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Precision livestock farming: real-time estimation of daily protein deposition in growing–finishing pigs

Published online by Cambridge University Press:  25 June 2020

A. Remus Open the ORCID record for A. Remus [Opens in a new window] ,
L. Hauschild ,
S. Methot  and
C. Pomar
A. Remus
Affiliation:
Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, 2000 College Street, Sherbrooke, QuébecJ1M 0C8, Canada
L. Hauschild
Affiliation:
Animal Science Department, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista (Unesp), Via de Acesso Prof. Paulo Donato Castelane, Jaboticabal, SP14883-108, Brazil
S. Methot
Affiliation:
Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, 2000 College Street, Sherbrooke, QuébecJ1M 0C8, Canada
C. Pomar*
Affiliation:
Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, 2000 College Street, Sherbrooke, QuébecJ1M 0C8, Canada Animal Science Department, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista (Unesp), Via de Acesso Prof. Paulo Donato Castelane, Jaboticabal, SP14883-108, Brazil
*
†E-mail: [email protected]
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Abstract

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.

Keywords

modellingprecision feedingprotein deposition ratesgrowth compositionprecision nutrition
Type
Research Article
Information
animal , Volume 14 , Issue S2: 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT) , August 2020 , pp. s360 - s370
Copyright
© Her Majesty the Queen in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada and The Author(s), 2020. Published by Cambridge University Press on behalf of The Animal Consortium

