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Poultry Lighting: LED Bulbs Provide Energy Saving and Durability

By
Jim
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Lighting significantly impacts poultry production. Too little lighting during brooding or too much during grow-out can result in lost performance and profits. Solid side wall,tunnel-ventilated poultry barns are more energy efficient to operate but do require artificial lighting sources.The expense of lighting a poultry barn with 60-watt in can-descent bulbs can be as much as 30%-40% of the electrical operating cost($100-$250 a flock/ barn depending on barn size and flock age to market). Therefore, lighting improvements should not only be beneficial for the birds but also energy efficient to minimize production costs.

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Lameness in Poultry – How Much is it Costing Your Poultry Operation?

By
Jim
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Lameness is a growing problem in modern poultry production that decreases bird performance and operation profitability. One of the major contributors to the worldwide poultry lameness issue is Bacterial Chondronecrosis and Osteomyelitis (BCO). BCO typically affects at least 1.5 percent of heavy broilers after 30 days of age, and an epidemic outbreak can impact up to 15 percent of a flock.

What is Bacterial Chondronecrosis with Osteomyelitis (BCO)?

BCO is caused by a bacterial infection in sites prone to microfractures (technically known as osteochondrosis), such as the proximal femoral and tibial growth plates, the radial zone supporting the growth of articular cartilage and the flexible thoracic vertebrae that are typically subjected to extreme torque and mechanical stress. The rapid growth of the bird within the first 30 days of life requires consistent and strong growth plates and articular cartilage. If there are not enough nutrients at the right time or trauma is accumulated this could results in thick, fragile growth plates that are susceptible to osteochondrosis, leading to a bacterial infection of the bone. Typically, the infection reaches these susceptible growth plates via pathogens that circulate in the blood stream after penetrating the gastrointestinal tract or respiratory system.

The infection restricts the birds’ natural movement, often reducing feed and water intake, and is a significant factor in morbidity and mortality. BCO often requires the use of an antibiotic treatment strategy, but recent research also concludes that a healthy gastrointestinal tract may help prevent or limit BCO.

Dr. Robert Wideman Jr. with the Center of Excellence for Poultry Science at the University of Arkansas is a widely recognized expert on BCO. He created a mechanical stress model using wire flooring to demonstrate the role stress, footing instability and a weak immune system can play in the development of BCO.

“BCO is a devastating lameness issue,” says Dr. Wideman. “The birds go from being clinically healthy one day to showing a hesitancy to stand up the next day. In three to four days the bird is completely incapacitated and upon post-mortem examination, they have large bacterial abscesses in the femoral head, tibial head and vertebrae.”

Significant mortality occurs starting around day 28, meaning the poultry operation is not only losing birds due to the infection, but these birds have consumed considerable feed and that is also a cost to the operation.

Strategies to Help Prevent or Manage BCO

Management is key. Poultry producers need to increase flock monitoring activities since BCO spreads rapidly, and take steps to enhance overall flock health.

“Recent epidemiological evidence suggests many BCO outbreaks are initiated by low-level vertical transmission of pathogenic bacteria, followed by a horizontal bird-to-bird spread in the broiler house,” says Dr. Wideman. “It appears very likely that in many cases the newly hatched chicks are carrying BCO pathogens from the hatchery to the broiler barns.”

This means hatchery sanitation is important in preventing potential contamination of the chicks. Floor eggs and soiled eggs should never be sent to the hatchery. Hatchery temperature management pre- and post-hatch must be monitored, as heat stress in the hatchers or post-hatch can make chicks more susceptible to BCO. Excessively high brooding temperatures also have been associated with subsequent susceptibility to BCO.

Dr. Wideman also recommends newly hatched chicks taken directly from the hatchery should be evaluated for existing femoral head necrosis and bacterial contamination of the spleen, as these are indices of vertical transmission.

Once the chicks are in the broiler house, it is important to focus on water quality and sanitation, as pathogens linked to BCO can be present as biofilms in water lines and drinkers. Maintaining excellent litter quality is important, as footing instability — demonstrated in Dr. Wideman’s research — can lead to growth plate micro-trauma and subsequent bacterial infection in the growth plates.

Poultry Nutrition can Reduce Spread of BCO

Since BCO follows a horizontal bird-to-bird spread, this requires an oral ingestion of bacterial pathogens — mainly from excreta in the litter but perhaps also from contaminated drinkers or nipples — followed by colonization of the bacteria in the gastrointestinal tract and translocation across the intestinal epithelium into the blood stream.

The lining of the intestinal tract is comprised of a layer of epithelial cells. These cells are bound to each other by complex protein structures, the main ones called “tight junctions,” and their role is to prevent bacteria, pathogens and toxins from passing through the intestinal lining and into the bloodstream.

Factors, such as heat stress, bacteria, feed contaminants, etc., can weaken the quality of the tight junctions, leading to a syndrome called “leaky gut.” This negative impact — leaky gut — allows molecules such as bacteria, pathogens and their toxins to pass in between the epithelial cells, resulting in cell damage or inflammation of the intestine.

Dr. Wideman’s research has revealed that probiotics and trace minerals help to tighten these cell junctions limiting the translocation of the bacterial pathogens from the gastrointestinal tract into the blood stream. Probiotics can reduce the vertical transmission of pathogens by broiler breeder hens, thereby reducing the likelihood that chicks will carry those pathogens into the broiler barns.

Performance trace minerals, such as zinc, copper and manganese also reduce the susceptibility of broilers to BCO by improving the tight junction integrity of the epithelial cells in the gastrointestinal tract and by helping to provide a more rapid immune response to the presence of bacterial pathogens.

In addition, extracellular copper is a key co-factor required by the enzyme Lysyl oxidase that is responsible for crosslinking — strengthening — collagen fibers within the cartilaginous portions of the growth plates. Zinc and manganese also play important roles on bone matrix formation and bone remodeling. Research also indicates that enhanced collagen crosslinking leads to lower incidences of osteochondritic micro-fracturing, and thus fewer wound sites available in the growth plates for bacterial infection. Dr. Wideman’s research also indicates that lower BCO lesion severity scores typically are observed when broilers are provided performance trace minerals.

To learn more about inflammation in poultry and how it may impact your operation, contact your Zinpro representative today.

Reducing salmonella risk in table egg production

By
Jim
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Quick facts

  • Purchase chicks from hatcheries in the United States Sanitation Monitored program and pullets from sources with salmonella prevention and control programs.
  • Control rodents, insects and wild birds on your farm.
  • Clean, wash and disinfectant poultry houses between flocks.
  • Monitor bacteria on your farm through laboratory testing.
  • Attend to feed quality control and proper feed storage.
  • Properly wash and store eggs to prevent salmonella contamination.
  • Have a strong and strict biosecurity program for your farm.

Chick and pullet replacements

Chickens are very prone to salmonellosis at two ages.

  • 1 to 14 days of age
  • When pullets move to laying houses

Optimal nutrition and care can help keep your birds healthy and reduce the risk of salmonella at these ages.

  • Purchase your chicks from hatcheries taking part in the United States Sanitation Monitored program. Get your pullets from sources with good salmonella prevention and control program.
  • Have a reputable hauler for your pullets. Make sure the pullets travel in clean coops and trucks. Undisinfected coops commonly carry salmonella.

Vaccines

Bacterin can stop vertical transmission of salmonella in turkeys. Salmonella vaccination research is underway at the Universities of Maine and Minnesota and other places. Early signs suggest that bacterins also reduces the spread of salmonella in chickens via feces and eggs.

Some companies produce bacterin for commercial use.

Controlling rodents, insects and wild birds

Vectors are organisms that can spread disease. Vector control throughout your flock’s life is key to reducing salmonella.

  • Work routinely with a licensed professional rodent and insect exterminator.
  • Be sure that personnel practice strict biosecurity steps for their clothing, equipment and vehicles.
  • Make sure service providers have a good vector control record with poultry operations.

Rodents

Rodent feces can contain infectious amounts of salmonella. Mouse feces, common in feed troughs, may amplify salmonella disease in poultry. Rodents also carry disease to near and distant houses and farms.

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  • Remove all cover for rodents inside and outside the poultry house. This may include:
    • Shrubs or tall grass
    • Garbage or construction debris
    • Broken equipment
    • Burrows under the foundation
  • Set up a rodent barrier around the outside of the poultry house.
  • Seal all entrance holes inside and outside the building.
    • Fix and close siding sheet seams.
    • Make sure doors and door frames fit tight.
  • Seal holes and cement cracks in manure pits.
  • Make sure rodents don’t reside in reused filler flats, which can move to and from farms during egg delivery.
  • Secure feed bins and sheds at night. Clean up dead birds and broken eggs daily.

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Caution: All baits are harmful to rodents, chickens, animals and people.

Preparing to bait
  • Remove all feed from feeders right after house depopulation, so rodents promptly go to the bait.
  • Remove all other food sources such as spilled feed, broken eggs and dead birds.
  • Check the outside of buildings often for rodent holes.
Selecting bait

Warfarin, diphacinone and pival are rat poisons that work by thinning the blood. To be effective, rats must receive these poisons in multiple doses over several days. Thus, poison works best if you use it routinely, every two weeks.

Newer blood thinners contain brodifacoum and bromadiolone. These may cause death three to five days after a single feeding. You can use single-dose rat poison at any time, especially right after house depopulation.

Make sure to stock enough rat poison to meet the long-term need of the farm.

Placing bait
  • Place baits to avoid contaminating feed and eggs or coming in contact with poultry and humans.
    • Don’t place bait loosely on the ground in high-traffic areas. People may carry it on their shoes and contaminate sensitive areas.
  • Save bait by only baiting holes with rodent activity.
  1. Fill all rodent holes with dirt or paper.
  2. Check for open holes later.
  3. Only bait the open holes.
  • Follow the manufacturer’s directions when placing the bait.
  • Control attic rodents by making a hatch for attic access and bait with a high-wax single-dose poison at least once yearly.
  • After controlling rodents, inspect and keep up permanent bait sites every two weeks. Record the location and numbers of trapped mice and maintain these records.

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Insects

Using several practices to control insects reduces the chance of insects adapting to a single method.

For example

  • Keep manure well ventilated and dry.
  • Prevent water leaks and remove wet areas.
  • If possible, use biological control methods (fly parasites and predators).
  • Use different types of insecticides.

Applying insecticide

  1. Clean and disinfect the poultry house floor.
  2. Once the floor dries, apply an approved insecticide to the floor, support poles and walls.
  3. Always follow the manufacturer’s safety precautions.

You can use synergized pyrethrins (pyrethrin plus piperonyl butoxide) in automatic spray systems inside poultry houses. These quickly knockdown flying insects, have short residual times and have low toxicity in mammals. Don’t apply these insecticides more than twice weekly, especially if you use a spray system.

Wild birds and pets

  • Avoid feed spills outside the buildings and clean up any spills right away.
  • Buildings should keep out wild birds and prevent them from sitting under eaves or on blinds.
  • Keep pets out of pullet and layer houses.

Cleaning facilities

Always clean pullet and layer houses between flocks to reduce possible buildup of disease agents, such as salmonella.

Clean facilities as soon as you remove the birds if any tested positive for salmonella. Cleaning will prevent replacements from contamination.

Good cleaning programs need to:

  • Be put in place across the entire farm
  • Have proper equipment
  • Have professional training

Cleaning conventional facilities presents a big challenge due to the following.

  • Facility size and complexity
  • Wooden construction materials are harder to disinfect than smooth metal surfaces
  • Plastic and fibrous egg handling surfaces are harder to disinfect than smooth metal surfaces

These problems decrease how effective cleaning plans are. In the past, many used formaldehyde to help disinfect porous surfaces. Although effective against salmonella, its use comes with human safety concerns, poor product availability and regulatory policies. Other options may help disinfect porous surfaces.

  • Other fumigants
  • Heat-enhanced disinfectants
  • High-pressure sprays or disinfectant foams
  • Sealants to reduce the rough surface of wood

Step-by-step cleaning

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Remove all dead and live birds from the building. This includes all escaped birds in the deep pit or outside. Start vector control procedures right away during bird removal.

