Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.

A review of thermal stress in cattle

Cattle control body temperature in a narrow range over varying climatic conditions. Endogenous body heat is generated by metabolism, digestion and activity. Radiation is the primary external source of heat transfer into the body of cattle. Cattle homeothermy uses behavioural and physiological controls to manage radiation, convection, conduction, and evaporative exchange of heat between the body and the environment, noting that evaporative mechanisms almost exclusively transfer body heat to the environment. Cattle control radiation by shade seeking (hot) and shelter (cold) and by huddling or standing further apart, noting there are intrinsic breed and age differences in radiative transfer potential. The temperature gradient between the skin and the external environment and wind speed (convection) determines heat transfer by these means. Cattle control these mechanisms by managing blood flow to the periphery (physiology), by shelter-seeking and standing/lying activity in the short term (behaviourally) and by modifying their coats and adjusting their metabolic rates in the longer term (acclimatisation). Evaporative heat loss in cattle is primarily from sweating, with some respiratory contribution, and is the primary mechanism for dissipating excess heat when environmental temperatures exceed skin temperature (~36°C). Cattle tend to be better adapted to cooler rather than hotter external conditions, with Bos indicus breeds more adapted to hotter conditions than Bos taurus. Management can minimise the risk of thermal stress by ensuring appropriate breeds of suitably acclimatised cattle, at appropriate stocking densities, fed appropriate diets (and water), and with access to suitable shelter and ventilation are better suited to their expected farm environment.

Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series

Adaptive management of large herbivores requires an understanding of how spatial-temporal fluctuations in forage biomass and quality influence animal performance. Advances in remote sensing have yielded information about the spatial-temporal dynamics of forage biomass, which in turn have informed rangeland management decisions such as stocking rate and paddock selection for free-ranging cattle. However, less is known about the spatial-temporal patterns of diet quality and their influence on large herbivore performance. This is due to infrequent concurrent ground observations of forage conditions with performance (e.g., mass gain), and previously limited satellite data at fine spatial and temporal scales. We combined multi-temporal field observations of diet quality (weekly) and mass gain (monthly) with satellite-derived phenological metrics (pseudo-daily, using data fusion and interpolation) to model daily mass gains of free-ranging yearling cattle in shortgrass steppe. We used this model to predict grazing season (mid-May to October) mass gains, a key management indicator, across 40 different paddocks grazed over a 10-year period (n = 138). We found strong relationships between diet quality and the satellite-derived phenological metrics, especially metrics related to the timing and rate of green-up and senescence. Satellite-derived diet quality estimates were strong predictors of monthly mass gains (R²  = 0.68) across a wide range of aboveground net herbaceous production. Season-long predictions of average daily gain and cattle off-mass had mean absolute errors of 8.9% and 2.9%, respectively. The model performed better temporally (across repeated observations in the same paddock) than spatially (across all paddocks within a given year), highlighting the need for accurate vegetation maps and robust field data collection across both space and time. This study demonstrates that free-ranging cattle performance in rangelands is strongly affected by diet quality, which is related to the timing of vegetation green-up and senescence. Senescing vegetation suppressed mass gains, even if adequate forage was available. The satellite-based pseudo-daily approach presented here offers new opportunities for adaptive management of large herbivores, such as identifying within-season triggers to move livestock among paddocks, predicting wildlife herd health, or timing the grazing season to better match earlier spring green-up caused by climate change and plant species invasion.

Protein Supplementation and Grazing Behavior for Cows on Differing Late-Season Rangeland Grazing Systems

Simple Summary

Cattle grazing late-season dormant rangeland are subject to impaired production due to reduced forage digestibility and a longer residence time of forage in the rumen, leading to reduced forage intake. It is a common practice to provide supplemental protein to help counteract these effects and to improve animal well-being and livestock production. Yet, the usage of supplements has been shown to interrupt and reduce the time spent grazing. These behavioral changes may vary with climate and the frequency and timing of strategic supplementation. The objective of this study was to evaluate how protein supplementation altered grazing behavior when used in both rotationally and continuously grazed dormant pastures. We utilized accelerometers (used in rockets to measure velocity in three directions and in smart phones to rotate the screen) to evaluate cattle behavior (via head movements) every 5 s on a 24 h basis. The cattle altered their grazing behavior in response to climate, supplementation status, and the grazing system. Cattle that were deprived of the protein supplement and stayed in the same continuously grazed pasture showed more restlessness in their behavior, spending more time walking from midnight to 8 a.m. Additionally, the harvest rate of dormant forage increased for the supplemented cattle.

Abstract

The objective was to determine if low- or high-residual feed intake (LRFI or HRFI, n = 24 for each) Hereford × Angus cows on continuously or rotationally grazed rangeland altered their grazing behavior when provided a protein supplement in late autumn. Treatments included continuously grazed, control (CCON, n = 12); continuously grazed, supplemented (CTRT, n = 12); rotationally grazed, control (RCON, n = 12); and rotationally grazed, supplemented pastures (RTRT, n = 12). Cows in each treatment had grazing time (GT), resting time (RT), and walking time (WLK) measured for 2 years with accelerometers. Bite rate (BR) was also measured. Time distributions of GT and RT differed by year (p < 0.05), being influenced by colder temperatures in 2016. Cattle in 2016 spent more time grazing during early morning and late evening (p < 0.05) and rested more during the day (p < 0.05). In 2017, cattle in the CCON treatment walked more (p < 0.05) during early morning time periods than did the CTRT cattle, indicative of search grazing. All supplemented cattle had greater BR (p < 0.05) than control cattle in 2017. Cattle with increased nutritional demands alter grazing behavior in a compensatory fashion when grazing late-season rangelands.

Application of the California Net Energy System to grazed forage: feed values and requirements

The California Net Energy System (CNES) has been successfully used for many years to generate estimates of grazing animal energy requirements, supplemental needs, and energy value of grazed forage diets. Compared to pen feeding situations, validation of feed nutritive value estimates or animal performance projections are extremely difficult in grazing animals because many of the system inputs are constantly changing. A major difficulty in applying this or any energy accounting system in the field is acquiring accurate estimates of forage intake. We discuss the various equations available to estimate forage intake for grazing animals with emphasis on beef cows. Progress has been made in recent years although there remains substantial discrepancy among various equations, particularly in the upper range of forage digestibility. Validation work and further development is needed in this area. For lactating cows, our conclusion is that the adjustment of intake for milk production (0.2 kg increase in forage intake per kg of milk produced) needs to be increased to a minimum of 0.35. A particular challenge with the CNES for grazing beef cows is the dramatic interaction that can occur between genetic potential for production traits and nutrient availability. Examples from literature are provided and a case study is presented demonstrating that energy requirements are dynamic and depend on nutrients available in grazing systems. The CNES is a useful tool in grazing beef cattle management although there remains substantial opportunity and need to improve inputs and validate the system in grazing situations.