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Precision livestock farming: real-time estimation of daily protein deposition in growing–finishing pigs
- A. Remus, L. Hauschild, S. Methot, C. Pomar
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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.
Toward better estimates of the real-time individual amino acid requirements of growing-finishing pigs showing deviations from their typical feeding patterns
- L. Hauschild, A. R. Kristensen, I. Andretta, A. Remus, L. S. Santos, C. Pomar
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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.
Simulated amino acid requirements of growing pigs differ between current factorial methods
- A. Remus, L. Hauschild, C. Pomar
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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.
Environmental impacts of precision feeding programs applied in pig production
- I. Andretta, L. Hauschild, M. Kipper, P. G. S. Pires, C. Pomar
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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.
Meta-analytical study of productive and nutritional interactions of mycotoxins in growing pigs
- I. Andretta, M. Kipper, C. R. Lehnen, L. Hauschild, M. M. Vale, P. A. Lovatto
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A meta-analysis was carried out in order to study the association of mycotoxins with performance and organ weights in growing pigs. A total of 85 articles published between 1968 and 2010 were used, totaling 1012 treatments and 13 196 animals. The meta-analysis followed three sequential analyses: graphical, correlation and variance–covariance. The presence of mycotoxins in diets was seen to reduce the feed intake by 18% and the weight gain in 21% compared with the control group. Deoxynivalenol and aflatoxins were the mycotoxins with the greatest impact on the feed intake and growth of pigs, reducing by 26% and 16% in the feed intake and by 26% and 22% in the weight gain. The mycotoxin concentration in diets and the animal age at challenge were the variables that more improved the coefficient of determination in equations for estimating the effect of mycotoxins on weight gain. The mycotoxin effect on growth proved to be greater in younger animals. In addition, the residual analysis showed that the greater part of the variation in weight gain was explained by the variation in feed intake (87%). The protein and methionine levels in diets could influence the feed intake and the weight gain in challenged animals. The weight gain in challenged pigs showed a positive correlation with the methionine level in diets (0.68). The mycotoxin effect on growth was greater in males compared with the effect on females. The reduction in weight gain was of 15% in the female group and 19% in the male group. Mycotoxin presence in pig diets has interfered in the relative weight of the liver, the kidneys and the heart. Mycotoxins have an influence on performance and organ weight in pigs. However, the magnitude of the effects varies with the type and concentration of mycotoxin, sex and the animal age, as well as nutritional factors.
Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs
- L. Hauschild, C. Pomar, P. A. Lovatto
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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.