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Using simulation models to predict feed intake: Phenotypic and genetic relationships between observed and predicted values in cattle

USDA, ARS, US Meat Animal Research Center, Clay Center, NE

	Abstract

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION IMPLICATIONS LITERATURE CITED

 
The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFR_mcg, CFR_mc, CFR_g, CFR_mg, and CFR_m, respectively) and DECI (DFR_mcg, DFR_mc, DFR_g, DFR_mg, and DFR_m, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) x CFR_mcg [R² = 0.44, residual SD (s_y.x) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) x DFR_mcg (R² = 0.53, s_y.x = 0.612 kg/d). Heritability of OFI was 0.27 ± 0.12, and heritabilities ranged from 0.33 ± 0.12 to 0.41 ± 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFR_mcg, CFR_mc, CFR_g, CFR_mg, CFR_m, DFR_mcg, DFR_mc, DFR_g, DFR_mg, and DFR_m were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P < 0.001 for all phenotypic correlations); and 0.95 ± 0.07, 0.82 ± 0.13, 0.89 ± 0.09, 0.95 ± 0.07, 0.91 ± 0.09, 0.96 ± 0.07, 0.89 ± 0.09, 0.88 ± 0.09, 0.96 ± 0.06, and 0.96 ± 0.07, respectively. Phenotypic and genetic correlations between CFR_mcg and DFR_mcg, CFR_mc and DFR_mc, CFR_g and DFR_g, CFR_mg and DFR_mg, and CFR_m and DFR_m were 0.98, 0.94, 0.99, 0.98, and 0.95 (P < 0.001 for all phenotypic correlations), and 0.99 ± 0.004, 0.98 ± 0.017, 0.99 ± 0.004, 0.99 ± 0.005, and 0.97 ± 0.021, respectively. The strong genetic relationships between OFI and CFR_mcg, CFR_mg, DFR_mcg, and DFR_mg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFR_mcg, and DFR_mcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency.

Key Words: cattle • feed intake • mathematical model • nutrient requirement

	INTRODUCTION

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION IMPLICATIONS LITERATURE CITED

 
Feed costs were found to represent about 66 and 77% of the total cost of gain in calf and yearling beef cattle finishing systems (Anderson et al., 2005); hence, any improvement in gain or feed efficiency has the potential to result in increased profits. Fox et al. (2001) simulated the impact of a 10% improvement in rate of gain or a 10% improvement in feed efficiency on profits for an average steer that gained 272 kg of BW to finish at 532 kg of BW at low choice grade. Simulated results showed that the improvement in rate of gain increased profits by 18%, whereas the improvement in feed efficiency increased profits by 43%.

Cattle finished in commercial feedlots represent a potential population that can be used to progeny test beef sires for feed efficiency. These cattle are fed high concentrate diets in large group pens and may have individual growth performance and carcass data. Selection for feed efficiency requires a measurement of individual DMI, and it is possible that biological models may be programmed to predict the individual DMI required for cattle to achieve their individual feedlot performance data.

The Decision Evaluator for the Cattle Industry (DECI; Williams and Jenkins, 2003a,b) and the Cornell Net Carbohydrate and Protein System (Fox et al., 2004) are 2 publicly available biological models that can predict animal performance when DMI and nutrient supply are known. The DECI model is capable of working in a reverse manner to predict the DMI and nutrient supply required for an animal to achieve a known level of performance, and the Cornell Value Discovery System (CVDS; Tedeschi et al., 2004) was developed as a separate model from the Cornell Net Carbohydrate and Protein System framework for this purpose.

The objectives of this study are to evaluate the accuracy of DECI and CVDS in predicting individual DMI and to evaluate the feasibility of using these predicted individual DMI data in animal genetic evaluations.

	MATERIALS AND METHODS

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION IMPLICATIONS LITERATURE CITED

 
Experimental Data

Individual postweaning records of feed consumption, growth, and carcass traits were obtained for 504 steers produced in phase 1 and phase 2 of a comprehensive heterosis experiment with Hereford, Angus, and Shorthorn cattle. This experiment was initiated in 1957 at the Fort Robinson Beef Cattle Research Station, Crawford, NE, and was described by Gregory et al. (1965). The experimental design and distribution of the 286 crossbred and straightbred steers from straightbred dams and numbers of sires used in phase 1 are given in Table 1. The experimental design and distribution of the 218 2-breed and 3-breed cross steers, and the numbers of sires used in phase 2, are given in Table 2. Phase 1 calves were born in 1961, 1962, and 1963, and phase 2 calves were born in 1963, 1964, and 1965. The calving season was from February 10 to May 1; most of the calves were born between February 20 and the end of March.

