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Precision Agriculture

, Volume 20, Issue 4, pp 746–766 | Cite as

Synthesis of proximal sensing, terrain analysis, and parent material information for available micronutrient prediction in tropical soils

  • M. H. P. Pelegrino
  • D. C. WeindorfEmail author
  • S. H. G. Silva
  • M. D. de Menezes
  • G. C. Poggere
  • L. R. G. Guilherme
  • N. Curi
Article

Abstract

In developing countries, the use of proximal and remotely sensed data is of critical importance as a less expensive means of obtaining soils information. While proximal sensor approaches such as portable X-ray fluorescence (pXRF) spectrometry are becoming increasingly used to predict soil properties worldwide, remotely sensed data has also been used for terrain analysis in recent decades with the aid of powerful interpretive algorithms. The aims of this work were to apply a random forest algorithm to model and predict the available contents of Fe, Cu, Mn, and Zn from pXRF data in addition to terrain attributes (TAs) with 5 and 10 m spatial resolution and parent material information. The data were used separately and together in an area with high variability of parent materials. Soil samples (n = 153) were collected, analyzed by pXRF, and subjected to laboratory analyses to determine the available contents of Fe, Cu, Mn, and Zn. Twelve TAs were generated from digital elevation models (DEM). These data were divided into five datasets (or random forest inputs): pXRF data; TA 5 m data; TA 10 m; pXRF + TA 5 m; and pXRF + TA 10 m. Predictions were performed to assess the importance of such variables. Models were validated with an independent set of samples. Finally, the best models were spatially rendered to cover the entire study area and maps were also validated. The combination of pXRF data and TA covariates in addition to parent material information allowed accurate predictions of available Fe, Mn, Cu, and Zn through the random forest algorithm. Parent material information improved the predictions. Pixel size of 10 m resolution promoted better results than 5 m resolution. Available Fe contents were better predicted using only TA data. For the spatial prediction of available micronutrients, validation of maps resulted in R2 of 0.88, RMSE of 59.97 mg kg−1 and ME of 24.00 mg kg−1 for Fe; 0.85, 29.65 mg kg−1, 9.70 mg kg−1 for Mn, 0.64, 3.11 mg kg−1, 0.71 mg kg−1 for Zn and 0.82, 1.17 mg kg−1, 0.43 mg kg−1 for Cu, respectively. Available micronutrient contents can be accurately predicted using pXRF data in association with terrain and parent material information.

Keywords

Portable X-ray fluorescence spectrometer Random forest Soil property prediction Tropical soils Brazil 

Notes

Acknowledgements

The authors would like to thank CNPq, CAPES and FAPEMIG Brazilian agencies for providing the financial support necessary for carrying out this work. The authors gratefully acknowledge the contributions of the BL Allen Endowment in Pedology at Texas Tech University in conducting this research.

