Sustainable Southwest Beef Project Annual Meeting Poster Session 2022

Ruby Harris-Gavin <jrharrisgavin@ucsb.edu>

UC Santa Barbara

Shelemia Nyamuryekung'e

Glenn Duff

The Sustainable Southwest Beef Project is partnering with ranchers and stakeholders to develop a Digital Ranching Platform aimed at informing decision making of ranch-level management tasks and climate -resilient livestock systems. This aspirational management approach fuses traditional statistical and advanced data science with visualization dashboards to inform key indicators of animal welfare and ranch resources. The system collects large streams of real-time data, which is logged and transmitted through a network of high throughput sensors, gateways and cloud computing services. Internet of Things infrastructure includes field sensors and high throughput GPS sensors, and accelerometers mounted on animals, operating on a Long Range Wide Area Network (LoRaWAN) solar or grid powered and using Ethernet, WiFi backhaul, or GSM communication. Software engineering and IT project components are focused on unifying a web-based dashboard and server application for visualization and retrieval of computed data and configuration of field devices and sensors. Utilities include improvements of operational efficiencies through near- to real-time tracking and scouting of livestock, rapid animal welfare assessments, remote monitoring of rain gauge tipping buckets and tracking of water level in cattle drinking troughs. Procedures seek to facilitate the harmonization (i.e. common feature representation) and curation of varying streams of data (i.e. erroneous sensor data and missing data points) prior to implementing machine learning for advance classification and prediction objectives. Future analytics will aim to merge patterns of animal data with insights on animal activity budgets, early warnings of breeding status, and faulty animal health or grazing performance. Unobtrusive scoring of cattle body condition is being collected using machine learning classifiers via high throughput video imagery of infrared depth cameras. Pilot case studies suggested important utilities of the system and have reveled several areas for improvement of existing sensor and network infrastructure and applications, which will be discussed in this symposium.

Andres Perea

1Department of Animal and Range Science, New Mexico State University, Las Cruces, NM; 2USDA-ARS Jornada Experimental Range Las Cruces, NM

Digital ranching, Precision livestock farming, animal sensors, LoRa WAN

ABSTRACT There is interest in the acceptance and implementation of precision ranching (a management strategy that gathers, process, and analyze temporal, spatial, and individual data and combines it with other information to support management decision according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.), due to the potential to contribute to the broader objective of meeting the growing demand for cattle products, ensuring the sustainability of primary production, based on a more accurate and resource-efficient technique to forage and livestock management, are major benefits that boost the desire to embrace this advanced way of ranching. The purpose of this study is to investigate and analyze the costs and benefits of precision ranching on ranches in some selected counties in New Mexico, using a cost and benefit analysis tool to estimate the various outcomes from costs incurred when precision ranching is been implemented. Using a Monte Carlo simulation model for sensitivity analysis, this study will investigate all the costs involved in precision ranching by carefully looking at cost of managing the units. Analysis of the cost to determine the benefits of implementing precision ranching and whether every dollar spent is worth the investment on the selected ranches in New Mexico.

Isaac Appiah

New Mexico State University

Precision ranching, Productivity, Break-even analysis

As greenhouse gases drive global warming, there is growing interest in using soil as a carbon sink. Adaptively managed grazing that encourages root production may promote soil organic carbon storage. Managed grazing could change how plants allocate carbon towards root production. Alternatively, grazing could influence ecosystem root production by promoting the persistence of species that constitutively produce more roots. Therefore, in order to understand how grazing affects root production, we need to examine how grazing influences both individual plants and rangeland plant communities. By combining studies of root responses to grazing in the greenhouse and in the field, we can understand how different grazing regimes affect soil carbon in different contexts.

