Progress report for LNC23-492
Project Information
Cover crops have been gaining popularity as a practice implemented by producers in the Northern Great Plains to improve soil health, increase soil nutrients and soil microbial populations, reduce variability in crop yields, increase crop yields, reduce soil erosion, and increase forage options for livestock. In recent years, producers have expressed an increased interest in integrated crop livestock systems (ICLS) and best management practices for grazing these systems. Strip grazing is one of the best management practices being promoted to improve harvest efficiency and nutrient distribution. However, there is no research showing this practice improves harvest efficiency and soil health in ICLS. In addition, this practice requires a significant input of labor on the part of the producer. The objectives of this study are to evaluate strip grazing and grazing technologies on soil health, animal performance, animal behavior and economic feasibility within ICLSs. To evaluate the effects of strip grazing an annual forage will be subjected to 1) strip grazing and 2) continuous grazing. Additionally, three approaches to strip grazing will be evaluated 1) manual fence movement, 2) automated fence movement and 3) virtual fence. Project results will be disseminated through cafe talks, workshops, tours, bulletins, news articles, videos, and social media. This project will provide valuable information on the effects of different grazing management strategies and grazing technologies in ICLSs, assisting producers in making management decisions.
Study objectives are to evaluate strip grazing and grazing technologies on soil health, animal performance and behavior, and economic feasibility within ICLSs. To evaluate strip grazing an annual forage will be 1) strip grazed and 2) continuous grazed. Three approaches to strip grazing will be evaluated 1) manual fence movement, 2) automated fence movement and 3) virtual fence. This project will provide valuable information on the effects of different grazing management strategies and technologies in ICLSs, assisting producers in making management decisions. Project results will be disseminated through cafe talks, workshops, tours, bulletins, news articles, videos, and social media.
Virtual fencing (VF) is a novel wireless technology used for managing the movement of grazing livestock. With VF technologies, a GPS receiver fitted to livestock, typically as a collar, communicates the position of the animal through long range wide area network (LoRaWAN) or cellular data back to a management program on a manager’s computer or mobile device. Using this program, the manager remotely establishes borders according to his or her management goals. Auditory or electrical stimulus designed to deter further movement are administered to the animal upon approaching or entering these boundaries.
Many of the drawbacks of conventional fencing can be avoided with VF. Virtual fencing can reduce labor and material costs while providing increased adaptability and flexibility to grazing livestock management (de Avila et al., 2025; Hoag et al., 2025). The ability to quickly shift grazing boundaries allows managers to adapt to changing pasture conditions, weather events, or fires (Boyd et al., 2022). Areas sensitive to grazing disturbance can be easily managed without the need to install and remove physical fencing (Campbell et al., 2019). In addition to comparing costs of VF to the costs of building new fencing, VF technologies become more cost effective on a per use basis, by allowing more intensive grazing strategies without the need for increased labor or materials (Hoag et al., 2025). Moving to more intensive grazing strategies can further increase the value of VF by increasing forage utilization. Forage utilization is increased as intensive grazing practices reduce trampling and wasted forage (Davies-Jenkins et al., 2024). Furthermore, VF can provide animal location data, allowing near-real time monitoring and insights that provide indirect benefits to grazing systems (Antaya et al., 2025).
Much of the current research and applications of VF is used within large-area grazing systems and rangelands. The reduced labor in moving cattle with VF may lead to useful adaptations to smaller scale intensive grazing systems, such as strip grazing within cover crop systems. However, the efficacy and accuracy of VF within smaller areas is unknown. The objectives of this on-going study are: 1) determine the efficacy of VF within small-area strip grazing 2) evaluate the effects to grazing efficiency through different grazing technologies, 3) determine differences in grazing livestock distribution from grazing technologies.
Research
This demonstration project is testing the following hypotheses:
H1: Strip grazing of an annual forage will improve grazing efficiency, resulting in increased grazing days and stocking rate compared to continuous grazing.
H2: Strip grazing of an annual forage will improve animal and nutrient distribution across fields, resulting in greater benefits to soil health over continuous grazing.
H3: The use of virtual fence and automated fence movement will achieve the same level of animal containment and harvest efficiency as a manual fence.
