Precision Agriculture

, Volume 9, Issue 6, pp 367–390 | Cite as

Soil heterogeneity at the field scale: a challenge for precision crop protection

  • Stefan Patzold
  • Franz Michael Mertens
  • Ludger Bornemann
  • Britta Koleczek
  • Jonas Franke
  • Hannes Feilhauer
  • Gerhard Welp


Crop protection seldom takes into account soil heterogeneity at the field scale. Yet, variable site characteristics affect the incidence of pests as well as the efficacy and fate of pesticides in soil. This article reviews crucial starting points for incorporating soil information into precision crop protection (PCP). At present, the lack of adequate field maps is a major drawback. Conventional soil analyses are too expensive to capture soil heterogeneity at the field scale with the required spatial resolution. Therefore, we discuss alternative procedures exemplified by our own results concerning (i) minimally and non-invasive sensor techniques for the estimation of soil properties, (ii) the evidence of soil heterogeneity with respect to PCP, and (iii) current possibilities for incorporation of high resolution soil information into crop protection decisions. Soil organic carbon (SOC) and soil texture are extremely interesting for PCP. Their determination with minimally invasive techniques requires the sampling of soils, because the sensors must be used in the laboratory. However, this technique delivers precise information at low cost. We accurately determined SOC in the near-infrared. In the mid-infrared, texture and lime content were also exactly quantified. Non-invasive sensors require less effort. The airborne HyMap sensor was suitable for the detection of variability in SOC at high resolution, thus promising further progress regarding SOC data acquisition from bare soil. The apparent electrical conductivity as measured by an EM38 sensor was shown to be a suitable proxy for soil texture and layering. A survey of arable fields near Bonn (Germany) revealed widespread within-field heterogeneity of texture-related ECa, SOC and other characteristics. Maps of herbicide sorption and application rate were derived from sensor data, showing that optimal herbicide dosage is strongly governed by soil variability. A phytoassay with isoproturon confirmed the reliability of spatially varied herbicide application rates. Mapping areas with an enhanced leaching risk within fields allows them to be kept free of pesticides with related regulatory restrictions. We conclude that the use of information on soil heterogeneity within the concept of PCP is beneficial, both economically and ecologically.


Pesticide Herbicide Soil organic carbon Texture Variability NIRS MIRS ECa Hyperspectral sensor Map 



The authors are indebted to Jürgen Lamp, Institute of Plant Nutrition and Soil Science and Eiko Thiessen, Institute of Agricultural Engineering, both from the University of Kiel and Michael Herbst, ICG IV, Research Centre Jülich, for the provision of sensors and support in data processing. The paper presents data from different projects funded by the German Research Foundation (DFG Research Training Group 722 “Use of Information Technologies for Precision Crop Protection”; Transregional Collaborative Research Centre 32 “Patterns in Soil-Vegetation-Atmosphere Systems”) and the RheinEnergie AG, Cologne.


