Índices de reflectancia espectral de pigmentos en hojas de cultivos andinos
Spectral-reflectance indices of pigments in leaves of Andean crops
DOI:
https://doi.org/10.15446/acag.v72n1.106493Palabras clave:
antocianina, carotenoides, Chenopodium quinoa, clorofila, Solanum tuberosum, Zea mays (es)anthocyanin, carotenoids, Chenopodium quinoa, chlorophyll, Solanum tuberosum, Zea mays (en)
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Para esta investigación se estudiaron los índices de reflectancia espectral de pigmentos (clorofila, antocianina y carotenoides) contenidos en hojas de 6 variedades de cultivos andinos registrados en el Instituto Nacional de Innovación Agraria (INIA) de Ayacucho, Perú: maíz de grano blanco (MB) INIA 620 Wari y maíz de grano y tusa de color morado INIA 615 Negro Canaán (MM) (Zea mays); tubérculos de papa color blanca (PB) de la variedad Yungay y tubérculos de papa de color roja (PR) INIA 316 Roja Ayacuchana (Solanum tuberosum); y quinua de grano blanco (QB) de la variedad Blanca de Junín y de grano rojo (QR) INIA 620 Pasankalla (Chenopodium quinoa). Los índices se determinaron a partir de datos de reflectancia espectral R(λ) entre 350 y 2500 nm, obtenidos mediante el espectrorradiómetro ASD FieldSpec 4, entre el 17 de febrero y el 9 de marzo de 2020, tiempo dividido en tres periodos bien definidos (inicial, crítico y final). Las medidas directas de reflectancia R(λ) en la región visible mostraron una mayor presencia de antocianinas en la quinua roja (QR) que en el resto de cultivos. Los 4 índices de clorofila calculados (SR, NDCI,ChlRE, Chlgreen) tienen el mismo comportamiento hacia el descenso para cada cultivo estudiado, por lo que puede utilizarse cualquiera de ellos en la cuantificación del contenido de clorofila. La quinua roja, a diferencia de los otros, mostró una tendencia al incremento en la última medición. Para los índices de antocianinas y carotenoides los índices utilizados muestran también el mismo comportamiento en cada cultivo, es decir, tendencia a la disminución o estabilización, como en la QB, QR Y PR. En el caso del índice de la razón carotenoides/clorofila (Car/Chl) no se da la misma tendencia en cada cultivo; sin embargo, el índice CClHE es el que mejor se acomoda en los 6 cultivos, por mostrar más estacionariedad para todos los cultivos. No obstante, es recomendable validar su uso para cada cultivo.
In this study, spectral reflectance indices of pigments (chlorophyll, anthocyanin, and carotenoids) contained in leaves of six varieties of Andean crops registered in the National Institute of Agrarian Innovation (INIA) of Ayacucho, Peru, were studied: white grain maize (MB) INIA 620, Wari and purple corn and cob INIA 615 Black Canaan (MM) (Zea mays), white potato tubers (PB) of the Yungay variety and red potato tubers (PR) INIA 316 Ayacuchana Red (Solanum tuberosum), and white grain quinoa (QB) of the Blanca de Junín variety and red grain (QR) INIA 620 Pasankalla (Chenopodium quinoa). These indices were determined from spectral reflectance data R(λ) between 350 and 2500 nm, obtained using the ASD FieldSpec 4 spectroradiometer between February 17th and March 9th, 2020, the time span was divided into three well-defined periods (initial, critical, and final). Direct measurements of reflectance R(λ) in the visible region showed a greater presence of anthocyanins in QR red quinoa than in the other crops. The four calculated chlorophyll indices (SR, NDCI, ChlRE, Chlgreen) had the same downward behavior for each crop studied, and any of them can be used to quantify the chlorophyll content. Red quinoa, unlike the others, showed an increasing trend in the last measurement. For the anthocyanin and carotenoid indices, they showed the same behavior in each crop, that is, a tendency to decrease or stabilize as in QB, QR and PR. In the case of the ratio index of carotenoids/chlorophyll (Car/ ), the same trend did not occur in each crop, however, the CClHE index is the one that best accommodated to the six crops, as it showed more stationarity for all of them. Nevertheless, it is advisable to validate its use for each crop.