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  • Clean fans and other air inlets from the outside.
  • Moving from top to bottom, clean up the dust inside the building. For example, remove dust from the ceiling, beams, walls, cages etc.
  • Promptly open feeder lines and remove all feed including feed inside troughs.
  • Open egg conveyance equipment at the front of the building and remove all dust and egg debris. Remove broken and soiled parts that you can’t clean.
  • Do your best to remove manure from dropping boards.
  • Remove all litter and manure from floor or cage houses, including augers and pit ends.
    • Be aware of National Institute for Occupational Safety and Health alerts on manure pit hazards.
    • If you can, fill trailers with manure inside the house and cover it before hauling to a disposal or composting site. Manure should not be spread near poultry facilities.
  • Remove egg belts and sweep away all debris above and below the belt.
  • Remove all debris and items not needed from the entire building.
  • Turn off power to electrical equipment prior to cleaning. Use compressed air or brushes to clean non-removable motors, switches, etc. Take extra care to keep sprays out of electric motors. Use duct tape to cover the motor slots before wet cleaning and disinfection. Remove the tape after wet cleaning and disinfecting.

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Soaking and washing
  1. Soften dirt in heavily soiled areas. Use a low-pressure (200 to 300 pounds per square inch) sprayer, which delivers 10 to 30 gallons per minute. Hot water and cleaners can help loosen debris and films that salmonella can grow in.
  2. While washing, start at the back and work toward the front. Spray the ceiling first, then the walls and lastly the floor. Thoroughly clean everything.
  3. Use sprayer attachments and nozzles that allow you to wash hard-to-reach places.
  • Aim for 750 to 2000 pounds per square inch.
  • High pressure requires extra care and safety clothing. Pressure sprayers can cut human skin like a knife. Always follow the manufacturer’s instructions for use.
  • Be sure to wash under the troughs and hidden surfaces of chains and augers.
  • Clean the egg elevator completely. Check that each angle (from under the pit and from behind rollers) is clean. Remove all traces of egg.
  • Wash storage and egg rooms, egg coolers, hallways, break, wash and restrooms.
  • Clean any other areas by hand if you haven’t cleaned them already.
Rinsing

A final rinse reduces residues of cleaning chemicals. Make sure to remove puddles right away to prevent bacteria from growing in them.

Repairs

Make all repairs after rinsing including:

  • Filling floor cracks
  • Repairing door frames
  • Replacing damaged panels
  • Repairing manure, egg handling and other equipment
Dirt floors

Add 3 to 6 inches of clean soil to houses with dirt floor pits to decrease risk of disease in new flocks. We don’t know the value of this for preventing salmonella.

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Have a third-party inspect your facility after wet cleaning and repairs. This may be done by an outside authority or by an in-house, unbiased employee in quality control.

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Start disinfecting within 24 hours of rinsing. Disinfectants are only effective on clean surfaces.

Heat enhancement

All disinfectants work best at temperatures over 65 F. Temperatures for chlorine- and iodine-based disinfectants shouldn’t exceed 110 F.

Dangerous mixtures

Always follow the manufacturer’s instructions for human and flock safety. Avoid adding chemicals to disinfectants without manufacturer approval. Adding chemicals can dangerously reduce product efficacy.

Disinfectants
  • Apply one gallon of diluted disinfectant to about 100 to 150 square feet of surface area.
    • To determine how much disinfectant you need, find the total surface area of the floor, ceiling and walls. Add 30 percent to this area to allow for cage surfaces.
  • Follow manufacturer instructions to apply disinfectants.
    • Use a pressure sprayer (500 to 1000 pounds per square inch) to help force disinfectants into wood pores and cracks. Move from back to front and from top to bottom.
  • Dirt floors are almost impossible to fully disinfect. Apply disinfectant to dirt floors at one gallon of diluted disinfectant per 10 square feet.
  • Disinfect egg handling equipment following equipment and disinfectant manufacturer recommendations.
    • Disinfecting egg belts using steam, vats of water at pasteurization temperatures, or soaking could help but efficacy or adverse effects on the belt aren’t known.
  • Clean feed bins, boots, augers, hoppers and carts. Sanitize water-lines.
    • Improper use of sanitizing agents can plug lines or damage metal and nonmetallic parts of watering systems.
    • Check with your farm’s water handling equipment manufacturer before using any specific chlorine or other treatments of your wells or water lines.
  • Promptly dry the building. You can use bullet space heaters to speed drying in cold or damp climates.
  • Test your facility for salmonella following disinfection. Make sure results are negative before placing chicks or ready-to-lay hens in your facility.
Formaldehyde and formalin

WARNING: Formaldehyde and formalin are dangerous chemicals. Always, contact state/federal (EPA, OSHA, FDA) authorities and licensed professionals before considering use. You will need gas masks, protective clothing and rescue plans when using these.

In the past, producers used formaldehyde (formalin) to disinfectant for salmonella. Producers also used formaldehyde fumigation (gas) as a final crack- and pore-cleaning step if relative humidity was at least 70 percent and temperatures were at least 70 F.

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Preparations for restarting

  1. Replace disposable items with new ones (for example, sponges on egg conveyor equipment).
  2. Repair and adjust your egg handling and conveyance system from hen to cooler.
  3. Remove old water filters. Clean and disinfect casing and install new filters.
  4. Restock restrooms and portable toilets with soap and paper towels.
  5. Make sure that all electrical equipment work properly.
  6. Clean all equipment used for washing and disinfecting the facility and store them in a clean, secure space.

Monitoring bacteria

You must monitor bacteria through laboratory testing to complete your quality control program. Monitoring helps keep track of how well you’re reducing risk. Lawyers suggest that knowledge of a problem is better than none.

Collecting samples

Sampling often requires on-the-spot judgements. How you collect samples is more important than how many you collect. Poor sampling or laboratory methods can result in a false negative reading. Choose a laboratory that follows good salmonella culture methods.

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You can sample numerous surfaces including:

  • Ceilings, walls and floors
  • Fan housings and blades
  • Cages
  • Waterers and feed troughs
  • Manure scrapers
  • Egg belts, rollers and sponges
How to collect samples

Use 33 or 44 inch multi-layered, lightly-moistened gauze pads. Gauze pads allow you to forcefully wipe large (22 foot) areas. A damp pad allows particles to stick better.

Only use cotton-tipped swabs for sampling hard-to-reach places.

Always wear sterile disposable gloves when sampling. Promptly refrigerate samples at 35 to 38 F.

Sampling of rodents is a prime sampling strategy.

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Drag swabs are gauze pads connected to a cord that are drawn over droppings and litter. They produce results that reflect the salmonella gut carrier or organ infection status of chickens.

How to collect samples

Draw two gauze pads connected to cord over fresh droppings along the full length of each row.

  • Use one two-pad drag swab set per row in caged pullet or layer houses.
  • Drag two or three two-pad drag swab sets over the litter surface if you keep your flocks on litter. Draw the swabs over litter at pen ends, sides and center.
    • Make sure each two-pad drag has contact with the litter for at least five to six minutes.

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Monitoring plans and schedules

State laws, regulations and policies differ on the privacy of voluntary monitoring to help gain research, disease and in-house quality control data. Positive results during any bacteria monitoring times (see table 1) may present complex fiscal, legal and ethical issues. The same may be true for if you don’t monitor. Work with professionals (legal, underwriter and veterinary) to develop monitoring programs and choose from the following examples for pullet and layer flocks.

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Table 2. Examples of chick/pullet monitoring times, locations and purposes

Time/Age Location Purpose
0 to 1 daya,b Chick transport papers, chick feces, cull chicks and dead chicks Detection of breeder or hatchery transmitted salmonella
2 weeksc Dropping boards (cage reared) or litter surfaces (floor reared) Detection of infection after period of high susceptibility
10 to 16 weeks Droppings or drag swabs of manure (litter) surfaces Detection of infection prior to movement to layer facilities
2 to 3 days after decontamination (C & D) of pullet facility Building/equipment surfaces, fan blades, etc. Evaluation of C & D operation prior to housing new chicks

aA laboratory manual detailing sampling and culture procedures and a magazine update on culture media improvements have recently been published.

bAn additional test for salmonella in one-day-old hatchlings is described in the mentioned laboratory manual (Chapter 1, page 5).

cAt any age, bacteriological examination of culls, fresh dead, and trapped mice especially, are used to enhance detection efficiency.

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Table 3. Examples of layer monitoring times, locations and purposes

Time/Age Location Purpose
10 – 12 weeks prior to depopulation of layer housea,b,c,d Droppings, or drag swabs of manure (litter), and building/equipment surfaces. Egg belts and elevators Fan blades Cages Walls Other Detection of infection with adequate time for decontamination (C & D) and vaccination of pullets
2 – 3 days after C & D 2 – 3 days after C & D Building/equipment surfaces as listed above Evaluation of C & D operation prior to housing new pullets

aA laboratory manual detailing sampling and culture procedures and a magazine update on culture media improvements have recently been published.

bUse of cull eggs and/or blood (serum) samples are currently being evaluated as additional or alternative monitoring tools.

cAt any age, bacteriological examination of culls, fresh dead, and trapped mice especially, are used to enhance detection efficiency.

dMore frequent monitoring during lay has also been suggested to increase the likelihood of prompt detection of contamination.

Egg handling

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Protect eggshell strength

Salmonella can get into weak shells more easily. Avoid weak shells by:

  • Having properly formulated feed at all ages.
    • For example, supplement enough calcium and vitamin D3 levels.
  • Vaccinating for infectious bronchitis and Newcastle disease that weak the shell.

Gather eggs frequently

Eggs exposed to temperatures of 80 to 90 F promote bacterial growth and increase salmonella risk.

Properly wash eggs

In-line washing systems

Wash the eggs and cool to 45 F or less. Use only potable water with a maximum iron content of 2 parts per million and a minimum wash water temperature of 90 F.

Nest run systems

Cool the eggs right away to 60 F until you wash them. This will avoid cracks during washing. If the egg and wash water temperatures differ by 50 F or more, you may need to pre-warm the eggs before washing. Make sure the wash and rinse solutions are 10 to 15 F warmer than the eggs.

Use sanitizers following the manufacturer’s recommendations.

After washing, cool the eggs to 45 F or less if sweating can be controlled.

Provide good storage and transport temperatures

Eggs should remain at 45 F during storage and transport.

Control inventory

Rotate the product at all levels of distribution, warehousing, sale and home or institutional use. This inventory control decreases the likelihood of salmonella growth in the product.

Have proper food labels

State on egg carton labels that eggs are a perishable (will spoil) food and require the same care, including cooking, as other animal source foods. More informed consumers benefits both producers and their customers. Store eggs in their original carton in the main section of the refrigerator and not in the door shelf, where it can reach 60 F.

Don’t reuse fiber flats and egg cartons. Reusing these can save you money but they can lead to disease spread. Plastic flats are more ideal for reuse, but you must wash and disinfect them after each use.

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Salmonella can contaminate both the egg shell surface and the inside of the egg. Salmonella contaminates the inside of an egg either before the egg fully forms or by entering the shell.

Proper egg washing and sanitizing can rid egg shell surfaces of salmonella but not salmonella inside the egg. Cool temperatures play a key role in preventing further salmonella growth inside eggs.

Egg white contains products that help kill or stop the growth of bacteria. These natural products become less effective as the egg whites age. Cool temperatures help slow egg white aging and thus, help it control bacterial growth.

Cold temperatures alone can also prevent or reduce the growth of salmonella organisms. Research shows that Salmonella enteritidis put into eggs, didn’t grow at 40 F, but did grow at 50 F. Thus, by reducing egg temperature to 45 F or lower, you can reduce the risk of salmonella growth.

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Feed and water

Many salmonella types have been found in feed and feed ingredients. You must prevent salmonella contamination after manufacturing. Take care in selecting feed suppliers and in shipping and storing feed.

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  • Obtain feed from mills that follow the guidelines:
    • Recommended Salmonella Control for Processors of Livestock and Poultry Feeds, published in September, 1988 by the American Feed Industry Association, 1501 Wilson Blvd., Suite 1100, Arlington, VA 22209.
  • Use animal protein ingredients from rendering plants taking part in the Animal Protein Producers Industry (APPI) Salmonella Reduction Education Program.
  • Keep feed ingredients and finished feed at all stages of manufacture and storage dry.

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  • Keep rodents out of feed.
  • Keep feed dry.
  • Seek advice from your nutritionist and veterinarian before using anti-salmonella feed additives.
    • These differ in effectiveness and mode of action and may be subject to Food and Drug Administration (FDA) regulatory control.
  • Start your own bank of feed samples.
    • Feed banks and testing promote quality control.
    • Store samples in a clean, dry place at room temperature.