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References

Andretta, I, Pomar, C, Rivest, J, Pomar, J, Lovatto, P and Radünz Neto, J 2014. The impact of feeding growing–finishing pigs with daily tailored diets using precision feeding techniques on animal performance, nutrient utilization, and body and carcass composition. Journal of Animal science 92, 3925–3936.10.2527/jas.2014-7643CrossRefGoogle ScholarPubMed
Andretta, I, Pomar, C, Rivest, J, Pomar, J and Radünz, J 2016. Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs. Animal 10, 1137–1147.CrossRefGoogle ScholarPubMed
Cangar, Ö, Aerts, J-M, Vranken, E and Berckmans, D 2008. Effects of different target trajectories on the broiler performance in growth control. Poultry Science 87, 2196–2207.CrossRefGoogle ScholarPubMed
Cloutier, L, Létourneau-Montminy, M-P, Bernier, J, Pomar, J and Pomar, C 2016. Effect of a lysine depletion–repletion protocol on the compensatory growth of growing-finishing pigs. Journal of Animal Science 94, 255–266.CrossRefGoogle ScholarPubMed
Cloutier, L, Pomar, C, Montminy, ML, Bernier, J and Pomar, J 2015. Evaluation of a method estimating real-time individual lysine requirements in two lines of growing–finishing pigs. Animal 9, 561–568.CrossRefGoogle ScholarPubMed
de Lange, CFM, Morel, PCH and Birkett, SH 2003. Modeling chemical and physical body composition of the growing pig. Journal of Animal Science 81, E159–E165.Google Scholar
Gonzalo, E 2017. Consequences of a dietary phosphorus and calcium depletion and repletion strategy in growing-finishing pigs. PhD thesis, Laval University, Québec, QC, Canada.Google Scholar
Gonzalo, E, Létourneau-Montminy, MP, Narcy, A, Bernier, JF and Pomar, C 2018. Consequences of dietary calcium and phosphorus depletion and repletion feeding sequences on growth performance and body composition of growing pigs. Animal 12, 1165–1173.CrossRefGoogle ScholarPubMed
Green, D and Whittemore, C 2005. Calibration and sensitivity analysis of a model of the growing pig for weight gain and composition. Agricultural Systems 84, 279–295.10.1016/j.agsy.2004.06.017CrossRefGoogle Scholar
Hauschild, L, Lovatto, PA, Pomar, J and Pomar, C 2012. Development of sustainable precision farming systems for swine: estimating real-time individual amino acid requirements in growing-finishing pigs. Journal of Animal Science 90, 2255–2263.CrossRefGoogle ScholarPubMed
Hauschild, L, Pomar, C and Lovatto, PA 2010. Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs. Animal 4, 714–723. doi: 10.1017/S1751731109991546CrossRefGoogle ScholarPubMed
International Organization for Standardization I 1993. Statistics – vocabulary and symbols part l: probability and general statistical terms. International Organization for Standardization, Geneva, Switzerland.Google Scholar
Kipper, M, Marcoux, M, Andretta, I and Pomar, C 2018. Repeatability and reproducibility of measurements obtained by dual-energy X-ray absorptiometry on pig carcasses. Journal of Animal Science 96, 2027–2037.CrossRefGoogle ScholarPubMed
Kipper, M, Marcoux, M, Andretta, I and Pomar, C 2019. Calibration of dual-energy x-ray absorptiometry estimating pig body composition. In 6th EAAP International symposium on energy and protein metabolism and nutrition (ed. Chizzotti, ML), pp. 427–429. Wageningen Academic Publishers, Belo Horizonte, Minas Gerais, Brazil.10.3920/978-90-8686-891-9_132CrossRefGoogle Scholar
Lin, LIK 1992. Assay validation using the concordance correlation coefficient. Biometrics 48, 599–604.10.2307/2532314CrossRefGoogle Scholar
Mahan, DC and Shields, RC Jr 1998. Essential and nonessential amino acid composition of pigs from birth to 145 kilograms of body weight, and comparison to other studies. Journal of Animal Science 76, 513–521.CrossRefGoogle ScholarPubMed
Moeller, SJ 2002. Evolution and use of ultrasonic technology in the swine industry. Journal of Animal Science 80, E19–E27.CrossRefGoogle Scholar
Möhn, S, Gillis, AM, Moughan, PJ and de Lange, CFM 2000. Influence of dietary lysine and energy intakes on body protein deposition and lysine utilization in the growing pig. Journal of Animal Science 78, 1510–1519.CrossRefGoogle ScholarPubMed
Niemi, JK, Sevón-Aimonen, M-L, Pietola, K and Stalder, KJ 2010. The value of precision feeding technologies for grow–finish swine. Livestock Science 129, 13–23.10.1016/j.livsci.2009.12.006CrossRefGoogle Scholar
Nutrient Requirement Council (NRC) 2012. Nutrient requirements of swine, 12th edition. The National Academies Press, Washington, DC, USA.Google Scholar
Pomar, C 2014. The utilization of mathematical models to improve swine production. In Congresso Latino Americano De Nutrição Animal, 23–26 September 2014, Estância de São Pedro, Brazil, pp. 23–26.Google Scholar
Pomar, C, Kipper, M and Marcoux, M 2017. Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research. Revista Brasileira de Zootecnia 46, 621–629.CrossRefGoogle Scholar
