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ANIMAL PRODUCTION |
* Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta T6G 2P5, Canada;
and
Alberta Agriculture, Food and Rural Development, Western Forage Beef Group, Lacombe Research Centre, Lacombe, Alberta T4L 1W1, Canada; and
and
Agriculture and Agri-Food Canada, Lethbridge Research Center, Lethbridge, Alberta T1J 4B1, Canada
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionLiterature Cited |
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Key Words: Beef Cattle • Carcass Merit • Efficiency • Performance
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionLiterature Cited |
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), residual feed intake (RFI; Koch et al., 1963), maintenance efficiency (Webster et al., 1974), cow-calf efficiency (Archer et al., 1999), partial efficiency of growth (PEG; Kellner, 1909), relative growth rate (RGR; Fitzhugh and Taylor, 1971), and Kleiber ratio (KR; Kleiber, 1947). Although FCR has been used extensively in the past, measures of FCR are greatly influenced by growth rate and composition of gain. Thus, selection against FCR might have unfavorable effects on overall production system efficiency (Barlow, 1984; Archer et al., 1999).
Recently, RFI (the difference between an animal’s actual feed intake and its expected feed intake based on its BW and growth over a specified period) has been shown to have greater potential as an index of energetic efficiency for beef cattle (Herd and Bishop, 2000; Liu et al., 2000; Arthur et al., 2001a). However, differences in RFI are also associated with small differences in carcass fatness (Richardson et al., 2001; Basarab et al., 2003), carcass leanness (Herd and Bishop, 2000; Arthur et al., 2001b), and meat quality (McDonagh et al., 2001), which may not be entirely desirable to the beef cattle industry. Whereas recent literature has established the relationship between RFI and FCR, little is known about how RFI compares with other proposed measures of efficiency, such as PEG, RGR, and KR (Arthur et al., 2001a), in terms of relationships to feed intake, growth rate, body composition, and carcass merit. This study was conducted to compare RFI to other measures of efficiency in terms of relationships with growth, feed intake, and ultrasound and carcass merit of hybrid cattle.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionLiterature Cited |
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).
Cows and heifers were bred in multiple-sire breeding groups on pasture, and the sire of each calf was later determined in a parentage test using a panel of bovine microsatellite markers. The animals weighed 325 kg (SD = 41.4) and were 248 d (SD = 10) of age on test. All animals in the study were cared for according to the guidelines of the CCAC (1993). Animals were randomly assembled into two contemporary test groups (G1 and G2); G1 comprised 67 steers and 19 bulls, and G2 comprised 64 steers. Each animal was identified by means of a plastic tag located in the left ear. All animals had been vaccinated for bovine viral diarrhea and clostridial diseases and treated with a pour-on parasiticide before entry into the test. The animals were given ad libitum access to a backgrounding diet of mainly alfalfa-brome hay with oats, supplemented with corn grain and feedlot mineral supplement to promote a growth rate of just under 1.0 kg/d for approximately 30 d. This period was followed by a 30-d pretest adjustment period in which the amount of corn in the backgrounding diet was adjusted up gradually to introduce the animals to the test diet and the feeding system. This was done to allow them to adapt to the diet and learn to feed from the test facility.
During the test period, a total mixed finishing ration (test diet) composed of (as-fed basis) approximately 80.0% dry-rolled corn, 13.5% alfalfa hay pellet, 5% feedlot supplement (32% CP beef mineral supplement), and 1.5% canola oil (Table 1) was fed ad libitum. Animals also had ad libitum access to clean drinking water throughout the study. No growth-promoting implants were administered. Before entry into the testing facility, each animal was fitted with a passive radio frequency transponder button (Allflex U.S.A., Inc., Dallas/Fort Worth, TX) encased in plastic ear tags at a position 5 to 6 cm from the base of the right ear with the button on the inside. The test facility was housed in a shed with one long side open to provide access to 10 feeding bunks. The animals were housed in a large outdoor pen with straw provided as bedding, small amounts of which may have been eaten to promote rumination. Evidence from previous studies (Archer et al., 1997) in which straw was deliberately fed to animals (instead of as a bedding) indicate that the daily energy intake from straw provided as bedding would be insignificant. Individual animal intakes of straw were therefore not measured and were excluded from calculation of intake.