Simulated Data

The DECI and CVDS models were parameterized using individual steer growth and carcass information to predict the average daily feed DMI required by individual steers to achieve the body composition and growth performance in the observed data. This individual feed requirement is the sum of feed required for maintenance and the observed BW gain over the experimental period. In both models, the maintenance requirement was adjusted for cold stress with equations published by Fox et al. (2004). The only climatic data that were available were average monthly temperatures for Fort Robinson, NE, and hourly wind speed data from Scottsbluff, NE, for 1961 to 1965. These data were used in calculating the cold stress maintenance adjustments. Experimental data serving as inputs for both models were dietary ME density, beginning BW, ending BW, and ADG for the experimental period. Inputs that were not in the experimental data were beginning body composition and ending body composition.

Body composition at the end of the experimental period in terms of ether-extractable lipid (FAT) and fat-free matter (FFM) was calculated from the individual animal experimental data for fat trim, HCW, cold carcass weight, and bone weight, as discussed by Williams et al. (1995). Growth and body composition of individual animals from birth to the beginning of the experiment were simulated with the CVDS and DECI models to obtain estimates of individual animal body composition at the beginning of the experiment. In these simulations, the body composition at birth was assumed to be 3% FAT and 97% FFM, and the experimental individual animal data on birth weight, weaning weight, weight at the beginning of the experiment, ADG from birth to weaning, and ADG from weaning to the beginning of the experiment were used as inputs.

The Cornell Value Discovery System.

At the beginning of the simulation, the ending BW and composition of each steer was used to calculate the ending empty BW (EBW) and ending empty body fat percent (EBFP). Each steer’s ending EBW was adjusted to a target EBFP of 28%, and this adjusted EBW was divided by 0.891 to convert it to an adjusted final shrunk BW at 28% FAT (AFBW), as follows:

Animal differences in mature BW affect the composition of gain at a particular weight, and to account for this effect, a size-scaling procedure was used to adjust the daily shrunk BW of each steer to an equivalent shrunk BW (EQSBW). This procedure calculated the EQSBW by multiplying the daily shrunk BW by a ratio of the standard reference animal BW (478 kg) to AFBW. The EQSBW was used to predict energy requirements for growth on a daily basis.

Simulation of each animal was begun with an initial estimate of DMI, and equations from Tedeschi et al. (2004) were used to calculate feed for maintenance, feed for gain, energy for gain, and ADG. The DMI was iterated each day until the predicted daily ADG was the same as the observed ADG. In calculating energy for gain, an initial estimate for NE_g that was based on the ME density of the diet was first used, and this estimate was updated each round of iteration using information on protein retention. At the end of the simulation, the final BW was the same as the observed BW, but the fat weight may have been different because convergence was based on ADG and not on body composition.

The predicted value for DMI, when the observed and predicted ADG converged, was the DMI required for maintenance, cold stress, and ADG. Daily DMI requirements were summed and divided by the days on feed, and this average daily feed DMI was referred to as the CVDS feed intake required for maintenance, cold stress, and ADG (CFR_mcg). Average daily feed DMI were also predicted for maintenance and ADG (CFR_mg), maintenance and cold stress (CFR_mc), maintenance (CFR_m), and ADG (CFR_g).

The Decision Evaluator for the Cattle Industry.

The CVDS model is based on empirical equations for which analytical solutions are easily obtained; in contrast, the DECI model is based on 13 differential equations that are numerically integrated on a daily basis to obtain solutions. Compared with the CVDS model, a different approach was used in simulating each steer with the DECI model. In this case, the observed ADG was used as the main input, and 2 parameters that determined body composition were iterated until the predicted ending BW and composition were the same as that observed. The 2 parameters were a fattening parameter (THETA) and FFM at maturity (FFM_mat), and breed averages (Williams et al., 1995) were used as initial values for these 2 parameters. Both THETA and FFM_mat were negatively correlated with fatness, and in making adjustments, FFM_mat was changed at twice the percentage change as that of THETA.

