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Can precision livestock farming optimize farm labor?Can precision livestock farming optimize farm labor?

Technologies can help farmers do a better job of managing their workforce and improving their herds.

February 16, 2021

5 Min Read
Lot of Holstein Cow eating
ADOPTING TECH: Recent advances in cost, quality of equipment, machine learning and cloud-based systems, and the fact that skilled farm labor is becoming harder to find, all are factors generating additional interest from livestock producers. BulentBARIS/Getty Images

Precision Livestock Farming technologies, which employ sensors to help manage farm operations, are at an early stage of adoption in livestock production. Recent advances in cost, quality of equipment, machine learning and cloud-based systems, and the fact that skilled farm labor is becoming harder to find, all are factors generating additional interest from livestock producers.

One feature most PLF capabilities share is the ability to operate continually without regular human intervention. This allows farm staff to monitor individual animals and focus on activities such as administering treatments or assisting in maternity instead of routine tasks like pen watching.

Reviewed in this article are various PLF technologies and their perceived impact on livestock health and farm labor for dairy and swine farms.

Automatic feeding systems

Feeding animals optimally to achieve their genetic potential, along with labor, are the costliest daily inputs on farms. Automatic calf feeding systems exemplify an established PLF technology. AFS seamlessly allows producers to maximize animal performance and fine-tune individualized feeding plans for calves as they develop and grow.

Many auto-feeders are equipped with scales that allow the operator to track a calf’s growth. This technology also allows a proactive approach to disease identification and prevention.

A Minnesota study showed that sick calves change their meal intervals and drinking speed, among other feeding behaviors, before they show clinical signs. A Michigan farmer using this technology noted that it helps identify calves that might need extra attention sooner, preventing small issues from significantly affecting the calf program.

Implementing AFS, for example, might alleviate the need for two hours of time to wash and sanitize buckets, and redirect employees’ time to added value procedures such as processing born calves in a timely way or observing calves more closely. 

Other PLF technology that increases the observation of susceptibility, or early stages of disease, include sensors to track rumination activity using sounds or rumen motion.

It has been shown that changes in rumination time or patterns can identify animals that are decreasing intake or showing other physiological changes. In a recent study, cows with metritis were identified by the aid of sensors measuring rumination.

Lameness identification

Lameness has a negative effect on animal performance and is a major welfare concern for both the swine and dairy industries. Identifying lame animals accurately and early requires both trained labor and facilities that provide a clear view for scoring.

However, when using PLF sensors, a computer can be trained to recognize, with high reliability, the gait patterns of livestock experiencing lameness. To do this, computers receiving video-images collected over time, quantify movement patterns and identify abnormal movements that reflect potential lameness. This allows employees to spend less time scoring animal movement and prioritize time to treat affected animals.

In dairy studies, multiple research groups are exploring the reliability of accelerometers, barn cameras, pressure plates or a combination of these technologies. Some automatic milking systems use a balance system to measure leg load of a cow while she is standing in the milking robot unit, then analyze the data to identify animals showing signs of potential lameness.

In the swine industry, features such as step length, speed of motion, standing time, stride length, and signs of tenderness are tracked using PLF sensors such as computers, cameras and accelerometers. This feature information can be coupled with benchmarking data from reproductive performance, or to tracking animal location to direct employees to make informed decisions about treatment or culling.

Estrus detection

Another area where PLF technologies can affect labor input is estrus detection. Automating this process could save operation labor costs by decreasing the time it takes to complete this task in both swine and dairy herds. For large group gestation systems, this can supplement a breeding technician’s observations.

Companies such as Ro-Main use sensors to capture sow behaviors associated with estrus, such as time standing in the presence of a boar to predict the best timing for insemination. Research has shown that when this technology was complemented by worker supervision, farrowing rate improved.

In dairy cattle, PLF sensors — including activity monitors, pressure sensors, camera systems and lasers —have been used to measure walking, mounting and standing activity to determine estrus behavior.

These approaches show promise as they not only reduce investment of employees’ time but also are noninvasive processes that reduce animal handling. Additional cattle and swine data will improve computer learning and accuracy of detection while reducing labor input costs required for heat checks and daily tail marking. 

Direct human attention

Most experts in the field of PLF technology agree that machines, regardless of how sophisticated, will not replace managers or skilled animal handlers on farms. Instead, they will help farmers do a better job managing their workforce and improving their herds — in many cases by identifying small problems before they grow into big ones.

Improved returns on investment in PLF technologies will likely track its benefits to nutrition, air quality and disease, while also addressing demands of an increasingly health-conscious consumer.

These benefits will come largely from the ability of these technologies to detect problems and direct human attention to them faster and more efficiently.

A key cost (which is also a benefit), in addition to equipment, is management and staff training in PLF data management. On many farms, that training process is already underway in response to automated feed, water, HVAC and biosecurity systems. 

Here are some factors driving interest and demand for PLF:

  • increasing global demand for animal protein

  • labor shortage (managers, trained animal handlers)

  • need for lower input costs to maintain farm profits

  • expanding farmer awareness of PLF and its benefits

  • public demand to lower antibiotic use in food animals

  • public demand to improve animal welfare

  • public demand to lower carbon footprint of livestock

  • public demand for greater transparency in food production

  • advances in sensor and cloud-based technologies

Mangual is an MSU Extension dairy educator. He can be contacted at [email protected] or 616-994-4581. Benjamin is an MSU assistant professor and swine Extension veterinarian. She can be reached at [email protected] or 517-614-8875. Thompson is an MSU Extension swine management educator. He can be contacted at [email protected] or 269-832-8403 (cell) and 517-279-6414 (office).

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