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Making the Web equal Profit Surfing for Genetics. Dorian Garrick & Mark Enns Department of Animal Sciences Colorado State University. Surfing for Genetics. Surfing for Convenience Surfing to Support Decisions based on your own Customized Computations. Convenience.

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Making the web equal profit surfing for genetics l.jpg

Making the Web equal ProfitSurfing for Genetics

Dorian Garrick & Mark Enns

Department of Animal Sciences

Colorado State University


Surfing for genetics l.jpg

Surfing for Genetics

  • Surfing for Convenience

  • Surfing to Support Decisions based on your own Customized Computations


Convenience l.jpg

Convenience

  • Finding a Particular Bull/Breed/Breeder

  • Sort Orders

    • Finding extreme bulls for some attribute

  • Filters

    • Finding bulls with particular combinations of attributes

  • Up-to-date EPD and ACC information


Customized computations l.jpg

Customized Computations

  • Interpretation of Threshold Traits

  • Interactions between ERTs

  • Assessment of Nutritional Implications

  • Assessment of Financial Implications

    • Perhaps also Economic Implications

  • Accounting for Risk

  • Multibreed Evaluation & Crossbreeding


Interpretation of a typical epd l.jpg

Interpretation of a Typical EPD

W W D = 50 lb

W W D = 20 lb


Interpretation of a typical epd6 l.jpg

Interpretation of a Typical EPD

W W D = 50 lb

W W D = 20 lb

Herd 1Average 500 lbAverage 530


Interpretation of a typical epd7 l.jpg

Interpretation of a Typical EPD

W W D = 50 lb

W W D = 20 lb

Herd 1Average 500 lbAverage 530

Herd 2Average 550 lbAverage 580


Interpretation of threshold traits l.jpg

Interpretation of Threshold Traits

  • Calving Ease EPD

  • Stayability EDP

  • Heifer Pregnancy EPD


Underlying scores to preg rate l.jpg

Underlying Scores to Preg Rate

Difficult to get pregnant

Easy to get pregnant

Average


Underlying scores to preg rate10 l.jpg

Underlying Scores to Preg Rate

Difficult to get pregnant

Easy to get pregnant

Average

Suppose 20% heifers are open

And 80% heifers are pregnant


Underlying scores to preg rate11 l.jpg

Underlying Scores to Preg Rate

Pregnant Heifers

Heifers not in calf

20%

Difficult to get pregnant

Easy to get pregnant

Average


Underlying scores to preg rate12 l.jpg

Underlying Scores to Preg Rate

Pregnant Heifers

Heifers not in calf

20%

Truncn pt = 0.84s

Threshold


Underlying scores to preg rate13 l.jpg

Underlying Scores to Preg Rate

Pregnant Heifers

Heifers not in calf

20%

Truncn pt = 0.84s

0.38


Underlying scores to preg rate14 l.jpg

Underlying Scores to Preg Rate

Pregnant Heifers

Heifers not in calf

20%

Truncn pt = 0.84s

12%

Truncn pt = 0.84 +0.38/1.17=1.165

Phenotypic s.d. = 1.17

0.38


Underlying scores to preg rate15 l.jpg

Underlying Scores to Preg Rate

10%

Truncn pt = 1.28s

5.5%

Truncn pt = 1.28 +0.38/1.17=1.605

Phenotypic s.d. = 1.17

0.38


Sensitive to the average l.jpg

Sensitive to the Average

  • An underlying EPD of 0.38 for heifer pregnancy would increase pregnancy rate

    • By 8.0% if average pregnancy rate is 80%

    • By 4.5% if the average is 90%

  • Phenotypic “interpretation” of underlying threshold scores depends upon the mean

  • Published values are at a mean of 50%


Solution l.jpg

Solution

  • Publish values that are hard to interpret

  • OR Publish tables of EPDs relevant to different average levels of performance

    • Calving Ease:

      • First Calf:75, 80, 85, 90, 95%

      • Mixed Age:95, 99%

    • Stayability: 40, 45, 50, 55, 60%

    • Heifer Pregnancy: 75, 80, 85, 90, 95%


Solutions cont l.jpg

Solutions (cont)

  • OR Use web-based decision support

    • User defined average levels of performance

    • Compute the

      • number of pregnant vs open heifers

      • number of easy vs difficult calvings

      • Likely age structure of the herd

        • Number of replacement heifers required

        • Number of cull yearlings and mixed age cows


Suppose our goal is incr sale wt l.jpg

Suppose our goal is incr sale wt

W W D = 20 lb

W W D = 50 lb

Base+30 lb

Base


Interactions between erts l.jpg

Interactions between ERTs

  • WWD EPD + 30 lb (all other EPDs equal)

    • gives +30 lb weanlings


Interactions between erts21 l.jpg

Interactions between ERTs

  • WWD EPD + 30 lb (all other EPDs equal)

    • gives +30 lb weanlings

    • gives +22 lb weanling sale wt “per cow” in a “typical” self-replacing herd


Interactions between erts22 l.jpg

Interactions between ERTs

  • WWD EPD + 30 lb (all other EPDs equal)

    • gives +30 lb weanlings

    • gives +22 lb weanling sale wt “per cow” in a “typical” self-replacing herd

  • Increasing ST +8 will give another +8 lb


Interactions between erts23 l.jpg

Interactions between ERTs

  • WWD EPD + 30 lb (all other EPDs equal)

    • gives +30 lb weanlings

    • gives +22 lb weanling sale wt “per cow” in a “typical” self-replacing herd

  • Increasing ST +8 will give another +8 lb

  • Increasing HPG +12 will give another + 3lb


Interactions between erts24 l.jpg

Interactions between ERTs

  • WWD EPD + 30 lb (all other EPDs equal)

