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Estimating productivity and nutritive value of Marandu palisadegrass using a proximal canopy reflectance sensor

Published online by Cambridge University Press:  28 July 2022

José Ricardo Macedo Pezzopane Open the ORCID record for José Ricardo Macedo Pezzopane [Opens in a new window] ,
Alberto Carlos de Campos Bernardi ,
Cristiam Bosi ,
Orlando Sengling ,
Willian Lucas Bonani ,
Henrique Bauab Brunetti  and
Patricia Menezes Santos
José Ricardo Macedo Pezzopane*
Affiliation:
Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, PO Box: 339, 13563-776, São Carlos, SP, Brazil
Alberto Carlos de Campos Bernardi
Affiliation:
Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, PO Box: 339, 13563-776, São Carlos, SP, Brazil
Cristiam Bosi
Affiliation:
Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, PO Box: 339, 13563-776, São Carlos, SP, Brazil
Orlando Sengling
Affiliation:
Universidade Federal de Lavras, P.O. Box 3037, Zip Code 37200-000. Lavras, MG. Brazil
Willian Lucas Bonani
Affiliation:
UNIARA, Rua Carlos Gomes, 1338 – Centro, 14801-340, Araraquara, SP, Brazil
Henrique Bauab Brunetti
Affiliation:
Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, PO Box: 339, 13563-776, São Carlos, SP, Brazil
Patricia Menezes Santos
Affiliation:
Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, PO Box: 339, 13563-776, São Carlos, SP, Brazil
*
*Corresponding author. Email: [email protected]
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Abstract

In intensive livestock production systems, estimating forage production and its nutritive value can assist farmers in optimizing pasture management, stocking rate, and feed supplementation to animals. In this study, we aimed to use vegetation indices, determined using a proximal canopy reflectance sensor, to estimate the forage mass, crude protein content, and nitrogen in live forage of Marandu palisadegrass (Urochloa brizantha). Pasture canopy reflectance was measured at three wavelengths (670, 720, and 760 nm) using a Crop Circle device equipped with an ACS-430 sensor. Total forage mass, plant-part composition, leaf area index (LAI), and crude protein content were assessed during 14 growth cycles in a pasture under four management regimes, comprising different combinations of two N fertilization rates and two irrigation schedules. For each forage assessment, pasture canopy reflectance data were used to calculate the following vegetation indices: normalized difference vegetation index, normalized difference red edge, simple ratio index (SRI), modified simple ratio, and chlorophyll index. In addition, we also performed analyses of the linear and exponential regressions between vegetation indices and total forage mass, leaf + stem mass, leaf mass, LAI, crude protein content, and nitrogen in live forage. The best estimates were achieved for total forage mass, leaf + stem mass, leaf mass, and nitrogen in live forage using SRI (R2 values between 0.72 and 0.79). When estimating pasture productive variables (total forage mass, leaf + stem mass, leaf mass, and LAI) from SRI, the equations showed R2 values between 0.69 (leaf mass) and 0.74 (LAI) and relative errors ranging from 19% to 21%. For each of the water and nitrogen supply conditions evaluated, this index facilitated the monitoring of forage mass time series and nitrogen in live forage and the extraction of this nutrient by the pasture.

Keywords

Leaf area indexCrop CircleTropical pastureCrude proteinReflectance
Type
Research Article
Information
Experimental Agriculture , Volume 58 , 2022 , e28
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Albayrak, S. (2008). Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in Sainfoin Pasture. Sensors 8, 7275–7286. https://doi.org/10.3390/s8117275 CrossRefGoogle Scholar
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998). Crop Evapotranspiration –Guidelines for Computing Crop Water Requirements. Rome, Italy: FAO, pp. 300.Google Scholar
Amaral, L.R., Molin, J.P., Portz, G., Finazzi, F.B. and Cortinove, L. (2015). Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status. Precision Agriculture 16, 15–28. https://doi.org/10.1007/s11119-014-9377-2.CrossRefGoogle Scholar
