[1] "C:/Users/jsteibel/OneDrive/Documents/Berkshire/Prog"Berkshire 2024 progeny testing
Data formatting, checking
Start reading historical data and current placement data
Read master data
Join placement, phenotype
Meat quality data includes many spreadsheets that are imported and compiled into one single table. I took care to link all records by the ID (last 4 digitst of RFID)
[1] "Pen"
[2] "ID"
[3] "Kill Date"
[4] "Kill Group"
[5] "Sequence Number"
[6] "Sex"
[7] "On-Test Block"
[8] "On-Test Date"
[9] "Penn State On-Test Wt (kg)"
[10] "Penn State Live Weight (kg)"
[11] "LK converted Live Weight (kg to lb)"
[12] "Ohio State converted Live Weight (kg to lb)"
[13] "Ohio State Live Weight (lb)"
[14] "Transport Shrink (%)"
[15] "Hot Carcass Weight (lb)"
[16] "Dressing Percentage (%)"
[17] "Carcass Length (cm)"
[18] "Backfat (in)"
[19] "Loin Eye Area (sq. in.)"
[20] "Ultimate pH"
[21] "Minolta Sequence"
[22] "L*"
[23] "a*"
[24] "b*"
[25] "Hunter L"
[26] "Hunter a"
[27] "Hunter b"
[28] "Y"
[29] "NPB Color"
[30] "NPB Marbling"
[31] "NPPC Firmness"
[32] "Intramuscular Fat IMF (%)"
[33] "Purge Loss (%)"
[34] "Cooking Loss (%)"
[35] "WB Shear Force (kg)"
[36] "Sensory Tenderness (1-10 scale)"
[37] "Sensory Juiciness (1-10 scale)"
[38] "Sensory Chewiness (1-10 scale)"
[39] "Sensory Pork Flavor (1-10 scale)" [1] "ID" "Visual Tag" "Tag ID"
[4] "Pen" "Sex" "Entry Name"
[7] "Complete Sire Name" "ABA reg. #" "Front"
[10] "Rear" THis is by far the longest data wranging operation including three tables: Placement, weight and MQ. Several steps take place here:
select variables in each table
rename variables as needed
compute ADG for the whole test period (april-august weights)
Transform Share force to starprobe units using equation from ISU paper (Figure 4): Instron_kg=WBSF_KG*0.8143+3.4416, please see ABA reports Cybox folder.
Compute LEA from provided data
Note: How LEA was computed: Take 2 columns from LEA spreadsheet (Dots and Initial), LEA=Dots/20+Initial. It has been confirmed by PU
Finally, raw data is saved.
Pigs per SIRE
Eliminate sires that don’t have to be included according to Lance’s indications
# A tibble: 10 × 2
Sire cnt
<fct> <dbl>
1 DKNS3 Django 7-3 (A) 8
2 DKNS3 Django 7-3 (B) 8
3 FKB1 Lakota 17-4 8
4 FKB2 SEAL Team 6 26-8 (A) 6
5 FKB2 SEAL Team 6 26-8 (B) 7
6 JBM2 Machine 30-1 8
7 KURO Saki II 95-9 8
8 KURO1 Rumbolds Peter Lad 14 15-9 8
9 KURO2 Black Stallion 58-9 7
10 KURO3 Rosette Count 11-9 8Compare this year’s results to data up to 2023
This is important to make sure we are working in the same range for all phenotypes and that we can join data for computing correction factors.
The following phenotypes are compared between the two datasets:
[1] "Year" "ID" "Pen" "Breeder"
[5] "Sire" "Gender" "On_Test_Date" "On_Test_WT"
[9] "Off_Test_Date" "Off_Test_Weight" "On-Test_Days" "On-Test_ADG"
[13] "Soundness_Score" "Market_Date" "Market_Weight" "Carcass_BF10"
[17] "Carcass_LEA" "48h_Loin_pH" "Visual_Color" "Visual_Marbling"
[21] "Visual_Firmness" "Minolta_Y" "Minolta_L*" "Hunter_L"
[25] "Thaw_Purge" "Cook_Loss" "Juiciness_Score" "Tenderness_Score"
[29] "Instron_kg" "Percent_IMF" The comparison is on the basis of the mean value per year and sex of the pigs in the progeny test. It is EXTREMELY important that ABA reviews these results and flag anything that may not make sense. We are on time to make adjustments as long as sire evaluations are not published.
2024 previous
1292 10084 
Compare means
This table shows the mean value (average) for Barrows (B) and Gilts (G) in the current test (2024) and in previous tests.
Again: extremely important to check this.
Observations of some results: It is extremely important to review all this carefully before proceeding
I can provide a full comparison in case it is useful for a discussion.
Full comparison between years
Not showing this anymore, but it is available
Computing corrections factors and adjustments
Records need to be adjusted for sex effects. To accomplish this, for each phenotype I fit a model that includes: Sire (random effect), Off_Test_Date (random effect), Sex and Off_Test_Weight.