References

  1. Adhikari, K., Kheir, R. B., Greve, M. B., Bøcher, P. K., Malone, B. P., Minasny, B., et al. (2013). High-resolution 3-D mapping of soil texture in Denmark. Soil Science Society of America Journal, 77(3), 860.  https://doi.org/10.2136/sssaj2012.0275.CrossRefGoogle Scholar
  2. Akpa, S. I. C., Odeh, I. O. A., Bishop, T. F. A., & Hartemink, A. E. (2014). Digital mapping of soil particle-size fractions for Nigeria. Soil Science Society of America Journal, 78(6), 1953–1966.  https://doi.org/10.2136/sssaj2014.05.0202.CrossRefGoogle Scholar
  3. Araujo, M. A., Pedroso, A. V., Amaral, D. C., & Zinn, Y. L. (2014). Paragênese mineral de solos desenvolvidos de diferentes litologias na região sul de Minas Gerais. Revista Brasileira de Ciencia do Solo, 38(1), 11–25.  https://doi.org/10.1590/S0100-06832014000100002.CrossRefGoogle Scholar
  4. Arrouays, D., Lagacherie, P., & Hartemink, A. E. (2017). Digital soil mapping across the globe. Geoderma Regional, 9, 1–4.  https://doi.org/10.1016/j.geodrs.2017.03.002.CrossRefGoogle Scholar
  5. Bhering, S. B., da Chagas, C., Junior, W., Pereira, N. R., Filho, B. C., & Pinheiro, H. S. K. (2016). Mapeamento digital de areia, argila e carbono orgânico por modelos Random Forest sob diferentes resoluções espaciais. Pesquisa Agropecuaria Brasileira, 51(9), 1359–1370.  https://doi.org/10.1590/S0100-204X2016000900035.CrossRefGoogle Scholar
  6. Bishop, T. F. A., Horta, A., & Karunaratne, S. B. (2015). Validation of digital soil maps at different spatial supports. Geoderma, 242, 238–249.CrossRefGoogle Scholar
  7. Borghi, E., Avanzi, J. C., Bortolon, L., Luchiari Junior, A., & Bortolon, E. S. O. (2016). Adoption and use of precision agriculture in Brazil: Perception of growers and service dealership. Journal of Agricultural Science, 8(11), 89.  https://doi.org/10.5539/jas.v8n11p89.CrossRefGoogle Scholar
  8. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.  https://doi.org/10.1023/A:1010933404324.CrossRefGoogle Scholar
  9. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees (1st ed.). New York: Chapman and Hall/CRC.Google Scholar
  10. Camargo, F. A. D. O., Santos, G. D. A., & Zonta, E. (1999). Alterações eletroquímicas em solos inundados. Ciência Rural, 29(1), 171–180.  https://doi.org/10.1590/S0103-84781999000100032.CrossRefGoogle Scholar
  11. Carvalho Junior, W., Calderano Filho, B., Chagas, C. D. S., Bhering, S. B., Pereira, N. R., & Pinheiro, H. S. K. (2016). Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas. Pesquisa Agropecuaria Brasileira, 51(9), 1428–1437.  https://doi.org/10.1590/s0100-204x2016000900041.CrossRefGoogle Scholar
  12. Cavazzi, S., Corstanje, R., Mayr, T., Hannam, J., & Fealy, R. (2013). Are fine resolution digital elevation models always the best choice in digital soil mapping? Geoderma, 195–196, 111–121.  https://doi.org/10.1016/j.geoderma.2012.11.020.CrossRefGoogle Scholar
  13. Chakraborty, S., Man, T., Paulette, L., Deb, S., Li, B., Weindorf, D. C., et al. (2017). Rapid assessment of smelter/mining soil contamination via portable X-ray fluorescence spectrometry and indicator kriging. Geoderma, 306(June), 108–119.  https://doi.org/10.1016/j.geoderma.2017.07.003.CrossRefGoogle Scholar
  14. Collard, F., Kempen, B., Heuvelink, G. B. M., Saby, N. P., Richer de Forges, A. C., Lehmann, S., et al. (2014). Refining a reconnaissance soil map by calibrating regression models with data from the same map (Normandy, France). Geoderma Regional, 1, 21–30.  https://doi.org/10.1016/j.geodrs.2014.07.001.CrossRefGoogle Scholar
