Pattern of Influencing Economic and Technical Factors on Egg Shell Quality for Waste Reduction: A Case Study of Egg Production Units in Khorasan Razavi Province of Iran

Authors

1 Instructor, Department of Agricultural Economics, Islamic Azad University, Mashhad, Iran

2 Assistant Professor, Department of Animal Science, Islamic Azad University, Mashhad, Iran

3 Assistant Professor of Agricultural Economics, Department of Agricultural Economics, Islamic Azad University, Mashhad, Iran

Abstract

This study aimed at investigating the economic and technical factors and how they affect the reduction of egg waste in all egg production units in Khorasan Razavi province of Iran in 2014 by using the Structural Equation Model based on Partial Least Squares and the proposed conceptual model. For this purpose, variables were grouped in three categories including managerial, environmental and nutritional factors. The results showed that the greatest effects on reduction of egg waste were respectively related to managerial (0.840), environmental (0.654) and nutritional (0.123) factors. In this conceptual model, as shown by the results, 0.857 percent of the changes in egg waste were predicted by the Latent Variables. As many of these factors fell into the group of management factors, it might be possible to control them in short term. Among the managerial factors, the variables of main managerial occupation, manager's experience, molting program age, type of ventilation and external parasitic contamination were found to be the most important factors affecting the reduction of egg waste through the Latent variables of management. Finally, it was recommended to appoint experienced managers with the related knowledge to select the appropriate time for molting age and external parasitic control.

Keywords

References

  1. Agricultural Statistics (2017). Ministry of Agriculture, Deputy Director of Planning and Economics, Information and Communication Technology Center. (Persian)
  2. Afsharmanesh, M., Pourreza, J. and Samie, A. (2001). Effect of different levels of calcium and vitamin D3 on eggshell quality traits. Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources), 5 (2):147-156. (Persian)
  3. Amani Saribagloo, J., Gholamali Lavasani, M., Ejei,J. and Khezri Azar, H. (2011). The relationship between cultural values and individual variables with computer use among university students. Quarterly Journal of Behavioral Sciences (JBS), 5 (15): 1-10. (Persian)
  4. Chin, W.W., Marcolin, B. L. and Newsted, P. R. (1996). A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and voice mail emotion/adoption study. Proceedings of the 17th International Conference on Information Systems, Cleveland, Ohio. December, 1996.
  5. Dijkstra, T.K. and Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2): 297-316.
  6. FAO (2018). http://www.fao.org/faostat/en/#data
  7. Fornell, C. and Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50.
  8. Gheshlagh Olyaee, M. and Janmohamadi, H. (2009). Factors affecting quality and quality of eggshells, Poultry Industry. http://www.itpoultry.com (Persian)
  9. Hair, J.F., Hult, T.M., Ringle, C.M. and Sarstedt, M. (2016). Partial least squares structural equation modeling (PLS-SEM). Tehran: NegaheDanesh Publications. (Persian)
  10. Hair, J.F., Marko Sarstedt, Christian, M. Ringle and Jeannette, A. Mena (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40 (3): 414 - 433.
  11. Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1): 107-120.
  12. Houman, H.A. (2014) Structural equation modeling using laser software. Tehran: Samt Publications. (Persian)
  13. Lohmoller, J.B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica.
  14. Mohsenin, Sh. and Esfidani, M.R. (2017). Structural equations based on partial least squares approach using Smart-PLS software. Tehran: Ketab Mehraban Nashr Publications. (Persian)
  15. Noubakht, A., Shivazad, M., Chamani, M., and Safamehr A.R. (2008).The effects of dietary electrolyte balance on performance and eggshell quality of laying hens exposed to heat stress and thermo neutral condition in early laying period. Quarterly Agroecology Journal (Journal of New Agricultural, Science) 3(9): 79-88. (Persian)
  16. Roberts, R. and Ball, W. (2004). Egg quality guidelines for the Australian egg industry. Australian Egg Corporation Limited Publication. 03 / 19, 32 pp. 
  17. Sabri, A. and Wan Mohamad, A.B.W.A. (2014). The importance-performance matrix analysis in partial least square structural equation modeling (PLS-SEM) with smartpls 2.0 M3. International Journal of Mathematical Research, 3(1): 1-14.
  18. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. M. and Lauro, C. (2005). PLS path modeling. Computational Statixtics and Date Analysis, 48(1): 159-205.
  19. Wan Mohamad, A.B.W.A. (2013). A comparison of partial least square structural equation modeling (PLS-SEM) and covariance based structural equation modeling (CB-SEM) for confirmatory factor analysis. International Journal of Engineering Science and Innovative Technology (IJESIT), 2 (5).
  20. Wold, H.O.A. (1974). Causal flows with latent variables: partings of the ways in the light of NIPALS modeling. European Economic Review, 5(1): 67-86.

 

Agricultural Economics and Development
Volume 27, Issue 2 - Serial Number 106
October 2019
Pages 109-132

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