Using the Small Ruminant Nutrition System to develop and evaluate an alternative approach to estimating the dry matter intake of goats when accounting for ruminal fiber stratification.

The first objective of this research was to assess the ability of the Small Ruminant Nutrition System (SRNS) mechanistic model to predict metabolizable energy intake (MEI) and milk yield (MY) when using a heterogeneous fiber pool scenario (GnG1), compared with a traditional, homogeneous scenario (G1). The second objective was to evaluate an alternative approach to estimating the dry matter intake (DMI) of goats to be used in the SRNS model. The GnG1 scenario considers an age-dependent fractional transference rate for fiber particles from the first ruminal fiber pool (raft) to an escapable pool (λr), and that this second ruminal fiber pool (i.e., escapable pool) follows an age-independent fractional escape rate for fiber particles (ke). Scenario G1 adopted only a single fractional passage rate (kp). All parameters were estimated individually by using equations published in the literature, except for 2 passage rate equations in the G1 scenario: 1 developed with sheep data (G1-S) and another developed with goat data (G1-G). The alternative approach to estimating DMI was based on an optimization process using a series of dietary constraints. The DMI, MEI, and MY estimated for the GnG1 and G1 scenarios were compared with the results of an independent dataset (n=327) that contained information regarding DMI, MEI, MY, and milk and dietary compositions. The evaluation of the scenarios was performed using the coefficient of determination (R(2)) between the observed and predicted values, mean bias (MB), bias correction factor (Cb), and concordance correlation coefficient. The MEI estimated by the GnG1 scenario yielded precise and accurate values (R(2) = 082; MB = 0.21 Mcal/d; Cb = 0.98) similar to those of the G1-S (R(2) = 0.85; MB = 0.10 Mcal/d; Cb=0.99) and G1-G (R(2) = 0.84; MB = 0.18 Mcal/d; Cb = 0.98) scenarios. The results were also similar for the MY, but a substantial MB was found as follows: GnG1 (R(2) = 0.74; MB = 0.70 kg/d; Cb = 0.79), G1-S (R(2) = 0.71; MB = 0.58 kg/d(1); Cb = 0.85) and G1-G (R(2) = 0.71; MB = 0.65 kg/d; Cb = 0.82). The alternative approach for DMI prediction provided better results with the G1-G scenario (R(2)=0.88; MB = -71.67 g/d; Cb = 0.98). We concluded that the GnG1 scenario is valid within mechanistic models such as the SRNS and that the alternative approach for estimating DMI is reasonable and can be used in diet formulations for goats.

Using body composition to determine weight at maturity of male and female Saanen goats.

The objective of this study is to provide approaches to determine mature weight of female and intact and castrated male Saanen goats using body composition data. Our database combined 7 comparative slaughter studies and comprised 244 individual records of body composition of intact male ( = 94), female ( = 71), and castrated male ( = 79) Saanen goats weighing from 4.6 to 51.0 kg BW. Nonlinear regressions were fitted to predict empty body water, fat (EBF), protein (EBP), and ash, expressed as amounts and percentages of the empty BW (EBW) and water-free EBW. Candidate equations were selected on the basis of preliminary graphical examination of the observed body composition of the database, and the best one to describe the data was selected on the basis of convergence achievement with coherent biological interpretation. The selected nonlinear functions were the allometric function (Y = ß × EBW) to describe the EBF content and the exponential function (Y = ß × × EBW) to describe EBP content in the water-free matter basis. None of the tested nonlinear functions were able to describe ash content, possibly because of its large variation. Mature weight was assumed to be the weight when net protein deposition (i.e., accretion minus degradation) tended to zero. The EBP (percentage of water-free EBW) plotted against the EBW using the exponential function enabled us to estimate the mature weight of intact and castrated males and females as 83.9, 33.6, and 26.4 kg EBW, respectively, indicating that the decrease of protein accretion of intact males approaches zero later than in females and castrated males during growth. Replacing these mature EBW estimates in the allometric function to describe the fat content in the EBW, we estimated that at maturity, castrated males and females had 21.6% and 22.4% EBF, whereas intact males had 36.8% EBF, which may not be biologically acceptable because it is too high. On the other hand, assuming that a goat matures at 22% EBF, one can backward estimate mature EBW of 42.6, 34.9, and 26.0 kg for intact and castrated males and females, respectively. This study indicated that fat percentage in the body may be used to describe maturity, as long as dietary challenges are not imposed on the animals. In addition, our results confirmed that female Saanen goats reach maturity at a lighter weight than males.

Assessment of the heterogeneous ruminal fiber pool and development of a mathematical approach for predicting the mean retention time of feeds in goats.

The objectives of this study were to evaluate ruminal fiber stratification and to develop a mathematical approach for predicting the mean retention time (MRT) of forage and concentrates in goats. A dataset from 3 studies was used that contained information regarding fiber and lignin intake as well as ruminal content and the kinetics of fiber passage for forage and concentrates. The kinetic information was obtained through pulse dose and the fecal concentration measurement of forage and concentrate markers in the same animals that were used to measure ruminal content. The evaluation of heterogeneous fiber pools in the rumen was performed using the Lucas' test assumptions, and the marker excretion profiles were interpreted using a model known in the literature as GNG1. The GNG1 model assumes an age-dependent fractional rate for the transfer of particles from the raft to the escapable pool in the rumen (λ(r); h(-1)) and an age-independent fractional rate for the escape of particles from the escapable pool to the remaining parts of the stomach (k(e); h(-1)). The equations used to predict the MRT for forage and concentrate fiber were developed using stepwise regression. A sensitivity analysis was conducted using a Monte Carlo simulation to investigate the relationships between the dependent and independent variables and between forage and concentrate passage rates. The Lucas' test yields goodness-of-fit estimates for NDF analysis; however, the homogeneous fiber pool approach could not be applied because a positive intercept (P < 0.05) was identified for lignin ruminal content. The stepwise regression model for MRT estimation had an approximate coefficient of determination and a root mean square error (RMSE) for forage of 0.53 and 9.78 h, respectively, and for concentrate of 0.49 and 5.86 h, respectively. The sensitivity analysis yielded a mean rate of passage (k(p)) value for forage of 0.0322 h(-1) (0.0158 to 0.0556 h(-1)) with 99% confidence interval. For the concentrate, the mean k(p) value was of 0.0334 h(-1) (0.0146 to 0.0570 h(-1)). A heterogeneous ruminal fiber pool should be assumed for goats fed diets with considerable fiber contents. The results of the sensitivity analysis indicated that both λ(r) and k(e) are of similar importance to the rate of passage in goats. The rates of passage of forage and concentrates in goats present a high degree of overlap and are closely related.