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Precision Agriculture

, Volume 20, Issue 1, pp 56–77 | Cite as

Fine-tuning of wheat (Triticum aestivum, L.) variable nitrogen rate by combining crop sensing and management zones approaches in southern Brazil

  • R. A. SchwalbertEmail author
  • T. J. C. Amado
  • G. B. Reimche
  • F. Gebert
Article
  • 206 Downloads

Abstract

The integration of crop sensors and management zones aiming at fine-tuning variable nitrogen rate (VNR) is a technological alternative to increase nitrogen use efficiency (NUE). The main objective of this study was to delimit management zones with different response to nitrogen input and using simulations to compare different wheat fertilization strategies: (a) traditional N management (constant rate), (b) VNR based on crop remote sensing, (c) VNR based on management zones and, (d) integrated approach combining management zones and crop sensing. This study was conducted in three steps: (i) delineation of management by overlapping maize grain yield maps, topographic features and soil apparent electrical conductivity; (ii) field experiments aiming at checking plant N uptake, N use efficiency (NUE) and wheat yield responses to N rates within each MZ—the N rates ranged from 0 to 120 kg ha−1 in field 1 and from 0 to 160 kg ha−1 in field 2- and, (iii) simulations of the four different strategies of wheat N fertilization. Both fields considered in this study were located in Carazinho, southern Brazil. The main outcomes were (1) significant N rates by management zones interactions for plant N uptake (P < 0.05) and for wheat grain yield (P < 0.05); (2) lower N fertilization response and NUE in low yield zone compared to medium and high yield zones and (3) superior performance of the integrated approach combining management zones and remote crop sensing.

Keywords

Precision agriculture Nitrogen use efficiency Spatial variability Remote sensing In-season prescription 

Supplementary material

11119_2018_9581_MOESM1_ESM.tif (1.2 mb)
Supplementary Figure 1. Regression analysis between plant N uptake at flowering stage and wheat grain yield (a and b) (HYZ and MYZ are pooled together because statistically they presented the same relationship) and between vegetation index obtained with crop sensor and plant N uptake (both at Z31) (c and d) for two field-years (2013-2014). *Significant at the 0.001 % level. Supplementary material 1 (TIFF 1182 kb)