Referencias
Apaza, V.; Cáceres, G.; Estrada, R. y Pinedo, R. (2013). Catálogo de variedades comerciales de quinua en el Perú. Lima: Instituto Nacional de Innovación Agraria - INIA; Organización de las Naciones Unidas para la Agricultura y la Alimentación - FAO. http://repositorio.inia.gob.pe/handle/20.500.12955/76
Baret, F.; Houlè, V. y Gué, M. (2007). Quantification of plant stress using remote sensing observations and crop models: The case of nitrogen management. Journal of Experimental Botany, 58(4), 869-880. https://doi.org/10.1093/jxb/erl231 DOI: https://doi.org/10.1093/jxb/erl231
Covshoff, S. (ed.). (2018). Photosynthesis. Methods and protocols. Methods in Molecular Biology, vol. 1770. Human Press. https://doi.org/10.1007/978-1-4939-7786-4 DOI: https://doi.org/10.1007/978-1-4939-7786-4
Croce, R.; Van Grondelle, R.; Van Amerongen, H. y Van Stokkum, I. (eds.). (2018). Light harvesting in photosynthesis. Boca Raton: CRC Press. https://doi.org/10.1201/9781351242899 DOI: https://doi.org/10.1201/9781351242899
Fu, Y.; Yang, G.; Pu, R.; Li, Z.; Li, H.; Xu, X.; Song, X.; Yang, X. y Zhao, C. (2021). An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives. European Journal of Agronomy, 124, 126241. https://doi.org/10.1016/J.EJA.2021.126241 DOI: https://doi.org/10.1016/j.eja.2021.126241
Ghosh, M.; Swain, D. K.; Jha, M. K. y Tewari, V. K. (2013). Precision nitrogen management using chlorophyll meter for improving growth, productivity and N use efficiency of rice in subtropical climate. Journal of Agricultural Science, 5(2), 253-266. https://doi.org/10.5539/JAS.V5N2P253 DOI: https://doi.org/10.5539/jas.v5n2p253
Gitelson, A. y Solovchenko, A. (2017). Generic algorithms for estimating foliar pigment content. Geophysical Research Letters, 44(18), 9293-9298. https://doi.org/10.1002/2017GL074799 DOI: https://doi.org/10.1002/2017GL074799
Gitelson, A. A.; Chivkunova, O. B. y Merzlyak, M. N. (2009). Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. American Journal of Botany, 96(10), 1861-1868. https://doi.org/10.3732/AJB.0800395 DOI: https://doi.org/10.3732/ajb.0800395
Gitelson, A. A.; Gritz, Y. y Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271-282. https://doi.org/10.1078/0176-1617-00887 DOI: https://doi.org/10.1078/0176-1617-00887
Gitelson, A. A.; Keydan, G. P. y Merzlyak, M. N. (2006). Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33(11). https://doi.org/10.1029/2006GL026457 DOI: https://doi.org/10.1029/2006GL026457
Gitelson, A. A.; Merzlyak, M. N. y Chivkunova, O. B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and photobiology, 74(1), 38-45. https://doi.org/10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2 DOI: https://doi.org/10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2
Gitelson, A. A.; Merzlyak, M. N. y Lichtenthaler, H. K. (1996). Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 148(3-4), 501-508. https://doi.org/10.1016/S0176-1617(96)80285-9 DOI: https://doi.org/10.1016/S0176-1617(96)80285-9
He, C.; Sun, J.; Chen, Y.; Wang, L.; Shi, S.; Qiu, F.; Wang, S. y Tagesson, T. (2023). A new vegetation index combination forllLeaf carotenoid-to-chlorophyll ratio: Minimizing the effect of their correlation. International Journal of Digital Earth, 16(1). https://doi.org/10.1080/17538947.2023.2168772 DOI: https://doi.org/10.1080/17538947.2023.2168772
Hu, J. G.; Zhang, L. J.; Sheng, Y. Y.; Wang, K. R.; Shi, Y. L.; Liang, Y. R. y Zheng, X. Q. (2020). Screening tea hybrid with abundant anthocyanins and investigating the effect of tea processing on foliar anthocyanins in tea. Folia Horticulturae, 32(2), 279-290. https://doi.org/10.2478/fhort-2020-0025 DOI: https://doi.org/10.2478/fhort-2020-0025