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). The in vivo digestibility of the diet was determined using feed intake and fecal output data of three Suffolk sheep (107.7, SD = 6.1 kg BW) tested at the Metabolic Research Unit of the University of Alberta, Edmonton. Daily feed intake, orts, and fecal output were measured on each sheep for 1 wk after they had been gradually brought to full feed (ad libitum intake with approximately 5% orts). Samples of the feed, orts, and feces for each animal were composited and frozen until analyzed. Samples were initially dried at 65°C, and then at 100°C in a forced-air oven to a constant weight to determine DM. Feed intake was 1.70 ± 0.26 kg/d on a DM basis. The GE of the various samples was determined by bomb calorimetry using an automatically controlled Parr adiabatic calorimeter (model 141, Parr Instruments Co., Moline, IL). The DE of the diet was computed from the GE of the feed and data obtained from the in vivo digestibility test and converted to ME using the NRC (1996) equation of ME = DE x 0.82. The NRC (1996) equations were also used to estimate the NEm and NEg of the diet.
Collection of Feed Intake Data
Feed intake was measured for each animal with the GrowSafe automated feeding system (GrowSafe Systems Ltd., Airdrie, Alberta, Canada), which has been validated and used previously (Basarab et al., 2003). Briefly, the system consisted of 10 feed tub units, a data-logging reader panel connected to each feed node, a personal computer, and GrowSafe Data Acquisition and Analysis software. Wireless communication (model 4000 R/F generator) allowed for the transfer of data between the acquisition unit and a desktop computer in an office located approximately 100 m away. Daily feed intake for each animal as recorded by the GrowSafe system was determined using specially customized software (Feed Intake from Raw GrowSafe Data; Basarab et al., 2003). Feed intake data were collected over approximately 84 d for each of the G1 and G2 tests. The feed efficiency traits for both G1 and G2, however, were computed using data from 70 d of testing based on evidence from previous studies (Arthur et al., 2001b). There were two occasions in test G1 and three occasions in test G2 when the automatic monitoring system failed to function due to power failure or failure of a main computer board. Data collected on those days were excluded from all subsequent analyses.
Growth, Ultrasound, and Carcass Data Collection
Weight measurements of all animals were taken weekly, and hip height, ultrasound measurements of 12th-/13th-rib fat depth, LM area, and marbling score were taken every 28 d. Ultrasound measurements were taken with an Aloka 500V real-time ultrasound with a 17-cm, 3.5-MHz linear array transducer (Overseas Monitor Corp. Ltd., Richmond, BC, Canada), using procedures detailed by Brethour (1992). At the end of the feed efficiency tests, steers were weighed (final BW) and shipped to a commercial packing plant, where they were slaughtered the following day and standard industry carcass data were collected after a 24-h chill at –4°C. Carcass data were not collected on 20 steers (the 10 most efficient and 10 least efficient steers) selected at the end of each test for indirect calorimetry and metabolic studies at the Metabolic Research Unit of the University of Alberta.
Traits and their Derivation
Each animal’s ADG during the test was computed as the coefficient of the linear regression of weight (kg) on time (d) using the PROC REG of SAS (SAS Inst., Inc., Cary, NC). The MWT of each animal over the test period was computed as the mid-point BW0.75. The total DMI of each animal over the 70-d test period was computed as the sum total of each animal’s daily DMI. This information was used to compute the metabolizable energy intake (MEI) per unit of MWT. The total ME consumption of each animal was computed as the total DMI over the test period multiplied by the ME content of the diet. This value was then divided by 10 to produce a value in kilograms of standardized intake with an energy density of approximately 10 MJ of ME/kg of DM. Standardization of the energy density of the test diet was necessary to allow for comparisons with results of associations between RFI and other traits from the current study to other independent studies previously reported (Arthur et al. 2001a; Richardson et al., 2001; Basarab et al., 2003). The total standardized DMI of each animal was divided by the test period of 70 d to give an average standardized DMI. The expected feed intake of each animal over the test period was predicted by using ADG and MWT to model average standardized DMI for each test group using PROC REG of SAS. A separate model was fitted for each contemporary group.