Daily individual animal feed requirements for maintenance and gain were obtained according to Williams and Jenkins (2003a,b), and these requirements, together with feed requirements for cold stress, were summed for the entire experimental period. The total DMI for the experimental period was divided by the number of days fed, and this average daily required DMI was referred to as the DECI feed intake requirement for maintenance, cold stress, and gain (DFR_mcg). Average daily feed DMI were also predicted for maintenance and ADG (DFR_mg), maintenance and cold stress (DFR_mc), maintenance (DFR_m), and ADG (DFR_g).

Statistical Analysis

Descriptive statistics for observed average daily DMI (OFI), and predicted measures of average daily required DMI were computed with the Means procedure of SAS (SAS Inst. Inc., Cary, NC). The accuracy of CVDS and DECI in predicting average daily DMI was evaluated by comparing CFR_mcg and DFR_mcg with OFI. Linear regressions were calculated between OFI and CFR_mcg, and between OFI and DFR_mcg, allowing the intercepts to be calculated; then in a second analysis the intercepts were forced through the origin. The second regression model tested how closely the predictions followed the observed animal response (i.e., the line where predicted equals observed). When the regression is forced through the origin, the SE of the dependent variable estimate (s_y.x) is an estimate of the precision of the predicted values over the range of observations, and the regression coefficient is an estimate of the bias.

Heritability and Genetic Correlations Estimates

Estimates of (co)variance components, heritabilities, and genetic correlations were made for the traits in Table 3, yearling weight (YWT), final weight (FWT), and ADG using the MTDFREML programs (Boldman et al., 1995). Fixed effects were genotype defined by the combination of sire breed and dam breed or dam crossbreed (15 combinations), year of birth (1961 to 1965), age of dam (2 to 6 yr of age), and a covariate defined by age at the beginning of the test period (mean = 239 d; range = 170 to 275 d). Random sources of variation were direct genetic and residual effects. An animal model was used for direct genetic effects. There were 947 animals in the pedigree. All animals with data had sire and dam identified. Maternal sire and dam also were known for animals born in phase 2 of the experiment.

	RESULTS AND DISCUSSION

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION IMPLICATIONS LITERATURE CITED

 
Summary of Observed and Predicted Data

The mean, CV, minimum and maximum values for OFI of steers, and predicted measures of average daily required DMI are shown in Table 3. Compared with the OFI mean, the DFR_mcg mean was very similar, but the CFR_mcg mean was about 3.5% lower. Mean values and CV for DFR_g and CFR_g were about the same, suggesting that the CVDS and DECI performed about the same in predicting the average daily required DMI for gain. The difference in means between DFR_mcg and CFR_mcg is mainly due to the difference in means for DFR_m and CFR_m because the difference in requirements for cold stress (DFR_mc – DFR_m; CFR_mc – CFR_m) were small. These results suggest that the CVDS model may be underpredicting maintenance requirements compared with the DECI model. Predicted average daily required DMI variables showed a similar variability compared with OFI, except for average daily required DMI for gain, and this agrees with a CV of 17% for ADG in the observed data.

Prediction of Individual DM Required

The relationship between OFI and CFR_mcg is illustrated in Figure 1, and the relationship between OFI and DFR_mcg is illustrated in Figure 2. Results of regressions of OFI on CFR_mcg and OFI on DFR_mcg are reported in Table 4. The intercept and slope for the regression of OFI on CFR_mcg were very similar to the values obtained by Guiroy et al. (2001) for the regression of OFI on daily DM required predicted with the CVDS model, except that the R² was smaller in this study. The CVDS and DECI models accounted for 44.3 and 53.4% of the variation in OFI, respectively. The s_y.x summarizes all the deviations of predicted from observed values into one statistic and is useful in comparing the precision of different models of the same dependent variable. In this case low s_y.x values would indicate greater precision. For the regressions of OFI on CFR_mcg and OFI on DFR_mcg that were forced through the origin, the s_y.x of the predicted values about OFI values were 0.683 and 0.633 kg, respectively. These results are similar to a s_y.x value of 0.58 kg obtained by Rayburn and Fox (1990) for a regression of observed on predicted DMI that was forced through the origin. The CVDS model underpredicted OFI with an average bias of 3.4%, and the DECI model overpredicted OFI with an average bias of 0.4%. These results suggest that both models were not very accurate phenotypically in predicting the individual average daily required DMI.

View larger version (13K): [in this window] [in a new window]   Figure 1. Relationship between observed average daily DMI (OFI) and average daily required DMI for maintenance, cold stress, and ADG of steers predicted using the Cornell Value Discovery System. Dashed line y = x indicates the position of the perfect fit between observed and model predicted values. The data used were from 502 individually fed steers produced in a heterosis experiment (Olson et al., 1978a,b).

