Abstract
The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared reflection (MIR) from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.
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Baxter, S. J., & Oliver, M. A. (2005). The spatial prediction of soil mineral N and potentially available N using elevation. Geoderma, 128, 325–339.
Bendor, E., & Banin, A. (1995). Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Science Society of America Journal, 59, 364–372.
Bhatti, A. U., Mulla, D. J., & Frazier, B. E. (1991). Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and Thematic Mapper images. Remote Sensing Environment, 37, 181–191.
Broge, N. H., Thomsen, A. G., & Greve, M. H. (2004). Prediction of topsoil organic matter and clay content from measurements of spectral reflectance and electrical conductivity. Acta Agriculturae Scandinavica Section B-Soil and Plant Science, 54, 232–240.
Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Science Society of America Journal, 65, 480–490.
Delin, S., & Söderström, M. (2003). Performance of soil electrical conductivity and different methods for mapping soil data from a small dataset. Acta Agriculturae Scandinavica, Section B. Soil and Plant Science, 52, 127–135.
Frogbrook, Z. L., & Oliver, M. A. (2000). The effects of sampling on the accuracy of predictions of soils properties for precision agriculture. In G. B. M. Heuvelink & M. J. P. M. Lemmens (Eds.), Accuracy 2000. Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences in Amsterdam, July 2000, Delft, Netherlands. Delft University Press, Delft, The Netherlands, pp. 225–232.
Fystro, G. (2002). The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis-NIR spectroscopy and comparative methods. Plant and Soil, 246, 139–149.
Gee, G. W., & Bauder, J. W. (1986). Particle-size analysis. In Klute A (Ed.), Physical and mineralogical methods. Madison, WI, USA:Soil Science Society of America.
Gustafsson, K. (1999). Models for precision application of lime. In J.V. Stafford (Ed.), Precision Agriculture ‘99. Part 1. 2nd European Conference on Precision Agriculture, Odense, Denmark 1999. SCI, UK, pp. 175–180.
Islam, K., Singh, B., & McBratney, A. (2003). Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy. Australian Journal of Soil Research, 41, 1101–1114.
Martens, H., & Naes, T. (1989). Multivariate calibration. Chichester, UK: John Wiley & Sons, 419 pp.
McBratney, A. B., Odeh, I. O. A., Bishop, T. F. A., Dunbar, M. S., & Shatar, T. M. (2000). An overview of pedometric techniques for use in soil survey. Geoderma, 97, 293–327.
Mulla, D. J., Betty, M., & Sekely, A. C. (2001). Evaluation of remote sensing and targeted soil sampling for variable rate application of lime. In P. C. Robert, R. H. Rust, & W. E. Larsen (Eds.), Precision Agriculture, Proceedings of the 5th International Conference. ASA-CSSA-SSSA, Madison, WI.
Oliver, M. A., Frogbrook, Z., Webster, R., & Dawson, C. J. (1997). A rational strategy for determining the number of cores for bulked sampling of soil. In J.V. Stafford (Ed), Precision Agriculture ‘97. Vol. 1. Spatial Variability in Soil and Crop. 1st European Conference on Precision Agriculture, Warwick University, UK. BIOS Scientific Publishers Ltd., UK, pp. 155–162.
Olsson, D., & Söderström, M. (2003). An automated method to locate optimal soil sampling sites using ancillary data. In A. Werner & A. Jarfe (Eds.), Program book of the joint conference of ECPA-ECPLF, Berlin. Poster at the 4th European Conference on Precision Agriculture. Wageningen Academic Publishers, The Netherlands, p. 649. (http://www.agrovast.se/precision/soilmap).
Reeves, J. B., McCarty, G. W., & Meisinger, J. J. (1999). Near infrared reflectance spectroscopy for the analysis of agricultural soils. Journal of near Infrared Spectroscopy, 7, 179–193.
Savitzky, A., & Golay, M. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36, 1627–1639.
Sørensen, L. K., & Dalsgaard, S. (2005). Determination of clay and other soil properties by near infrared spectroscopy. Soil Science Society of America Journal, 69, 159–167.
Stenberg, B., Jonsson, A., & Börjesson, T. (2002). Near infrared technology for soil analysis with implications for precision agriculture. In A. Davies & R. Cho (Eds.), Near Infrared Spectroscopy: Proceedings of the 10th International Conference, Kyongju S. Korea. NIR Publications, Chichester, UK, pp. 279–284.
Udelhoven, T., Emmerling, C., & Jarmer, T. (2003). Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study. Plant and Soil, 251, 319–329.
Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil properties. Journal of soil science, 43, 177–192.
Wetterlind, J., Stenberg, B., & Söderström, M. (2006). New strategy for farm-soil mapping using NIR to increase sample point density. In G. R. Burling-Claridge, S. E. Holroyd & R. M. W. Sumner (Eds.), 12th International Conference, Auckland, New Zealand. New Zealand Near Infrared Spectroscopy Society Incorporated.
Acknowledgements
We wish to thank Jarl Ryberg for allowing us to use his fields and the Swedish Farmers’ Foundation for Agricultural Research (SLF) for funding the work. We also want to thank Anita Dellsén, Lisbet Norberg, Jan-Olov Gustavsson and Kristina Gustavsson for important help with the extensive soil sampling.
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Wetterlind, J., Stenberg, B. & Söderström, M. The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agric 9, 57–69 (2008). https://doi.org/10.1007/s11119-007-9051-z
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DOI: https://doi.org/10.1007/s11119-007-9051-z