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Delineation of specific management areas for coffee cultivation based on the soil–relief relationship and numerical classification

Abstract

Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31.7 ha from which samples were collected at depths of 0.00–0.20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30.4l ha−1) and best coffee beverage quality (61 sacks ha−1). Based on the results, we believe that multivariate analysis, geostatistics and the soil–relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production.

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References

  1. Alves, M. C. A., Silva, F. M., Moraes, J. C., Pozza, E. A., Oliveira, M. S., Souza, J. C. S., et al. (2011). Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee agroecosystem. Precision Agriculture, 12(1), 18–31.

    Article  Google Scholar 

  2. Assad, E. D., Pinto, H. S., Zullo, J, Jr., & Ávila, A. M. H. (2004). Impacto das mudanças climáticas no zoneamento agroclimático do café no Brasil. Pesquisa Agropecuária Brasileira, 39(11), 1057–1064.

    Article  Google Scholar 

  3. Barbieri, D. M., Marques, J, Jr., Alleoni, L. R. F., Garbuio, F. J., & Camargo, L. A. (2009). Hillslope curvature, clay mineralogy, and phosphorus adsorption in an Alfisol cultivated with sugarcane. Scientia Agricola, 66(6), 819–826.

    Article  CAS  Google Scholar 

  4. Bogaert, P., & D’or, D. (2002). Estimating soil properties from thematic soil maps, the bayesian maximum entropy approach. Soil Science Society of America Journal, 66(5), 1492–1500.

    Article  CAS  Google Scholar 

  5. Brito, L. F., Marques, J, Jr., Pereira, G. T., & Souza, Z. M. (2009). Soil CO2 emission of sugarcane fields as affected by topography. Scientia Agricola, 66(1), 77–83.

    Article  CAS  Google Scholar 

  6. Brunner, A. C., Park, S. J., Ruecker, G. R., Dikau, R., & Vlek, P. L. G. (2004). Catenary soil development influencing erosion susceptibility along a hillslope in Uganda. Catena, 58(1), 1–22.

    Article  Google Scholar 

  7. Bundt, M., Kretzschmar, S., Zech, W., & Wilcke, W. (1997). Seasonal dynamics of nutrients in leaves and xylem sap of coffee plants as related to different soil compartments. Plant and Soil, 197(1), 157–166.

    Article  CAS  Google Scholar 

  8. Camargo, L. A., Marques, J, Jr., Pereira, G. T., & Horvat, R. A. (2008). Variabilidade espacial de atributos mineralógicos de um Latossolo sob diferentes formas de relevo. I-Mineralogia da fração argila. Revista Brasileira de Ciência do Solo, 32(6), 2269–2277 (in Portuguese).

    Article  CAS  Google Scholar 

  9. 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(5), 1501–1511.

    Article  Google Scholar 

  10. Caramori, P. H., Caviglione, J. H., Wrege, M. S., Gonçalves, S. L., Faria, R. T., Androcioli Filho, A., et al. (2001). Climatic risk zoning for coffee (Coffea arabica L.) in Paraná state, Brazil. Revista Brasileira de Agrometeorologia, 9(3), 486–494.

    Google Scholar 

  11. Carvalho, W, Jr., Schaefer, C. E. G. R., Chagas, C. S. Y., & Fernandes Filho, E. I. (2008). Análise multivariada de Argissolos da faixa atlântica brasileira. Revista Brasileira de Ciência do Solo, 32(1), 2081–2090 (in Portuguese).

    Article  Google Scholar 

  12. Carvalho, L. G., Sediyama, G. C., Cecon, P. R., & Alves, H. M. R. (2004). Modelo de regressão para a previsão de produdividade de cafeeiros no Estado de Minas Gerais. Revista Brasileira de Engenharia Agrícola e Ambiental, 8(2–3), 204–211 (in Portuguese).