Everything but Sex are so called “nuisance” effects (effects we want to account for but that we are not interested in yet). Sex, on the other hand, is our effect of interest. We decide if there is a sex difference or not and then we figure out the correction factors for each phenotype such that corrected phenotypes will be comparable regardless of the sex of the tested pig.
Check sire sample sizes and mean phenotypes
All results are available upon request
# A tibble: 10 × 3
Sire mins maxs
<fct> <int> <int>
1 DKNS3 Django 7-3 (A) 8 8
2 DKNS3 Django 7-3 (B) 7 8
3 FKB1 Lakota 17-4 8 8
4 FKB2 SEAL Team 6 26-8 (A) 6 6
5 FKB2 SEAL Team 6 26-8 (B) 7 7
6 JBM2 Machine 30-1 8 8
7 KURO Saki II 95-9 8 8
8 KURO1 Rumbolds Peter Lad 14 15-9 8 8
9 KURO2 Black Stallion 58-9 7 7
10 KURO3 Rosette Count 11-9 8 8Sex Correction factor using all data
Results available upon request
#long_joint
rst<-joint_DT%>%
group_modify(~ lmer(value~(1|Sire)+(1|Off_Test_Date)+Gender+Off_Test_Weight,data= .x)%>%
emmeans(.,specs=~Gender)%>%pairs()%>%as.data.frame())%>%
mutate(Barrow=-(p.value<0.05)*estimate/2,Gilt=(p.value<0.05)*estimate/2)%>%
select(Phenotype,contrast,Barrow, Gilt)boundary (singular) fit: see help('isSingular')datatable(rst)%>%formatRound(columns=c('Barrow','Gilt'), digits=3)Sex correction factors using only 2024 data
I repeated calculations of the sex correction factors for 3 datasets: 1) all data (above) 2) all data up to 2023, Used by Matt Ritter last year and not shown 3) only 2024 data (below)
General conclusion from these results: Using all data for computing sex adjustment vs using only 2024 data gives similar factors, but, as expected, the factors based on a very small dataset (2024) often are not significant and end up on a zero correction factor.
One particularly different result this year is the one for meat color
ABA should discuss this with Steibel to confirm that initial choice of using all data is still acceptable Using the sex correction factors from all data for now on.
NPPC’s equations, used before for every Berkshire evaluation
- Adjusted ADG=ADG+0.005*(90-ON-Test-weight). Note: BF and Weight in lb
- Adjusted BF10=BF10+(290-Market_Weight)*(BF10/(Market_Weight-c)) note: c=30 for barrows and 5 for gilts
- Adjusted LEA=LEA+(290-Market_Weight)*(LEA/(Market_Weight+155))
# A tibble: 700 × 31
Gender yr Off_Test_Date Year ID Pen Breeder Sire On_Test_Date
<fct> <fct> <fct> <fct> <chr> <fct> <chr> <fct> <chr>
1 B previous 42798 2017 36 1 Brummer KURO… 42693
2 B previous 42798 2017 37 1 Brummer KURO… 42693
3 B previous 42820 2017 38 1 Brummer KURO… 42693
4 G previous 42820 2017 39 1 Brummer KURO… 42693
5 G previous 42798 2017 40 1 Brummer KURO… 42693
6 B previous 42784 2017 41 1 Brummer KURO… 42693
7 B previous 42784 2017 42 1 Brummer KURO… 42693
8 B previous 42784 2017 43 1 Brummer KURO… 42693
9 B previous 42784 2017 8717 2 Evergreen… EHB5… 42679
10 B previous 42798 2017 8719 2 Evergreen… EHB5… 42679
# ℹ 690 more rows
# ℹ 22 more variables: On_Test_WT <dbl>, Off_Test_Weight <dbl>,
# `On-Test_Days` <dbl>, Market_Date <chr>, Market_Weight <dbl>,
# `On-Test_ADG` <dbl>, Soundness_Score <dbl>, Carcass_LEA <dbl>,
# `48h_Loin_pH` <dbl>, Visual_Color <dbl>, Visual_Marbling <dbl>,
# Visual_Firmness <dbl>, Minolta_Y <dbl>, `Minolta_L*` <dbl>, Hunter_L <dbl>,
# Thaw_Purge <dbl>, Cook_Loss <dbl>, Juiciness_Score <dbl>, …
Sex adjustments using previously computed coefficients (historical data)
Explained above. Here I include some graphics to show the effect of these corrections
Per-sire summary
In this section, I prepared the data for computing sire summaries and also corrected by year mean, which is needed for the selection indexes. This table is sortable and searchable.
Compute selection indexes using ABA index equations
Indexes are computed and joined to the individual phenotype summaries. Two tables are presented: 1) table only for indexes. 2) A wide table for all evaluated traits.
Producer-specific reports
Per-breeder individual reports are generated for all pigs that finished the test, but they are written to breeder-specific excell files for sharing with each breeder individually.