Skip to main content
SpringerLink
Log in

Taking into account change of support when merging heterogeneous spatial data for field partition

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

The paper describes a geostatistical approach for combining multi-source data with different support for field delineation into homogeneous soil zones. It takes into account change of support explicitly given the critical influence of spatial resolution on the statistical characteristics of estimates. Geophysical and hyperspectral data were used in combination with soil chemical properties measured in the laboratory on 50 samples collected in a field cropped with Tomato (Solanum lycopersicum, L. cv San Marzano). The approach consisted in performing Gaussian anamorphosis with support correction, multi-collocated block cokriging and factorial block cokriging to jointly analyse all data. Two regionalised factors at different spatial scales were retained to split the field into homogeneous zones for site-specific management. The results emphasize the impact of spatial scale on site-specific management.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Aïchi, H., Fouad, Y., Walter, C., Viscarra Rossel, R. A., Lili Chabaane, Z., & Sanaa, M. (2009). Regional predictions of soil organic carbon content from spectral reflectance measurements. Biosystems Engineering, 104(3), 442–446. https://doi.org/10.1016/j.biosystemseng.2009.08.002.

    Article  Google Scholar 

  • Armstrong, M. (1998). Basic linear geostatistics. Berlin, Heidelberg, Germany: Springer, Berlin Heidelberg. https://doi.org/10.1007/978-3-642-58727-6.

    Book  Google Scholar 

  • Bremner, J. M. (1996). Nitrogen‐total. In D. L. Sparks (Ed.), Methods of Soil Analysis. Part 3. Chemical Methods (pp. 1085–1121). Madison, WI: American Society of Agronomy and Soil Science Society of America.

    Google Scholar 

  • Burgess, T. M., & Webster, R. (1980). Optimal interpolation and isarithmic mapping of soil properties: II Block kriging. Journal of Soil Science, 31(2), 333–341. https://doi.org/10.1111/j.1365-2389.1980.tb02085.x.

    Article  Google Scholar 

  • Buttafuoco, G., Castrignanò, A., Cucci, G., Lacolla, G., & Lucà, F. (2017). Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field. Precision Agriculture, 18(1), 37–58. https://doi.org/10.1007/s11119-016-9462-9.

    Article  Google Scholar 

  • Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R. A., Terribile, F., et al. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture. CATENA, 167, 293–304. https://doi.org/10.1016/j.catena.2018.05.011.

    Article  Google Scholar 

  • Castrignanò, A., Quarto, R., Venezia, A., & Buttafuoco, G. (2019). A comparison between mixed support kriging and block cokriging for modelling and combining spatial data with different support. Precision Agriculture, 20(2), 193–213. https://doi.org/10.1007/s11119-018-09630-w.

    Article  Google Scholar 

  • Castrignanò, A., Wong, M. T. F., Stelluti, M., De Benedetto, D., & Sollitto, D. (2012). Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation. Geoderma, 175–176, 78–89. https://doi.org/10.1016/j.geoderma.2012.01.013.

    Article  CAS  Google Scholar 

  • Castrignanò, A., Giugliarini, L., Risaliti, R., & Martinelli, N. (2000). Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma, 97(1–2), 39–60. https://doi.org/10.1016/S0016-7061(00)00025-2.

    Article  Google Scholar 

  • Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., et al. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors (Switzerland), 17(12), 2794. https://doi.org/10.3390/s17122794.

    Article  Google Scholar 

  • Castrignanò, A., Costantini, E. A. C., Barbetti, R., & Sollitto, D. (2009). Accounting for extensive topographic and pedologic secondary information to improve soil mapping. CATENA, 77(1), 28–38. https://doi.org/10.1016/j.catena.2008.12.004.

    Article  Google Scholar 

  • Cavallo, G., De Benedetto, D., Castrignanò, A., Quarto, R., Vonella, A. V., & Buttafuoco, G. (2016). Use of geophysical data for assessing 3D soil variation in a durum wheat field and their association with crop yield. Biosystems Engineering, 152, 28–40. https://doi.org/10.1016/j.biosystemseng.2016.07.002.

