Skip to main content
SpringerLink
Log in

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

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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 

  • Behrens, T., & Scholten, T. (2006). Digital soil mapping in Germany—A review. Journal of Plant Nutrition and Soil Science, 169, 434–443.

    Article  CAS  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Corwin, D. L., & Lesch, S. M. (2005). Applications of apparent soil electrical conductivity in precision agriculture. Computers and Electronics in Agriculture, 46, 11–43.

    Article  Google Scholar 

  • Cousens, R., & Mortimer, M. (1995). Dynamics of weed populations. London: Cambridge University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dordas, C. (2008). Role of nutrients in controlling plant diseases in sustainable agriculture. A review. Agronomy for Sustainable Development, 28, 33–46.

    Article  CAS  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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 

  • Ehlert, D., & Dammer, K.-H. (2006). Widescale testing of the crop-meter for site-specific farming. Precision Agriculture, 7, 101–115.

    Article  Google Scholar 

  • Franke, J., & Menz, G. (2007). Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture, 8, 161–172.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • ISO 10694. (1995). Soil quality—Determination of organic and total carbon after dry combustion (elemental analysis). Berlin: Beuth-Verlag.

    Google Scholar 

  • ISO 11277. (2002). Soil quality—Determination of particle size distribution in mineral soil material—Method by sieving and sedimentation. Berlin: Beuth-Verlag.

    Google Scholar 

  • ISO 13878. (1998). Soil quality—Determination of total nitrogen content by dry combustion (elemental analysis). Berlin: Beuth-Verlag.

    Google Scholar 

  • 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 

  • Kah, M., Beulke, S., & Brown, C. D. (2007). Factors influencing degradation of pesticides in soil. Journal of Agricultural and Food Chemistry, 55, 4487–4492.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Martens, H., & Naes, T. (1991). Multivariate calibration (438 pp). Chichester, UK: Wiley.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • McBratney, A. B., Santos, M. L. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117, 3–52.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Minasny, B., McBratney, A. B., & Whelan, B. M. (2005). VESPER version 1.62. Australian Centre for Precision Agriculture. http://www.usyd.edu.au/su/agric/acpa. Accessed 30 June 2008.

  • Nombela, G., Navas, A., & Bello, A. (1994). Structure of the nematofauna in Spanish Mediterranean continental soils. Biology and Fertility of Soils, 18, 183–192.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  CAS  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Spliid, N. H., & Køppen, B. (1998). Occurrence of pesticides in Danish shallow ground water. Chemosphere, 37, 1307–1316.

    Article  PubMed  CAS  Google Scholar 

  • Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76, 267–275.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Vandenberghe, J., & van Overmeeren, R. A. (1999). Ground penetrating radar images of selected fluvial deposits in the Netherlands. Sedimentary Geology, 128, 245–270.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Walter, A. M., Christensen, S., & Simmelsgaard, S. E. (2002). Spatial correlation between weed species densities and soil properties. Weed Research, 42, 26–38.

    Article  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil properties. Journal of Soil Science, 43, 177–192.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • 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.

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Institute of Crop Science and Resource Conservation, Division Soil Science, University of Bonn, Nussallee 13, 53115, Bonn, Germany

    Stefan Patzold, Franz Michael Mertens, Ludger Bornemann, Britta Koleczek & Gerhard Welp

  2. Center for Remote Sensing of Land Surfaces, University of Bonn, Walter-Flex-Str. 3, 53113, Bonn, Germany

    Jonas Franke

  3. Institute of Geography, Vegetation Geography, University of Bonn, Meckenheimer Allee 166, 53115, Bonn, Germany

    Hannes Feilhauer

Authors
  1. Stefan Patzold
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Franz Michael Mertens
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ludger Bornemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Britta Koleczek
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jonas Franke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hannes Feilhauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gerhard Welp
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan Patzold.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Patzold, S., Mertens, F.M., Bornemann, L. et al. Soil heterogeneity at the field scale: a challenge for precision crop protection. Precision Agric 9, 367–390 (2008). https://doi.org/10.1007/s11119-008-9077-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-008-9077-x

Keywords

Search

Navigation