Source: SOUTH DAKOTA STATE UNIVERSITY submitted to NRP
PRECISION RANCHING: APPLICATION OF EMERGING TECHNOLOGY TO IMPROVE RANGE MANAGEMENT AND OPTIMIZE CATTLE PERFORMANCE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1028285
Grant No.
2022-69014-36670
Cumulative Award Amt.
$1,000,000.00
Proposal No.
2021-10904
Multistate No.
(N/A)
Project Start Date
Feb 1, 2022
Project End Date
Jan 31, 2027
Grant Year
2022
Program Code
[A1261]- Inter-Disciplinary Engagement in Animal Systems
Recipient Organization
SOUTH DAKOTA STATE UNIVERSITY
PO BOX 2275A
BROOKINGS,SD 57007
Performing Department
Animal Science
Non Technical Summary
Emerging technologies are a potential avenue to improve beef cattle performance and efficiency, but lack in-depth research. Understanding beef cattle performance and efficiency is necessary to address human food needs, enhance environmental quality, effectively use nonrenewable resources and on-farm resources, and help sustain farm/ranch economic viability.The overall hypothesis of this work is that the use of precision grazing technology will enhance animal performance and efficiency, while increasing environmental capacity. Specific objectives are to: 1) implement precision grazing using emerging technologies to evaluate cattle, environment, and economic responses; 2) develop and integrate satellite imagery to further enhance precision grazing; and 3) deliver science-based, objective knowledge to producers and consumers regarding these emerging technologies.We expect that cattle in a precision grazing system will have lower intestinal greenhouse gas emissions, greater average daily gain, improved feed conversion ratios, and more desirable carcass traits compared to cattle in a simple continuous grazing system. We also expect that in a precision grazing system, forage utilization will be uniform, and the carbon balance will be better regulated than compared to a simple continuous grazing system. Anticipated impacts of this project include increased producer adoption of emerging technologies, improved animal performance and efficiency, and therefore higher quality cattle and more economic viability for producers. This work will develop management approaches to improve the economic and environmental sustainability of beef production, and provide a greater understanding and communication of those management strategies to producers and consumers.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
30733991070100%
Knowledge Area
307 - Animal Management Systems;

Subject Of Investigation
3399 - Beef cattle, general/other;

