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Applications of Remote Sensing for Livestock and Grazing Land Management

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 33870

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Special Issue Editors

Dr. Matthew Reeves

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Guest Editor

USDA Forest Service, Rocky Mountain Research Station, Missoula, MT 59801, USA
Interests: climate change effects on rangelands; decision support tools; adaptation
Special Issues, Collections and Topics in MDPI journals

Dr. Humberto L. Perotto-Baldivieso

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Guest Editor

Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, 305 Horticulture/Forest Science Building (HFSB), College Station, TX 77843-2138, USA
Interests: landscape ecology; remote sensing; spatial ecology; drones
Special Issues, Collections and Topics in MDPI journals

Prof. Luciano A. Gonzalez

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Guest Editor

Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, 380 Werombi Rd, Camden, NSW 2570, Australia
Interests: precision livestock production, remote monitoring and modelling of livestock systems, animal behavior and welfare, animal nutrition

Dr. Edward C. Rhodes

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Guest Editor

Texas Water Resources Institute, Texas A&M AgriLife Research, Texas A&M University, 570 John Kimbrough Blvd, Suite 150, 2260 TAMU, College Station, TX 77843, USA
Interests: remote sensing of rangeland and surface water; landscape ecology; land-use/land-cover; rangeland production modeling; watershed assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is focused on the application of remote sensing to aid livestock and grazing land management. Here, Grazing lands include rangelands, pastures and grazed forest. Tremendous increases in the number of ground-, air- and space-borne instruments offer unprecedented opportunities to assist livestock and rangeland management. Nearly all aspects of monitoring herd movement, vegetation conditions, water availability, weather, soil, and developing quantitative risk management strategies use remote sensing in some manner to improve outcomes.

We welcome research that examines the use of remote sensing technology, at any scale, to improve management outcomes in land or livestock management. The special issue should encompass a wide variety of foci including:

1) Remote sensing technologies and methodologies of grazing lands, and livestock using on-ground static sensors, aerial vehicles, ground vehicles, or satellite platforms.

2) Remote measurement of vegetation for determination of productivity, growth, biomass, nutritive quality, carbon and nitrogen stocks and fluxes.

3) Remote Monitoring of soil and water characteristics as relevant for livestock production and vegetation management.

4) Remote measurement of animal behavior, location and movement using for example GNSS, accelerometers, or high-resolution videography

5) Remote measurement of animal forage intake, nutrient excretion, performance, and body composition

6) Decision support systems based on remote sensing information and mathematical modeling

Dr. Matt Reeves
Dr. Humberto L. Perotto-Baldivieso
Prof. Luciano A. Gonzalez
Mr. Edward C. Rhodes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote monitoring
Rangeland ecology
Pastures
Livestock
Animal movement
Risk management
Productivity
Soil
Water

Published Papers (10 papers)

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Editorial

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11 pages, 299 KiB

Open AccessEditorial

Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management

by Edward C. Rhodes, Humberto L. Perotto-Baldivieso, Matthew C. Reeves and Luciano A. Gonzalez

Remote Sens. 2022, 14(8), 1882; https://doi.org/10.3390/rs14081882 - 14 Apr 2022

Cited by 2 | Viewed by 2404

Abstract

The use of geospatial sciences and technologies for the management of grazinglands has fostered a plethora of applications related to ecology, wildlife, vegetation science, forage productivity and quality, and animal husbandry. Some of the earliest use of remote sensing dates to the proliferation of aerial photography in the 1930s. Today, remote sensing using satellite imagery, global navigation satellite systems (GNSS), and internet-connected devices and sensors allow for real- and near real-time modeling and observation of grazingland resources. In this special issue of Remote Sensing, we introduce nine original publications focusing on varying aspects of grazingland management, such as animal health and telemetry, climate change, soil moisture, herbaceous biomass, and vegetation phenology. The work in this issue spans a diverse range of scale from satellite to unmanned aerial systems imagery, as well as ground-based measurements from mounted cameras, telemetry devices, and datalogging devices. Remote sensing-based technologies continue to evolve, allowing us to address critical issues facing grazingland management such as climate change, restoration, forage abundance and quality, and animal behavior, production, and welfare. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

Research

Jump to: Editorial

12 pages, 2263 KiB

Open AccessArticle

Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage

by Jorge Gonzalo N. Irisarri, Martin Durante, Justin D. Derner, Martin Oesterheld and David J. Augustine

