Two classification methods for developing and interpreting productivity zones using site properties

Nicolás Martín, Germán Bollero, Newell R. Kitchen, Alexandra N. Kravchenko, Ken Sudduth, William J. Wiebold, Don Bullock

Research output: Contribution to journalArticlepeer-review


Crop performance is often shown as areas of differing grain yield. Many producers utilize simple GIS color ramping techniques to produce visual yield maps with delineated clusters. However, a more quantitative approach such as an unsupervised clustering procedure is generally used by scientists since it is much less arbitrary. Intuitively the yield clusters are due to soil and terrain properties, but there is no clear criterion for the delineation. We compared the effectiveness of two delineation or classification procedures: quadratic discriminant analysis (QDA) and k-nearest neighbor discriminant analysis (k-NN) for the study of how yield temporal patterns relate to site properties. This study used three production fields, one in Monticello, IL, and two in Centralia, MO. Clusters were defined using maize (Zea mays L.) and soybean (Glycine max (L.) Merr.) yield from three seasons. The k-NN had greater and more consistent successful classification rates than did QDA. Classification success rate varied from 0.465 to 0.790 for QDA while the k-NN classification rate varied from 0.794 to 0.874. This shows that areas of certain temporal yield patterns correspond to areas of specific site properties. Although profiles of site properties differ by crop and production field, areas of consistent low maize yield had greater shallow electrical conductivity (ECshallow), than those of consistent high maize yield. Furthermore, areas of consistent high soybean yield had lower soil reflectance than those areas of consistent low yields.

Original languageEnglish (US)
Pages (from-to)357-371
Number of pages15
JournalPlant and Soil
Issue number1-2
StatePublished - Oct 2006


  • Quadratic discriminant analysis
  • Site properties
  • Yield temporal patterns
  • k-means clustering
  • k-nearest neighbor discriminant analysis

ASJC Scopus subject areas

  • Soil Science
  • Plant Science


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