Benchaar, C., Pomar, C. and Chiquette, J. 2001. Evaluation of dietary strategies to reduce methane production in ruminants: A modelling approach. Can. J. Anim. Sci. 81: 563-574. The objective of this study was to use the modelling approach to assess the effectiveness of different existing nutritional strategies to reduce methane production from ruminants. For this purpose, a modified version of a mechanistic and dynamic model of rumen digestion was used. Simulated strategies included: dry matter intake (DMI), forage to concentrate ratio, nature of concentrate (fibrous vs. starchy concentrate), type of starch (slowly vs. rapidly degraded), forage species (legume vs. grass), forage maturity, forage preservation method (dried vs. ensiled), forage processing, and upgrading and supplementation of poor quality forages (straw). This study showed that mathematical modelling is a valuable tool to evaluate the impact of a given dietary manipulation not only on methanogenesis but also on the metabolism of the whole rumen system. Depending on the nature of the intervention, methane production can be reduced by 10 to 40%. Increasing DMI and the proportion of concentrate in the diet reduced methane production (-7 and -40%). Methane production was also decreased with the replacement of fibrous concentrate with starchy concentrate (-22% ) and with the utilization of less ruminally degradable starch (-17%). The use of more digestible forage (less mature and processed forage) resulted in a reduction of methane production (-15 and -21%). Methane production was lower with legume than with grass forage (-28%), and with silage compared to hay (-20%). Supplementation or ammoniation of straw did not reduce methane losses, but had a positive impact on the efficiency of rumen metabolism. The modelling approach demonstrated that reduction of methane production from ruminants is a complex challenge. Implementation of any strategy must take into account the possible consequences on the efficiency of the entire rumen system. Key words: Ruminants, methane reduction, modelling approach Benchaar, C., Pomar, C. et Chiquette, J. 2001. Evaluation de stratégies alimentaires pour réduire la production de méthane chez les ruminants: approche modélisatrice. Can. J. Anim. Sci. 81: 563-574. L'objectif de cette étude est d'utiliser la modélisation mathématique pour évaluer et expliquer l'impact de différentes stratégies alimentaires appliquées pour réduire la production de méthane chez les ruminants. Pour cela, une version modifiée d'un modèle dynamique et mécaniste de la digestion ruminale a été utilisée. Les différentes stratégies alimentaires évaluées sont: le niveau d'ingestion, la proportion de concentré dans la ration, la nature du concentré (fibreux vs. amylacé), la dégradabilité ruminale de l'amidon (lentement vs. rapidement dégradable), l'espèce (légumineuse vs. graminée), la maturité et la méthode de conservation du fourrage, et le traitement chimique et la supplémentation de la paille. Les résultats des simulations obtenus montrent que la prod...

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.

Empirical and factorial methods are currently used to estimate nutrient requirements for domestic animals. The purpose of this study was to estimate the nutrient requirements of a given pig population using the empirical and factorial methods; to establish the relationship between the requirements estimated with these two methods; and to study the limitations of the methods when used to determine the level of a nutrient needed to optimize individual and population responses of growing pigs. A systematic analysis was carried out on optimal lysine-to-net-energy (Lys : NE) ratios estimated by the empirical and factorial methods using a modified InraPorc R growth model. Sixty-eight pigs were individually simulated based on detailed experimental data. In the empirical method, population responses were estimated by feeding pigs with 11 diets of different Lys : NE ratios. Average daily gain and feed conversion ratio were the chosen performance criteria. These variables were combined with economic information to estimate the economic responses. In the factorial method, the Lys : NE ratio for each animal was estimated by model inversion. Optimal Lys : NE ratios estimated for growing pigs (25 to 105 kg) differed between the empirical and the factorial method. When the average pig is taken to represent a population, the factorial method does not permit estimation of the Lys : NE ratio that maximizes the response of heterogeneous populations in a given time or weight interval. Although optimal population responses are obtained by the empirical method, the estimated requirements are fixed and cannot be used for other growth periods or populations. This study demonstrates that the two methods commonly used to estimate nutrient requirements provide different nutrient recommendations and have important limitations that should be considered when the goal is to optimize the response of individuals or pig populations.