View larger version (13K): [in this window] [in a new window]   Figure 2. Relationship between observed average daily DMI (OFI) and average daily required DMI for maintenance, cold stress, and ADG of steers predicted using the Decision Evaluator for the Cattle Industry. Dashed line y = x indicates the position of the perfect fit between observed and model predicted values. The data used were from 502 individually fed steers produced in a heterosis experiment (Olson et al., 1978a,b).

Heritability estimates for OFI and predicted measures of average daily required DMI, and phenotypic and genetic correlations between OFI and predicted measures of average daily required DMI are shown in Table 5. Heritability values were very similar for each measure of average daily required DMI predicted with the CVDS model and the DECI model. The heritability for OFI (0.27 ± 0.12) was similar to values reported for beef cattle by Koots et al. (1994a), Arthur et al. (2001b), and Robinson and Oddy (2004). Phenotypic correlations between OFI and each of the predicted measures of average daily required DMI were greater for the DECI model compared with the CVDS model. The lowest phenotypic and genetic correlations were obtained with CFR_mc and DFR_mc for both models, which suggest that the predicted feed required for maintenance and cold stress may not be good indicators of overall feed requirements.

Genetic and phenotypic correlations between each measure for average daily required DMI predicted with the CVDS and DECI models are reported in Table 6. Except for correlations between CFR_mc and DFR_mc and between CFR_m and DFR_m, all other phenotypic and genetic correlations were high. The lower phenotypic and genetic correlations between CFR_mc and DFR_mc and between CFR_m and DFR_m are a result of differences in calculating maintenance requirements between the 2 models. The high phenotypic and genetic correlation between CFR_g and DFR_g suggest that both models perform about the same in predicting requirements for ADG. The high phenotypic and genetic correlation between CFR_mcg and DFR_mcg suggest there may be very little difference between CVDS and DECI in predicting the average daily required DMI.

The differences between lower phenotypic and greater genetic correlations for OFI and FWT are similar to those between phenotypic and genetic correlations for OFI and predicted DMI in Table 5. Lower phenotypic and greater genetic correlations were also observed by Koots et al. (1994b) and Arthur et al. (2001b) for DMI and growth traits. Reasons for the differences between phenotypic and genetic correlations could be errors in measurement of OFI from using an individual feeder system or in the input measurements used for prediction. Measurement error is expected to contribute to phenotypic variance but not to the covariance between traits.

The high genetic correlations for FWT with OFI, CFR_mcg, and DFR_mcg indicate that there may be little opportunity for genetic variance in feed efficiency, both in the experimental and predicted data. Several researchers (Herd and Bishop, 2000; Arthur et al., 2001a,b) have found genetic variance in feed efficiency (residual feed intake and feed conversion ratio) in different sets of experimental data, and Arthur et al. (2001a, b) reported lower genetic correlations of 0.83 ± 0.04 and 0.56 ± 0.09 for OFI with 400-d BW and 15-mo BW, respectively, in their experimental data. This suggests that the approach of using predicted required DMI in genetic evaluations needs to be further evaluated in populations where genetic variance in feed efficiency exists.

	IMPLICATIONS

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION IMPLICATIONS LITERATURE CITED

 
Use of biological models to predict feed intake could provide the beef cattle industry with a cost effective approach to genetically improve feed efficiency using data frequently collected by the industry. The high genetic correlations between observed and predicted feed intakes obtained in this study indicate that this technology would be potentially useful to estimate breeding values for feed efficiency with nearly the same accuracy but at a considerably lower cost than breeding values based entirely on individual feeding records. The technology would also facilitate evaluation of a much larger number of animals, and this could potentially have a greater impact on response to selection for feed efficiency than selection based only on individual feeding records. However, high genetic correlations for final weight with observed and predicted feed intakes suggests that the technology needs to be further evaluated in populations with genetic variance in feed efficiency.

¹ Corresponding author: williams{at}email.marc.usda.gov

Received for publication September 2, 2005. Accepted for publication January 18, 2006.

	LITERATURE CITED

Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION IMPLICATIONS LITERATURE CITED

 


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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Williams, C. B. Articles by Ferrell, C. L. Search for Related Content PubMed PubMed Citation Articles by Williams, C. B. Articles by Ferrell, C. L.