Regression modeling nitrogen fertilization requirement for maize crop by combining spectral reflectance and agronomic efficiency

References

• Acock, B., and M. C. Acock. 1991. Potential for using long-term field research data to develop and validate crop simulators. Agronomy Journal 83 (1):56–61. doi: 10.2134/agronj1991.00021962008300010015x.
   Web of Science ®Google Scholar
• Alvares, C. A., J. L. Stape, P. C. Sentelhas, J. L. Moraes Gonçalves, and G. Sparovek. 2013. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 22 (6):711–28. doi: 10.1127/0941-2948/2013/0507.
   Web of Science ®Google Scholar
• Alvarez, V. V. H. 1994. Avaliação da fertilidade do solo: Superfície de respostas – modelos aproximativos para expressar a relação fator-resposta. [Evaluation of soil fertility: Response surface - approximate models to express the factor-response relationship]. Viçosa, Minas Gerais, Brasil: UFV.
   Google Scholar
• Anandhi, A. 2016. Growing degree days–Ecosystem indicator for changing diurnal temperatures and their impact on corn growth stages in Kansas. Ecological Indicators 61:149–58. doi: 10.1016/j.ecolind.2015.08.023.
   Web of Science ®Google Scholar
• Bayer, C., J. Gomes, J. A. Zanatta, F. C. B. Vieira, M. C. Piccolo, J. Dieckow, and J. Six. 2015. Soil nitrous oxide emissions as affected by long-term tillage, cropping systems and nitrogen fertilization in Southern Brazil. Soil and Tillage Research 146:213–22. doi: 10.1016/j.still.2014.10.011.
   Web of Science ®Google Scholar
• Benckiser, G., T. Schartel, and A. Weiske. 2015. Control of NO3− and N2O emissions in agroecosystems: A review. Agronomy for Sustainable Development 35 (3):1059–74. doi: 10.1007/s13593-015-0296-z.
   Google Scholar
• Bragagnolo, J., T. J. C. Amado, and R. P. Bortolotto. 2016. Use efficiency of variable rate of nitrogen prescribed by optical sensor in corn. Revista Ceres 63 (1):103–11. doi: 10.1590/0034-737X201663010014.
   Google Scholar
• Bragagnolo, J., T. J. C. Amado, R. D. S. Nicoloso, J. Jasper, J. Kunz, and T. D. G. Teixeira. 2013. Optical crop sensor for variable-rate nitrogen fertilization in corn: I-plant nutrition and dry matter production. Revista Brasileira de Ciência Do Solo 37 (5):1288–98. doi: 10.1590/S0100-06832013000500018.
   Web of Science ®Google Scholar
• Breunig, F. M., L. S. Galvão, A. R. Formaggio, and J. C. N. Epiphanio. 2013. Influence of data acquisition geometry on soybean spectral response simulated by the prosail model. Engenharia Agrícola 33 (1):176–87. doi: 10.1590/S0100-69162013000100018.
   Web of Science ®Google Scholar
• Bushong, J. T., J. L. Mullock, D. B. Arnall, and W. R. Raun. 2018. Effect of nitrogen fertilizer source on corn (Zea mays L.) optical sensor response index values in a rain-fed environment. Journal of Plant Nutrition 41 (9):1172–83. doi: 10.1080/01904167.2018.1434202.
   Web of Science ®Google Scholar
• Chen, B., E. Liu, Q. Tian, C. Yan, and Y. Zhang. 2014. Soil nitrogen dynamics and crop residues. A review. Agronomy for Sustainable Development 34 (2):429–42. doi: 10.1007/s13593-014-0207-8.
   Web of Science ®Google Scholar
• Colaço, A. F., and R. G. V. Bramley. 2018. Do crop sensors promote improved nitrogen management in grain crops. ? Field Crops Research 218:126–40. doi: 10.1016/j.fcr.2018.01.007.