Jacob Weverka

University of California, Santa Barbara

Soil Carbon, Grazing, Roots, Microbial Activity

Beef production in the southwestern United States is projected to experience increasingly warmer and drier climate in the future. Adaptation strategies to these future conditions are needed without compromising environmental quality or profitability. Options include the use of desert-adapted beef cattle biotypes, such as Rarámuri Criollo cattle and Criollo crossbreeding with more traditional British breeds. Currently most calves raised in the Southwest are grain finished with feed often from irrigated crops produced in the sensitive Ogallala Aquifer region. A viable alternative may be grass finishing with rainfed forage in the desert rangeland of the southwest or in the temperate grassland of the Central Plains. We compared the environmental impacts and economics of current production systems using Angus cattle raised in the Southwest and grain-finished on feedlots in the Texas Panhandle with Criollo cattle and crosses (Criollo x Angus) finished on high-grain or high-grass diets. Current and alternative supply chain strategies were simulated using the Integrated Farm System Model to determine effects on farm-gate life cycle intensities of greenhouse gas emissions, fossil energy use, nitrogen losses, blue water consumption and production costs, using representative (appropriate soils, climate, and management) ranch and feedlot operations. Regardless of finishing options, Criollo x Angus cattle had the best environmental and economic outcomes, followed by pure Criollo cattle and then Angus. The crossbreed combined the desert adapted grazing behavior of Criollo cows and calves with heavier final carcasses from Angus genetics. Considering the combination of breed and finishing options, crossbred cattle with grass finishing in the Southwest or in the Central Plains outperformed on most environmental variables and production costs, mostly due to reduced external input requirements (primarily feed). A downside was greater carbon emission compared to grain finishing due to greater methane emissions from high forage diets and an extended time to finish.

Matthew McIntosh

1 New Mexico State University, Jornada Experimental Range 2 USDA-ARS, Pasture Systems and Watershed Management Research Unit 3 Evergreen Livestock & Ranching LLC 4 New Mexico State University, Extension Animal Sciences and Natural Resources 5 New Mexico State University, Clayton Livestock Research Center 6 USDA-ARS, Jornada Experimental Range

Determining co-benefits and tradeoffs among management strategies is increasingly necessary in rangeland systems where management approaches seek to improve livestock production and conserve natural resources, but where spatiotemporal heterogeneity of ecological systems complicates the teasing apart of such tactics. The Sustainable Southwest Beef Coordinated Agricultural Project (SW Beef CAP) and the USDA-ARS Long Term Agroecosystem Network seeks to develop performance indicators to measure potential of novel and business as usual beef management production systems in the southwestern US. Our team aims to evaluate performance of the integration of heritage cattle genetics and precision ranching systems versus more conventional approaches that include conventional beef cattle genetics and no precision ranching systems. Our vision for future southwestern beef production systems is that they will be sustainable, resilient, climate smart, and circular, thus an indicator framework could be an effective method for determining how well each treatment/ management strategy is working. The SW beef CAP plans to evaluate how these treatments affect the status of five attributes of sustainability and resilience of beef production systems: Human health, Ecological health, Production efficiency, Social cohesion, and Economic growth. We are currently developing benchmarks (or desired conditions) associated with indicators of each attribute, which will allow us to assess how well each treatment performs in terms of preferred outcomes. For example, one key indicator of Ecological health is Biotic integrity, which we plan to measure as a combination of three metrics: perennial grass cover, bare ground %, and shrub canopy cover; these metrics will be measured across experimental treatments and sights on an inter-annual basis over the next two decades using on-the-ground and remotely sensed monitoring methods to establish treatment-derived changes. This methodology will allow us to compared treatments against each other, but also against pre-defined benchmarks, hence they will be able to indicate the degree to which a treatment is meeting expectations and what the tradeoffs and co-benefits of two management strategies are.

Matthew McIntosh

USDA-ARS Jornada Experimental Range

indicators

Grazer manure represents a substantial labile carbon and nutrient flow into cattle rangelands bolstering both plant and microbial communities. My study focuses on the microbial communities supported by root exudate - the downstream labile carbon produced by plant roots. Root exudation may subsidize enzyme synthesis allowing microbes to decompose previously unavailable soil carbon—a mechanism of soil priming. But where are soil C stocks most at risk of decomposition by these communities and how might varying concentrations of labile carbon exuded by roots influence microbial exoenzyme synthesis? To answer these questions we present a novel system synthesizing micro-dialysis to simulate root exudation with planar optode technology to allow spatial characterization of microbial decomposition. Improving our understanding of the microscale inter-play between root exudation, microbial communities, and soil carbon decomposition may help rangeland managers make informed macroscale decisions with our soil carbon stocks in mind.

