The Effect of Grazing Intensity and Sward Heterogeneity on the Movement Behavior of Suckler Cows on Semi-natural Grassland

How does grazing pressure affect feed intake and behavior of livestock in a meadow steppe in northern China and their coupling relationship

Livestock feeding behavior and intake play a crucial role in influencing grassland health and productivity. A comprehensive investigation into livestock feeding behavior and intake can effectively elucidate the interactions and impacts of livestock and grasslands, providing scientific evidence and technical support for the formulation and implementation of sustainable grassland development strategies. Based on a long-term controlled grazing experiment platform conducted over 13 years, the feeding behavior and forage intake of cattle under different grazing intensities were observed and analyzed. Additionally, we used GPS sensors to study cattle grazing behavior trends. Using Mantel's test, we analyzed the relationship between cattle movement distance, forage intake, and environmental factors. The results demonstrated that cattle forage intake decreased with increasing grazing intensity. Forage intake peaked at the end of July and beginning of August, with the highest efficiency observed in August. Moreover, under light grazing intensity, cattle exhibited greater fluctuations in forage intake than those under moderate and heavy grazing intensity. Cattle movement levels increased with higher grazing intensity, and during the period of lush grass growth, cattle displayed significantly higher movement levels than during grass senescence. The accuracy of the behavior determination model based on cattle velocity ranged from 60 to 80 %. Using this model, we found that under heavy grazing conditions, cattle spent significantly more time roaming than under light and moderate grazing. Conversely, under light grazing conditions, cattle spent significantly more time feeding. A negative correlation was identified between cattle forage intake and movement distance. Cattle's forage intake was significantly positively correlated with grass height and grass biomass and significantly negatively correlated with stocking rate and movement distance. Thorough research on livestock feeding behavior and intake offers scientific evidence and technical support for formulating and implementing sustainable grassland development strategies.

Heifers don't care: no evidence of negative impact on animal welfare of growing heifers when using virtual fences compared to physical fences for grazing

Virtual fencing (VF) represents a way to simplify traditional pasture management with its high labour and cost requirements for fencing and to make better use of the 'beneficial' agronomic and ecological effects of livestock grazing. In this study, the VF technology (® Nofence, AS, Batnfjordsøra Norway) was used with Fleckvieh heifers to investigate possible welfare impacts on the animals compared to conventionally fenced animals when they were trained to respond correctly to the system. The Nofence® collars (attached to the neck of the heifers) send acoustic signals as a warning when the animals approach the VF line, which was set up by GPS coordinates within the Nofence®-App, followed by an electric pulse when they do not stop or return. The heifers had no experience with VF prior to the study. Two treatments (VF versus physical fencing (PF)) were applied to six groups of four heifers each (three groups per treatment) over three 12-day time replicates. One VF line separated the pasture of the VF group into an accessible or non-accessible area. The control group had a PF line. Both groups were equipped with Nofence® collars (deactivated for the PF group). The trial took place on two adjacent paddocks of 1 000 m² each following a 12-day schedule which was divided into three sections: visual support of the VF line by a physical barrier (first 2 days), only virtual border without visual support, moving the VF line (on day 8). Each time replicate followed the next successively on different paddocks with two new groups of heifers, which were grazed 5 h daily. During the whole experiment, the behaviour of each of the four animals per group was continuously observed; 2 h a.m., 2 h p.m. Exclusion by the VF line was effective in our trial. None of the heifers crossed the virtual boundary, i.e. the time spent in exclusion zone was zero. The heifers received 2.70 ± 2.63 acoustic signals and 0.30 ± 0.36 electric pulses (mean ± SD) per heifer and hour during all time replicates. Main cattle behaviour on pasture was not affected by the fencing system. Live weight gain, herbage consumption and faecal cortisol metabolites also revealed no significant differences. The duration until the heifers restarted grazing after an electric pulse from the Nofence® collar was significantly shorter than after an electric pulse from the physical fence. We can summarise that in our study, cattle well-being on pasture was not negatively affected by VF compared to PF.

Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows

In precision grazing, pasture allocation decisions are made continuously to ensure demand-based feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up.