    • gives +30 lb weanlings

    • gives +22 lb weanling sale wt “per cow” in a “typical” self-replacing herd

  • Increasing ST +8 will give another +8 lb

  • Increasing HPG +12 will give another + 3lb

  • Increasing CED +11 will give another +1 lb


Interactions between erts25 l.jpg

Interactions between ERTs

  • Many ERT interact in a system context

  • Total sale weight at weaning is altered by WWD, WWM, STAY, HPG, CED, CEM, ME (plus BW & YWT)

  • The impact of any one EPD on the change in sale weight depends upon all the other EPDs and the average levels of performance


Assessment of nutritional other input implications l.jpg

Assessment of Nutritional (& other input) Implications

  • Feed requirements are influenced by the

    • Expected maintenance requirements

    • Expected requirements for gain

    • Deviation from our expectations (known as residual feed intake or RFI)

  • Changing any of WWD, WWM, STAY, HPG, CED, CEM, ME, BW, YW will alter whole herd feed requirements


Assessment of financial implications l.jpg

Assessment of Financial Implications

  • Changes in profit can be calculated from change in income × priceschange in expenses × costs

    • Straightforward (but tedious) arithmetic

  • Prices & Costs can be obtained on a financial basis or an economic basis

    • What is the cost of feed in an extensive cow-calf grazing operation?


Economic cost of feed l.jpg

Economic Cost of Feed

  • Feed “cost” can be calculated from its “opportunity” cost

    • Measure of what net income would be given up if you had less feed (and less cows)

    • Can be calculated from comparing the system profit of two herds of alternative genotypes with different stocking rates such that each consume the same amount of feed


Solutions l.jpg

Solutions

  • Leave it to bull buyers to struggle thru facts & calculations

  • OR put it all together via the web

    Website ert.agsci.colostate.edu


Current philosophical approach l.jpg

Current Philosophical Approach

Perturbed Situation

Base Situation

Current

(equilibrium)

Cow

Herd

(EPD

&

Performance)

Like

Merit

Bulls

X

Base Cow-calf outputs & inputs


Current philosophical approach35 l.jpg

Current Philosophical Approach

Perturbed Situation

Base Situation

Current

Cow

Herd

(EPD)

Chosen

Bulls

X

Current

(equilibrium)

Cow

Herd

(EPD

&

Performance)

Like

Merit

Bulls

Daughter

(equilibrium)

Cow

Herd

(EPD

& Base mean

Performance)

X

Base Cow-calf outputs & inputs


Current philosophical approach36 l.jpg

Current Philosophical Approach

Perturbed Situation

Base Situation

Current

Cow

Herd

(EPD)

Chosen

Bulls

X

Current

(equilibrium)

Cow

Herd

(EPD

&

Performance)

Like

Merit

Bulls

Daughter

(equilibrium)

Cow

Herd

(EPD

& base mean

Performance)

Like

Merit

Bulls

X

X

Perturbed Cow-calf outputs & inputs

Base Cow-calf outputs & inputs


Accounting for risk l.jpg

Accounting for Risk

Consider the following three bulls

Bull#prog AccProfit

Lima 300.5 to 0.6$908

Sierra 2400.7 to 0.8$729

Bravo37000.8 to 0.95$648


Accounting for risk39 l.jpg

Accounting for Risk

  • On average, true EPD is equally likely to be greater or lesser than published

  • ACC allows us to quantify the extent to which the estimate may vary from true

  • Considering just BW, WW, YW, Milk, ME and not (in this example) CED, CETM, HPG, ST we can compute many possible “realizations” of each bull


Extra profit per 30 daughter years l.jpg

Extra Profit per 30 daughter-years

3700 progeny

$648


Accounting for risk41 l.jpg

Accounting for Risk

3700 progeny

240 progeny

$729


Accounting for risk42 l.jpg

Accounting for Risk

3700 progeny

240 progeny

30 progeny

$908


Solution43 l.jpg

Solution

  • Publish an “expected change” table

  • OR provide web options for quantifying risk (prototype available this Fall)


Multibreed evaluation xbreeding l.jpg

Multibreed Evaluation & Xbreeding

*A1 & S3 are breed average EPDs


Multibreed evaluation xbreeding45 l.jpg

Multibreed Evaluation & Xbreeding

#Angus Base

*A1 & S3 are breed average EPDs


Multibreed evaluation xbreeding46 l.jpg

Multibreed Evaluation & Xbreeding


Multibreed evaluation xbreeding47 l.jpg

Multibreed Evaluation & Xbreeding


Multibreed evaluation xbreeding48 l.jpg

Multibreed Evaluation & Xbreeding


Multibreed evaluation xbreeding49 l.jpg

Multibreed Evaluation & Xbreeding


Multibreed evaluation xbreeding50 l.jpg

Multibreed Evaluation & Xbreeding


Multibreed evaluation xbreeding51 l.jpg

Multibreed Evaluation & Xbreeding


Multibreed evaluation xbreeding52 l.jpg

Multibreed Evaluation & Xbreeding


Solution53 l.jpg

Solution

  • Publish within-breed EPDs

    • Let users find breed adjustments & heterosis

  • Publish multibreed EPDs

    • Let users deal with heterosis coefficients & heterosis

  • Publish all EPDs on a multibreed base with heterosis factors included according to the breed of dam for every ERT

  • Use web-based decision support


Summary l.jpg

Summary

  • To date, the major value of the web has been convenience

  • In future, the web will provide an interface to knowledge (eg nutritional requirements and heterosis factors) and information that for customized calculations to support your decisions

  • Better decision support will give better decisions (eg more profit)


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