Remote Sens. 2022, 14(4), 854; https://doi.org/10.3390/rs14040854 - 11 Feb 2022

Cited by 2 | Viewed by 2778

Abstract

In the Great Plains of central North America, sustainable livestock production is dependent on matching the timing of forage availability and quality with animal intake demands. Advances in remote sensing technology provide accurate information for forage quantity. However, similar efforts for forage quality are lacking. Crude protein (CP) content is one of the most relevant forage quality determinants of individual animal intake, especially below an 8% threshold for growing animals. In a set of shortgrass steppe paddocks with contrasting botanical composition, we (1) modeled the spatiotemporal variation in field estimates of CP content against seven spectral MODIS bands, and (2) used the model to assess the risk of reaching the 8% CP content threshold during the grazing season for paddocks with light, moderate, or heavy grazing intensities for the last 22 years (2000–2021). Our calibrated model explained up to 69% of the spatiotemporal variation in CP content. Different from previous investigations, our model was partially independent of NDVI, as it included the green and red portions of the spectrum as direct predictors of CP content. From 2000 to 2021, the model predicted that CP content was a limiting factor for growth of yearling cattle in 80% of the years for about 60% of the mid-May to October grazing season. The risk of forage quality being below the CP content threshold increases as the grazing season progresses, suggesting that ranchers across this rangeland region could benefit from remotely sensed CP content to proactively remove yearling cattle earlier than the traditional October date or to strategically provide supplemental protein sources to grazing cattle. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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27 pages, 10155 KiB

Open AccessArticle

Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands

by Markéta Poděbradská, Bruce K. Wylie, Deborah J. Bathke, Yared A. Bayissa, Devendra Dahal, Justin D. Derner, Philip A. Fay, Michael J. Hayes, Walter H. Schacht, Jerry D. Volesky, Pradeep Wagle and Brian D. Wardlow

Remote Sens. 2022, 14(1), 4; https://doi.org/10.3390/rs14010004 - 21 Dec 2021

Cited by 10 | Viewed by 3488

Abstract

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha⁻¹ year⁻¹) using a newly-developed relation with the Soil Survey Geographic Database range production data (R² = 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R² = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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14 pages, 1854 KiB

Open AccessArticle

The Relationship between Satellite-Derived Vegetation Indices and Live Weight Changes of Beef Cattle in Extensive Grazing Conditions

by Christie Pearson, Patrick Filippi and Luciano A. González

Remote Sens. 2021, 13(20), 4132; https://doi.org/10.3390/rs13204132 - 15 Oct 2021

Cited by 7 | Viewed by 2050

Abstract

The live weight (LW) and live weight change (LWC) of cattle in extensive beef production is associated with pasture availability and quality. The remote monitoring of pastures and cattle LWC can be achieved with a combination of satellite imagery and walk-over-weighing (WoW) stations. The objective of the present study is to determine the association, if any, between vegetation indices (VIs) (pasture availability) and the LWC of beef cattle in an extensive breeding operation in Northern Australia. The study also tests a suite of VIs along with variables such as rainfall and Julian day to predict the LWC of breeding cows. The VIs were calculated from Sentinel-2 satellite imagery over a 2-year period from a paddock with 378 cattle. Animal LW was measured remotely using a weighing scale at the water point. The relationship between VIs, the LWC, and LW was assessed using linear mixed-effects regression models and random forest modelling. Findings demonstrate that all VIs calculated had a significant positive relationship with the LWC and LW (p < 0.001). Machine learning predictive modelling showed that the LWC of breeding cows could be predicted from VIs, Julian day, and rainfall information, with a Lin’s Concordance Correlation Coefficient of 0.62 when using the leave-one-month-out cross-validation. The LW and LWC were greater during the wet season when VIs were higher compared to the dry season (p < 0.001). Results suggest that the remote monitoring of pasture availability, the LWC and LW is possible under extensive grazing conditions. Further, the use of VIs and other readily available data such as rainfall can be used to predict the LWC of a breeding herd in extensive conditions. Such information could be used to increase the productivity and land management in extensive beef production. The integration of these data streams offers great potential to improve the monitoring, management, and productivity of grazing or cropping enterprises. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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19 pages, 3600 KiB

Open AccessCommunication

Changes in Meadow Phenology in Response to Grazing Management at Multiple Scales of Measurement

by William Richardson, Tamzen K. Stringham, Wade Lieurance and Keirith A. Snyder

Remote Sens. 2021, 13(20), 4028; https://doi.org/10.3390/rs13204028 - 09 Oct 2021