Demonstration Design
The use of annual forages is increasing in the Northern Great Plains as a source of supplemental feed and grazing. Grazing management practices, such as strip grazing, have the potential to improve grazing efficiency and boost soil health within ICLSs. Incorporating grazing technologies may reduce labor and enhance adaptation of grazing management practices in ICLS. To evaluate the effects of strip grazing an annual forage will be subjected to 1) strip grazing and 2) continuous grazing. Additionally, three approaches to strip grazing will be evaluated 1) manual fence movement, 2) automated fence movement and 3) virtual fence. A three-year randomized block design with 4 replications across 2 states will be used to test four grazing and technology treatments: 1) continuous grazing, 2) strip graze × manual fence, 3) strip graze x automated fence and 4) strip graze × virtual fence. A non-grazed treatment will serve as the control. These treatments will be imposed for 3 years.
NDSU and Nebraska Extension will partner with four Research Extension Centers across the two states to establish demonstration sites to showcase the research to producers across the region. Demonstration sites will be established on the NDSU Main Campus, NDSU Central Grasslands Research Extension Center, NDSU Carrington Research Extension Center, and Eastern Nebraska Research and Extension Center. The project will be conducted at a field scale to provide a real-world perspective of the effects strip grazing and application of grazing technologies in ICLSs. At each location, a 16-ha block of cropland will be selected to test the four grazing treatments against the control using a randomized complete block design with each location acting as a block.
Forage Establishment
The annual forage crops selected to be grown will include a mixture of warm- and cool-season cereal grains, broadleaves and legumes. A diverse mix that includes both warm and cool season species was selected to reduce the risk associated with establishing a cover crop in semi-arid climates. The planned mix for host sites includes oats, sorghum sudangrass, sunflower, radish, turnip, and forage pea seeded at a rate of 25, 2.5, 1.5, 1.1, 0.8, and 10 kg/ha, respectively. The seed will be planted at a depth of ¾". The cover crop will be planted by mid-June at the North Dakota locations and by mid-July at the Nebraska location. This should allow all sites to accumulate a similar number of growing degree days and achieve similar forage production by September 1.
Livestock and Grazing Management
Animals assigned to the virtual fence treatment will be trained prior to the grazing period. The training period will take place inside a pen containing physical fence boundaries. The animals will be fitted with the VF collars and two conterminous sides of the pen will be assigned as virtual boundaries. For days 1 to 3 of training, the virtual perimeter will be defined by an auditory cue zone that extends 30 feet inward from the physical perimeter and an electrical stimulus zone that extends 15 feet inward from the physical perimeter. For days 4 and 5 of training, the auditory and electrical zones will be expanded by 20 to 30 feet each. Following the fifth day of training, animals will be ready to begin the study. The training period described for this experiment is published by Boyd et al. 2022 (DOI: 10.1016/j.rama.2022.01.001) using the same virtual fence technology.
Carrying capacities will be determined based on available forage production and estimated harvest efficiency of 50%. Forage production of the annual forage crop will be estimated by clipping twelve 0.25-m2 quadrats per experimental treatment and the control. Only the top portion of tuber plants will be included in the assessment because consumption of the lower portion is minimal or variable among animals. Clipping for peak biomass production will be completed during the last week of August, prior to grazing. Stocking rate will be set based on the estimated intake of yearling beef cattle with a targeted grazing period of 60 days and a minimum of 5 head per 4 ha paddock. Animals in the strip grazed treatments will be provided a new strip and the continuous grazed treatment will be removed when harvest efficiency is visually estimated 50% removal of forage. Post grazing treatments will be clipped to determine harvest efficiency.
Electric poly-wire and step-in posts will be utilized as portable cross-fence to strip graze livestock in the manual fence and automated fence movement treatments. The automated fence movement will consist of an automated gate that will be timed to open when animals are to be moved to the next strip. VF animals will be moved using the VF software to remove a fence and the audio cues and electric stimuli associated with that the VF. All grazing treatments will have an electric perimeter fence.
Soil Sampling
Soil samples will be collected to characterize physical and chemical properties. Analysis of soil physical properties include bulk density, infiltration and soil aggregate stability. Analysis of soil chemical properties include soil nutrients, pH and organic matter. Soil samples will be collected in the spring of 2024 prior to treatment implementation and following the 2026 grazing period to assess treatment effects. Soil samples will be stratified based on soil type across and within sites to the extent possible to reduce variability in the soils data.