References

  1. Adamchuk, V. I., Viscarra Rossel, R. A., Sudduth, K. A., & Lammers, P. S. (2011). Sensor fusion for precision agriculture. In C. Thomas (Ed.), Sensor fusion—foundation and applications (pp. 27–40). Rijeka: InTech.Google Scholar
  2. Afifi, A., & Clark, V. (1996). Discriminant analysis. Computer-Aided Multivariate Analysis (pp. 243–280). New York: Springer.CrossRefGoogle Scholar
  3. Amado, T. J. C., Mielniczuk, J., & Aita, C. (2002). Recomendação de adubação nitrogenada para o milho no RS e SC Adaptada ao uso de culturas de cobertura do solo, sob sistema plantio direto. Revista Brasileira de Ciência do Solo, 26(1), 241–248.CrossRefGoogle Scholar
  4. Amaral, L. R., Molin, J. P., Portz, G., Finazzi, F. B., & Cortinove, L. (2014). Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status. Precision Agriculture, 16(1), 15–28.  https://doi.org/10.1007/s11119-014-9377-2.CrossRefGoogle Scholar
  5. Bachmaier, M., & Gandorfer, M. (2009). A conceptual framework for judging the precision agriculture hypothesis with regard to site-specific nitrogen application. Precision Agriculture, 10(2), 95–110.  https://doi.org/10.1007/s11119-008-9069-x.CrossRefGoogle Scholar
  6. Bernaards, C. A., & Jennrich, I. R. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement., 65, 676–696.CrossRefGoogle Scholar
  7. Berntsen, J., Thomsen, A., Schelde, K., Hansen, O. M., Knudsen, L., Broge, N., et al. (2006). Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat. Precision Agriculture, 7(2), 65–83.  https://doi.org/10.1007/s11119-006-9000-2.CrossRefGoogle Scholar
  8. Bragagnolo, J., Amado, T. J. C., da Nicoloso, R. S., Jasper, J., Kunz, J., & de Teixeira, T. G. (2013a). 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), 1288–1298.  https://doi.org/10.1590/S0100-06832013000500018.CrossRefGoogle Scholar
  9. Bragagnolo, J., Amado, T. J. C., da Nicoloso, R. S., Santi, A. L., Fiorin, J. E., & Tabaldi, F. (2013b). Optical crop sensor for variable-rate nitrogen fertilization in corn: II—Indices of fertilizer efficiency and corn yield. Revista Brasileira de Ciência do Solo, 37(5), 1299–1309.  https://doi.org/10.1590/S0100-06832013000500019.CrossRefGoogle Scholar
  10. Bredemeier, C., Variani, C., Almeida, D., & Rosa, A. T. (2013). Estimativa do potencial produtivo em trigo utilizando sensor óptico ativo para adubação nitrogenada em taxa variável. Ciência Rural, 43(7), 1147–1154.  https://doi.org/10.1590/S0103-84782013005000080.CrossRefGoogle Scholar
  11. Cassman, K., Dobermann, A., & Walters, D. (2002). Agroecosystems, nitrogen-use efficiency, and nitrogen management. AMBIO: A Journal of the Human Environment, 31(2), 132.CrossRefGoogle Scholar
  12. Changere, A., & Lal, R. (1997). Slope position and erosional effects on soil properties and corn production on a Miamian soil in central Ohio. Journal of Sustainable Agriculture., 11, 5–21.CrossRefGoogle Scholar
  13. Clay, D. E., Kim, K. I., Chang, J., Clay, S. A., & Dalsted, K. (2006). Characterizing water and nitrogen stress in corn using remote sensing. Agronomy Journal, 98(3), 579–587.  https://doi.org/10.2134/agronj2005.0204.CrossRefGoogle Scholar
  14. Cui, Z., Chen, X., Miao, Y., Li, F., Zhang, F., Li, J., et al. (2008). On-farm evaluation of winter wheat yield response to residual soil nitrate-N in North China Plain. Agronomy Journal, 100(6), 1527.  https://doi.org/10.2134/agronj2008.0005.CrossRefGoogle Scholar
  15. Dobermann, A. (2005). Nitrogen use efficiency–state of the art.Google Scholar
  16. Doerge, T. (1999). Defining management zones for precision farming. Crop Insights, 8(21), 1–5.Google Scholar
  17. Ebeid, M. M., Lal, R., Hall, G. F., & Miller, E. (1995). Erosion effects on soil properties and soybean yield of a Miamian soil in Western Ohio in a season with below normal rainfall. Soil technology, 85(2), 97–108.CrossRefGoogle Scholar
  18. Fraisse, C., Sudduth, K., & Kitchen, N. (2001). Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Transactions of the ASAE, 44(1), 155.CrossRefGoogle Scholar
  19. Fridgen, J., & Kitchen, N. (2004). Management zone analyst (MZA). Agronomy Journal, 96(1), 101–108.Google Scholar
  20. Fulton, J. P., Winstead, A., Shaw, J. N., Rodekhor, D., & Brodbeck, C. J. (2013). A case study for variable-rate seeding of corn and cotton in the Tennessee valley of Alabama. Journal of Chemical Information and Modeling, 53(9), 1689–1699.  https://doi.org/10.1017/CBO9781107415324.004.Google Scholar