Field Of Science
1070 - Ecology;
Goals / Objectives
Our long-term goal is to evaluate and integrate emerging management technologies on a ranch and landscape level to develop a suite of tools and efficiency metrics that beef producers can use to optimize individual animal efficiency and range conditions. This will ultimately improve their personal efficiency, work-life balance, profit margins, and sustainability for future generations. We will accomplish our long-term goal with three main objectives:Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence).Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices.
Project Methods
Study Areas. Tasks 1A and 1B will occur at the SDSU Cottonwood Field Station (CWFS; grazing). The CWFS is located in west-central South Dakotawithin aMixed Grass Prairieecosystem, andis composed primarily of native C3 ( Nassella viridula and Pascopyrumsmithii) and C4 (Boutelouagracilisand Buchloe dactyloides) grasses. Task 1C will occur at the SDSU Southeast Research Farm (Beresford, SD; Feedlot). Objectives 1 and 2 will use three groups of yearling Angus steers (n = 135 per group/yr; 3yr, n = 405 total)Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. Task 1A: Build a decision support tool (DST) that integrates ecological and cattle weight data with ruminant nutrition and grazing equations to inform virtually fenced pasture rotations.Precision grazing will be implemented on the CWFS long-term grazing study. This study includes six different pastures (P) as a randomized complete block with low, moderate, and high grazing intensities (n=3) in 2 replicate blocks (grazed 3.5 months). Our experimental design will therefore be a factorial of 2 grazing management treatments [continuous grazing (CG) and precision grazing (PG)] and 3 grazing intensities. The CG will be season-long grazing without precision tools and the PG will use virtual fencing rotations informed by the Dynamic Grazing System (DGS) model. The DGS model will be built using differential equations using Vensim™ informed by precision measures of observed steer weight (lb/d), climate (temperature and precipitation; MESONET, a network of climate stations currently being enhanced in SD), and forage data (lb/ac, quality). The DGS model will be formulated to dynamically adjust each subsequent PG paddock. The DGS estimates of forage production (lb/ac) will be evaluated for accuracy and precision against historical CWFS data.Task 1B: Evaluate the difference between continuous grazing (CG) and precision grazing (PG) on individual animal efficiency, behavior, and environmental impacts. For both PG and CG treatments, precision measurement technologies will be used to determine the impact of PG on animal behavior, performance, vegetation response, and GHG emissions compared to CG.Animal Behavior. All steers will be outfitted with Vence collars. Livestock behavior metrics will be measured using GPS enabled collars within each experimental P (n=21). Spatial statistics will be used to test the effectiveness of the PG treatment to distribute grazing more evenly throughout P. Collars will be outfitted with a high-frequency accelerometer (n=5/trt) to record locations at 1-minute intervals. Existing models will be used to classify livestock behavior (graze or non-graze). Using GPS and accelerometer data, daily grazing and resting time, and distance traveled will be calculated to evaluate behavior differences between foraging and resting time, and travel distance based on grazing management treatments.Emissions. GreenFeed systems will be used to monitor CH4 and CO2 emissions from steers within the CG and PG treatments. Two GreenFeed systems will be rotated weekly between treatment P (3-week interval). Gas exchangedata will be coupled with average daily gain data collected from the SmartScales. Feed conversion (gain:feed) will be calculated daily to determine individual feed efficiency, and daily herd average emissions will be calculated to determine differences between treatments. A SmartScale will measure daily individual steer body weight (BW) in each P. Net Ecosystem Exchange (NEE; i.e., CO2 flux, temperature and incident sunlight) will be measured weekly from May and September (n=10/trt). Using these data, season-long estimates of NEE and respiration will be modeled. Soil carbon will be measured monthly during summer grazing (3 years).Utilization. Utilization will be determined for the CG P and within each of the virtual paddocks. Utilization within the PG treatment will be measured by clipping biomass in 5 randomly located 0.25 m2 plots in each paddock before and after grazing. Livestock exclusion cages will be used to measure utilization within the CG treatment. Grazing efficiency will be calculated as average dry matter intake (DMI) as a proportion of total biomass disappearance.Task 1C: Determine CG and PG impact on subsequent individual feedlot performance and carcass traits.Following the grazing season, steers will be delivered to the SDSU Southeast Research Farm for finishing to evaluate carryover effects on animal responses. On arrival, steers will be adapted to a high concentrate finishing diet and fed until slaughter. They will be placed in 12 pens (n=8 per pen; 2 pens per treatment P) and DMI will be recorded daily for each pen. SmartScale and GreenFeed instruments will be deployed in feedlot pens. All cattle will be tracked individually through the harvesting process. Steers will be harvested at a commercial packing facility and hot carcass weight will be collected. Following carcass chilling, standard carcass data will be recorded per individual and used to calculate yield and quality grade according to USDA guidelines. In addition to animal performance data, input costs and carcass value will be recorded to inform the economic analyses of the CG and PG.Task 1D: Compare CG and PG herd level outcomes related to environmental and economic measures. Environmental. We will compare environmental impact between CG and PG by calculating differences in carbon emitted and sequestered at a herd level from steer weaning to finishing. Measured data include enteric GHG, days on grazing, days on feed, NEE (CO2), and Active-C (g/kg) from weaning to slaughter of each steer group at an individual animal level. These daily emission rates will be aggregated into daily and final (weaning to slaughter) cumulative totals for each steer, which will then be totaled for each steer group (groups 1-3) to derive the total daily and final herd emissions specifically from enteric methane production. This will result in daily herd emission rates and herd emission totals. Daily and total NEE and change in Active C will be calculated for each respective grazing year.Economics. We will compare the net present value for the CG and PG treatments over the experimental years, which can be calculated using: discount factor, average stocking rate, purchase and selling prices and weights, and costs (including precision technology). Based on changes in soil C sequestered and GHG emissions resulting from the different treatments/intensities, a C value (and GHG value) will be estimated and the economic returns with C credits taken into consideration will also be compared between treatments.Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence).We will develop the remote sensing-optimal grazing algorithm using topographic maps and daily Planet satellite imagery. To facilitate C3 and C4 grass classification, training site polygons will be mapped in each experimental P representing a range of grass coverage. Three 0.25 m2 plot frames will be randomly placed in each training polygon to measure the percentage of C3 and C4 grasses. Machine learning will be used to predict C3 and C4 composition, and validated to test model accuracy. Models will be used to predict thematic raster maps of C3:C4 grass composition of each P and then be integrated into the DGS model to inform PG.Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices.We will conduct a Precision Animal Learning Extension program using extension programming methods to facilitate producer engagement in the program, enhance producer knowledge, and provide consumer interaction activities.