  1. Altieri, M. A., & Nicholls, C. I. (2003). Soil fertility management and insect pests: Harmonizing soil and plant health in agroecosystems. Soil and Tillage Research, 72, 203–211.CrossRefGoogle Scholar
  2. Avendaño, F., Pierce, F. J., & Schabenberger, O. (2004). The spatial distribution of soybean cyst nematode in relation to soil texture and soil map unit. Agronomy Journal, 96, 181–194.Google Scholar
  3. Behrens, T., & Scholten, T. (2006). Digital soil mapping in Germany—A review. Journal of Plant Nutrition and Soil Science, 169, 434–443.CrossRefGoogle Scholar
  4. Bornemann, L., Welp, G., Brodowski, S., Rodionov, A., & Amelung, W. (2008). Rapid assessment of black carbon in soil organic matter using mid-infrared spectroscopy. Organic Geochemistry, accepted, doi:  10.1016/j.orggeochem.2008.07.012.
  5. 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, 393–404.CrossRefGoogle Scholar
  6. Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., et al. (1994). Field-scale variability of soil properties in Central Iowa soils. Soil Science Society of America Journal, 58, 1501–1511.Google Scholar
  7. Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R., Jr. (2001). Near-infrared reflectance spectroscopy—Principal components regression analysis of soil samples. Soil Science Society of America Journal, 65, 480–490.Google Scholar
  8. Cooke, C. M., Shaw, G., & Collins, C. D. (2004). Determination of solid-liquid partition coefficients (Kd) for the herbicides isoproturon and trifluralin in five UK agricultural soils. Environmental Pollution, 132, 541–552.PubMedCrossRefGoogle Scholar
  9. Corwin, D. L., & Lesch, S. M. (2005). Applications of apparent soil electrical conductivity in precision agriculture. Computers and Electronics in Agriculture, 46, 11–43.CrossRefGoogle Scholar
  10. Cousens, R., & Mortimer, M. (1995). Dynamics of weed populations. London: Cambridge University Press.Google Scholar
  11. De Vos, B., Vandecasteele, B., Deckers, J., & Muys, B. (2005). Capability of loss-on-ignition as a predictor of total organic carbon in non-calcareous forest soils. Communications in Soil Science and Plant Analysis, 36, 2899–2921.CrossRefGoogle Scholar
  12. Dicke, D., Gerhards, R., Büchse, A., & Hurle, K. (2007). Modeling spatial and temporal dynamics of Chenopodium album L. under the influence of site-specific weed control. Crop Protection, 26, 206–211.CrossRefGoogle Scholar
  13. Dordas, C. (2008). Role of nutrients in controlling plant diseases in sustainable agriculture. A review. Agronomy for Sustainable Development, 28, 33–46.CrossRefGoogle Scholar
  14. Dörfler, U., Cao, G., Grundmann, S., & Schroll, R. (2006). Influence of a heavy rainfall event on the leaching of [14C]isoproturon and its degradation products in outdoor lysimeters. Environmental Pollution, 144, 695–702.PubMedCrossRefGoogle Scholar
  15. Dunker, M., & Nordmeyer, H. (2000). Reasons for the distribution of weed species on arable fields—Field and greenhouse experiments concerning the influence of soil properties. Journal of Plant Diseases and Protection, Special Issue, 17, 55–62.Google Scholar
  16. Ehlert, D., & Dammer, K.-H. (2006). Widescale testing of the crop-meter for site-specific farming. Precision Agriculture, 7, 101–115.CrossRefGoogle Scholar
  17. Franke, J., & Menz, G. (2007). Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture, 8, 161–172.CrossRefGoogle Scholar
  18. Gebhardt, S., Schellberg, J., Lock, R., & Kühbauch, W. (2006). Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Precision Agriculture, 7, 165–178.CrossRefGoogle Scholar
  19. Gerhards, R., & Oebel, H. (2006). Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Research, 46, 185–193.CrossRefGoogle Scholar
  20. Goovaerts, P. (1998). Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biology and Fertility of Soils, 27, 315–334.CrossRefGoogle Scholar
  21. Haaland, D. M., & Thomas, E. V. (1988). Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information. Analytical Chemistry, 60, 1193–1202.CrossRefGoogle Scholar
  22. ISO 10694. (1995). Soil quality—Determination of organic and total carbon after dry combustion (elemental analysis). Berlin: Beuth-Verlag.Google Scholar
