factors that altogether impair the epithelial integrity, resulting in weaker tight junctions, e.g., physiological aging of epithelium, mechanical epithelial stress due to milking, and experienced clinical or subclinical mastitis. Mastitis is also known to affect the udder synthesis ability, so our intention through this study was to evaluate if there is a cumulative and lasting effect of mammary gland inflammation(s) on milk yield (MY) and LC. For this purpose, we used diagnoses of clinical mastitis and milk data of Austrian Fleckvieh cows to evaluate the effect of cumulative mastitis events on LC and MY. Only mastitis diagnoses recorded by trained veterinarians were used. Finally, we investigated if cumulative mastitis is a heritable trait and whether it is genetically correlated with either LC or MY. Estimates were obtained using univariate and bivariate linear animal models. A significant reduction in LC and MY was observed in cows that suffered from mastitis compared with those that did not experience udder inflammation. The h2 of cumulative mastitis is promising and much greater (0.09) than the h2 of the binary event itself (≤0.03). The genetic correlations between cumulative mastitis with LC and MY were negative, suggesting that cows with a great genetic merit for MY and LC are expected to be more resistant to repeated inflammations and less recidivist. When we used number of lifetime SCC peaks (≥200,000 or 400,000 cells/mL) to calculate cumulative inflammation events, h2 was even higher (up to 0.38), implying that subclinical mastitis also has a relevant negative impact on both LC and MY. Finally, the present study demonstrated how repeated mastitis events can permanently affect the mammary gland epithelial integrity and synthesis ability, and that the number of cumulative mastitis is a promising phenotype to be used in selection index in combination with other indicator traits toward more resistant and resilient mammary glands

Ketosis risk derived from mid-infrared predicted traits and its relationship with herd milk yield, health and fertility

Milk analysis using mid-infrared spectroscopy (MIR) is a fast and inexpensive way of examining milk samples on a large scale for fat, protein, lactose, urea and many other novel traits. A new indicator trait for ketosis, KetoMIR, which is based on clinical ketosis diagnoses and MIR-predicted traits, was developed by the Regional State Association for Performance and Quality Inspection in Animal Breeding of Baden Württemberg in 2015. The KetoMIR result is available for each cow at milk recording during the first 120 days in milk and presented to farmers in three classes: 1 = low ketosis risk, 2 = moderate ketosis risk and 3 = high ketosis risk. The aim of the current study was to analyze the phenotypic relationships between KetoMIR and milk yield, fertility and health at the herd level. Annual herd reports from 12,909 herds with an average herd size of 27 cows were available for the analyses. Overall, the mean incidence of ketosis (KetoMIR risk class 2 or 3) at the herd level was 14.0%. Farms with the lowest ketosis risk (≤10% of cows in the herd with a moderate or high ketosis risk) differed in all variables from the farms with the highest ketosis risk (>50% of cows in the herd with a moderate or high ketosis risk). The increased ketosis risk based on KetoMIR was associated with lower average herd milk yield (-1,975 kg milk). Mean herd somatic cell count in first and higher lactations was increased by 60,500 and 134,400 cells/ml, respectively. The interval from calving to first service was prolonged by +36.5 days, as was the calving interval with +58.2 days. The newly developed KetoMIR trait may be used in ketosis prevention programs

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

Submitted 2020-07-14 | Accepted 2020-08-18 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.224-232Pregnancy assessment is a very important tool for the reproductive management in efficient and profitable dairy farms. Nowadays, mid-infrared (MIR) spectroscopy is the method of choice in the routine milk recording system for quality control and to determine standard milk components. Since it is well known that there are changes in milk yield and composition during pregnancy, the aim of this study was to develop a discriminant model to predict the pregnancy state from routinely recorded MIR spectral data. The data for this study was from the Austrian milk recording system. Test day records of Fleckvieh, Brown Swiss and Holstein Friesian cows between 3 and 305 days of lactation were included in the study. As predictor variables, the first derivative of 212 selected MIR spectral wavenumbers were used. The data set contained roughly 400,000 records from around 40,000 cows and was randomly split into calibration and validation set by farm. Prediction was done with Partial Least Square Discriminant Analysis. Indicators of model fit were sensitivity, specificity, balanced accuracy and Area Under Receiver Operating Characteristic Curve (AUC). In a first approach, one discriminant model for all cows across the whole lactation and gestation lengths was applied. The sensitivity and specificity of this model in validation were 0.856 and 0.836, respectively. Splitting up the results for different lactation stages showed that the model was not able to predict pregnant cases before the third month of lactation and vice versa not able to predict non-pregnancy after the third month of lactation. Consequently, in the second approach a prediction model for each different (expected) pregnancy stage and lactation stage was developed. Balanced accuracies ranged from 0.523 to 0.918. 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Invited review: Using data from sensors and other precision farming technologies to enhance the sustainability of dairy cattle breeding programs

The increased uptake of sensor technologies and precision farming tools for the dairy cattle sector is enabling real-time monitoring of animal health, welfare,and productivity. These digital advancements provide high-frequency, objective, and large-scale phenotypic data for breeding purposes. This review explores thepotential of sensor-derived data to improve genetic and genomic evaluations in dairy cattle and outlines key challenges, opportunities, and approaches associated with their implementation. While these data streams have great potential for genetic evaluations, their integration into national and international breeding programs remains limited due to fragmentation across sensor brands, lack of standardization, and challenges related to data accessibility, data access and portability rights, business interests, and governance. A crucial aspect of leveraging digital technologies in dairy cattle breeding is data harmonization and integration. We highlight the importance of establishing standardized data collection and data sharing protocols, implementing robust quality control and data cleaning methodologies, as well as defining novel sensor-based traits and estimating their genetic background. In this context, we compiled heritability estimates for novel traits derived from data recorded by sensors and other technologies in dairy cattle populations. The development of phenomics in breeding programs, which involves integrating multisource data—including sensor-based, genomic, and managementinformation—will be key to accelerating genetic progress, especially for traits related to animal welfare, health, resilience, and efficiency. This review presentsa roadmap for the effective use of sensor-derived data in genetic evaluations, advocating for centralized data infrastructures, transparent data-sharing agreements, and the role of different stakeholders from academia and industry, including organizations such as the International Committee on Animal Recording (ICAR) in establishing global standards and guidelines. By addressingthese challenges, dairy breeding programs can fully harness precision dairy farming technologies to enhance production and environmental efficiency, improve animal health and welfare, and drive sustainable genetic advancements in the dairy cattle sector