    Article  Google Scholar 

  • Chilès, J.-P., & Delfiner, P. (2012). Geostatistics: Modeling spatial uncertainty (2nd ed.). Hoboken, NJ, USA: Wiley. https://doi.org/10.1002/9781118136188.

    Book  Google Scholar 

  • Cobo, J. G., Dercon, G., Yekeye, T., Chapungu, L., Kadzere, C., Murwira, A., et al. (2010). Integration of mid-infrared spectroscopy and geostatistics in the assessment of soil spatial variability at landscape level. Geoderma, 158(3–4), 398–411. https://doi.org/10.1016/j.geoderma.2010.06.013.

    Article  CAS  Google Scholar 

  • Conforti, M., Matteucci, G., & Buttafuoco, G. (2018). Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties. Journal of Soils and Sediments, 18(3), 1009–1019. https://doi.org/10.1007/s11368-017-1766-5.

    Article  CAS  Google Scholar 

  • Corwin, D. (2005). Geospatial Measurement of Apparent Soil Electrical Conductivity for Characterizing Soil Spatial Variability. In J. Alvarez-Benedi & R. Munoz-Carpena (Eds.), Soil-water-solute process characterization an integrated approach (pp. 639–672). Boca Raton, FL, USA: CRC Press. https://doi.org/10.1201/9781420032086.ch18.

  • Corwin, Dennis L. (2013). Site-specific management and delineating management zones. In M. Oliver, T. Bishop, & B. P. Marchant (Eds.), Precision agriculture for sustainability and environmental protection (Vol. 9780203128, pp. 135–157). London, UK: Routledge. https://doi.org/10.4324/9780203128329.

  • Daniels, J. J., Ehsani, M. R., & Allred, B. J. (2008). Ground-Penetrating Radar Methods (GPR). In B. J. Allred, J. J. Daniels, & M. R. Ehsani (Eds.), Handbook of agricultural geophysics (pp. 129–146). Boca Raton, FL, USA: CRC Press.

    Google Scholar 

  • De Benedetto, D., Castrignano, A., Sollitto, D., Modugno, F., Buttafuoco, G., &, Lo Papa G. (2012). Integrating geophysical and geostatistical techniques to map the spatial variation of clay. Geoderma, 171–172, 53–63. https://doi.org/https://doi.org/10.1016/j.geoderma.2011.05.005.

  • Doolittle, J. A., & Brevik, E. C. (2014). The use of electromagnetic induction techniques in soils studies. Geoderma, 223–225, 33–45. https://doi.org/10.1016/J.geoderma.2014.01.027.

    Article  Google Scholar 

  • Emery, X. (2007). On some consistency conditions for geostatistical change-of-support models. Mathematical Geology, 39(2), 205–223. https://doi.org/10.1007/s11004-006-9073-5.

    Article  Google Scholar 

  • Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327, 828–831. https://doi.org/10.1126/science.1183899.

    Article  CAS  PubMed  Google Scholar 

  • Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Applied geostatistics series. New York, USA: Oxford University Press.

  • Gotway, C. A., & Young, L. J. (2002). Combining incompatible spatial data. Journal of the American Statistical Association, 97(458), 632–648. https://doi.org/10.1198/016214502760047140.

    Article  Google Scholar 

  • Huang, H. (2005). Depth of investigation for small broadband electromagnetic sensors. Geophysics, 70(6), G135. https://doi.org/10.1190/1.2122412.

    Article  Google Scholar 

  • Jackson, J. E. (2003). A user’s guide to principal components. New York, USA: Wiley.

    Google Scholar 

  • Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. London, UK: Academic Press.

