The first two columns record the herd code (herd of origin) and the identification number or name for each goat measured (see Figure 1). Capricorn’s method is to use the last number of the year born (in this case “9” for 1999) as the first number of the tag number. Color of ear tag can be used to indicate sire but this year, sire and dam numbers were written on the inside of the ear tag before the kid was tagged. In past years a blue ear tag “B” was used to indicate progeny of a heavily fleeced Boer goat. Bucks and does are turned out into a communal herd after the National Western Stock Show in January so kids born after June 15 are not ear tagged.
In order for the database to work, every kid born that year must be measured. This is because much of Pattie and Restall’s work is based upon the individuals’ deviation from the mean of the herd. It is of no use to measure and test just the crème of the crop. Deviation from the mean is the magic term that allows the identification of truly superior animals. It also establishes the herd averages that are so important to Estimated Breeding Value (EBV) calculations. So every kid born every year should be measured.
Again in Figure 1, columns three and four indicate gender and age. Columns five through eight describe the bodies of the goats measured. A small livestock scale is the optimal machine to measure liveweight. Bathroom scales can be used if the weight of the holder is tared out. Hanging scales have been used to great success in the past. If none of the above is available, heartgirth can be substituted for liveweight with a slight modification in the spreadsheet. Currently, data is being collected that will allow the production of a weight tape that will automatically convert heartgirth to pounds or kilograms of bodyweight, but at this time, sufficient data to correctly correlate such a tape do not exist. Heartgirth is also measured independently as is length of body and height at withers. Originally, the CW researchers defined length of body as the length from the withers to the hipbone. However, Mongolian researchers pointed out that a better, more consistently accurate measure of body length is best taken from the point of the shoulder to the pin bone. CW has implemented this change. Height at withers is measured from the ground to the highest point on the wither measured at the scapula. All measurements are to be recorded in inches. The spreadsheet will later convert these measurements into metric.
Frame is not only visually assessed (Column 10) but a ratio is later calculated by combining the length, height and girth measurements and dividing the sum by the bodyweight. This results in a “combined frame” measurement that can be assessed across the population. Scrotal circumference is also measured and recorded in column 9. Condition is a very important component of the description of the goat. A well-known phenomenon called “hunger fineness” exists. If a goat is starved, it will grow finer fiber than its genetic package dictates. Since it is not feasible to separate environmental factors from genetic ones when assessing a goat, it is very important to identify goats that are significantly thinner than other and to take that condition into account when making comparisons within the population as a whole. For both frame and condition, goats that are “framey” and fat are scored a three on a scale of three. Puny or poor goats are given a score of one. Fine goats that are also thin are penalized and fine goats that are fat are rewarded.
Birth type is the number of siblings a goat has. Twins are scored as a two, singles a one and multiples as three, four or five. Pattie and Restall report that singles are 5.9% heavier than twins, had 11.3% greater down weights, and fiber that was 1.3% longer and 3.3% coarser than twins. They did not report data comparing twins to triplets so all multiple births are treated the same. In order to compare a single born kid to a twin, the twin must have a handicap, based upon the above research results, added to its score. For example, a single kid with a down length of 1.5 inches will be compared to a twin with a down length of 1.25 inches by adding 1.3% to the twin’s measurement (0.01625 inches). The computer spreadsheet does this automatically by reading the birth type entry and adding the appropriate amount to twins and triplets. It is assumed that all kids, whether twin or singles will eventually grow out to their genetic potential so handicaps are applied only to kid populations. It is very important to not discriminate against a twin just because he is slightly smaller than his single born cousin. Using twin born bucks maintains the productivity of his female offspring because the tendency towards multiple ovulations is genetic. Boer goats do not twin as regularly as cashmere goats and since Boers are both large and well fed, this must be due to selection programs that did not value productivity.