Residual feed intake was then computed for each animal as the deviation of the predicted daily DMI from actual daily DMI according to (Arthur et al., 2001a). Standard deviations above and below the mean were used to group animals into high (>0.5 SD), medium (±0.5 SD), and low RFI (<0.5 SD). Feed conversion ratio of each animal was computed as the ratio of daily DMI to ADG. Partial efficiency of growth above maintenance of each animal was computed as the ratio of ADG to the difference between average daily DMI and expected DMI for maintenance (DMIm; Brody, 1935; Arthur et al., 2001a), where DMIm was computed according to NRC (1996). The RGR of each animal (growth relative to instantaneous body size) was computed as the percentage of the difference between the log of final BW and initial BW over the number of days on test (Fitzhugh and Taylor, 1971). Kleiber ratio was computed as the ratio of ADG to MWT (Bergh et al., 1992; Arthur et al., 2001a).
Gain in hip height, backfat, LM area, and marbling score were predicted from regression equations of ultrasound measurements upon time (days) for each individual animal. Carcass traits were evaluated according to the Canadian beef carcass grading system (Agriculture Canada, 1992). Carcass weight of each animal was determined as the weight of the left and right halves of the carcass after 24-h chill at –4°C. Carcass grade fat was measured at the 12th/13th rib of each carcass. Carcass marbling score is a measure of i.m. fat and can be classified as follows: 1 to <2 units = trace marbling (Canada A quality grade); 2 to <3 units = slight marbling (Canada AA quality grade); 3 to <4 units = small to moderate marbling (Canada AAA quality grade); and 
4 units = slightly abundant or more marbling (Canada Prime). Lean meat yield is an estimate of saleable meat and was calculated according to Jones et al. (1984). Yield grade is the proportion of lean meat and is classified as follows: 1 = 59%; 2 = 54 to <59%; and 3 = 
54%.
Statistical Analysis
In the current study, performance, efficiency, and ultrasound data from 150 steers and bulls and carcass data from 109 steers were used. Effect of RFI group on measures of growth, feed intake, energetic efficiency, and ultrasound measurements was analyzed by least-squares procedures using PROC MIXED of SAS. The statistical model used included fixed effects due to RFI group (high, medium, and low), sire breed (Angus, Charolais, and Hybrid), test group (G1 and G2), sex of animal (bull and steer), and all possible interactions. All interaction terms that were not significant (P > 0.10) were subsequently excluded from the final model.
A random animal effect was included in the final model for all traits. Age of dam and start age of animal on test were included in the model as linear covariates. Effect of RFI group on carcass test traits was analyzed by least squares with a similar model but excluded the fixed effect of sex because carcass data was collected on steers only. All F-tests of fixed effects were carried out using Type III sums of squares. The error term for sire breed was animal nested within sire breed. Phenotypic correlations among various test traits were computed using PROC CORR of SAS with the partial correlation option to adjust for the fixed effects of sire breed, test group and sex.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionLiterature Cited |
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). Residual feed intake averaged 0.00 kg/d (SD = 0.83) and ranged from –2.25 (most efficient) to 2.61 kg/d (least efficient), a difference of 4.86 kg of feed per day. Residual feed intake, PEG, and MWT did not differ among animals of different sire breeds. Average daily gain was different among sire breeds (P < 0.05) and was lower for Charolais-sired (1.36 ± 0.03 kg/d) than for Angus- (1.42 ± 0.03 kg/d) or Hybrid- (1.48 ± 0.04 kg/d) sired animals. Both RGR and KR, respectively, were different (P < 0.05) among breeds and were lower for Charolais-sired (0.15 ± 0.003 and 0.016) than for Angus- (0.17 ± 0.003 and 0.017) or Hybrid- (0.19 ± 0.004 and 0.018) sired calves.
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To determine the effects of pretest environment on measures of performance and efficiency, simple regression equations were computed between various test traits and start age or start weight on test. This analysis showed that variation in initial BW of animal on test was significant (P < 0.01) and accounted for 17.1% of ADG, 98.1% metabolic weight, 11.65% of FCR, 55.8% of DMI, 8.6% of PEG, 25.5% of RGR, and 8.0% of KR, but was unrelated to RFI. Similarly, although initial age on test was significant (P < 0.05) and accounted for 4.6% of ADG, 51.3% of metabolic weight, 10% of FCR, 24.3% of DMI, 4.2% of PEG, 18.8% of RGR, and 8.7% of KR, it was unrelated to RFI. Differences among high, medium-, and low-RFI animals in feed intake and other measures of energetic efficiency for animals in the study are shown in Table 2, whereas Table 3 shows the partial phenotypic correlations among measures of growth, intake, and efficiency.