Jacquemoud, S. y Ustin, S. (2019). Leaf optical properties. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108686457 DOI: https://doi.org/10.1017/9781108686457
Kong, W.; Huang, W.; Zhou, X.; Song, X. y Casa, R. (2016). Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data. Journal of Applied Remote Sensing, 10(2), 026035. https://doi.org/10.1117/1.JRS.10.026035 DOI: https://doi.org/10.1117/1.JRS.10.026035
Li, W.; Sun, Z.; Lu, S. y Omasa, K. (2019). Estimation of the leaf chlorophyll content using multiangular spectral reflectance factor. Plant, Cell & Environment, 42(11), 3152-3165. https://doi.org/10.1111/PCE.13605 DOI: https://doi.org/10.1111/pce.13605
Lichtenthaler, H. K.; Gitelson, A. A. y Lang, M. (1996). Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements. Journal of Plant Physiology, 148(3-4), 483-493. https://doi.org/10.1016/S0176-1617(96)80283-5 DOI: https://doi.org/10.1016/S0176-1617(96)80283-5
Lu, Y.; Zhang, X.; Cui, Y.; Chao, Y.; Song, G.; Nie, C. y Wang, L. (2023). Response of different varieties of maize to nitrogen stress and diagnosis of leaf nitrogen using hyperspectral data. Scientific Reports, 13(1), 5890. https://doi.org/10.1038/s41598-023-31887-z DOI: https://doi.org/10.1038/s41598-023-31887-z
Maslova, T. G.; Markovskaya, E. F. y Slemnev, N. N. (2021). Functions of carotenoids in leaves of higher plants (review). Biology Bulletin Reviews 11(5), 476-487. https://doi.org/10.1134/S2079086421050078 DOI: https://doi.org/10.1134/S2079086421050078
Merzlyak, M. N.; Gitelson, A. A.; Chivkunova, O. B. y Rakitin, V. Y. (1999). Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106(1), 135-141. https://doi.org/10.1034/J.1399-3054.1999.106119.X DOI: https://doi.org/10.1034/j.1399-3054.1999.106119.x
Mendoza-Tafolla, R. O.; Juárez-López, P.; Ontiveros-Capurata, R. E.; Alia-Tejacal, I.; Guillén-Sánchez, D.; Villegas-Torres, Ó. G. y Chávez-Bárcenas, A. T. (2022). Estimación de la concentración de clorofila, nitrógeno y biomasa en arúgula (Eruca sativa mill.) mediante mediciones portátiles no destructivas. Bioagro, 34(2), 151-162. https://doi.org/10.51372/bioagro342.5 DOI: https://doi.org/10.51372/bioagro342.5
Miao, Y.; Mulla, D. J.; Randall, G. W.; Vetsch, J. A. y Vintila, R. (2009). Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture, 10(1), 45-62. https://doi.org/10.1007/s11119-008-9091-z DOI: https://doi.org/10.1007/s11119-008-9091-z
Obeidat, W.; Ávila, L.; Earl, H. y Lukens, L. (2018). Leaf spectral reflectance of maize seedlings and its relationship to cold tolerance. Crop Science, 58(6), 2569-2580. https://doi.org/10.2135/CROPSCI2018.02.0115 DOI: https://doi.org/10.2135/cropsci2018.02.0115
Padilla, F. M.; de Souza, R.; Peña-Fleitas, M. T.; Grasso, R.; Gallardo, M. y Thompson, R. B. (2019). Influence of time of day on measurement with chlorophyll meters and canopy reflectance sensors of different crop N status. Precision Agriculture, 20(6), 1087-1106. https://doi.org/10.2135/cropsci2018.02.0115 DOI: https://doi.org/10.1007/s11119-019-09641-1
Pietrini, F. y Massacci, A. (1998). Leaf anthocyanin content changes in Zea mays L. grown at low temperature: Significance for the relationship between the quantum yield of PS II and the apparent quantum yield of CO2 assimilation. Photosynthesis Research, 58(3), 213-219. https://doi.org/10.1023/A:1006152610137 DOI: https://doi.org/10.1023/A:1006152610137
Press, W. H.; Teukolsky, S.; Vetterling, W. T. y Flannery, B. P. (2007). Numerical recipes. The art of scientific somputing (3° ed.). Cambridge: Cambridge University Press.
