Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

Summary: Background: Testosterone plays an important role in the regulation of glucose metabolism. While earlier studies have shown that it has a protective effect in males, unfavorable effects of testosterone on glucose metabolism have been reported in females; however, whether there is a sex-specific relationship between testosterone and glucose metabolism in patients with prediabetes has not been investigated in detail hitherto. Methods: This cross-sectional analysis investigated 423 males and 287 females with diagnosed prediabetes. Detailed assessment of their metabolic profiles was performed, including a 2‑h oral glucose tolerance test (OGTT), HbA1c levels, calculation of insulin resistance with homeostatic model assessment for insulin resistance (HOMA-IR), assessment of lipid metabolism, anthropometric parameters and the fatty liver index (FLI). By using Spearman’s correlation test, we investigated the sex-specific relationship between testosterone and metabolism in the prediabetic individuals. Results: In the present study, prediabetic females (mean age 58.6 years, confidence interval [CI: 57.6 y; 59.5 y]) were characterized by lower fasting plasma glucose levels (104.2 mg/dl [CI: 103.0 mg/dl; 105.4 mg/dl] vs. 106.9 mg/dl [CI: 106.0 mg/dl; 107.8 mg/dl]) and a lower FLI (49.5 [CI: 45.7; 53.2] vs. 58.8 [CI: 55.8; 61.8]), but presented with a higher risk of developing manifest type 2 diabetes in the next 10 years (FINDRISK score: 17.6 [CI: 17.1; 18.1] vs. 16.1 [CI: 15.7; 16.5]) when compared to prediabetic males (mean age: 58.04 years [CI: 57.0 y; 59.1 y]). Testosterone was negatively related to insulin resistance (HOMA-IR: Spearman’s ρ: −0.33, p < 0.01), 2‑h stimulated glucose levels during the OGTT (ρ = −0.18, p < 0.01), HbA1c levels (ρ = −0.13, p < 0.05), FLI and BMI in prediabetic males; however, no relationship between testosterone and metabolic parameters could be found in prediabetic females. Conclusion: The increase of testosterone levels in males was related to a more favorable glucose metabolism, including lower HbA1c, lower stimulated glucose levels and higher insulin sensitivity; however, in prediabetic females, testosterone was not related to glucose metabolism.

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

In the domain of precision livestock farming, the integration of diverse data sources is crucial for advancing sustainability and evaluating the implications of farm management practices on cow health. Addressing the challenge of data heterogeneity and management diversity, we propose a key-feature-based clustering method. This approach, merging knowledge-driven feature selection with unsupervised machine learning, enables the systematic investigation of management effects on cow health by forming distinct clusters for analysis. Utilizing data from 3,284 Austrian farms, including 80 features related to feeding, milking, housing, and technology systems, and health information for 56,000 cows, we show how this methodology can be applied to study the impact of technological systems on cow health resulting from the incidence of veterinary diagnoses. Our analysis successfully identified 14 distinct clusters, further divided into four main groups based on their level of technological integration in farm management: “SMART,” “TRADITIONAL,” “AMS (automatic milking system),” and “SENSOR.” We found that “SMART” farms, which integrate both AMS and sensor systems, exhibited a minimally higher disease risk for milk fever (OR 1.09) but lower risks for fertility disorders and udder diseases, indicating a general trend toward reduced disease risks. In contrast, farms with “TRADITIONAL” management, without AMS and sensor systems, showed the lowest risk for milk fever but the highest risk of udder disease (OR 1.12) and a minimally higher incidence of fertility disorders (OR 1.07). Furthermore, across all four groups, we observed that organic farming practices were associated with a reduced incidence of milk fever, udder issues, and particularly fertility diagnoses. However, the size of the effect varied by cluster, highlighting the complex and multifactorial nature of the relationship between farm management practices and disease risk. The study highlights the effectiveness of the key-feature-based clustering approach for high-dimensional data analyses aimed at comparing different management practices and exploring their complex relationships. The adaptable analytical framework of this approach makes it a promising tool for planning optimizing sustainable and efficient animal husbandry practices.

The sustainable future of dairy farming will depend on how trade-offs between environmental impact, economic viability, and animal welfare are managed. Dairy production contributes significantly not only to human nutrition but also to greenhouse gas (GHG) emissions, ammonia release, and water pollution. Comprehensive sustainability assessments are essential for addressing these impacts, also in light of evolving regulations like the EU Corporate Sustainability Reporting Directive. However, existing research on sustainable dairy farming and intensification often overlooks trade-offs with other ecological aspects like biodiversity, economic viability, or animal welfare. This study evaluated the sustainability performance of Austrian dairy farms using a tool called NEU.rind, which combines life cycle assessment (LCA) with other indicators. Applied to 170 dairy farms, the tool identified four sustainability clusters across the dimensions of environmental conditions, efficiency, animal health, and sustainability: (1) Alpine farms (high cow longevity, medium-to-high emissions per kg milk), (2) efficient low-input farms (low emissions, high cow longevity), (3) high-output lowland farms (high productivity, lower animal welfare), and (4) input-intensive lowland farms (high emissions, especially per hectare; inefficient use of resources). The analysis revealed fundamental trade-offs between production intensity, environmental impact, and animal welfare, particularly when comparing product-based (per kg milk) versus hectare-based indicators. Key improvement strategies include increasing the use of regional feed and pasture as well as adapting manure management. For policymakers, these findings underline the importance of site-specific sustainability assessments and the need for region-specific incentive schemes that reward both environmental efficiency and animal health performance. In this context, NEU.rind provides farm-specific recommendations with minimal data input, making sustainability assessments practical and feasible.