A soil core sampler with hammer attachment will be used to measure bulk density at a depth of 0-15 cm. Samples will be weighed, then oven-dried at 105℃ for 24 hours. In calculating bulk density, the weight of the oven-dried soil will be divided by the volume of the ring to determine g/cm3. Soil infiltration will be determined by utilizing the Cornell Sprinkle Infiltrometer system (van Es and Shindelbeck, 2003). Soil aggregate stability samples will be collected with a tiling spade to a depth of 0-15 cm. A manual wet sieving method by Six et al. (1998) was used to develop an automated method for accessing aggregate stability. Due to variation in soil across locations, the sand correction procedure described by Mikha and Rice (2004) will be applied to each sample to remove the sand fraction from the water stable aggregates total.
Soil N, C, P, K, Na, pH, and organic matter will be determined from samples collected at depths of 0-15 and 15-30 cm and analyzed at AgVise Laboratories. Samples will be collected every square acre in a grid to analyze nutrient distribution among grazing treatments.
Livestock Performance
Cattle performance will be determined by measuring body weight, body condition score, and subcutaneous body fat thickness. Body weights will be taken over 2 consecutive days at the initiation and termination of the grazing portion of the study. Body condition score and subcutaneous body fat thickness will be measured at the initiation and termination of the grazing portion of the study by a trained technician using a scale of 1 to 9. Body fat thickness will be determined using ultrasonography between the 12th and 13th rib and rump fat.
Livestock Behavior
Livestock behavior will be evaluated for all treatments by combining GPS and accelerometer data to determine movement patterns and grazing activity throughout the duration of the experimental period. Prior to the start of grazing, each animal (~5 per paddock) will be fit with a GPS-enabled collar to collect location data at 5-minute intervals. A separate tri-axial accelerometer will be mounted to a halter which will be placed on the animal (3 per paddock) to collect acceleration data in the x-, y-, and z- planes at 12.5 Hz intervals. The accelerometers store data on-board and the devices will be collected at the end of the grazing study and data analyzed. Data derived from GPS coordinates will be utilized to calculate daily distance traveled and movement patterns in relation to pasture location and soil characteristics. Acceleration data will be aggregated into several time epochs to evaluate and predict resting, grazing, and walking activity through machine learning techniques.
In addition to the data derived from GPS and accelerometer technologies, the VF animals will be evaluated on their behavioral interactions with the VF boundaries. When an animal approaches the virtual fence boundary, a warning sound is given to alert the animal that they are close to moving beyond the boundary. If the animal travels beyond the boundary, a shock is given to motivate the animal to return to the virtual fence area. The virtual fence system can track these interactions. Therefore, data will be collected to determine the number of animals receiving alarm sounds, shocks, and daily total alarm durations. Fencing success rate (% of animals successfully contained within the virtual fence) will also be determined throughout the experimental period, particularly in response to virtual fence movement related to strip grazing.
Data Analysis
The demonstrations will be set-up utilizing a randomized complete block design with each location acting as a block to assess changes in soil physical properties, soil chemical properties, forage production and livestock performance among treatments. Animal behavior (time spent grazing, resting, and walking) using tracking data will be analyzed by descriptive statistics to assess changes in daily activity. Near-real-time collar data analysis (GPS location, and VF triggered audio and electric stimuli) will be aggregated daily to assess VF efficacy in animal management throughout the grazing period. The above data will be organized, processed and analyzed via various data analytic tools including Python, RStudio and SAS (version 9.4). The relationship between animal distribution over the grazing period and soil nutrient distribution will be analyzed in ArcGIS Pro.
Economic Analysis
Economic analyses will be conducted on all treatments to determine feasibility. Methodologies utilized to complete a thorough analysis of each treatment will include partial budgeting, capital budgeting and multi-criteria decision analysis (MCDA). Partial budgeting will assess the trade-offs of costs and/or revenues among the different grazing treatments and technologies and against the baseline system to determine the economic advantages and disadvantages of each of the practices. Capital budgeting, or investment analysis, will be used to assess the feasibility of investing in technology to implement new production practices. Scenario analysis will supplement the investment analysis to provide a robust picture of possible returns on investment and risk associated with differing payback periods. Finally, as acknowledged throughout this proposal, several key decision criteria may be difficult to assign a market value to and will need to be included in the treatment comparisons using modern MCDA techniques where non-market and market values can be traded off with one another in a structured decision support system.