Pomar, C, Pomar, J, Dubeau, F, Joannopoulos, E and Dussault, JP 2014. The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed costs in growing–finishing pigs. Animal 8, 704–713.10.1017/S1751731114000408CrossRefGoogle ScholarPubMed
Pomar, C, Pomar, J, Rivest, J, Cloutier, L, Letourneau-Montminy, MP, Andretta, I and Hauschild, L 2015. In estimating real-time individual amino acid requirements in growing-finishing. In Nutritional modelling for pigs and poultry (ed. Sakomura, NK, Gous, RM, Kyriazakis, I and Hauschild, L), pp. 157–174. CABI Publishing, Wallingford, UK.Google Scholar
Pomar, C and Remus, A 2019. Precision pig feeding: a breakthrough toward sustainability. Animal Frontiers 9, 52–59.10.1093/af/vfz006CrossRefGoogle ScholarPubMed
Porter, T, Kebreab, E, Darmani Kuhi, H, Lopez, S, Strathe, AB and France, J 2010. Flexible alternatives to the Gompertz equation for describing growth with age in turkey hens. Poultry Science 89, 371–378.10.3382/ps.2009-00141CrossRefGoogle ScholarPubMed
Remus, A, Hauschild, L, Corrent, E, Létourneau-Montminy, M-P and Pomar, C 2019a. Pigs receiving daily tailored diets using precision-feeding techniques have different threonine requirements than pigs fed in conventional phase-feeding systems. Journal of Animal Science and Biotechnology 10, 16.10.1186/s40104-019-0328-7CrossRefGoogle ScholarPubMed
Remus, A, Hauschild, L, Létourneau-Montminy, M-P, Corrent, E and Pomar, C 2020b. The ideal protein profile for late-finishing pigs in precision feeding systems: threonine. Animal Feed Science Technology 265, 114500.CrossRefGoogle Scholar
Remus, A, Hauschild, L, Méthot, S and Pomar, C 2019c. Sustainable precision feeding: real-time estimation of body protein mass in growing-finishing pigs. In 70th Annual Meetingg of the European Federation of Animal Science (EAAP), Wageningen Academic Publishers, Ghent, Belgium, pp. 713.Google Scholar
Remus, A, Méthot, S, Hauschild, L and Pomar, C 2019b. Sustainable precision livestock farming: calibrating the real-time estimation of daily protein gain in growing-finishing pigs. Advances in Animal Biosciences 10, 350.Google Scholar
Remus, A, del Castillo, JRE and Pomar, C 2020a. Improving the estimation of amino acid requirements to maximize nitrogen retention in precision feeding for growing-finishing pigs. Animal, https://doi.org//10.1017/S1751731120000798, Published online by Cambridge University Press: 22 April 2020.CrossRefGoogle ScholarPubMed
Roush, WB, Dozier, 3rd WA and Branton, SL 2006. Comparison of gompertz and neural network models of broiler growth. Poultry Science 85, 749–797. doi: 10.1093/ps/85.4.794CrossRefGoogle ScholarPubMed
Schinckel, A and De Lange, C 1996. Characterization of growth parameters needed as inputs for pig growth models. Journal of Animal Science 74, 2021–2036.CrossRefGoogle ScholarPubMed
Schinckel, AP, Mahan, DC, Wiseman, TG and Einstein, ME 2008. Growth of protein, moisture, lipid, and ash of two genetic lines of barrows and gilts from twenty to one hundred twenty-five kilograms of body weight1. Journal of Animal Science 86, 460–471.CrossRefGoogle Scholar
Schinckel, AP, Preckel, PV and Einstein, ME 1996. Prediction of daily protein accretion rates of pigs from estimates of fat-free lean gain between 20 and 120 kilograms live weight2. Journal of Animal Science 74, 498–503.10.2527/1996.743498xCrossRefGoogle Scholar
St-Pierre, NR 2001. Invited review: integrating quantitative findings from multiple studies using mixed model methodology. Journal of Dairy Science 84, 741–755.CrossRefGoogle ScholarPubMed
Thornley, JH and France, J 2007. Growth functions. In Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences (ed. Thornley, JHM and France, J), pp. 136–169, Cabi, Wallingford, UK.CrossRefGoogle Scholar
Tillett, RD, Frost, AR and Welch, SK 2002. AP—Animal production technology: predicting sensor placement targets on pigs using image analysis. Biosystems Engineering 81, 453–463.10.1006/bioe.2001.0018CrossRefGoogle Scholar
van Milgen, J, Valancogne, A, Dubois, S, Dourmad, J-Y, Sève, B and Noblet, J 2008. InraPorc: a model and decision support tool for the nutrition of growing pigs. Animal Feed Science and Technology 143, 387–405.CrossRefGoogle Scholar
Vautier, B, Quiniou, N, Van Milgen, J and Brossard, L 2013. Accounting for variability among individual pigs in deterministic growth models. Animal 7, 1265–1273.CrossRefGoogle ScholarPubMed
Wellock, I, Emmans, G and Kyriazakis, I 2004. Modeling the effects of stressors on the performance of populations of pigs. Journal of Animal Science 82, 2442–2450.CrossRefGoogle ScholarPubMed
Whittemore, CT and Fawcett, RH 1974. Model responses of the growing pig to the dietary intake of energy and protein. Animal Science 19, 221–231.10.1017/S0003356100022789CrossRefGoogle Scholar
Whittemore, CT, Tullis, JB and Emmans, GC 1988. Protein growth in pigs. Animal Production 46, 437–445.CrossRefGoogle Scholar
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