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Skeleton bones, distinguished by trabecular and cortical bone tissue content, exhibit varied growth and composition, in response to modified dietary calcium and phosphorus levels. The study investigated how gilts adapt their individual bone and bone region mineralisation kinetics in response to changing intake of Ca and P. A total of 24 gilts were fed according to a two-phase (Depletion (D) 60–95 and Repletion (R) 95–140 kg BW, respectively). During the D phase, gilts were fed either 60% (D60) or 100% (D100) of the estimated P requirement. Subsequently, during the R phase, half of the gilts from each D diet were fed either 100% (R100) or 160% (R160) of the estimated P requirement according to a 2 × 2 factorial arrangement. Bone mineral content (BMC) was assessed in the whole body, individual bones (femur and lumbar spine L2–L4), and bone regions (head, front legs, trunk, pelvis, femur, and hind legs) every 2 weeks using dual-energy X-ray absorptiometry (DXA). At 95 kg BW, gilts fed D60 showed reduced BMC and BMC/BW ratio in all studied sites compared to those fed D100 (P

Methionine (Met) is an essential amino acid that can be supplied in different chemical forms: DL-Met, L-Met, and OH-Met. This study aimed (i) to model and compare the utilization efficiency of Met for protein deposition (PD) from all sources and (ii) to determine the efficacy and efficiency of these three free Met sources in average daily gain (ADG) of post-weaning pigs fed at or below the Met requirement. A systematic review of the literature resulted in 1 898 papers being screened for title and abstract, with 24 papers meeting the inclusion criteria. The resulting database containing 208 treatment means was used. Prior to model development, the standardized ileal digestible (SID) Met requirements in percentage in the diet were determined using initial and final BW according to the NRC (2012). Data from piglets fed above the SID Met requirements were excluded from the database prior to statistical analysis. Linear mixed-effects regression models predicting ADG as a function of free Met source and SID methionine intake (Meti) or methionine + cysteine intake (Met + cysi) were used to evaluate the efficacy and efficiency of free Met source for weight gain. Moreover, Met retention was modeled assuming that 16% of ADG is deposited as PD, and that Met accounts for 2% of PD. Met utilization efficiency was calculated as Meti after maintenance divided by Met retained in PD. Met utilization efficiency was 77% for the basal diet, decreased (P 0.05). However, the efficacy (ADG per unit of SID Meti) of free Met sources for weight gain differed between piglets fed L and DL-Met (P 0.05) between piglets fed DL and OH-Met or OH and L-Met. On average, piglets fed L-Met gained 40.3 g/d more weight per unit of increase in SID Meti than those fed DL-Met (model 4; P = 0.05). The efficacy of free Met sources for ADG was also compared using SID Met + cysi as covariable. Piglets fed L- (+11.7 g/d; P = 0.02) or OH-Met (+11.5 g/d; P = 0.04) gained more weight per gram of SID Met + cysi compared to those fed DL-Met. In conclusion, although the efficacy of DL- and L-Met for ADG differed, the efficiency for PD of L-, DL-, and OH-Met were not different in piglets fed at or below Meti requirement.

This study investigated the ability of replacement gilts to adapt their calcium and phosphorus utilization and their kinetics in bone mineralization to compensate for modified intake of these nutrients by applying a novel Ca and P depletion and repletion strategy. A total of 24 gilts were fed according to a two-phase feeding program. In the first phase, gilts (60–95 kg BW) were fed ad libitum a depletion diet providing either 60% (D60; 1.2 g digestible P/kg) or 100% (D100; 2.1 g digestible P/kg) of the estimated P requirement. In the second phase, gilts (95–140 kg BW) were fed restrictively (aim: 700–750 g/d BW gain) a repletion diet. Half of the gilts from each depletion diet were randomly assigned to either a control diet or a high-P diet (R100 and R160; with 2.1 and 3.5 g digestible P/kg, respectively) according to a 2 × 2 factorial design, resulting in four treatments: D60-R100, D60-R160, D100-R100 and D100-R160. Dual-energy X-ray absorptiometry was used to measure whole-body bone mineral content (BMC), bone mineral density (BMD) and lean and fat tissue mass on each gilt at 2-week intervals. The depletion and repletion diets, fed for 5 and 8 weeks, respectively, did not influence growth performance. The D60 gilts had a reduced BMC and BMD from the second week onwards and ended (95 kg BW) with 9% lower values than D100 gilts (P