Baker, D.W. and Saweyer, J.E. (2010). Using active canopy sensors to quantify corn nitrogen stress and nitrogen application rate. Agronomy Journal 102, 965–971. https://doi:10.2134/agronj2010.0004 Google Scholar
Ball, D.M., Collins, M., Lacefield, G.D., Martin, N.P., Mertens, D.A., Olson, K.E., Putnam, D.H., Undersander, D.J. and Wolf, M.W. (2001). Understanding Forage Quality. Park Ridge, Illinois, USA: American Farm Bureau Federation Publication 1-01, p. 21.Google Scholar
Bosi, C., Sentelhas, P.C., Huth, N.I., Pezzopane, J.R.M., Andreucci, M.P. and Santos, P.M. (2020). APSIM-tropical pasture: A model for simulating perennial tropical grass growth and its parameterisation for palisade grass (Brachiaria brizantha). Agricultural Systems 184, 1–13. https://doi.org/10.1016/j.agsy.2020.102917 CrossRefGoogle Scholar
Calderano Filho, B., Santos, H.G., Fonseca, O.O.M., Santos, R.D., Primavesi, O. and Primavesi, A.C. (1998). The Soils of Canchim Farm, Southeast Livestock Research Center, São Carlos, SP: Semi-detailed Survey, Properties and Potential (in Portuguese). Embrapa-CNPS/São Carlos: Embrapa-CPPSE, Rio de Janeiro (Embrapa—CNPS. Boletim de Pesquisa, 7 and Embrapa—CPPSE. Boletim de Pesquisa, 2).Google Scholar
Cao, Q., Miaoa, Y., Wanga, H., Huanga, S., Chenga, S., Khoslaa, R. and Jiangaa, R. (2013). Non-destructive estimation of rice plant nitrogen status with crop circle multispectral active canopy sensor. Field Crops Research 154, 133–144. https://doi.org/10.1016/j.fcr.2013.08.005 CrossRefGoogle Scholar
Chen, J.M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing 22, 229–242. https://doi.org/10.1080/07038992.1996.10855178.CrossRefGoogle Scholar
Ciganda, V., Gitelson, A. and Schepers, J. (2009). Non-destructive determination of maize leaf and canopy chlorophyll content. Journal of Plant Physiology 166, 157–167, https://doi:10.1016/j.jplph.2008.03.004 CrossRefGoogle ScholarPubMed
Edirisinghe, A., Hill, M.J., Donald, G.E. and Hyder, M. (2011). Quantitative mapping of pasture biomass using satellite imagery, International Journal of Remote Sensing 32, 2699–2724, https://DOI:10.1080/01431161003743181 CrossRefGoogle Scholar
Ferner, J., Linstadter, A., Sudekum, K.H. and Schmidtlein, S. (2015). Spectral indicators of forage quality in West Africa tropical savannas. International Journal of Applied Earth Observation and Geoinformation 41, 99–106. https://doi.org/10.1016/j.jag.2015.04.019 CrossRefGoogle Scholar
Flynn, S.E., Dougherty, C.T. and Wendroth, O. (2008). Assessment of pasture biomass with the normalized difference vegetation index from active ground-based sensors. Agronomy Journal, 100, 144–121. https://doi.org/10.2134/agronj2006.0363 CrossRefGoogle Scholar
Gardner, A. L. (1986). Pasture research techniques and applicability of results in production systems (in Portuguese). Brasília, IICA/EMBRAPA – CNPGL. 197 p.Google Scholar
Gitelson, A.A., Viña, A., Ciganda, V., Rundquist, D.C. and Arkebauer, T.J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32, L08403. https://doi.org/10.1029/2005GL022688 CrossRefGoogle Scholar
Hanna, M., Steyn-Ross, D. and Steyn-Ross, M. (1999). Estimating biomass for New Zealand pasture using optical remote sensing techniques. Geocarto International 14, 89–94.CrossRefGoogle Scholar
Hirata, M. (2000). Quantifying spatial heterogeneity in herbage mass and consumption in pastures. Journal of Range Management 53, 315–321.CrossRefGoogle Scholar
Holland, Scientific. (2013). Crop Circle ACS-430 Multi-Spectral Crop Canopy Sensor [Internet]. Lincoln, NE: Holland Scientific, Inc. Available at http://hollandscientific.com/portfolio/crop-circle-acs-430/ (accessed 1 January 2017).Google Scholar
Laca, E.A., Demment, M.W., Winckel, J. and Kie, J.G. (1989). Comparison of weight estimate and rising-plate meter methods to measure herbage mass of a mountain meadow. Journal of Range Management 42, 71–75.CrossRefGoogle Scholar
Li, Z. and Guo, X. (2010). A suitable vegetation index for quantifying temporal variation of leaf area index (LAI) in semiarid mixed grassland. Canadian Journal of Remote Sensing 36, 709–721. https://doi.org/10.5589/m11-002 CrossRefGoogle Scholar
