Agriculture Victoria

Dairy cows are highly susceptible to heat stress, which has a detrimental effect on milk production, fertility performance, and animal welfare. These adverse effects will increase as dairy-producing regions continue to be affected by climate change. To address this, Australia implemented a genetic evaluation for heat tolerance in 2017, which was updated in 2024. The objective of this study was to assess the relationship between heat tolerance solutions from the official genetic evaluation and other important traits in Australian Holsteins and Jerseys. In addition to heat tolerance solution, genetic correlations were estimated for mean yield (cow solution for intercept) of milk, fat, and protein yields with 11 conformation traits, 2 body size composites, and 2 fitness traits using a multivariate animal model. To compare the correlation of heat tolerance solution with the traits considered in this study, we also calculated the correlations of heat tolerance adjusted for mean milk, fat, or protein production. Of the conformation traits considered, angularity had the strongest genetic correlation with the heat tolerance solution for milk of -0.26 ± 0.03 and -0.38 ± 0.04 for Holstein and Jersey, respectively. Comparatively, when the genetic correlation between angularity and the adjusted heat tolerance solution was corrected for milk, the correlations reduced to -0.11 ± 0.06 for Holstein and -0.12 ± 0.14 for Jersey. Both calving interval and survival had strong genetic correlations with the heat tolerance solution compared with other traits studied. When comparing heat tolerance solutions with heat tolerance solutions adjusted for mean production, the effect of milk, fat, and protein yield on the relationship between heat tolerance and conformation traits is driven by milk, fat, and protein yield. Production strongly influences the relationship of the current Australian phenotype for heat tolerance due to the calculation of heat tolerance phenotype via yield deviation under heat stress. Given these results, conformation traits have minimal value as a predictor of heat tolerance in Australian Holstein and Jersey cattle.

Dairy cattle, particularly in Australia where dairy farming is predominantly pasture-based, are experiencing an increased incidence of heat stress through rising global temperatures, causing a detrimental impact on productivity and welfare. Improving the thermotolerance of dairy cows through genetic selection is a potential proactive solution for mitigating the impact of heat stress for the dairy industry. Although heat tolerance breeding values for milk yield traits have been available to Australian dairy producers since 2017, considerable potential remains to develop genetic evaluation for heat tolerance of fertility and understanding of the underlying genetic architecture for heat tolerance. The objective of this study was to investigate the effect of heat stress measured as temperature-humidity index (THI) on fertility traits and to identify genomic regions associated with heat tolerance in 2 specific fertility traits: first service nonreturn rate (FNRR) and successful calving rate to first service (SCRFS). In this study, we assembled more than 892K phenotypic records of Holstein cows with THI and fertility traits and identified specific periods with respect to service day. The effect of heat stress on fertility was assessed using a random regression sire model for estimating the change in genetic variance of fertility across various THI and obtaining heat tolerance solution (slope) for sires. Also, whole genome sequence GWAS were undertaken based on heat tolerance phenotypes of 5K Holstein bulls with at least 10 daughters with fertility data. The assessment of the different THI definitions based on different numbers of days before and after artificial service days on the fertility traits showed that the most prominent effect of THI on fertility outcomes was observed for THI 7 d preceding service (including the service day) and 6 d after service day. Heat tolerance (HT) traits with respect to FNRR and SCRFS are lowly heritable and ranged from 0.01 to 0.04 under moderate THI conditions ranging between 60 and 70. However, as the THI exceeded 70, the heritability increased to up to 0.08, indicating increased genetic variance as THI increased. Genetic correlations between extreme THI ranges were as low as 0.13, while correlations between consecutive THI ranges reached up to 0.98. This finding suggests the presence of genotype-by-environment interactions due to heat stress. Notable variation in heat stress sensitivity among sires was also observed for HT fertility. In total, 553 sequence variants were significantly associated with HT fertility, and 52 of them were identified as independent QTL. Some of the QTL regions were located near or within the genes that are involved in oxidative stress, inflammation, and fertility (e.g., TRPC5, CDK5RAP2, MGAT1, COMMD10, PRR7, GRK6, CUGBP1, MAFG, HERC2, NAPRT, HSD17B12, THRB, and EEF1D). The findings in this study will further aid in understanding genetic architecture and provide valuable information for improving the accuracy of genomic prediction of HT in dairy cattle.

