Precision Livestock Farming in Pasture-Based Dairy Systems: Monitoring Grazing Behavior

Gadzama, I.U.ᵃ,* and Ray, Sᵇ

ᵃDepartment of Primary Industries and Regional Development, WA 6743 Australia 

ᵇOlam Orchards Australia Pty Ltd, Mildura, VIC 3549 Australia

Corresponding author: [email protected]

Introduction

Pasture-based dairy systems are becoming more common worldwide, and precision dairy technologies can greatly benefit grazing dairy producers. While most research on these technologies has focused on confinement systems (Pereira et al., 2020; Heins et al., 2023), they are also valuable for pasture-based systems. 

Fresh pasture is a major part of dairy cow diets in temperate regions. Dairy cows spend over 50% of their time grazing and ruminating (Figure 1), which requires significant time and energy (Pollock et al., 2022). However, achieving high dry matter intake is challenging, making it hard to meet the nutritional needs of high-production cows (Pollock et al., 2022). Factors like pasture quality, cow breed, and pasture quantity influence an animal’s ability to meet its nutritional requirements (Pollock et al., 2022). 

Figure 1. Cows grazing in a pasture-based dairy system

Animal Performance is Influenced By:

• Genetics
• Diet
• Reproductive status
• Health
• Overall farm management

Why Precision Livestock Farming (PLF)

Direct observation of cow behavior is labor-intensive and error-prone (Penning, 1983). Precision Livestock Farming (PLF) technologies are automated, non-invasive methods essential for monitoring individual cow behavior in large herds, improving overall herd performance (Figure 2). These tools provide real-time data on cow behavior, milk production, temperature, estrus, and disease prediction, helping farmers enhance animal welfare, feed intake, health, reproduction, and overall farm management (Werner et al., 2018; Pereira et al., 2020; Schmelinga et al., 2021; Boschma, 2024). 

 PLF tools can monitor behaviors like eating, ruminating, calving, lying, walking, and urinating. While effective in confined systems, their accuracy in outdoor grazing systems needs more validation. Monitoring an individual animal's feeding behavior, health, and welfare, especially in pasture-based systems, is time-consuming. PLF technologies can help dairy farmers monitor cow behavior, such as predicting feeding and rumination patterns and detecting estrus, health issues, and activity levels (Schmelinga et al., 2021; Boschma, 2024). 

Cow monitoring systems provide valuable data that could help farmers make informed decisions, improve farm efficiency, save labor, and offer peace of mind (Figure 2; Boschma, 2024). For example, a CowManager ear sensor system can immediately alert farmers when an animal is in heat or sick and allows easy handling of ear tags and data sharing (Boschma, 2024).

Schmelinga et al. (2021) tested a collar-based prototype with a 3D accelerometer and gyroscope on 11 cows across three farms. Using camera data to train the model, they achieved 97.4% accuracy in detecting rumination. The system accurately predicted 97.1% of rumination bouts and worked well both on pasture and in stables.

Iqbal et al. (2021) validated a behavior monitoring collar (AfiCollar device) for grazing dairy cows in New Zealand. The collar effectively monitored cow behavior, aiding in better farm management and improved dairy cow performance.

Pereira et al. (2020) showed that ear-attached accelerometers (Smartbow GmbH) accurately tracked grazing behavior in pasture-based systems. Although the algorithm isn’t commercially available yet, it shows great promise for helping farmers manage their dairy herds more effectively. Dairy farmers could use PLF technologies to manage their herds better, ensuring efficient feeding, improved health, and overall better performance.

Figure 2. An example of a cow monitoring sensor (RumiWatch noseband)

Adapted from Zehner et al. (2017)

Factors Influencing Grazing Behaviors in Dairy Cows

• Feed availability
• Periods of deprivation
• Feed delivery frequency
• Lactation stage
• Parity (number of births)
• Milk output

Pasture allocation frequency (PAF) in rotational grazing can impact hunger, feed availability, and competition, altering grazing and ruminating patterns and nutrient intake (Pollock et al., 2022).

In commercial herds, cows with different milk yields and lactation stages graze together, leading to competition and variability in intake rates and pasture quality. Primiparous cows (first-time mothers) often face high competition and are considered subordinate due to their lower weight and fewer lactations (Phillips et al., 2002; Hussein et al., 2016). In indoor systems, where competition is intense, cows change their feeding times, eating less after fresh feed delivery to avoid aggression but end up consuming lower-quality feed (DeVries et al., 2004).

