G. Kee Jim, D.V.M.; Calvin W. Booker, D.V.M., M.Vet.Sc.; Brian K. Wildman, D.V.M.; Tye Perrett, B.Sc.Agr., D.V.M.; Oliver C. Schunicht, D.V.M., B.Sc.;

G. Kee Jim, D.V.M.; Calvin W. Booker, D.V.M., M.Vet.Sc.; Brian K. Wildman, D.V.M.; Tye Perrett, B.Sc.Agr., D.V.M.;

Oliver C. Schunicht, D.V.M., B.Sc.; R. Kent. Fenton, D.V.M.; Sherry J. Hannon, D.V.M., M.Vet.Sc., Ph.D.; Colleen Pollock, D.V.M., M.Vet.Sc.;

Luis O. Burciaga-Robles, M.V.Z., M.C., Ph.D.; Robert. E. Peterson, B.Sc., M.Sc., Ph.D.,

Veterinary and Production Consultant TeamFHMS

Individual Animal Management: The Principal Driver For Improved Profitability In Feedlot Production

General

• Individual Animal Management (IAM) in feedlot production has been advocated by FHMS for several years because it can significantly improve feedlot profitability

• Changing from managing at the pen level to managing at the individual animal level allows for the optimization of procurement of feeder animals, production processes and marketing

• Purchase the “right animal”, apply the “most appropriate” production technologies, and sell at the “correct” end-point in the marketing scenario that returns maximum revenue

• The implementation of IAM requires individual animal identification, data collection, execution algorithms, and appropriate physical infrastructure

Objective of IAM

• Maximize the net profitability of each animal through optimization of:

» Procurement practices

» Production practices

» Marketing practices

History of Individual Animal Management

1. Animal Health

» Animal health was the first production process to be fully managed at the individual animal level

– Evolution from uniquely identifying “sick” animals to uniquely identifying all animals on arrival at the feedlot

– Correlation of tag sequence to processing group

– Correlation of processing group to a buying location/order buyer

• Animal Health

» Processing (arrival), health and movement events recorded at the individual animal level

» Epidemiological analysis of individual animal based health data led to significant advances in disease control including

a) Health risk categorization system for animals arriving at the feedlot

b) Early identification of “wreck pens”

c) Defining health intervention strategies

History of Individual Animal Management …continued


d) Evaluation of metaphylactic and therapeutic antimicrobial approaches

e) Refining pen checking techniques

f) Models to predict final morbidity and mortality at various points in the feeding period


2. Selection/Sale of Slaughter AnimalsMethod 1 - Sell the entire pen when the average weight of

the pen reached the target weight (based on historical performance estimates for average daily gain)

Method 2 - Visually sort individual animals into “loads” based on “degree of finish”

» Visual sorting methods were driven by packer discounts for poorer yield grades and the paradigm that poorer yield grade in a group of cattle was accompanied by a dramatic decline in feed conversion (DM:G)

» Visual sorting was/is a highly subjective process conducted by the resident fat cattle “guru”

» Visual sorting did not withstand scientific scrutiny

Drivers of IAM Approach

1. Emergence of marketing “grids” with premiums and discounts applied to yield grade, quality grade, and weight of each carcass

» Opportunity for increased selling price by producing carcasses with more desirable characteristics

Drivers of IAM Approach...continued

2. Increased dry matter (feed) costs

» Creates strong incentive to sell animals before feed conversion is adversely affected

» Emphasizes the need to identify the impact of and application strategy for performance enhancement products such as growth implants, repartitioning agents, ionophores, liver abscess control agents, etc.

Drivers of IAM Approach...continued

3. Increasing complexity of technology/product application

» Repartitioning agents

» Growth implants protocols

» Recognition of “cattle types”

» Identification of various “interactions”

• Market animals at a target carcass weight by using weight management procedures (based on individually weighing animals at the time of terminal implant administration and identifying marketing groups) so that each pen/lot would have several slaughter groups (3-6 groups)

• Weight management procedures at the time of terminal implant administration

• Animals could remain in the same initial pen, identified with different colored tags and each tag color would be marketed at a specified date

• Animals could be comingled with animals from other pens to create large groups or full pens of animals with the same marketing date

Basic Weight Management “the low hanging fruit of IAM”

Basic Weight Management “the low hanging fruit of IAM”…continued

• 310 Winter Calves

» Arrival Weight = 624.6 lbs.

