@proceedings {JRN55365,
	title = {Precision grazing: State of the science and opportunity for user feedback on new technologies},
	year = {2022},
	month = {3/29/2028},
	abstract = {Precision farming technologies are an emerging management platform aimed at digitizing crop and animal production practices to enable detection and mediation of real-time environmental and welfare concerns and to equip producers with affordable, accurate, and easy-to-use tools to improve production sustainability. The goal of this symposium is to highlight current state-of-the-art precision grazing and digital ranching tools, techniques, and platforms under study and in development for application on extensive rangelands. The session will be broadly targeted toward scientists, government agencies, and producers, all of whom are presumed to benefit from precision grazing technologies. The morning session of the Precision Grazing symposium will feature a suite of rangeland scientists investigating various precision grazing tools including virtual fencing, tri-axial accelerometers, and grazing detection instruments, as well as producer outlooks on digital ranching necessities. The afternoon session will feature work conducted by the Sustainable Southwest Beef Coordinated Agricultural Project which is aimed at developing an integrated precision grazing system and dashboard that can track cattle and sheep welfare and geolocation, body condition scores, and other ranch infrastructure in near real time in various production systems.},
	keywords = {feedback, new technologies, opportunity, Precision grazing, science, state},
	author = {Matthew M. McIntosh and S. Spiegal and Skye Aney and Jean Steiner}
}
@conference {JRN55355,
	title = {Testing of a LoRa-WAN digital ranching system on desert rangelands: Some practical experiences},
	booktitle = {2021 NMSU ACES BEEF, DAIRY AND LIVESTOCK UPDATE Presented by NMSU Cooperative Extension Service},
	year = {2022},
	month = {1/25/2022},
	abstract = {Precision livestock farming (PLF) is an emerging agricultural strategy that incorporates sensors and data analytics to inform management decisions (Neethirajan, 2017; Tedeschi et al., 2021). Though common in household and more intensive livestock and crop production systems, Internet of Things (IoT) tools are in earliest stages of development for extensive beef and sheep production systems on arid rangeland primarily due to limited infrastructure and lack of Wi-Fi, cellular (e.g. 3 and 4G), or satellite network access. Yet Long Range Wide Area Networks (Lora-WAN) could offer a high efficiency and low-cost solution to this hindrance as they promise near-real time long-range coverage (e.g. > 6 mi), a strong signal, long battery life, and customizable data collection units (e.g. widgets or sensors), compared to other conventional network types (Bocquier et al., 2014). Precision livestock technologies could provide New Mexican livestock producers novel tools for realtime monitoring of animal location and activity, asset tracking, and infrastructure monitoring in the face of a dwindling workforce and harsher climate (Spiegal et al., 2020).
This study aimed to test a PLF LoRa-WAN-based monitoring system across four pastures (12,000 ac) of Chihuahuan Desert rangeland. We report the installation protocol, maintenance, and practical applications associated with the system.},
	keywords = {beef cattle, digital agriculture, GPS-tracking, Precision grazing, Precision livestock farming, rain gauge sensor, water level sensor},
	url = {files/bibliography/21-059.pdf},
	author = {Matthew M. McIntosh and Andr{\'e}s F. Cibils and S. Nyamuryekung{\textquoteright}e and Richard E. Estell and Andrew Cox and Danielle Duni and S. A. Utsumi and Qixu Gong and Tony Waterhouse and J P Holland and Huiping Cao and LE Boucheron and H. Chen and S. Spiegal}
}
@article {JRN55342,
	title = {Can cattle geolocation data yield behavior-based criteria to inform precision grazing systems on rangeland?},
	journal = {Livestock Science},
	volume = {255},
	year = {2021},
	month = {12/5/2021},
	pages = {104801},
	abstract = {A key challenge of precision grazing systems is identifying behavior anomalies associated with situations of reduced animal production and wellbeing. We determined typical ranges of diel variation of movement and activity patterns of steers on rangeland to identify metrics that could serve as sensitive indicators of behavior anomalies. Seventeen Raramuri Criollo or Criollo crossbred yearling steers weighing 318 {\textpm} 9.3 kg (winter; W) or 358 {\textpm} 8.4 kg (late summer; LS) were fitted with GPS collars that recorded animal location at 5-min intervals. Steers grazed a 3,215-ha rangeland pasture for approximately 67 d in W or LS of 2016 and 2017. GPS data were used to derive 22 commonly monitored behavior variables. Means and day-to-day variation (CV\%) of all behavior metrics were calculated for each animal as well as linear correlations between the CV of each behavior and ADG. Daily time spent resting or grazing exhibited the least day-to-day variation in both W and LS (CV resting =10.8 and 9.9\%, respectively; CV grazing =13.8 and 14.8\%, respectively); predawn area explored (CV =240.8\%) and time spent at drinkers (CV =336.6\%) exhibited the most daily variation in W and LS, respectively. During W, increasing day-to-day variation in daytime distance traveled and area explored, as well as daily time spent traveling were associated with increasing ADG (r = 0.56 to 0.58; P < 0.05). In LS, steers with greater CV for 24-h area explored, time spent traveling, or daytime distance traveled tended to gain less weight (r =-0.77 to -0.84; P < 0.01), while steers with more flexible 24 hour path sinuosity tended to gain more weight (r = 0.93; P < 0.01). Behavior metrics more closely associated with forage intake processes, such as daily time spent grazing or resting, exhibited lowest diel variation levels and could be used to diagnose non-normal behavior of cattle on rangeland.},
	keywords = {Average daily weight gain, beef cattle, GPS monitoring, Precision grazing, Precision livestock ranching},
	doi = {10.1016/j.livsci.2021.104801},
	url = {files/bibliography/21-053.pdf},
	author = {Matthew M. McIntosh and Andr{\'e}s F. Cibils and Richard E. Estell and Qixu Gong and Huiping Cao and Amy L. Gonzalez and S. Nyamuryekung{\textquoteright}e and S. Spiegal}
}