Mannetje, L. (2000). Measuring biomass of grassland vegetation. In Mannetje, L. and Jones, R.M. (eds.), Field and Laboratory Methods for Grassland and Animal Production Research.   Wallingford: CABI Publishing, pp. 151–177.CrossRefGoogle Scholar
Martha Junior, G.B., Alves, E. and Contini, E. (2012). Land-saving approaches and beef production growth in Brazil. Agricultural Systems 110, 173–177. https://doi.org/10.1016/j.agsy.2012.03.001 CrossRefGoogle Scholar
Martins, R.N., Pinto, F.A.C., Queiroz, D.M., Valente, D.S.M. and Rosas, J.T.F. (2020). A novel vegetation index for coffee ripeness monitoring using aerial imagery. Remote Sensing 2021, 263–279. https://doi.org/10.3390/rs13020263 Google Scholar
Moleele, N., Ringrose, S., Arnberg, W., Lunden, B. and Vanderpost, C. (2001). Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands. International Journal of Remote Sensing 22, 741–756. https://DOI:10.1080/01431160051060147 CrossRefGoogle Scholar
Muñoz-Huerta, R.F., Guevara-Gonzalez,, R.G., Contreras-Medina, L.M., Torres-Pacheco, I., Prado-Olivarez, J. and Ocampo-Velazquez, R.V. (2013). A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors 13, 10823–10843. https://doi.org/10.3390/s130810823 CrossRefGoogle ScholarPubMed
Ogura, S. and Hirata, M. (2001). Two-dimensional monitoring of spatial distribution of herbage mass in a bahia grass (Paspalum notatum Flugge) pasture grazed with cattle. Japanese Journal of Grassland Science 47, 453–459. https://doi.org/10.14941/grass.47.453 Google Scholar
Parente, L. and Ferreira, L. (2018). Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS Images from 2000 to 2016. Remote Sensing 10, 1–14. https://doi.org/10.3390/rs10040606.CrossRefGoogle Scholar
Parsons, A.J., Johnson, I.R. and Harvey, A. (1988). Use of a model to optimize the interaction between frequency and severity of intermittent defoliation to provide a fundamental comparison of the continuous and intermittent defoliation of grass. Grass and Forage Science 43, 49–59. https://doi.org/10.1111/j.1365-2494.1988.tb02140.x CrossRefGoogle Scholar
Paruelo, J.M., Epstein, H.E., Lauenroth, W.K. and Burke, I.C. (1997). Anpp estimates from NDVI for the central grassland region of the United States. Ecology 78, 953–958. https://doi.org/10.1890/0012-9658(1997)078[0953:AEFNFT]2.0.CO;2 CrossRefGoogle Scholar
Pezzopane, J.R.M., Bernardi, A.C.C., Bosi, C., Crippa, P.H., Santos, P.M. and Nardachione, E.C. (2019). Assessment of Piatã palisadegrass forage mass in integrated livestock production systems using a proximal canopy reflectance sensor. European Journal of Agronomy 103, 130–139. https://doi.org/10.1016/j.eja.2018.12.005 CrossRefGoogle Scholar
Pezzopane, J.R.M., Santos, P.M., Evangelista, S.R.M., Bosi, C., Cavalcante, A.C.R., Bettiol, G.M., Gomide, C.A.M. and Pellegrino, G.Q. (2016). Panicum maximum cv. Tanzânia: climate trends and regional pasture production in Brazil. Grass and Forage Science 72, 104–117. https://doi.org/10.1111/gfs.12229 CrossRefGoogle Scholar
Prasad, B., Carver, B.F., Stone, M.L., Babar, M.A., Raun, W.R. and Klatt, A.R. (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Science 47, 1426–1440. https://doi:10.2135/cropsci2006.07.0492 CrossRefGoogle Scholar
Pullanagari, R.R., Yule, I.J., Tuohy, M.P., Hedley, M.J., Dynes, R.A. and King, W.M. (2012). Proximal sensing of the seasonal variability of pasture nutritive value using multispectralradiometry. Grass and Forage Science 68, 110–119. https://doi:10.1111/j.1365-2494.2012.00877.x CrossRefGoogle Scholar
Rodriguez, D, Fitzgerald, G.J., Belford, R. and Christensen, L. (2006). Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop ecophysiological concepts. Australian Journal Aricultural Research 57, 781–789. https://doi.org/10.1071/AR05361.CrossRefGoogle Scholar
Rodriguez, J.A.E., Diaz-Ambrona, C.G.H. and Alfonso, A.M.T. (2014). Selección de índices de vegetación para la estimación de la producción herbácea en dehesas. Revistas Pastos 44, 6–18.Google Scholar
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. and Harlan, J.C. (1974). Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation. Greenbelt, MD, USA: NASA/GSFC, Type III Final Report, pp. 1–371.Google Scholar
