Nitrogen (N) leaching losses from grazed pasture systems pose a risk to the environment with mitigation strategies urgently required to achieve regulatory limits. There has been increased interest in alternative forages to manipulate excretion of urinary‐N of livestock. This review summarises research on key forage attributes which affect the pattern of urine‐N excretion. The literature showed an opportunity to alter both N concentration and moisture concentration of forages to reduce urine patch N loading. Complementary mitigations of extending the grazing rotation and reducing fertiliser were tested in simulations in addition to a diuresis effect on nitrate leaching in pastoral dairy systems. Findings suggested that forage species alone could not substantially reduce (by more than 20%) nitrate leaching. Combining all forage, management and systems‐based solutions led to the greatest reductions in nitrate leaching, 59 and 31% for Waikato and Canterbury. Nonetheless, reductions in N loss came at the expense of pasture productivity which will likely affect profitability and farmer adoption of this suite of solutions in the absence of other drivers. The role of plant secondary metabolites in microbial dynamics has implications on N recycling in soil and rumen environments and this was identified as an area for further research.

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals.

The goals of the current study were to investigate the effects of including chicory (Cichorium intybus L.) into the traditional feeding regime of ryegrass/white clover (Lolium perenne L./Trifolium repens L.), and time of its allocation on milk production, rumen fermentation, and FA composition of milk and rumen digesta of dairy cows. Nine groups of four cows were allocated one of three replicated feeding regimes: (1) ryegrass/white clover only (RGWC), (2) ryegrass/white clover + morning allocation of chicory (CHAM), and (3) ryegrass/white clover + afternoon allocation of chicory (CHPM). One cow per group had a rumen cannulae fitted. Treatment did not affect total grazing time or estimated dry matter intake, but cows ruminated more when fed RGWC than chicory. Allocating chicory in the afternoon elevated milk production compared with RGWC and CHAM. Milk from cows grazing chicory contained greater concentrations of polyunsaturated FA (PUFA) such as C18:3 c9, 12, 15 and C18:2 c9, 12 than those on RGWC. As with milk, rumen digesta concentration of PUFA increased when cows grazed on chicory rather than RGWC, which corresponded with lower concentrations of intermediate vaccenic and biohydrogenation end-product stearic acid for cows grazing on chicory. Mean ruminal pH was lower for cows offered chicory than those on RGWC, reflecting greater rumen concentrations of volatile fatty acids (VFA) for cows fed chicory. Allocating chicory during the afternoon is a useful strategy that can translate to improved milk production. The lower rumen pH, lower concentration of vaccenic and stearic acids, and elevated concentration of PUFA in the rumen of cows fed chicory suggest reduced biohydrogenation and may explain the elevated concentration of PUFA in the milk of cows fed chicory compared with those fed RGWC.

With dairy cattle farming under pressure to lower its environmental footprint, it is important to find effective on-farm proxies for evaluation and monitoring of management practices aimed at reducing the risk of nitrogen (N) losses and optimizing N use efficiency of dairy farm systems. Urinary N (UN) is regarded as the most potent source of N emissions. In contrast to confinement systems, there have been few studies from pasture-based systems associating on-farm animal and nutritional factors with UN output. Thus, the aims of this meta-analysis were to collate a database from pasture-based research in order to (1) investigate the associations of management, dietary, and animal variables with MUN concentration and daily UN output; (2) describe the MUN-UN association; and (3) assess whether animal, management, and dietary factors influence the relationship. We developed a dataset consisting of 95 observations representing 919 lactating dairy cattle fed pasture-based diets, which was compiled from 32 unique research publications that reported both MUN and UN output. Multilevel, mixed meta-analysis regression techniques were used to analyze the data. Initially, all variables were assessed as the sole fixed effect in a 2-level random effects model, accounting for within-publication heterogeneity. Meta-regression techniques were then used to assess the relationship of all variables with MUN and UN output, respectively, accounting for 3 sources of variability: the sampling error of the individual observation, within-publication heterogeneity, and among-publication heterogeneity. At the univariable level, despite more than 10 dietary, animal, or management variables being significantly associated with MUN, none explained a large amount of the MUN variation. The variables that explained the greatest amount of variation were dietary CP content and the ratio of nitrogen to ME content, which explained about 33% and 31% of the variation in MUN concentrations, respectively. Combining factors in multiple regressions improved the model fit, such that the variation within publications explained by dietary CP and N intake increased to 40.0% in the final multiple meta-regression model. For UN output, individual variables explained a greater proportion of variance reported among observations, compared with MUN, whereby diet CP content (pseudo R² = 66.1%), N-to-ME intake ratio (pseudo R² = 64.0%), N intake (pseudo R² = 58.3%), and MUN (pseudo R² = 43.5%) explained the greatest amount of the total variation. Milk urea nitrogen, N intake, and DMI were associated with UN output in the final multiple meta-regression model. Substantial heterogeneity existed in both MUN and UN among publications, with among-publication heterogeneity accounting for 73.4% of all the variation noted in MUN, and 88.6% of all the variation in UN output. As such, the meta-analyses could not predict MUN and UN to any great extent. It is recommended that a consistent approach to measuring and reporting MUN concentrations and UN output be carried out for all future research in pasture-based systems.