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Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field

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Abstract

The paper proposes a geostatistical approach for delineating management zones (MZs) based on multivariate geostatistics, showing the use of polygon kriging to compare durum wheat yield among the different MZs (polygons). The study site was a durum wheat field in southern Italy and yield was measured over three crop seasons. The first regionalized factor, calculated with factorial cokriging, was used to partition the field into three iso-frequency classes (MZs). For each MZ, the expected value and standard deviation of yield were estimated with polygon kriging over the three crop seasons. The yield variation was only in part related to soil properties but most of it might be ascribable to different patterns of meteorological conditions. Both components of variation (plant and soil) in a cropping system should then be taken into account for an effective management of rainfed durum wheat in precision agriculture. The proposed approach proved multivariate Geostatistics to be effective for MZ delineation even if further testing is required under different cropping systems and management.

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References

  • Barca, E., Castrignanò, A., Buttafuoco, G., De Benedetto, D., & Passarella, G. (2015). Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing. Environmental Monitoring and Assessment, 187(1), 187–422.

    Google Scholar 

  • Bleines, C., Deraisme, J., Geoffrey, F., Jeannée, N., Perseval, S., Rambert, F., et al. (2010). Isatis software manual (10th ed.). Avon: Géovariances & Ecole des Mines de Paris.

    Google Scholar 

  • Buttafuoco, G., Castrignanò, A., Colecchia, A. S., & Ricca, N. (2010). Delineation of management zones using soil properties and a multivariate geostatistical approach. Italian Journal of Agronomy, 5, 323–332.

    Article  Google Scholar 

  • Buttafuoco, G., Castrignanò, A., Cucci, G., Rinaldi, M., & Ruggieri, S. (2015). An approach to delineate management zones in a durum wheat field: Validation using remote sensing and yield mapping. In J. V. Stafford (Ed.), Proceedings of the 10th European Conference on Precision Agriculture, Precision Agriculture’15 (pp. 241–247). The Netherlands: Wageningen Academic Publishers. doi:10.3920/978-90-8686-814-8.

  • 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, 39–60.

    Article  Google Scholar 

  • Chilès, J.-P., & Delfiner, P. (2012). Geostatistics: modeling spatial uncertainty (2nd ed.). New York: Wiley.

    Book  Google Scholar 

  • Diacono, M., Castrignanò, A., Troccoli, A., De Benedetto, D., Basso, B., & Rubino, P. (2012). Spatial and temporal variability of wheat grain yield and quality in a Mediterranean environment: a multivariate geostatistical approach. Field Crops Research, 13, 49–62.

    Article  Google Scholar 

  • Diacono, M., Castrignanò, A., Vitti, C., Stellacci, A. M., Marino, L., Cocozza, C., et al. (2014). An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture. Precision Agriculture, 15, 479–498.

    Article  Google Scholar 

  • Doerge, T. A. (1999). Management zone concepts [Online]. Available at http://www.ipni.net/publication/ssmg.nsf/0/C0D052F04A53E0BF852579E500761AE3/$FILE/SSMG-02.pdf. Accessed 14 June 2016.

  • Frogbrook, Z. L., & Oliver, M. A. (2007). Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data. Soil Use and Management, 23, 40–51.

    Article  Google Scholar 

  • Geovariances. (2015). ISATIS software: technical references release 2015 (p. 220). Paris: Geovariances and Ecole des Mines de Paris.

    Google Scholar 

  • Goovaerts, P. (1992). Factorial kriging analysis: a useful tool for exploring the structure of multivariate spatial soil information. Journal of Soil Science, 43, 597–619.

    Article  Google Scholar 

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

    Google Scholar 

  • Goovaerts, P. (2008). Kriging and semivariograms deconvolution in the presence of irregular geographical units. Mathematical Geosciences, 40, 101–128. doi:10.1007/s11004-007-9129-1.

    Article  Google Scholar 

  • Goovaerts, P., Jacquez, G. M., & Greilling, D. (2005). Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms: a simulation study. Geographical Analysis, 37(2), 152–182.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jiang, H.-L., Liu, G.-S., Liu, S.-D., Li, E.-H., Wang, R., Yang, Y.-F., et al. (2012). Delineation of site-specific management zones based on soil properties for a hillside field in central China. Archives of Agronomy and Soil Science, 58, 1075–1090.

    Article  CAS  Google Scholar 

  • Khosla, R., & Shaver, T. (2001). Zoning in on nitrogen needs. Colorado State University Agronomy Newsletter, 21, 24–26.

    Google Scholar 

  • Matheron, G. (1982). Pour une analyse krigeante des données régionalisées (For a kriging analysis of regionalised data). Report N-732, Centro de Géostatistiques. Fontainebleau: École des Mines de Paris.

  • Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F., & MacDonald, L. (2005). Spatial variability of measured soil properties across site-specific management zones. Soil Science Society of America Journal, 69, 1572–1579.

    Article  CAS  Google Scholar 

  • Oliver, M. A., Webster, R., Lajaunie, C., Muir, K. R., Parkes, S. E., Cameron, A. H., et al. (1998). Binomial cokriging for estimating and mapping the risk of childhood cancer. IMA Journal of Mathematics Applied in Medicine and Biology, 15(3), 279–297.

    Article  CAS  PubMed  Google Scholar 

  • Pagliai, M. (Ed.). (1997). Metodi di Analisi Fisica del Suolo (Physical Methods of Soil Analysis). Milan: Italian Ministry of Agriculture, Franco Angeli. (in Italian).

    Google Scholar 

  • Soil Survey Staff. (2010). Keys to soil taxonomy (11th ed.). Washington, DC: USDA, US Department of Agriculture, Natural Resources Conservation Service.

    Google Scholar 

  • Violante, P. (Ed.). (2000). Metodi di Analisi Chimica del Suolo (Chemical Methods of Soil Analysis). Milan: Italian Ministry of Agriculture. Franco Angeli. (in Italian).

    Google Scholar 

  • Wackernagel, H. (2003). Multivariate Geostatistics: an introduction with applications (3rd ed., p. 388). Berlin: Springer-Verlag.

    Book  Google Scholar 

  • Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (2nd ed., p. 330). Chichester: Wiley.

    Book  Google Scholar 

  • Zarco-Tejada, P. J., Hubbard, N., Loudjani, P. (2014). Precision agriculture: an opportunity for EU farmers-potential support with the CAP 2014-2020: in-depth analysis. [Online]. Available at http://www.europarl.europa.eu/RegData/etudes/note/join/2014/529049/IPOL-AGRI_NT%282014%29529049_EN.pdf. Accessed 14 June 2016.

  • Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture: a worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132.

    Article  Google Scholar 

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

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  • DOI: https://doi.org/10.1007/s11119-016-9462-9

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