Progress 02/01/24 to 01/31/25

Outputs
Target Audience:Our target audiences include researchers, Extension agents, governmental agencies (USDA, Forest Service, NRCS, BLM), agriculture producers, and the public. Specific producers include beef cattle ranchers in the cow-calf, stocker/backgrounding, and feedlot phases. Researchers, Extension, and agency personnel include those applying precision livestock technology. Recruited 14 beef cattle producers into the BeefSD Extension program through interviews. Hosted a producer focused BeefSD Exention program kickoff meeting in Pierre, South Dakota a "gate to plate" focus. Hosted a National Animal Nutrition Program workshop for over 100 industry nutritionist, students, and professionals which shared key scientific findings and provide nutrition model training at the annual meeting for the America Society of Animal Science Hosted a workshop for over 200 industry nutritionist, students, and professionals which shared key scientific findings and provide nutrition model training at the annual meeting for the Brazilian Society of Animal Science. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training activities: Through our research and Extension activities we were able to provide training on virtual fencing, precision weighing, and enteric emissions measurements to Hadley Dotts, Aletta Hussman, and Kali-Jo Bentz (M.S. students), Elias Moreno, and Walter Wafula (Ph.D. students), Students have also learned the basics of mathematical modeling using precision livestock data. Professional development:Activities included student [Hadley Dotts, Aletta Hussman, and Kali-Jo Bentz (M.S. students), Elias Moreno, and Walter Wafula (Ph.D. students)] knowledge gain on how to present precision livestock technology to broad audiences (industry, producers, consumers). How have the results been disseminated to communities of interest?We have disseminated information about our activities through SDSU Extension programming efforts, specifically the BeefSD program class 7. This helps producers take the research and data and apply it to enhance their production operations. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. Implement precision livestock technologies in early April on yearling steers to enhance livestock adoption of technologies at the South Dakota State University Cottonwood Field Station (long-term stocking pastures). We will then conduct the field trial using about 135 steers and rangeland measuring methods. We will then send the steers to the feedlot phase for finishing and slaughter to collect performance and carcass data. We will also document costs to acquire the information needed for the economic analysis of precision verses continuous grazing. We will also document costs to acquire the information needed for the economic analysis of precision verses continuous grazing. Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence). For objective 2 we plan to repeat our collection of forage samples throughout the growing season and separate them by species. These samples will also undergo nutrient analyses. Global position system (GPS) points and polygons (spatial areas) will be documented to link forage samples to specific spatial locations to train remote sensing algorithms. Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices. During the next year, class 7 of the SDSU Extension program will take real producers (n=14) to comprehensive training and field trips. The producers will also be mentored by leading ranchers from our established Precision Animal Learning (PAL) group.

Impacts
What was accomplished under these goals? Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. 30% Accomplished In year three we implemented the experiment and captured individual animal, forage, and soil data from April 15, 2024-February 12, 2025. The 2024 cohort of steers were trained to the GreenFeed pasture unit and grazed successfully. Enteric emissions data was obtained from grazing and feedlot phases; 12,000and 30,000 individual points, respectively. We were also able to collect performance and data on grazing behavior. Virtual fencing collars were used to implement rotational grazing and had a high retention rate and performed with a high-level of efficacy. Steers were successfully developed until slaughter and an industry funded grant (Iowa Beef Council) allowed us to buy back striploins for additional analyses that will directly benefit producers and beef markets. All project objectives were met for 2024. Impact Statement: This has a two-fold impact. 1) We have been able to share knowledge with researchers who are currently or plan to utilize precision livestock technology on extensive rangeland systems and collect enteric emissions data. 2) We have been able to disseminate this information to producers or agencies seeking to deploy similar precision livestock technologies on extensive rangeland systems. 3)We have built, to our knowledge, the most comprehensive beef stocker and feedlot steer precision dataset in the world. This data is being shared with producers, leveraged with genetic companies, and will have immediate and future implications to making U.S. beef systems more cost-effective and competitive. Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence). 40% Accomplished. In 2024, our Ph.D. student (Walter Wafula) was able to collect 96 GPS located points in the grazing study pasture areas. These samples have been separated by cool and warm season grasses and are being entered into a database. This data will be used to develop the training dataset for grass identification for the remote sensing algorithm. Impact Statement: Developing remote sensing tools to inform precision livestock management on extensive rangeland beef systems has a huge potential to accelerate remote sensing and grazing research. Further, turnkey products that can be shared with livestock producers through SDSU Extension will facilitate rancher use and adoption to improve grazing management decisions. Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices. 25% Accomplished. Outputs and Outcomes for 2/1/2024 to 1/31/2025 We began advertising and recruitment for our Extension and outreach program, beefSD, during the summer 2024. Interviews were conducted with potential participants via Zoom in September. The program commenced with a kick-off meeting November 13-4, 2025. We are proud to have 14 beef cattle producers in the program, representing cow-calf operations across the state of South Dakota. The kick-off meeting focused on sharing the program's vision of educating producers through a "gate to plate" program. Presentations were provided on adaptive management, the role of technology in beef production, systems thinking, goal setting, and program expectations. The participants responded favorably to the content and enjoyed networking with each other. We hosted our first webinar with the participants on December 18, which focused on feeding technologies. In addition, the Extension team held regular meetings to develop curriculum for the beefSD program and to develop our next steps for the PAL member engagement. Our engagement with the beefSD participants is ongoing, as we use Google Classroom to provide educational materials, post webinar recordings, and assign homework to the participants, such as working on their goals. Future curriculum in early 2025 for the beefSD program will focus on a case study workshop at an established operation that will focus on adaptive management and using every acre for what it is best suited for. We will also host a webinar on drought forecasts and management. Additional programming will occur throughout the year and will be conveyed in future reports. Our team will also be re-engaging with the PAL members in the latter half of 2025. Impact Statement: We have 14 participants in the beefSD program. We will be conducting an early program evaluation in the first quarter of 2025 to determine participants' interest in specific topics and further tailor the curriculum to their needs. Evaluation results will be conveyed in future reports.