  15. Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., et al. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7), 1991–2007.  https://doi.org/10.5194/gmd-8-1991-2015.CrossRefGoogle Scholar
  16. Curi, N., Silva, S. H. G., Poggere, G. C., & de Menezes, M. D. (2017). Mapeamento de solos e magnetismo no campus da UFLA como traçadores ambientais (1st ed.). Lavras: Editora UFLA.Google Scholar
  17. da Chagas, S. C., de Carvalho Junior, W., Bhering, S. B., & Calderano Filho, B. (2016). Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. Catena, 139, 232–240.  https://doi.org/10.1016/j.catena.2016.01.001.CrossRefGoogle Scholar
  18. Dantas, A. A. A., de Carvalho, L. G., & Ferreira, E. (2007). Classificação e tendências climáticas em Lavras, MG. Ciência e Agrotecnologia, 31(6), 1862–1866.  https://doi.org/10.1590/S1413-70542007000600039.CrossRefGoogle Scholar
  19. De Menezes, M. D., Silva, S. H. G., De Mello, C. R., Owens, P. R., & Curi, N. (2016). Spatial prediction of soil properties in two contrasting physiographic regions in Brazil. Scientia Agricola, 73(3), 274–285.  https://doi.org/10.1590/0103-9016-2015-0071.CrossRefGoogle Scholar
  20. de Menezes, M. D., Silva, S. H. G., de Mello, C. R., Owens, P. R., & Curi, N. (2018). Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds. Scientia Agricola, 75(2), 144–153.  https://doi.org/10.1590/1678-992x-2016-0097.CrossRefGoogle Scholar
  21. Duda, B. M., Weindorf, D. C., Chakraborty, S., Li, B., Man, T., Paulette, L., et al. (2017). Soil characterization across catenas via advanced proximal sensors. Geoderma, 298, 78–91.  https://doi.org/10.1016/j.geoderma.2017.03.017.CrossRefGoogle Scholar
  22. Fageria, N., & Stone, L. (2008). Micronutrient deficiency problems in South America. In Brian J. Alloway (Ed.), Micronutrient deficiencies in global crop production (pp. 245–266). Dordrecht: Springer.CrossRefGoogle Scholar
  23. Florinsky, I., Eilers, R., Manning, G., & Fuller, L. (2002). Prediction of soil properties by digital terrain modelling. Environmental Modelling & Software, 17(3), 295–311.  https://doi.org/10.1016/S1364-8152(01)00067-6.CrossRefGoogle Scholar
  24. Forkuor, G., Hounkpatin, O. K. L., Welp, G., & Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in South-Western Burkina Faso: A comparison of machine learning and multiple linear regression models. PLoS ONE, 12(1), e0170478.  https://doi.org/10.1371/journal.pone.0170478.CrossRefGoogle Scholar
  25. Gessler, P. E., Moore, I. D., McKenzie, N. J., & Ryan, P. J. (1995). Soil-landscape modelling and spatial prediction of soil attributes. International journal of geographical information systems, 9(4), 421–432.  https://doi.org/10.1080/02693799508902047.CrossRefGoogle Scholar
  26. Giasson, E., Clarke, R. T., Inda Junior, A. V., Merten, G. H., & Tornquist, C. G. (2006). Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil. Scientia Agricola.  https://doi.org/10.1590/S0103-90162006000300008.Google Scholar
  27. Gray, J. M., Bishop, T. F. A., & Wilford, J. R. (2016). Lithology and soil relationships for soil modelling and mapping. Catena, 147, 429–440.  https://doi.org/10.1016/j.catena.2016.07.045.CrossRefGoogle Scholar
  28. Hengl, T. (2006). Finding the right pixel size. Computers & Geosciences, 32(9), 1283–1298.  https://doi.org/10.1016/j.cageo.2005.11.008.CrossRefGoogle Scholar
  29. Hengl, T., Leenaars, J. G. B., Shepherd, K. D., Walsh, M. G., Heuvelink, G. B. M., Mamo, T., et al. (2017). Soil nutrient maps of Sub-Saharan Africa: Assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutrient Cycling in Agroecosystems, 109(1), 77–102.  https://doi.org/10.1007/s10705-017-9870-x.CrossRefGoogle Scholar
