The assessment of poultry welfare using partial least squares-path modelling (PLS-SEM): a modeling the effects of physical activity and stress on growth

Year 2021, , 109 - 119, 24.03.2021

Hamza Yalçin , Zeki Doğan , Sedat Şen , Mehmet Çetin , Mehmet Göçmen , Abdurrahim Koçyiğit

https://doi.org/10.29050/harranziraat.842308

Abstract

Poultry welfare is closely related to human health and animal production which is a sensitive process. While several statistical methods are available to measure observed variables (stocking density), unobserved (latent) variables such as emotional, psychological situations are typically analyzed using factor analysis. In recent years, structural equation modeling (SEM) has been used effectively in many fields such as agriculture and livestock. SEM provides an estimation of relationships between latent variables that cannot be measured directly. Most of the studies published on poultry welfare highlighted the need to determine causal relationships between latent variables (growth, physical activity, biological stress). Here, we demonstrate that PLS-SEM analysis was used to examine the relationship between biological stress, physical activity, and growth in livestock. A three-factor model was conducted with 96 animals. The PLS-SEM results revealed that 65.2% of growth can be predicted by the independent variables (R2 = 0.652), and that physical activity (γ = 0.698) was found to have more effect than biological stress (γ = 0.176) on growth. Also, physical activity was determined as the main variable for the evaluation of biological stress (γ = 0.546, R2 = 0.298) and growth. Overall, the research showed the suitability of SEM for the assessment of growth and biological stress.

Keywords

Growth, physical activity, PLS-SEM, poultry, welfare

Kısmi en küçük kareler-yol modellemesi (VT-YEM) kullanılarak kanatlı refahının değerlendirilmesi: fiziksel aktivite ve stresin büyüme üzerindeki etkilerinin modellenmesi

Abstract

Kanatlı refahı, insan sağlığı ve hassas bir süreç olan hayvan üretimi ile yakından ilgilidir. Gözlemlenen değişkenleri (yerleşim sıklığı) ölçmek için çeşitli istatistiksel yöntemler mevcut olsa da duygusal, psikolojik durumlar gibi doğrudan gözlemlenmemiş (gizil) değişkenler tipik olarak faktör analizi kullanılarak analiz edilir. Son yıllarda yapısal eşitlik modellemesi (YEM) tarım ve hayvancılık gibi birçok alanda etkin bir şekilde kullanılmaktadır. YEM, doğrudan ölçülemeyen gizli değişkenler arasındaki ilişkilerin bir tahminini sağlar. Kanatlı hayvan refahı üzerine yayınlanan çalışmaların çoğu, gizil değişkenler (büyüme, fiziksel aktivite, biyolojik stres) arasındaki nedensel ilişkileri belirleme ihtiyacını vurgulamaktadır. Burada, çiftlik hayvanlarında biyolojik stres, fiziksel aktivite ve büyüme arasındaki ilişkinin incelenmesi için varyans tabanlı (VT)-YEM analizi kullanılmıştır. Çalışmada, 96 hayvanın bulunduğu üç faktörlü bir model oluşturulmuştur. VT-YEM sonuçları, büyümenin %65.2’sinin bağımsız değişkenler (R2 = 0.652) tarafından tahmin edilebildiğini ve fiziksel aktivitenin (γ = 0.698) büyüme üzerinde biyolojik stresten (γ = 0.176) daha fazla etkiye sahip olduğunu ortaya koymuştur. Ayrıca biyolojik stres (γ = 0.546, R2 = 0.298) ve büyümenin değerlendirilmesinde fiziksel aktivite ana değişken olarak belirlenmiştir. Genel olarak araştırma, YEM’in büyüme ve biyolojik stresin değerlendirilmesi için uygunluğunu göstermiştir.

