Using count data models to determine the factors affecting farmers' quantity decisions of precision farming technology adoption

By Tamer Isgin, A. Bilgiç, D. Lynn Forster, M. Batte. 2008

181 citations recorded by the source corpus.

Subfields: General Agricultural and Biological Sciences, Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/ebb3c807c4d89c3a09a84507412a94336366d47b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Agricultural and Biological Sciences

1. Y. Tey
2. M. Brindal

In Plant Science

1. Athanasios Balafoutis

Citations in the corpus

• Factors influencing the adoption of precision agricultural technologies: a review for policy implications (2012, background): “…described an ‘‘after the event’’ nature of an inquiry, was made up by 10 studies: Robertson et al. (2012), Walton et al. (2008), Larson et al. (2008), Isgin et al. (2008), Roberts et al. (2004), Daberkow and McBride (2003), Fernandez-Cornejo et al. (2002), Roberts et al. (2002), Khanna (2001)…”
• Influencing factors and incentives on the intention to adopt precision agricultural technologies within arable farming systems (2019, methodology): “A count regression modelling approach was applied as the technologies represent count outcomes for the individual farms (Isgin et al., 2008; Paxton et al., 2011; Castle et al., 2016).”