Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

Real-Time Monitoring of Grazing Cattle Using LORA-WAN Sensors to Improve Precision in Detecting Animal Welfare Implications via Daily Distance Walked Metrics

Animal welfare monitoring relies on sensor accuracy for detecting changes in animal well-being. We compared the distance calculations based on global positioning system (GPS) data alone or combined with motion data from triaxial accelerometers. The assessment involved static trackers placed outdoors or indoors vs. trackers mounted on cows grazing on pasture. Trackers communicated motion data at 1 min intervals and GPS positions at 15 min intervals for seven days. Daily distance walked was determined using the following: (1) raw GPS data (RawDist), (2) data with erroneous GPS locations removed (CorrectedDist), or (3) data with erroneous GPS locations removed, combined with the exclusion of GPS data associated with no motion reading (CorrectedDist_Act). Distances were analyzed via one-way ANOVA to compare the effects of tracker placement (Indoor, Outdoor, or Animal). No difference was detected between the tracker placement for RawDist. The computation of CorrectedDist differed between the tracker placements. However, due to the random error of GPS measurements, CorrectedDist for Indoor static trackers differed from zero. The walking distance calculated by CorrectedDist_Act differed between the tracker placements, with distances for static trackers not differing from zero. The fusion of GPS and accelerometer data better detected animal welfare implications related to immobility in grazing cattle.

Phenomics as an approach to Comparative Developmental Physiology

The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.

Practical Experiences of a Smart Livestock Location Monitoring System Leveraging GNSS, LoRaWAN and Cloud Services

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud, and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver, and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost, and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration, and number of devices. The results are analyzed and discussed for packet delivery ratio, energy consumption, localization accuracy, battery discharge measurement, and delay.

Review: An overview of beef production from pasture and feedlot globally, as demand for beef and the need for sustainable practices increase

Beef is a high-quality source of protein that also can provide highly desirable eating experiences, and demand is increasing globally. Sustainability of beef industries requires high on-farm efficiency and productivity, and efficient value-chains that reward achievement of target-market specifications. These factors also contribute to reduced environmental and animal welfare impacts necessary for provenance and social licence to operate. This review provides an overview of beef industries, beef production, and beef production systems globally, including more productive and efficient industries, systems and practices. Extensive beef production systems typically include pasture-based cow-calf and stocker-backgrounding or grow-out systems, and pasture or feedlot finishing. Cattle in pasture-based systems are subject to high levels of environmental variation to which specific genotypes are better suited. Strategic nutritional supplementation can be provided within these systems to overcome deficiencies in the amount and quality of pasture- or forage-based feed for the breeding herd and for younger offspring prior to a finishing period. More intensive systems can maintain more control over nutrition and the environment and are more typically used for beef and veal from dairy breeds, crosses between beef and dairy breeds, and during finishing of beef cattle to assure product quality and specifications. Cull cows and heifers from beef seedstock and cow-calf operations and dairy enterprises that are mostly sent directly to abattoirs are also important in beef production. Beef production systems that use beef breeds should target appropriate genotypes and high productivity relative to maintenance for the breeding herd and for growing and finishing cattle. This maximizes income and limits input costs particularly feed costs which may be 60% or more of production costs. Digital and other technologies that enable rapid capture and use of environmental and cattle performance data, even within extensive systems, should enhance beef industry productivity, efficiency, animal welfare and sustainability.

Understanding intake on pastures: how, why, and a way forward

An assessment of dietary intake is a critical component of animal nutrition. Consumption of feed resources is the basis upon which feeding strategies and grazing management are based. Yet, as far back as 1948, researchers have lauded the trials and tribulations of estimation of the phenomenon, especially when focused on grazing animals and pasture resources. The grazing environment presents a unique situation in which the feed resource is not provided to the animal but, rather, the animal operates as the mechanism of harvest. Therefore, tools for estimation must be developed, validated, and applied to the scenario. There are a plethora of methods currently in use for the estimation of intake, ranging from manual measurement of herbage disappearance to digital technologies and sensors, each of which come with its share of advantages and disadvantages. In order to more firmly grasp these concepts and provide a discussion on the future of this estimation, the Forages and Pastures Symposium at the 2020 ASAS-CSAS-WSASAS Annual Meeting was dedicated to this topic. This review summarizes the presentations in that symposium and offers further insight into where we have come from and where we are going in the estimation of intake for grazing livestock.

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

Performance of Angus weaner heifers varying in residual feed intake-feedlot estimated breeding values grazing severely drought-affected pasture

Context Beef industry productivity and profitability would be enhanced by improved efficiency at pasture. Our research is evaluating performance at pasture of Angus heifers divergent in estimated breeding values for residual feed intake determined from feedlot data (RFI-f-EBV) under a range of grazing conditions. Aims To determine whether Low- and High-RFI-f-EBV cattle differ in their growth response when pasture quality and availability become limiting to performance. Methods Eight-month-old heifers (n = 40) weaned at 6 months of age grazed within two replicates of 20, each with 10 low feedlot-efficiency (High-RFI-f-EBV) and 10 high feedlot-efficiency (Low-RFI-f-EBV) heifers. Each replicate grazed each of eight 1.25-ha paddocks comprising severely drought-affected, low-quality (mean dry-matter (DM) digestibility 44.1%, crude protein 7.3% DM, and 6.1 MJ metabolisable energy/kg DM) mixed perennial and annual native temperate grasses at 7-day intervals during repeated 28-day cycles, with Phase 1 with 2834 kg DM/ha and Phase 2 with 1890 kg DM/ha mean starting biomass. Heifers were yard-weighed weekly on nine occasions during the 8-week study. Key results During Phase 1 of grazing, the heifers gained 6.2 kg liveweight (LW) and during Phase 2 of grazing they lost 10 kg LW on average. Differences in LW between the RFI-f-EBV groups were not evident at the start or end of the study. However, over the 56 days of study, average daily change in LW calculated from the difference between starting and final LW was higher for Low-RFI-f-EBV heifers than for High-RFI-f-EBV heifers (–33 vs –127 g/day, s.e.m. = 41 g/day, P = 0.026). A similar result was evident when average daily LW change was determined from regression of LW on the day of study (–6 vs –96 g/day, s.e.m. = 41 g/day, P = 0.033). No significant interactions between grazing Phase and RFI-EBV group were evident for the growth responses. Conclusions Higher feedlot-efficiency (Low-RFI-f-EBV) weaner heifers maintained LW somewhat better than lower feedlot-efficiency (High-RFI-f-EBV) heifers, as the nutritional availability at pasture became more limiting. Implications Low-RFI-f-EBV weaner heifers may be more nutritionally resilient than are High-RFI-f-EBV heifers under drought conditions and, hence, may require less supplementary feed to maintain growth performance.

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data 'as is', further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.