Agrivoltaics offer a sustainable solution to the growing competition between food and energy production. However, their adoption is often constrained by the design and operation challenges associated with optimising the complex trade-off between crop yield and photovoltaic (PV) output. Digital twins can mitigate these risks, yet most agricultural digital twins operate as fragmented digital shadows, lacking high-fidelity modelling, advanced simulation, and bidirectional control capabilities. This study presents a comprehensive, end-to-end digital twin framework to address these limitations. The framework integrates a high-resolution 3D orchard model, reconstructed via UAV photogrammetry, with a CesiumJS-based web interface linked to a modular IoT architecture built on Node-RED, Message Queuing Telemetry Transport (MQTT) protocol and InfluxDB for real-time monitoring and control. A PV simulation engine supports the design, simulation and optimisation of agrivoltaic systems. Bidirectional communication was validated through remote actuation of a physical solar tracker, demonstrating integration among the 3D environment, sensor data and control systems to achieve a closed-loop digital twin. Simulation analyses suggested that panel orientation and row spacing exert a dominant influence on crop-level light distribution. Simulation results demonstrated that a 90° azimuth configuration achieved the highest daily energy yield of 53.97 kWh but reduced peak crop-level irradiance to 205 W/m2. In contrast, the baseline 0° configuration offered a balanced output of 40.86 kWh with a peak light availability of 338 W/m2. The validated, interoperable digital twin architecture provides a reference model for the design, simulation, monitoring and control of an agrivoltaic system, reducing investment uncertainty and supporting sustainable food–energy co-production.

Polyphenols represent a large and structurally diverse family of plant secondary metabolites with bioactive properties. In ruminants, these compounds can influence rumen fermentation, microbial ecology, and nutrient metabolism, offering potential benefits for animal health, productivity, and environmental sustainability. This review synthesizes evidence on the fate of dietary polyphenols in dairy cows from ingestion to their possible secretion into milk. It outlines the main dietary sources and classes of polyphenols, their microbial biotransformations in the rumen, and subsequent host metabolism involving absorption, conjugation, and systemic circulation. Particular attention is given to the mechanisms of mammary uptake and secretion, where most compounds appear as conjugated metabolites such as glucuronides, sulfates, and urolithins rather than parent forms. Although the transfer efficiency from feed to milk is typically low, consistent detection of isoflavone derivatives, phenyl-γ-valerolactones, urolithins, and hippuric acid demonstrates the feasibility of diet-to-milk modulation. Factors affecting bioavailability and transfer include polyphenol structure, dietary matrix, dose, rumen microbiota composition, animal physiology, and feed processing. Advances in high-resolution analytical techniques have improved the characterization of these low-abundance metabolites, yet large variability among studies persists. In vivo studies indicate that polyphenol-derived metabolites in bovine milk occur at low ng/mL to low µg/mL levels, with compounds such as equol, enterolactone, urolithins, phenyl-γ-valerolactones and phenolic acids typically detected in the sub-micromolar range. Overall, dietary polyphenols offer promising opportunities to improve ruminant health and produce milk with enhanced functional quality, but quantitative and mechanistic research is still required to optimize feeding strategies and understand their contribution to milk bioactivity.

Including soybean hulls (SH) in ruminant diets increase the carbon circularity of livestock systems. We evaluated whether SH could partially replace corn grain (CG) in the diet of lactating cows (≈7.5% of the total diet). Six Holstein multiparous cows were fed a 50:50 (dry matter basis) corn-silage to concentrate diet for two 25-day periods in three replicated 2 × 2 Latin Squares using linear mixed models. Feed intake, milk yield and composition, N excretion, CH4 production, and ruminal microbial profiles were measured. Enteric CH4 was measured in respiration chambers. The DMI of cows fed SH (28 kg DM/d) was greater (p = 0.034) than those fed CG (26 kg DM/d), but no differences were observed in milk production (p = 0.557) or enteric CH4 emission intensity (g/kg milk, p = 0.387). Enteric CH4 emissions tended to be greater in SH-fed cows (484 g/d) than CG-fed cows (456 g/d; p = 0.075), and N excretion was significantly greater in SH than CG cows (p < 0.001). No mayor shifts in ruminal microbiota or overall ruminal environment were observed. In conclusion, SH can partially replace CG in diets of lactating dairy cows without affecting production, but potential negative environmental impacts should be considered.