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). Dry matter intake was correlated (P < 0.001) with ADG, MWT, FCR, PEG, RGR, and KR. Feed conversion ratio was correlated (P < 0.001) with ADG, PEG, RGR, and KR, but not with MWT. Partial efficiency of growth was correlated (P < 0.001) with RGR and KR. In addition, RGR and KR were highly correlated (P < 0.001)
Correlations of RFI with ultrasound measures of LM area, marbling score, and hip height in this study were not different from zero (Tables 4 and 5). In addition, correlations of RFI with final BW, carcass weight, LM area, and marbling score were not different from zero. However, significant correlations (P < 0.01) were observed between RFI and estimated gain in ultrasound backfat, carcass grade fat, and yield grade. Weak, but significant, correlations (P < 0.05) were also observed between RFI and average ultrasound backfat thickness and carcass lean meat yield (P < 0.05; Tables 4 and 5). The relationships of PEG and FCR with hip height and ultrasound or carcass traits were not different from the corresponding relationships of these traits with RFI. Partial efficiency of growth was correlated with ultrasound marbling score, gain in backfat, final ultrasound backfat, grade fat, lean meat yield, and yield grade. Feed conversion ratio was correlated with gain in backfat, final ultrasound backfat, grade fat, lean meat yield, and yield grade.
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). Correlations of ADG, MWT, and DMI with hip height, and ultrasound and carcass traits were generally different from zero (P < 0.05) except correlations of ADG and MWT with gain in ultrasound backfat and carcass yield grade. |
Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionLiterature Cited |
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). Sire breed differences in ADG and BW observed in the current study were surprising as the Charolais breed is generally known to be of a larger frame and higher mature BW than Angus (Wheeler et al., 1996). Laborde et al. (2001) observed that large-framed Simmental crossbred steers did not differ significantly in ADG from Angus crossbred steers and required 71 d more on feed to finish at heavier BW. Wheeler et al. (1996) observed differences in BW between Angus and Charolais crossbred steers when taken to a constant age, but not to a constant carcass weight or fatness. It seems that the performance of the crossbred progeny of larger frame cattle may depend on age and phase of production. Sire breed differences in carcass and ultrasound traits (data not shown) were, however, consistent with differences reported in previous studies (Wheeler et al., 1996).
The lack of significant differences between bulls and steers in ADG and MWT in the current study was surprising, as gender differences in growth and BW have long been observed in cattle (Webster et al., 1982) and it would be generally expected that bulls would grow relatively faster than steers when fed together. However, the number of bulls available for comparison (n = 19) in this study was relatively low and the presence of a few poor-performing bulls may obviously have had some effects on the gender differences reported in this study. Gender differences in feed intake (16% higher in steers), FCR (7% higher in steers), PEG (11% lower in steers), and RFI (13% higher in steers), without differences in growth rate or BW, observed in this study suggest that steers generally consume higher amounts of feed, but are less efficient than bulls. These differences may be irrespective of their growth rate or BW. The gender differences observed for different measures of energetic efficiency in the current study show that RGR and KR might not detect obvious differences in energetic efficiency between animals. The results also show that, compared with PEG and RFI, FCR may generally underestimate differences between animals in energetic efficiency, probably due to the effects of growth and BW on feed intake (Archer et al., 1999). These results also suggest that a separate regression model for computing feed efficiency, especially RFI, must be fitted for animals of different gender within the same contemporary group (Arthur et al., 2001b).
The observed significant effects of start weight and start age on measures of growth, intake, and all measures of efficiency (excluding RFI) are consistent with earlier reports that RFI should generally not be affected by differences between animals in growth rates and maturity patterns (Archer et al., 1999; Liu et al., 2000). This finding is also consistent with the lack of a relationship between RFI and ADG or MWT in this study (Tables 3 and 4), as there is a general agreement in the literature that RFI computed from regression should be phenotypically independent of its component traits (Arthur et al., 2001b). This finding may also indicate that RFI, compared with the other measures of efficiency, may be unaffected by pretest environmental variations in age and BW, or even age of dam, making it a much more robust index for the comparison of data across environments and from different contemporary groups (Herd and Bishop, 2000).