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Competitive exclusion (CE) refers to the natural gut bacteria that protect the bird from pathogens. You can buy and feed CE to your flocks.

Prompt post-hatch founding of CE gut bacteria, with a clean environment, may help reduce the risks of salmonella growth in the gut of normally prone, young fowl. CE cultures seem to help speed the growth of a possible protective gut bacteria. Probiotics aren’t quite the same as CE cultures.

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Routinely chlorinating poultry drinking water to at least 1 to 1.5 parts per million of free chlorine level reduces the spread of salmonella.

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Biosecurity

Biosecurity practices that prevent most diseases are equally good within a salmonella risk reduction program. Salmonella infects flocks when a virus or disease agent weakens your flock’s natural defense. Thus every step in biosecurity is an investment in flock survival.

People can also aid in spreading salmonella to chickens and eggs. Thus, all farm workers must have good hygiene. Provide clean, working toilets with hand washing and drying facilities to serve all employees.

Provide training materials such as videos or pamphlets to employees at all levels. Review such materials regularly and add practices as you see fit for your farm.

Biosecurity checklists

Checklists for flock caretakers and farm managers

Flock caretakers

You can post this list in all poultry houses. Consider printing large, clear posters.

  • Watch for, correct and report right away any rodent, insect, wild bird or pet problems.
    • Rats and mice are especially important!
  • Check daily for quick, secure removal of all dead and dying birds.
  • Have disinfectant soap available for personnel handling chickens or eggs.
  • Don’t go into the poultry house after hunting.
  • Keep egg belts, elevators, etc. in proper adjustment. Regularly clean and sanitize.
  • Wear clean clothing.
Farm managers
  • State in contracts and check that all pullet deliveries are made in clean and disinfected coops and trucks.
  • Make sure all visitors, farm executives and others wear biosecure clothing.
  • Ban caretakers from having any poultry flocks at home.

Spent hen removal

  • Make sure all racks are clean before they enter the poultry house.
  • Be sure the driver dresses in clean clothing before going into the poultry house.

Kim, C. J., D. A. Emery, H. Rinke, K. V. Nagaraja, and D. A. Halvorson. Effect of time and temperature on growth of Salmonella enteritidis in experimentally inoculated eggs. Avian Dis. 33:735-742. 1989.

“Biosecurity for Poultry Lock Diseases Out.” 1987. (Brunet).

Diseases of Poultry. Ninth ed. 1991. Chapter 30, “External Parasites and Poultry Pests.” (J. J. Arends). Pp. 727-730.

1991 United States Animal Health Association Salmonella Committee “Integrated Guidelines for Table Egg Producers” with input from members of the Minnesota Poultry Industries Association.

David Halvorson, emeritus professor, College of Veterinary Medicine

Reducing salmonella risk in table egg production

By
Jim
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Three common water sanitation programs

Clean sanitized water is one of the most critical, but unfortunately often overlooked, elements in allowing a flock to perform to its full potential. Clean water reduces stress on the animals and helps to prevent common health issues such as Bordetella, E.coli, and Salmonella.

The three most common sanitation systems are chlorine with acidification, chlorine dioxide, and hydrogen peroxide. Each have their own positive and negative aspects. To make sure the sanitizer is working properly, it is recommended to take water samples routinely. Simply go to the end of your line and get a clean, non-contaminated water sample with your sanitizer present in the water system.

water sample test end of line.jpg

Chlorine with Acidification

Pros

  • Cost effective option
  • Effective when used with less challenged water

Cons

  • Not as effective in the presence of organic matter – cannot remove biofilm
  • Corrosive to equipment when overused

Chlorine with acidification is a system that destroys organisms in the water using Hypochlorus acid (HOCI) and Hypochlorite ion (OCI). You must use two different pump systems with using both chlorine and acidification: one to pump in the acid and then one to pump in the chlorine. You should never mix the products together in the same open tank. When using chlorine with acidification, it is important to measure ORP levels.

Recommended values

  • ORP should be ≥ 750
  • Total chlorine (3-5ppm) with 5.5 – 6.5 pH

Chlorine Dioxide

Pros

  • Very effective when used with challenged water (i.e. well water)
  • Can eliminate biofilm
  • Can eliminate odors from high sulfur/mineral water
  • OMRI (Organic Materials Review Institute) approved and can be used in an organic system.

Cons

  • When delivered manually, on site activation is time consuming and must be completed in a well-ventilated area
  • Automatic systems can be more expensive system

Chlorine dioxide is a strong sanitizer made up of sodium chlorite and inorganic acid. It is an unstable form of chlorine that rapidly breaks down into harmless water and salt. The conversion from chlorite to is achieved by mixing the chlorite solution with citric acid, transforming the chlorite to chlorine dioxide (CL02). Simply mixing the two components together without adequate “dwell time” for activation results in little to no CLO2 being generated.

Delivery Methods

AANE (Automatic Activation No Electric) system

AANE system.pngThe best way to activate chlorite is by having a pump system such as an AANE (Automatic Activation No Electric) system that automatically mixes the acid and chlorite solution. This kind of system gives the solution appropriate time to activate and introduces the chlorine dioxide into the water system at your desired rate. When using a pump system use, between 1 and 5 ppm. The greater the health challenge, the higher ppm you want to use.

Manual

Alternatively, you can also mix the acid and chlorite solution manually into a separate bucket. Once you achieve activation you can then administer the product by mixing the desired rate into a stock solution and inject the product through a medicator or inject the activated solution through a pump. When using a 1:128 medicator mix 2 to 3 ounces of chlorine dioxide (5%) per gallon of stock solution. 5% chlorine dioxide products are more cost effective and overall work better than the 2% chlorine dioxide products. Activated product needs to be kept in a well-covered container to hold efficacy and the product should be covered up once initial activation is complete. Activated solution in a well-covered container will last 7 to 10 days.

Recommended values

  • Desired levels of total chlorine should be 6-10 ppm with free chlorine at 3-5 ppm.

palintest example.png

Hydrogen Peroxide

Pros

  • Very powerful oxidizer
  • Non corrosive to your equipment
  • Can be ready to use (no mixing required)

Cons

  • Must be very carefully handled for safety reasons
  • Cannot be used in combination with chlorine
  • Must be stored properly away from sunlight

Hydrogen peroxide products can also be a very effective tool to sanitize your water system. Like chlorine dioxide, hydrogen peroxide is a powerful oxidizer, up to two times more powerful than chlorine.

If you are mixing product into a stock solution and injecting the peroxide through a 1:128 medicator, 3 to 4 ounces per gallon of 50% peroxide can generally be used to achieve 25 to 50 ppm. If you are using a pump system, simply adjust your equipment to inject 25 to 50 ppm of product. The important thing to remember about hydrogen peroxide is that it should only be run for 4 to 5 consecutive days and then rotate back to your chlorine or sodium chlorite product for 4 to 5 days.

Recommended values

  • 25-50 ppm of available peroxide. In certain health challenged situations, 100 ppm can be used, but this is considered a treatment level and should only be used for shorter durations.

Make sure whatever sanitizing program you choose, it should fit well into your system and should be effective at reaching the recommended values. For more information on water sanitation, contact a Hybrid representative.

Broiler Production:Considerations for Potential Growers

By
Jim
-

Download the complete article

Contract broiler production is concentrated in the eastern tier of Oklahoma counties. The success of contract broiler production in eastern Oklahoma is directly related to the success of poultry companies (integrators) located in Arkansas. Eastern Oklahoma has benefited from the integrators’ expansion to capitalize on increased consumer demand for poultry products.

Download the complete article

Introduction to Poultry Nutrition

By
Jim
-

The science of nutrition involves providing a balance of nutrients that best meets the animals needs for growth, maintenance, egg production, etc. For economic reasons, this supply of nutrients should be at least cost, and so we must supply only enough for requirements, without there being any major excesses. It is very difficult and very expensive to supply all nutrients at the exact nutrient needs – rather we have to oversupply some nutrients in practical situations, in an attempt to meet the limiting nutrients. In poultry diets these limiting nutrients are usually energy and some of the essential amino acids, such as methionine and lysine. In formulating diets the following nutrients are considered:

  • Energy
  • Protein
  • Fat
  • Vitamins
  • Minerals
  • Water

With the exception of water, these nutrients are provided by the ingredients that make up the diet. Ingredients are classified as:

  • Cereals
  • Animal Proteins
  • Vegetables Proteins
  • Vegetable Fats
  • Animal Fats
  • Micro Minerals
  • Macro Minerals
  • Vitamin Premixes

Each of these separate types of ingredient provides a specific quantity and quality of nutrients to the diet. Balancing these ingredients to produce the diet formulation (recipe) relies on the skill of the nutritionist.

In order to produce a diet, the nutritionist must know the birds needs and the composition of the ingredients.

Formulation = Balance needs vs ingredients vs costs.

The following nutrients are considered both for the birds needs and for the composition of the various ingredients:

Protein

Measure as crude protein, which is simply nitrogen x 6.25.
Component amino acids are the important part of protein.
There are 10 amino acids that are essential to the bird:

  • Methionine
  • Lysine
  • Tryptophan
  • Threonine

Protein and amino acids are supplied by ingredients such as:

  • Soybean meal
  • Canola meal
  • Cottonseed meal

All contain toxins that must be destroyed by heat treatment.
Other protein sources are from animals, and are generally of better amino acid content, but are expensive:

  • Meat meal
  • Poultry by-product meal
  • Fish meal

Energy

The most expensive nutrient in a diet, but is difficult to measure and there is no guarantee with the feed.

Energy is important because it governs feed intake.

  • high energy —> low feed intake
  • low energy —> high feed intake

Sources of energy – everything in the diet other than minerals.

Units – Calorie, or Kilocalorie

Metabolizable energy = Energy intake as feed minus energy appearing in urine and feces. Therefore can only measure with a chicken trial, therefore expensive ($1,000/assay).

Sources:

  • Corn
  • Soybean meal
  • Fat
  • Wheat
  • Meat meal
  • Barley

Fiber largely indigestible – cecal microbes?

  • Influences manure consistency.
  • Problem with some ingredients such as wheat, barley – enzymes.

Vitamins

All supplied as synthetics.

  • Fat soluble – A, D3, E, K.
  • Water soluble – B vitamins eg. Riboflavin, biotin

Cost about $2-5/tonne.

Exception is choline, which is added separately.

Generous safety factor 2-10x requirement.

Storage loss -> time, temperature, humidity

Minerals

  • Macro –> Calcium, Phosphorus
  • Micro –> Copper, Zinc, Manganese, Iron, Iodine, Selenium
  • Salt –> Sodium, Chloride

Fats

Not really an essential nutrient, other than linoleic acid (fatty acid).

  • Animal fats – hard, inexpensive. Problems with digestion by young birds.
  • Vegetable oils – liquid, expensive

Pellet quality, dustiness of feed

For more information:
Toll Free: 1-877-424-1300
E-mail: ag.info.omafra@ontario.ca

Evaluation of the Combined Effects of Air Movement and Reduced House Relative Humidity on Bird Health and Welfare in the Early Phase of Commercial Broiler Production

By
Jim
-

Institution: University of Georgia

Principal Investigator: Michael Czarick
University of Georgia
Poultry Science Department
110 Cedar Street
Athens, GA 30602

In today’s broiler industry producers are relying less on antibiotics and putting more attention on animal welfare than ever before. One result of this trend is that growers are being challenged to improve litter management. High litter moisture (>35%) has been correlated with increased risk factors related to bird health and welfare. Litter moisture can be reduced through proper drinker management and ventilation to maintain a low house relative humidity (Rh <50%). Though proper drinker management does not place a financial burden on the grower, the same cannot be said about ventilation rates required to maintain a low Rh. Depending on conditions, decreasing Rh by just 20% could increase heating costs by 45% due to the higher ventilation rates required.

A possible alternative to primarily using ventilation to control litter moisture could be maintaining a moderate Rh level (50-60%) and increasing air movement over the litter through the use of circulation fans. Traditionally, circulation fans have been used primarily to minimize temperature stratification, improve temperature uniformity and conserve energy. Circulation fan systems designed to meet these objectives do not typically produce a significant level of air movement at floor level which limits litter drying. However, to obtain the level of litter drying required to optimize bird health and welfare, a greater level of air movement at floor level is likely needed.