Figure 2 highlights the conformation section of the datasheet (columns 13 through 20), a new addition to the database. Pattie and Restall recommend that up to 25% of a population be culled on conformational defects no matter how fabulous their fiber. Columns 13 through 20 allow the individual assessment of seven conformational points; teeth and head, head and neck, shoulder and spring of rib, chest and forelegs, rump and hindlegs, feet and genitals. Each is a very important component of a correctly conformed goat and deficiencies in any of these can affect the survivability, reproductivity and productivity of a goat. The assessor ranks each component and assigns it a ranking on a scale of 1 to 3, three being above average, two average and one deficient. The spreadsheet calculates the mean of all assigned values and identifies the bottom 25%. The word “yes” or “no” will appear in column 20 in answer to the question “cull?”, to identify those goats that should be culled on body alone. Remember that bad teeth, winged shoulders, fallen pasterns and steep rumps all are conformational defects that are highly heritable and they are defects that can and do influence the well-being of a goat. Their presence should trigger the elimination of the goat from the gene pool one way or another. If nothing else, the database forces a producer to look at each of these areas critically.
Pedigrees are perhaps the most important aspect of this database. Eventually when the data is incorporated into a Microsoft Access( based database, lines can be tracked and identified based on the pedigrees of individual animals. It is very important to include herd codes so herds of origin can be accurately traced.
Color is recorded by using a four to six letter code. Guard hair color is indicated by the letters Bl (black) Br (brown), Gy (grey), Rd (red) or white (Wh or WC). Fiber color is indicated by similar letters. Black goats that exhibit badger faced striping are indicated by the addition of Ab, spots or white stripes should be indicated by the use of Sp or St. A complete illustration of the various color variations and their abbreviations is available.
Figure 3 magnifies fiber characteristics section of the datasheet. Length of fiber is assessed at three points on each goat; at the neck, midside and rump about 3 inches from the dorsal line. At this time, measurements are to be recorded in inches but the program must later convert these measurements to metric. Metric based spreadsheets are available.
The next four columns are dedicated to the assessment of style. Style is the frequency of crimp or undulations in an individual fiber. There has been a lot of talk about style assessment lately. Pattie and Restall tested a number of Australian “experts” a number of years ago at a major Australian goat show. Each was asked to assess some fleeces using a scale of 1 to 4. Their correlations were less than optimal. Other reports have attempted to assess style on scales ranging from 1 to 5 and, incredibly, 1 to 7. Some claim to have computerized correlations. The CW researchers concluded that offering a greater of number of choices to assessors only decreases the correlation between them. The objective here is to have everyone who is looking at style, no matter how experienced or inexperienced, be able to come up with the same assessment of style consistently. In order to do this, we’ve got to keep it simple. Consequently, CW has embraced a scale of 1 to 3 to assess style. Figure 4 is a matrix that illustrates the expected style from fine, medium and coarse fiber. Animals with medium or coarse fiber should not be penalized on style because the number of undulations per unit measure is less than a goat with fine fiber. Style is style and allowances should be made for coarser goats. Not all goats can have super fine fiber and fabulous crimp. Fleeces with good style are given a score of 3, average fleeces rate a 2 while fleeces with no style rank as a 1. Nuances in style can be addressed by adding a “+” or a “-” to the numeric score to indicate “running out” (“-“) or “slightly better than” (“+”) assessments. Mostly straight fiber with just some undulations can be given a ranking of 1+ in order to differentiate it from absolutely straight fiber, which would be ranked as a 1. The spreadsheet will either add or subtract .33 to the numeric score to reflect these nuances. So, a 3.33 indicates a score of “3+” while a 1.66 indicates a score of “2-“. There is no such thing as style 4.
The ranking of fine fiber as a 3 and coarse fiber as a 1 is a habit that is not easily broken. Historically, fiber classers such as Hugh Hopkins, formerly of Fortè Cashmere and Terry Sim, formerly of Cashmere America, have used the higher number to indicate finer fiber. But, when head classer James Barton of Cashmere America identifies a classed line of fine fiber, he uses the number 1 as do all professional line classers. At this time, it is just too difficult to retrain American cashmere growers to use the number 1 to describe fine fiber off a goat’s back so the CW spreadsheet will use a 3 to describe fine fiber (15 to 16 micron), a 2 to describe medium (16.5 to 17.5 micron) and a 1 to describe coarse (18 to 19 micron) fiber. Again, a “+” or a “-” can be used to describe super fine (3+) fiber or fiber that is medium but pushing towards coarse (2-). The spreadsheet will convert these assessments into microns until such time as fiber diameter is objectively tested.