  21. Guastaferro, F., Castrignanò, A., de Benedetto, D., Sollitto, D., Troccoli, A., & Cafarelli, B. (2010). A comparison of different algorithms for the delineation of management zones. Precision Agriculture, 11(6), 600–620.  https://doi.org/10.1007/s11119-010-9183-4.CrossRefGoogle Scholar
  22. Holland, K., & Schepers, J. (2010). Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agronomy Journal, 102(5), 1415–1424.CrossRefGoogle Scholar
  23. Holland, K. H., & Schepers, J. S. (2013). Use of a virtual-reference concept to interpret active crop canopy sensor data. Precision Agriculture, 14(1), 71–85.  https://doi.org/10.1007/s11119-012-9301-6.CrossRefGoogle Scholar
  24. Hörbe, T. A. N., Amado, T. J. C., Ferreira, A. O., & Alba, P. J. (2013). Optimization of corn plant population according to management zones in Southern Brazil. Precision Agriculture, 14(4), 450–465.  https://doi.org/10.1007/s11119-013-9308-7.CrossRefGoogle Scholar
  25. Hurley, T. M., Malzer, G. L., & Kilian, B. (2004). Estimating site-specific nitrogen crop response functions. Agronomy Journal, 96(5), 1331.  https://doi.org/10.2134/agronj2004.1331.CrossRefGoogle Scholar
  26. Inman, D., Khosla, R., Westfall, D. G., & Reich, R. (2005). Nitrogen uptake across site specific management zones in irrigated corn production systems. Agronomy Journal, 97(1), 169–176.  https://doi.org/10.2134/agronj2005.0169.CrossRefGoogle Scholar
  27. Iqbal, J., & Read, J. (2005). Relationships between soil–landscape and dryland cotton lint yield. Soil Science Society of America Journal.  https://doi.org/10.2136/sssaj2004.0178.Google Scholar
  28. Jaynes, D. B., Kaspar, T. C., & Colvin, T. S. (2011). Economically optimal nitrogen rates of corn: Management zones delineated from soil and terrain attributes. Agronomy Journal, 103(4), 1026–1035.  https://doi.org/10.2134/agronj2010.0472.CrossRefGoogle Scholar
  29. Jørgensen, J., & Jørgensen, R. (2007). Uniformity of wheat yield and quality using sensor assisted application of nitrogen. Precision Agriculture, 8(1), 63–73.  https://doi.org/10.1007/s11119-006-9029-2.CrossRefGoogle Scholar
  30. Kaffka, S. R., Lesch, S. M., Bali, K. M., & Corwin, D. L. (2005). Site-specific management in salt-affected sugar beet fields using electromagnetic induction. Computers and Electronics in Agriculture.  https://doi.org/10.1016/j.compag.2004.11.013.Google Scholar
  31. Kaspar, T. C., Colvin, T. S., Jaynes, D. B., Karlen, D. L., James, D. E., Meek, D. W., et al. (2003). Relationship between 6 years of corn yields and terrain attributes. Precision Agriculture, 4(1), 87–101.  https://doi.org/10.1023/A:1021867123125.CrossRefGoogle Scholar
  32. Khosla, R., Fleming, K., Delgado, J., Shaver, T. M., & Westfall, D. G. (2002). Use of site-specific management zones to improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation, 57(6), 513–518.Google Scholar
  33. Khosla, R., Inman, D., Westfall, D. G., Reich, R. M., Frasier, M., Mzuku, M., et al. (2008). A synthesis of multi-disciplinary research in precision agriculture: Site-specific management zones in the semi-arid western Great Plains of the USA. Precision Agriculture, 9, 85–100.  https://doi.org/10.1007/s11119-008-9057-1.CrossRefGoogle Scholar
  34. Kitchen, N., & Drummond, S. (2003). Soil electrical conductivity and topography related to yield for three contrasting soil–crop systems. Agronomy Journal.  https://doi.org/10.2134/agronj2003.4830.Google Scholar
  35. Kitchen, N. R., Sudduth, K. A., Drummond, S. T., Scharf, P. C., Palm, H. L., Roberts, D. F., et al. (2010). Ground-based canopy reflectance sensing for variable-rate nitrogen corn fertilization. Agronomy Journal, 102(1), 71–84.  https://doi.org/10.2134/agronj2009.0114.CrossRefGoogle Scholar
  36. Koch, B., Khosla, R., & Frasier, W. (2004). Economic feasibility of variable-rate nitrogen application utilizing site-specific management zones. Agronomy Journal.  https://doi.org/10.2134/agronj2004.1572.Google Scholar
  37. Kravchenko, A. N., & Bullock, D. G. (2000). Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92(1), 75.  https://doi.org/10.2134/agronj2000.92175x.CrossRefGoogle Scholar