The objective of this study was to evaluate the combined effects of maintaining a moderate house Rh level (50-60%) and moderate level of air movement (150 feet/minute) across the floor on litter moisture, paw health and coccidia sporulation. A total of five flocks were studied on two commercial broiler farms (two houses per farm). One house on each farm did not use circulation fans (control), and an adjacent house (treatment) was equipped with eight 24-inch 1/3 horsepower circulation fans that operated continuously throughout the flock. Both houses on each farm were managed similarly and were ventilated to maintain a moderate Rh of 50-60%.

The study demonstrated that the combination of maintaining a house Rh between 50-60% and an average velocity at floor level of 150 feet/minute resulted in a more consistent environment throughout the house. During cold weather, when temperature uniformity tends to be more problematic, the treatment house temperatures differed by less than 5°F over 99% of the time, while control house temperatures varied less than 5°F only 50% of the time.

Thermal images showed areas beneath tube heaters in control houses often exceeded 120°F during colder weather, while in the treatment houses floor temperatures ranged between 85°F to 100°F. These differences in floor temperatures led to uneven bird distribution in the control house. Birds often gathered near sidewalls to avoid areas under the heaters in control houses, while birds were more evenly distributed in treatment houses. Differences in bird distribution influenced litter moisture profiles. By three weeks, litter moisture was often 20-25% in treatment houses versus 25-35% in control houses. Furthermore, sidewalls in treatment houses tended to be less than 25% in moisture versus >30% in control houses during the cooler times of the year.

With drier litter, footpad lesions were typically lower in treatment houses. By the end of each flock, usually less than 30% of birds scored showed signs of severe lesions in the treatment houses whereas over 50% of birds displayed signs of severe lesions in control houses. If a paw value of $1.00/pound is assumed, the treatment effect could have the potential to save up to $3,000 per year for a 25,000-bird house growing a 4.5-pound bird.

No significant differences in coccidia sporulation were noted between control and treatment houses, which demonstrated that drier litter conditions in the treatment house had no negative impact on oocyst sporulation rates.

The producers observed that ammonia levels were consistently lower in the treatment house. Ammonia measurements taken over the first four weeks of one flock found an approximate 50% reduction in ammonia concentrations in the treatment house versus control (15-25 ppm vs. 30-40 ppm).

Genetics of adaptation in modern chicken

By
Jim
-

Source: Plos

Abstract

We carried out whole genome resequencing of 127 chicken including red jungle fowl and multiple populations of commercial broilers and layers to perform a systematic screening of adaptive changes in modern chicken (Gallus gallus domesticus). We uncovered >21 million high quality SNPs of which 34% are newly detected variants. This panel comprises >115,000 predicted amino-acid altering substitutions as well as 1,100 SNPs predicted to be stop-gain or -loss, several of which reach high frequencies. Signatures of selection were investigated both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during domestication and breed development. Contrasting wild and domestic chicken we confirmed selection at the BCO2 and TSHR loci and identified 34 putative sweeps co-localized with ALX1, KITLG, EPGR, IGF1, DLK1, JPT2, CRAMP1, and GLI3, among others. Analysis of enrichment between groups of wild vs. commercials and broilers vs. layers revealed a further panel of candidate genes including CORIN, SKIV2L2 implicated in pigmentation and LEPR, MEGF10 and SPEF2, suggestive of production-oriented selection. SNPs with marked allele frequency differences between wild and domestic chicken showed a highly significant deficiency in the proportion of amino-acid altering mutations (P<2.5×10−6). The results contribute to the understanding of major genetic changes that took place during the evolution of modern chickens and in poultry breeding.

Author summary

Domestic chickens (Gallus gallus domesticus) provide a critical resource for animal proteins for human nutrition worldwide. Chickens were primarily domesticated from the red jungle fowl (Gallus gallus gallus), a bird that still runs wild in most of Southeast Asia. Human driven selection during domestication and subsequent specialization into meat type (broilers) and egg layer (layers) birds has left detectable signatures of selection within the genome of modern chicken. In this study, we performed whole genome sequencing of 127 chicken including the red jungle fowl and multiple populations of commercial broilers and layers to perform a systematic screening of adaptive changes in modern chicken. Analysis of selection provided a comprehensive list of several tens of independent loci that are likely to have contributed to domestication or improving production. SNP by SNP comparison of allele frequency between groups of wild and domestic chicken showed a highly significant deficiency of the proportion of amino acid altering mutations. This implies that commercial birds have undergone purifying selection reducing the frequency of deleterious variants.

Citation: Qanbari S, Rubin C-J, Maqbool K, Weigend S, Weigend A, Geibel J, et al. (2019) Genetics of adaptation in modern chicken. PLoS Genet 15(4): e1007989. https://doi.org/10.1371/journal.pgen.1007989

Editor: Tosso Leeb, University of Bern, SWITZERLAND

Received: July 19, 2018; Accepted: January 28, 2019; Published: April 29, 2019

Copyright: © 2019 Qanbari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data is deposited in the European Nucleotide Archive (ENA) with the accession number (PRJEB30270).

Funding: This research is financially supported by grants from the German Research Foundation (DFG, No. QA55/1-1). The sampling of the Richardson red jungle fowl was supported by the Department of Energy under Award Number DE-EM0004391 to the University of Georgia Research Foundation, and benefitted critically from help from Tom Condon. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The modern chicken (Gallus gallus domesticus) was domesticated from the red jungle fowl (RJF) [1], but with some contributions from at least one other closely related species, the grey jungle fowl [2]. Domestic chicken segregate into several hundreds of distinct breeds distributed across the world. During the last century, the domestic chicken has been developed into a major food source by adapting the genome to specialized egg laying (layers) and fast-growing meat birds (broilers) whose productivity far exceeds their wild ancestor as well as the domestic chicken present only 100 years ago. Most modern commercial layers produce ~300 eggs in a year while the RJF usually lay a single clutch of 5–9 eggs per year. Modern broilers rapidly reach a body weight of 4–5 kg compared to the ~1 kg weight of an adult RJF male [3]. The commercial broiler and layer suppliers produce more than 70 billion birds annually to meet current worldwide consumer demands of more than 120 million tons of meat and over 1.2 trillion eggs [4].

The increasing productivity has been achieved through intensive directional selection on production traits over several tens of generations in purebred populations of limited effective population size followed by crossbreeding strategies in the generation of production animals. Maximizing yield however, has been at the price of reduced immunity and accompanied by a number of undesirable traits [5]. These negative effects may either be the result of pleiotropy of genes under selection for increased productivity, hitch-hiking of unfavourable alleles with the alleles under selection, or genetic drift. Understanding the nature of adaptive forces acting on the genome of commercial chicken provides insight into the complex relationship between production, disease and genes while opening up new directions for further improvement of this important farm animal, that is essential for global food security.

The domestic chicken is an excellent model to investigate the genetics of adaptation, as it involves transformation of the ancestral red jungle fowl into a domesticated bird. Furthermore, parallel populations of broilers and layers were independently established from earlier multi-purpose populations by several breeding companies selecting for very similar breeding goals during the last hundred years. This scenario allows us to explore if the same alleles are responsible for the selection response in these parallel populations. In this study, we conducted a systematic comparison of genomic sequence variation from multiple populations of broilers and layers, versus each other and versus RJF to identify genes that underwent selection during domestication and the subsequent specialization of domestic chicken into broiler and layer lines. We report the discovery and characterization of over 21 million SNPs, 34% of which were not previously described. Analysis of selection provides a comprehensive list of candidate loci underlying domestication and/or changes in production-relevant traits. We further report a highly significant (P<2.5×10−6) deficiency of amino-acid altering mutations among those showing strong genetic differentiation between RJF and commercial birds.

Results and discussion

Detecting millions of high-quality SNPs

The bioinformatics analysis using the described criteria detected ∼26.3 million putative SNPs and INDELs. After filtering, over 21 million high-quality bi-allelic SNPs were retained that were either segregating or fixed for a non-reference within a population. The retained variants were distributed in the genome with an average density of 1 SNP every ~50 bases. About 34% of these SNPs (n = 7,146,382) had not been reported before. The number of SNPs detected in each population varied between 7.6 and 17.4 million (Table 1). For the layer lines, the proportion of segregating variants was rather low, with an average of 57% of total variation, while the corresponding average for the broilers was 65%. RJFt alone carries 86% of all detected variants. These results show that layers have lost a considerable amount of the genetic diversity present in their wild ancestor as also indicated by the significantly lower levels of nucleotide diversity (π) in LRs (0.15–0.20%) compared with that observed in RJFt (0.40%; Table 1), although the possibility exists that the nucleotide diversity in RJFt is somewhat inflated if multiple subpopulations in northern Thailand was sampled. The low nucleotide diversity of RJFi (0.13%) is presumably due to the fact that this population has been maintained as a small, closed breeding population for many years. The observed reduction in nucleotide diversity in the layer lines is mainly attributed to small number of founders and many generations of mating within closed lines of limited population size, but also partly due to the effect of linked selection.

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Table 1. Summary statistics for chicken whole genome resequencing.

https://doi.org/10.1371/journal.pgen.1007989.t001

We detected 115,107 amino acid-altering SNPs of which 17% were predicted by SIFT to be evolutionary intolerant (SIFT scores = 0.00–0.05), 215,810 synonymous variants, 588,491 variants within untranslated regions and 1,100 stop-gain or -loss variants. An unknown fraction of these will have functional consequences.

Allele frequency spectrum

The comparison of the allele frequency profiles of wild and commercial populations reveals substantial differences (Fig 1A; S2 Fig). In wild birds (RJFt), the distribution of allele frequencies shows a marked overrepresentation of infrequent alleles which is consistent with the pattern observed for high-quality data in many other organisms including human and cattle populations [6, 7]. In contrast, commercial populations, particularly layers (S2 Fig), show a substantially smaller proportion of rare alleles that can be attributed to the smaller effective population size caused by recent selective breeding leading to loss of rare alleles. A subtle excess in the proportion of missense relative to synonymous mutations is evident among rare alleles, presumably caused by selection reducing the allele frequency of slightly deleterious mutations [6, 8].

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Fig 1. Analysis of SNP diversity.

(A) Comparison of the minor allele frequency spectrum of coding sequences in RJFt and commercial populations. (B) Visualization of the distribution of population-specific and group-specific variants detected from individual sequencing only. Each triangle represents the number (103) of variants exclusively segregating or detected in the corresponding population and overlapping sections denote group-specific variants. (C) Heatmap of the allele frequency distribution of population-specific variants. (D) Principal component analysis of chicken populations. Populations are coded as RJFt = red jungle fowl (Thailand), RJFi = red jungle fowl (India), BL = Brown layer, WL = White layer, RWp = Rhode-White pool, BRA = Broiler line A, BRB = Broiler line B and BRpD = Broiler line pool D, BRs = three commercial broiler lines (BRA, BRB and BRpD). show a substantially smaller proportion of rare alleles that can be attributed to the smaller effective population size caused by recent selective breeding leading to loss of rare alleles.

https://doi.org/10.1371/journal.pgen.1007989.g001

Fig 1B shows the distribution of population- and group-specific variants detected from individual sequencing only. Out of the >18 million variants detected in RJFt, as many as 4.4 million were unique to this population (Fig 1B). This suggests loss of genetic diversity during domestication and breeding, although this might be partly due to genetic differences between the RJF birds used in this study and the ancestral population(s) of red jungle fowl that contributed to chicken domestication. We compared the distributions of population-specific SNPs among commercial and wild chicken to investigate differences in the frequency patterns (Fig 1C). With the exception of the inbred RJFi population, the layer lines exhibit higher frequencies of population-specific alleles. This may be a consequence of a more narrow genetic basis and successive generations of selective breeding in commercial populations to enhance the frequency of favourable alleles. A good proportion of these loci are probably dragged to higher frequencies due to linkage with the target loci under selection [9]. Summary statistics of group-specific variants discovered exclusively in the layer and broiler lines are presented in supplementary Tables 1–4.

