Precision livestock farming

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Precision livestock farming (PLF) is a set of electronic tools for managing livestock. It involves automated monitoring of animals to improve their production/reproduction, health and welfare, and impact on the environment. PLF tracks large animals, such as cows, "per animal"; however, it tracks animals like poultry "per flock". The whole flock in a house is tracked as one animal, especially in broilers.

PLF technologies include cameras, microphones, and other sensors for tracking livestock, as well as computer software. The results can be quantitative, qualitative and/or addressing sustainability.

Goals[edit]

PLF involves the monitoring of each individual animal, or the use of objective measurements on the animals, using signal analysis algorithms and statistical analysis. These techniques are applied in part with the goal of regaining an advantage of older, smaller-scale farming, namely detailed knowledge of individual animals. Before large farms became the norm, most farmers knew each of their animals by name. Moreover, a farmer could typically point out who the parents were and sum up other important characteristics. Each animal was approached as an individual. In the past three decades, farms have multiplied in scale, with highly automated processes for feeding and other tasks. Consequently, farmers currently are forced to work with many more animals to make their living out of livestock farming and work with average values per group. Variety has become an impediment to increasing economies of scale.

Using information technology, farmers can record numerous attributes of each animal, such as pedigree, age, reproduction, growth, health, feed conversion, killing out percentage (carcass weight as percentage of its live weight) and meat quality. Animal welfare, infection, aggression, weight, feed and water intake are variables that today can be monitored by PLF. Culling can now be done on the basis of reproduction values, plus killing out percentage, plus meat quality, plus health. The result is significantly higher reproduction outcomes, with each newborn also contributing to a higher meat value.

In addition to these economic goals, precision livestock farming supports societal goals: food of high quality and general safety, animal farming that is efficient but also sustainable, animal health and well-being, and a small ecological footprint of livestock production.[1]

Economic livestock farming[edit]

Due to academic studies, the requirements of an animal are well known for each phase of its life and individual physical demands[citation needed]. These requirements allow the precise preparation of an optimal feed to support the animal. The requirements are oriented on the required nutrition – providing more nutrition than required make no economic sense, but providing less nutrients can be negative to the health of the animal.[2]

Quality and safety[edit]

Economic goals are an important factor in livestock farming, but not the only one. Legal bodies (such as the government and industrial bodies) set quality standards that are legally binding to any livestock producing company. In addition, societal standards are followed.[3]

'Quality' in this context includes:

  • the quality of used ingredients
  • the quality of animal keeping
  • the quality of the processes

One example for issues with quality of ingredients is the (nowadays often illegal) use of meat and bone meal for ruminant animals.

Ecological livestock farming[edit]

Selecting the "right" ingredients can have a positive effect on the environment pollution. It has been shown that optimizing the feed this can reduce nitrogen and phosphorus found in the excrements of pigs.[4]

Tools[edit]

PLF starts with consistently collecting information about each animal. For this, there are several technologies: unique ID, electronic wearables to identify illness and other issues, software, cameras, etc.

Each animal requires a unique number (typically by means of an ear tag). This can be utilized through a visual ID, passive electronic ID tag or even an active electronic ID tag. For example, at birth, the farmer selects "Birth" from the menu on the reader, after which the interactive screen requests the user to read the tag of the mother. Next, tags are inserted in the ears of newborns and read. With this simple action, important information is recorded, such as:

  • who is the mother
  • how many siblings did she deliver
  • what is the gender of each sibling
  • what is the date of birth

Electronic wearables such as an active smart ear tag can get data from individual animals such as temperature and activity patterns. This data can be utilized in identification of illness, heat stress, oestrous, etc. This enables individualized care for the animals and methods to lower stress upon them. The end result is judicious use of drug treatments and nutrition to bolster healthy growth. This provides livestock producers with the tools to identify sick animals sooner and more accurately. This early detection leads to reduction in costs by lowering re-treatment rate and death loss, and getting animals back to peak performance faster.

Data recorded by the farmer or collected by sensors is then gathered by software. Although there has been software used that was run on a single computer, it has become more common for the software to connect to the internet, so that much of the data processing can happen on a remote server. Having the software connected to the internet can also make it easier to look up information about a particular animal. Due to high computational requirements, PLF requires computer-supported tools. The following types (available for PCs and via Internet) are available:

  • Induction/processing software applications (a necessity for use with electronic active ID tags)
  • Automated livestock administration software
  • Reproduction optimization software
  • Feed formulation software
  • Quality management software

Examples in different industries[edit]

Dairy Industry[edit]

Robotic Milkers[edit]

Automatic cattle feeder[5]

In Automatic milking, a robotic milker can be used for precision management of dairy cattle. The main advantages are time savings, greater production, a record of valuable information, and diversion of abnormal milk. There are many brands of robots available including Lely, DeLavel.

Automatic Feeders[edit]

An automatic feeder is a tool used to automatically provide feed to cattle. It is composed of a robot (either on a rail system or self-propelled) that will feed the cattle at designated times. The robot mixes the feed ration and will deliver the correct amount.

Activity Collars[edit]

Activity collars are like fitbits for cows. Some wearable devices help farmers with estrous detection as well as other adverse health events or conditions.

Inline Milk Sensors[edit]

Inline milk sensors help farmers identify variation of components in the milk. Some sensors are relatively simple technologies that measure properties like electrical conductivity. Other devices use automated sampling and reagents to provide a different measure to inform management decisions.

