برآورد پارامتر و تجزیه و تحلیل حساسیت از مدل های رسوب چربی در گوشت گاو، گاوهای پرواری با استفاده از acslXtreme

The Davis Growth Model (a dynamic steer growth model encompassing 4 fat deposition models) is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict P8 fat (mm) in beef cattle to assist beef producers meet market specifications. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat (kg) is calculated from the net energy available after accounting for energy needs for maintenance and protein synthesis. Total body fat (kg) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral). This paper reports on the parameter estimation and sensitivity analysis of the DNA (deoxyribonucleic acid) logistic growth equations and the fat deposition first-order differential equations in the Davis Growth Model using acslXtreme (Hunstville, AL, USA, Xcellon). The DNA and fat deposition parameter coefficients were found to be important determinants of model function; the DNA parameter coefficients with days on feed >100 days and the fat deposition parameter coefficients for all days on feed. The generalized NL2SOL optimization algorithm had the fastest processing time and the minimum number of objective function evaluations when estimating the 4 fat deposition parameter coefficients with 2 observed values (initial and final fat). The subcutaneous fat parameter coefficient did indicate a metabolic difference for frame sizes. The results look promising and the prototype Davis Growth Model has the potential to assist the beef industry meet market specifications.

The Davis Growth Model, a dynamic steer growth model [9] that includes 4 fat deposition models [11] is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict beef cattle fatness in the field [4]. Predicting beef cattle fatness will assist beef producers manage cattle to meet stringent market specifications that are related to both weight and fatness for domestic and international markets. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat is calculated from the net energy available after accounting for energy needs for maintenance and protein synthesis. Total body fat (FAT) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral) (Fig. 1). Three of the fat depots are then converted to carcass characteristics: intramuscular fat (IMF, kg) to IMF as a percentage (%), subcutaneous fat (kg) to 12/13th rib fat (mm) and subsequently to P8 fat (B.J. Walmsley, unpublished results), and visceral fat (kg) to kidney, pelvic, and heart fat (KPH, %) [8]. The 4th fat depot, intermuscular fat, is not converted to any carcass characteristic. Each of the 4 fat depots is derived by a first-order differential equation. This paper describes the parameter estimation and sensitivity analysis of the DNA logistic growth equations and fat deposition first-order differential equations using acslXtreme (Hunstville, AL, USA, Xcellon). Data from Robelin [10] and Cianzio et al. [2] were used to parameterize the DNA equations and data from a meta-analysis study [7] were used to parameterize the fat deposition equations. The objectives of this study were: (1) describe the fat deposition models; (2) parameterize the DNA logistic growth equations and fat deposition first-order differential equations and; (3) conduct a sensitivity analysis of the parameters.

The results from this study indicate that the DNA (kDNAj) and fat deposition (kFATj) parameter coefficients are sensitive and important determinants of model function in the prediction of fat (Fj; kg) in each of the fat depots. This paper found that the generalized NL2SOL optimization algorithm, in acslXtreme, had the fastest processing time and the minimum number of objective function evaluations when estimating the parameter coefficients of the first-order differential fat deposition equations with 2 observed values (initial and final fat) in each of the fat depots. The generalized NL2SOL optimization algorithm also predicted fat at slaughter (i.e., final fat) for each of the fat depots (Fj; kg) with a MSEP = 0. The statistical analysis of the fat deposition (kFATj) parameters indicate that they are not metabolically different for non-implanted and implanted steers in all fat depots and not different for intermuscular, intramuscular, and visceral fat depots for frame size at the level of aggregation used to simulate fat deposition in beef steers. The subcutaneous fat parameter coefficient did, however, indicate a metabolic difference for frame size therefore a non-linear relationship was developed for kFAT3 to adjust for differences in frame size. Availability of data is a limiting factor because studies on the cellularity of bovine tissues are scarce. Nevertheless, the DNA parameter coefficient sensitivity analysis of model responses when DOF > 100 days and the DNA fat partition of parameter coefficients indicated that the DNA parameter coefficients are important determinants of model function. The parameterization of the fat deposition first-order differential equations indicated that the parameter coefficients were sensitive to model responses for all DOF and the fat depot partition of parameter coefficients indicated that the fat depot parameter coefficients are also important determinants of model function. The DNA logistic growth curves (Fig. 2) were developed, as mentioned above, with a limited amount of data and therefore may not accurately represent the shape of the relationship. This study highlights that further research in bovine adipose cellularity is required to develop and challenge DNA models. As additional data becomes available the DNA relationship may show that a secondary ‘wave’ of hyperplasia occurs as animals fatten. This secondary ‘wave’ of hyperplasia may follow a more episodic pattern. Future research may also show that there are differences in hypertrophy between breeds. A preliminary analysis of the field calculator that predicts P8 fat has shown some promising results (V.H., Oddy, unpublished results). The empirical equation in the field calculator was however, developed with the subcutaneous fat proportion of FAT fixed at 20%. This study and the parameterization of the DNA logistic growth equations and the first-order differential fat deposition equations are dynamic not static! Therefore, the future development has the potential to improve the prediction of fat in the Davis Growth Model. The simulated diameters (Table 7) indicate some discrepancies with the limited amount of data that is available. Further development of the fat deposition models may correct this apparent anomaly.