Quantifying adoption intensity for weed-resistance management practices and its determinants among U.S. Soybean, corn, and cotton farmers

Fengxia Dong, Paul D. Mitchell, Terrance M. Hurley, George B Frisvold

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Using data envelopment analysis with principal components, we calculate an adoption-intensity index for herbicide-resistance best management practices (BMPs). Empirical results for over 1,100 farmers in twenty-two U.S. states suggest that many farmers could improve their herbicideresistance BMP adoption. Two-limit truncated regression results show that higher yields and a greater proportion of acres planted with Roundup ReadyR seeds motivate weed BMP adoption. While soybean and corn farmers have lower adoption intensity than cotton farmers, farmer educational attainment and greater concern for herbicide effectiveness and for human and environmental safety are found to help increase the adoption of weed BMPs.

Original languageEnglish (US)
Pages (from-to)42-61
Number of pages20
JournalJournal of Agricultural and Resource Economics
Volume41
Issue number1
StatePublished - Jan 1 2016

Fingerprint

resistance management
best management practices
cotton
weeds
soybeans
farmers
corn
herbicide resistance
data analysis
herbicides
Weeds
Soybean
Farmers
Corn
Cotton
Management practices
Best management practices
seeds

Keywords

  • Adoption Intensity
  • Best Management Practices
  • Common-Weight Data Envelopment Analysis
  • Herbicide-Resistance Management
  • Polychoric Non-Negative Principal Component Analysis
  • Weed-Resistance Management

ASJC Scopus subject areas

  • Economics and Econometrics
  • Agronomy and Crop Science
  • Animal Science and Zoology

Cite this

Quantifying adoption intensity for weed-resistance management practices and its determinants among U.S. Soybean, corn, and cotton farmers. / Dong, Fengxia; Mitchell, Paul D.; Hurley, Terrance M.; Frisvold, George B.

In: Journal of Agricultural and Resource Economics, Vol. 41, No. 1, 01.01.2016, p. 42-61.

Research output: Contribution to journalArticle

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