Significant differences in the estimation of amino acid requirements exist between the available factorial methods. This study aimed to compare current factorial models used to estimate the individual and population standardised ileal digestible (SID) lysine (Lys) requirements of growing pigs during a 26-day feeding phase. Individual daily feed intake and BW data from 40 high-performance pigs (25-kg initial BW) were smoothed by linear regression. Body weight gain was constant (regression slope not different from 0) for all the pigs. The CV of the SID Lys requirements ranged from 22% at the beginning of the trial to 8% at the end. The population Brazilian tables (BT-2017) and National Research Council (NRC-2012) SID Lys requirements for the average pig were 16% higher than the average requirement estimated by the individual precision-feeding model (IPF), but similar to the estimated for the population assuming that population requirements are those of the 80th-percentile pig of the population (IPF-80). Meaning that, the IPF-80, BT-2017, and NRC-2012 models would yield similar recommendations when pigs are group-fed in conventional multi-phase systems. Additionally, the IPF-80 estimates are independent of the phase length, whereas the BT-2017 and NRC-2012 models use average population values in the middle of the feeding phase for the calculations and therefore, conventional requirement estimations decrease as the length of the feeding phase increases. In conclusion, the BT-2017 and NRC-2012 methods were calibrated for maximum population responses, which explains why these methods yield higher values than those estimated for the average pig by the IPF model. This study shows the limitations of conventional factorial methods to estimate amino acid requirements for precision-feeding systems.

Precision feeding requires a mathematical model to estimate standardized ileal digestible (SID) lysine (Lys) requirements (SIDLysR) in real time. However, this type of model requires constant calibration updates. The objective of this study was to review the calibration of the model used to estimate the real-time Lys requirements of individual growing-finishing pigs. A digestibility trial (n = 10) was conducted to evaluate amino acids digestibility during the growing and finishing phases. Additionally, 120 pigs were used in two 28-day growth experiments conducted as completely randomized design with growing (25 ± 2.1 kg BW, n = 60; 10 pigs per treatment) or finishing barrows (68.1 ± 6 kg BW, n = 60; 10 pigs per treatment). In each experiment, the pigs were divided into six equal treatment groups and fed 60%, 70%, 80%, 90%, 100% or 110% of their estimated individual SIDLysR. The Lys requirement of each pig was estimated daily using a real-time model. Body composition was measured with dual-energy X-ray densitometry on day 1 and 28 of the experiments. Average daily feed intake increased quadratically (P < 0.05) during both growth phases. Maximum average daily gain (ADG) (0.98 kg) and maximum protein deposition (PD; 170 g/day) were observed in growing pigs fed 100% of the estimated SIDLysR (P < 0.001). During the growing period, PD in BW gain (17% to 19%) and N efficiency (52% to 65%) increased linearly (P < 0.01) with increasing inclusion rates of SID Lys. Finishing pigs had maximum ADG (1.2 kg/day) when they were fed 100% of the requirements. However, the amount of protein in BW gain (13% to 16%) and N efficiency (40% to 55%) increased linearly (P < 0.01) with increasing inclusion rates of SID Lys. In conclusion, the model proposed for precision feeding is correctly calibrated to predict SIDLysR that maximize PD and ADG of average pigs from 25 to 50 kg BW. Still, there is an opportunity to improve the estimation of SIDLysR and N retention in individual pigs by better representing the individual proportion of protein in BW gain and the factors controlling the efficiency of Lys utilization in individual pigs.