Sanderson, M.A., Rotz, C.A., Fultz, S.W. and Rayburn, E.B. (2001). Estimating forage ass with a commercial capacitance meter, rising plate meter, and pasture ruler. Agronomy Journal 93, 1281–1286. https://doi.org/10.2134/agronj2001.1281.CrossRefGoogle Scholar
Schellberg, J., Hill, M.J., Gerhards, R., Rothmund, M. and Braun, M. (2008). Precision agriculture on grassland: applications, perspectives and constraints. European Journal of Agronomy 29, 59–71. https://doi.org/10.1016/j.eja.2008.05.005.CrossRefGoogle Scholar
Scrivner, J.H., Center, D.M. and Jones, M.B. (1986). A rising plate meter for estimating production and utilization. Journal of Range Management 39, 475–477.CrossRefGoogle Scholar
Serrano, J., Peça, J., Marques Da Silva, J., and Shahidian, S. (2011). Calibration of a capacitance probe for measurement and mapping of dry matter yield in Mediterranean pastures. Precision Agriculture 12, 860–875. https://doi:10.1007/s11119-011-9227-4 CrossRefGoogle Scholar
Serrano, J., Shahidian, S. and Marques Da Silva, J. (2016a). Monitoring pasture variability: Optical OptRx® crop sensor versus grassmaster II capacitance probe. Environmental Monitoring and Assessment 188, 1–17. https://doi:10.1007/s10661-016-5126-5.CrossRefGoogle ScholarPubMed
Serrano, J., Shahidian, S. and Marques Da Silva, J. (2016b). Calibration of grassMaster II to estimate green and dry matter yield in Mediterranean pastures: Effect of pasture moisture content. Crop and Pasture Science 67, 780–791. https://doi:10.1071/CP15319.CrossRefGoogle Scholar
Serrano, J., Shahidian, S., Silva, J.M., Sales-Baptista, E., Oliveira, I.F., Castro, J.L., Pereira, A., Abreu, M.C., Machado, E. and Carvalho, M. (2018). Tree influence on soil and pasture: contribution of proximal sensing to pasture productivity and quality estimation in montado ecosystems. International Journal of Remote Sensing 39, 4801–4829, https://DOI:10.1080/01431161.2017.1404166.CrossRefGoogle Scholar
Starks, P.J., Coleman, S.W. and Phillips, W.A. (2004). Determination of forage chemical composition using remote sensing. Journal of Range Management 57, 635–640.CrossRefGoogle Scholar
Starks, P.J., Zhao, D., Phillips, W.A. and Coleman, S.W. (2006). Herbage mass, nutritive value and canopy spectral reflectance of bermudagrass pastures. Grass Forage Science 61, 101–111. https://doi.org/10.1111/j.1365-2494.2006.00514.x CrossRefGoogle Scholar
Teal, R.K., Tubana, B., Girma, K., Freeman, K.W., Arnall, D.B., Walsh, O. and Raun, W.R. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal 98, 1488–1494. https://doi.org/10.2134/agronj2006.0103.CrossRefGoogle Scholar
Thornthwaite, C.W. and Mather, J.R. (1955). The Water Balance. USA: Drexel Institute of Technology, Centerton, p. 104.Google Scholar
Todd, S.W., Hoffer, R.M. and Michunas, D.G. (1998). Biomass estimation on grazed and ungrazed rangeland using spectral indices. International Journal of Remote Sensing 19, 427–438. https://doi.org/10.1080/014311698216071 CrossRefGoogle Scholar
Trotter, M.G., Schneider, D., Lamb, D., Edwards, C. and McPhee, M. (2012). Examining the potential for active optical sensors to provide biomass estimation in improved and native pastures. Proceedings of the 16th Australian Agronomy Conference “Capturing Opportunities and Overcoming Obstacles in Australian Agronomy”, University of New England 14th–18th October, Australia.Google Scholar
Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127–150. https://doi.org/10.1016/0034-4257(79)90013-0 CrossRefGoogle Scholar
Wachendorf, M., Fricke, T. and Astor, T. (2017). Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands. Grass and Forage Science 73, 1–14. https://DOI:10.1111/gfs.12312 CrossRefGoogle Scholar
Willmott, C.J. (1981). On the validation of models. Physical Geography 2, 184–194. https://doi.org/10.1080/02723646.1981.10642213 CrossRefGoogle Scholar
Yang, Y.H., Fang, J.Y., Pan, Y.D. and Ji, C.J. (2009). Aboveground biomass in Tibetan grasslands. Journal of Arid Environment 73, 91–95. https://doi.org/10.1016/j.jaridenv.2008.09.027 CrossRefGoogle Scholar
Zhao, D., Starks, P.J., Brown, M.A., Phillips, W.A. and Coleman, S.W. (2007). Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassland Science 53, 39–49. https://doi.org/10.1111/j.1744-697X.2007.00072.x.CrossRefGoogle Scholar