Maxi Navarrette

University of California, Santa Barbara — Ecology, Evolution, and Marine Biology

Soil Carbon, Root Priming

Exotic African oryx are expanding their range and having increased impacts on ranches and public land. The objectives of this study are to provide information to land managers on oryx foraging behavior and how they interact with cattle. We collected occupancy data using images captured by 40 Cuddeback E2 Long-range IR trail cameras located in 4 pastures of the Chihuahuan Desert Rangeland Research Center. Data was collected for 3 months before, during, and following 3 months of cattle grazing within the study area and during the same season of the following year. LPI data collected at camera locations link oryx occupancy with vegetative foliar cover. We will report oryx occupancy changes of the study area before, during, and after cattle use and whether oryx occupancy varies with vegetation cover. We expect our findings could guide decisions by land managers and agencies by improving the knowledge base of oryx interactions with livestock operations and the landscape.

Micah Funk

New Mexico State University, Jornada Experimental Range

Oryx, Wildlife interactions, Camera trap

Body condition score (BCS) has been a useful metric for estimating bovine health as a function of subcutaneous fat for a number of years. While useful, the metric is time consuming to compute and is subject to user bias. Utilizing BCS on a large number of cattle for long periods at a time is often not feasible, and skilled professionals are often not available to small scale ranchers. Presented is an analysis of the feasibility of using convolutional neural networks, as well as support vector machines trained on depth images, to estimate the body condition score of Criollo cattle at the Jornada Experimental Range (JER). Criollo cattle have been studied on the JER since 2005, and present new challenges for BCS estimation due to their natural body structure. In order to address these challenges, previously used techniques are examined and modified to use more advanced machine learning algorithms. Additionally, previously used networks are applied to new data sets in order to test their real-world applications on beef cattle.

Santiago Utsumi

New Mexico State University

This study investigated the integration of Long Range Wide Area Network (LoRa WAN) communication technology and sensors for use as Internet of Things (IoT) platform for Precision Livestock-Farming (PLF) applications. The research was conducted at New Mexico State University’s Clayton Livestock Research Centre. The functionality of LoRA WAN communication technology and performance of LoRa WAN motion and GPS sensors were tested using static sensors that were placed either, a) outdoors and at incremental distances from the LoRa WAN gateway antenna (Field, n=6), or b) housed indoors and close to the same LoRa WAN gateway antenna (Indoor, n=5). Accelerometer data, reported as motion intensity index, and GPS location were acquired, transmitted and logged at 1 and 15 minute intervals, respectively. We evaluated the tracker's GPS accuracy (GPSBias as the euclidean distance between the actual and projected tracker location) and variables associated with the tracker’s data transmission capabilities. The results indicate that field trackers had a greater accuracy for remote sensing of GPS locations compared to indoor trackers facing increasing communication interference to acquire satellite signals (GPSBias; 5.20 vs. 17.76 m; P<0.01). Overall, the trackers and deployments appeared to have a comparable GPS accuracy to other tracking devices and systems available in the market. The total data packets that were successfully transmitted were similar between the indoor and field trackers, but the number of data packets that were processed varied between the two deployments (P=0.02). Due to the static deployment of indoor and field trackers, activity data was almost non-existent for most devices. However, same trackers embedded on collars that were mounted on mature cattle showed clear diurnal patterns consistent with time budgets exerted by grazing cattle. The pilot testing of GPS and accelerometer sensors using LoRa WAN technology revealed reasonable sensor sensitivity and reliability for integration in PLF platforms.

Shelemia Nyamuryekung'e

New Mexico State University

Precision Livestock Farming (PLF), Precision Livestock Ranching (PLR), Internet of Things (IoT), Long Range Wide Area Network (LoRa WAN)