Abstract
Crop yield variations are strongly influenced by the spatial and temporal availabilities of water and nitrogen in the soil during the crop growth season. To estimate the quantities and distributions of water and nitrogen within a given soil, process-oriented soil models have often been used. These models require detailed information about the soil characteristics and profile architecture (e.g., soil depth, clay content, bulk density, field capacity and wilting point), but high resolution information about these soil properties, both vertically and laterally, is difficult to obtain through conventional approaches. However, on-the-go electrical resistivity tomography (ERT) measurements of the soil and data inversion tools have recently improved the lateral resolutions of the vertically distributed measurable information. Using these techniques, nearly 19,000 virtual soil profiles with defined layer depths were successfully created for a 30 ha silty cropped soil over loamy and sandy substrates in Central Germany, which were used to initialise the CArbon and Nitrogen DYnamics (CANDY) model. The soil clay content was derived from the electrical resistivity (ER) and the collected soil samples using a simple linear regression approach (the mean R2 of clay = 0.39). The additional required structural and hydrological properties were derived from pedotransfer functions. The modelling results, derived soil texture distributions and original ER data were compared with the spatial winter wheat yield distribution in a relatively dry year using regression and boundary line analysis. The yield variation was best explained by the simulated soil water content (R2 = 0.18) during the grain filling and was additionally validated by the measured soil water content with a root mean square error (RMSE) of 7.5 Vol%.
Similar content being viewed by others
Abbreviations
- CV %:
-
Coefficient of variation
- ERT:
-
Electrical resistivity tomography
- ERa:
-
Apparent electrical resistivity
- PA:
-
Precision agriculture
- PSD:
-
Particle size distribution
- PTF:
-
Pedotransfer function
- SOM:
-
Soil organic matter
- STD:
-
Soil texture distribution
- STC:
-
Soil texture class
References
Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71–91. https://doi.org/10.1016/j.compag.2004.03.002.
Ad hoc AG Boden (2005). Bodenkundliche Kartieranleitung (5ed.). Stuttgart, Germany: Schweitzerbart.
Allred, B. J., Daniels, J. J., & Ehsani, M. R. (2008). Handbook of agricultural geophysics. New York, USA: CRC Press.
Altermann, M., Rinklebe, J., Merbach, I., Korschens, M., Langer, U., & Hofmann, B. (2005). Chernozem—soil of the year 2005. Journal of Plant Nutrition and Soil Science, 168(6), 725–740. https://doi.org/10.1002/jpln.200521814.
Batchelor, W. D., Basso, B., & Paz, J. O. (2002). Examples of strategies to analyze spatial and temporal yield variability using crop models. European Journal of Agronomy, 18(1–2), 141–158. https://doi.org/10.1016/S1161-0301(02)00101-6.
Berzsenyi, Z., Győrffy, B., & Lap, D. (2000). Effect of crop rotation and fertilisation on maize and wheat yields and yield stability in a long-term experiment. European Journal of Agronomy, 13(2–3), 225–244. https://doi.org/10.1016/S1161-0301(00)00076-9.
Besson, A., Cousin, I., Samouelian, A., Boizard, H., & Richard, G. (2004). Structural heterogeneity of the soil tilled layer as characterized by 2D electrical resistivity surveying. Soil & Tillage Research, 79(2), 239–249. https://doi.org/10.1016/j.still.2004.07.012.
Brevik, E. C., Fenton, T. E., & Lazari, A. (2006). Soil electrical conductivity as a function of soil water content and implications for soil mapping. Precision Agriculture, 7(6), 393–404. https://doi.org/10.1007/s11119-006-9021-x.
Brooks, R. H., & Corey, A. T. (1964). Hydraulic properties of porous media. In Hydrolgical Paper (Vol. 3). Fort Collins, CO, USA: Colorado State University.
Core Team, R. (2016). A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical conductivity to precision agriculture: Theory, principles, and guidelines. Agronomy Journal, 95(3), 455–471. https://doi.org/10.2134/agronj2003.4550.