  30. Heung, B., Ho, H. C., Zhang, J., Knudby, A., Bulmer, C. E., & Schmidt, M. G. (2016). An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma, 265, 62–77.  https://doi.org/10.1016/j.geoderma.2015.11.014.CrossRefGoogle Scholar
  31. Horta, A., Malone, B., Stockmann, U., Minasny, B., Bishop, T. F., et al. (2015). Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review. Geoderma, 241–242, 180–209.  https://doi.org/10.1016/j.geoderma.2014.11.024.CrossRefGoogle Scholar
  32. Hunt, A. M. W., & Speakman, R. J. (2015). Portable XRF analysis of archaeological sediments and ceramics. Journal of Archaeological Science, 53, 628–638.  https://doi.org/10.1016/j.jas.2014.11.031.CrossRefGoogle Scholar
  33. Jackson, M. L. (1958). Soil chemical analysis (1st ed.). Englewood Cliffs: Prentice-Hall Inc.Google Scholar
  34. Jakob, S., Gloaguen, R., & Laukamp, C. (2016). Remote sensing-based exploration of structurally-related mineralizations around Mount Isa, Queensland, Australia. Remote Sensing.  https://doi.org/10.3390/rs8050358.Google Scholar
  35. Kämpf, N., Marques, J. J., & Curi, N. (2012). Mineralogia de Solos Brasileiros. In Pedologia Fundamentos (p. 343). Viçosa, MG: SBCS.Google Scholar
  36. Lacerda, M. P. C., Andrade, H., & Quemeneur, J. J. G. (2002). Pedogeoquimica em perfis de alteração na região de Lavras (MG)—Elementos maiores—óxidos constituintes. Revista Brasileira de Ciência do Solo, 26(3), 87–102.CrossRefGoogle Scholar
  37. Lagacherie, P., & McBratney, A. B. (2006). Spatial soil information systems and spatial soil inference systems: perspectives for digital soil mapping. In P. Lagacherie, A. B. McBratney, & M. Voltz (Eds.), Digital soil mapping an introductory perspective (p. 658). Amsterdam: Elsevier.Google Scholar
  38. Lecours, V., Devillers, R., Simms, A. E., Lucieer, V. L., & Brown, C. J. (2017). Towards a framework for terrain attribute selection in environmental studies. Environmental Modelling and Software, 89, 19–30.  https://doi.org/10.1016/j.envsoft.2016.11.027.CrossRefGoogle Scholar
  39. Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2, 18–22.  https://doi.org/10.1023/A:1010933404324.Google Scholar
  40. Lopes, A. S., & Guilherme, L. R. G. (2016). A career perspective on soil management in the Cerrado region of Brazil. Advances in Agronomy, 137, 1–72.  https://doi.org/10.1016/bs.agron.2015.12.004.CrossRefGoogle Scholar
  41. Marques, J., Jr., Curi, N., & Lima, J. (1992). Evolução diferenciada de Latossolo Vermelho-Amarelo e Latossolo Vermelho em função da litologia gnáissica na região de Lavras (MG). Revista Brasileira de Ciência do Solo, 16, 235–240.Google Scholar
  42. Mashimbye, Z. E., De Clercq, W. P., & Van Niekerk, A. (2014). An evaluation of digital elevation models (DEMs) for delineating land components. Geoderma, 213, 312–319.  https://doi.org/10.1016/j.geoderma.2013.08.023.CrossRefGoogle Scholar
  43. Maynard, J. J., & Johnson, M. G. (2014). Scale-dependency of LiDAR derived terrain attributes in quantitative soil-landscape modeling: Effects of grid resolution vs. neighborhood extent. Geoderma, 230–231, 29–40.  https://doi.org/10.1016/j.geoderma.2014.03.021.CrossRefGoogle Scholar
  44. McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma.  https://doi.org/10.1016/s0016-7061(03)00223-4.Google Scholar
  45. McGladdery, C., Weindorf, D. C., Chakraborty, S., Li, B., Paulette, L., Podar, D., et al. (2018). Elemental assessment of vegetation via portable X-ray fluorescence (PXRF) spectrometry. Journal of Environmental Management, 210, 210–225.CrossRefGoogle Scholar
  46. Mehlich, A. (1953). Determination of P, Ca, Mg, K, Na and NH4 (p. 195). Raleigh: North Carolina Soil Testing Division.Google Scholar
  47. Mokarram, M., & Hojati, M. (2017). Morphometric analysis of stream as one of resources for agricultural lands irrigation using high spatial resolution of digital elevation model (DEM). Computers and Electronics in Agriculture, 142, 190–200.  https://doi.org/10.1016/j.compag.2017.09.001.CrossRefGoogle Scholar
  48. Moore, I. D., Gessler, P. E., Nielsen, G. A., & Peterson, G. A. (1993). Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57(2), 443–452.  https://doi.org/10.2136/sssaj1993.572NPb.CrossRefGoogle Scholar
  49. Paulette, L., Man, T., Weindorf, D. C., & Person, T. (2015). Rapid assessment of soil and contaminant variability via portable x-ray fluorescence spectroscopy: Copşa Mică, Romania. Geoderma, 243–244, 130–140.  https://doi.org/10.1016/j.geoderma.2014.12.025.CrossRefGoogle Scholar
  50. Pearson, D., Chakraborty, S., Duda, B., Li, B., Weindorf, D. C., Deb, S., et al. (2017). Water analysis via portable X-ray fluorescence spectrometry. Journal of Hydrology, 544, 172–179.  https://doi.org/10.1016/j.jhydrol.2016.11.018.CrossRefGoogle Scholar
  51. Pelegrino, M. H. P., Silva, S. H. G., de Menezes, M. D., da Silva, E., Owens, P. R., & Curi, N. (2016). Mapping soils in two watersheds using legacy data and extrapolation for similar surrounding areas. Ciência e Agrotecnologia, 40(5), 534–546.  https://doi.org/10.1590/1413-70542016405011416.CrossRefGoogle Scholar
  52. Penížek, V., Zádorová, T., Kodešová, R., & Vaněk, A. (2016). Influence of elevation data resolution on spatial prediction of colluvial soils in a luvisol region. PLoS ONE, 11(11), 1–18.  https://doi.org/10.1371/journal.pone.0165699.Google Scholar
  53. Peters, J., De Baets, B., Verhoest, N. E. C., Samson, R., Degroeve, S., Becker, P. De, et al. (2007). Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling, 207(2–4), 304–318.  https://doi.org/10.1016/j.ecolmodel.2007.05.011.CrossRefGoogle Scholar
  54. Reidinger, S., Ramsey, M. H., & Hartley, S. E. (2012). Rapid and accurate analyses of silicon and phosphorus in plants using a portable X-ray fluorescence spectrometer. New Phytologist, 195(3), 699–706.  https://doi.org/10.1111/j.1469-8137.2012.04179.x.CrossRefGoogle Scholar
  55. Resende, M., Curi, N., Rezende, S. B., Corrêa, G. F., & Ker, J. C. (2014). Pedologia: Base para distinção de ambientes (6th ed.). Lavras: Editora UFLA.Google Scholar
  56. Ribeiro, B. T., Silva, S. H. G., Silva, E. A., & Guilherme, L. R. G. (2017). Portable X-ray fluorescence (pXRF) applications in tropical Soil Science. Ciência e Agrotecnologia, 41(3), 245–254.  https://doi.org/10.1590/1413-70542017413000117.CrossRefGoogle Scholar
  57. Ryan, J. G., Shervais, J. W., Li, Y., Reagan, M. K., Li, H. Y., Heaton, D., et al. (2017). Application of a handheld X-ray fluorescence spectrometer for real-time, high-density quantitative analysis of drilled igneous rocks and sediments during IODP Expedition 352. Chemical Geology, 451, 55–66.  https://doi.org/10.1016/j.chemgeo.2017.01.007.CrossRefGoogle Scholar
  58. Shaetzl, R., & Anderson, S. (2005). Soils: Genesis and geomorphology (1st ed.). New York: Cambridge University Press.CrossRefGoogle Scholar
  59. Shangguan, W., Hengl, T., de Jesus, J. M., Yuan, H., & Dai, Y. (2014). Mapping the global depth to bedrock for land suface modeling. Journal of Advances in Modeling Earth Systems, 6, 513–526.  https://doi.org/10.1002/2013MS000282.Received.CrossRefGoogle Scholar
  60. Sharma, A., Weindorf, D. C., Man, T., Aldabaa, A. A. A., & Chakraborty, S. (2014). Characterizing soils via portable X-ray fluorescence spectrometer: 3. Soil reaction (pH). Geoderma, 232–234, 141–147.  https://doi.org/10.1016/j.geoderma.2014.05.005.CrossRefGoogle Scholar