Cover and yield are assessed by judging the percentage of body surface growing fiber and the ratio of down length to guard hair length. Goats that are 90% covered by cashmere (leaving only the legs uncovered) are ranked as a cover of 3. If the neck and belly are slick, a ranking of 2 should be used. If the neck, shoulder, belly and legs are slick, cover is assessed as a 1. Yield is assessed on shorn fleece or on live goats on a scale of 1 to 3. Yield 1 indicates that the fiber is less than half the length of the guard hair. Yield 2 indicates that the fiber is equally as long as the guard hair. Yield 3 indicates fiber that exceeds the length of guard hair. Again, “+” and “-” can be used to fine tune assessments. Combed fleeces should be assessed as 7, 8 or 9 to indicate 60% to 70%, 70% to 80% or 80% to 90% yield. The yield assessment will be used by the spreadsheet to calculate down production when it is multiplied by the total fleece weight, either shorn or combed.
This concludes the data collection section of the database. All other columns reveal numbers that are either calculated by the spreadsheet or assigned by the assessor. Figure 5 illustrates the data calculation area of the spreadsheet. The first five groups of numbers are indices designed by the CW researchers to rank animals on the basis of their deviation from the group mean for frame, style, down length, down production, and fineness. Handicaps for twins are added to their value and the index calculated resulting in either a positive or a negative number being calculated for each individual. Animals that rank high in their group will have the highest positive number. Each index has an adjacent column labeled “ordinals” in which the relative order of each animal is noted. The animal with the lowest positive score is given an ordinal of “1” while the animal with the highest negative score is given an ordinal of “-1”. Depending upon how many animals are above average (receiving a positive score) and below average (negative score), either positive or negative ordinals are assigned to each goat depending upon its position. Animals with tie scores are given identical ordinals. Since down production was not measured, values for down production were not calculated. The sum of ordinals will reflect each animal’s accumulative standing within the group. The CW Index rank column will identify the top 10 goats within the group and identify them as #1, #2, etc. Those goats with the highest total number of ordinals will be ranked first. To aid the reader in following this line of thinking, two individual goats’ data will be followed. The first is goat P9503, we’ll call him Capricorn Marcus. The second goat is a little, late born Boer cross, NT1. We’ll call him Capricorn Stoney. Both of their data lines are highlighted for easy reference during this discussion. As you can see, “Marcus” received an ordinal of “12” for frame while little “Stoney” received a “-10”. This means that Marcus was the top goat in the group when the sum of his heartgirth, length and height was divided into his bodyweight. Little Stoney was almost last. However, when it came to style, Marcus was given an ordinal of 3, meaning his was the third lowest positive number (seven goats scored higher) and Stoney was given a -7, meaning his index score was the seventh lowest negative number. This means that Marcus was not the most stylish buck in the group (NT3) was, but he was above average. And neither was Stoney the least stylish buck, P9517 was. But let’s press on.
The CW Index for down length scored Marcus as a 3, meaning his was the third lowest index. Ten bucks had fiber that exceeded his. Stoney on the other hand had a positive index score but it was the lowest one. The final CW Index, that for fineness because production was not measured, showed Marcus to have the third lowest positive score and Stoney to have the second lowest positive score. In other words they were pretty close. But, when you add up all the ordinals assigned to each buck, Marcus comes out on top with 21 points while little Stoney could only gather minus 14 points, meaning his negative scores exceeded his positive one by 14 points. Also, Marcus ended up being the buck with the highest total of ordinal points and was then ranked 1st in the overall CW Index. He wasn’t the most stylish or the one with the longest fiber, or even the largest. But he did strike a happy medium between these characteristics and was awarded the rank of 1st.