Dabas, M., Decriaud, J. P., Ducomet, G., Hesse, A., Mounir, A., & Tabbagh, A. (1994). Continuous recording of resistivity with towed arrays for systematic mapping of buried structures at shallow depths. Revue d’Archéométrie, 18, 13–17.
Dualem. (2005). DUALEM-1S and DUALEM-2S User’s Manual. Milton, Ontario, Canada: Dualem, Inc.
Eghball, B., Schepers, J. S., Negahban, M., & Schlemmer, M. R. (2003). Spatial and Temporal Variability of Soil Nitrate and Corn Yield Joint contribution of the USDA-ARS and the Univ. of Nebraska Agric. Res. Div., Lincoln, NE, as paper no. 13618. Agronomy Journal, 95(2), 339–346. https://doi.org/10.2134/agronj2003.3390.
Eissmann, L. (1994). Grundzüge der Quartärgeologie Mitteldeutschlands - Sachsen, Sachsen-Anhalt, Südbrandenburg, Thüringen (Features of the Quaternary Geology of Central Germany - Saxonia, Saxonia-Anhalt, South Brandenburg, Thuringia). In L. Eissmann & T. Litt (Eds.), Das Quartär Mitteldeutschlands - Ein Leitfaden und Exkursionsführer (The Quaternary in Central Germany - A guideline and excursion guide), DEUQUA-Tagung in Leipzig 1994, 7 (pp. 55–135). Altenburg, Germany: Altenburger Naturwissenschaftliche Forschung.
Franko, U., Oelschlagel, B., & Schenk, S. (1995). Simulation of temperature, water and nitrogen dynamics using the model candy. Ecological Modelling, 81(1–3), 213–222. https://doi.org/10.1016/0304-3800(94)00172-E.
Franko, U., Oelschlägel, B., Schenk, S., Puhlmann, M., Kuka, K., Bönecke, E., et al. (2015). CANDY manual—description of background (p. 50). UFZ, Halle, Germany: Helmholtz-Centre for Environmental Research—UFZ.
Fukue, M., Minato, T., Horibe, H., & Taya, N. (1999). The micro-structures of clay given by resistivity measurements. Engineering Geology, 54(1–2), 43–53. https://doi.org/10.1016/S0013-7952(99)00060-5.
Gebbers, R., & Lück, E. (2005). Comparison of geoelectrical methods for soil mapping. Precision Agriculture, 5, 473–479.
Godwin, R. J., & Miller, P. C. H. (2003). A review of the technologies for mapping within-field variability. Biosystems Engineering, 84(4), 393–407. https://doi.org/10.1016/S1537-5110(02)00283-0.
Grisso, R., Alley, M., Holshouser, D., & Thomason, W. (2009). Precision farming tools: Soil electrical conductivity. Richmond, USA: Virginia Cooperative Extension.
Haase, D., Fink, J., Haase, G., Ruske, R., Pecsi, M., Richter, H., et al. (2007). Loess in Europe—its spatial distribution based on a European Loess Map, scale 1: 2,500,000. Quaternary Science Reviews, 26(9–10), 1301–1312. https://doi.org/10.1016/j.quascirev.2007.02.003.
Hartemink, A. E., & Minasny, B. (2014). Towards digital soil morphometrics. Geoderma, 230, 305–317. https://doi.org/10.1016/j.geoderma.2014.03.008.
Heuvelink, G. B. M., Brown, J. D., & van Loon, E. E. (2007). A probabilistic framework for representing and simulating uncertain environmental variables. International Journal of Geographical Information Science, 21(5), 497–513. https://doi.org/10.1080/13658810601063951.
Kaspar, T. C., Pulido, D. J., Fenton, T. E., Colvin, T. S., Karlen, D. L., Jaynes, D. B., et al. (2004). Relationship of corn and soybean yield to soil and terrain properties. Agronomy Journal, 96(3), 700–709. https://doi.org/10.2134/agronj2004.0700.
Knoth, W. (1992). Geological overview map of Sachsen-Anhalt 1:400 000. Halle (Saale), Germany: Geologisches Landesamt Sachsen-Anhalt.
Koenker, R. (2005). Quantile regression (Vol. 38). New York, USA: Cambridge University Press.