  23. ISO 11277. (2002). Soil quality—Determination of particle size distribution in mineral soil material—Method by sieving and sedimentation. Berlin: Beuth-Verlag.Google Scholar
  24. ISO 13878. (1998). Soil quality—Determination of total nitrogen content by dry combustion (elemental analysis). Berlin: Beuth-Verlag.Google Scholar
  25. Jacobi, J., Backes, M., Kühbauch, W., & Plümer, L. (2006). Identification of weeds in remote-sensed images on the basis of differences in spectral reflectance. Journal of Plant Diseases and Protection, Special Issue, 20, 241–248.Google Scholar
  26. Kah, M., Beulke, S., & Brown, C. D. (2007). Factors influencing degradation of pesticides in soil. Journal of Agricultural and Food Chemistry, 55, 4487–4492.PubMedCrossRefGoogle Scholar
  27. Kerry, R., & Oliver, M. A. (2008). Determining nugget:sill ratios of standardized variograms from aerial photographs to krige sparse soil data. Precision Agriculture, 9, 33–56.CrossRefGoogle Scholar
  28. Lagacherie, P., Baret, F., Feret, J.-B., Netto, J. M., & Robbez-Masson, J. M. (2008). Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment, 112, 825–835.CrossRefGoogle Scholar
  29. Martens, H., & Naes, T. (1991). Multivariate calibration (438 pp). Chichester, UK: Wiley.Google Scholar
  30. Mateille, T., Duponnois, R., & Diop, M. T. (1995). Influence des facteurs telluriques abiotiques et de la plante hôte sur l’infection des nématodes phytoparasites du genre Meloidogyne par l’actinomycète parasitoide Pastoria penetrans. Agronomie, 15, 581–591.CrossRefGoogle Scholar
  31. McBratney, A. B., Minasny, B., & Viscarra Rossel, R. (2006). Spectral soil analysis and inference systems. A powerful combination for solving the soil data crisis. Geoderma, 136, 272–278.CrossRefGoogle Scholar
  32. McBratney, A. B., Santos, M. L. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117, 3–52.CrossRefGoogle Scholar
  33. Mertens, F. M., Pätzold, S., & Welp, G. (2008). Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity. Journal of Plant Nutrition and Soil Science, 171, 146–154.CrossRefGoogle Scholar
  34. Minasny, B., McBratney, A. B., & Whelan, B. M. (2005). VESPER version 1.62. Australian Centre for Precision Agriculture. Accessed 30 June 2008.
  35. Nombela, G., Navas, A., & Bello, A. (1994). Structure of the nematofauna in Spanish Mediterranean continental soils. Biology and Fertility of Soils, 18, 183–192.CrossRefGoogle Scholar
  36. Nordmeyer, H., & Dunker, M. (1999). Variable weed densities and soil properties in a weed mapping concept for patchy weed control. In J. V. Stafford (Ed.), Precision Agriculture 1999: Proceedings of the 2nd European Conference on Precision Agriculture (pp. 453–462). Sheffield, UK: Sheffield Academic Press.Google Scholar
  37. Nordmeyer, H., & Häusler, A. (2004). Einfluss von Bodeneigenschaften auf die Segetalflora von Ackerflächen. (Impact of soil properties on weed distribution within agricultural fields.). Journal of Plant Nutrition and Soil Science, 167, 328–336.CrossRefGoogle Scholar
  38. OECD (Organisation for Economic Co-operation, Development). (2000). Adsorption—Desorption using a batch equilibrium method. OECD Guideline for Testing Chemicals, 106, 1–42.Google Scholar
  39. Pätzold, S., & Brümmer, G. W. (2003). Influence of microbial activity and soil moisture on herbicide immobilization in soils. Journal of Plant Nutrition and Soil Science, 166, 336–344.CrossRefGoogle Scholar
  40. Renaud, F. G., Brown, C. D., Fryer, C. J., & Walker, A. (2004). A lysimeter experiment to investigate temporal changes in the availability of pesticide residues for leaching. Environmental Pollution, 131, 81–91.PubMedCrossRefGoogle Scholar
  41. Rider, T. W., Vogel, J. W., Dille, J. A., Dhuyvetter, K. C., & Kastens, T. L. (2006). An economic evaluation of site-specific herbicide application. Precision Agriculture, 7, 379–392.CrossRefGoogle Scholar
  42. Ritter, C., Dicke, D., Weis, M., Oebel, H., Piepho, H. P., Buerchse, A., et al. (2008). An on-farm approach to quantify yield variation and to derive decision rules for site-specific weed management. Precision Agriculture, 9, 133–146.CrossRefGoogle Scholar