Describing the feeding behavior of pigs is difficult given the large day-to-day variations observed for a given animal. The objectives of this study were to create an index that integrates the information from several components of feeding behavior in order to account for intra-animal variation within a day and between days, and to evaluate the capability of this index to study the impact of relevant nutritional factors affecting feeding behavior. Feed intake information from 160 pigs during the last 28 d of the growing phase from three studies was used. For each pig, the sum of the absolute values of the deviation areas between the regression line of the relative cumulative feed intake and the observed cumulative feed intake was used to calculate the weekly index measuring the irregularity of feed intake (IIFI). Spearman’s correlations of IIFI with the number of daily meals (r = −0.42; P

Pigs exposed to stressors might change their daily typical feeding intake pattern. The objective of this study was to develop a method for the early identification of deviations from an individual pig’s typical feeding patterns. In addition, a general approach was proposed to model feed intake and real-time individual nutrient requirements for pigs with atypical feeding patterns. First, a dynamic linear model (DLM) was proposed to model the typical daily feed intake (DFI) and daily gain (DG) patterns of pigs. Individual DFI and DG dynamics are described by a univariate DLM in conjunction with Kalman filtering. A standardized tabular cumulative sum (CUMSUM) control chart was applied to the forecast errors generated by DLM to activate an alarm when a pig showed deviations from its typical feeding patterns. The relative feed intake (RFI) during a challenge period was calculated. For that, the forecasted individual pig DFI is expressed as its highest DFI relative to the intake during pre-challenge period. Finally, the DLM and RFI approaches were integrated into the actual precision-feeding model (original model) to estimate real-time individual nutrient requirements for pigs with atypical feeding patterns. This general approach was evaluated with data from two studies (130 pigs, at 35.25 ± 3.9 kg of initial BW) that investigated during 84 days the effect of precision-feeding systems for growing-finishing pigs. The proposed general approach to estimating real-time individual nutrient requirements (updated model) was evaluated by comparing its estimates with those generated by the original model. For 11 individuals out of 130, the DLM did not fit the observed data well in a specific period, resulting in an increase in the sum of standardized forecast errors and in the number of time steps that the model needed to adapt to the new patterns. This poor fit can be identified by the increase in the CUMSUM with a consequent alarm generated. The results of this study show that the updated model made it possible to reduce intra-individual variation for the estimated lysine requirements in comparison with the original model, especially for individuals with atypical feeding patterns. In conclusion, the DLM in conjunction with CUMSUM could be used as a tool for the online monitoring of DFI for growing-finishing pigs. Moreover, the proposed general approach allows the estimation of real-time amino acid requirements and accounts for the reduced feed intake and growth potential of pigs with atypical feeding patterns.

Precision feeding using real-time models to estimate daily tailored diets can potentially increase nutrient utilization efficiency. However, to improve the estimation of amino acid requirements for growing–finishing pigs, it is necessary to accurately estimate the real-time body protein (BP) mass. The aim of this study was to predict individual BP over time in order to obtain individual daily protein content of the gain (i.e., protein deposition/daily gain, PD/DG) to be integrated into a real-time model used for precision feeding. Two databases were used in this study: one for the development of the equations for the model and the other for model evaluation. For the equations, data from 79 barrows (25 to 144 kg BW) were used to estimate the parameters for a Gompertz function and a mixed linear-quadratic regression. Individual BP predictions obtained by dual X-ray absorptiometry were regressed as a function of BW. Individual pig BP estimates were obtained by linear-quadratic regression using the MIXED procedure of SAS, considering pig measurements repeated in time. Individual Gompertz curves were obtained using the NLMIXED procedure of SAS. Both procedures generate an average or a general model, which was assessed for accuracy with the database used to generate the equations. Coefficients of concordance and determination were both 0.99, and the RMSE was 0.21 kg for the linear-quadratic regression. The Gompertz curve coefficients of concordance and determination were both 0.99, and the RMSE was 0.36 kg. In sequence, the linear-quadratic regression and Gompertz curve were evaluated in an independent data set (488 observations; 21 to 126 kg BW). The linear-quadratic regression to predict BP mass was accurate (mean absolute percentage error (MAPE) = 2.5%; bias = 0.03); the Gompertz model performed worse (MAPE = 3.9%; bias = 0.04) than the linear-quadratic regression. When using the derivative of these equations to predict PD/DG, the linear-quadratic regression was more accurate (MAPE = 4.8%, bias = 0.17%) compared to the Gompertz (MAPE = 10.6%, bias = −0.99%) mainly due to the linear decrease in PD/DG in the observed data. Further analysis using individual pig data showed that the goodness of fit of PD/DG curve depends on the individual shape of the growth curve, with either the Gompertz or the linear-quadratic regression being more accurate for specific individuals. Therefore, both approaches are provided to allow end users to select the model that best fits their needs. The proposed update of the empirical component of the original model, using either linear-quadratic regression or the Gompertz function, is able to predict BP in real-time with good accuracy.

