Event dependence and heterogeneity in the adoption of precision farming technologies: A case of US cotton production

Krishna P. Paudel, Ashok K. Mishra, Mahesh Pandit, Eduardo Segarra

Research output: Contribution to journalArticlepeer-review

Abstract

This study aims to examine event dependence and heterogeneity in the adoption of precision farming (PF) technologies. The study uses farm-level data and a conditional frailty model to estimate the empirical model. A novelty of this study is the introduction of a group level heterogeneity in the traditional conditional frailty model. The simulation model shows that the conditional frailty model addresses both event dependence and heterogeneity related issues in technology adoption. Results indicate that farmers with large farms, a higher share of total cultivated farmland, a higher percentage of income from farming, and farmers using computers for farm management are more likely to adopt PF technologies early on after a technology becomes available. Further, cotton producers who think that PF technology would be valuable in the future and those receiving farming information from university publications are more likely to adopt PF technologies soon after the technologies become available.

Original languageEnglish
Article number105979
JournalComputers and Electronics in Agriculture
Volume181
DOIs
StatePublished - Feb 2021

Keywords

  • Conditional frailty
  • Event dependence
  • Heterogeneity
  • Time-varying component

Fingerprint Dive into the research topics of 'Event dependence and heterogeneity in the adoption of precision farming technologies: A case of US cotton production'. Together they form a unique fingerprint.

Cite this