Nitrogen variable rate in pastures using optical sensors
DOI:
https://doi.org/10.5433/1679-0359.2019v40n6Supl2p2917Keywords:
Precision agriculture, NSI, Vegetation index, Brachiaria brizantha, Xaraés grass.Abstract
The use of optical sensors to identify the nutritional needs of agricultural crops has been the subject of several studies using precision agriculture techniques. In this work, we sought to overcome the lack of research evaluating the use of these techniques in the management of nitrogen (N) fertilizer in pastures. We evaluated the methodology of the nitrogen sufficiency index (NSI) in N management at variable rates (VR) using a portable chlorophyll meter. In addition, the use of color vegetation indices generated from a digital camera was evaluated as a low-cost alternative. The work was conducted in four management cycles at different times of year, evaluating the productivity and quality of Brachiaria brizantha cv. Xaraés grass. Three NSIs (0.85, 0.90 and 0.95) were evaluated, applying complementary doses of N according to the response of monitored plots using a chlorophyll meter and comparing the productivity and leaf N content of these treatments to the reference treatment (TREF), which received a single dose of N (150 kg ha-1). Together with these treatments, plots without N application (control) were analyzed, totaling five treatments with six replications in a completely randomized design. The dry mass productivity and N leaf concentration of the VR treatments were statistically equal to TREF in all management cycles (P < 0.05). Most color vegetation indices correlated significantly (P < 0.05) to the chlorophyll readings. The use of NSI methodology in pastures allows the same productivity gains, with significant input savings. In addition, the use of digital cameras presents itself as a viable alternative to monitoring the N status in pastures.Downloads
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References
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FONTES, J. G. G.; FAGUNDES, J. L.; BACKES, A. A.; BARBOSA, L. T.; CERQUEIRA, E. S. A.; SILVA, L. M. da; MORAIS, J. A. S.; VIEIRA, J. S. Herbage accumulation in Brachiaria brizantha cultivars submitted to defoliation intensities. Semina: Ciências Agrárias, Londrina, v. 35, n. 3, p. 1425-1438, maio/jun. 2014. DOI: 10.5433/1679-0359.2014v35n3p1425.
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HE, L.; SONG, X.; FENG, W.; GUO, B. B.; ZHANG, Y. S.; WANG, Y. H.; WANG, C. Y.; GUO, T. C. Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data. Remote Sensing of Environment, New York, v. 174, n. 1, p. 122-133, 2016. DOI: 10.1016/j.rse.2015.12.007.
HUNT JR., E. R.; DORAISWAMY, P. C.; MCMURTREY, J. E.; DAUGHTRY, C. S. T.; PERRY, E. M.; AKHMEDOV, B. A visible band index for remote sensing leaf chlorophyll content at the Canopy scale. International Journal of Applied Earth Observation and Geoinformation, Enschede, v. 21, n. 1, p. 103-112, 2013. DOI: 10.1016/j.jag.2012.07.020.
INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA - IBGE. Censo Agro 2017. Accessed at: https://censos.ibge.gov.br/agro/2017/templates/censo_agro/resultadosagro/estabelecimentos.html. Accessed at: 31 jan. 2019.
JINWEN, L. Determination of canopy’s average SPAD readings based on the analysis of digital images. Agrotechnology, Hyderabad, v. 3, n. 1, p. 1-4, 2014. DOI: 10.4172/2168-9881.100012.
LI, F.; MISTELE, B.; HU, Y.; CHEN, X.; SCHMIDHALTER, U. Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. European Journal of Agronomy, Montpellier, v. 52, part B, p. 198-209, 2014. DOI: 10.1016/j.eja.2013.09.006.
LI, Y.; CHEN, D.; WALKER, C. N.; ANGUS, J. F. Estimating the nitrogen status of crops using a digital camera. Field Crops Research, Amsterdam, v. 118, n. 3, p. 221-227, 2010. DOI: 10.1016/j.fcr.2010.05.011.
MAO, W.; WANG, Y. Real-time detection of between-row weeds using machine vision. American Society of Agricultural Engineers. St. Joseph: The Society for Agricultural, Food, and Biological Systems, 2003. DOI: 10.13031/2013.15381.
MATLAB version R2009b. Natick: The MathWorks Inc., Massachusetts, USA. 2009.
MUTANGA, O.; SKIDMORE, A. K.; PRINS, H. H. T. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment, New York, v. 89, n. 3, p. 393-408, 2004. DOI: 10.1016/j.rse.2003.11.001.
NETTO, A. T.; CAMPOSTRINI, E.; OLIVEIRA, J. G. de; BRESSAN-SMITH, R. E. Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Scientia Horticulturae, Amsterdam, v. 104, n. 2, p. 199-209, 2005. DOI: 10.1016/j.scienta.2004.08.013.
ORGANIZAÇÃO DAS NAÇÕES UNIDAS - ONU. Brasil na COP 21. 2015. Available at: http://www.mma.gov.br/clima/convencao-das-nacoes-unidas/acordo-de-paris. Accessed at: 7 jan. 2017.
PAGOLA, M.; ORTIZ, R.; IRIGOYEN, I.; BUSTINCE, H.; BARRENECHEA, E.; APARICIO-TEJO, P.; LAMSFUS, C.; LASA, B. New method to assess barley nitrogen nutrition status based on image colour analysis: comparison with SPAD-502. Computers and Electronics in Agriculture, Amsterdam, v. 65, n. 2, p. 213-218, 2009. DOI: 10.1016/j.compag.2008.10.003.
PEDREIRA, B. C. E.; PEDREIRA, C. G. S.; SILVA, S. C. da. Acúmulo de forragem durante a rebrotação de capim-xaraés submetido a três estratégias de desfolhação. Revista Brasileira de Zootecnia, Viçosa, MG, v. 38, n. 4, p. 618-625, 2009. DOI: 10.1590/S1516-35982009000400005.
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2019-09-30
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Corrêdo, L. de P., Pinto, F. de A. de C., Queiroz, D. S., Valente, D. S. M., & Villar, F. M. de M. (2019). Nitrogen variable rate in pastures using optical sensors. Semina: Ciências Agrárias, 40(6Supl2), 2917–2932. https://doi.org/10.5433/1679-0359.2019v40n6Supl2p2917
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