Phosphorus (P) is an essential nutrient in livestock feed but can pollute waterways. In order for pig production to become less of a threat to the environment, excreta must contain as little P as possible or be efficiently used by plants. This must be achieved without decreasing the livestock performance. Phosphorus and calcium (Ca) deposition in the bones of growing pigs must be optimised without affecting the muscle gain. This requires precision feeding based on cutting-edge techniques of diet formulation throughout the animal growth phase. Modelling and data mining have become important tools in this quest. In this study, a mechanistic model taking into account the distribution of P between bone and soft tissues was compared to the established factorial models (INRA (Jondreville and Dourmad, 2005) and NRC (National Research Council, 2012)) that predict P (apparent total tract digestible, ATTD-P; or standardised total tract digestible, STTD-P) and Ca (total and STTD) requirements as a function of BW and protein deposition. The requirements for different bone mineralisation scenarios, namely, 100% and 85% of the genetic potential, were compared with these two models. Sobol indices were used to estimate the relative impact of growth-related parameters on mineral requirements at 30, 60 and 120 kg of BW. The INRA showed the highest value of ATTD-P requirement between 29 and 103 kg of BW (6%) and lower for lighter and higher BW. Similarly, the model for 85% bone mineralisation showed lower STTD-P requirement than NRC between 29 and 93 kg of BW (7%) and higher for lighter and higher BW. Contrary to other models, the Ca requirement of the proposed model is not fixed in relation to P. It increases from 95 kg of BW while the others decrease. The INRA showed the highest Ca requirements. The model Ca requirements for 100% bone mineralisation are higher than NRC from 20 to 38 kg of BW similar until 70 kg of BW and then higher again. For 85% objective, the model showed lower Ca requirements from 25 to 82 kg of BW and higher for lighter and higher BW. The potential Ca deposition in bones is the most sensitive parameter (84% to 100% of the variance) of both ATTD-P and Ca at 30, 60 and 120 kg. The second most sensitive parameter is the protein deposition, explaining 1% to 15% of the ATTD-P variance. Studies such as this one will help to usher in a new era of sustainable and eco-friendly livestock production.

The effect of a calcium (Ca) and phosphorus (P) depletion and repletion strategy was studied in four consecutive feeding phases of 28 days each. In all, 60 castrated male pigs (14±1.6 kg initial BW) received 60% (low (L) diet; depletion) or 100% (control (C) diet; repletion) of their Ca and digestible P requirements according to six feeding sequences (CCCC, CCCL, CLCC, CCLC, LCLC and LLLL; subsequent letters indicate the diet received in phases 1, 2, 3 and 4, respectively). Pigs bone mineral content in whole-body (BMCb) and lumbar vertebrae L2 to L4 (BMCv) was measured in every feeding phase by dual-energy X-ray absorptiometry. Growth performance was slightly (

