Prediction of within-herd differences in total feed intake between growing pigs

Transcription

1 Prediction of within-herd differences in total feed intake between growing pigs J. R. Brisbane 1 Canadian Centre for Swine Improvement, Building 54, Central Experimental Farm, Ottawa, Ontario, Canada K1A 0C6. Received 27 July 2000, accepted 15 May Brisbane, J. R Prediction of within-herd differences in total feed intake between growing pigs. Can. J. Anim. Sci. 82: Electronic feeders for measurement of individual feed intake in group-penned pigs have been available for many years, and have been used to measure and select breeding pigs for feed efficiency. The cost per feeder is usually too high to measure intake of all selection candidates in a nucleus herd over the whole grower-finisher period. If the feed intake of each candidate is measured over only part of the period, this would allow more pigs to be measured per feeder in a given time. This paper documents an analysis of test station data on Yorkshire, Landrace, Duroc and crossbred pigs, estimating the accuracy of prediction of total feed intake based on intake measured over different parts of the grower-finisher period. Feed intake measured over only about 2 wk from 80 to 90 kg liveweight explained 50% of the variance in total feed intake from 30 to 100 kg liveweight, in a dataset independent from the one used to derive the parameters of the prediction. Of all possible periods spanning 10 kg of liveweight gain, this measurement period was the most accurate, and coincided with the period of maximum growth rate. It is concluded that total feed intake over the grower finisher period can be predicted with useful accuracy, using feed intake measured over a period of about 2 wk or 10 kg of liveweight gain from around 80 to 90 kg. The gain in accuracy achieved by measuring pigs over multiple periods (e.g., from 50 to 60 kg and from 80 to 90 kg) is much smaller than the initial benefit of recording over just 10 kg of gain. Key words: Swine, feed efficiency, feed conversion Brisbane, J. R Prévision de la variation intra-troupeau de la prise alimentaire chez les porcs d élevage. Can. J. Anim. Sci. 82: Les distributeurs électroniques permettant de mesurer la prise alimentaire des porcs élevés en groupe existent depuis de nombreuses années. On s en sert notamment pour déterminer l indice de consommation et sélectionner les sujets de reproduction en conséquence. Le prix des distributeurs électroniques est souvent trop élevé pour qu on mesure la prise alimentaire des porcs d un futur troupeau durant la période d engraissement complète. On pourrait jauger plus d animaux par distributeur si on déterminait la prise alimentaire des sujets pendant une partie de la période d engraissement seulement. L article que voici analyse les données expérimentales sur des porcs Yorkshire, Landrace, Duroc et hybrides et estime la précision de la prise alimentaire globale prévue. Ces données reposent sur les mesures relevées à divers moments de la période d engraissement. La prise alimentaire mesurée au cours des deux semaines durant lesquelles le poids vif de l animal passe de 80 à 90 kg explique 50 % de la variance de la prise alimentaire totale durant la période où le poids vif de l animal passe de 30 à 100 kg, pour un jeu de données distinct de celui d où ont été dérivés les paramètres de la prévision. Entre toutes les périodes envisageables durant lesquelles l animal grossit de 10 kg, c est la précédente qui s avère la plus précise et elle coïncide avec celle où l animal connaît le taux de croissance le plus important. On en conclut qu il est possible de prédire la prise alimentaire totale durant l engraissement avec une précision suffisante à partir de la quantité d aliments ingérée au cours d une période d environ deux semaines, correspondant à un gain de poids de 10 kg, quand l animal grossit de 80 à 90 kg. La précision additionnelle obtenue quand les porcs sont jaugés sur plusieurs périodes (par ex., de 50 à 60 kg et de 80 à 90 kg) est beaucoup plus faible que celle obtenue quand on se borne à relever les données durant un gain de poids de 10 kg seulement. Feed is a major cost in pig production, and feed efficiency is one of the most important objectives for genetic improvement of swine. Faster-growing pigs and leaner pigs consume less feed over the grower-finisher period than average pigs, and selection for growth and leanness usually leads to improvements in feed efficiency without direct selection for feed efficiency. However, the measurement of individual Mots clés: Porcins, indice de consommation, valorisation des aliments feed intake in group-penned pigs has long been a reality in many breeding programs (Bampton 1992) and it is recognised that selection on direct feed efficiency records offers the opportunity for increased genetic improvement (Mrode and Kennedy 1993). Electronic feeders are sufficiently expensive to prohibit measurement of individual feed intake of all selection candidates over the whole grower-finisher 1 Current address: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G Abbreviations: ADG, average daily gain; CDPQ, Céntre de développement du porc du Québec Inc; CP, crude protein; ME, metabolisable energy; OSI, Ontario Swine Improvement Inc; SEW, segregated early weaning

