A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina

Precision Agriculture volume 5, pages579–600(2004)Cite this article

Abstract

The gap between data analysis and site-specific recommendations has been identified as one of the key constraints on widespread adoption of precision agriculture technology. This disparity is in part due to the fact that analytical techniques available to understand crop GIS layers have lagged behind development of data gathering and storage technologies. Yield monitor, sensor and other spatially dense agronomic data is often autocorrelated, and this dependence among neighboring observations violates the assumptions of classical statistical analysis. Consequently, reliability of estimates may be compromised. Spatial regression analysis is one way to more fully exploit the information contained in spatially dense data. Spatial regression techniques can also adjust for bias and inefficiency caused by spatial autocorrelation. The objective of this paper is to compare four spatial regression methods that explicitly incorporate spatial correlation in the economic analysis of variable rate technology: (1) a regression approach adopted from the spatial econometric literature; (2) a polynomial trend regression approach; (3) a classical nearest neighbor analysis; and (4) a geostatistical approach. The data used in the analysis is from a variable rate nitrogen trial in the Córdoba Province, Argentina, 1999. The spatial regression approaches offered stronger statistical evidence of spatial heterogeneity of corn yield response to nitrogen than ordinary least squares. The spatial econometric analysis can be implemented on relatively small data sets that do not have enough observations for estimation of the semivariogram required by geostatistics. The nearest neighbor and polynomial trend analyses can be implemented with ordinary least squares routines that are available in GIS software. The main result of this study is that conclusions drawn from marginal analyses of this variable rate nitrogen trial were similar for each of the spatial regression models, although the assumptions about spatial process in each model are quite different.

This is a preview of subscription content, log in to check access.

References

  1. L. Anselin (1988) Spatial Econometrics: Methods and Models Kluwer Academic Publishers Dordrecht, The Netherlands

    Google Scholar 

  2. L. Anselin (1992) Space Stat Tutorial University of California Santa Barbara, CA, USA

    Google Scholar 

  3. B. R. Beattie C. R. Taylor (1985) The Economics of Production Wiley New York, USA

    Google Scholar 

  4. M. D. Boehlje V. R. Eidman (1984) Farm Management Wiley New York, USA

    Google Scholar 

  5. >Bongiovanni, R. and Lowenberg-DeBoer, J., 2000. Nitrogen Management in Corn Using Site-Specific Crop Response Estimates From a Spatial Regression Model. In: Proceedings of the 5th International Conference on Precision Agriculture, edited by P. Robert, R. Rust, and W. Larson, (ASA-CSSA-SSSA, Madison, Wisconsin, USA), CD-ROM.

  6. Bongiovanni, R. G. and Lowenberg-DeBoer, J. 2002. Economic Of Nitrogen Response Variability Over Space And Time: Results From The 1999–2001 Field Trials In Argentina. In: Proceedings of the 6th International Conference on Precision Agriculture, edited by P. Robert, R. Rust. and W. Larson (ASA-CSSA-SSSA, Madison, Wisconsin, USA) CD-ROM.

  7. C. Brownie D. T. Bowman J. W. Burton (1993) ArticleTitleEstimating spatial variation in analysis of data from yield trials: a comparison of methods Agronomy Journal 85 1244–1253

    Google Scholar 

  8. D. S. Bullock J. Lowenberg-DeBoer S. Swinton (2002) ArticleTitleAdding value to spatially managed inputs by understanding site-specific yield response Agricultural Economics 1679 1–13

    Google Scholar 

  9. G. Cassella R. L. Berger (1990) Statistical Inference Duxbury Press Belmont, California, USA

    Google Scholar 

  10. C. Castillo G. Espósito J. Gesumaría G. Tellería R. Balboa (1998) Respuesta A La Fertilización Del Cultivo De Maíz En Siembra Directa en Río Cuarto, “Recommendations for the Fertilization of No-Till Corn in Rio Cuarto” National University Río Cuarto-CREA, Argentina

    Google Scholar 

  11. N. A. C. Cressie (1993) Statistics for Spatial Data Wiley New York, USA

    Google Scholar 

  12. Daberkow, S. G., Fernandez-Cornejo, J. and Padgitt, M. 2002. Precision agriculture technology diffusion: current status and future prospects. In: Proceedings of the 6th International Conference on Precision Agriculture, edited by P. C. Robert (ASA-CSSA-SSSA, Madison, Wisconsin, USA), CD-ROM.

