Precision Agriculture

, Volume 6, Issue 1, pp 87–110 | Cite as

Soil Test, Aerial Image and Yield Data as Inputs for Site-specific Fertility and Hybrid Management Under Maize

  • Antoni Magri
  • Harold M. Van Es
  • Michael A. Glos
  • William J. Cox


Several potential sources of information exist to support precision management of crop inputs. This study evaluated soil test data, bare-soil remote sensing imagery and yield monitor information for their potential contributions to precision management of maize (Zea mays L.). Data were collected from five farmer-managed fields in Central New York in 1999, 2000, and 2001. Geostatistical techniques were used to analyze the spatial structure of soil fertility (pH, P, K, NO3 and organic matter content) and yield variables (yield, hybrid response and N fertilization response), while remote sensing imagery was processed using principal component analysis. Geographic information system (GIS) spatial data processing and correlation analyses were used to evaluate relationships in the data. Organic matter content, pH, P, and K were highly consistent over time and showed high to moderate levels of spatial autocorrelation, suggesting that grid soil sampling at 2.5–5.5 ha scale may be used as a basis for defining fertility management zones. Soil nitrate levels were strongly influenced by seasonal weather conditions and showed low potential for site-specific N management. Aerial image data were correlated to soil organic matter content and in some cases to yield, mainly through the effect of drainage patterns. Aerial image data were not well correlated with soil fertility indicators, and therefore were not useful for defining fertility management zones. Yield response to hybrid selection and nitrogen fertilization rates were highly variable among years, and showed little justification for site-specific management. In conclusion, we recommend grid-based management of lime, P, and K, but no justification existed within our limited study area for site-specific N or hybrid management.


precision agriculture remote sensing site-specific management field variability hybrid 


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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Antoni Magri
    • 1
  • Harold M. Van Es
    • 1
  • Michael A. Glos
    • 1
  • William J. Cox
    • 1
  1. 1.Department of Crop and Soil SciencesCornell UniversityIthacaUSA

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