2 284 CANADIAN JOURNAL OF ANIMAL SCIENCE period. However, it may be possible to predict feed intake of each candidate over the whole period based on its intake over part of the period, and then incorporate these predictions into the selection criterion for genetic improvement. This could be a cost-efficient method of increasing rates of genetic improvement in feed efficiency. A review of the scientific literature produced numerous studies on various aspects of feed intake, such as housing, behavioural and social effects (e.g., Von Felde et al. 1996; Hall et al. 1998), the modelling of feed intake using mathematical functions of days on test or liveweight (e.g., Andersen and Pedersen 1996; de Lange et al. 1997; Rivest et al. 1999), and relationships with growth and fatness (e.g., Kennedy et al. 1993; Mrode and Kennedy 1993). Electronic feeders tend to have errors where on some days they do not record the correct feed intake, and this leads to the need for methods to estimate feed intake on the days when the correct intake is missing. This has received some attention in the literature. Eissen et al. (1999) fitted a linear regression, a third-order polynomial, and a non-linear function to feed intake as a function of days on test, and for each model they calculated predicted intake on the day of the missing record. They tested the three methods by deleting known daily intake records from the data and then measuring the accuracy of predicting the deleted data. Among these methods, they found linear regression to be the most accurate. However, no published studies were found in the literature describing methods to predict feed intake over the complete grower-finisher period using feed intake data over a short part of the growing period. The objective of this paper is to use an analysis of individual feed intake data to derive least-squares regression-based predictions of total feed intake over the grower-finisher period, from feed intake over part of the period. MATERIALS AND METHODS Data Individual daily feed intake recorded over the entire grower-finisher period was obtained from the Céntre de développement du porc du Québec Inc. (CDPQ), and from Ontario Swine Improvement Inc. (OSI). At both companies, animals were cared for under the guidelines of the Canadian Council on Animal Care (CCAC 1993) CDPQ Data and Testing Protocol The CDPQ data contained records from 1283 pigs from four fills of the central test station (referred to as tests 6, 7, 8, and 9) at Deschambault, Quebec. Yorkshire, Duroc and Landrace breeds were represented in the data. The tests took place from December 1997 to October Piglets were transferred from breeders herds in Quebec to Deschambault at 10 to 14 d old. Tests 6 and 7 included pigs from breeders located in other provinces. Ten pigs of mixed breeds and sex and grouped by liveweight such that the maximum range was 0.5 kg were placed in SEW pens. All piglets received an oxytetracycline injection upon arrival, as well as typical preventative antibiotics in the nursery feed. They were vaccinated against Mycoplasma hyopneumoniae at 5 and 7 wk after arrival and transferred to test facilities at an average liveweight of 25 kg. They were given a week to become familiar with the feeders, before recording began. Each test pen contained 12 to 14 pigs of the same sex, with about 0.9 m 2 of space per pig. Animals were fed using INSENTEC IVOG electronic feeding stations. It has been noted (de Haer et al. 1992) that the IVOG feeding station has a much more open entrance than other common electronic feeding systems, and this allows more competition among the pigs, such as occurs in commercial pen feeding situations. Pen flooring was entirely concrete slats. Diet ingredients were mainly ground maize, soybean meal and wheat and were formulated to 88% dry matter. Three-phase feeding was used with a diet formulated for 18% crude protein (CP) and 1.22% lysine until the pigs reached 50 kg liveweight, 16% CP and 1.05% lysine from 50 to 75 kg liveweight, and 15% CP and 0.94% lysine thereafter. Diets varied only slightly among tests. Table 1 shows the number of animals by breed, sex and test. The daily intake records collected by CDPQ included some records where the feed intake for a pig on a particular day was missing. Three hundred and seventy missing records arose on a single day due to a power outage. Also, some feeders had malfunctions, which caused them to record an obviously incorrect (extreme) daily intake figure on certain days. CDPQ staff identified about 400 of these records by plotting the daily intake of each pig against days on test, identifying outliers by eye and substituting estimated values based on the average of the preceding 3 d and following 3 d (J. Rivest, pers. commun.). Each animal was weighed at the beginning of test, at the end of test, and at roughly one-third and two-thirds of the way through each test. The first and last weighings were usually on the same day as the first and last daily feed intake records. Average ages at each weighing were 68, 100, 125, and 160 d (Table 2). In test 6, a temporary problem with some feeders may have restricted the feed intake of some pens. Also, in test 7, average daily gain (ADG) may have been slightly lower than in other tests because of health problems at the beginning of the test. Pigs were taken off test at an average weight close to 105 kg. Table 2 shows the means, standard deviations and ranges for important age, weight and ADG statistics in the CDPQ data. The average ADG increased from 0.82 kg d 1 between the first two weighings to 0.96 kg d 1 between the last two weighings. Although pigs were weighed only four times, a liveweight was assigned to each daily intake record by linear interpolation (i.e., assuming a constant ADG) between adjacent weights. In order to obtain a dataset with complete daily intake and liveweight information over the range from 30 to 100 kg liveweight, for some pigs where the first weight was above 30 kg, the weight and daily intake information had to be extrapolated down to 30 kg, and for some pigs where the last weight was below 100 kg, the weight and daily intake information had to be extrapolated up to 100 kg. The extrapolation of daily weights down to 30 kg was done using the ADG between the first two weights and the extrapolation of daily weights up to 100 kg was done using the ADG between the last two weights. In order to extrapolate daily feed intakes down to