Richardson, A. D.; Duigan, S. P. y Berlyn, G. P. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist, 153(1), 185-194. https://doi.org/10.1046/J.0028-646X.2001.00289.X DOI: https://doi.org/10.1046/j.0028-646X.2001.00289.x
Savitzky, A. y Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639. https://pubs.acs.org/doi/10.1021/ac60214a047 DOI: https://doi.org/10.1021/ac60214a047
Stetsenko, L. A.; Pashkovsky, P. P.; Voloshin, R. A.; Kreslavski, V. D.; Kuznetsov, V. V. y Allakhverdiev, S. I. (2020). Role of anthocyanin and carotenoids in the adaptation of the photosynthetic apparatus of purple-and green-leaved cultivars of sweet basil (Ocimum basilicum) to high-intensity light. Photosynthetica, 58(4), 890-901. https://doi.org/10.32615/PS.2020.048 DOI: https://doi.org/10.32615/ps.2020.048
Sun, T.; Rao, S.; Zhou, X. y Li, L. (2022). Plant carotenoids: Recent advances and future perspectives. Molecular Horticulture, 2(1), 1-21. https://doi.org/10.1186/S43897-022-00023-2 DOI: https://doi.org/10.1186/s43897-022-00023-2
Wong, C. Y. S.; D’Odorico, P.; Arain, M. A. y Ensminger, I. (2020). Tracking the phenology of photosynthesis using carotenoid-sensitive and near-infrared reflectance vegetation indices in a temperate evergreen and mixed deciduous forest. New Phytologist, 226(6), 1682-1695. https://doi.org/10.1111/NPH.16479 DOI: https://doi.org/10.1111/nph.16479
Xue, L. y Yang, L. (2009). Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 97-106. https://doi.org/10.1016/J.ISPRSJPRS.2008.06.002 DOI: https://doi.org/10.1016/j.isprsjprs.2008.06.002
Zheng, X. T.; Yu, Z. C.; Tang, J. W.; Cai, M. L.; Chen, Y. L.; Yang, C. W.; Chow, W. S. y Peng, C. L. (2021). The major photoprotective role of anthocyanins in leaves of Arabidopsis thaliana under long-term high light treatment: Antioxidant or light attenuator? Photosynthesis Research, 149(1-2), 25-40. https://doi.org/10.1007/S11120-020-00761-8 DOI: https://doi.org/10.1007/s11120-020-00761-8
Zhou, X.; Huang, W.; Kong, W.; Ye, H.; Dong, Y. y Casa, R. (2017). Assessment of leaf carotenoids content with a new carotenoid index: Development and validation on experimental and model data. International Journal of Applied Earth Observation and Geoinformation, 57, 24-35. https://doi.org/10.1016/J.JAG.2016.12.005 DOI: https://doi.org/10.1016/j.jag.2016.12.005
Zhou, X.; Huang, W.; Zhang, J.; Kong, W.; Casa, R. y Huang, Y. (2019). A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status. International Journal of Applied Earth Observation and Geoinformation, 76, 128-142. https://doi.org/10.1016/J.JAG.2018.10.012 DOI: https://doi.org/10.1016/j.jag.2018.10.012
Zulfiqar, S.; Sharif, S.; Saeed, M. y Tahir, A. (2021). Role of carotenoids in photosynthesis. En Carotenoids: structure and function in the human body (pp. 147-187). Springer. https://doi.org/10.1007/978-3-030-46459-2_5 DOI: https://doi.org/10.1007/978-3-030-46459-2_5
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