Keywords

Büyüme, fiziksel aktivite, refah, VT-YEM

References

• Alves, J. G. B., & Alves, G. V. (2019). Effects of physical activity on children’s growth. Jornal de Pediatria, 95, 72–78. https://doi.org/10.1016/j.jped.2018.11.003
• Anonymous. (2018). Antioksidan Mineral ve Vitaminler. http://www.ivek.org.tr/calismalar/antioksidan-mineral-ve-vitaminler
• Augère-Granier, M.-L. (2019). The EU Poultry Meat and Egg Sector. Main Features Challenges and Prospects.
• Beski, S. S. M., Swick, R. A., & Iji, P. A. (2015). Specialized protein products in broiler chicken nutrition: A review. Animal Nutrition, 1(2), 47–53. https://doi.org/10.1016/j.aninu.2015.05.005
• Bradshaw, R. H., Kirkden, R. D., & Broom, D. M. (2002). A review of the aetiology and pathology of leg weakness in broilers in relation to welfare. Avian and Poultry Biology Reviews, 13(2), 45–103. https://doi.org/10.3184/147020602783698421
• Celi, P., & Gabai, G. (2015). Oxidant/Antioxidant Balance in Animal Nutrition and Health: The Role of Protein Oxidation. Frontiers in Veterinary Science, 2:48. https://doi.org/10.3389/fvets.2015.00048
• Çetin, M., & Göçmen, M. (2015). Kanatlı Hayvanların Beslenmesinde Antibiyotiklere Alternatif Olarak Kekik (Thyme) Kullanmanın Etkileri. Harran Tarım ve Gıda Bilimleri Dergisi, 17(3), 35–40. https://dergipark.org.tr/tr/pub/harranziraat/194211
• Çetin, M., Yurtseven, S., Koçyiğit, A., Temamoğullari, F., Altaş, M. G., Özyildiz, Z., Yilmaz, R., & Taşkin, A. (2019). Kurutulmuş Pelin (Artemisia Absinthium) Otunun Etlik Piliçlerin Performans, Karkas Özellikleri ve Biyokimyasal Parametreler Üzerine Etkisi. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 22(2), 409–417. https://doi.org/10.18016/ksutarimdoga.vi.551016
• Cheah, J.-H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM. International Journal of Contemporary Hospitality Management, 30(11), 3192–3210. https://doi.org/10.1108/IJCHM-10-2017-0649
• Collins, L., & Part, C. (2013). Modelling Farm Animal Welfare. Animals, 3(2), 416–441. https://doi.org/10.3390/ani3020416
• Costantino, A., Fabrizio, E., Ghiggini, A., & Bariani, M. (2018). Climate control in broiler houses: A thermal model for the calculation of the energy use and indoor environmental conditions. Energy and Buildings, 169, 110–126. https://doi.org/10.1016/j.enbuild.2018.03.056
• Drews, J., Czycholl, I., Junge, W., & Krieter, J. (2018). An evaluation of efficiency in dairy production using structural equation modelling. The Journal of Agricultural Science, 156(8), 996–1004. https://doi.org/10.1017/S0021859618001041
• Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of Partial Least Squares (Vincenzo Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (eds.)). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-32827-8
• Fang, A., Li, K., Li, H., Guo, M., He, J., Shen, X., & Song, J. (2017). Low Habitual Dietary Calcium and Linear Growth from Adolescence to Young Adulthood: results from the China Health and Nutrition Survey. Scientific Reports, 7(1), 9111. https://doi.org/10.1038/s41598-017-08943-6
• Farr, J. N., Laddu, D. R., Blew, R. M., Lee, V. R., & Going, S. B. (2013). Effects of Physical Activity and Muscle Quality on Bone Development in Girls. Medicine & Science in Sports & Exercise, 45(12), 2332–2340. https://doi.org/10.1249/MSS.0b013e31829c32fe
• Friedewald, W. T., Levy, R. I., & Fredrickson, D. S. (1972). Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry, 18(6), 499–502. https://doi.org/10.1093/clinchem/18.6.499