3 Table 1. Number of animals in the data by breed, sex and test Z CDPQ z BRISBANE PREDICTION OF TOTAL FEED INTAKE IN GROWING PIGS 285 Test 6 Test 7 Test 8 Test 9 OSI y Total Gilts Barrows Boars Yorkshire Landrace Duroc Yorkshire Duroc Total z CDPQ: Céntre de développement du porc du Québec Inc. y OSI: Ontario Swine Improvement Inc. Table 2. Summary statistics of age, weight, and ADG in the edited data Variable or trait Mean SD Range CV (%) CDPQ Age on test (d, at first daily intake record) to Age at first weight (d) to Age at last weight (d) to First weight (kg) to Last weight (kg) to Average ADG between first and second weights (kg d 1 ) to Average ADG between second and third weights (kg d 1 ) to Average ADG between third and fourth weights (kg d 1 ) to Age off test (d, at last daily intake record) to OSI Age on test (d, at first daily intake record) to Age at first weight (d) to First weight (kg) to Number of weights per pig to Age at last weight (d) to Last weight (kg) to Age off test (d, at last daily intake record) to ADG between first and last weights (kg d 1 ) to kg it was assumed that feed intake on each day prior to recording was equal to the average of the first six daily feed intake records on the pig. In order to extrapolate daily feed intakes up to 100 kg it was assumed that feed intake on each day after recording ended was equal to the average of the last six daily feed intake records on the pig. This resulted in a total of daily intake records, of which only around 1300 or 1.2% were estimated by extrapolation or interpolation. At the end of the first phase of feeding, the metabolisable energy (ME) content of the diet was reduced from MJ kg 1 to MJ kg 1. The daily intake data were pre-adjusted to the intake that would give the same ME intake if the energy content of the diet was MJ kg 1. This involved multiplying the daily intakes of the first diet by a factor of (i.e., 14.55/14.00). This adjustment was done based on the assumption that the pig s feed intake varies inversely with the ME content of the feed (C. de Lange and J. Chesnais, pers. commun.). OSI Data and Testing Protocol Data originated from animals bred at Ridgetown College from purebred Yorkshire sows, using AI with either Yorkshire or Duroc semen. Piglets were weaned at 4 to 5 wk of age and placed in a nursery. One gilt and one boar from each litter were transferred to the testing facility at a liveweight of 25 kg. They were allowed 1 wk to become accustomed to the feeders before testing began. The test facility consisted of partially slatted pens with 8 to 14 pigs in each with the sexes mixed and an average density of around 1.2 m 2 per pig. Each pen was fed using an electronic feeder (FIRE System, Osborne Industries, KS). Pigs were fed a grower diet (20% CP, 1% lysine) up to 65 kg, and a finisher diet (19% CP, 0.6% lysine) thereafter. Both diets were based on ground corn, soyabean and wheat. The data did not contain distinct test groups. The test facility was managed on a continuous flow basis, with a small group of animals going on test every month to 6 wk from January 1998 to July 1999, and coming off test between April 1998 and October 1999, when their average weight was close to 105 kg. Prior to editing, there were 196 barrows and 193 gilts, of which 197 were purebred Yorkshires and 204 were Yorkshire-Duroc F 1 crosses. There was some editing of the feed intake data from OSI prior to its being received for use in this analysis. Firstly, all daily intake values less than zero or greater than 5 kg were set to missing values. Then daily feed intake was plotted against days on test for each individual pig, a line was drawn through the points by eye, and

4 286 CANADIAN JOURNAL OF ANIMAL SCIENCE any record more than 1 kg above or below the line was set to a missing value (C. Aker, pers. commun.). These outliers were assumed to have been caused by an error in the computer of the electronic feeder. After the data were received for use in the analysis described in this paper, seven animals were edited out of the data because they had less than three liveweights in total, because they had a first weight greater than 70 kg, or because they had a weight loss of more than 10 kg. Table 1 shows the number of animals in the edited data by breed and sex. After editing, the first weight averaged 43 kg and the last weight averaged 103 kg (Table 2). Table 2 shows that the coefficients of variation for many of the age and weight traits were much larger in the OSI data than in CDPQ data. The continuous flow system used for the testing probably contributed to increased variation in performance on test. Continuous-flow systems have been seen to give increased variation compared to all-in all-out systems (Clark et al. 1995). The energy levels were13.79 MJ kg 1 and MJ kg 1 for the grower and finisher diets, respectively. As with the CDPQ data, the daily intakes were pre-adjusted to the intake that would give the same ME intake if the energy content of the diet was kg, assuming that feed intake varies inversely with diet ME content. This involved multiplying the daily intakes up to 65 kg liveweight by a factor of (13.79/14.00) and subsequent daily intakes by factor of (13.89/14.00). Extrapolation was used to obtain liveweight and feed intake data over the complete 30 to 100 kg period, in the same way as described for the CDPQ data. This resulted in a total of daily intake records, of which 13% were estimated by extrapolation or interpolation. Almost all of these estimated records arose as a result of pigs not starting feed intake recording until they were heavier than 30 kg. Missing values as a result of feeder malfunctions comprised a small proportion. The first weight recorded on each pig was usually well above 30 kg and often quite a lot higher. Statistical Methods Least-squares regression was used to develop equations that were used to predict total feed intake over the whole growth period from intake(s) over part(s) of the period. Growth periods were defined based on weight ranges rather than age ranges (e.g., 60 to 70 kg liveweight rather than 90 to 110 d of age). Feed intake over a fixed weight range depends both on ADG and daily feed intake, and is a better measure of feed efficiency than feed intake over a fixed period of time. Each prediction can be written as: Predicted total feed intake = a + b x where x is a vector of length N containing the feed intakes of the animal over N periods, b is a vector of partial regression coefficients of total intake on intake over each period, and a is an intercept. Based on the expected coefficients of the least squares regression equations, b can be estimated as: where V is an N N variance-covariance matrix of intake over the N periods, H is a vector of covariances of intakes over the N periods with total intake. The intercept a, is estimated as a = (M TOTAL b.m) where M is the vector of predicted intakes for the N periods for the appropriate breed and sex, M TOTAL is the predicted total intake for the appropriate breed and sex. The elements of V, H and M were estimated as described in the following section, using daily feed intake data of tests 8 and 9 from CDPQ. This is referred to as the prediction dataset. Prediction equations were tested on two independent datasets, the first being tests 6 and 7 from CDPQ, and the second being the Ridgetown test station data from OSI. These two datasets are collectively referred to as the test data. Estimating the Prediction Equations Each daily intake record in the prediction data was assigned to one of seven consecutive 10-kg weight periods ranging from 30 kg to 100 kg, based on the animal s interpolated weight. Total feed intake of each animal within each period was calculated by summing its daily intakes for the period. Total feed intake of each animal across all periods from 30 to 100 kg was calculated as the sum of the total intake for the seven periods There were three breeds, three sexes and a total of 578 animals in the prediction data (CDPQ tests 8 and 9 shown in Table 1). The number of animals in each breed-sex class varied from 12 to 110, with entire males being the most poorly represented. A univariate linear model including all effects of breed, sex and test (P < 0.05), was fitted to total feed intake for each of the seven periods as well as total feed intake from 30 to 100 kg. Breed and sex least squares means were calculated from each of the eight models. Residual correlations were calculated among intakes over each of the seven periods and total feed intake from 30 to 100 kg. This was also done separately for each breed and sex. Tests for differences in correlations between breeds and sexes were made using Fisher s transformation (Snedecor and Cochran 1989). The 7 7 matrix, R, of correlations among the residuals for the seven periods, pooled across breed, sex and test, was smoothed. The smoothed value was calculated as the average of the original value, its neighbours in the same column and its neighbours in the same row, excluding diagonal elements. More precisely, element (i,j) of the smoothed matrix, R *, where i < j, was calculated as: 3 3 ri+ h 2, j + rij, + k 2 * h= 12, < ( h+ i) < ( j+ 2) k= 12, < ( k+ j) < ( i+ 2) rij =, h= 12, < ( h+ i) < ( j+ 2) k= 12, < ( k+ j) < ( i+ 2) b = V 1 H, i = 1, 6; j = i+ 1, 7 (1)

5 BRISBANE PREDICTION OF TOTAL FEED INTAKE IN GROWING PIGS 287 where R * is defined as an upper triangular matrix. The variables h and k are integers from 1 to 3 defining elements of R used in the smoothing, subject to the restrictions 2 < (h + i) < (j + 2) and 2 < (k + j) < (i + 2). The restrictions avoid situations where I < 1, j < 1, and j i. For example, for i = 3 and j = 5, the smoothed value is the sum of elements (3, 4), (3, 5), (3, 6), (2, 5), (3, 5), and (4, 5) divided by 6, but for i = 1 and j = 5, the smoothed value is the sum of elements (1, 4), (1, 5), (1, 6), (1, 5) and (2, 5) divided by 5. Tests for heterogeneous variance were used between breeds and sexes, for each period and for total feed intake from 30 to 100 kg. Where only two breeds or sexes were involved, a standard F-ratio test was used, and where there were more than two, Bartlett s test (Snedecor and Cochran 1989) was used. The diagonal elements of V were estimated by the variances of the residuals from a linear model fitted to the feed intakes, pooled across breed and sex. These parameters were the same for all breeds and sexes. The off-diagonals of V were covariances calculated by multiplying the smoothed correlations (Eq. 1) by the product of the pooled standard deviations. Elements of H are the covariances between the residuals from the linear model fitted to feed intake for each period and the residuals from the linear model fitted to total feed intake from 30 to 100 kg, also pooled across breed and sex. The means used in M and M TOTAL were the model predictions for the breed and sex, so that the means were different for the different breeds and sexes. Pooling of V and H gives the same regression coefficients, b, for all breeds and sexes. Breed and sex-specific estimates of M and M TOTAL give different estimates of the intercept term, a, for each breed and sex. Both the pooling of V and H and the use of breed and sex-specific estimates of M and M TOTAL were based on the presence, or otherwise, of statistically significant differences in the relevant parameter estimates between breeds and sexes in the data. The ADG of each animal for period i was calculated as 10/n i where n i is the number of daily intake records of the animal in period i. The ADG of each animal from 30 to 100 kg was calculated as 70/N where N is the total number of daily intake records of the animal. The average daily intake for each animal within each period was calculated by dividing its total intake for the period by n i, and the average daily intake for each animal across all periods was calculated by dividing its total intake by N. The same linear model as fitted to feed intake was also fitted to ADG for each of the seven periods, and to ADG from 30 to 100 kg, and breed and sex least squares means were calculated for each trait. A similar model was fitted to average daily intake for each period and to average daily intake across all periods, except that this model included the fixed effect of individual animals. This model tends to have heterogeneous variance because a slower