Publications

  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Brennan, J. R., L. Parsons, I., Harrison, M., & Menendez III, H. M. (2024). Development of an application programming interface to automate downloading and processing of precision livestock data. Translational Animal Science, 8, txae092. Contributed to data collection, writing, editing, and funding. https://doi.org/10.1093/tas/txae092
  • Type: Other Status: Published Year Published: 2024 Citation: B.M. DeBruin, E.V. Moreno, I.L. Parsons, J.R. Brennan, H.M. Menendez, K.E. Ehlert, J.R. Jaeger, K.R. Underwood, J.K. Grubbs, C.E. Bakker, Z.K. Smith, K.C. Olson, A.D. Blair. 2024. Benchmarking the pounds of beef produced per unit of methane during the stocker phase across different pasture systems. South Dakota Beef Industry Council.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Menendez III, H.M., Turner, B.L., Atzori, A.S., Brennan, J.R., Parsons, I.L., Velasquez-Moreno, E.R., Husmann, A.L. Dotts, H.A., Guarnido-Lopez, P., and Tedeschi, L.O. (2024). NANP: Hands-On 1: Applying system dynamics to develop Flight Simulators for sustainable animal production. American Society of Animal Science Annual Meeting, July 21, 2024, Calgary, Canada. (https://doi.org/10.1093/jas/skae234.074)
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Menendez III, H.M. NANP: Round-Table II-Discussion. American Society of Animal Science Annual Meeting, July 21, 2024, Calgary, Canada.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Brennan, J. and Menendez, H. 2024. Machine learning for behavior analyses. Invited symposium. Society for Range Management-Reno, NV
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Brennan, J. and Menendez, H.M. 2024. Utilizing APIs for precision livestock data processing. C-Lock GreenFeed Certification Course. Rapid City -SD.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Menendez III, H.M. 2024. Development of Climate Smart Beef and Bison Commodities in the US Northern Great PlainsTurner. Data-Driven Intelligent Agricultural Systems Symposium. Texas A&M College Station, February 9, 2024. Invited* Keynote*
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Menendez III, H.M., and Blair, A.D. 2024. Relevance of Research (Part 2): A Systems Perspective. Turner Management Team Meeting/National Bison Association Winter Conference. January 15-19, 2024. Invited*
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Mazo, S.B., Moreno-Pulgar�n, L.F.F., Agudelo, J.F.R., Guar�n-Montoya, J.F. and Menendez III, H.M. (2024). An electronic device for enteric methane emissions monitoring. American Society of Animal Science Annual Meeting, July 22, 2024, Calgary, Canada. (https://doi.org/10.1093/jas/skae234.350) *ORAL
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Husmann, A.L., Velasquez-Moreno, E.R., Brennan, J.R., Smith, Z.K., Olson, K., Blair, A., Wang, T. Leffler, J., Wafula, W., Parsons, I.L., Dotts, H.A., Guarnido-Lopez, P., Tedeschi, L.O., Menendez III, H.M. (2024). Evaluating the effects of grazing native rangeland on enteric emissions. American Society of Animal Science Annual Meeting, July 24, 2024, Calgary, Canada. https://doi.org/10.1093/jas/skae234.366 *ORAL
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Velasquez-Moreno, E.R., Menendez III, H.M., Brennan, J.R., Husmann, A.L., Dotts, H.A., Olson, K., Blair, A., Ehlert, K., Wang, T., Leffler, J., Wafula, W., Parsons, I.L., Guarnido-Lopez, P., Tedeschi, L.O., Smith, Z.K. (2024). Assessing the carry-over effects of precision livestock technology on steer performance and carcass characteristics. American Society of Animal Science Annual Meeting, July 22, 2024, Calgary, Canada. https://doi.org/10.1093/jas/skae234.177 *ORAL
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Zuidema, D., Cammack, K.M., Blair, A., Menendez III, H.M.*, Brennan, J.R., Graham, C., and Short, R.A. (2024). Establishing producer research sites for the development of beef and bison climate-smart agriculture. American Society of Animal Science Annual Meeting, July 22, 2024, Calgary, Canada. https://doi.org/10.1093/jas/skae234.349 *ORAL
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Parsons, I.L., Brennan, J.R., Menendez III, H.M., Velasquez Moreno, E.R., Husmann, A.L., and Dotts, H.A.. (2024). Allocating distribution of pasture utilization across the grazing landscape in grazing steers equipped with virtual fencing collars. American Society of Animal Science Annual Meeting, July 22, 2024, Calgary, Canada. https://doi.org/10.1093/jas/skae234.363 *ORAL
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Guarnido-Lopez, P., Menendez III, H.M., Tedeschi, L.O. (2024). Factors influencing greenhouse gas measurements in beef cattle: Understanding GreenFeed results. American Society of Animal Science Annual Meeting, July 22, 2024, Calgary, Canada. https://doi.org/10.1093/jas/skae234.106 *ORAL
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Husmann, A.L. Brennan, J.R., Velasquez-Moreno, E.R., Smith, Z.K., Leffler, J., Ehlert, K., and Menendez III, H.M. (2024). Evaluating the effect of a phenology-based timeline on the interpretation of enteric methane emission results. American Society of Animal Science Annual Meeting, July 24, 2024, Calgary, Canada. (https://doi.org/10.1093/jas/skae234.362)
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Velasquez-Moreno, E.R., Brennan, J.R., Vandermark, L.R., Ehlert, K., Husmann, A.L., Dotts, H.A., Olson, K., Blair, A., Wang, T. Leffler, J., Wafula, W., Parsons, I.L., Smith, Z.K., and Menendez III, H.M. (2024). Impact of virtual fence technology on yearling steer behavior and performance. American Society of Animal Science Annual Meeting, July 22, 2024, Calgary, Canada. https://doi.org/10.1093/jas/skae234.003*ORAL