Kravchenko, A. N., Thelen, K. D., Bullock, D. G., & Miller, N. R. (2003). Relationship among crop grain yield, topography, and soil electrical conductivity studied with cross-correlograms. Agronomy Journal, 95(5), 1132–1139. https://doi.org/10.2134/agronj2003.1132.
Kruger, J., Franko, U., Fank, J., Stelzl, E., Dietrich, P., Pohle, M., et al. (2013). Linking geophysics and soil function modeling-an application study for biomass production. Vadose Zone Journal, 12(4), 1–13. https://doi.org/10.2136/vzj2013.01.0015.
Lark, R. M. (1997). An empirical method for describing the joint effects of environmental and other variables on crop yield. Annals of Applied Biology, 131(1), 141–159. https://doi.org/10.1111/j.1744-7348.1997.tb05402.x.
Loke, M. H., & Barker, R. D. (1995). Least-squares deconvolution of apparent resistivity pseudosections. Geophysics, 60(6), 1682–1690. https://doi.org/10.1190/1.1443900.
Loke, M. H., & Barker, R. D. (1996). Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-Newton method. Geophysical Prospecting, 44(1), 131–152. https://doi.org/10.1111/j.1365-2478.1996.tb00142.x.
Lueck, E., & Ruehlmann, J. (2013). Resistivity mapping with GEOPHILUS ELECTRICUS—information about lateral and vertical soil heterogeneity. Geoderma, 199, 2–11. https://doi.org/10.1016/j.geoderma.2012.11.009.
Lund, E., Christy, C., & Drummond, P. (1999). Practical applications of soil electrical conductivity mapping. Precision Agriculture, 99, 771–779.
Machado, S., Bynum, E. D., Archer, T. L., Lascano, R. J., Wilson, L. T., Bordovsky, J., et al. (2002). Spatial and temporal variability of corn growth and grain yield. Crop Science, 42(5), 1564–1576. https://doi.org/10.2135/cropsci2002.1564.
Manzoni, S., & Porporato, A. (2009). Soil carbon and nitrogen mineralization: Theory and models across scales. Soil Biology & Biochemistry, 41(7), 1355–1379. https://doi.org/10.1016/j.soilbio.2009.02.031.
McBratney, A. B., Minasny, B., Cattle, S. R., & Vervoort, R. W. (2002). From pedotransfer functions to soil inference systems. Geoderma, 109(1–2), 41–73.
McNeill, J. (1980). Electromagnetic terrain conductivity measurement at low induction numbers. Ontario, Canada: Geonics Limited.
Menke, W. (2012). Geophysical data analysis: Discrete inverse theory: MATLAB edition (Vol. 45). Oxford, UK: Academic press.
Pan, L., Adamchuk, V. I., Prasher, S., Gebbers, R., Taylor, R. S., & Dabas, M. (2014). Vertical soil profiling using a galvanic contact resistivity scanning approach. Sensors, 14(7), 13243–13255. https://doi.org/10.3390/s140713243.
Pellerin, L., & Wannamaker, P. E. (2005). Multi-dimensional electromagnetic modeling and inversion with application to near-surface earth investigations. Computers and Electronics in Agriculture, 46(1–3), 71–102. https://doi.org/10.1016/j.compag.2004.11.017.
Piikki, K., Wetterlind, J., Soderstrom, M., & Stenberg, B. (2015). Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal sensor data obtained from different sensing methods. Precision Agriculture, 16(1), 29–45. https://doi.org/10.1007/s11119-014-9381-6.
Pracilio, G., Asseng, S., Cook, S. E., Hodgson, G., Wong, M. T. F., Adams, M. L., et al. (2003). Estimating spatially variable deep drainage across a central-eastern wheatbelt catchment, Western Australia. Australian Journal of Agricultural Research, 54(8), 789–802. https://doi.org/10.1071/Ar02084.
Radic, T. (2014). SIP Rabbit—for precision agriculture measurements. http://www.radic-research.de/Flyer_Rabbit_171114.pdf, Accessed Dec 15, 2017
Rawls, W. J., & Brakensiek, D. L. (1985). Prediction of soil water properties for hydrologic modeling. In Watershed management in the eighties, 1985 (pp. 293–299). St Joseph, USA, American Society Agricultural Engineers.
