Context Dairy farming occurs across a wide range of production systems globally, but all share a common requirement, namely, their development and sustainability depend on meeting fundamental animal wellbeing needs, such as shelter, nutrition, and comfort, within varied social, cultural, economic, and regulatory contexts.Aim A conceptual typology for global dairy farm systems is proposed, designed to enable evaluation across any system from an animal wellbeing perspective.Methods The typology is structured around eight primary system components that influence the adult dairy cows' physiological and psychological wellbeing, including living environment, diet, genetic selection for resilience, contact with people, degree of behavioural autonomy, group size, milking interval and cow-calf interaction. These components are organised into an attribute hierarchy, visually represented in a matrix that progresses from lower to higher levels of human intervention, and a shift in welfare risks from physiological to psychological domains. The typology was applied to representative dairy systems in New Zealand, the United States, Ireland, and China.Key results Despite limitations in cross-jurisdictional data availability, it successfully highlighted key similarities and differences in wellbeing-related system attributes. Further refinement, particularly in measuring components such as the degree of human contact, could enhance its precision and applicability. The framework was also tested against future scenarios, demonstrating its potential to identify farm system attributes that align with varying and changing welfare expectations.Conclusions This exercise supports the broader utility of the typology in system-level assessments, including those not explicitly focused on animal welfare, while ensuring that wellbeing remains a central consideration.Implications Overall, the attribute hierarchy provides a cow-centric and operationally flexible tool for characterising and evaluating global dairy farm systems.
Heat stress (HS) negatively affects the health, reproduction, and milk production of dairy cows, indicating a major challenge to animal welfare and farm profitability. Although environmental and physiological indicators are commonly used to monitor HS, cellular biomarkers such as heat shock protein 70 (HSP70) offer a promising approach for detecting HS at the molecular level. This research aimed to evaluate and compare HSP70 levels in the milk, blood, and saliva of lactating dairy cows across seasons, assess their environmental responsiveness using the temperature-humidity index (THI) as the standard environmental indicator of HS under temperate Australian conditions, and investigate the potential of milk HSP70 as a noninvasive biomarker of HS at the cellular level. Twenty multiparous Holstein Friesian cows were monitored across 4 seasons (summer, autumn, winter, and spring) over 1 yr, and HSP70 concentrations were measured using a competitive ELISA system. Weather data, including THI, as well as milk yield and composition traits, were also recorded. Results showed significant seasonal variation in HSP70 levels, with the highest concentrations observed in summer across all sample types. Blood exhibited the highest (416.45 ng/mL) HSP70 concentrations overall, followed by saliva (304.89 ng/mL) and milk (279.02 ng/mL). Milk HSP70 showed the highest variability (CV = 53.76%), indicating that additional factors beyond HS may influence its expression, whereas blood and saliva appeared to be more consistent. The associations between HSP70 concentrations in biological fluids were initially evaluated using Pearson correlation analysis. To account for repeated measurements in cows and identify key predictors, linear mixed-effects models with cow identification as a random effect were then fitted. Moderate positive correlations were found between the concentration of milk HSP70 and both blood (r = 0.58) and saliva HSP70 (r = 0.53), indicating that milk HSP70 may reflect systemic stress responses in dairy cows. Multivariable linear mixed models identified average THI as a significant predictor of HSP70 concentrations across fluids. In the milk HSP70 model, DIM was also a significant predictor, and milk yield was not. Overall, this study provides preliminary evidence that milk HSP70 may have potential as a noninvasive indicator of HS in dairy cows. These findings also contribute to a better understanding of HS physiology and may inform future approaches for herd-level monitoring and management under climate change.