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Table 2. Pairwise FST between populations.

https://doi.org/10.1371/journal.pgen.1007989.t002

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Table 3. List of putative selective sweeps revealed by FST analysis.

https://doi.org/10.1371/journal.pgen.1007989.t003

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Table 4. A list of candidate genes harbouring missense mutations with ΔAF > 0.7 in two contrasts ‘RJFs vs. Coms’ and ‘BRs vs. LRs’.

https://doi.org/10.1371/journal.pgen.1007989.t004

Principal component analysis of genetic relationships

We performed a comprehensive analysis of genetic similarity based on genotypes from >21 million SNPs. As expected, individually sequenced birds from the same population clustered together (Fig 1D; S3 Fig). The white (WL) and brown (BL) laying birds clustered distantly, although they are both layers, a result consistent with previous data [10]. Rhode White (RWp) is a layer breed developed by crossing white and brown layers and is located in the middle of the plot. The clusters of RJFs from Thailand and India were in fairly close proximity to one other. Broilers showed a strong clustering in the middle of the plot, probably due to the common ancestor of all, rooted back to the Cornish breed [11]. These results provide important background information for our attempts to identify loci under selection in the domestic populations.

Detecting selective sweeps

a. Analysis of genetic differentiation.

The level of genetic differentiation varies among chromosomes, annotation categories as well as groups of birds (S4 Fig). To detect putative selective sweeps, we first searched the genome for regions with high degrees of differentiation between groups (RJF, LRs and BRs). Across the genome we observed the largest FST values in contrasts between populations with the lowest nucleotide diversities reflecting genetic drift (Tables 1 and 2). FST values were estimated in sliding 40 kb windows along the genome in steps of 20 kb. The size of a selective sweep depends on multiple factors such as the local recombination rate, selection intensity, and the number of generations that passed from the time when a favourable mutation arose and it became fixed. Thus, selective sweeps vary in size due to several variables, making it difficult to determine an optimal window size in which to search for signatures of selection. Thus, we cannot rule out that our approach may have failed to detect sweeps that would have been detected using other fixed or variable window sizes. The distribution of window-ZFST values are plotted in S4C Fig for all comparisons. Since only windows with >10 SNPs were analysed, the number of windows available for analysis varied from 46,146 to 46,150 per comparison (S5–S8 Tables).

The profile of FST also varied among comparisons and chromosomes (S4 Fig), which complicates defining a threshold to distinguish true selective sweeps from regions showing genetic differentiation due to genetic drift. We therefore defined putative sweeps as those reaching a ZFST score ≥ 6, as these were in the extreme upper end of the distribution (S4C Fig). We however believe that loci further down the list still merit further examination in follow-up studies. All windows with ZFST ≥ 4 in any of the comparisons are listed in S9 Table.

Only ~0.13% of the windows (n = 60) had a ZFST score ≥ 6 in the ‘RJFs vs. Coms’ comparison, and the corresponding fractions were ~0.05% for ‘BRs vs. LRs’ (n = 41), ~0.03% for RJFs vs. LRs (n = 66) and ~0.07% for ‘RJFs vs. BRs’ (n = 90). In total, 31 putative sweeps were mapped with ZFST-scores exceeding the threshold at least in one of the contrasts (Table 3). We used the yellow skin (BCO2) locus [2] and the TSHR locus [12] as proofs of principle showing that our approach can reveal established sweeps. We observed an FST value of 0.65 (ZFST = 7.0) over BCO2 (Fig 2A) and the localization perfectly overlapped the previously defined sweeps. The window harbouring the TSHR locus showed an FST value of 0.34 (ZFST = 3.4) in the ‘RJFs vs. Coms’ contrast residing within 9% of top differentiated windows (S6 Table). Another signal (ZFST = 10.6) overlapping a previously detected sweep was mapped on chromosome 1 over IGF1, which encodes insulin-like growth factor 1, an important growth factor associated with body size in dogs [13]. This signal appeared in three out of four contrasts where RJFs were included and were maximum when wild birds were compared against broilers. Several recent studies have reported QTLs associated with chicken growth traits in this region [14].

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Fig 2. Genome-wide visualization of candidate selective sweeps.

Each dot represents a 40 kb window in steps of 20 kb along the genome. (A) ZFST scores in different contrasts of chicken populations. Candidate genes are indicated for each signal. Signals marked by a star represent regions lacking annotated genes. (B) Manhattan plot of the Z|ΔPi| scores between two RJF and four commercial populations. (C) High resolution illustration of putative sweeps on GGA14. The heatmap visualizes the region as FST values among multiple populations and groups, where the genes or known elements overlapping the candidate sweeps are indicated underneath.

https://doi.org/10.1371/journal.pgen.1007989.g002

In total, eleven putative sweeps including IGF1 had ZFST -scores more extreme than that of yellow skin/BCO2 (Table 3), four of which were localized in regions lacking annotated genes. Other signals overlapped with HNF4G, TBXAS1, GLP1R and GJD2. A particularly interesting signal was found in the comparison of RJF/Commercials, and was localized at the distal end of GGA14 (ZFST = 9.65) coinciding a gene-rich region. This signal was further supported by analysis of the differences in nucleotide diversity between wild and domestic chicken (ΔPi) that revealed a high degree of fixation in domestic chicken in this window on GGA14 (see section ‘Analysis of fixation’ and Fig 2B), therefore we decided to further evaluate this signal.

A closer look at the GGA14 sweep (Fig 2C) revealed three separate signals emerging from the region. The window with the strongest signal (ZFST = 9.65) occurs in a window with a very high SNP density (nSNPs = 1,798) and overlaps the genes JPT2 (Jupiter microtubule associated homolog 2) and CRAMP1 (cramped chromatin regulator homolog 1). The signal reflects strong genetic differentiation between RJF and all domestic chickens (Fig 2). JPT2 (also known as HN1L) shows high sequence conservation among vertebrates and are proposed to be involved in embryo development [15]. A study in Drosophila melanogaster showed that a CRAMP1 homolog is involved in epigenetic regulation of gene expression [16].

The second signal (GGA14: 14.28–14.32 Mb, ZFST = 8.4) overlaps cyclin F (CCNF) and the third signal (GGA14:14.78–14.82, ZFST = 9.8) hits VPS35L (Vacuolar protein sorting-associated protein 35 like).

We explored these genes for function-altering mutations and identified 6 highly differentiated SNPs (ΔAF ≥ 0.7) between RJF and commercial populations, all residing in CCNF, annotated as missense mutations, (S10 Table), one of which was predicted to be deleterious.

b. Analysis of fixation.

To extend the analysis of loci under selection during domestication, we compared the level of nucleotide diversity between wild birds and commercial lines. For this analysis, we included the six populations comprising sequence data from single individuals (see Table 1). We computed absolute values of the difference in nucleotide diversity (ΔPi) between groups of wild vs. commercial birds (RJFs vs. Coms) in every window and normalized the results by using a Z-score normalization (ZΔPi = (ΔPiwin−ΔPigenome)/σ(ΔPigenome)). The most outstanding signal of Z|ΔPi| occurs on GGA14 overlapping the sweep signal encompassing the JPT2 and CRAMP1L genes (Fig 2).

In a further step we estimated nucleotide diversity for groups of birds as well as all six populations of RJFs and commercials (S5A and S5B Fig). The latter scan may identify adaptive selection that happened prior to domestication in those cases where there is no significant genetic differentiation between populations but a reduction in nucleotide diversity in all of them. Density plots indicate no outlying signal at the negative tail of the diversity distributions implying the absence of aberrant local diversity across the genome, an observation that emerges from genomic distribution of diversity scores as well (see S5C Fig). At the local scale however, we noticed extensively swept regions that persisted across multiple consecutive windows and span over hundreds of kilobases (Fig 3). Two particularly interesting selective sweeps that are present in all populations overlap the genes for ALX Homeobox 1 (ALX1) and KIT Ligand (KITLG) on GGA1. The reason for classifying these as two separate sweeps is that they are separated by a highly variable region. The ALX1 is responsible for beak shape variation among Darwin’s finches [17]. The KITLG is a major determinant of pigmentation, which plays an important role in camouflage and sexual display [18]. As shown in Fig 3A, this is a fairly large region with an unusually low nucleotide diversity and we cannot rule out the possible involvement of other genes residing in the region contributing to the observed pattern. However, the two emerging valleys of homozygosity are evidently centred over ALX1 and KITLG. The results suggest that beak morphology and pigmentation traits may have been under selection in chicken prior to domestication. Another noticeable sweep is located on GGA2 spanning over a ~3.5 Mb region harbouring 25 genes (Fig 3B).

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Fig 3. A graphical illustration of regions with extremely low nucleotide diversity across populations on GGA1 and GGA2.

In panel A, two regions of high homozygosity are centered over ALX1 and KITLG on GGA1. Red and green rectangles, respectively display the chromosomal positions of ALX1 and KITLG. Panel B visualizes an extensive putative sweep on GGA2 overlapping the EGFR locus. Each dot represents a 40 kb window. The standard errors of ZPi-scores in each window across scans are smoothed over the region in grey. Nucleotide diversity was estimated for RJFs (two red jungle fowl populations), Coms (four commercial lines), BRs (the two commercial broiler lines, BRA and BRB), LRs (two layer populations, BL and WL) and ALL (all six populations of RJFs and commercials).

https://doi.org/10.1371/journal.pgen.1007989.g003

Genomic enrichment of functional variants

The extensive SNP data combined with annotation information for each single site enabled us to explore the genomic distribution of sequence polymorphisms showing strong genetic differentiation between wild and domestic chicken as well as between broilers and layers. We carried out enrichment analyses to identify categories of SNPs showing differentiation between groups of birds. The absolute allele frequency difference (ΔAF) was calculated for different categories of SNPs in four contrasts (1) RJFs vs. Coms, (2) BRs vs. LRs, (3) RJFs vs. BRs and (4) RJFs vs. LRs and these ΔAF-values were sorted into 10 bins of allele frequency (ΔAF 0–0.1, etc.) to test for possible enrichment of variants in different annotation categories among SNPs showing strong differentiation. In all contrasts the great majority of SNPs showed a ΔAF<0.10 (Figs 4 and S6, S11–S14 Tables). This implies lack of differentiation between groups of birds at most loci, whereas a small percentage of variants, including those under selection showed highly significant differentiation.

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Fig 4. Analysis of enrichment for different categories of SNPs.

Panel A and B represent the contrasts ‘RJFs vs. Coms’ and ‘BRs vs. LRs’, respectively. UpDwStream stands for sites residing 5 kb up- and downstream of genes. The black line represents the total number of SNPs in each ΔAF bin and colored lines represent log2 fold changes of the observed SNP count for each category in each bin against the expected SNP count.

https://doi.org/10.1371/journal.pgen.1007989.g004

The intensity of adaptive and purifying selection varies across the genome according to the functional properties; as such intergenic sequences evolve relatively more freely than protein-coding sequences. We observed a marked decline in relative abundance of missense substitutions showing a steady decrease above ΔAF = 0.2 in all contrasts (Fig 4). SNPs with marked allele frequency differences (ΔAF≥0.7) between wild and commercial chicken demonstrate a highly significant deficiency of missense mutations (P<2.5×10−6). We argue that this sharp decline in the proportion of differentiated missense substitutions represents purifying selection that reduces the frequency of slightly deleterious mutations affecting production and/or health. Thus, SNPs showing strong genetic differentiation between wild and domestic chickens are enriched for selectively neutral variants that have changed in frequency due to genetic drift as indicated by the enrichment of intergenic SNPs (P<0.0001) among variants with ΔAF>0.7.This result is in sharp contrast to recently reported data for the Atlantic herring where a similar analysis of high ΔAF SNPs showed a highly significant enrichment of missense mutations and other functionally important variants in a species with huge population size and a minimum amount of genetic drift [19].

The increase of log2 values for the contrast RJF vs. Coms and the flat curve for BRs vs. LRs (Fig 4) indicate most likely that a fraction of the missense mutations has been under positive selection during domestication. Therefore, we decided to focus on the highly differentiated missense variants (e.g., ΔAF>0.70), which were only 262 and 188 in the contrast ‘RJFs vs. Coms’ and ‘BRs vs. LRs’, respectively. All strongly differentiated missense variants in all four contrasts are compiled in S15–S18 Tables. Within the list of high ΔAF SNPs we observed multiple missense variants occurring in the same gene. For example, the 262 missense substitutions with ΔAF ≥ 0.70 in the RJFs vs. Coms contrast occur in only 189 different genes and the corresponding figure for the contrast BRs vs. LRs is 188 missense substitutions in 150 genes. This result may reflect hitchhiking or possibly the evolution of alleles composed of multiple causal variants affecting the function of the same gene as previously documented in domestic animals [20].

We used the hypergeometric test of FUNC [21] to perform a gene ontology enrichment analysis based on the list of all genes embedding differentiated missense mutations and found no significant overrepresentation of any particular biological process. Nevertheless, we noted that some of these variants occur in genes affecting domestication or production-related traits (Table 4). However, as most genes have pleiotropic effects, selection may possibly act on other functional effects of these genes than those highlighted here. In the following sections, we highlight some results from these analyses.

Evolution of pigmentation traits from wild to domestic type is one of the most striking changes during domestication [20]. Traits associated with visual appearance in domestic chicken have been artificially selected for aesthetic reasons and as a trademark in establishing distinct breeds. In the enrichment analysis of ‘RJFs vs. Coms’, two of the missense mutations with the highest ΔAF occur in the CORIN (AFRJFs = 0.09 and AFComs = 0.96) and in Ski2 Like RNA Helicase 2 (SKIV2L2, AFRJFs = 0.71 and AFComs = 0.00) genes. CORIN is a modifier of Agouti signalling protein (ASIP) in dermal papilla and its absence causes ASIP activity being prolonged leading to lighter coat color in mice [22]. SKIV2L2 regulates melanoblast proliferation during early stages of melanocyte regeneration [23]. Thus, both genes are involved in the pigmentation process. However, no genotype-phenotype association has yet been established for CORIN and SKIV2L2 in chicken.

Among the top ΔAF alleles in the ‘RJFs vs. Coms’ contrast is the gene encoding sperm flagellar protein 2 (SPEF2, AFRJFs = 0.03 and AFComs = 0.82). SPEF2 is implicated in feather development. In contrast to the modern chicken, jungle fowl use feathers for flight and thermoregulation, both of which are more crucial in wild birds than in commercial chicken maintained in a controlled environment. However, thermoinsulation must have been an important trait in domestic chicken in the past when birds were kept in unheated stables in cold climate. Furthermore, SPEF2 is a major candidate gene for chicken early- and late-feathering [24], which is an economically important trait in the poultry industry since it can be used to sex chickens, and likely another reason for the differentiation of this mutation through linked selection. Two other notable mutations in this contrast overlapped the GLI Family Zinc Finger 3 (GLI3, AFRJFs = 0.03 and AFComs = 0.79) and the Kinesin Family Member 7 (KIF7, AFRJFs = 0.03 and AFComs = 0.82) genes, both involved in Sonic hedgehog (Shh) signaling pathway that controls the normal shaping of many tissues and organs during embryogenesis including limb and wing development [25, 26]. Further genetic and functional studies of these allelic variants are necessary to verify their possible contribution to chicken domestication.

Coding SNPs with ΔAF≥0.7 in the contrast between BRs vs. LRs also included interesting candidate mutations. For example, a missense mutation of extreme ΔAF (AFBRs = 0.14 and AFLRs = 0.86), occur in the Leptin receptor gene (Table 4), whose function in regulating feed intake and body weight is well documented in mammals [27, 28] whereas the role of leptin-leptin receptor interaction for metabolic regulation in birds is not yet clear [29]. Another particularly interesting substitution in this contrast overlaps the multiple epidermal growth factor 10 gene (MEGF10, AFBRs = 0.82 and AFLRs = 0.00) on GGA8, known to function as a myogenic regulator of satellite cells in skeletal muscle [30]. Mutations in MEGF10 have previously been shown to cause an unusual combination of dystrophic and myopathic features leading to the weak muscles in humans [30, 31], suggesting that the mutation reported here may affect muscle growth in broilers. The fact that different broiler lines have a high frequency of the variant allele at this locus is consistent with this suggestion. Other notable mutations in this contrast were found in the IGSF10 gene implicated in age at puberty [32] and PLEKHM1 with a suggested role in osteoporosis [33].

This paper reports the discovery and characterization of over 20 million SNPs from the chicken genome with the goal to delineate those with potential functional consequences—either having adaptive advantages or deleterious effects. To our knowledge, this is so far the largest study of its kind in chicken as a large number of individuals have been sequenced and a large number of sequence variants were detected. As many as 34% (n = 7,146,383) of the SNPs had not been reported before. The results revealed a subtle differentiation between wild and modern chicken at most loci, whereas a small percentage of loci showed strong differentiation. Analysis of selection provided a comprehensive list of several tens of independent loci that are likely to have contributed to domestication or improving production. We confirmed strong differentiation between red jungle fowl and domestic chickens at the previously reported BCO2 and TSHR loci. We identified 34 putative selective sweeps co-localized with, among others, KITLG, ALX1, IGF1, DLK1, JPT2 and CRAMP1. Single SNP contrasts between groups of birds revealed several highly differentiated coding variants, in genes such as CORIN and SKIV2L2 involved in pigmentation and LEPR, MEGF10 and SPEF2 possibly affecting traits relevant for animal production. SNPs with marked allele frequency differences between wild and domestic chicken showed a highly significant deficiency of the proportion of missense mutations (P<2.5×10−6).

Methods

Ethics statement

Samples were either taken from a DNA bank established at Friedrich-Loeffler-Institut during the EC project AVIANDIV (1998–2000; EC Contract No. BIO4-CT98-0342, https://aviandiv.fli.de) or as part of the SYNBREED project (2009–2014, Funding ID: 0315526; http://www.synbreed.tum.de/) where sampling was done in strict accordance to the German Animal Welfare regulations (33.9-42502-05-10A064) and with written consent of the animal owners.

Genetic material

Three groups of birds were included in the study (1) red jungle fowls (Gallus gallus gallus, RJFs), (2) broilers (BRs) and (3) layers (LRs) (Table 1). The RJFs were sampled from two geographical regions, Thailand (RJFt) and India (RJFi). The RJFt consisted of 25 DNA samples collected within a European collaborative research project AVIANDIV (https://aviandiv.fli.de/). RJFt was randomly down-sampled from ~150 RJFs caught in northern Thailand in 1997 and maintained since with random mating over four flocks; given the place and date, the RJFt samples likely have seen some contamination from domestic or feral populations prior to collection [34]. The DNA samples from RJFt were collected in 1999. For further information on the behaviour and morphology of these birds we refer to the AVIANDIV project webpage. The RJFi population involved 10 individuals of the Richardson line, originating from RJF caught in India in the 1960´s. This population has been extensively studied [35–37], and appears to have been established from a wild population prior to major genetic contamination of red jungle fowl populations, such that it may represent a unique RJF line that is at least largely free of influence from domestic stocks. The second and third group of birds represent commercial chicken, comprising three broiler and three layer populations, respectively. The broilers (BRs) were represented by 20 DNA samples of each of two lines (BRA and BRB) established independently and previously collected as part of the AVIANDIV project. BRA was a sire line belonging to the company Indian River International (Texas) established in 1980 and closed since with a breeding population size of >10,000 birds. BRB was another sire line originally from France, developed in 1970 with a breeding population size varying between 10,000 to 70,000. The broiler group further involved a pooled sample of 25 birds from AVIANDIV’s broiler sire line D, hereafter denoted BRpD. This is a sire line originally from UK, established in 1974 and closed since with unknown population size. In the layer group (LRs), data from 25 birds each from purebred white (WL) and brown (BL) egg laying populations, sequenced in the frame of the SYNBREED project (http://www.synbreed.tum.de/index.php?id=2), were included. WL and BL birds represent parental lines of the LOHMANN Tierzucht GmbH that are originally established from White Leghorn and Rhode Island Red, respectively. Moreover, we used pooled sequence data of 48 birds from Rhode Island White (RWp), a crossbred layer population collected by the AVIANDIV project.

DNA sequencing, alignment and variant calling

Sequencing libraries of 300–500 bp fragments were constructed for each individual sample using Illumina Nextera Library preparation kits. Sequencing of RJFt, BRA and BRB was conducted using an Illumina HiSeq 2500 machine and 2×126 bp paired-end reads were generated. RJFi, WL and BL along with the three DNA pools (RWp, BRpB and BRpD) were sequenced with 2×101 bp paired-end reads (see Table 1). All reads were mapped against the reference genome assembly Galgal5 [38] using the Burrows-Wheeler aligner (bwa-0.7.12) [39]. Duplicate reads were masked during pre-processing using the Picard tool set (version 2.0.1).

We identified SNPs following the recommendations of best practices workflow for variant discovery analysis using GATK [40]. Briefly, after recalibrating for base quality scores, BAM files were fed into the GATK-HaplotypeCaller tool which is capable of calling SNPs and INDELs simultaneously via local de-novo assembly of haplotypes in a region. After generating 127 GVCF files for individual and pooled samples, they were called simultaneously using the GenotypeGVCFs module. Raw vcf files were then filtered and used for downstream analyses. S1 Fig presents a summary of SNPs called based on different sequencing parameters.

Data preparation

The number of detected variants was 26,290,203 which included 3,442,027 INDELs and 1,024,944 multi-allelic sites. Raw vcf files from both individuals and pools were filtered primarily based on the following parameters. Variants were removed with QualByDepth (QD) < 4.0, 300 > depth > 2200, Quality < 30, mapping quality (MQ) < 40.0, MQRankSum < -10, ReadPosRankSum < -7.0, Fisher Strand > 60.0, ReadPosRankSum > 7, BaseQRankSum < -6, BaseQRankSum > 6″. Cluster Size and ClusterWindowSize were set to 4 and 10, respectively. For the subsequent analyses we used only bi-allelic SNPs on autosomes and chromosomes W and Z. In total, 21,190,795 SNPs were retained for downstream analysis.

Analysis of population structure and relatedness

The R packages SNPRelate and gdsfmt [41] were used for principal component analysis of relatedness using identity-by-descent measures estimated from all SNPs.

Annotation of genetic variants

SnpEff (v.3.4) [42] was used to annotate variants according to their functional categorization which included the following categories 5 kb up- and down-stream of a gene, intergenic, missense, synonymous, intronic, 3′ untranslated regions, 5′ untranslated regions, stop gain and stop loss. Variants in the up- and down-stream regions and in the 3′ UTR, 5′ UTR regions were merged into the single categories.

The online tool Ensemble Variant Effect Predictor (VEP, webpage: http://www.ensembl.org/info/docs/tools/vep/index.html)) [43], was used to predict SIFT-scores for amino-acid altering substitutions.

Enrichment analysis

The enrichment analysis was conducted as previously described in [44] for four contrasts (1) RJFs vs. commercial and (2) BRs vs. LRs, (3) RJFs vs. BRs and (4) RJFs vs. LRs. First we estimated the allele frequency (AF) of each SNP based on the proportion of high-quality reads supporting the non-reference allele. To ensure an unbiased estimation of AF several filters were employed to remove low quality SNPs and uncertain genotypes. In individually sequenced populations, loci with genotype quality < 20 were set to no.call and allele frequencies were estimated only for sites with >50% of the individuals genotyped. Because of low coverage, we treated the population RJFi as a pool in this analysis. In all pools SNPs with allelic depth <50% of mean coverage were set to no.call. Then, for each contrast, allele frequencies of intra-group populations were averaged and used to estimate the absolute value of allele frequency difference (ΔAF) for every single variant. The SNPs were then sorted into different bins of ΔAF (e.g., 0–0.1, >0.1–0.2, etc.) representing the allele frequency difference between populations. The expected number of SNPs for each category in each bin was calculated as p(category) X n(bin), where p(category) is the proportion of a specific SNP category in the entire genome and n(bin) is the total number of SNPs in a given bin. Finally, log2 fold changes of the observed SNP count for each category in each bin were compared against the expected SNP count and statistical significance of the deviations from the expected values was tested with a standard χ2 test.

Detecting selective sweeps

Evidence of positive selection was investigated in two steps. First, we explored differentiation of loci between the following combinations of populations. (1) RJFs vs. Commercials, (2) BRs vs. LRs, (3) RJFs vs. BRs and (4) RJFs vs. LRs. We estimated FST [45] for each of these four contrasts. To reduce locus-to-locus variation in the inference of selection we averaged single SNP values for sliding windows of 40 kb with 20 kb overlap across chicken chromosomes. Window-based FST values were then normalized and windows in the outlier tail ZFST > 6 were identified as selection candidates for domestication and genetic improvement in commercial populations.