  38. Ladha, J., Pathak, H., & Krupnik, T. (2005). Efficiency of fertilizer nitrogen in cereal production: retrospects and prospects. Advances in Agronomy.  https://doi.org/10.1016/S0065-2113(05)87003-8.Google Scholar
  39. Lambert, D. (2006). Economic analysis of spatial-temporal patterns in corn and soybean response to nitrogen and phosphorus. Agronomy Journal.  https://doi.org/10.2134/agronj2005.0005.Google Scholar
  40. Link, A., Jasper, J., & Olfs, H. W. (2004). Variable nitrogen fertilisation by tractor-mounted remote sensing. Controlling Nitrogen Flows and Losses, 188.Google Scholar
  41. Longchamps, L., & Khosla, R. (2015). Improving N use efficiency by integrating soil and crop properties for variable rate N management. Precision agriculture.  https://doi.org/10.3920/978-90-8686-814-8_30.Google Scholar
  42. Mamo, M., Malzer, G. L., Mulla, D. J., Huggins, D. R., & Strock, J. (2003). Spatial and temporal variation in economically optimum nitrogen rate for corn. Agronomy Journal, 95(4), 958–964.  https://doi.org/10.2134/agronj2003.9580.CrossRefGoogle Scholar
  43. Marques da Silva, J. R., & Silva, L. L. (2008). Evaluation of the relationship between maize yield spatial and temporal variability and different topographic attributes. Biosystems Engineering, 101(2), 183–190.  https://doi.org/10.1016/j.biosystemseng.2008.07.003.CrossRefGoogle Scholar
  44. Mayfield, A. H., & Trengove, S. P. (2009). Grain yield and protein responses in wheat using the N-Sensor for variable rate N application. Crop. Pasture Sci., 60(9), 818–823.  https://doi.org/10.1071/CP08344.CrossRefGoogle Scholar
  45. Nielsen, R. (2006). Loss Mechanisms and Nitrogen Use Efficiency. In Purdue nitrogen management workshops (pp. 1–5).Google Scholar
  46. Odeh, I. O., Chittleborough, D. J., & McBratney, A. B. (1992). Fuzzy-c-means and kriging for mapping soil as a continuous system. Soil Science Society of America Journal, 56(6), 1848–1854.CrossRefGoogle Scholar
  47. Osborne, S. L., Schepers, J. S., Francis, D. D., & Schlemmer, M. R. (2002a). Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen- and water-stressed corn. Crop Science, 42(1), 165–171.  https://doi.org/10.2135/cropsci2002.0165.CrossRefGoogle Scholar
  48. Osborne, S. L., Schepers, J. S., Francis, D. D., & Schlemmer, M. R. (2002b). Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agronomy Journal, 94(6), 1215–1221.  https://doi.org/10.2134/agronj2002.1215.CrossRefGoogle Scholar
  49. Pebesma, E. J. (2004). Multivariable geostatistics in S: The gstat package. Computers & Geosciences, 30, 683–691.CrossRefGoogle Scholar
  50. Peralta, N. R., & Costa, J. L. (2013). Delineation of management zones with soil apparent electrical conductivity to improve nutrient management. Computers and Electronics in Agriculture, 99, 218–226.  https://doi.org/10.1016/j.compag.2013.09.014.CrossRefGoogle Scholar
  51. Peralta, N. R., Costa, J. L., Balzarini, M., Castro Franco, M., Córdoba, M., & Bullock, D. (2015). Delineation of management zones to improve nitrogen management of wheat. Computers and Electronics in Agriculture.  https://doi.org/10.1016/j.compag.2014.10.017.Google Scholar
  52. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team. (2015). nlme: Linear and nonlinear mixed effects models.Google Scholar
  53. R Core Team. (2017). R: A Language and Environment for Statistical Computing. Vienna, Austria. https://www.r-project.org/.
  54. Raun, W. R., & Johnson, G. V. (1999). Improving nitrogen use efficiency for cereal production. Agronomy Journal.  https://doi.org/10.2134/agronj1999.00021962009100030001x.Google Scholar
  55. Raun, W. R., Solie, J. B., Stone, M. L., Martin, K. L., Freeman, K. W., Mullen, R. W., et al. (2005). Optical sensor-based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis, 36(19–20), 2759–2781.  https://doi.org/10.1080/00103620500303988.CrossRefGoogle Scholar
  56. Revelle, W. (2015). Psych: Procedures for Psychological, Psychometric, and Personality Research. Illinois: Evanston.Google Scholar
  57. Roberts, D. F., Ferguson, R. B., Kitchen, N. R., Adamchuk, V. I., & Shanahan, J. F. (2012). Relationships between soil-based management zones and canopy sensing for corn Nitrogen management. Agronomy Journal, 104(1), 119–129.  https://doi.org/10.2134/agronj2011.0044.CrossRefGoogle Scholar
