Advertisement

Precision Agriculture

, Volume 14, Issue 2, pp 201–214 | Cite as

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

  • Maria Gabriela Baracat Sanchez
  • José MarquesJr.
  • Diego Silva SiqueiraEmail author
  • Livia Arantes Camargo
  • Gener Tadeu Pereira
Article

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.

Keywords

Drink quality Spatial variability Multivariate analysis 

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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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).CrossRefGoogle 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.CrossRefGoogle 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).CrossRefGoogle 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).CrossRefGoogle 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).Google Scholar
  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).CrossRefGoogle 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.CrossRefGoogle 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).CrossRefGoogle Scholar
  19. Legros, J. P. (2006). Mapping of the soil. Translated from French by Sarma, V. A. K. Enfield: New Hampshire. Science Publishers, 411p.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  25. Montgomery, D. R. (2003). Predicting landscape scale erosion rates using digital elevation models. Comptes Rendus Geoscience, 335(16), 1121–1130.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  31. Pennock, D. J. (2003). Terrain attributes, landform segmentation, and soil redistribution. Soil and Tillage Research, 69(1–2), 15–26.CrossRefGoogle 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.CrossRefPubMedGoogle 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).CrossRefGoogle 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).CrossRefGoogle Scholar
  35. Schwertmann, U. (1991). Solubility and dissolution of iron oxides. Plant and Soil, 130(1–2), 1–25.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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).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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Maria Gabriela Baracat Sanchez
    • 1
  • José MarquesJr.
    • 1
  • Diego Silva Siqueira
    • 1
    Email author
  • Livia Arantes Camargo
    • 1
  • Gener Tadeu Pereira
    • 2
  1. 1.Dept. Solos e Adubos, Faculdade de Ciências Agrárias e VeterináriasUnesp - Univ Estadual PaulistaJaboticabalBrazil
  2. 2.Dept. Ciências Exatas, Faculdade de Ciências Agrárias e VeterináriasUnesp - Univ Estadual PaulistaJaboticabalBrazil

Personalised recommendations