The ability of birds to modify dietary phosphorus utilisation when fed with low-phosphorus and calcium (Ca) diets was studied using different sequences of dietary phosphorus and Ca restriction (depletion) and recovery (repletion) during the grower and the finisher phases. A total of 3600 Ross 708 broilers were randomly divided into 10 replicate pens per treatment (60 per pen, six pens per block). Chicks were fed a common starter diet from days 0 to 10, then a grower control diet (C: 0.90% Ca, 0.39% non-phytate phosphorus, nPP), mid-level diet (M: 0.71% Ca, 0.35% nPP) or low Ca and nPP diet (L: 0.60% Ca, 0.30% nPP) from days 11 to 21, followed by a finisher diet C, M or L containing, respectively, 0.85%, 0.57% or 0.48% Ca and 0.35%, 0.29% or 0.24% nPP from days 22 to 37. Six treatment sequences were tested: CC, MM, LL, ML, LC and LM. Bone mineral content by dual-energy X-ray, tibia ash, toe ash weight and tibia breaking strength were measured on days 21 and 37. No significant effect was observed on growth performance throughout the experiment. Diet L reduced bone mineral content, breaking strength, tibia and toe ash by 9%, 13%, 11% and 10%, respectively, on day 21 (compared with diet C, for linear effect, P

This study was undertaken to evaluate the effect that switching from conventional to precision feeding systems during the growing-finishing phase would have on the potential environmental impact of Brazilian pig production. Standard life-cycle assessment procedures were used, with a cradle-to-farm gate boundary. The inputs and outputs of each interface of the life cycle (production of feed ingredients, processing in the feed industry, transportation and animal rearing) were organized in a model. Grain production was independently characterized in the Central-West and South regions of Brazil, whereas the pigs were raised in the South region. Three feeding programs were applied for growing-finishing pigs: conventional phase feeding by group (CON); precision daily feeding by group (PFG) (whole herd fed the same daily adjusted diet); and precision daily feeding by individual (PFI) (diets adjusted daily to match individual nutrient requirements). Raising pigs (1 t pig BW at farm gate) in South Brazil under the CON feeding program using grain cultivated in the same region led to emissions of 1840 kg of CO2-eq, 13.1 kg of PO4-eq and 32.2 kg of SO2-eq. Simulations using grain from the Central-West region showed a greater climate change impact. Compared with the previous scenario, a 17% increase in climate change impact was found when simulating with soybeans produced in Central-West Brazil, whereas a 28% increase was observed when simulating with corn and soybeans from Central-West Brazil. Compared with the CON feeding program, the PFG and PFI programs reduced the potential environmental impact. Applying the PFG program mitigated the potential climate change impact and eutrophication by up to 4%, and acidification impact by up to 3% compared with the CON program. Making a further adjustment by feeding pigs according to their individual nutrient requirements mitigated the potential climate change impact by up to 6% and the potential eutrophication and acidification impact by up to 5% compared with the CON program. The greatest environmental gains associated with the adoption of precision feeding were observed when the diet combined soybeans from Central-West Brazil with corn produced in Southern Brazil. The results clearly show that precision feeding is an effective approach for improving the environmental sustainability of Brazilian pig production.

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

The implementation of precision feeding in growing–finishing facilities requires accurate estimates of the animals’ nutrient requirements. The objectives of the current study was to validate a method for estimating the real-time individual standardized ileal digestible (SID) lysine (Lys) requirements of growing–finishing pigs and the ability of this method to estimate the Lys requirements of pigs with different feed intake and growth patterns. Seventy-five pigs from a terminal cross and 72 pigs from a maternal cross were used in two 28-day experimental phases beginning at 25.8 (±2.5) and 73.3 (±5.2) kg BW, respectively. Treatments were randomly assigned to pigs within each experimental phase according to a 2×4 factorial design in which the two genetic lines and four dietary SID Lys levels (70%, 85%, 100% and 115% of the requirements estimated by the factorial method developed for precision feeding) were the main factors. Individual pigs’ Lys requirements were estimated daily using a factorial approach based on their feed intake, BW and weight gain patterns. From 25 to 50 kg BW, this method slightly underestimated the pigs’ SID Lys requirements, given that maximum protein deposition and weight gain were achieved at 115% of SID Lys requirements. However, the best gain-to-feed ratio (G : F) was obtained at a level of 85% or more of the estimated Lys requirement. From 70 to 100 kg, the method adequately estimated the pigs’ individual requirements, given that maximum performance was achieved at 100% of Lys requirements. Terminal line pigs ate more (P=0.04) during the first experimental phase and tended to eat more (P=0.10) during the second phase than the maternal line pigs but both genetic lines had similar ADG and protein deposition rates during the two phases. The factorial method used in this study to estimate individual daily SID Lys requirements was able to accommodate the small genetic differences in feed intake, and it was concluded that this method can be used in precision feeding systems without adjustments. However, the method’s ability to accommodate large genetic differences in feed intake and protein deposition patterns needs to be studied further.