• Gholami, M., Chamani, M., Seidavi, A., Sadeghi, A. A., & Aminafschar, M. (2020). Effects of stocking density and environmental conditions on performance, immunity, carcase characteristics, blood constitutes, and economical parameters of cobb 500 strain broiler chickens. Italian Journal of Animal Science, 19(1), 524–535. https://doi.org/10.1080/1828051X.2020.1757522
• Hair, J., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
• Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE.
• Hair, J., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
• Heringstad, B., Wu, X. L., & Gianola, D. (2009). Inferring relationships between health and fertility in Norwegian Red cows using recursive models. Journal of Dairy Science, 92(4), 1778–1784. https://doi.org/10.3168/jds.2008-1535
• Huttunen, J. K. (1982). Physical activity and plasma lipids and lipoproteins. Annals of Clinical Research, 14 Suppl 3, 124–129. http://www.ncbi.nlm.nih.gov/pubmed/6983325
• Ingenbleek, P. T. M., Immink, V. M., Spoolder, H. A. M., Bokma, M. H., & Keeling, L. J. (2012). EU animal welfare policy: Developing a comprehensive policy framework. Food Policy, 37(6), 690–699. https://doi.org/10.1016/j.foodpol.2012.07.001
• Krugmann, K. L., Mieloch, F. J., Krieter, J., & Czycholl, I. (2020). Investigation of influence of growing pigs’ positive affective state on behavioral and physiological parameters using structural equation modeling. Journal of Animal Science, 98(2), skaa028. https://doi.org/10.1093/jas/skaa028
• Kryeziu, A. J., Kamberi, M., Muji, S., Mestani, N., & Berisha, S. (2018). Carcass traits of broilers as affected by different stocking density and sex. Bulgarian Journal of Agricultural Science, 24(6), 1097–1103.
• Lykkesfeldt, J., & Svendsen, O. (2007). Oxidants and antioxidants in disease: Oxidative stress in farm animals. The Veterinary Journal, 173(3), 502–511. https://doi.org/10.1016/j.tvjl.2006.06.005
• Ma, Z., Zhang, J., Ma, H., Dai, B., Zheng, L., Miao, J., & Zhang, Y. (2014). The influence of dietary taurine and reduced housing density on hepatic functions in laying hens. Poultry Science, 93(7), 1724–1736. https://doi.org/10.3382/ps.2013-03654
• Mann, S., Beedie, C., & Jimenez, A. (2014). Differential Effects of Aerobic Exercise, Resistance Training and Combined Exercise Modalities on Cholesterol and the Lipid Profile: Review, Synthesis and Recommendations. Sports Medicine, 44(2), 211–221. https://doi.org/10.1007/s40279-013-0110-5
• Mello, H. H. de C., Gomes, P. C., Rostagno, H. S., Albino, L. F. T., da Rocha, T. C., de Almeida, R. L., & Calderano, A. A. (2012). Dietary requirements of available phosphorus in growing broiler chickens at a constant calcium: Available phosphorus ratio. Revista Brasileira de Zootecnia, 41(11), 2323–2328. https://doi.org/10.1590/S1516-35982012001100004
• Mench, J. A. (2018). Advances in agricultural animal welfare: Science and practice. Woodhead Publishing.
• Park, B. S., Um, K. H., Park, S. O., & Zammit, V. A. (2018). Effect of stocking density on behavioral traits, blood biochemical parameters and immune responses in meat ducks exposed to heat stress. Archives Animal Breeding, 61(4), 425–432. https://doi.org/10.5194/aab-61-425-2018
• Pedersen, I. J., Tahamtani, F. M., Forkman, B., Young, J. F., Poulsen, H. D., & Riber, A. B. (2020). Effects of environmental enrichment on health and bone characteristics of fast growing broiler chickens. Poultry Science, 99(4), 1946–1955. https://doi.org/10.1016/j.psj.2019.11.061
• Proszkowiec-Weglarz, M., & Angel, R. (2013). Calcium and phosphorus metabolism in broilers: Effect of homeostatic mechanism on calcium and phosphorus digestibility. Journal of Applied Poultry Research, 22(3), 609–627. https://doi.org/10.3382/japr.2012-00743