Benefits of Monitoring Dairy Cow Grazing Behavior

• Improved Efficiency: Investigating variations in cow behavior throughout lactation can enhance farm efficiency and help select more efficient animals.
• Health Monitoring: Daily grazing and rumination times reveal eating patterns and health issues. Lower grazing time may indicate a cow is unhealthy or lame. Consistent changes in rumination time can signal health problems.
• Herd Management: Identifying cows with high grazing time but low productivity, or vice versa, helps develop a more efficient herd.
• Feed Management: Monitoring seasonal grazing patterns helps manage feed resources and prevent shortages.
• Estrus Detection: Monitoring behavior during estrus improves detection and conception rates.
• Overall Welfare: Monitoring behavior improves cow welfare and quality of life, contributing to better overall farm efficiency.

How Grazing Behavior Affects Energy Expenditure in Dairy Cows

Grazing behavior significantly impacts the energy expenditure and nutrient intake of dairy cows, which in turn affects milk production. Lactating cows can spend up to 15 hours a day eating and ruminating, which requires a lot of energy (Abrahamse et al., 2008; Vance et al., 2012). For instance, eating and ruminating cost 30 and 9 J/min/kg of body weight, respectively (Susenbeth et al., 1998). Due to increased physical activity, grazing animals need 25-50% more energy than housed animals (Osuji, 1974). Increased competition for resources can also lead to more grazing and rumination by younger cows (primiparous) compared to older cows (multiparous), indicating higher energy expenditure (Pollock et al., 2022). Additionally, grazing on soft or waterlogged ground increases energy expenditure compared to firm ground (Pollock et al., 2022). Understanding these variations can help develop efficient feeding strategies and improve cow performance. 

Grazing and ruminating are essential for nutrient capture and animal performance but require significant time and energy. For example, cows chew more during rumination with high pasture allowance (Dale et al., 2018) and feed longer right after fresh feed delivery (DeVries et al., 2005). In addition, cows show diurnal feeding patterns when offered fresh pasture every 12, 24, or 36 hours, grazing mostly during the day (90%) and ruminating at night (73%) (Pollock et al., 2022). Farmers can improve feeding efficiency and overall cow performance by understanding and managing these behaviors.

Why Understanding Animal Behavior is Essential for Farm Management

Understanding animal behavior is crucial for effective management and improving the quality of life for livestock. Here are the key reasons:

• Identifying and Treating Sick Animals: Monitoring changes in behavior can help detect illnesses early, allowing for timely treatment.
• Selecting Better Breeders: Observing behavior helps choose animals with desirable breeding traits.
• Designing Appropriate Housing: Knowledge of animal behavior aids in creating housing that meets their needs and reduces stress.
• Handling Herds with Minimal Stress: Understanding behavior ensures that animals are handled in ways that minimize stress and improve welfare.

In grazing-based systems, two behaviors are particularly important:

• Grazing Behavior: This indicates grass intake and eating patterns, which are influenced by factors such as grass type, weather, and social status within the herd.
• Rumination Behavior: This reflects digestive efficiency, fiber intake, and overall health, varying with the quality and type of grass.

Challenges in the Use of Precision Technologies 

• Variability in Pasture Quality: Pasture quality can vary significantly due to factors like weather, soil fertility, and plant species composition, making it challenging to maintain consistent feed intake.
• Cost and Accessibility: Advanced measurement tools and software can be expensive, which may limit their use among smaller farms.
• Data Integration: Combining data from different sensors and systems into a cohesive and usable format can be challenging.
• Implementation Cost: Implementing these technologies can be expensive, which may be a barrier for some farmers.

Conclusion

Understanding cow behavior is crucial for enhancing dairy farm efficiency and productivity. Technologies like motion sensors, vision analysis, and machine learning are revolutionizing how we monitor and understand grazing behavior in dairy cows. These tools allow farmers to monitor behaviors such as grazing and rumination, providing valuable information for making informed decisions about feed management, production efficiency, health interventions, and overall herd management. Ultimately, this leads to improved animal welfare and performance in pasture-based dairy systems.