  58. Santi, A. L., Amado, T. J. C., Cherubin, M. R., Martin, T. N., Pires, J. L., Della, L. P., et al. (2012). Análise de componentes principais de atributos químicos e físicos do solo limitantes à produtividade de graos. Pesquisa Agropecuaria Brasileira, 47(9), 1346–1357.  https://doi.org/10.1590/S0100-204X2012000900020.CrossRefGoogle Scholar
  59. Scharf, P. C., Kitchen, N. R., Sudduth, K. A., Davis, J. G., Hubbard, V. C., & Lory, J. A. (2005). Field-scale variability in optimal nitrogen fertilizer rate for corn. Precision Agriculture, 97, 452–461.Google Scholar
  60. Schepers, A. R., Shanahan, J. F., Liebig, M., Schepers, J. S., Johnson, S. H., & Luchiari, A. (2004). Appropriateness of Management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 96(1), 195–203.  https://doi.org/10.2134/agronj2004.0195.CrossRefGoogle Scholar
  61. Schnug, E., Panten, K., & Haneklaus, S. (1998). Sampling and nutrient recommendations—The future. Communications in Soil Science and Plant Analysis, 29(11–14), 1455–1462.  https://doi.org/10.1080/00103629809370042.CrossRefGoogle Scholar
  62. Shanahan, J. F., Doerge, T. A., Johnson, J. J., & Vigil, M. F. (2004). Feasibility of site-specific management of corn hybrids and plant densities in the great plains. Precision Agriculture, 5(3), 207–225.  https://doi.org/10.1023/B:PRAG.0000032762.72510.10.CrossRefGoogle Scholar
  63. Shanahan, J. F., Kitchen, N. R., Raun, W. R., & Schepers, J. S. (2008). Responsive in-season nitrogen management for cereals. Computers and Electronics in Agriculture, 61(1), 51–62.  https://doi.org/10.1016/j.compag.2007.06.006.CrossRefGoogle Scholar
  64. Shiratsuchi, L., Ferguson, R., Shanahan, J., Adamchuk, V., Rundquist, D., Marx, D., et al. (2011). Water and nitrogen effects on active canopy sensor vegetation indices. Agronomy Journal, 103(6), 1815.  https://doi.org/10.2134/agronj2011.0199.CrossRefGoogle Scholar
  65. Solari, F., Shanahan, J. F., Ferguson, R. B., & Adamchuk, V. I. (2010). An active sensor algorithm for corn nitrogen recommendations based on a chlorophyll meter algorithm. Agronomy Journal, 102(4), 1090–1098.  https://doi.org/10.2134/agronj2010.0009.CrossRefGoogle Scholar
  66. Solie, J. B., Monroe, Dean, Raun, W. R., & Stone, M. L. (2012). Generalized algorithm for variable-rate nitrogen application in cereal grains. Agronomy Journal, 104, 378–387.  https://doi.org/10.2134/agronj2011.0249.CrossRefGoogle Scholar
  67. Sudduth, K. A., & Drummond, S. T. (2007). Yield editor: Software for removing errors from crop yield maps. Agronomy Journal, 99, 1471–1482.CrossRefGoogle Scholar
  68. Tedesco, M., Gianello, C., & Bissani, C. (1995). Análises de solo, plantas e outros materiais. Porto Alegre: Universidade Federal do Rio Grande do Sul.Google Scholar
  69. Tilling, A. K., O’Leary, G. J., Ferwerda, J. G., Jones, S. D., Fitzgerald, G. J., Rodriguez, D., et al. (2007). Remote sensing of nitrogen and water stress in wheat. Field Crops Research, 104, 77–85.  https://doi.org/10.1016/j.fcr.2007.03.023.CrossRefGoogle Scholar
  70. Van Evert, F. K., Van Der Schans, D. A., Van Geel, W. C. A., Malda, J. T., & Vona, V. (2013). From theory to practice: Using canopy reflectance to determine sidedress N rate in potatoes (pp. 119–127). Wageningen Academic Publishers: Wageningen.  https://doi.org/10.3920/978-90-8686-778-3_13.Google Scholar
  71. Vrindts, E., Reyniers, M., Darius, P., De baerdemaeker, J., Gilot, M., Sadaoui, Y., et al. (2003). Analysis of soil and crop properties for precision agriculture for winter wheat. Biosystems Engineering, 85(2), 141–152.  https://doi.org/10.1016/S1537-5110(03)00040-0.CrossRefGoogle Scholar
  72. Zillmann, E., Graeff, S., Link, J., Batchelor, W. D., & Claupein, W. (2006). Assessment of cereal nitrogen requirements derived by optical on-the-go sensors on heterogeneous soils. Agronomy Journal, 98, 682–690.  https://doi.org/10.2134/agronj2005.0253.CrossRefGoogle Scholar

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Authors and Affiliations

  • R. A. Schwalbert
    • 1
    Email author
  • T. J. C. Amado
    • 2
  • G. B. Reimche
    • 2
  • F. Gebert
    • 1
  1. 1.Agricultural Engineering DepartmentFederal University of Santa MariaSanta MariaBrazil
  2. 2.Soil Science DepartmentFederal University of Santa MariaSanta MariaBrazil

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