Adding to the global burden of high pathogenicity avian influenza (HPAI) H5N1, an unprecedented five HPAI H7 outbreaks occurred globally in 2024. Of these, three occurred in southeast Australia, with the independent emergence of HPAI H7N9, H7N8, and H7N3, resulting in the destruction of 2 million poultry. Historical data demonstrates that H7 outbreaks in Australia do not occur randomly, rather, there is a strong association between the timing of the previous H7 outbreaks and rainfall patterns in southeastern Australia. We aimed to address a hypothesis wherein prior to H7 outbreaks in poultry, there was a detectable change in H7 prevalence and/or virus diversity in wild bird populations. We addressed this using virological and serological surveillance data generated from multiple programs. Despite the collection of thousands of samples, there was only weak evidence to support our hypothesis, which provides strong incentive to evaluate current surveillance approaches for the purposes of risk prediction. However, in alignment with a previous analysis, there is strong support for a relationship between H7 outbreak probability and rainfall patterns across southeast Australia. Overall, improved understanding of the ecology and evolution of H5 and H7 viruses in wild bird reservoirs is pivotal to global disease preparedness and response.

Commercial dairy farms face major challenges in safeguarding animal welfare and overall farm sustainability from environmental heat stressors. As climate change drives increased temperatures in many places, it is essential to predict the potential effects of heat stress on dairy cows to mitigate the adverse impact. This study aimed to develop an eXtreme Gradient Boosting (XGBoost) machine learning model to predict the daily milk production of 3,369 lactating dairy cows under different climatic conditions across 10 different commercial dairy farms in Australia. The duration of the dataset covered early 2019 to mid-2023, with seasonal variations. The model considered a total of 8 input parameters combining the physiological properties of cows as well as temporal and climate variables. In this study, the temperature-humidity index (THI) was incorporated, using a novel approach in which THI mean values of 5 accumulating days were considered. The model considered a mean daily THI ≥55 as a threshold point to identify a potential heat stress day (THI ≥60) and then combined the THI mean of 2 d before and 2 d after to incorporate the before- and after-effects of a potential heat stress day, defined as THI composite. The model was evaluated using combined farm data, regional farm data, and leave-one-farm-out validations, achieving high predictive accuracy (R2 up to 0.73; Lin's concordance correlation coefficient up to 0.84). The THI composite metric improved prediction accuracy by up to 21% compared with conventional rolling THI averages, demonstrating its value in forecasting milk yield under heat stress conditions. The model from this study offers a foundation for strategic planning using projected climate data to mitigate future heat-related impacts on dairy productivity.

Inclusive assessment is essential in higher education to ensure that all students, regardless of background, ability, or circumstance, have equitable opportunities to demonstrate their learning. In high-stakes disciplines such as pharmacology and other medical sciences, where assessment outcomes significantly influence academic progression and professional qualification, embedding inclusive practices is particularly important. Equity, diversity, and inclusion (EDI) have emerged as central principles in shaping contemporary educational approaches, especially in how we design and deliver assessments. Integrating EDI into pharmacology assessment is critical for creating learning environments that are fair, accessible and responsive to the needs of a diverse student body, including individuals from varied cultural and socio-economic backgrounds, those with disabilities and neurodivergent learners. This review examines the theoretical underpinnings and practical applications of inclusive assessment within pharmacology and related health science disciplines and integrates practical examples to highlight innovative strategies that promote equitable and pedagogically sound evaluation of student learning. In doing so, the paper also acknowledges the systemic and institutional challenges that may impede implementation and proposes evidence-informed, actionable recommendations to address these barriers. The overarching aim is to support educators in advancing inclusive assessment practices that not only foster academic success and fairness but also contribute to the development of a diverse, capable health-care workforce equipped to navigate the complex health challenges of the 21st century.