Further Research

More research is needed to validate PLF tools to ensure they accurately monitor dairy cow behavior. It's also important to study how factors like breed, lactation number, seasonal patterns, and lactation stage affect grazing time. This understanding will help improve farm management and optimize the use of PLF technologies.

References

• Abrahamse, P. A., Dijkstra, J., Vlaeminck, B., & Tamminga, S. (2008). Frequent allocation of rotationally grazed dairy cows changes grazing behavior and improves productivity. Journal of Dairy Science, 91(5), 2033-2045.
• Boschma, C. (2024). The impact of plug-and-play on pasture-based farms. Dairy News Australia. Retrieved from https://www.dairynewsaustralia.com.au/news/the-impact-of-plug-and-play-on-pasture-based-farms/
• Dale, A. J., Laidlaw, A. S., McGettrick, S., Gordon, A., & Ferris, C. P. (2018). The effect of grazing intensity on the performance of high‐yielding dairy cows. Grass and forage science, 73(3), 798-810. 
• DeVries, T. J., Von Keyserlingk, M. A. G., & Weary, D. M. (2004). Effect of feeding space on the inter-cow distance, aggression, and feeding behavior of free-stall housed lactating dairy cows. Journal of Dairy Science, 87(5), 1432-1438.
• DeVries, T. J., Von Keyserlingk, M. A. G., & Beauchemin, K. A. (2005). Frequency of feed delivery affects the behavior of lactating dairy cows. Journal of Dairy Science, 88(10), 3553-3562.
• Heins, B. J., Pereira, G. M., & Sharpe, K. T. (2023). Precision technologies to improve dairy grazing systems. JDS communications, 4(4), 318-323.
• Hussein, A. N., Al-Marashdeh, O., Bryant, R. H., & Edwards, G. (2016). Relationship between social dominance and milk production of dairy cows grazing pasture. New Zealand Society of Animal Production.
• Iqbal, M. W., Draganova, I., Morel, P., & Morris, S. (2021). Can cow behaviour influence performance? The Journal, 24-28. Retrieved from https://www.afimilk.com/app/uploads/PLF-Massey.pdf
• Osuji, P. (1974). The physiology of eating and the energy expenditure of the ruminant at pasture. Rangeland Ecology & Management, 27(6), 437–443.
• Penning, P. D. (1983). A technique to record automatically some aspects of grazing and ruminating behaviour in sheep. Grass and Forage Science, 38(2), 89-96.
• Pereira, G. M., Heins, B. J., O'Brien, B., McDonagh, A., Lidauer, L., & Kickinger, F. (2020). Validation of an ear tag–based accelerometer system for detecting grazing behavior of dairy cows. Journal of Dairy Science, 103(4), 3529-3544.
• Phillips, C. J. C., & Rind, M. I. (2002). The effects of social dominance on the production and behavior of grazing dairy cows offered forage supplements. Journal of Dairy Science, 85(1), 51-59.
• Pollock, J. G., Gordon, A. W., Huson, K. M., & McConnell, D. A. (2022). The effect of frequency of fresh pasture allocation on the feeding behaviour of high production dairy cows. Animals, 12(3), 243.
• Schmelinga, L., Elmamoozc, G., Nicklasc, D., Thurnera, S., & Rauchb, E. (2021). Evaluation of a sensor-based system for monitoring rumination in dairy cows with access to pasture. Agricultural Engineering AgEng2021, 543.
• Susenbeth, A., Mayer, R., Koehler, B., & Neumann, O. (1998). Energy requirement for eating in cattle. Journal of Animal Science, 76(10), 2701-2705.
• Vance, E. R., Ferris, C. P., Elliott, C. T., & Kilpatrick, D. J. (2012). A comparison of the feeding and grazing behaviour of primiparous Holstein-Friesian and Jersey× Holstein-Friesian dairy cows. Irish Journal of Agricultural and Food Research, 45-61.
• Werner, J., Leso, L., Umstatter, C., Niederhauser, J., Kennedy, E., Geoghegan, A., ... & O’Brien, B. (2018). Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows. Journal of neuroscience methods, 300, 138-146.
• Zehner, N., Umstätter, C., Niederhauser, J. J., & Schick, M. (2017). System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows. Computers and Electronics in Agriculture, 136, 31-41.