Rhoades, J. D., Manteghi, N. A., Shouse, P. J., & Alves, W. J. (1989). Soil electrical-conductivity and soil-salinity—new formulations and calibrations. Soil Science Society of America Journal, 53(2), 433–439. https://doi.org/10.2136/sssaj1989.03615995005300020020x.
Robert, P. (1993). Characterization of soil conditions at the field level for soil specific management. Geoderma, 60(1), 57–72. https://doi.org/10.1016/0016-7061(93)90018-G.
Rodriguez-Perez, J. R., Plant, R. E., Lambert, J. J., & Smart, D. R. (2011). Using apparent soil electrical conductivity (ECa) to characterize vineyard soils of high clay content. Precision Agriculture, 12(6), 775–794. https://doi.org/10.1007/s11119-011-9220-y.
Roy, A., & Apparao, A. (1971). Depth of investigation in direct current methods. Geophysics, 36(5), 943–959. https://doi.org/10.1190/1.1440226.
Ruehlmann, J., & Korschens, M. (2009). Calculating the effect of soil organic matter concentration on soil bulk density. Soil Science Society of America Journal, 73(3), 876–885. https://doi.org/10.2136/sssaj2007.0149.
Rühlmann, J., Körschens, M., & Graefe, J. (2006). A new approach to calculate the particle density of soils considering properties of the soil organic matter and the mineral matrix. Geoderma, 130(3–4), 272–283. https://doi.org/10.1016/j.geoderma.2005.01.024.
Saey, T., De Smedt, P., Delefortrie, S., de Vijver, E. V., & Van Meirvenne, M. (2015). Comparing one- and two-dimensional EMI conductivity inverse modeling procedures for characterizing a two-layered soil. Geoderma, 241, 12–23. https://doi.org/10.1016/j.geoderma.2014.10.020.
Saey, T., Simpson, D., Vermeersch, H., Cockx, L., & Van Meirvenne, M. (2009). Comparing the EM38DD and DUALEM-21S sensors for depth-to-clay mapping. Soil Science Society of America Journal, 73(1), 7–12. https://doi.org/10.2136/sssaj2008.0079.
Samouelian, A., Cousin, I., Tabbagh, A., Bruand, A., & Richard, G. (2005). Electrical resistivity survey in soil science: a review. Soil & Tillage Research, 83(2), 173–193. https://doi.org/10.1016/j.still.2004.10.004.
Schamper, C., Rejiba, F., & Guerin, R. (2012). 1D single-site and laterally constrained inversion of multifrequency and multicomponent ground-based electromagnetic induction data—application to the investigation of a near-surface clayey overburden. Geophysics, 77(4), Wb19–Wb35, https://doi.org/10.1190/geo2011-0358.1.
Shahandeh, H., Wright, A. L., Hons, F. M., & Lascano, R. J. (2005). Spatial and temporal variation of soil nitrogen parameters related to soil texture and corn yield. Agronomy Journal, 97(3), 772–782. https://doi.org/10.2134/agronj2004.0287.
Shatar, T. M., & McBratney, A. B. (2004). Boundary-line analysis of field-scale yield response to soil properties. Journal of Agricultural Science, 142(5), 553–560. https://doi.org/10.1017/S0021859604004642.
Smith, P., Andren, O., Brussaard, L., Dangerfield, M., Ekschmitt, K., Lavelle, P., et al. (1998). Soil biota and global change at the ecosystem level: describing soil biota in mathematical models. Global Change Biology, 4(7), 773–784. https://doi.org/10.1046/j.1365-2486.1998.00193.x.
Smith, P., Smith, J. U., Powlson, D. S., McGill, W. B., Arah, J. R. M., Chertov, O. G., et al. (1997). A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma, 81(1–2), 153–225. https://doi.org/10.1016/S0016-7061(97)00087-6.