Meat Industry[edit]

EID / RFID / Electronic Identification / Electronic Ear Tags[edit]

Radio Frequency ID (commonly known as RFID or EID) is applied in cattle, pigs, sheep, goats, deer and other types of livestock for individual identification. In more and more countries, RFID or EID is mandatory for certain species. For example, Australia has made EID compulsory for cattle, as has New Zealand for deer, and the EU for sheep and goats. EID makes identification of individual animals much less error-prone. This enhances traceability, but it also provides other benefits such as reproduction tracking (pedigree, progeny and productivity), automatic weighing and drafting.

Smart Ear Tags[edit]

Cattle hide their symptoms of illness from humans due to their predatory response. The result is that illness is detected late and not very accurately utilizing conventional methods.[citation needed] Smart cattle ear tags get behavioural and biometric data from cattle 24 hours a day/7 days a week allowing managers to see the exact animals that need more attention regarding their health. This is effective in identifying illness earlier and more accurately than visual observation allows.

Swine Industry[edit]

There are many tools available to closely monitor animals in the swine industry. Size is an important factor in swine production.

Automated Weight Detection Cameras[edit]

Automated weight detection cameras can be used to calculate the pig's weight without a scale.[6] These cameras can have an accuracy of less than 1.5 kilograms.[6]

Microphones to Detect Respiratory Problems[edit]

In the swine industry, the presence of respiratory problems must be closely monitored. There are multiple pathogens that can cause infection, however, enzootic pneumonia is one of the most common respiratory diseases in pigs caused by Mycoplasma hyopneumoniae and other bacteria.[7] This is an airborne disease that can be easily spread due to the proximity of the pigs in the herd. Early detection is important in using fewer antibiotics and minimising economic loss due to appetite loss of pigs.[6] A common symptom of this is chronic coughing.[8] A microphone can be used to detect the sound of coughing in the herd and raise an alert to the farmer.

Climate Control[edit]

Thermal stress is connected to reduced performance, illness, and mortality.[9] Depending on geographical location, and the types of animals will require different heating or ventilation systems. Broilers, laying hens, and piglets like to be kept warm.[10] Sensors can be used to constantly receive data about the climate control in the livestock houses and the automatic feeding systems. The behaviour of animals can also be monitored.[11]

Poultry Industry[edit]

In the poultry industry, unfavourable climate conditions increase the chances of behavioural, respiratory, and digestive disorders in the birds.[12] Thermometers should be used to ensure proper temperatures, and animals should be closely monitored for signs of unsatisfactory climate.[12]

Quantitative Methods, towards scientifically based management of livestock farming[edit]

The development of quantitative methods for livestock production includes mathematical modelling based in plant-herbivore or predator-prey models to forecast and optimise meet production. An example is the Predator-Prey Grassland Livestock Model (PPGL)[13] to address the dynamics of the combined grass-animals system as a predator-prey dynamical system. This PPGL model has been used to simulate the effect of forage deficiency on the farm's economic performance.[14]

References[edit]

  1. ^ Daniel Berckmans: Automatic On-Line Monitoring of Animals by Precision Livestock International Society for Animal Hygiène - Saint-Malo - 2004
  2. ^ Gene M. Pesti, Bill R. Miller: Animal feed formulation: economics and computer applications Springer, 1993 - ISBN 978-0-442-01335-6
  3. ^ Frank T. Jones: Quality Control in Feed Manufacturing Feedstuffs Reference Issue and Buyers - 2001
  4. ^ Mark S. Honeyman: Environment-friendly swine feed formulation to reduce nitrogen and phosphorus excretion American Journal of Alternative Agriculture - Volume 8, pp. 128-132 - 1993
  5. ^ "File:Automatic cattle feeder - geograph.org.uk - 428330.jpg", Wikipedia, retrieved 2019-04-02
  6. ^ a b c Vranken, E (2017). "Precision livestock farming for pigs". Animal Frontiers. 7 (1): 32–37. doi:10.2527/af.2017.0106.
  7. ^ Luehrs; Siegenthaler; Grützner; Grosse; Beilage; Kuhnert; Nathues (2017). "The occurrence of Mycoplasma hyorhinis infections in fattening pigs and association with clinical signs and pathological lesions of Enzootic Pneumonia". Veterinary Microbiology. 203: 1–5. doi:10.1016/j.vetmic.2017.02.001. PMID 28619130.
  8. ^ "Farm Health Online – Animal Health and Welfare Knowledge Hub – Respiratory Disease in Pigs". Retrieved 2019-03-24.
  9. ^ Fournal, S.; Rosseau, A.; Laberge, B. (2017). "Rethinking environment control strategy of confined animal housing systems through precision livestock farming". Biosystems Engineering. 155: 96–123. doi:10.1016/j.biosystemseng.2016.12.005.
  10. ^ Costantino, Fabrizio; Ghiggini; Bariani (2018). "Climate control in broiler houses: A thermal model for the calculation of energy use and indoor environmental conditions". Energy & Buildings. 169: 110–126. doi:10.1016/j.enbuild.2018.03.056. S2CID 115755562.
  11. ^ "Smart farming: a revolutionary system by Fancom for farmers". Fancom BV. Retrieved 2020-04-10.
  12. ^ a b "Climate in poultry houses". Poultry Hub. Retrieved 2019-03-27.
  13. ^ Dieguez, F., Fort, H (2017). "Towards scientifically based management of extensive livestock farming in terms of ecological predator-prey modeling". Agricultural Systems. 153: 127–137. doi:10.1016/j.agsy.2017.01.021. ISSN 0308-521X.
  14. ^ Dieguez, Francisco; Fort, Hugo (2019). "An application of a dynamical model with ecological predator-prey approach to extensive livestock farming in uruguay: Economical assessment on forage deficiency". Journal of Dynamics & Games. 6 (2): 119. doi:10.3934/jdg.2019009.