growing animal has more records in each period and therefore its mean daily intake for the period has a lower variance than the mean for a faster growing animal. However, the purpose was to calculate breed and sex least squares means rather than exact hypothesis testing. Eleven prediction equations were calculated. The first seven equations predicted total intake from intake over each of the seven periods individually, and the other four equations predicted total intake from intake over different combinations of the beginning (period 1), middle (period 4) and end of the growth curve. Period 6 (80 90 kg) was chosen as a period toward the end of the curve, rather than period 7 ( kg) because feed intake over period 7 had a correlation with total feed intake, which was markedly lower than that for period 6. The four predictions were based on: (1) periods 1 and 4 (i.e., kg and kg: the beginning and middle of the growth curve), (2) periods 4 and 6 (i.e., kg and kg: the middle and end of the growth curve), (3) periods 1 and 6 (i.e., kg and kg: the beginning and end of the growth curve), (4) periods 1, 4 and 6 (i.e., kg, kg, and kg: the beginning, middle and end of the growth curve). Testing the Prediction Equations The predictions were applied to the test datasets. This involved calculating total feed intake over each period of weight gain, using the same procedure as described above, then applying the regression coefficients to the intakes and comparing the predictions of intake over the entire kg period to the observed total intakes. The intercept terms used in the predictions for the Yorkshire Duroc F 1 animals in the OSI data were the average of the Yorkshire and Duroc intercepts estimated from the prediction data. The mean bias and coefficient of determination (R 2 ) was calculated separately for each breed and sex within the CDPQ dataset and within the OSI dataset. The mean bias was calculated as the mean of the predicted values minus the mean of the observed values. Breed and sex least-squares means for total feed intake, average daily feed intake, and ADG were calculated for each period of weight gain and for the overall period from 30 to 100 kg. RESULTS AND DISCUSSION The prediction data showed significant (P < 0.01) effects of sex on total feed intake (Table 3) and on intake over each of the 10 kg periods of weight gain. There were also significant (P < 0.01) effects of breed and test on total feed intake (Table 3) and on intake over some of the periods, but not all of them. Interaction effects were generally not significant (P > 0.05) in this study. A model was fitted including main effects of breed, sex and test, and Table 3 shows leastsquares means for breed and sex from that model. Boars consumed 7% less (P < 0.01) total feed than gilts over the entire kg period. The average feed-efficiency of barrows was slightly less (P < 0.01) than that of gilts. When boars were included, sex differences were much larger than breed differences, although breed differences were similar in magnitude to the difference between gilts and barrows. Animals with lower total feed intakes from 30 to 100 kg may not necessarily have lower daily feed intakes if they have higher ADG over this period. There were no boars in the test data. Feed intake increased (i.e., feed efficiency decreased) with each successive 10-kg period of weight gain

6 288 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 3. Least-squares means of total feed intake, ADG and average daily feed intake from 30 to 100 kg z Breed Sex Trait Yorkshire Duroc Landrace Duroc Yorkshire Barrows Gilts Boars CDPQ prediction dataset Total feed intake (kg) 171.3ab 175.0a 169.5b 178.5a 174.7b 162.5c ADG (kg d 1 ) 0.88ab 0.85a 0.91b 0.92a 0.85b 0.89ab Average daily intake (kg) 2.15a 2.13a 2.20a 2.33a 2.10b 2.05b CDPQ test dataset Total feed intake (kg) 179.7a 180.8a 182.2a 183.2a 178.6b ADG (kg d 1 ) 0.85a 0.85a 0.86a 0.89a 0.83b Average daily intake (kg d 1 ) 2.18a 2.20a 2.24a 2.32a 2.10b OSI Total feed intake (kg) 166.2a 171.2b 170.4a 167.1a ADG (kg d 1 ) 1.02a 1.00a 0.98a 1.03b Average daily intake (kg d 1 ) 2.35a 2.47b 2.49a 2.33b z Standard errors are from 0.2 to 2.0 kg for total intake, from 0.01 to 0.03 kg d 1 for ADG, and from 0.01 to 0.04 for average daily intake. a c Breed means within a row which do not share a common letter are significantly (P < 0.01) different and sex means within a row which do not share a common letter are significantly (P < 0.01) different. throughout the growth curve in the prediction data. ADG and average daily intake both increased with each successive 10-kg period of weight gain throughout the growth curve in the prediction data, except that ADG reached a maximum in period 6 (80 to 90 kg), and declined thereafter. In the CDPQ test data, there were no significant breed differences in total feed intake, ADG, or average daily intake. In OSI data, ADG and average daily intake were both higher than in CDPQ data, and total feed intake from 30 to 100 kg was lower than in CDPQ data. In OSI data only one of the purebreds (Yorkshire) was represented and it had a significantly lower daily intake than the Yorkshire Duroc F 1 genotype. While in barrows, ADG was only 8 to 10% higher in OSI data than in CDPQ data, in gilts it was 20 to 25% higher. Higher ADG in OSI data was largely offset by higher average daily intake, especially in barrows. For both barrows and gilts, feed intake from 30 to 100 kg was 5 to 7% lower in OSI data than in CDPQ data. The differences in these traits may have been due to management effects such as type of weaning or dietary protein content. Table 4 shows the correlation matrix of feed intake over each of the seven periods and total feed intake, pooled across breed and sex for the prediction data. The correlations with total intake ( kg) were not smoothed. Standard deviation of total intake over each 10-kg period of weight gain increased as the pigs grew, as did the mean. Correlation between intake over each period and total intake from 30 to 100 kg increased