Ruminants may contribute to global warming through the release of methane gas by enteric fermentation. Until now, methane emissions from ruminants were estimated using simple regression equations. The objective of this study was to compare the capacity of dynamic and mechanistic models to that of regression equations to predict methane production from dairy cows. The updated version of the model of Baldwin et al. and a modified version of the model of Dijkstra et al. and the regression equations of Blaxter and Clapperton and Moe and Tyrrell were challenged with 32 experimental diets selected from 13 publications. The predictive capacity of mechanistic models and regression equations was evaluated by comparing predicted and observed methane production using regression analysis. Results of regression showed better prediction of methane production with mechanistic models than with regression equations. The modified model of Dijkstra et al. predicted methane production with the higher R2 (.71) and the smaller error of prediction (19.87% of the observed mean). The model of Baldwin et al. predicted methane production with a similar R2 (.70) but a higher error of prediction (36.93%). However, a large proportion of this error can be eliminated by a correction factor. Predictions using the equations of Moe and Tyrrell and Blaxter and Clapperton were poor (R2 = .42 and .57; error of prediction = 33.72% and 22.93%, respectively). This study demonstrated that from a large variation in diet composition, mechanistic models allow the prediction of methane production more accurately than simple regression equations.
Considerable progress has been made in the nutritional modelling of growth. Most models typically predict (or analyse) the response of a single animal. However, the response to nutrients of a single, representative animal is likely to be different from the response of the herd. To address the variation in response between animals, a stochastic approach towards nutritional modelling is required. In the present study, an analysis method is presented to describe growth and feed intake curves of individual pigs within a population of 192 pigs. This method was developed to allow end-users of InraPorc (a nutritional model predicting and analysing growth in pigs) to easily characterise their animals based on observed data and then use the model to test different scenarios. First, growth and intake data were curve-fitted to characterise individual pigs in terms of BW (Gompertz function of age) and feed intake (power function of BW) by a set of five parameters, having a biological or technico-economical meaning. This information was then used to create a population of virtual pigs in InraPorc, having the same feed intake and growth characteristics as those observed in the population. After determination of the mean lysine (Lys) requirement curve of the population, simulations were carried out for each virtual pig using different feeding strategies (i.e. 1, 2, 3 or 10 diets) and Lys supply (ranging from 70% to 130% of the mean requirement of the population). Because of the phenotypic variation between pigs and the common feeding strategies that were applied to the population, the Lys requirement of each individual pig was not always met. The percentage of pigs for which the Lys requirement was met increased concomitantly with increasing Lys supply, but decreased with increasing number of diets used. Simulated daily gain increased and feed conversion ratio decreased with increasing Lys supply ( P , 0.001) according to a curvilinear-plateau relationship. Simulated performance was close to maximum when the Lys supply was 110% of the mean population requirement and did not depend on the number of diets used. At this level of Lys supply, the coefficient of variation of simulated daily gain was minimal and close to 10%, which appears to be a phenotypic characteristic of this population. At lower Lys supplies, simulated performance decreased and variability of daily gain increased with an increasing number of diets ( P , 0.001). Knowledge of nutrient requirements becomes more critical when a greater number of diets are used. This study shows the limitations of using a deterministic model to estimate the nutrient requirements of a population of pigs. A stochastic approach can be used provided that relationships between the most relevant model parameters are known.
The impact of moving from conventional to precision feeding systems in growing-finishing pig operations on animal performance, nutrient utilization, and body and carcass composition was studied. Fifteen animals per treatment for a total of 60 pigs of 41.2 (SE = 0.5) kg of BW were used in a performance trial (84 d) with 4 treatments: a 3-phase (3P) feeding program obtained by blending fixed proportions of feeds A (high nutrient density) and B (low nutrient density); a 3-phase commercial (COM) feeding program; and 2 daily-phase feeding programs in which the blended proportions of feeds A and B were adjusted daily to meet the estimated nutritional requirements of the group (multiphase-group feeding, MPG) or of each pig individually (multiphase-individual feeding, MPI). Daily feed intake was recorded each day and pigs were weighed weekly during the trial. Body composition was assessed at the beginning of the trial and every 28 d by dual-energy X-ray densitometry. Nitrogen and phosphorus excretion was estimated as the difference between retention and intake. Organ, carcass, and primal cut measurements were taken after slaughter. The COM feeding program reduced (P < 0.05) ADFI and improved G:F rate in relation to other treatments. The MPG and MPI programs showed values for ADFI, ADG, G:F, final BW, and nitrogen and phosphorus retention that were similar to those obtained for the 3P feeding program. However, compared with the 3P treatment, the MPI feeding program reduced the standardized ileal digestible lysine intake by 27%, the estimated nitrogen excretion by 22%, and the estimated phosphorus excretion by 27% (P < 0.05). Organs, carcass, and primal cut weights did not differ among treatments. Feeding growing-finishing pigs with daily tailored diets using precision feeding techniques is an effective approach to reduce nutrient excretion without compromising pig performance or carcass composition.
This study was developed to assess the impact on performance, nutrient balance, serum parameters and feeding costs resulting from the switching of conventional to precision-feeding programs for growing-finishing pigs. A total of 70 pigs (30.4 ± 2.2 kg BW) were used in a performance trial (84 days). The five treatments used in this experiment were a three-phase group-feeding program (control) obtained with fixed blending proportions of feeds A (high nutrient density) and B (low nutrient density); against four individual daily-phase feeding programs in which the blending proportions of feeds A and B were updated daily to meet 110%, 100%, 90% or 80% of the lysine requirements estimated using a mathematical model. Feed intake was recorded automatically by a computerized device in the feeders, and the pigs were weighed weekly during the project. Body composition traits were estimated by scanning with an ultrasound device and densitometer every 28 days. Nitrogen and phosphorus excretions were calculated by the difference between retention (obtained from densitometer measurements) and intake. Feeding costs were assessed using 2013 ingredient cost data. Feed intake, feed efficiency, back fat thickness, body fat mass and serum contents of total protein and phosphorus were similar among treatments. Feeding pigs in a daily-basis program providing 110%, 100% or 90% of the estimated individual lysine requirements also did not influence BW, body protein mass, weight gain and nitrogen retention in comparison with the animals in the group-feeding program. However, feeding pigs individually with diets tailored to match 100% of nutrient requirements made it possible to reduce ( P < 0.05) digestible lysine intake by 26%, estimated nitrogen excretion by 30% and feeding costs by US$7.60/pig (−10%) relative to group feeding. Precision feeding is an effective approach to make pig production more sustainable without compromising growth performance.Keywords: nutrition, nutrient requirements, precision feeding, protein, swine ImplicationsPresent study investigated the impact of using a mathematical model estimating real-time daily lysine requirements in a sustainable precision-feeding program for growing pigs. Results clearly indicate that this is an effective approach for reducing nutrient intake, nutrient excretion and feeding costs. Feeding pigs individually with daily tailored diets that provide 100% of estimated requirements can reduce lysine intake by 26% and nitrogen excretion by 30% without compromising the pig performance. The proposed precisionfeeding system represents a paradigm shift in pig production, as it takes into account between-animal differences in nutrient requirements within a population and their dynamic evolution over time.
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