  13. Finck, C. 2001. The root of all yields. Farm Journal Field Tests, July/August. http://www.flexharrow.com/content/whatsnew/stlkchpprartcle.html (Accessed 03/29/04).

  14. T. C. Helms R. A. Scott J. J. Hammond (1999) ArticleTitleIntrablock variance among duplicate treatments for nearest-neighbor analyses Agronomy Journal 91 317–320

    Google Scholar 

  15. Hurley, T. M., Malzer, G. and Kilian, B. 2004. Estimating site-specific nitrogen crop response functions: a conceptual framework and geostatistical model. Staff Paper 04-2 (Department of applied Economics College of Agricultural, Food, and Environmental Sciences, University Of Minnesota, USA) http://agecon.lib.umn.edu/cgi-bin/view.pl (Accessed 3/29/04).

  16. Kessler, M. C. and Lowenberg-DeBoer, J. 1998. Regression analysis of yield monitor data and its use in fine-tuning crop decisions. In: Precision Agriculture: Proceedings of the 4th International Conference, edited by P. C. Robert, R. H. Rust and W. E. Larson (ASA/CSSA/SSSA, Madison, Wisconsin, USA), pp. 821–828.

  17. H. J. Kirk F. L. Haynes R. J. Monroe (1980) ArticleTitleApplication of trend analysis to horticultural field trials Journal of the American Horticultural Society 105 189–193

    Google Scholar 

  18. Lambert, D. M., Lowenberg-DeBoer, J. and Bongiovanni, R. 2002. Spatial regression, an alternative statistical analysis for landscape scale on-farm trials: case study of variable rate nitrogen application in Argentina. In: Proceedings of the 6th International Conference on Precision Agriculture, edited by P. C. Robert (ASA-CSSA-SSSA, Madison, Wisconsin, USA), CD-ROM.

  19. R. M. Lark H. C. Wheeler (2003) ArticleTitleA method to investigate within-field variation of the response of combinable crops to input Agronomy Journal 95 1093–1104

    Google Scholar 

  20. R. C. Little G. A. Milliken W. Stroup R. D. Wolfinger (1996) SAS System for Mixed Models SAS Institute Inc. Cary, NC, USA

    Google Scholar 

  21. Papadakis, J. S. 1937. Méthode statistique pour des experiences sur champs (“Statistical Methods for Field Experiments. Bulletin of the Institute of Plant Improvement”) Bulletin de l‘Institut de l’Amelioration des Plantes Thessaloniki (Greece), p. 23.

  22. InstitutionalAuthorNameSAS (2000) Version 8.0 The SAS Institute, Incorporated. Cary, NC, USA

    Google Scholar 

  23. O. Schabenberger F. J. Pierce (2002) Contemporary Statistical Models for the Plant and Soil Sciences CRC Press Boca Raton, FL, USA

    Google Scholar 

  24. W. Stroup P. S. Baenziger D. K. Mulitze (1994) ArticleTitleRemoving spatial variation from wheat yield trials: a comparison of methods Crop Science 34 62–66

    Google Scholar 

  25. R. N. Tamura L. A. Nelson G. C. Naderman (1988) ArticleTitleAn investigation of the validity and usefulness of trend analysis for field plot data Agronomy Journal 80 712–718

    Google Scholar 

  26. J. Vollmann J. Winkler C. N Fritz H. Grausgruber P. Ruckenbauer (2000) ArticleTitleSpatial field variations in soybean ( Glycine Max [L.] Merr.) performance trials affect agronomic characters and seed composition European Journal of 12 13–22

    Google Scholar 

Download references

Author information

Affiliations

  1. Department of Agricultural Economics/Site-Specific Management Center, Purdue University, 47097–2056, 403 W State Street, West Lafayette, Indiana, USA

    Dayton M. Lambert & James Lowenberg-Deboer

  2. National Institute for Agricultural Technology (INTA), 5988, Ruta 9 km 636, Manfredi, Argentina

    Rodolfo Bongiovanni

Authors
  1. Dayton M. Lambert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James Lowenberg-Deboer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rodolfo Bongiovanni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James Lowenberg-Deboer.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lambert, D.M., Lowenberg-Deboer, J. & Bongiovanni, R. A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina. Precision Agric 5, 579–600 (2004). https://doi.org/10.1007/s11119-004-6344-3

Download citation

Keywords

  • yield monitor data
  • variable rate nitrogen
  • spatial regression models
  • negative spatial correlation