Cited by 6 | Viewed by 2162

Abstract

Riparian and ground-water dependent ecosystems found in the Great Basin of North America are heavily utilized by livestock and wildlife throughout the year. Due to this constant pressure, grazing can be a major influence on many groundwater dependent resources. It is important for land managers to understand how intensity and timing of grazing affect the temporal availability of these commodities (i.e., biodiversity, water filtration, forage, habitat). Shifts in forage or water availability could potentially be harmful for fauna that rely on them at specific times of the year. Seven meadow communities, each consisting of three distinct vegetative communities, were grazed at three intensities to determine the relationship between grazing management and phenological timing of vegetation. The agreement of on-the-ground measurements, near-surface digital cameras (phenocams), and satellite-based indices of greenness was examined for a two-year period (2019–2020) over these grazing and vegetative community gradients. Field determined phenology, phenocam Green Chromatic Coordinate (GCC), and Landsat Normalized Difference Vegetation Index (NDVI) were all highly correlated and the relationship did not change across the treatments. Timing of growth varied in these ecosystems depending on yearly precipitation and vegetative type. Communities dominated by mesic sedges had growing seasons which stopped earlier in the year. Heavier grazing regimes, however, did not equate to significant changes in growing season. Ultimately, shifts in phenology occurred and were successfully monitored at various spatial and temporal scales. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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26 pages, 5222 KiB

Open AccessArticle

Complementary Differences in Primary Production and Phenology among Vegetation Types Increase Ecosystem Resilience to Climate Change and Grazing Pressure in an Iconic Mediterranean Ecosystem

by Juan Miguel Giralt-Rueda and Luis Santamaria

Remote Sens. 2021, 13(19), 3920; https://doi.org/10.3390/rs13193920 - 30 Sep 2021

Cited by 6 | Viewed by 2440

Abstract

Plant primary production is a key factor in ecosystem dynamics. In environments with high climatic variability such as the Mediterranean region, plant primary production shows strong seasonal and inter-annual fluctuations, which both drive and interplay with herbivore grazing. Knowledge on the responses of different vegetation types to the variability in both rainfall and grazing pressure by wild and domestic ungulates is a necessary starting point for the sustainable management of these ecosystems. In this work we combine a 15 year series of remote sensing data on plant production (NDVI) with meteorological (daily precipitation data) and ungulate abundance (annual counts of four species of wild and domestic ungulates: red deer, fallow deer, cattle, and horses) in an iconic protected area (the Doñana National Park, SW Spain) to (i) estimate the impact of intra- and inter-annual variation in rainfall and herbivore pressure on primary production, for each of four main vegetation types; and (ii) evaluate the potential impact of different policy (i.e., herbivore management) strategies under expected climate change scenarios. Our results show that the production of different vegetation types differed strongly in their responses to phenology (a surrogate of the effect of climatology on vegetation development), water availability (rainfall accumulated until the phenological peak), and grazing pressure. Although the density of domestic ungulates shows a linear, negative effect on the primary production of three of the four vegetation types, differences in primary production and phenology among vegetation types increase ecosystem resilience to both climatological variability and grazing pressure. Such resilience may, however, be reduced under the conditions predicted by climate change models, if the moderate predicted reduction in rainfall levels combines with moderate to high densities of domestic ungulates, resulting in important reductions in primary production that may compromise plant regeneration, leading to irreversible degradation. New management strategies taking advantage of habitat heterogeneity and phenological alternation, more flexible stocking rates, and the redistribution of management units should be considered to mitigate these effects. The use of available remote sensing data and techniques in combination with statistical models represents a valuable tool for developing, monitoring, and refining such strategies. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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17 pages, 3180 KiB

Open AccessArticle

Intravaginal Devices and GNSS Collars with Satellite Communication to Detect Calving Events in Extensive Beef Production in Northern Australia

by Christie Pearson, Lucy Lush and Luciano A. González

Remote Sens. 2020, 12(23), 3963; https://doi.org/10.3390/rs12233963 - 03 Dec 2020