Progress 02/01/23 to 01/31/24

Outputs
Target Audience:Our target audiences include researchers, Extension agents, governmental agencies (USDA, Forest Service, NRCS, BLM), agriculture producers, and the public. Specific producers include beef cattle ranchers in the cow-calf, stocker/backgrounding, and feedlot phases. Researchers, Extension, and agency personnel include those applying precision livestock technology. We hosted a Precision Field School at the South Dakota State University Cottonwood Field Station (near Phillip, SD) and in the Wall Community Center in Wall, South Dakota. We hosted a Precision Animal Learning group advisory meeting at the South Dakota State University Cottonwood Field Station (near Phillip, SD) and in the Wall Community Center in Wall, South Dakota. Hosted a virtual fencing data workshop at Iowa State University and at a virtual fencing Inservice meeting (Boise, Idaho). Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training activities: Through our research and Extension activities we were able to provide training on virtual fencing, precision weighing, and enteric emissions measurements to Logan Vandermark, Lily McFadden, Hadley Dotts, Aletta Hussman, and Anna Dagel (M.S. students), Elias Moreno and Walter Wafula (Ph.D. students), Veterinarian student Katie Evans, intern Taylor Gills, producers, and agencies employees (USDA-NRCS). Students have also learned the basics of mathematical modeling using precision livestock data. Professional development:Activities included student [Logan Vandermark, Lily McFadden, Hadley Dotts, Aletta Hussman, and Anna Dagel (M.S. students), Elias Moreno and Walter Wafula (Ph.D. students), veterinarian student-Katie Evans, and intern Taylor Gill] knowledge gain on how to present precision livestock technology to broad audiences (industry, producers, consumers). How have the results been disseminated to communities of interest?We have disseminated information about our activities through SDSU Extension programming efforts, specifically the Precision Field School. At the field school participants saw and handled the precision technologies and participated in live demonstrations. Our Precision Animal Learning Day allowed the public, who are not normally aware of these research activities, to gain knowledge about what precision livestock technology is and its intended purpose for ranching, ultimately translating to beef products purchased by consumers. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. Implement precision livestock technologies in early April on yearling steers to enhance livestock adoption of technologies at the South Dakota State University Cottonwood Field Station (long-term stocking pastures). We will then conduct the field trial using about 135 steers and rangeland measuring methods. We will then send the steers to the feedlot phase for finishing and slaughter to collect performance and carcass data. We will also document costs to acquire the information needed for the economic analysis of precision verses continuous grazing. We will also document costs to acquire the information needed for the economic analysis of precision verses continuous grazing. Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence). For objective 2 we plan to repeat our collection of forage samples throughout the growing season and separate them by species. These samples will also undergo nutrient analyses. Global position system (GPS) points and polygons (spatial areas) will be documented to link forage samples to specific spatial locations to train remote sensing algorithms. Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices. During the next year, we will open applications for Class 7 in early July, with an anticipated start date of the program in September 2024. We will conduct Survey #1 with the Class 7 participants prior to the start of the program to determine pre-program knowledge and management practices.