Generally, most of the phenotypic correlations among growth, feed intake, and feed efficiency reported in this study were significantly different from zero. These results are consistent with genetic and phenotypic correlations reported in the literature (Arthur et al., 2001a; Basarab et al., 2003). Strong phenotypic and genetic relationships between feed intake, growth, and body size imply that one-sided selection for faster growth rate and higher finish weights would lead to higher maintenance energy requirements and higher overall feed consumption, especially in mature breeding animals (Archer et al., 1999). Similarly, a one-sided selection against feed intake may lead to decreases in growth and BW at maturity, which may be undesirable for the feedlot sector of the industry. The correlations among ADG, DMI, MEI, and FCR are similar to published estimates, as reviewed by Koots et al. (1994), as well as those reported by Liu et al. (2000) and Arthur et al. (2001a,b).
Nonetheless, these correlations are in contrast with several earlier reports (Gill et al., 1986; Meissner et al., 1995; Gibb and McAllister, 1999) that indicated that the correlations between feed intake and gain or between intake and FCR are generally poor in feedlot cattle. Perhaps, recent developments in technological expertise for the estimation of feed consumption and animal performance using automated individual animal feeding systems (such as the radio frequency-based GrowSafe system used in this study) could increase our ability to accurately measure animal performance. The phenotypic relationships among ADG, DMI, MEI, and FCR obtained in the current study (Table 3), as well as phenotypic and genetic relationships obtained in other studies (Liu et al., 2000; Arthur et al., 2001a,b), indicate that selection against FCR would decrease the amount of feed required for growth and thus be very beneficial to the feedlot operator. However, a strong correlation of FCR with growth raises questions in terms of its value to the improvement of overall production system efficiency as it may also lead to direct increases in mature BW, resulting in an increase in the cost of maintaining breeding herds (Barlow, 1984; Archer et al., 1999).
To the best of our knowledge, the only report in the literature comparing RFI to PEG in terms of genetic and phenotypic relationships with cattle performance is the study by Arthur et al. (2001a). The relationship of RFI and PEG with each other and with ADG, MWT, and DMI obtained in the study by Arthur et al. (2001a) as well as in this study may indicate that selection for PEG or against RFI would be similarly beneficial in terms of the correlated decrease in feed intake, with little effect (PEG) or no effect (RFI) on growth or on body size. Arthur et al. (2001a) explain that indices of efficiency that incorporate measures of growth and metabolic body size seek to capture the variations among animals in energy utilization for growth and maintenance. This ensures that the cattle resulting from this form of selection would potentially be efficient both as feedlot animals and in the breeding herd.
The phenotypic associations reported here indicate that animals with high RFI generally have high FCR (17.2% higher) and consume more feed (15% higher), despite the lack of differences in ADG or BW. This difference in energy intake is even higher when expressed in terms of the metabolic weight of animals. The associations also showed that the PEG above maintenance of high-RFI animals was 27% compared with 32% in medium-RFI and 38% in low-RFI animals. A high correlation between RFI and PEG observed in this study is not surprising as both traits incorporate components of feed intake due to maintenance and growth. Arthur et al. (2001a) reported strong genetic and phenotypic correlations between RFI and PEG. These findings therefore indicate that responses to selection for PEG would be similar to the expected responses to selection against RFI. However, unlike RFI, PEG showed a small but significant correlation with ADG in the current study indicating that at least in some animals, higher PEG may be phenotypically related to increased growth rate and subsequently BW.
This observation is in contrast to that of Arthur et al. (2001a), who observed that PEG was not related to rate of growth. In this study, expected feed intake for maintenance (required for computing PEG) was computed from feeding standards equations and may explain the significant correlation between PEG and ADG. This may be true because observations have shown that even RFI may not be phenotypically independent of growth and body size when expected feed intake is computed from feeding standards formulas, instead of from regression equations (Arthur et al., 2001b). Thus, the difference in findings between the current study and the study by Arthur et al. (2001a) may be related to the fact that feed requirements for maintenance in the latter study were computed using the French feeding standards formulas.