Cited by 6 | Viewed by 2836

Abstract

Observing calves at birth may help to identify risk factors for, and reduce, calf loss in extensive beef systems. The objectives of this study were to: (1) evaluate two commercial satellite birth alert systems to enable the observation of newborn calves and (2) assess behavioral changes of cows around calving. Vaginal Implant Transmitters (VIT) paired with Global Navigation Satellite System (GNSS) collars were worn by 20 cows in Trial 1 and 10 cows in Trial 2 to identify birthing events. The VIT and GNSS collars contained a temperature sensor, accelerometer, and very high frequency (VHF) to communicate with a handheld tracker, and ultra-high frequency (UHF) for communication between the VIT and GNSS collar, which had two-way communication using Iridium satellites. A change (Brand 1) or drop (Brand 2) in temperature of more than 3 °C and inactivity triggered the VIT to communicate an expelled alert to the collar, which transmitted the birth alert information via Iridium (device ID, date, time and geolocation of the GNSS collar at expulsion). Cows and calves were tracked in the paddock following a birth alert to assess their health and status. Overall, true birth alerts occurred in only 27.6% of devices. Cows remained active on the day of calving travelling 5.54 ± 4.11 and 5.00 ± 2.80 km/day compared to 6.45 ± 2.79 and 6.12 ± 2.30 km/d on days when calving did not occur for Trial 1 and 2, respectively (mean ± SD). Average activity of the accelerometer X- and Y-axis on calving day was reduced by 15%–20% compared to other days in Trial 1 (p < 0.05) but not in Trial 2 (p > 0.05). Results suggest that these two birth alert systems are not suitable for use in extensive systems and the further development of the technology is required. Cows in the current trials remained active on the day of, and after, calving, indicating that a faster, real-time alert system and communication protocol would be required to achieve the aim of finding newborn calves. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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17 pages, 6702 KiB

Open AccessArticle

Use of a Satellite-Based Aridity Index to Monitor Decreased Soil Water Content and Grass Growth in Grasslands of North-East Asia

by Reiji Kimura and Masao Moriyama

Remote Sens. 2020, 12(21), 3556; https://doi.org/10.3390/rs12213556 - 30 Oct 2020

Cited by 6 | Viewed by 2753

Abstract

Numerous simulation studies of the effect of global warming on arid regions have indicated that increases in temperature and decreases in precipitation will trigger water shortages, drought, and further aridification. In north-east Asia, especially China and Mongolia, the area of degraded land has increased since 2000. Land use in arid regions is mainly natural grasslands for grazing. Growth in this land use is limited by the precipitation amount and intensity. To develop sustainable management of grasslands, it is essential to examine the relationship between water consumption and the growth patterns of the grasses. This study examined the applicability of a satellite-based aridity index (SbAI) as a way to measure the water consumption and growth of grasslands in China and Mongolia. The effective cumulative reciprocal SbAI was strongly correlated with the cumulative decreased soil water content in the root zone and changes in the normalized difference vegetation index in Shenmu, China. Application of the effective cumulative reciprocal SbAI to grasslands in Mongolia and in north-east Asia revealed a high correlation between the effective cumulative reciprocal SbAI and changes in the normalized difference vegetation index (NDVI). The effective cumulative reciprocal SbAI might be suitable for the detection of water consumption and growth in grasslands from satellite data alone. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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13 pages, 4413 KiB

Open AccessArticle

A Pilot Study to Estimate Forage Mass from Unmanned Aerial Vehicles in a Semi-Arid Rangeland

by Alexandria M. DiMaggio, Humberto L. Perotto-Baldivieso, J. Alfonso Ortega-S., Chase Walther, Karelys N. Labrador-Rodriguez, Michael T. Page, Jose de la Luz Martinez, Sandra Rideout-Hanzak, Brent C. Hedquist and David B. Wester

Remote Sens. 2020, 12(15), 2431; https://doi.org/10.3390/rs12152431 - 29 Jul 2020

Cited by 11 | Viewed by 3579

Abstract

The application of unmanned aerial vehicles (UAVs) in the monitoring and management of rangelands has exponentially increased in recent years due to the miniaturization of sensors, ability to capture imagery with high spatial resolution, lower altitude platforms, and the ease of flying UAVs in remote environments. The aim of this research was to develop a method to estimate forage mass in rangelands using high-resolution imagery derived from the UAV using a South Texas pasture as a pilot site. The specific objectives of this research were to (1) evaluate the feasibility of quantifying forage mass in semi-arid rangelands using a double sampling technique with high-resolution imagery and (2) to compare the effect of altitude on forage mass estimation. Orthoimagery and digital surface models (DSM) with a resolution <1.5 cm were acquired with an UAV at altitudes of 30, 40, and 50 m above ground level (AGL) in Duval County, Texas. Field forage mass data were regressed on volumes obtained from a DSM. Our results show that volumes estimated with UAV data and forage mass as measured in the field have a significant relationship at all flight altitudes with best results at 30-m AGL (r² = 0.65) and 50-m AGL (r² = 0.63). Furthermore, the use of UAVs would allow one to collect a large number of samples using a non-destructive method to estimate available forage for grazing animals. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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13 pages, 465 KiB

Open AccessArticle

Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

by Jamie Barwick, David William Lamb, Robin Dobos, Mitchell Welch, Derek Schneider and Mark Trotter

Remote Sens. 2020, 12(4), 646; https://doi.org/10.3390/rs12040646 - 15 Feb 2020

Cited by 37 | Viewed by 4584

Abstract

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management)

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