Plant and Soil

, Volume 288, Issue 1–2, pp 357–371 | Cite as

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
Original Paper


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.


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


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Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Nicolás Martín
    • 1
  • Germán Bollero
    • 2
  • Newell R. Kitchen
    • 3
  • Alexandra N. Kravchenko
    • 4
  • Ken Sudduth
    • 3
  • William J. Wiebold
    • 5
  • Don Bullock
    • 2
  1. 1.Syngenta, Inc.StantonUSA
  2. 2.Crop SciencesUniversity of IllinoisUrbanaUSA
  3. 3.USDA-ARS Cropping Systems and Water Quality Research Unit University of MissouriColumbiaUSA
  4. 4.Department of Crop and Soil SciencesMichigan State UniversityEast LansingUSA
  5. 5.Division of Plant SciencesUniversity of MissouriColumbiaUSA

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