as pigs grew, except for the correlation between intake in period 7 ( kg) and total intake, which was lower than those for periods 5 and 6. Observed correlations between periods tend to decrease as periods become further apart, although this is not always the case. The smoothing procedure calculates an average of each element of the correlation matrix and adjacent elements. However, there are less adjacent elements available for elements on the edge of the matrix and next to the diagonal. As a consequence of this, smoothed correlations next to the diagonal are probably biased downward and those next to the edges of the table are probably biased upward. However, it is assumed that smoothing probably increases the accuracy of correlation estimates. For the CDPQ test data, correlations and SD (results not shown) were very similar to those in Table 4. However, in the OSI test data, correlations between intake over periods 1, 2, and 3 and total intake were higher (results not shown) than in the CDPQ data. In the OSI data it was common for a pig to have no liveweight records until it reached 50 or 60 kg. Feed intake from 30 kg up to when the pig was first weighed was estimated from subsequent feed intake and ADG. This caused estimated intake over the first few periods to be highly correlated to observed intake over the later periods. Hence the correlation matrix based on the CDPQ data was expected to be more realistic. There was no strong evidence of differences in the variance of feed intake (over a fixed period of weight gain) between breeds or sexes within the same dataset (Table 5). For all periods, feed intake had a higher SD in OSI data than in CDPQ data, and for total intake from 30 to 100 kg, the SD was 80% higher in OSI data. Since the SD were calculated from model residuals (i.e., after removal of significant breed, sex and test effects), the difference in the amount of variation between OSI and CDPQ data was likely caused by management factors. In the prediction dataset, correlations between feed intake over each 10-kg period of weight gain and total feed intake from 30 to 100 kg ranged from 0.31 to 0.76 for different periods, breeds and sexes (results not shown). Correlations in the CDPQ test data were similar (results not shown). There was no strong evidence of differences in these correlations between breeds or sexes in any dataset. The average bias (predicted value observed value) and coefficient of determination (squared correlation between predicted and observed values, denoted R 2 ) were estimated for each prediction in both test datasets by breed (Table 6) and sex (Table 7). For the CDPQ data, average biases of predictions based on single periods ranged from 0.8 kg to

7 BRISBANE PREDICTION OF TOTAL FEED INTAKE IN GROWING PIGS 289 Table 4. Feed intake residual correlations Z in the prediction dataset Period y Total intake Period (30 100kg) Total intake ( kg) z These are the correlations among model residuals pooled across breed, sex and test, after removing all significant breed, sex and test effects. Standard deviations are on the diagonal, observed correlations are above the diagonal, and smoothed correlations are below. y The seven periods are the seven consecutive periods of 10 kg liveweight gain from 30 kg to 100 kg (i.e., 30 to 40 kg, 40 to 50 kg, 90 to 100 kg). Table 5. Standard deviation of total feed intake from 30 to 100 kg, by breed and sex, with number of records in parentheses a b CDPQ prediction data CDPQ test data OSI test data Breed Duroc 13.7a (91) 13.1a (173) Landrace 14.2a (223) 16.1a (237) Yorkshire 14.4a (264) 15.3a (275) 24.7a (190) Duroc Yorkshire 29.4b (202) Sex Barrows 14.6a (244) 15.4a (343) 27.0a (190) Gilts 14.2a (266) 14.7a (342) 27.5a (202) Boars 12.4a (68) a, b Breed standard deviations within a column which do not share a common letter are significantly (P < 0.01) different and sex standard deviations within a column which do not share a common letter are significantly (P < 0.01) different. 5.1 kg. Assuming total intake has a mean of 180 kg and a standard deviation of 15 kg, the biases are up to 3% of the mean or 0.34 standard deviations. In OSI data, biases of predictions based on single periods ranged from +0.4 kg to +6.9 kg. By sex, in CDPQ data mean biases of single-period predictions ranged from 5.6 kg to 2.4 kg in gilts and from 5.1 kg to +0.1 kg in barrows. In OSI data, mean biases of single-period predictions ranged from 0.8 kg to +4.0 kg in gilts and from +1.6 kg to kg in barrows. As might be expected, the bias of each multiple-period prediction in Tables 6 and 7 was lower than the average of the biases of the predictions using each of the single periods used in the multiple-period prediction. In CDPQ test data, averaged across breeds, R 2 values for single-period predictions ranged from 0.17 to 0.52 (Table 6). Period 6 (from 80 to 90 kg) was the most accurate period to measure, with an average R 2 value of Period 6 was also the period of maximum ADG (results not shown). In OSI test data, averaged across breeds, R 2 values for single-period predictions ranged from 0.40 to 0.58 (Table 6). Periods 2 and 3 (40 to 60 kg) were the most accurate periods, whereas they had relatively low accuracy in the CDPQ data. The high accuracy of periods 2 and 3 in the OSI data was likely an artefact caused by the lack of early liveweight and feed intake data described above. R 2 values for two-period predictions in CDPQ test data, averaged across breeds, ranged from 0.50 to 0.71 (Table 6). The most accurate pair of periods was 4 and 6, giving an average R 2 of R 2 values for two-period predictions in OSI test data, averaged across breeds, ranged from 0.63 to 0.72 (Table 6), but these results were influenced by problems with the data. Tables 6 and 7 show no consistent differences in R 2 between breeds or sexes in either dataset. Based on the CDPQ data, the average R 2 can be increased from 0.71 to 0.82 if periods 1, 4 and 6 are used instead of only periods 4 and 6. In practice with limited test capacity, use of