    Article  Google Scholar 

  13. Cooley, W. W., & Lohnes, P. R. (1971). Multivariate data analysis. New York: Wiley.

    Google Scholar 

  14. Embrapa—Empresa Brasileira de Pesquisa Agropecuária. Centro Nacional de Pesquisa de Solos. (1999). Sistema Brasileiro de Classificação de Solos. Rio de Janeiro, p. 412 (i.e. in Portuguese).

  15. Evangelista, A. W. P., Carvalho, L. G., & Sediyama, G. C. (2002). Zoneamento climático associado ao potencial produtivo da cultura do café no Estado de Minas Gerais. Revista Brasileira de Engenharia Agrícola e Ambiental, 6(3), 445–452 (in Portuguese).

    Article  Google Scholar 

  16. Islam, K., Mcbratney, A., & Singh, B. (2005). Rapid estimation of soil variability from the convex hull biplot area of topsoil ultra-violet, visible and near-infrared diffuse reflectance spectra. Geoderma, 128(3–4), 249–257.

    Article  CAS  Google Scholar 

  17. Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  18. Leão, M. G. A., Marques, J, Jr., Souza, Z. M., Siqueira, D. S., & Pereira, G. T. (2010). O relevo na interpretação da variabilidade espacial dos teores de nutrientes em folha de citros. Revista Brasileira de Engenharia Agricola e Ambiental, 14(11), 1152–1159 (in Portuguese).

    Article  Google Scholar 

  19. Legros, J. P. (2006). Mapping of the soil. Translated from French by Sarma, V. A. K. Enfield: New Hampshire. Science Publishers, 411p.

  20. Martín, N. F., Bollero, G. A., & Bullock, D. G. (2005). Associations between field characteristics and soybean plant performance using canonical correlation analysis. Plant and Soil, 273(1–2), 39–55.

    Article  Google Scholar 

  21. Maule, R. F., Mazza, J. A., & Martha, G. B., Jr. (2001). Productivity of sugarcane cultivars in different soils and harvesting periods. Scientia Agrícola, 58(2), 295–301 (i.e. in Portuguese).

    Google Scholar 

  22. McBratney, A. B., Odeh, I. O. A., Bishop, T. F. A., Dunbar, M. S., & Shatar, T. M. (2000). An overview of pedometric techniques for use in soil survey. Geoderma, 97(3–4), 293–327.

    Article  Google Scholar 

  23. Mcbratney, A. B., & Webster, R. (1986). Choosing functions for semi-variograms of soil properties and fitting them to sampling estimates. Soil Science Society of America Journal, 37(4), 617–639.

    Google Scholar 

  24. Montanari, R., Souza, G. S. A., Pereira, G. T., Marques, J, Jr., Siqueira, D. S., & Siqueira, G. M. (2012). The use of scaled semivariograms to plan soil sampling in sugarcane fields. Precision Agriculture, 13(5), 542–552.

    Article  Google Scholar 

  25. Montgomery, D. R. (2003). Predicting landscape scale erosion rates using digital elevation models. Comptes Rendus Geoscience, 335(16), 1121–1130.

    Article  Google Scholar 

  26. Nix, H. (1968). The assessment of biological productivity. In, Land Evaluation, Papers on a SCIRO Symposium G. A. Stewart, Ed., Macmillan of Australia, pp. 77–87.

  27. Odeh, I. O. A., Chittleborough, D. J., & Mcbratney, A. B. (1991). Elucidation of soil–landform interrelationships by canonical ordination analysis. Geoderma, 49(1–2), 1–32.

    Article  Google Scholar 

  28. Odlare, M., Svensson, K., & Pellb, M. (2005). Near infrared reflectance spectroscopy for assessment of spatial soil variation in an agricultural field. Geoderma, 126(3–4), 193–202.

    Article  CAS  Google Scholar 

  29. Officer, S. J., Kravchenko, A., Bollero, G. A., Sudduth, K. A., Kitchen, N. R., Wiebold, W. J., et al. (2007). Caracterização geofísica do solo para uso em agricultura de precisão. Revista Brasileira de Geofisica, 25(3), 340.