Impacts
What was accomplished under these goals? Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. 20% Accomplished In year two we implemented the experiment and captured individual animal, forage, and soil data from April 15, 2023-Januaury 15, 2024. The 2023 cohort of steers were trained to the GreenFeed pasture unit and grazed successfully. Enteric emissions data was obtained from grazing and feedlot phases; 8,000 and 30,000 individual points, respectively. We were also able to collect performance and data on grazing behavior. Virtual fencing collars were used to implement rotational grazing and had a 96% retention rate and performed with a high-level of efficacy. Steers were successfully developed until slaughter. All project objectives were met for 2023 except for the accelerometer data. There was a backlog on accelerometers from the manufacturer. Data has been processed and will be presented at national and international meetings in 2024. Impact Statement: The first year of data collection was very challenging. This is because animals do not stay within a proximity to the GreenFeed in wide open pastures compared to confined settings. Our success provides a template for others to be successful in collecting this type of data. This has a two-fold impact. 1) We have been able to share knowledge with researchers who are currently or plan to utilize precision livestock technology on extensive rangeland systems and collect enteric emissions data. 2) We have been able to disseminate this information to producers or agencies seeking to deploy similar precision livestock technologies on extensive rangeland systems. Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence). 25% Accomplished. Our Ph.D. student (Walter Wafula) was able to collect 96 GPS located points in the grazing study pasture areas. These samples have been separated by cool and warm season grasses and are being entered into a database. This data will be used to develop the training dataset for grass identification for the remote sensing algorithm. Impact Statement: Developing remote sensing tools to inform precision livestock management on extensive rangeland beef systems has a huge potential to accelerate remote sensing and grazing research. Further, turnkey products that can be shared with livestock producers through SDSU Extension will facilitate rancher use and adoption to improve grazing management decisions. Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices. 25% Accomplished. We hosted our first PAL group meeting with our 10 PAL members on September 15, 2024. This consisted of a morning session at the SDSU Cottonwood Field Station to tour and demonstrate the various precision livestock management technology being implemented at the station. The afternoon session focused on how this research and technology can be applied, including challenges, benefits, and data insights. The PAL members were excited about the research and potential applications and asked questions pertaining to the economics of the equipment, how it fits in with pasture rotations, and they provided several new ideas regarding estimating water intake from cattle, and phone apps that could provide notifications of animal health, remote monitoring of stock tank levels and water quality, etc. The Extension team on this grant has met several times to develop the curriculum for BeefSD Class 7, which will be focused on capturing the early majority producers. Our team has met to discuss the educational components of Class 7 that will focus on: instructional workshops, mentor relationships, online learning, and out of state trips. Impact Statement: There were 10 direct contacts at the first PAL group meeting. No formal evaluation was conducted; however, PAL members remarked that they were excited to serve in an advisory role for this project, and that they appreciated the tour at the SDSU Cottonwood Field Station and the afternoon session.