In the second step, we searched the genome for regions with high degrees of fixation. To this purpose, the nucleotide diversity (Pi) was compared between RJF and commercial birds as a signature of selection during domestication. Different window sizes were tested but did not change the consistent picture of the signals. A window size of 40 kb was selected in accordance to the differentiation analysis. The Pi values were then normalized. Analysis of fixation involved six populations for which individually sequenced data were available. As such, nucleotide diversity was estimated for RJFs (two red jungle fowl populations), commercials (four commercial lines), broilers (the two commercial broiler lines, BRA and BRB), LRs (two layer populations, BL and WL) and ALL (all six populations of RJFs and commercials).

Gene ontology enrichment analyses, contrasting differentiated genes against a genomic background gene set, were performed using the hypergeometric test of FUNC [21].

Supporting information

pgen.1007989.s001.docx
Table S1. The frequency distribution of layer-specific SNPs (segregating only in white
layer, brown layer and Rhode Island White)
in different annotation categories.
Bin*
BinCoun
t
UpDw
UTR
Intergeni
c
Missens
e
Syn
Intronic
StopG
StopL
0-0.1
905
224
23
513
20
14
330
0
0
0.1-0.2
972
227
28
399
8
7
532
0
0
0.2-0.3
1002
175
14
410
6
5
557
0
0
0.3-0.4
1025
199
34
401
5
10
571
0
0
0.4-0.5
773
152
22
326
9
10
406
0
0
0.5-0.6
448
95
13
158
6
6
258
0
0
0.6-0.7
199
40
15
73
2
1
112
0
0
0.7-0.8
64
11
2
27
0
0
35
0
0
0.8-0.9
16
9
0
11
0
0
5
0
0
0.9-1
5
1
1
1
0
1
1
0
0
Sum
5409
1133
152
2319
56
54
2807
0
0
*Bins of a
verage allele frequency estimated across
three layer populations
for 5409 layers-specific
variants.
figshare

 

 

(DOCX)

S1 Table. The frequency distribution of layer-specific SNPs (segregating only in white layer, brown layer and Rhode Island White) in different annotation categories.

https://doi.org/10.1371/journal.pgen.1007989.s001

(DOCX)

S2 Table. List of layer-specific missense variants and corresponding genes (mean AF*>0.5).

https://doi.org/10.1371/journal.pgen.1007989.s002

(DOCX)

S3 Table. The frequency distribution of broiler-specific SNPs (segregating only in BRA, BRB and BRpD) in different annotation categories.

https://doi.org/10.1371/journal.pgen.1007989.s003

(DOCX)

S4 Table. List of broiler-specific missense SNPs and corresponding genes (mean AF*>0.5).

https://doi.org/10.1371/journal.pgen.1007989.s004

(DOCX)

S5 Table. Genome-wide FST-scores averaged over 40 kb windows between wild and commercial birds (RJFs vs. Coms).

https://doi.org/10.1371/journal.pgen.1007989.s005

(XLS)

S6 Table. Genome-wide FST-scores averaged over 40 kb windows between broilers and layers (BRs vs. LRs).

https://doi.org/10.1371/journal.pgen.1007989.s006

(XLS)

S7 Table. Genome-wide FST-scores averaged over 40 kb windows between wild birds and broilers (RJFs vs. BRs).

https://doi.org/10.1371/journal.pgen.1007989.s007

(XLS)

S8 Table. Genome-wide FST-scores averaged over 40 kb windows between wild birds and layers (RJFs vs. LRs).

https://doi.org/10.1371/journal.pgen.1007989.s008

(XLS)

S9 Table. The list of windows exceeding ZFST ≥ 4 in either differentiation comparison.

https://doi.org/10.1371/journal.pgen.1007989.s009

(XLS)

S10 Table. List of missense substitution revealed in the top putative selective sweep on GGA14.

https://doi.org/10.1371/journal.pgen.1007989.s010

(DOCX)

S11 Table. Distribution of SNPs with functional annotation in different delta allele frequency bins between two wild and six commercial populations (‘RJFs vs. Commercials’).

https://doi.org/10.1371/journal.pgen.1007989.s011

(DOCX)

S12 Table. Distribution of SNPs with functional annotation in the delta allele frequency bins between three broiler and three layer populations (BRs vs. LRs).

https://doi.org/10.1371/journal.pgen.1007989.s012

(DOCX)

S13 Table. Distribution of SNPs with functional annotation in different delta allele frequency bins between two wild and three layer populations (RJFs vs. LRs).

https://doi.org/10.1371/journal.pgen.1007989.s013

(DOCX)

S14 Table. Distribution of SNPs with functional annotation in different delta allele frequency bins between two wild and four broiler populations (RJFs vs. BRs).

https://doi.org/10.1371/journal.pgen.1007989.s014

(DOCX)

S15 Table. Differentiated missense variants (ΔAF>0.70) in the contrast RJF vs. commercials.

https://doi.org/10.1371/journal.pgen.1007989.s015

(XLSX)

S16 Table. Differentiated missense variants (ΔAF>0.70) in the contrast broilers vs. layers.

https://doi.org/10.1371/journal.pgen.1007989.s016

(XLSX)

S17 Table. Differentiated missense variants (ΔAF>0.70) in the contrast RJF vs. broilers.

https://doi.org/10.1371/journal.pgen.1007989.s017

(XLSX)

S18 Table. Differentiated missense variants (ΔAF>0.70) in the contrast RJF vs. layers.

https://doi.org/10.1371/journal.pgen.1007989.s018

(XLSX)

S1 Fig. Summary statistics of detected polymorphism.

SNPs called according to the best practices workflow using GATK (McKenna et al., 2010). The quality parameters shown from left to right and top to bottom are: Phred Quality Score; Allele Frequency; Depth of Coverage; Base Quality Rank Sum; Clipping Rank Sum; Excess of Heterozygosity; Fisher Strand; Inbreeding Coefficient; Maximum Likelihood Expectation for the Allele counts; Maximum Likelihood Expectation for the Allele Frequency; Mapping Quality; Mapping Quality Rank Sum; Quality by Depth; Z-score from Wilcoxon rank sum test of Alt vs. Ref read position bias; Strand Odds Ratio and Allelic Number in called genotypes.

https://doi.org/10.1371/journal.pgen.1007989.s019

(TIF)

S2 Fig. Allele frequency spectrum of SNPs with different annotation categories.

Colored lines depict the distribution of alternative allele for SNPs in different annotation categories.

https://doi.org/10.1371/journal.pgen.1007989.s020

(TIF)

S3 Fig. Clustering individuals based on genetic similarity.

https://doi.org/10.1371/journal.pgen.1007989.s021

(TIF)

S4 Fig. Analysis of genetic differentiation.

(A) FST among chromosomes in different contrasts of differentiation. The average FST values varied both among chromosomes and between autosomes and chromosome Z. (B) Average FST for different categories of SNPs. Function-altering variants such as stop-gain or loss as well as missense mutations show lower degrees of differentiation than other annotation categories. (C) Distribution of ZFST-scores averaged over 40 kb windows in different contrasts.

https://doi.org/10.1371/journal.pgen.1007989.s022

(TIF)

S5 Fig. Analysis of fixation.

Panel A and B, respectively displays distribution of number of variants and ZPi scores estimated in 40 kb windows in steps of 20 kb in different groups of birds. Panel C provides a schematic representation of the genome-wide nucleotide diversity (ZPi-scores). Nucleotide diversity are estimated only for the six individually sequenced populations. Each dot represents a ZPi-score for a 40 kb window.

https://doi.org/10.1371/journal.pgen.1007989.s023

(TIF)

S6 Fig. Analysis of enrichment for SNPs in different annotation categories in relation to delta allele frequencies (ΔAF).

Panel A and B represent the contrasts ‘RJFs vs. LRs’ and ‘RJFs vs. BRs’, respectively. The Y axis represents number of SNPs. The black line represents the total number of SNPs in each ΔAF bin and the colored lines represent log2-fold changes of the observed SNP count for each category in each bin against the expected SNP count. UpDwStream stands for SNPs residing 5 kb up or downstream of genes.

https://doi.org/10.1371/journal.pgen.1007989.s024

(TIF)

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Marel Q2 2019: On track with solid sales and EBIT growth

By
Jim
-

Financial highlights Q2 2019

  • Orders received were EUR 311.2m (2Q18: 291.1m).
  • Revenues were EUR 326.5m (2Q18: 296.7m).
  • Adjusted EBIT* was EUR 49.6m (2Q18: 43.2m), translating to an EBIT* margin of 15.2% (2Q18: 14.6%).
  • Net result was EUR 34.3m (2Q18: 29.5m).
  • Basic earnings per share (EPS) were EUR 5.16 cents (2Q18: 4.31 cents).
  • Cash flow from operating activities before interest and tax in the quarter was EUR 22.3m (2Q18: 56.4m).
  • Net debt/EBITDA was x0.6 at quarter-end (1Q19: x2.2). Targeted capital structure is x2-3 net debt/EBITDA.
  • The order book was EUR 459.4m (1Q19 474.7m and 2Q18: 523.2m).

*Operating income adjusted for purchase price allocation (PPA) costs related to acquisitions

Árni Oddur Thórdarson, CEO

“The second quarter was an eventful one for Marel. Clearly, the highlight was the offering and listing of 15% of new share capital on Euronext Amsterdam, while at the same time delivering growth and solid operational results.  

Revenues reached EUR 327 million, an increase of 10% compared with the same period last year. EBIT was up by 15% and the margin was 15.2% compared with 14.6% in second quarter last year.

Orders received were EUR 311 million, up 7% compared with the second quarter of 2018 and marginally  down compared to the strong first quarter of this year. We note a dynamic shift in the market with growth in greenfield sales in Asia and China, while new large project sales were softer in Europe and N-America. Maintenance and modernization of production facilities by our customers in Europe and N-America however is on track.      

We are very pleased with the endorsement by high quality cornerstone investors as well as the strong levels of support and interest from both retail and international institutional investment communities in the offering of shares and dual-listing on Euronext Amsterdam, complementing our Icelandic listing. In total more than 4,700 investors participated in the offering compared to around 2,500 shareholders prior to the dual-listing. The Euronext listing will support the next phase of our growth, as liquid and tradable shares are an important acquisition currency.

We continue to drive growth through innovation and global market presence, complemented by acquisitions. We are on track with our 2026 growth targets of reaching EUR 3 billion in revenues and best-in-class profitability. In partnership with our customers we are transforming the way food is processed, with a focus on food safety, traceability, sustainability and affordability.”

Financial performance

Orders received at good level

Orders received in the second quarter were EUR 311.2m, down 3.7% QoQ and up 6.9% compared to second quarter of 2018. The book-to-bill ratio was 0.95 in 2Q19 compared to 0.99 in 1Q19.

Overall, Marel’s commercial position continues to benefit from its full-line offering and steady launch of innovative high-tech equipment and software for smarter processing. Global reach and focus on full-line offering across the poultry, meat and fish industries counterbalances fluctuations in operations.

Markets continue to remain strong with some shifts in demand between markets within the industries and geographies. A higher proportion of new orders are coming from markets outside of Europe and North America. These are markets where it is more difficult to estimate the timing of fully financially secured orders compared to established markets. Orders received from Asia in particular China continue to be on the rise.

The impact of African Swine Fever (ASF) is severe on the overall pork industry and pork supply, however accelerates further new investments with a focus on automation and traceability in China which Marel can support. ASF is also leading to further imports and investments in the poultry industry serving the Chinese market. With Marel´s proprietary software platform Innova our customers have full traceability from live animal intake to the finished product dispatch.

The order book in the second quarter was EUR 459.4m, compared to EUR 474.7m in 1Q19 and EUR 523.2m 2Q18. This equals 36% of 12-month trailing revenues.

Greenfields and large projects with longer lead times constitute the vast majority of the order book while services, spares and standard equipment run faster through the system with shorter lead times.

Delivering strong revenue growth and resilient profitability

Revenues were strong in the quarter and totaled EUR 326.5m in 2Q19, similar levels as in 1Q and up 10.0% compared to second quarter of 2018.

EBIT* up by 14.8% YoY. EBIT* margin of 15.2% in 2Q19 (14.6% 2Q18).

With one of the largest installed base worldwide, a significant proportion of Marel’s revenues derive from recurring service and spare parts business or around 35% in 2Q19. Installed base is growing following high greenfield sales in past years. Inventories in fast moving and critical parts in spares have been built up in order to shorten lead times and better serve our customers.