    Article  Google Scholar 

  30. Panosso, A. R., Marques, J, Jr., Pereira, G. T., Jr, & La Scala, N. (2009). Spatial and temporal variability of soil CO2 emission in a sugarcane area under green and slash-and-burn managements. Soil and Tillage Research, 105(2), 275–282.

    Article  Google Scholar 

  31. Pennock, D. J. (2003). Terrain attributes, landform segmentation, and soil redistribution. Soil and Tillage Research, 69(1–2), 15–26.

    Article  Google Scholar 

  32. Römheld, V., & Marschner, H. (1983). Mechanism of iron uptake by peanut plants. I. Fe(III) reduction, chelate splitting, and release of phenolics. Plant Physiology, 71(4), 949–954.

    Article  PubMed  Google Scholar 

  33. Sanchez, R. B., Marques, J, Jr., Pereira, G. T., & Souza, Z. M. (2005). Variabilidade espacial de propriedades de Latossolo e da produção de café em diferentes superfícies geomórficas. Revista Brasileira de Engenharia Agrícola e Ambiental, 9(4), 489–495 (in Portuguese).

    Article  Google Scholar 

  34. Sanchez, R. B., Marques, J, Jr., Pereira, G. T., Souza, Z. M., & Martins Filho, M. V. (2009). Variabilidade espacial de atributos do solo e de fatores de erosão em diferentes pedoformas. Bragantia, 68(4), 873–884 (in Portuguese).

    Article  Google Scholar 

  35. Schwertmann, U. (1991). Solubility and dissolution of iron oxides. Plant and Soil, 130(1–2), 1–25.

    Article  CAS  Google Scholar 

  36. Silva, F. M., Menezes, Z., Pereira, C. A., Vieira, L. H., & Oliveira, E. (2008). Spatial variability of chemical attributes and coffee productivity in two harvests. Ciência Agrotecnica, 32, 231–241.

    Article  Google Scholar 

  37. Siqueira, D. S., Marques, J, Jr., Matias, S. S. R., Barrón, V., Torrent, J., Baffa, O., et al. (2010a). Correlation of properties of Brazilian Haplustalfs with magnetic susceptibility measurements. Soil Use and Management, 26(4), 425–431.

    Article  Google Scholar 

  38. Siqueira, D. S., Marques, J, Jr., & Pereira, G. T. (2010b). The use of landforms to predict the variability of soil and orange attributes. Geoderma, 155(1–2), 55–66.

    Article  Google Scholar 

  39. Specialty Coffee Association of America. (2009). Cupping Protocols. http://www.coffeeinstitute.org/resources/scaa-standards-and-protocols. Accessed Oct 18 2012.

  40. Uchimiya, M., & Stone, A. T. (2006). Redox reactions between iron and quinines: Thermodynamic constraints. Geochimica et Cosmochimica Acta, 70(6), 1388–1401.

    Article  CAS  Google Scholar 

  41. Vieira, S. R., Hatfield, J. L., Nielsen, D. R., & Biggar, J. W. (1983). Geostatistical theory and application to variability of some agronomical properties. Hilgardia, 51(3), 1–75.

    Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. Weill, M. A. M., Arruda, F. B., Oliveira, J. B., Donzeli, P. L., & Van Raij, B. (1999). Avaliação de fatores edafoclimáticos e do manejo na produção de cafeeiros (Coffea arabica L.) no oeste Paulista. Revista Brasileira de Ciência do Solo, 23(4), 891–901 (in Portuguese).

    CAS  Google Scholar 

  44. Zhang, H., & Zhang, G. L. (2005). Landscape-scale soil quality change under different farming systems of a tropical farm in Hainan China. Soil Use and Management, 21(1), 58–64.

    Article  CAS  Google Scholar 

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Correspondence to Diego Silva Siqueira.

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Sanchez, M.G.B., Marques, J., Siqueira, D.S. et al. Delineation of specific management areas for coffee cultivation based on the soil–relief relationship and numerical classification. Precision Agric 14, 201–214 (2013). https://doi.org/10.1007/s11119-012-9288-z

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Keywords

  • Drink quality
  • Spatial variability
  • Multivariate analysis