   Web of Science ®Google Scholar
• Da Ros, C. O., R. L. Salet, R. L. Porn, and J. N. C. Machado. 2003. Disponibilidade de nitrogênio e produtividade de milho e trigo com diferentes métodos de adubação nitrogenada no sistema plantio direto. [Effects of fertilization methods on soil nitrogen availability for wheat and corn production. Ciência Rural 33 (5):799–804. ]. doi: 10.1590/S0103-84782003000500002.
   Google Scholar
• Fageria, N. K., and V. C. Baligar. 2005. Enhancing nitrogen use efficiency in crop plants. Advances in Agronomy 88:97–185. doi: 10.1016/S0065-2113(05)88004-6.
   Web of Science ®Google Scholar
• Fontoura, S. M. V., and C. Bayer. 2009. Adubação nitrogenada para alto rendimento de milho em plantio direto na região Centro-Sul do Paraná. [Nitrogen-fertilizer recommendation for high corn yields under no-tillage in the South-Central region of Parana State, Brazil. Revista Brasileira de Ciência Do Solo 33 (6):1721–32. ]. doi: 10.1590/S0100-06832009000600021.
   Google Scholar
• Franzen, D., N. Kitchen, K. Holland, J. Schepers, and W. Raun. 2016. Algorithms for in-season nutrient management in cereals. Agronomy Journal 108 (5):1775–81. doi: 10.2134/agronj2016.01.0041.
   Web of Science ®Google Scholar
• Gabaldón-Leal, C., H. Webber, M. E. Otegui, G. A. Slafer, R. A. Ordóñez, T. Gaiser, I. J. Lorite, M. Ruiz-Ramos, and F. Ewert. 2016. Modelling the impact of heat stress on maize yield formation. Field Crops Research 198:226–37. doi: 10.1016/j.fcr.2016.08.013.
   Web of Science ®Google Scholar
• Hishi, T., R. Urakawa, N. Tashiro, Y. Maeda, and H. Shibata. 2014. Seasonality of factors controlling N mineralization rates among slope positions and aspects in cool-temperate deciduous natural forests and larch plantations. Biology and Fertility of Soils 50 (2):343–56. doi: 10.1007/s00374-013-0863-x.
   Web of Science ®Google Scholar
• Holland, K. H., and J. S. Schepers. 2010. Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agronomy Journal 102 (5):1415–24. doi: 10.2134/agronj2010.0015.
   Web of Science ®Google Scholar
• Hong, S. ‐D., J. S. Schepers, D. D. Francis, and M. R. Schlemmer. 2007. Comparison of ground-based remote sensors for evaluation of corn biomass affected by nitrogen stress. Communications in Soil Science and Plant Analysis 38 (15-16):2209–26. doi: 10.1080/00103620701549157.
   Web of Science ®Google Scholar
• Inman, D., R. Khosla, and T. Mayfield. 2005. On-the-go active remote sensing for efficient crop nitrogen management. Sensor Review 25 (3):209–14. doi: 10.1108/02602280510606499.
   Google Scholar
• Kipp, S., B. Mistele, and U. Schmidhalter. 2014. The performance of active spectral reflectance sensors as influenced by measuring distance, device temperature and light intensity. Computers and Electronics in Agriculture 100:24–33. doi: 10.1016/j.compag.2013.10.007.
   Web of Science ®Google Scholar
• Koch, B., R. Khosla, W. M. Frasier, D. G. Westfall, and D. Inman. 2004. Economic feasibility of variable rate nitrogen application utilizing site-specific management zones. Agronomy Journal 96 (6):1572–80. doi: 10.2134/agronj2004.1572.
   Web of Science ®Google Scholar
• Lambert, D. M., J. Lowenberg-Deboer, and G. L. Malzer. 2006. Economic analysis of spatial temporal patterns in corn and soybean response to nitrogen and phosphorus. Agronomy Journal 98 (1):43–54. doi: 10.2134/agronj2005.0005.