Outreach Activities
NDSU and UNL Extension will collaborate with producer advisory boards to establish four demonstration sites. These advisory boards are already in place and are used to guide research and outreach activities at each location. In year one the team will conduct a multistate survey assessing the use of technology in grazing systems. This information will be used by the project team to direct education and outreach efforts. A series of café talks, workshops, field days and tours will provide opportunities to disseminate results to producers, landowners, local organizations and other stakeholders. These events will help build a community of practice between producers, research and technical services providers interested in ICLSs and/or grazing technologies that will result in co-learning. Other outreach efforts include, but are not limited to, journal articles, extension publications, bulletins, news articles, videos and social media. In year three the team will host a multi-day workshop focused on technology adaptation within grazing systems. The workshop will include researchers and producers in the region sharing their experiences with the use of different technologies in both ICLSs and traditional grazing systems.
Cattle containment did not differ between grazing technologies (P = 0.533), with 77.5% containment for VF, 77.4% containment within AUTO, and 81.4% containment within MAN. Containment varied by location as well as by the individual animal. Additionally, containment rates were often reduced prior to the allocation of the next grazing strip as cattle encroached upon fence lines to seek preferred forages. Escapes prior to the allocation of the new strip were greatest within AUTO. The number of VF cues increased during days when the VF boundary moved, as animals learned where the boundaries were. Audible and electrical cues increased as cattle were motivated to cross boundaries as forage quality and quantity decreased.
Forage utilization was greatest (P = 0.012) within MAN at 34% which differed from CONT at -15%. The negative utilization in CONT is likely due to the large amount of trampling waste and high variability of remaining biomass. Utilization rates within AUTO and VF did not differ from MAN nor CONT with 25% and 14% respectively. Utilization rates were lower than expected, likely due to highly mature forage reducing palatability late in the grazing period. These differences in utilization did not statistically impact stocking rates (P = 0.094). The average stocking rates across all locations were 2.6 AUM/ac in AUTO, 2.5 AUM/ac in MAN, 2.3 AUM/ac in VF and 2.2 AUM/ac in CONT. While stocking rates did not statistically differ, grazing durations differed at each location. At CGREC, cattle in CONT and VF grazed for 37 days, while AUTO and MAN paddocks grazed for 48 days. Grazing at CREC concluded early due to continued entry to restricted sections increasing trampled forage. All treatments at CREC grazed 35 days, but standing forage within non-allocated strips was greatest in the VF treatment indicating restricted access by grazing cattle. Grazing duration was greatest at BCRT with 49 days in CONT and 60 days in VF, AUTO, and MAN. Across all locations, grazing duration did not statistically vary, although intensive grazing provided an additional eleven days of grazing or up to 0.5 AUM/ac.
Virtual fencing offers an adaptable and flexible strategy for managing grazing livestock with similar efficacy to conventional polywire. Strip grazing can increase forage utilization and stocking rates, creating a more efficient grazing system. The use of VF could aid in the adoption of intensive grazing practices due to the reduced labor required to manage livestock. Additionally, the value generated from alternative VF uses can aid in reducing the cost per use of the technology, increasing the value of the investment in VF infrastructure.
Education
NDSU and UNL Extension collaborated with producer advisory boards to establish four demonstration sites. These advisory boards are already in place and are used to guide research and outreach activities at each location. In year one the team will conducted a multistate survey assessing the use of technology in grazing systems. This information will be used by the project team to direct education and outreach efforts. A series of café talks, workshops, field days and tours will provide opportunities to disseminate results to producers, landowners, local organizations and other stakeholders. These events will help build a community of practice between producers, research and technical services providers interested in ICLSs and/or grazing technologies that will result in co-learning. Other outreach efforts include, but are not limited to, journal articles, extension publications, handouts, news articles, videos and social media. In year three the team will host a multi-day workshop focused on technology adaptation within grazing systems. The workshop will include researchers and producers in the region sharing their experiences with the use of different technologies in both ICLSs and traditional grazing systems.