» Day 63 Weight = 882.3 lbs. (ADG=3.39)

» Day 140 Weight = 1155.4 lbs. (ADG=3.36)

0

5

10

15

20

25

30

35

Body Weight Pounds

Nu

mb

er o

f H

ead

Day 0

Day 63

Day 140

2 per. Mov. Avg. (Day 0)



Average Weight:624.6 lbs. Average Weight:

1155.4 lbs.

Average Weight: 882.3 lbs.

Body Weight Profile

Basic Weight Management “the low hanging fruit of IAM”…continued

Subsequent Evolution of IAM

1. Refinement of Weight sorting algorithms

» Transition from static weight sorting based on predetermined “weight breaks” to proportional sorting systems designed to maximize pen occupancy

2. Increasing target carcass weight

» Observation that significantly increasing days on feed (DOF) within a reasonable range did not appear to adversely influence DM:G

» Research trials studying the effect of feeding cattle beyond conventional end points using sequential slaughter methodologies

Subsequent Evolution of IAM…continued

» Data demonstrating that at both the group and individual animal levels, slow gainers are not necessarily poor converters

3. Incorporation of phenotypic and health event data to modify marketing dates

» Health events (initial diagnosis, relapse diagnosis, designation as chronic) impact ADG and carcass characteristics

» Phenotypic data such bred type, color, linear measurements (height and weight), muscling score, etc.


4. Incorporation of genotypic data into the days on feed prediction model

» Collect blood sample

» Analyze in a “multi SNP” panel

» Use genetic information to improve sorting techniques

5. Provision of carcass data linked to individual animal

» Retrieval of detailed carcass data on large populations of animals without sending personnel to the packing plant

» Facilitated by an RFID tag that is cross referenced to feedlot tag


» In Canada, all cattle must be tagged with an RFID tag before leaving the herd of origin

» This linkage has facilitated IAM research and scientific validation of IAM strategies

6. Refining procurement strategies

» Evaluation of purchase location, cattle buyer, cattle type

7. Refining marketing strategies

» Analysis of grid options

» Grid negotiations


8. Improving the efficiency of beef production systems

» In North America, three basic systems

– a) directly to feedlot

– b) back grounding followed by feedlot

– c) backgrounding to grass to feedlot

» Potential that certain cattle types are best suited to a specific production system

Implementation of IAM

1. Algorithms

» Knowledge to create marketing groups or groups of animals to which differential production techniques will be applied

Implementation of IAM…continued

2. Software

» Chute side data collection

» Capable of driving execution of the sort algorithm (multiple attributes determine the sort group)

» Lot/pen/tag inventories to tie in with feedlot administrative software programs and individual animal carcass data

» Current feedlot administrative software systems do keep track of individual tags

» Complete redesign of administrative software systems to accommodate IAM (ParaDIAM project)

Implementation of IAM…continued

3. Physical infrastructure requirements

» Investment in new or upgraded handling/sorting facilities

» Smaller or more flexible pen capacities

» Effects on occupancy and effectiveness of proportional sorting

Continued Development Of IAM by FHMS

• Data collection and analysis will be the basis for ongoing development/evolution of IAM strategies to optimize feedlot performance and carcass production

• The impacts of IAM on feedlot occupancy, sorting, commingling, risk management strategies, etc., will be modeled to optimize cost-effectiveness

Continued Development Of IAM by FHMS

• New approaches will be investigated and scientific studies conducted to identify appropriate applications of IAM for procurement, production, and marketing

• A variety of research tools will be required to validate IAM strategies in commercial feedlot production scenarios

• IAM will be used as an integral component of producing/delivering specified quantities of carcasses with highly predictable characteristics as part of a integrated strategy to optimize packing plants return and feedlot production costs

Summary

• IAM can significantly improve net profitability

• Paradigm shift in feedlot management

• Requires a change in philosophy

• Software changes

• Physical infrastructure changes

• Buying and selling “by the pen” is financially contraindicated

Documents

G. Kee Jim, D.V.M.; Calvin W. Booker, D.V.M., M.Vet.Sc.; Brian K. Wildman, D.V.M.; Tye Perrett, B.Sc.Agr., D.V.M.; Oliver C. Schunicht, D.V.M., B.Sc.;