Marel Poultry continues to be the largest driver with 17.9% revenue growth YoY and EBIT of 20.2% in 2Q19. Marel Meat revenues were up 10.5% YoY and 11.2% EBIT*. Marel Fish revenues are down by 21.3% with low EBIT of 2.3%, mainly due to soft orders received in 2H 2018 and change in mix in orders, leading to a delay in revenue and profit recognition.

Gross profit margin was 39.9% (2Q18: 38.8%) and gross profit was EUR 130.2m in 2Q19, up 13.2% YoY compared to EUR 115.0m in 2Q18. Operational expenses are in line with management´s expectations.

Net result in 2Q19 was EUR 34.3m, up 6.5% QoQ and up 16.3% compared to second quarter of 2018. Basic earnings per share (EPS) were EUR 5.16 cents in the quarter (2Q18 EUR 4.31 cents), up 19.7% YoY.

Cash flow affected by timing and delivery of projects

The cash flow, both operational (EUR 22.3m, 2Q18: 56.4m) and free cash flow (EUR -1.7m, 2Q18: 34.8), was unusually low in the quarter.

Operational cash flow before taxes and investments is at a lower level mainly due an to increase in work-in-progress (net contract assets and liabilities) of EUR 25.9m and the increase in inventories of EUR 6.2m QoQ. The work-in-progress build up is a timing matter while the inventory build up is a special initiative to shorten lead times in spares and standard equipment’s.

Investments are at lower level than previous quarters with EUR 9.8m (2Q18: 12.6m) as facility investments are scaling down after a period of significant investments. Marel continues to invest in the business to prepare for future growth, including IT platforms and a EUR 1.8m minority investment in Worximity Technology Inc.

Tax payments are high in the quarter (EUR 16.0m, 2Q18: 9.0m) as a result of timing of payments, not actual changes in taxes or tax rates.

Net proceeds from the equity offering after deduction of transaction fees is EUR 351.8m.

Leverage is at x0.6 at the end of 2Q19 (1Q19: x2.2) following the share capital increase. Net debt decreased by EUR 345.8m between quarters and part of the cash from the equity issue was used to repay revolving loan facilities. Leverage is now well under the targeted capital structure (2-3x Net debt/EBITDA) highlighting the company´s ability to facilitate future strategic moves in line with its growth strategy.

Strategic investment in Worximity Technology Inc.

In June 2019, Marel acquired 14.3% interest in the Canadian software company Worximity Technology for CAD 2.5m (EUR 1.8m).  Marel will invest an additional CAD 2.5m in new share capital in the company in the next twelve months, bringing Marel’s total ownership to 25%.

Worximity offers real-time cloud data collection and analytics solutions and is compatible with Marel´s proprietary software platform Innova. Worximity has around 25 employees, servicing over 200 customers since its launch in 2012. The company mainly operates in meat, dairy and baked goods processing industries, that use Worximity software solutions to reduce downtime, increase throughput, improve quality and get better raw material yield.

Outlook

Market conditions have been exceptionally favorable in recent years but are currently more challenging in light of geopolitical uncertainty. Marel enjoys a balanced exposure to global economies and local markets through its global reach, innovative product portfolio and diversified business mix.

In the period 2017-2026, Marel is targeting 12% average annual revenue growth through market penetration and innovation, complemented by strategic partnerships and acquisitions.

  • Marel’s management expects 4-6% average annual market growth in the long term. Marel aims to grow organically faster than the market, driven by innovation and growing market penetration.
  • Maintaining solid operational performance and strong cash flow is expected to support 5-7% revenues growth on average by acquisition.
  • Marel’s management expects basic earnings per share (EPS) to grow faster than revenues.

Growth is not expected to be linear but based on opportunities and economic fluctuations. Operational results may vary from quarter to quarter due to general economic developments, fluctuations in orders received and timing of deliveries of larger systems.

Industry performance Q2 2019

Marel Poultry -Delivered 56% of total revenues and 20.2% EBIT in 2Q19.

Marel Poultry’s competitive position remains exceptionally strong on the back of its established full-line product range, including standard equipment and modules, and service and spare parts revenue from its large installed base worldwide.

Revenues for Marel Poultry in 2Q19 were up 17.9% YoY or EUR 182.5m (2Q18: 154.8m). EBIT was EUR 36.8m (2Q18: 26.9m) and the EBIT margin was 20.2% (2Q18: 17.4%). In comparison, the EBIT margin in 1Q19 was 17.6%.

Large orders booked in Vietnam, China, Canada and the US.  Europe and N-America markets are softer in new greenfields while demand has shifted to Asia, in particular in China.

High growth in maintenance revenues and pipeline building up in standard equipment to advance and modernize existing plants.

Rising demand in China is partly in response to the ASF and general global trend towards a balanced diet.

As announced on 10 May 2019 and coming into effect on 1 September 2019, Roger Claessens, current Director of Innovation Poultry will be taking over from Anton de Weerd, as EVP of Marel Poultry.

 

Marel Meat – Delivered 32% of total revenues and 11.2% EBIT* in 2Q19.

Marel Meat is a full-line supplier to the meat processing industry following acquisitions of MPS, with further bolt-on capabilities added with the acquisitions of Sulmaq and MAJA.

Revenues for Marel Meat in 2Q19 were EUR 104.1m, up 10.5% YoY (2Q18: EUR 94.2m). EBIT* was EUR 11.7m (2Q18: 12.1m) and the EBIT* margin was 11.2% (2Q18: 12.8%). In comparison, the EBIT* margin in 1Q19 was 12.6%.

Order intake was strong in Meat, especially in primary meat. Different from Poultry and Fish the order intake for Meat was strong in Europe as well as Russia.

Food supply and food security are a top priority in China.  Although total supply of pork is down in China, the demand for modern and advanced pork plants with traceability is increasing.

Standardization and modularization of the offering in primary meat and further cross and upselling of secondary and further processing solutions is a top priority. At a successful IFFA exhibition in May 2019 in Frankfurt Marel launched over 20 new solutions, covering the meat processing value chain.

Management is targeting medium and long-term EBIT* margin expansion for Marel Meat.

 

Marel Fish – Delivered 11% of total revenues and 2.3% EBIT in 2Q19.

Marel Fish predominantly consists of sales of solutions into wild whitefish and salmon. Marel is as well investing to serve the potential high growth in farmed whitefish segment.

Revenues for Marel Fish in 2Q19 were down 21.3% YoY or EUR 35.2m (2Q18: EUR 44.7m), mainly due to soft orders received in 2H 2018. Due to changes in the product mix there are delays in revenue and profit recognition. EBIT was EUR 0.8m (2Q18: 3.8m) and the EBIT margin was at 2.3% (2Q18: 8.5%). In comparison, the EBIT margin in 1Q19 was 7.4%.

Marel is systematically investing in innovation to become a full-line provider in those segments with closing application capabilities in primary processing and continue to introduce steady flow of innovative solutions in secondary processing.

Highlights in new solutions introduced to the market this year is the RobotBatcher to sort consumer products and various applications serving the white-farmed fish segment.

Management is targeting medium and long-term EBIT margin expansion for Marel Fish.

 

Investor Relations

Investor meeting and live webcast/conference call 25 July 2019

On Thursday 25 July 2019, at 8:30 am GMT (10:30 am CET), Marel will host an investor meeting where CEO Árni Oddur Thórdarson and CFO Linda Jónsdóttir will give an overview of the financial results and operational highlights in the second quarter.

The investor meeting will be held at the company’s headquarters: Austurhraun 9, Gardabaer, Iceland. Breakfast will be served from 8:00 am GMT.

Please note that the meeting will also be webcast live on www.marel.com/webcast and a recording will be available after the meeting on marel.com/IR.

Members of the investment community can join the conference call at:

  • IS: +354 8007508
  • NL: +31 207219496
  • UK: +44 3333009031
  • US: +1 8335268395.

Upcoming IR events

  • Kepler Cheuvreux – Autumn Conference Paris, 12 September 2019

Upcoming tradeshows

  • Marel Whitefish Showhow – Copenhagen, 25 September 2019
  • Marel Poultry Showhow – Copenhagen, 14 November 2019
  • Marel Seafood Showhow – Seattle, 22 November 2019

Financial Calendar

Marel will publish its interim and annual Condensed Consolidated Financial Statements according to the below financial calendar:

  • Q3 2019 – 23 October 2019
  • Q4 2019 – 5 February 2020
  • AGM – 4 March 2020

Financial results will be disclosed and published after market closing of both NASDAQ Iceland and Euronext Amsterdam.

For further information, please contact Marel Investor Relations via email IR@marel.com or tel. +354 563 8001.

About Marel

Marel (NASDAQ: MAREL; AEX: MAREL) is a leading global provider of advanced food processing equipment, systems, software and services to the poultry, meat and fish industries. Our united team of more than 6,000 employees in over 30 countries delivered EUR 1.2 billion in revenues in 2018 and operated at a 14.6% EBIT margin. Annually, Marel invests around 6% of revenues in innovation which translated to EUR 74 million in 2018. By continuously advancing food processing, we enable our customers to increase yield and throughput, ensure food safety and traceability, and improve sustainability in food production. Listed on NASDAQ Iceland since 1992, Marel had a public offering and listing of 15% of its shares on Euronext Amsterdam in June 2019.

Forward-looking statements

Statements in this press release that are not based on historical facts are forward-looking statements. Although such statements are based on management’s current estimates and expectations, forward-looking statements are inherently uncertain. We therefore caution the reader that there are a variety of factors that could cause business conditions and results to differ materially from what is contained in our forward-looking statements, and that we do not undertake to update any forward-looking statements. All forward-looking statements are qualified in their entirety by this cautionary statement.

Market share data

Statements regarding market share, including those regarding Marel’s competitive position, are based on outside sources such as research institutes, industry and dealer panels in combination with management estimates. Where information is not yet available to Marel, those statements may also be based on estimates and projections prepared by outside sources or management. Rankings are based on sales unless otherwise stated.

 

Cargill’s 2019 Annual Report Highlights Commitment To Sustainable Growth

By
Jim
-

Source: Cargill news release

Cargill’s 2019 annual report, published today, tells the story of how the company is reaching higher to create more value for customers, accelerate its growth and sustainably nourish the world.

“We are directing our insights, capabilities and resources toward answering some of the world’s biggest questions,” said David MacLennan, Cargill’s chairman and CEO. “Everyone at Cargill is relentlessly determined to transform what is possible in food, agriculture and nutrition.” In 2019, this included enhanced policies and commitments in support of the U.N. Sustainable Development Goals, including a strengthened human rights commitment and global forest policy.

During the past 12 months, Cargill’s global team leaned into a challenging business and geopolitical environment. By remaining agile and focused on what the company can control, Cargill advanced its corporate strategy and helped customers meet consumer demands in local markets.

As highlighted in the report, Cargill:

• Joined together with new partners, especially on the digital front: Along with other grain buyers, the company launched GrainBridge, a one-stop digital application where North American farmers can gain powerful new insights about how and when to market their crops. In Germany, Cargill also supported the World Food Programme Innovation Accelerator by mentoring startup firms seeking breakthroughs to end hunger.

• Expanded possibilities for customers: In the Netherlands, Cargill’s Anova asphalt rejuvenator boosted sustainability with an environmentally friendly way to pave roadways and cycling paths. In the U.S., a poultry customer turned to Cargill to create a supply chain for specialty feed ingredients, leading to new opportunities for farmers and fresh choices for consumers. U.S. farmers also gained access to Portfolio Builder, a new grain marketing contract that uses the power of diversification to help improve farm profitability over time.

• Raised prosperity for small-scale farmers: Cargill teamed up with Heifer International to launch the Hatching Hope Global Initiative, using poultry production to open new doors for women farmers and their communities. A four-year partnership with TechnoServe in India achieved its main target — reaching 5,000 farming households across 27 villages with tools to help them increase crop productivity, upgrade water and sanitation, and improve nutrition.

“We are proud of how far we have come in 154 years, and we know together we can achieve much more,” said David Dines, Cargill’s CFO. “Every day we challenge ourselves to reach higher.”

The 2019 annual report is available online at https://www.cargill.com/annual-report/. You also can learn more from @Cargill on Twitter.

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