  43. Sarmah, A. K., Muller, K., & Ahmad, R. (2004). Fate and behaviour of pesticides in the agroecosystem—A review with a New Zealand perspective. Australian Journal of Soil Research, 42, 125–154.CrossRefGoogle Scholar
  44. Selige, T., Bohner, J., & Schmidhalter, U. (2006). High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma, 136, 235–244.CrossRefGoogle Scholar
  45. Shepherd, K. D., & Walsh, M. G. (2002). Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal, 66, 988–998.Google Scholar
  46. Spliid, N. H., & Køppen, B. (1998). Occurrence of pesticides in Danish shallow ground water. Chemosphere, 37, 1307–1316.PubMedCrossRefGoogle Scholar
  47. Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76, 267–275.CrossRefGoogle Scholar
  48. Stevens, A., Van Wesemael, B., Vandenschrick, G., Touré, S., & Tychon, B. (2006). Detection of carbon stock change in agricultural soils using spectroscopic techniques. Soil Science Society of America Journal, 70, 844–850.CrossRefGoogle Scholar
  49. Sudduth, K. A., Drummond, S. T., & Kitchen, N. R. (2001). Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Computers and Electronics in Agriculture, 31, 239–264.CrossRefGoogle Scholar
  50. Thomas, J. M., & Cline, J. F. (1985). Modification of the Neubauer technique to assess toxicity of hazardous chemicals in soils. Environmental Toxicology and Chemistry, 4, 201–207.CrossRefGoogle Scholar
  51. Vandenberghe, J., & van Overmeeren, R. A. (1999). Ground penetrating radar images of selected fluvial deposits in the Netherlands. Sedimentary Geology, 128, 245–270.CrossRefGoogle Scholar
  52. Viscarra Rossell, R. A., & McBratney, A. B. (1998a). Laboratory evaluation of a proximal sensing technique for simultaneous measurement of soil clay and water content. Geoderma, 85, 19–39.CrossRefGoogle Scholar
  53. Viscarra Rossell, R. A., & McBratney, A. B. (1998b). Soil chemical analytical accuracy and costs: Implications from precision agriculture. Australian Journal of Experimental Agriculture, 38, 765–775.CrossRefGoogle Scholar
  54. Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J., & Skjemstad, J. O. (2006). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131(1–2), 59–75.CrossRefGoogle Scholar
  55. Walter, A. M., Christensen, S., & Simmelsgaard, S. E. (2002). Spatial correlation between weed species densities and soil properties. Weed Research, 42, 26–38.CrossRefGoogle Scholar
  56. Wauchope, R. D., Yeh, S., Linders, J. B., Kloskowski, R., Tanaka, K., Rubin, B., et al. (2002). Pesticide soil sorption parameters: Theory, measurement, uses, limitations and reliability. Pest Management Science, 58, 419–445.PubMedCrossRefGoogle Scholar
  57. Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil properties. Journal of Soil Science, 43, 177–192.CrossRefGoogle Scholar
  58. Wetterlind, J., Stenberg, B., & Soderstrom, M. (2008). The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agriculture, 9, 57–69.CrossRefGoogle Scholar
  59. Williams, M. W., Mortensen, D. A., Martin, A. R., & Marx, D. B. (2001). Within-field soil heterogeneity effects on herbicide mediated crop injury and weed biomass. Weed Science, 49, 798–805.CrossRefGoogle Scholar
  60. Williams, P. C., & Sobering, D. C. (1993). Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. Journal of Near Infrared Spectroscopy, 1, 25–32.Google Scholar
  61. Zens, I. (2000). Auftreten und Bekämpfung der späten Rübenfäule, verursacht durch Rhizoctonia solani. (Occurence and control of root and crown rot of sugar beet, caused by Rhizoctonia solani). Ph.D. thesis, University of Bonn, Germany.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Stefan Patzold
    • 1
  • Franz Michael Mertens
    • 1
  • Ludger Bornemann
    • 1
  • Britta Koleczek
    • 1
  • Jonas Franke
    • 2
  • Hannes Feilhauer
    • 3
  • Gerhard Welp
    • 1
  1. 1.Institute of Crop Science and Resource Conservation, Division Soil ScienceUniversity of BonnBonnGermany
  2. 2.Center for Remote Sensing of Land SurfacesUniversity of BonnBonnGermany
  3. 3.Institute of Geography, Vegetation GeographyUniversity of BonnBonnGermany

Personalised recommendations