Stockmann, U., Adams, M. A., Crawford, J. W., Field, D. J., Henakaarchchi, N., Jenkins, M., et al. (2013). The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems & Environment, 164, 80–99. https://doi.org/10.1016/j.agee.2012.10.001.
Stoorvogel, J., & Bouma, J. (2005). Precision agriculture: The solution to control nutrient emissions. In J. V. Stafford (Ed.), Proceedings of 5th European Conference on Precision Agriculture, pp. 47–55. Wageningen, Netherlands: Wageningen Academic Publishers.
Tabbagh, A., Dabas, M., Hesse, A., & Panissod, C. (2000). Soil resistivity: a non-invasive tool to map soil structure horizonation. Geoderma, 97(3–4), 393–404. https://doi.org/10.1016/S0016-7061(00)00047-1.
Terron, J. M., da Silva, J. R. M., Moral, F. J., & Garcia-Ferrer, A. (2011). Soil apparent electrical conductivity and geographically weighted regression for mapping soil. Precision Agriculture, 12(5), 750–761. https://doi.org/10.1007/s11119-011-9218-5.
Varvel, G. E. (2000). Crop rotation and nitrogen effects on normalized grain yields in a long-term study joint contribution of USDA-ARS and the Nebraska Agric. Res. Div., Journal Ser. no. 12880. Agronomy Journal, 92(5), 938–941. https://doi.org/10.2134/agronj2000.925938x.
Vereecken, H., Schnepf, A., Hopmans, J. W., Javaux, M., Or, D., Roose, D. O. T., et al. (2016). Modeling soil processes: Review, key challenges, and new perspectives. Vadose Zone Journal, 15(5), 1–57. https://doi.org/10.2136/vzj2015.09.0131.
Viscarra Rossel, R. A., Adamchuk, V. I., Sudduth, K. A., McKenzie, N. J., & Lobsey, C. (2011). Proximal soil sensing: an effective approach for soil measurements in space and time. Advances of Agronomy, 113, 237–282. Amsterdam, Netherlands: Academic Press.
Vitharana, U. W. A., Van Meirvenne, M., Cockx, L., & Bourgeois, J. (2006). Identifying potential management zones in a layered soil using several sources of ancillary information. Soil Use and Management, 22(4), 405–413. https://doi.org/10.1111/j.1475-2743.2006.00052.x.
Vitharana, U. W. A., Van Meirvenne, M., Simpson, D., Cockx, L., & De Baerdemaeker, J. (2008). Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area. Geoderma, 143(1–2), 206–215. https://doi.org/10.1016/j.geoderma.2007.11.003.
Wong, M. T. F., & Asseng, S. (2004). Fluctuations in spatial variability of wheat yield. In Proceedings of 4th International Crop Science Congress. Brisbane, Australia: http://www.regional.org.au/au/asa/2004/poster/1/4/1158_wongmt.htm, Accessed Dec 15, 2017
Wong, M. T. F., & Asseng, S. (2006). Determining the causes of spatial and temporal variability of wheat yields at sub-field scale using a new method of upscaling a crop model. Plant and Soil, 283(1–2), 203–215. https://doi.org/10.1007/s11104-006-0012-5.
Wong, M. T. F., Asseng, S., & Zhang, H. (2006). A flexible approach to managing variability in grain yield and nitrate leaching at within-field to farm scales. [journal article]. Precision Agriculture, 7(6), 405–417. https://doi.org/10.1007/s11119-006-9023-8.
Wosten, J. H. M., Pachepsky, Y. A., & Rawls, W. J. (2001). Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251(3–4), 123–150. https://doi.org/10.1016/S0022-1694(01)00464-4.
Acknowledgements
This research project was funded by the German Federal Office for Agriculture and Food (Bundesanstalt für Landwirtschaft und Ernährung—BLE), Project No. 2815410210. Special thanks go to soil specialist Marco Knötig from AgroSat, Baasdorf, Germany, for supporting the field operations.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Boenecke, E., Lueck, E., Ruehlmann, J. et al. Determining the within-field yield variability from seasonally changing soil conditions. Precision Agric 19, 750–769 (2018). https://doi.org/10.1007/s11119-017-9556-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-017-9556-z