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Menendez III, H. M., Brennan, J. R., Ehlert, K. A., & Parsons, I. L. (2023). Improving Dry Matter Intake Estimates Using Precision Body Weight on Cattle Grazed on Extensive Rangelands. Animals, 13(24), 3844.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Brennan, J.R., Menendez III, H.M., Ehlert, K., and Tedeschi, L.O. (2023). BOARD INVITED REVIEW: ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Making Sense of Big Data and Machine Learning: How Open Source Code can Advance Precision Livestock Agriculture. Journal of Animal Science.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Menendez, H.M., Brennan, J.R., and Ehlert, K. 2023. Precision beef dry matter intake estimation on extensive rangelands. 2nd U.S. Precision Livestock Farming Conference proceedings. Knoxville, TN.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Brennan, J.R., Menendez, H.M., Ehlert, K., Olson, K., and Rekabdarkolaee, H. 2023. Implications for daily weight data on beef cattle grazing extensive rangelands. 2nd U.S. Precision Livestock Farming Conference proceedings. Knoxville, TN.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Boylard, J., Ehlert, K., Brennan, J., Graham, C., Parsons, I., and Menendez III, H.M. 2024. The Impact of Long-Term Stocking Rates on Soil Moisture Content and Drought Resilience. Society for Range Management  Reno, NV.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Parsons, I.L., Brennan, J.R., Menendez III, H.M., Vandermark, L.R., Moreno, E., Ehlert, K., and Dotts, H. 2024. Cue Frequency and Animal Behavioral response to changing virtual boundaries in extensive grazing systems. Society for Range Management  Reno, NV.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Parsons, I.L., Menendez III, H.M., Vandermark, L.R., McFadden, J.R., Dagel, A.K., Ehlert, K., and Brennan, J.R. 2023. Precision Livestock Technologies to Measure Real-Time Drinking Behavior, Body Mass, and Growth in Steers Managed Using Virtual Fencing Technology in Extensive Pastures. American Society of Animal Scientists annual meeting  Albuquerque, NM.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Parsons, I.L., Menendez III, H.M., Ehlert, K., and Brennan, J.R. 2023. Precision Beef Dry Matter Intake Estimation on Extensive Rangelands. American Society of Animal Scientists annual meeting  Albuquerque, NM.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Jameson Brennan, Krista Ehlert, Alan Joshua Leffler, Hector Menendez, Hossein Moradi Rekabdarkolaee, Zach Smith. 2024. Integrating precision technology, machine learning, and animal nutrition models to inform grazing rotations in South Dakota.Society for Rangeland Management, Reno NV.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Brennan, J.R., Menendez III, H., and Ehlert, K. 2023. Integrating Multiple Data Streams and Models to Inform Precision Grazing Management in the Western United States. European Federation of Animal Science  Lyon, France. *Oral
  • Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Brennan, J.R., Antaya, A., and Menendez III, H.M. 2023. Virtual Fence Technology: From Raw Data Messages to Animal Energetics Models. Iowa State University of Science and Technology (ISU) for the Agricultural Genome to Phenome Initiative (AG2PI). INVITED**Oral
  • Type: Conference Papers and Presentations Status: Other Year Published: 2023 Citation: Moreno, E.V., Menendez III, H.M., McFadden, L.J., Parsons, I., Vandermark, L., Brennan, J.R., Ehlert, K, Blair, A., and Olson, K. 2023. Precision Ranching: Application of Emerging Technology to Improve Range Management and Optimize Cattle Performance. National Animal Nutrition Program, Washington, D.C. April, 12, 2023. 1 of 6 in the nation to receive poster slot and scholarship. *Poster


Progress 02/01/22 to 01/31/23

Outputs
Target Audience:Our target audiences include researchers, Extension agents, governmental agencies (USDA, Forest Service, NRCS, BLM), agriculture producers, and the general public. Specific producers include beef cattle ranchers in the cow-calf, stocker/backgrounding, and feedlot phases. Researchers, Extension, and agency personnel include those applying precision livestock technology. We hosted a Precision Field Day at the South Dakota State University Cottonwood Field Station (near Phillip, SD). We presented a Hands-on workshop on integrating real-time precision livestock data into dynamic models using R. Modeling Nutrient Digestion and Utilization in Farm Animals (MODNUT) Conference. September 21, 2022. Sardina, Italy. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training activities: Through our research and Extension activities we were able to provide training on virtual fencing, precision weighing, and enteric emissions measurements to Logan Vandermark, Lily McFadden, and Anna Dagel (M.S. students), producers, and agencies employees (USDA-NRCS). Students have also learned the basics of mathematical modeling using precision livestock data. Professional development:Activities included student (Logan Vandermark, Lily McFadden, and Anna Dagel) knowledge gain on how to present precision livestock technology to broad audiences (industry, producers, consumers). How have the results been disseminated to communities of interest?Although we have only completed our warm-up year we have disseminated information about our activities through SDSU Extension programming efforts, specifically the Precision Field Day. At the field day participants saw or handled the precision technologies and some live demonstrations. The general public, who are not normally aware of these research activities were able to gain knowledge about what precision livestock technology is and its intended purpose for ranching, ultimately translating to beef products purchased by consumers. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. For objective 1 we plan to implement precision livestock technologies in early April on the yearling steers to enhance livestock adoption of technologies. We will then conduct the field trial using about 135 steers and rangeland measuring methods. We will also document costs to acquire the information needed for the economic analysis of precision verses continuous grazing. Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence). For objective 2 we plan to collect forage samples throughout the growing season and separate them by species. These samples will also undergo nutrient analyses. Global position system (GPS) points and polygons (spatial areas) will be documented to link forage samples to specific spatial locations to train remote sensing algorithms. Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices. During the next year, we will host a Precision Animal Learning (PAL) meeting for the 12 producers we identify as PAL members. The PAL meeting will include an overview of the grant, demonstration of the precision livestock technology, and a roundtable discussion of how the research outcomes of the grant can best be disseminated for producers.