Heat waves (HWs) are increasing in frequency and severity in many regions under climate change, posing challenges to the welfare and productivity of dairy cows. Physiological and environmental indicators are widely used to assess heat stress (HS) in dairy cows; however, the short-term temporal dynamics and lagged responses of molecular stress biomarkers during HW events under production conditions remain poorly characterized. This study aimed to evaluate daily variation in milk HSP70 during a HW and to assess its association with the environmental temperature-humidity index (THI) and sensor-derived reticulorumen temperature (RRT). The research was carried out over 5 consecutive days of HW in a pasture-based dairy farm in Camden, New South Wales, Australia, investigating 20 clinically healthy, third-parity Holstein Friesian cows. Daily milk samples were obtained during afternoon milking for HSP70 assessment using competitive ELISA. Environmental data were recorded at 1-min intervals to calculate THI, while RRT was continuously monitored at 10-min intervals using rumen bolus sensors. Associations between THI, RRT, and milk HSP70 were analyzed using repeated-measures correlations and univariable linear mixed models across lag windows of up to 50 h before sample collection, with adjustment for multiple testing across lag intervals. Exploratory classification performance was evaluated using receiver operating characteristic (ROC) analysis. The results revealed delayed associations between milk HSP70 and both THI and RRT. Associations between milk HSP70 and THI were strongest within an approximate delayed response window of 40-50 h after heat exposure, with repeated-measures correlations ranging from 0.57 to 0.84. Corresponding mixed-model analyses remained significant after adjustment for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) procedure (FDR-adjusted P < 0.001), with R2c values ranging from 43.79% to 75.66%. Similarly, associations with RRT were strongest within a delayed response window of approximately 48-50 h after heat exposure, with repeated-measures correlations ranging from 0.51 to 0.62. Corresponding mixed-model analyses remained significant (FDR-adjusted P < 0.001), with R2c values ranging from 40.92% to 51.71%. In contrast, RRT responded rapidly to environmental heat load, showing the strongest associations with THI within the immediate response window of approximately 0-6 h (repeated-measures r = 0.57-0.67), while the corresponding mixed-model analyses remained significant (FDR-adjusted P < 0.001; R2c = 48.15-57.21%). An exploratory milk HSP70 cut-point of approximately 550 ng/mL was identified, with a sensitivity of 0.782 and specificity of 0.909 for RRT-defined HS. Overall, the findings indicate that milk HSP70 exhibits a delayed response pattern following HW exposure, consistent with cumulative cellular stress, and may provide supplementary insights alongside environmental and physiological indicators for evaluating prolonged HS in dairy cows in pasture-based systems.
Enteric methane is the dominant GHG emitted from pasture-based dairy systems, defined for this review as dairy systems in which grazed pasture makes up the majority of feed intake. This review summarizes the main drivers of methane emissions and the mitigation options available in these systems. Evidence indicates that cows offered high-nutritive-value perennial ryegrass pasture, characterized by high OM digestibility and ME, and moderate NDF content, have lower methane conversion factors (CH4 as a percentage of gross energy intake) and methane yields (g CH4/kg DMI) than values currently used by the Intergovernmental Panel on Climate Change and national inventories. Potential methane mitigation strategies for these pasture-based systems fall into 3 broad categories: technological interventions (such as feed additives or vaccines), genetics, and nutritional approaches. Some mitigations show promise; however, their effectiveness under grazing conditions is often limited by delivery constraints, variable responses, and uncertainty around cost, persistence, and whole-farm impacts such as economics and GHG life cycle assessment outcomes. Overall, evidence indicates that well-managed pasture-based dairy systems have lower baseline methane emissions than current inventory estimates. However, beyond this lower baseline, no mitigation strategies have yet demonstrated cost-effective, scalable, and consistent emissions reductions across these systems.
Pasture-based dairy systems offer a sustainable and cost-effective approach to milk production, particularly in temperate regions. This review synthesizes current knowledge on the nutritional profile of pastures and the nutritional management of grazing dairy cows, emphasizing the unique challenges and opportunities associated with using nonpasture feeds (i.e., grains, coproducts, or conserved forages: commonly referred to as supplements) to fill pasture deficits or increase milk production. Well-managed fresh pasture is a high-quality feed capable of supporting moderate milk production; it supplies an excellent profile of metabolizable AA and highly and rapidly digestible fiber. Its nutritional profile fluctuates, however, with regrowth stage and season, making precise diet management impractical. Our current understanding suggests that supplementary feeding (1) should first be employed to address deficits in ME during periods of low pasture growth; (2) at >30% of DMI can lead to nutrient imbalances, particularly in MP and AA; and (3) can reduce pasture utilization and farm profitability. There is no evidence to support individualized feeding strategies. Further research is required to understand the situations in which supplementary fat or crude protein and AA will increase milk production and to help better parameterize models for use with pasture-based systems.