• Rama Rao, S. V., Ravindra Reddy, V., & Ramasubba Reddy, V. (1999). Enhancement of phytate phosphorus availability in the diets of commercial broilers and layers. Animal Feed Science and Technology. https://doi.org/10.1016/S0377-8401(99)00020-6
• Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. SmartPLS GmbH.
• Simitzis, P. E., Kalogeraki, E., Goliomytis, M., Charismiadou, M. A., Triantaphyllopoulos, K., Ayoutanti, A., Niforou, K., Hager-Theodorides, A. L., & Deligeorgis, S. G. (2012). Impact of stocking density on broiler growth performance, meat characteristics, behavioural components and indicators of physiological and oxidative stress. British Poultry Science, 53(6), 721–730. https://doi.org/10.1080/00071668.2012.745930
• Simsek, U. G., Cerci, I. H., Dalkilic, B., Yilmaz, O., & Ciftci, M. (2009). Impact of stocking density and feeding regimen on broilers: Chicken meat composition, fatty acids, and serum cholesterol levels. Journal of Applied Poultry Research, 18(3), 514–520. https://doi.org/10.3382/japr.2008-00141
• Simsek, U. G., Dalkilic, B., Ciftci, M., & Yuce, A. (2009). The influences of different stocking densities on some welfare indicators, lipid peroxidation (MDA) and antioxidant enzyme activities (GSH, GSH-Px, CAT) in broiler chickens. Journal of Animal and Veterinary Advances, 8, 1568–1572
• Stamp Dawkins, M., Donnelly, C. A., & Jones, T. A. (2004). Chicken welfare is influenced more by housing conditions than by stocking density. Nature, 427(6972), 342–344. https://doi.org/10.1038/nature02226
• Stucchi, A. F., Terpstra, A. H. M., Foxall, T. L., Nicolosi, R. J., & Smith, S. C. (1991). The effect of exercise on plasma lipids and LDL subclass metabolism in miniature swine. Medicine and Science in Sports and Exercise, 23(5), 552–561. https://doi.org/10.1249/00005768-199105000-00007
• Swennen, Q., Decuypere, E., & Buyse, J. (2007). Implications of dietary macronutrients for growth and metabolism in broiler chickens. World’s Poultry Science Journal, 63(4), 541–556. https://doi.org/10.1017/S0043933907001602
• Tesseraud, S., Everaert, N., Boussaid-Om Ezzine, S., Collin, A., Métayer-Coustard, S., & Berri, C. (2011). Manipulating tissue metabolism by amino acids. World’s Poultry Science Journal, 67(2), 243–252. https://doi.org/10.1017/S0043933911000274
• Torun, B., & Viteri, F. E. (1994). Influence of exercise on linear growth. European Journal of Clinical Nutrition, 48(SUPPL. 1), 86–89.
• Villeneuve, B., Piffady, J., Valette, L., Souchon, Y., & Usseglio-Polatera, P. (2018). Direct and indirect effects of multiple stressors on stream invertebrates across watershed, reach and site scales: A structural equation modelling better informing on hydromorphological impacts. Science of the Total Environment, 612, 660–671. https://doi.org/10.1016/j.scitotenv.2017.08.197
• Weeks, C. A., Danbury, T. D., Davies, H. C., Hunt, P., & Kestin, S. C. (2000). The behaviour of broiler chickens and its modification by lameness. Applied Animal Behaviour Science, 67(1–2), 111–125. https://doi.org/10.1016/S0168-1591(99)00102-1
• Wei, Y. H., & Pang, C. Y. (2005). The role of mitochondria in the human ageing process. Bio Tech International, 17(3), 8–13.
• Wold, H. (1982). Soft modelling: the basic design and some extensions. In K. G. Joreskog & H. O. A. Wold (Eds.), Systems under indirect observation: Causality-structure-prediction: Vol. part II (pp. 1–54). Amsterdam, North-Holland.
• Wu, Y., Li, J., Qin, X., Sun, S., Xiao, Z., Dong, X., Shahid, M. S., Yin, D., & Yuan, J. (2018). Proteome and microbiota analysis reveals alterations of liver-gut axis under different stocking density of Peking ducks. PLOS ONE, 13(10), e0198985. https://doi.org/10.1371/journal.pone.0198985