Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Socioeconomic determinants of the level of mechanization of nurseries and greenhouses in the southern United States

Coastal Research and Extension Center, Mississippi State University, Biloxi, MS, United States

Special Editions: The next generation of Precision Horticulture Technologies

As horticulture production increases over time, growers are expected to improve efficiency, adopt appropriate technologies, improve working conditions and workers’ safety, and enhance markets. Mechanization decisions are made to maximize production under a least-cost combination of inputs including mechanization. The objective of this paper is to determine the socioeconomic factors influencing the level of mechanization among nurseries and greenhouses. The results will provide more profound insights into the empirical relationships between the level of mechanization and the economic and technical characteristics of nurseries or greenhouses. Results will also describe the influences of owners or operators’ characteristics on mechanizations decisions. The level of mechanization shows the extent by which nurseries or greenhouses have currently mechanized each of the significant workers’ tasks involved in the production of horticulture products. A regression equation was estimated using the socioeconomic database collected from a survey of 215 randomly selected wholesale nurseries and greenhouses in eight Southern states. The regression results explained 69% of the variation in the level of mechanization among participating nurseries or greenhouses. Younger owners or operators tend to approve of higher mechanization in horticulture operations. Significant differences in the levels of mechanization were observed among owners or operators with different levels of formal educational attainment. Workers’ tasks in greenhouse-only operations tend to be more mechanized than nursery-only operations. Workers’ tasks in operations with higher annual gross sales were more mechanized than smaller operations. The shortage of permanent or part-time workers would encourage owners or operators to shift to more mechanized horticulture production activities. Corporate-run horticulture organizations provided more mechanization options for their workers than the other business operations.
  Article Metrics


1. Hodges AW, Hall CR, Palma MA, et al. (2015) Economic contributions of the green industry in the United States in 2013. 25: 805–814.

2. Posadas BC, Knight PR, Coker CH, et al. (2012) Economic impacts of mechanization or automation on horticulture production firms sales, employment, and workers' earnings, safety, and retention. HortTechnology 22: 388–401.

3. Rogers EM (2003) Diffusion of Innovations. 5th ed. Free Press, New York (NY).

4. Feder G, Just RE, Zilberman D (1985) Adoption of agricultural innovations in developing countries: A survey. Econ Dev Cultural Change 33: 255–298.    

5. Caplan S, Tilt B, Hoheisel G, et al. (2014) Specialty crop growers' perspectives on adopting new technologies. Horttechnology 24: 81–87.

6. Debertin DL, Pagoulatos A, Aoun A (1982) Determinants of farm mechanization in Kentucky: An econometric analysis. North Cent J Agric Econ 4: 73–80.    

7. D'Souza G, Cyphers D, Phipps T (1993) Factors affecting the adoption of sustainable agricultural practices. Agric Resour Econ Rev 22: 159–165.    

8. Banerjee B, Martin SW, Roberts RK, et al. (2008) A binary logit estimation of factors affecting adoption of GPS guidance systems by cotton producers. J Agric Appl Econ 40: 345–355.    

9. Posadas BC, Knight PR, Coker RY, et al. (2008) Socioeconomic impact of automation on horticulture production firms in the northern Gulf of Mexico. HortTechnology 18: 697–704.

10. Posadas BC, Knight PR, Coker CH, et al. (2010) Socioeconomic characteristics of workers and working conditions in nurseries and greenhouses in the northern Gulf of Mexico states. Bulletin 1182, Mississippi agricultural and forestry experiment station. Available from: http://www.mafes.msstate.edu/publications/bulletins/b1182.pdf.

11. Posadas BC, Knight PR, Coker CH, et al. (2010) Operational characteristics of nurseries and greenhouses in the northern Gulf of Mexico states. Bulletin 1184, Mississippi agricultural and forestry experiment station. Available from: http://mafes.msstate.edu/publications/bulletins/b1184.pdf.

12. Coker RY, Posadas BC, Langlois SA, et al. (2015) Current mechanization systems among greenhouses and mixed operations. Bulletin 1208, Mississippi agricultural and forestry experiment station. Available from: http://www.mafes.msstate.edu/publications/bulletins/b1208.pdf.

13. Coker RY, Posadas BC, Langlois SA, et al. (2010) Current mechanization systems among nurseries and mixed operations. Bulletin 1189, Mississippi agricultural and forestry experiment station. Available from:


14. Posadas BC, Knight PR, Coker CH, et al. (2014) Hiring preferences of nurseries and greenhouses in U.S. Southern States. HortTechnology 24: 107–117.

15. Giacomelli G (2002) Greenhouse structures. Paper No. E-125933-04-01, the Controlled Environment Agricultural Center (CEAC), University of Arizona, Tucson, AZ. Available from: http://www.ea.gr/ep/organic/ACTIVITY%206/GREENHOUSES/Greenhouse%20Structures%20-%20CEAC.pdf.

16. Porter M (2002) Automation vs. mechanization. Greenhouse Product News. Available from: https://gpnmag.com/article/automation-vs-mechanization/.

17. Ling PP (1994) Introduction: From mechanization to the information highway. In: NRAES, Greenhouse systems: Automation, culture and environment, Proceedings from the Greenhouse Systems International Conference, July 20–22, 1994, New Brunswick, New Jersey, 5–8.

18. Rogers WH (1993) Regression standard errors in clustered samples. Stata Tech Bull 13: 19–23. Reprinted in Stata Tech Bull 3: 88–94.

19. Williams RL (2000) A note on robust variance estimation for clustered-correlated data. Biometrics 56: 645–646.    

20. Brown S (2018) Fewer farm workers lead to more mechanization. The Prairie Star. Available from: https://www.agupdate.com/theprairiestar/news/crop/fewer-farm-workers-leads-to-more-mechanization/article_2a4a7766-4808-11e8-8bf0-2b50f21f998b.html.

21. Charlton D, Taylor JE (2016) A declining farm workforce: Analysis of panel data from rural Mexico. Am J Agric Econ 98: 1158–1180.    

22. Castle MH, Lubben BD, Luck JD (2016) Factors influencing the adoption of precision agriculture technologies by Nebraska producers. Presentations, Working Papers, and Gray Literature: Agricultural Economics. Paper 49. Available from: http://digitalcommons.unl.edu/ageconworkpap/49.

23. Larson JA, Roberts RK, English BC, et al. (2008) Factors influencing adoption of remotely sensed imagery for site-specific management in cotton production. Precis Agric 9: 195–208.    

24. Daberkow SG, McBride WD (2003) Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US. Precis Agric 4: 163–177.    

25. Hall TJ, Dennis JH, Lopez RG, et al. (2009) Factors affecting growers' willingness to adopt sustainable floriculture practices. Hortscience Publ Am Soc Sci 44: 1346–1351.

26. Uematsu H, Mishra A (2010) Can education be a barrier to technology adoption? No. 61630, 2010 Annual Meeting, July 25–27, Denver, Colorado from Agricultural and Applied Economics Association. Available from: https://www.researchgate.net/publication/254383748_Can_Education_Be_a_Barrier_to_Technology_Adoption.

© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved