Inflection point position as a potential diagnostic tool for the estimation of sulfur concentration in Eucalyptus seedlings

References

• Adams, M. L., W. A. Norvell, W. D. Philpot, and J. H. Peverly. 2000a. Spectral detection of micronutrient deficiency in ‘Bragg’ soybean. Agronomy Journal 92 (2):261–8. doi: 10.2134/agronj2000.922261x.
   Web of Science ®Google Scholar
• Adams, M. L., W. A. Norvell, W. D. Philpot, and J. H. Peverly. 2000b. Toward the discrimination of manganese, zinc, copper, and iron deficiency in ‘Bragg’ soybean using spectral detection methods. Agronomy Journal 92 (2):268–74. doi: 10.2134/agronj2000.922268x.
   Web of Science ®Google Scholar
• Braga, M. D. M., A. E. Furtini Neto, A. H. Oliveira, and R. O. Batista. 2014. Sulfur effects on the development and quality of Australian cedar seedlings (In portuguese). Científica 42 (1):91–100. doi: 10.15361/1984-5529.2014v42n1p91-100.
   Google Scholar
• Buschmann, C., E. Nagel, K. Szabó, and L. Kocsányi. 1994. Spectrometer for fast measurements of in vivo reflectance, absorptance, and fluorescence in the visible and near infrared. Remote Sensing of Environment 48 (1):18–24. doi: 10.1016/0034-4257(94)90110-4.
   Web of Science ®Google Scholar
• Clark, R. B. 1975. Characterization of phosphatase of intact maize roots. Journal of Agricultural and Food Chemistry 23 (3):458–60. doi: 10.1021/jf60199a002.
   PubMed Web of Science ®Google Scholar
• Clevers, J., and A. A. Gitelson. 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoinformation 23:344–51. doi: 10.1016/j.jag.2012.10.008.
   Web of Science ®Google Scholar
• Demetriades-Shah, T. H., M. D. Steven, and J. A. Clark. 1990. High resolution derivative spectra in remote sensing. Remote Sensing of Environment 33 (1):55–64. doi: 10.1016/0034-4257(90)90055-Q.
   Web of Science ®Google Scholar
• Empresa Brasileira de Pesquisa Agropecuária – Embrapa. 2009. Manual deanálises químicas de solos, plantas e fertilizantes, 2nd ed., 628. Brasília: Informação Tecnológica.
   Google Scholar
• Gazola, R. D. N., Buzetti, S. Teixeira Filho, M. C. M. Dinalli, R. P. Moraes, M. L. T. Celestrino, T. D. S. Celestrino, T. S. Silva, P. H. Dupas. M., and E. 2015. Doses of N, P and K in the cultivation of eucalyptus in soil originally under Cerrado vegetation. Semina: Ciências Agrárias 36 (3Supl1):1895–911. doi: 10.5433/1679-0359.2015v36n3Supl1p1895.
   Web of Science ®Google Scholar
• IBÁ. 2020. [IBÁ] Indústria Brasileira de Árvores. Relatório Ibá 2019. https://iba.org/datafiles/publicacoes/relatorios/iba-relatorioanual2019.pdf.
   Google Scholar
• Jorgensen, R. N., L. K. Christensen, and R. Bro. 2007. Spectral reflectance at sub‐leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression. International Journal of Remote Sensing 28 (5):943–62. doi: 10.1080/01431160600735657.
   Web of Science ®Google Scholar
• Lichtenthaler, H. K. 1987. Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. Methods in Enzymology 148:350–82. doi: 10.1016/0076-6879(87)48036-1.
   Web of Science ®Google Scholar
• Mahajan, G. R., R. N. Pandey, R. N. Sahoo, V. K. Gupta, S. C. Datta, and D. Kumar. 2017. Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precision Agriculture 18 (5):736–61. doi: 10.1007/s11119-016-9485-2.
   Web of Science ®Google Scholar
• Marschner, P. 2012. Marschner’s mineral nutrition of higher plants. 3rd ed., 651. London: Academic Press.
   Google Scholar
• Masoni, A., L. Ercoli, and M. Mariotti. 1996. Spectral properties of leaves deficient in iron, sulfur, magnesium, and manganese. Agronomy Journal 88 (6):937–43. doi: 10.2134/agronj1996.00021962003600060015x.
   Web of Science ®Google Scholar
• Oliveira, L. F. R. D., M. L. R. D. Oliveira, F. S. Gomes, and R. C. Santana. 2017. Estimating foliar nitrogen in Eucalyptus using vegetation indexes. Scientia Agricola 74 (2):142–7. doi: 10.1590/1678-992x-2015-0477.
   Web of Science ®Google Scholar
• Oliveira, L. F. R. D., and R. C. Santana. 2020. Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression. Scientia Agricola 77 (6). doi: 10.1590/1678-992X-2018-0409.
   Web of Science ®Google Scholar
• Oliveira, L. F. R., and R. C. Santana. 2019. Eucalyptus leaf reflectance patterns on different days and periods of the day (In portuguese). Colloquium Agrariae 15 (1):43–50. doi: 10.5747/ca.2019.v15.n1.a269.
   Google Scholar
• Pacumbaba, R. O., and C. A. Beyl. 2011. Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies. Advances in Space Research 48 (1):32–42. doi: 10.1016/j.asr.2011.02.020.
   Web of Science ®Google Scholar
• Santos, E. F., N. S. Mateus, F. H. S. Rabêlo, F. G. Macedo, and J. Lavres. 2020. Diagnosing early disorders in Jatropha curcas to calcium, magnesium and sulfur deficiency. Journal of Plant Nutrition 43 (11):1604–16. doi: 10.1080/01904167.2020.1730899.
   Web of Science ®Google Scholar
• Savitzky, A., and M. J. E. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36 (8):1627–39. doi: 10.1021/ac60214a047.
   Web of Science ®Google Scholar
• Seber, G. A F., and A. J. Lee. 2003. Linear regression analysis, 582. Hoboken: John Wiley & Sons, Inc.
   Google Scholar
• Vieira, C. R., O. L. S. Weber, and J. F. Scaramuzza. 2016. Macronutrients omission on initial growth of Tabebuia ochraceae (In portuguese). Ambiência 12 (4):869–83. doi: 10.5935/ambiencia.2016.04.08.
   Google Scholar