The strong relationship between KR and RGR with ADG observed in this study (Table 3) was also observed by Arthur et al. (2001a). However, whereas Arthur et al. (2001a) observed that the relationships of KR and RGR with BW and feed intake might not be different from zero, the current study showed that KR was related to DMI and MEI. As well, RGR was correlated with MWT, DMI, and MEI. These findings suggest desirable phenotypic effects of KR and RGR in growing animals, but undesirable effects on energy intake (in terms of higher maintenance requirements) in older animals in which growth has virtually ceased. The implication of this is that the phenotypic relationship of KR and RGR with animal performance and efficiency may not be any different from the reported relationships with FCR.
The relationships between measures of growth and RFI, DMI, and FCR with measures of ultrasound and carcass merit obtained in this study generally agree with published estimates. However, to the best of our knowledge, there is no report in the literature comparing PEG, KR, or RGR to RFI with respect to effects on carcass merit. Koots et al. (1994) reported a significant genetic correlation between FCR and lean meat yield (r = –0.32). Herd and Bishop (2000) reported significant phenotypic and genetic correlations between RFI and carcass lean percent (r = –0.43 ± 0.23). In addition, Arthur et al. (2001b) reported weak and moderate phenotypic correlations between feed intake and backfat (r = 0.23) or LM area (r = 0.33), respectively. In the same study, however, phenotypic correlations of RFI or FCR and ultrasound measures of backfat or rib eye area were not different from zero. A study by Richardson et al. (2001) showed that a single generation of selection against RFI was accompanied by a small decrease in body fat content.
A recent serial slaughter study by Basarab et al. (2003) indicated that RFI computed from regression of ADG and MWT on intake showed weak but significant correlations with carcass fat (r = 0.14), carcass lean (r = –0.21), gain in backfat thickness (r = 0.22), gain in marbling score (r = 0.22), and empty body fat (r = 0.26). Differences in carcass merit, such as less marbling on efficient cattle, may not be considered a favorable response by the beef cattle industry. Thus, evidence from this and other studies generally point toward a potential for small (5 ± 2%) decrease in carcass fatness and rate of gain in s.c. fat coupled with a slight improvement in carcass lean meat yield and yield grade (4 to 5%) following selection against RFI. However, the results on the differences in carcass merit between high-, medium-, and low-RFI groups indicate that, whereas low RFI is associated with an increased lean meat yield and yield grade, the animals have more than adequate backfat thickness and do not stand any risk of being downgraded for lack of external fat cover. In addition, differences in marbling score among the various groups were not significant. The observed phenotypic relationships of PEG with carcass and ultrasound merit in this study are comparable to the relationships of RFI with carcass merit.
The reported differences in carcass merit and body composition in this and other studies for animals differing in RFI may account for only a small proportion of the observed variations in energetic efficiency between these animals. It has therefore been suggested that other sources of variation such as differences in heat increment (especially associated with feeding and visceral metabolism), level of feeding activity and feeding behavior, nutrient turnover, and digestive functions may account for part of the variation in RFI (Oddy and Herd, 2001; Richardson et al., 2001; Johnson et al., 2003). Further research efforts are therefore required to characterize the sources of variation between animals differing in RFI.
In conclusion, this study has indicated that, although RFI shows small but significant relationships with carcass fatness and leanness, the efficient animals had adequate carcass fatness and did not stand any risk of being downgraded for lack of external fatness. The phenotypic relationships of carcass and ultrasound merit with PEG and FCR in beef cattle may be similar to the relationships with RFI. Animals with low RFI may show significant decreases in the energy requirement for maintenance and increase the PEG above maintenance. Partial efficiency of growth may be similarly robust (compared with RFI) as a measure of energetic efficiency, but its potential relationships with growth rate may be a disadvantage to overall production efficiency in mature animals.
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Footnotes |
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2 Correspondence: S. S. Moore, 4-10 Ag/For Bldg. (phone: 780-492-0169; fax: 780-492-4265; e-mail: stephen.moore{at}ualberta.ca).
Received for publication December 4, 2003. Accepted for publication April 16, 2004.
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