two periods rather than three will allow 50% more pigs to be tested per year. Hence loss of selection intensity would probably outweigh the increase in accuracy and it is unlikely that the use of three periods will be desirable for genetic improvement reasons. When the correlation between intakes over different periods is positive, the increase in accuracy per unit increase in the length of the total recording period is higher for shorter periods, i.e., recording pigs for longer gives diminishing returns in increased accuracy. Thus if there are many selection candidates to be tested and few electronic feeders, as long as feed intakes over different parts of the grower-finisher period are positively correlated with each other, the average accuracy of prediction of total feed intake for all candidates is maximised by testing all the candidates and allocating each candidate an equal length of the period of recording, rather than by testing some candidates for a long period and not testing other candidates. However this strategy will probably not maximise the rate of genetic improvement of feed efficiency under selection for the following reasons. Firstly, when test capacity is low it is best to give

8 290 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 6. Average bias (kg) and coefficient of determination (R 2 ) of the predictions of feed intake by breed, in the two independent test datasets Period(s) Data set z Statistic Duroc Yorkshire Landrace Yorkshire Duroc Average 1 CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R and 4 CDPQ bias R OSI bias R and 6 CDPQ bias R OSI bias R and 6 CDPQ bias R OSI bias R , 4 and 6 CDPQ bias R OSI bias R z CDPQ denotes the CDPQ test data containing 173 Duroc, 275 Yorkshire and 237 Landrace pigs. OSI denotes the OSI test data containing 193 Yorkshire and 203 Yorkshire Duroc pigs. preference to males over females (Van Vleck et al 1987, p. 296), because this gives a higher selection intensity, because fewer males are required than females to breed the next generation of selection candidates. Secondly, it is best to give preference for testing to sire lines over dam line animals, since selection for market hog traits such as feed efficiency is more important in sire lines than dam lines (Smith 1964). De Vries and van der Steen (1990) used detailed computer simulations of pig breeding schemes with fourway crossing to find, for a fixed test capacity and number of sow places, how the test capacity and sow places should be distributed between sire and dam lines for maximum economic genetic improvement in commercial production. The test was assumed to produce measurements of growth rate and feed intake from 23 to 100 kg, in addition to estimated carcass lean yield. However, situations where feed intake was measured over only part of the 23 to 100 kg growth period in order to test more animals were not studied. They found that even though the dam lines should be twice as large as the sire lines in terms of the number of purebred sows and litters of selection candidates, equal numbers of young sire line and dam line animals should be tested for market hog traits. Thus they found the proportion of young pigs tested in sire lines should be twice as high as in dam lines. Thirdly, within lines, giving priority to testing animals of higher predicted genetic merit based on pedigree information also increases genetic improvement (Hopkins and James 1977; de Vries et al. 1990). For the type of testing studied in this paper, where pigs can be recorded on an electronic feeder for a relatively short part of the total growth period, it would be useful to derive the balance between the number of pigs tested and the length of the recording period

9 BRISBANE PREDICTION OF TOTAL FEED INTAKE IN GROWING PIGS 291 Table 7. Average bias (kg) and coefficient of determination (R 2 ) of the predictions of feed intake by sex, in the two independent test datasets Period (s) Data set z Statistic Gilts Barrows Average 1 CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R CDPQ bias R OSI bias R and 4 CDPQ bias R OSI bias R and 6 CDPQ bias R OSI bias R and 6 CDPQ bias R OSI bias R , 4 and 6 CDPQ bias R OSI bias R z CDPQ denotes the CDPQ test data containing 342 gilts and 343 barrows. OSI denotes the OSI test data containing 203 gilts and 193 barrows. for each pig which maximises genetic improvement. However, this would require estimation of genetic parameters followed by fairly detailed computer simulation, similar to that of de Vries and van der Steen (1990), in order to account for the effects described above. It is not practical to move the bulky and sensitive electronic feeders between pens, and consequently the pigs have to be moved to and from the feeder. This involves labour and disruption of the pigs, such that it is not practical to test for very short periods. However, if a breeder has many selection candidates and few electronic feeders at the farm, as is currently the case with Canadian breeders, since there are diminishing returns in increased accuracy as pigs are tested for longer, the best approach may be to keep the recording period as short as it is feasible to be on practical grounds (e.g., 10 kg of weight gain), in order to test as many candidates as possible. On the other hand, the disruption of the testing procedure may affect the feed intake of the pigs such that it is not representative of their intake under normal conditions, necessitating that the pigs be allowed an acclimatisation period, of perhaps a week, to become used to the feeder and their new pen before recording begins. The existence of this fixed period would mean that as the length or the test period is reduced, a smaller proportion of time is spent actually recording pigs. Firm conclusions on optimum testing procedures cannot be given without further research. The parameters (means, variances and covariances) used in the prediction may be affected by herd environments and management systems. It may not be possible to use predictions derived from test station data accurately in a herd with a different type of environment or feeding system. However the method is valid for comparing animals within the same