This does not mean he is the only one kept intact, however. More study is required. The following eight columns calculate the Estimated Breeding Values for each goat based upon Pattie and Restall’s work. Values reported indicate the unit measure in kgs, mms, gms and microns for bodyweight, down length, down production and micron diameter. These values are the EBV for each animal. Half of the calculated EBV can be expected to be passed along to the offspring. This is called the Predicted Progeny Value or PPV. If EBVs for both bucks and does are calculated, the sum of half of each parents’ EBVs will describe the Predicted Progeny Difference or PPD. Again, the buck with the highest EBV in each category has been assigned a ranking of #1, #2, etc. For example (see figure 6), Marcus ranked 1st in EBVbw for bodyweight, meaning that taking his down length, and micron diameter into consideration and knowing what Pattie and Restall have established about negative correlations between these characteristics, Marcus should be expected to add half of his EBVbw of 2.41 kgs (or 1.2 kgs) to his progeny’s average bodyweight. At the same time, using Marcus will also decrease his progeny’s average down length by half of his EBVln for down length (-2.51), meaning his progeny will have an average down length that is shorter by 1.25 mms than the average of their herd of origin. Using Marcus will also decrease down weight by 2.42 grams and will tend to decrease the mean fiber diameter of his offspring over that of the average MFD of the doe herd by 0.09 microns. So if a breeder wants to increase bodyweight without affecting micron diameter and is willing to sacrifice some production to do it, Marcus would be the buck of choice.
Looking at Stoney’s EBVs, it’s clear that using him would result in a decrease in bodyweight by .65 kgs, increase down length by 1.63 mms, increase down weight by 2.45 gms and increase diameter by .055 microns. Stoney ranked 8th in EBVs for length and 5th for down weight. Looking just at EBVs, a producer might consider Stoney if he/she wants to increase production and is not worried about bodyweight. But EBVs alone don’t tell the whole story at this stage of the game. Since the bucks are so young, they all exhibit fine fiber. A few more months of growth may change the picture a bit. This is why Pattie and Restall have developed a series of two-stage selection indices that will allow the producer to select the top 25% of a group of bucks. Continuing with our example, using Pattie and Restall’s Index 2 to hold diameter constant when down weight is not measured, Marcus squeaks into the top 10 while Stoney comes in 12th. Perhaps a producer would do well to look elsewhere for a buck to in crease down production other than at Stoney.
The next five columns are dedicated to converting English measurement to metric and adding birth type allowances to twins and triplets. Figure 7 highlights the columns necessary to calculating Pattie and Restall’s Selection Indices. There are five indices to choose from now and more can be designed by Pattie and Restall upon request. These are described in the dotted line boxes. Index 1 selects bucks that will hold liveweight and diameter constant. Index 2 selects bucks out of the same group that are best used for holding down diameter constant when down weight is not measured. Index 3 and Index 4 select bucks to reduce down diameter by .4 and .25 microns, respectively. Index 5 selects bucks that will maximize financial value. The multipliers for these indices are listed to the right. When an index is selected by placed its number in the heavily lined box at the top of the column, the spreadsheet automatically multiplies the corresponding set of multipliers with the buck’s deviation from the mean for the various characteristics. Changing the number in the heavily lined box will change the Index selected and the corresponding multipliers. It is an interesting exercise to select different indices and see which group are selected. Out of a group of 10 bucks, any one can be top of the group for varying breeding goals. Because these are Two Stage Indices, the Stage I Index will simply select the top ten bucks for the chosen goals. Then the breeder will need to have each buck objectively fiber tested in order to complete the Stage II Index. Using a Two-Stage Index saves the breeder money, as not all the kid buck population needs to be fiber tested.
The bucks in Figure 7 are ordered according to the Stage I Index for keeping liveweight and diameter constant. These rankings are noted in the Stage I ranking column. Look at the rankings assigned to the top ten bucks across the various indices. Depending upon the breeders’ goals, any of the top twelve bucks could be viable candidates. Note that buck #13 has been eliminated due to conformational faults.
The purpose of the CW database is to give the breeder tools which he/she can use to take his/her herd in a certain direction. It does not pretend to choose the best bucks as the question then becomes, best for whom? Breeders with light bodyweights will want to choose a different breeding buck than one who wishes to decrease his/her fiber diameter. Because the various interactions or correlations between and among the fiber and body characteristics, it is not possible to visually assess a buck and predict his influence upon his progeny. Only computers can manage this huge dataset and solve the incredibly long equations. It takes a breeder though to define his/her goals and be prepared to make a scientific choice, based upon measured or carefully assessed characteristics.
Copyright 1994 Capricorn Press. Not to be reproduced without express written consent of Capricorn Press.