The objective of this study was to develop and evaluate a mathematical model used to estimate the daily amino acid requirements of individual growing-finishing pigs. The model includes empirical and mechanistic model components. The empirical component estimates daily feed intake (DFI), BW, and daily gain (DG) based on individual pig information collected in real time. Based on DFI, BW, and DG estimates, the mechanistic component uses classic factorial equations to estimate the optimal concentration of amino acids that must be offered to each pig to meet its requirements. The model was evaluated with data from a study that investigated the effect of feeding pigs with a 3-phase or daily multiphase system. The DFI and BW values measured in this study were compared with those estimated by the empirical component of the model. The coherence of the values estimated by the mechanistic component was evaluated by analyzing if it followed a normal pattern of requirements. Lastly, the proposed model was evaluated by comparing its estimates with those generated by the existing growth model (InraPorc). The precision of the proposed model and InraPorc in estimating DFI and BW was evaluated through the mean absolute error. The empirical component results indicated that the DFI and BW trajectories of individual pigs fed ad libitum could be predicted 1 d (DFI) or 7 d (BW) ahead with the average mean absolute error of 12.45 and 1.85%, respectively. The average mean absolute error obtained with the InraPorc for the average individual of the population was 14.72% for DFI and 5.38% for BW. Major differences were observed when estimates from InraPorc were compared with individual observations. The proposed model, however, was effective in tracking the change in DFI and BW for each individual pig. The mechanistic model component estimated the optimal standardized ileal digestible Lys to NE ratio with reasonable between animal (average CV = 7%) and overtime (average CV = 14%) variation. Thus, the amino acid requirements estimated by model are animal- and time-dependent and follow, in real time, the individual DFI and BW growth patterns. The proposed model can follow the average feed intake and feed weight trajectory of each individual pig in real time with good accuracy. Based on these trajectories and using classical factorial equations, the model makes it possible to estimate dynamically the AA requirements of each animal, taking into account the intake and growth changes of the animal.

-The high cost of feed ingredients, the use of non-renewable sources of phosphate and the dramatic increase in the environmental load resulting from the excessive land application of manure are major challenges for the livestock industry. Precision feeding is proposed as an essential approach to improve the utilization of dietary nitrogen, phosphorus and other nutrients and thus reduce feeding costs and nutrient excretion. Precision feeding requires accurate knowledge of the nutritional value of feedstuffs and animal nutrient requirements, the formulation of diets in accordance with environmental constraints, and the gradual adjustment of the dietary nutrient supply to match the requirements of the animals. After the nutritional potential of feed ingredients has been precisely determined and has been improved by the addition of enzymes (e.g. phytases) or feed treatments, the addition of environmental objectives to the traditional feed formulation algorithms can promote the sustainability of the swine industry by reducing nutrient excretion in swine operations with small increases in feeding costs. Increasing the number of feeding phases can also contribute to significant reductions in nutrient excretion and feeding costs. However, the use of precision feeding techniques in which pigs are fed individually with daily tailored diets can further improve the efficiency with which pigs utilize dietary nutrients. Precision feeding involves the use of feeding techniques that allow the provision of the right amount of feed with the right composition at the right time to each pig in the herd. Using this approach, it has been estimated that feeding costs can be reduced by more than 4.6%, and nitrogen and phosphorus excretion can both be reduced by more than 38%. Moreover, the integration of precision feeding techniques into large-group production systems can provide real-time off-farm monitoring of feed and animals for optimal slaughter and production strategies, thus improving the environmental sustainability of pork production, animal well-being and meat-product quality.Key Words: animal variability, diet formulation, nutrient excretion, nutrient requirements, production cost Técnicas de alimentação de precisão em operações de suínos em crescimento-terminaçãoRESUMO -O custo elevado das matérias-primas, o uso de recursos não renováveis de fosfatos e o aumento da poluição ambiental resultante do excesso de aplicação de dejetos no meio ambiente têm sido considerado um dos principais problemas na produção animal. A alimentação de precisão é proposta como uma abordagem essencial para melhorar a utilização do nitrogênio, fósforo e outros nutrientes oriundos da dieta e reduzir assim o custo da dieta e a excreção de nutrientes. A alimentação de precisão requer um conhecimento do valor nutricional dos ingredientes, exigência nutricional dos animais, formulação das dietas de acordo com as restrições ambientais e do adequado ajuste da oferta de nutrientes com a exigência dos animais. O conhecimento do potencial nutricional...

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.

A mathematical model was developed from literature data to predict the volume and composition of pig's excreta (dry and organic matter, C, N, P, K, Cu and Zn contents), and the emission of greenhouse gases (CH 4 and CO 2 ) though respiration and from the intestinal tract, for each physiological stage (post-weaning and fattening pigs and lactating and gestating sows). The main sources of variation considered in the model are related to animal performances (feed efficiency, prolificacy, body weight gain, etc.), to water and nutrient intakes and to housing conditions (ambient temperature). Model predictions were validated by using 19 experimental studies, most of them performed in conditions close to those of commercial farms. Validation results showed that the model is precise and robust when predicting slurry volume ( R 2 5 0.96), slurry N ( R 2 5 0.91), P ( R 2 5 0.95) and to a lesser extent dry matter ( R 2 5 0.75) contents. Faeces and urine composition (minerals and macronutrients) can also be precisely assessed, provided the composition and the digestibility of the feed are well known. Sensitivity analysis showed strong differences in CH 4 emission and excretion amounts and composition according to physiological status, animal performance, temperature and diet composition. The model is an efficient tool to calculate nutrient balances at the animal level in commercial conditions, and to simulate the effect of production alternatives, such as feeding strategy or animal performance, on excreta production and composition. This is illustrated by simulations of three feeding strategies, which demonstrates important opportunities to limit environmental risks through diet manipulations.