  61. Sharma, A., Weindorf, D. C., Wang, D., & Chakraborty, S. (2015). Characterizing soils via portable X-ray fluorescence spectrometer: 4. Cation exchange capacity (CEC). Geoderma, 239, 130–134.  https://doi.org/10.1016/j.geoderma.2014.10.001.CrossRefGoogle Scholar
  62. Silva, S. H. G., de Menezes, M. D., de Mello, C. R., de Góes, H. T. P., Owens, P. R., & Curi, N. (2016a). Geomorphometric tool associated with soil types and properties spatial variability at watersheds under tropical conditions. Scientia Agricola.  https://doi.org/10.1590/0103-9016-2015-0293.Google Scholar
  63. Silva, S. H. G., Poggere, G. C., de Menezes, M. D., Carvalho, G. S., Guilherme, L. R. G., & Curi, N. (2016b). Proximal sensing and digital terrain models applied to digital soil mapping and modeling of Brazilian Latosols (Oxisols). Remote Sensing, 8, 614–635.  https://doi.org/10.3390/rs8080614.CrossRefGoogle Scholar
  64. Silva, S. H. G., Teixeira, A. F. S., Menezes, M. D., Guilherme, L. R. G., de Moreira, F. M., & Curi, N. (2017). Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence analyzer (pXRF). Ciência e Agrotecnologia, 41(6), 648–664.CrossRefGoogle Scholar
  65. Smith, M. P., Zhu, X., Burt, J. E., & Stiles, C. (2006). The effects of DEM resolution and neighborhood size on digital soil survey. Geoderma, 137(1–2), 58–69.  https://doi.org/10.1016/j.geoderma.2006.07.002.CrossRefGoogle Scholar
  66. Soil Survey Staff. (2014). Keys to soil taxonomy (12th ed.). USDA-NRCS. Retrieved from http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_051546.pdf
  67. Stockmann, U., Cattle, S. R., Minasny, B., & McBratney, A. B. (2016). Utilizing portable X-ray fluorescence spectrometry for in-field investigation of pedogenesis. Catena, 139, 220–231.  https://doi.org/10.1016/j.catena.2016.01.007.CrossRefGoogle Scholar
  68. Thompson, J., Roecker, S., Grunwald, S., & Owens, P. R. (2012). Digital soil mapping: Interactions with and applications for hydropedology. Hydropedology.  https://doi.org/10.1016/B978-0-12-386941-8.00021-6.Google Scholar
  69. Weindorf, D. C., Bakr, N., & Zhu, Y. (2014). Chapter One—advances in portable X-ray fluorescence (PXRF) for environmental, pedological, and agronomic applications. In A. Sparks (Ed.), Advances in agronomy (pp. 1–45). New York: Academic Press.Google Scholar
  70. Weindorf, D. C., Zhu, Y., Haggard, B., Lofton, J., Chakraborty, S., Bakr, N., et al. (2012). Enhanced pedon horizonation using portable X-ray fluorescence spectrometry. Soil Science Society of America Journal, 76(2), 522–531.  https://doi.org/10.2136/sssaj2011.0174.CrossRefGoogle Scholar
  71. Weindorf, D. C., & Chakraborty, S. (2016). Portable X-ray fluorescence spectrometry analysis of soils. In D. Hirmas (Ed.), Methods of soil analysis (pp. 1–8). Madison: Soil Science Society of America.Google Scholar
  72. Zhu, Y., Weindorf, D. C., & Zhang, W. (2011). Characterizing soils using a portable X-ray fluorescence spectrometer: 1. Soil texture. Geoderma, 167–168, 167–177.  https://doi.org/10.1016/j.geoderma.2011.08.010.CrossRefGoogle Scholar
  73. Zhu, H., Zhao, Y., Nan, F., Duan, Y., & Bi, R. (2016). Relative influence of soil chemistry and topography on soil available micronutrients by structural equation modeling, 16(4), 1038–1051.  https://doi.org/10.4067/S0718-95162016005000076.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departamento de Ciência do SoloUniversidade Federal de LavrasLavrasBrazil
  2. 2.Department of Plant and Soil ScienceTexas Tech UniversityLubbockUSA

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