10 292 CANADIAN JOURNAL OF ANIMAL SCIENCE herds and management systems and across herds and management systems under the assumption that the parameters used are correct for all of the herds and environments to which the predictions are applied. Future Work In future, this prediction method may be applied to routine performance test data from breeders herds, where each pig may begin and end feed intake recording at a different liveweight from other pigs. The methods described in this paper can still be used to obtain predictions of total feed intake, but the prediction equation (i.e., the coefficients denoted a and b in the methodology) will be different for each animal. It is convenient to define 70 units of 1 kg of weight gain from 30 kg to 100 kg, with unit i being the gain from (30 + i 1) kg to (30 + i) kg. Then let V * be the variance-covariance matrix of feed intake over each unit of gain, let H * be the vector of 70 covariances of intake over each unit with total intake, and let M * be the vector of 70 means of intake over each unit. Straight line interpolation can be used to obtain H * from H and to obtain M * from M. The same type of interpolation extended into two dimensions can be used to derive V * from V. For animal i, H * can be used to construct the vector of covariances of measured feed intake with total feed intake, denoted H i. H i has length N i where N i is the number of periods over which animal i is measured. For example, if the animal is recorded from 31 to 38 kg, from 70 to 75 kg, and finally from 90 to 95 kg, then N i = 3, but if the animal is recorded continuously from 50 to 100 kg then N i = 1. Similarly, V * can be used to construct the variance-covariance matrix, V i, of measured feed intakes of animal i. V i is unlikely to be full rank, but the regression coefficients of the prediction for animal i can be calculated as b i = V i H i, where V i is a generalised inverse of V i. The intercept can be calculated as a i = (M TOTAL b i M i ). The predicted feed intake for animal i is: Predicted feed intake = a i + b i x i where x i is the vector of measured feed intakes over each period. The accuracy of the prediction (i.e., the theoretical correlation between true and predicted values) is: ri = bvb i i i Var( Y) where Var(Y) is the variance of total feed intake, calculated as 1 V1, where 1 denotes a vector with all elements equal to unity. If there is enough reliable and randomly sampled data to estimate the effect of management differences between herds, the prediction method could be modified to account for these differences. Suppose a large number of pigs are measured in a herd over some period of weight gain, with a mean feed intake denoted x i which is significantly different from the mean intake denoted x 0 over the same period in the prediction dataset from the test station. The ratio of mean intake in the herd to mean intake in the station over that particular period is D = x i /x o. If it is assumed that the ratio of mean total intake in the herd from 30 to 100 kg to mean total intake in the station from 30 to 100 kg is also D, then it is appropriate to substitute D*M TOTAL for M TOTAL in Eq. 3 (J Chesnais, pers. commun.). This does not change the regression coefficients, b, and therefore does not change the predicted differences between animals in that herd. However, previously the intercept was a = (M TOTAL b.m), and now the intercept term for pigs in that herd is multiplied by a factor of D, becoming a = D*(M TOTAL b.m). The herd average is predicted without bias, given that the assumption stated above holds. CONCLUSIONS AND IMPLICATIONS The results suggest the most accurate single 10-kg period of feed intake measurement is from 80 to 90 kg, accounting for around 50% of the variance in total feed intake from 30 to 100 kg. Hence, where the number of electronic feeders limits the number of pigs that can be measured, a good level of accuracy can be obtained by measuring each pig for one relatively small part of the entire grower-finisher period. The most accurate pair of 10-kg periods to use was from 60 to 70 kg and from 80 to 90 kg, explaining around 70% of the variance in total feed intake. However, when intakes over different periods are positively correlated, the increase in accuracy (i.e., the R 2 of prediction of total intake) per unit increase in the length of the overall recording period is greatest when its length is shorter rather than longer. Hence if the number of electronic feeders is limiting, the average accuracy of all candidates will be increased by testing more pigs and testing each one for a shorter time compared to the situation where fewer pigs are tested and each one is tested for a longer time. The rate of genetic improvement in feed efficiency depends on the accuracy of prediction of breeding values, which depends on the genetic correlation between the measured feed intake and total feed intake over the whole growth period. This study looked only at phenotypic correlations between intake over different possible periods of measurement and total intake. It did not estimate the genetic correlation between intake measured over each period and total intake. Hence in order to make more valid conclusions about the effect of different testing schemes on genetic improvement it will be necessary to estimate the aforementioned genetic correlations. In terms of maximising genetic improvement, when test capacity is limiting, it is usually best to give priority for testing to males over females and to sire lines over dam lines. A breeder with many selection candidates and few feeders could decide upon the shortest recording period that is feasible, and when selecting pigs for testing, give preference to males over females and sire lines over dam lines. Future work was described, which would allow the prediction method to be applied to routine performance test