Development of sustainable precision farming systems for swine: Estimating real-time individual amino acid requirements in growing-finishing pigs

Abstract

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.

Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs

“…The amount of protein contained in the BW gain of each pig was estimated by regression analysis relating within each feeding period the observed performance variables and the protein content in live weight gain (Rivest J and Pomar C, unpublished data). This method of estimating nutrient requirements has been described previously (Hauschild et al, 2012;Pomar et al, 2014b) and validated in two previous studies (Zhang et al, 2012;Cloutier et al, 2014).…”

“…The required concentration of lysine was estimated individually for each pig in all treatments with a previously described mathematical model (Hauschild et al, 2012) using individual daily feed intake and the weekly BW information. In this model, the empirical component estimates the expected BW, feed intake and daily gain for the next day, whereas the mechanistic component uses these three estimates to calculate, with a factorial method, the Andretta, Pomar, Rivest, Pomar and Radünz optimal concentration of amino acids that should be offered that day to each pig in the herd so as to meet their requirements.…”

Application of a precision nutrition tool for growing and finishing pigs

“…High environmental temperatures reduce voluntary feed intake in pigs during heat stress (Quiniou et al, 2001;Huynh et al, 2005;Kiefer et al, 2010;Batista et al, 2011) because the intake mechanisms are regulated for the short and long-term processes, which influence the partition of energy between maintenance and deposition (Hauschild et al, 2010;Batista et al, 2011;Hauschild et al, 2012;Rossi et al, 2013;Warpechowski et al, 2014). The InraPorc ® deterministic model characterizes the pig in thermal comfort, in heat stress, or under other factors that influence the voluntary feed intake (Van Milgen et al, 2008;Hauschild et al, 2012;Rossi et al, 2013), but does not consider environmental effects directly. However, if the animal profile is adjusted in specific climatic conditions, its requirements and the nutritional adjustment are more accurate.…”

Applying precision feeding to improve the nitrogen efficiency of swine production: a review of possible practices and obstacles

“…While promoting better nutrient use, they fail to provide exactly what each animal requires. In this context, precision feeding is a modern approach in which pigs are fed individually using diets adjusted in real time according to their patterns of feed intake and growth (HAUSCHILD et al, 2012).…”

“…For this reason, precision feeding techniques have come under increasing study in recent years, and some researches have been conducted on the swine production chain (HAUSCHILD et al, 2012;POMAR et al, 2014;ANDRETTA et al, 2014MONTEIRO et al, 2016).…”