The effect of feeding pigs in a three-phase feeding (3PF) system or a daily-phase feeding (DPF) system on growth performance, body composition, and N and P excretions was studied on 8 pens of 10 pigs each. Feeds for the 3PF and DPF treatments were obtained by mixing two feeds, one with a high nutrient concentration and the other with a low nutrient concentration. The DPF pigs tended (P=0.08) to consume more feed (+3.7%) than the 3PF pigs, but only during the first feeding phase. The DPF pigs consumed 7.3% less protein (P

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® 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.

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 (CH4 and CO2) 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 (R2 = 0.96), slurry N (R2 = 0.91), P (R2 = 0.95) and to a lesser extent dry matter (R2 = 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 CH4 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.

Pollution relative to phosphorus excretion in poultry manure as well as the soaring prices of phosphate, a non-renewable resource, remain of major importance. Thus, a good understanding of bird response regarding dietary phosphorus (P) is a prerequisite to optimise the utilisation of this essential element in broiler diets. A database built from 15 experiments with 203 treatments was used to predict the response of 21-day-old broilers to dietary non-phytate P (NPP), taking into account the main factors of variation, calcium (Ca) and microbial phytase derived from Aspergillus niger, in terms of average daily feed intake (ADFI), average daily gain (ADG), gain to feed (G:F) and tibia ash concentration. All criteria evolve linearly (P < 0.001) and quadratically (P < 0.001) with dietary NPP concentration. Dietary Ca affected the intercept and linear component for ADG (P < 0.01), G:F (P < 0.05) and tibia ash concentration (P < 0.001), whereas for ADFI, it affected only the intercept (P < 0.01). Microbial phytase addition impacted on the intercept, the linear and the quadratic coefficient for ADFI (P < 0.01), ADG (P < 0.001) and G:F (P < 0.05), and on the intercept and the linear component (P < 0.001) for tibia ash concentration. An evaluation of these models was then performed on a database built from 28 experiments and 255 treatments that were not used to perform the models. Results showed that ADFI, ADG and Tibia ash concentration were predicted fairly well (slope and intercept did not deviate from 0 to 1, respectively), whereas this was not the case for G:F. The increase in dietary Ca concentration aggravated P deficiency for all criteria while phytase addition had a positive effect. The more P deficiency was marked, the more the bird response to ADFI, ADG, G:F and tibia ash concentration was exacerbated. It must also be considered that even if the decrease in dietary Ca may improve P utilisation, it could in turn become limiting for bone mineralisation. In conclusion, this meta-analysis provides ways to reduce dietary P in broiler diets without impairing performance, taking into account dietary Ca and microbial phytase.

Dans un contexte dans lequel le coût élevé des matières premières et l’utilisation de sources non renouvelables de phosphates sont des enjeux majeurs pour les productions animales, l’alimentation de précision est proposée comme une démarche essentielle à l’accroissement de l’efficacité d’utilisation de l’azote et du phosphore alimentaire, et par conséquent, une réduction de leur excrétion. Une alimentation de précision requiert de bien caractériser le potentiel nutritif des aliments, de déterminer avec précision les besoins nutritionnels des animaux, de prédire la réponse animale aux apports nutritionnels, de formuler d’aliments avec des contraintes environnementales, et d’ajuster progressivement les apports aux besoins décroissants des animaux. La mise en place d’une telle stratégie d’alimentation demande cependant d’accroître nos connaissances et d’améliorer nos méthodes de calcul afin de s’assurer que les formules alimentaires proposées soient celles qui optimiseront nos systèmes de production tant d’un point de vue animal, économique qu’environnemental.