    Google Scholar 

  • Longobardi, A., Buttafuoco, G., Caloiero, T., & Coscarelli, R. (2016). Spatial and temporal distribution of precipitation in a Mediterranean area (southern Italy). Environmental Earth Sciences, 75(3), 1–20. https://doi.org/10.1007/s12665-015-5045-8.

    Article  Google Scholar 

  • Martens, H., & Næs, T. (1989). Multivariate calibration. New York, USA: Wiley.

    Google Scholar 

  • Matheron, G. (1973). The intrinsic random functions and their applications. Advances in Applied Probability, 5(03), 439–468. https://doi.org/10.2307/1425829.

    Article  Google Scholar 

  • Matheron, G. (1971). The theory of regionalized variables and its applications. Les Cahiers du Centre de Morphlogie Mathematique (Vol. 5). Fontainebleau, France: Ecole Nationale Superieure des Mines de Paris.

  • Matheron, G. (1976). Forecasting block grade distributions: the transfer functions. In M. Guarascio, M. David, & C. J. Huijbregts (Eds.), Advanced geostatistics in the mining industry (pp. 237–251). Dordrecht, The Netherlands: Springer.

    Chapter  Google Scholar 

  • McBratney, A. B., Minasny, B., & Whelan, B. (2011). Defining proximal soil sensing. In V. I. Adamchuk & R. A. Viscarra Rossel (Eds.), The Second Global Workshop on Proximal Soil Sensing (pp. 144–146). Montreal, Canada: McGill University.

    Google Scholar 

  • Mulla, D. J. (2017). Spatial variability in precision agriculture. In S. Shashi, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS (pp. 2118–2125). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-23519-6_1652-1.

  • Myers, D. E. (1984). Co-Kriging—New developments. In G. Verly, M. David, A. G. Journel, & A. Marechal (Eds.), Geostatistics for natural resources characterization (pp. 295–305). Dordrecht, The Netherlands: Springer. https://doi.org/10.1007/978-94-009-3699-7_18.

  • Nanni, M. R., & Demattê, J. A. M. (2006). Spectral reflectance methodology in comparison to traditional soil analysis. Soil Science Society of America Journal, 70(2), 393–407. https://doi.org/10.2136/sssaj2003.0285.

    Article  CAS  Google Scholar 

  • Nelson, D. W., & Sommers, L. E. (1996). Total carbon, organic carbon, and organic matter. In D. L. Sparks (Ed.), Methods of soil analysis. Part 3. Chemical methods (pp. 961–1010). Madison, WI: American Society of Agronomy and Soil Science Society of America.

    Google Scholar 

  • Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., et al. (2015). Soil spectroscopy: An alternative to wet chemistry for soil monitoring. Advances in Agronomy, 132, 139–159. https://doi.org/10.1016/bs.agron.2015.02.002.

    Article  Google Scholar 

  • Olsen, S. R., & Sommers, L. E. (1982). Phosphorus. In A. L. Page, R. H. Miller, & D. R. Keeney (Eds.), Methods of soil analysis. Part 2. Chemical and microbiological properties (pp. 403–430). Madison, WI: American Society of Agronomy and Soil Science Society of America.

    Google Scholar 

  • Rivoirard, J., Freulon, X., Demange, C., & Lecureuil, A. (2014). Kriging, indicators, and nonlinear geostatistics. Journal of the Southern African Institute of Mining and Metallurgy, 114(3), 245–250.

    Google Scholar 

  • Rivoirard, J. (1994). Introduction to disjunctive kriging and non-linear geostatistics. Oxford, UK: Clarendon Press.

    Google Scholar 

  • Rivoirard, J. (2001). Which models for collocated cokriging? Mathematical Geology, 33(2), 117–131. https://doi.org/10.1023/A:1007530600152.

    Article  Google Scholar 

  • Romesburg, H. C. (2011) Cluster analysis: An introduction. In Lovric M (Ed.), International encyclopedia of statistical science (pp. 262–265). Berlin, Heidelberg, Germany: Springer.

  • Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010). Chapter Five—Visible and near infrared spectroscopy in soil science. In Advances in agronomy (Vol. 107, pp. 163–215). https://doi.org/10.1016/S0065-2113(10)07005-7.

  • Sudduth, K. A., Kitchen, N. R., Wiebold, W. J., Batchelor, W. D., Bollero, G. A., Bullock, D. G., et al. (2005). Relating apparent electrical conductivity to soil properties across the north-central USA. Computers and Electronics in Agriculture, 46(1–3), 263–283. https://doi.org/10.1016/j.compag.2004.11.010.

    Article  Google Scholar 

  • Thomas, G. W. (1982). Exchangeable cations. In A. L. Page, R. H. Miller, & D. R. Keeney (Eds.), Methods of soil analysis. Part 2. Chemical and microbiological properties (pp. 159–165). Madison, WI: American Society of Agronomy and Soil Science Society of America.

    Google Scholar 

  • Vågen, T. G., Shepherd, K. D., & Walsh, M. G. (2006). Sensing landscape level change in soil fertility following deforestation and conversion in the highlands of Madagascar using Vis-NIR spectroscopy. Geoderma, 133(3–4), 281–294. https://doi.org/10.1016/j.geoderma.2005.07.014.

    Article  CAS  Google Scholar 

  • Viscarra Rossel, R. A., & Behrens, T. (2010). Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158(1–2), 46–54. https://doi.org/10.1016/j.geoderma.2009.12.025.

    Article  CAS  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), 59–75. https://doi.org/10.1016/j.geoderma.2005.03.007.

    Article  CAS  Google Scholar 

  • Wackernagel, H. (2003). Multivariate geostatistics: an introduction with applications. Berlin, Germany: Springer.

    Book  Google Scholar 

  • Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. Chichester, UK: Wiley. https://doi.org/10.1002/9780470517277.

  • Zovko, M., Romić, D., Colombo, C., Di Iorio, E., Romić, M., Buttafuoco, G., et al. (2018). A geostatistical Vis-NIR spectroscopy index to assess the incipient soil salinization in the Neretva River valley, Croatia. Geoderma, 332, 60–72. https://doi.org/10.1016/j.geoderma.2018.07.005.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Financial support for this work comes from the project “M2Q” PON03PE_00180_1 co-funded by the National Operational Program for Research and Competitiveness (PON R&C) 2007–2013, through the European Regional Development Fund (ERDF) and national resource (Revolving Fund—Cohesion Action Plan MIUR). D.M. MIUR n. 738/05.03.2014. The authors thank the reviewers of this paper and Dr. John Stafford for providing constructive comments, which have contributed to the improvement of the published version.

Author information

Authors and Affiliations

  1. National Research Council of Italy - Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM), 87036, Rende, CS, Italy

    G. Buttafuoco & A. Castrignanò

  2. Earth and Geoenvironmental Sciences Department, University of Bari Aldo Moro, Bari, Italy

    R. Quarto

  3. PRO-GEO S.a.S., 76121, Barletta, Italy

    F. Quarto

  4. National Research Council of Italy - Research Institute for Geo-Hydrological Protection (IRPI), 87036, Rende, CS, Italy

    M. Conforti

  5. CREA Research Centre for Vegetable and Ornamental Crops, 84098, Pontecagnano, SA, Italy

    A. Venezia

  6. CREA Research Centre for Agriculture and Environment, 70125, Bari, Italy

    C. Vitti

Authors
  1. G. Buttafuoco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Quarto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Quarto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Conforti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Venezia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. C. Vitti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. A. Castrignanò
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Buttafuoco.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buttafuoco, G., Quarto, R., Quarto, F. et al. Taking into account change of support when merging heterogeneous spatial data for field partition. Precision Agric 22, 586–607 (2021). https://doi.org/10.1007/s11119-020-09781-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-020-09781-9

Keywords

Search

Navigation