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

Abstract

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.

Keywords

precision agriculture remote sensing site-specific management field variability hybrid 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arslan, S., Colvin, T. S. 2002An Evaluation of the response of yield monitors and combines to varying yieldsPrecision Agriculture3107122Google Scholar
  2. Blackmore, S., Moore, M. 1999Remedial correction of yield map dataPrecision Agriculture15366Google Scholar
  3. Cambardella, C. A., Karlen, D. L. 1999Spatial analysis of soil fertility parametersPrecision Agriculture1514Google Scholar
  4. Chang, C., Laird, D. A., Mausbach, M. J., Hurburgh, C. R.,Jr. 2001Near-infrared reflectance spectroscopy—Principal components regression analysis of soil propertiesSoil Science Society of America Journal65480490Google Scholar
  5. Cornell Cooperative Extension. 1998, 1999, and 2000. Cornell Guide for Integrated Field Crop Management (Cornell University, Ithaca, NY, USA). Google Scholar
  6. Cornell University Nutrient Analysis Laboratory. 2002. Nutrient Testing Information Web page. Online. http://www.css.cornell.edu/soiltest/Advanced.html (verified April, 2004). Google Scholar
  7. Cox, W. J., Cherney, D. J. R. 2002Evaluation of narrow-row corn forage in field-scale studiesAgronomy Journal94321325Google Scholar
  8. Ehsani, M. R., Upadhyaya, S. K., Slaughter, D., Shafii, S., Pelletier, M. 1999A NIR technique for rapid determination of soil mineral nitrogenPrecision Agriculture1217234Google Scholar
  9. Emerge. 2002. Digital Sensor System Brochure. Online.Google Scholar
  10. http://www.emergeweb.com/Public/info/emerge_DSS.pdf (verified April, 2004).Google Scholar
  11. Goovaerts, P. 1999Geostatistics in soil science: State-of-the-art and perspectivesGeoderma89145Google Scholar
  12. Kahabka, J. E., Es, H. M., McClenahan, E. J., Cox, W. J. 2004Spatial analysis of maize response to nitrogen fertilizer in Central New YorkPrecision Agriculture5463476Google Scholar
  13. Katsvairo, T. W., Cox, W. J., Es, H. M., Glos, M. A. 2003aSpatial yield response to two corn hybrids at two nitrogen levelsAgronomy Journal9510121022Google Scholar
  14. Katsvairo, T. W., Cox, W. J., Es, H. M. 2003bSpatial growth and soil responses of two corn hybrids to two N levelsAgronomy Journal9510001011Google Scholar
  15. Lobell, D. B., Asner, G. P. 2002Moisture effects on soil reflectanceSoil Science Society of America Journal66722727Google Scholar
  16. Magdoff, F. R. 1991Understanding the Magdoff pre-sidedress nitrate soil test for cornJournal of Production Agriculture4297305Google Scholar
  17. Magdoff, F. R. and van Es, H. M. 2000. Building Soils for Better Crops. 2nd Edn (Sustainable Agriculture Network, Beltsville, MD USA), Handbook Series, Book 4. 230 p.Google Scholar
  18. Magri, A. 2003Integral Evaluation of Soil Test, Aerial Image, Yield Monitor, and Soil Survey Data as Inputs for Precision Fertility Management Under MaizeCornell UniversityIthaca, NY USAM.S. thesisGoogle Scholar
  19. McBratney, A. B., Pringle, M. J. 1999Estimating average and proportional variograms of soil properties and their potential use in precision agriculturePrecision Agriculture1125152Google Scholar
  20. Muller, E., Décamps, H. 2000Modeling soil moisture-reflectanceRemote Sensing of Environment76173180Google Scholar
  21. Pioneer Hi-Bred International, Inc. 2002. Agronomic Traits Chart. Online.Google Scholar
  22. http://www.pioneer.com/Products/PrdGuide/EC/010/charts/TREC10AG.pdf (verified April, 2004).Google Scholar
  23. Senay, G. B., Ward, A. D., Lyon, J. D., Fausey, N. R., Nokes, S. E., Brown, L. C. 2000The relations between spectral data and water in a crop production environmentInternational Journal of Remote Sensing2118971910Google Scholar
  24. Sogbedji, J. M., Es, H. M., Klausner, S. D., Bouldin, D. R., Cox, W. J. 2001Spatial and temporal processes affecting nitrogen availability at the landscape scaleSoil & Tillage Research58233244Google Scholar
  25. Tomer, M. D., Anderson, J. L., Lamb, J. A. 1997Assesing corn yield and nitrogen uptake variability with digitized aerial infrared photographsPhotogrammetric Engineering and Remote Sensing63299306Google Scholar
  26. Trimble Navigation Limited. 1998. GPS Pathfinder Pro XR/XRS Datasheet and Specifications. Online. http://trl.trimble.com/dscgi/ds.py/Get/File-128929 (verified April, 2004). Google Scholar
  27. USDA, Soil Conservation Service in cooperation with Cornell University Agricultural Experiment Station. 1972. Soil Survey: Seneca County New York USA. 143 pages and maps.Google Scholar
  28. USDA, Soil Conservation Service in cooperation with Cornell University Agricultural Experiment Station. 1973. Soil Survey: Onondaga County New York USA. 235 pages and maps.Google Scholar
  29. Varvel, G. E., Schlemmer, M. R., Schepers, J. S. 1999Relationship between spectral data from aerial image and soil organic matter and phosphorous levelsPrecision Agriculture1291300Google Scholar
  30. Webster, R. 1984. Elucidation and characterization of spatial variation in soil using regionalized variable theory. In: Proceedings of the 2nd NATO ASI, Geostatistics for Natural Resource Characterization, NATO ASI Series. Series C: Mathematical and PhysicalSciences, edited by Verly, G. Daid, M., Journel, A.G. and Marechal, A. Reidel, Dordrecht, The Netherlands, v. 2, pp. 903–913. Google Scholar
  31. Webster, R. 2000Is soil variation random?Geoderma97149163CrossRefGoogle Scholar
  32. Whelan, B. M., McBratney, A. B. 2000The “Null Hypothesis” of precision agriculture managementPrecision Agriculture2265279Google Scholar
  33. Wollenahupt, N. C., Mulla, D. J. and Gotway Crawford, C. A. 1997. Soil sampling and interpolation techniques for mapping spatial variability of soil properties. In: The State of Site-Specific Management for Agriculture, edited by Pierce, F. J. and Sadler, E. J. (ASA-CSSA-SSSA, Madison, Wisconsin USA), pp. 19–53.Google Scholar

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

Personalised recommendations