Impacts
What was accomplished under these goals? Objective 1: Implement a precision grazing system and determine the impact on animal efficiency, forage production, the environment, and economics compared to a continuous grazing system. 20% Accomplished The first year of the grant was a planning year. We have had regular project meetings, recruited two Ph.D. students (one in Animal Science and the other is in Natural Resource Management). We have also implemented precision grazing using virtual fencing technology, precision weighing, and enteric emissions measurements on our long-term pastures in a warm-up year. This allowed our team to evaluate technology implementation successes and challenges to more effectively capture individual animal data in the 2023, 2024, and 2025 trial years. We have also collected financial information about each precision technology. Impact Statement: The warm-up year has allowed us to share knowledge gained through research and Extension programming. This has a two-fold impact. 1) We have been able to share knowledge with researchers who are currently or plan to utilize precision livestock technology on extensive rangeland systems. 2) We have been able to disseminate this information to producers or agencies seeking to deploy similar precision livestock technologies on extensive rangeland systems. Objective 2: Develop and validate a remote sensing (satellite imagery) algorithm to optimally inform the precision animal and land management tool (virtual fence). 5% Accomplished. We were able to recruit a Ph.D. student who will conduct this objective. We have gathered some additional preliminary data (e.g., forage and soil) on the study site and discussed the specific research plan to be implemented in the summers of 2023, 2024, and 2025. Impact Statement: Developing remote sensing tools to inform precision livestock management on extensive rangeland beef systems has a huge potential to accelerate remote sensing and grazing research. Further, turnkey products that can be shared with livestock producers through SDSU Extension will facilitate rancher use and adoption to improve grazing management decisions. Objective 3: Conduct Extension and outreach programs to inform producers about PG systems and technologies and communicate to consumers about environmentally sustainable practices. 5% Accomplished. We have had several Extension team planning meetings to develop a recruitment plan for the Precision Animal Learning (PAL) advisory group. We are in the beginning stages of reaching out to potential PAL members. In addition, we hosted a Precision Ranching Field Day in August 2022 at the SDSU Cottonwood Field Station (Cottonwood, SD). This field day demonstrated different precision livestock technologies such as precision supplementation, feeding, weighing, and virtual fencing. These activities included a field tour, lunch, and afternoon seminar series. The seminar series included more detail on precision equipment costs, setup, maintenance, data use, and implementation. Impact Statement:There were 35 direct contacts at the Precision Ranching Field Day. We conducted pre and post surveys. Evaluation data demonstrated that 85% of participants, who took the survey, increased their knowledge of precision livestock technology, and the majority of participants said they were "very interested" in potentially adopting precision livestock technology when it becomes more cost-effective for producers.

Publications

  • Type: Other Status: Published Year Published: 2022 Citation: Menendez III, H.M., Brennan, J.R., Gaillard, C., Ehlert, K., Quintana, J., Neethirajan, S., ... and Tedeschi, L.O. 2022. ASASNANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production. Journal of Animal Science. 100(6), skac160. BOARD INVITED REVIEW
  • Type: Conference Papers and Presentations Status: Other Year Published: 2022 Citation: Menendez III, H.M., Brennan, J.R., Dagel, A., Atzori, A.S., and Turner, B.L. 2022. Integrating real-time precision livestock data into dynamic models using R. Modeling Nutrient Digestion and Utilization in Farm Animals (MODNUT) Conference. September 21. Sardina, Italy.