   Web of Science ®Google Scholar
• Masvaya, E. N., J. Nyamangara, K. Descheemaeker, and K. E. Giller. 2017. Tillage, mulch and fertiliser impacts on soil nitrogen availability and maize production in semi-arid Zimbabwe. Soil and Tillage Research 168:125–32. doi: 10.1016/j.still.2016.12.007.
   Web of Science ®Google Scholar
• Povh, F. P., J. P. Molin, L. M. Gimenez, V. Pauletti, R. Molin, and J. V. Salvi. 2008. Comportamento do NDVI obtido por sensor ótico ativo em cereais. [Behavior of NDVI obtained from an active optical sensor in cereals. Pesquisa Agropecuária Brasileira 43 (8):1075–83. doi: 10.1590/S0100-204X2008000800018.
   Web of Science ®Google Scholar
• R Foundation for Statistical Computing. 2016. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Raun, W. R., J. B. Solie, G. V. Johnson, M. L. Stone, R. W. Mullen, K. W. Freeman, W. E. Thomason, and E. V. Lukina. 2002. Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal 94 (4):815–20. doi: 10.2134/agronj2002.8150.
   Web of Science ®Google Scholar
• Raun, W. R., J. B. Solie, M. L. Stone, K. L. Martin, K. W. Freeman, R. W. Mullen, H. Zhang, J. S. Schepers, and G. V. Johnson. 2005. Optical sensor‐based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis 36 (19-20):2759–81. doi: 10.1080/00103620500303988.
   Web of Science ®Google Scholar
• Raun, W. R., J. B. Solie, and M. L. Stone. 2011. Independence of yield potential and crop nitrogen response. Precision Agriculture 12 (4):508–18. doi: 10.1007/s11119-010-9196-z.
   Web of Science ®Google Scholar
• Rissini, A. L. L., J. Kawakami, and A. M. Genú. 2015. Índice de vegetação por diferença normalizada e produtividade de cultivares de trigo submetidas a doses de nitrogênio. [Normalized difference vegetation index and yield of wheat cultivars under different application rates of nitrogen. Revista Brasileira de Ciência Do Solo 39 (6):1703–13. ]. doi: 10.1590/01000683rbcs20140686.
   Google Scholar
• Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering. 1973. Monitoring vegetation systems in the great plains with ERTS (Earth Resources Technology Satellite). Proceedings of 3rd Earth Resources Technology Satellite Symposium, Greenbelt, 10-14 December, SP-351, 309–17.
   Google Scholar
• Sharma, L. K., H. Bu, A. Denton, and D. W. Franzen. 2015. Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, U.S.A. Sensors (Basel, Switzerland) 15 (11):27832–53. doi: 10.3390/s151127832.
   PubMed Web of Science ®Google Scholar
• Soil Survey Staff. 2013. Simplified guide to soil taxonomy. Lincoln, NE, USA: USDA Natural Resources Conservation Service, National Soil Survey Center.
   Google Scholar
• Solie, J. B., A. D. Monroe, W. R. Raun, and M. L. Stone. 2012. Generalized algorithm for variable-rate nitrogen application in cereal grains. Agronomy Journal 104 (2):378–87. doi: 10.2134/agronj2011.0249.
   Web of Science ®Google Scholar
• Thomason, W. E., S. B. Phillips, P. H. Davis, J. G. Warren, M. M. Alley, and M. S. Reiter. 2011. Variable nitrogen rate determination from plant spectral reflectance in soft red winter wheat. Precision Agriculture 12 (5):666–81. doi: 10.1007/s11119-010-9210-5.
   Web of Science ®Google Scholar
• Thornley, J. H. M., and I. R. Johnson. 2000. Plant and crop modelling: A mathematical approach to plant and crop physiology, 669. Hawthorne, CA, USA: The Blackburn Press.
   Google Scholar