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Use of fluorescence-based sensors to determine the nitrogen status of paddy rice

  • J. W. LI (a1) (a2) (a3), J. X. ZHANG (a1) (a2), Z. ZHAO (a1) (a2), X. D. LEI (a1) (a2), X. L. XU (a1) (a2), X. X. LU (a1) (a2), D. L. WENG (a4), Y. GAO (a5) and L. K. CAO (a1) (a2)...


The environmental concern about diffuse pollution from nitrogen (N) fertilizers has led to increased research on the diagnosis of crop N status. The SPAD chlorophyll (Chl) meter is the most commonly used tool for rice (Oryza sativa L.) N status diagnosis, but measurements are conducted at a specific point and readings are affected by different leaf positions. Many measurements per plant must be taken in order to increase the accuracy of N status diagnosis, which limits its application. The present paper attempts to determine rice N status at the canopy level using Multiplex®, a new hand-held optical fluorescence sensor. The fluorescence emission of rice leaves under light excitation was utilized by Multiplex® to non-destructively assess rice leaf Chl and phenolic compound content. A field experiment was conducted in 2011 using a completely randomized split-plot design, with main-plot treatments being six N fertilizer application rates and subplot treatments being different plant densities. Leaf Chl and phenolic compounds were evaluated using the ratio of far-red fluorescence (FRF) to red fluorescence (RF) emission under red light excitation (simple fluorescence ratio, SFR_R) (R2 = 0·35, P < 0·01) and the ratio of decadic logarithm of red to ultra-violet (UV) fluorescence emission (R2 = 0·30, P < 0·01), respectively. Both SPAD reading and fluorescence-based indices including flavonoids (FLAV), nitrogen balance index (NBI_R) and SFR_R could be used to predict rice leaf N contents. The canopy FLAV, SFR_R and NBI_R were all highly correlated to average SPAD readings (R2 > 0·70 in most cases, P < 0·01). Therefore, Multiplex® can be used as an alternative to SPAD to determine rice N status in paddy fields.


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Agati, G., Cerovic, Z. G., Marta, A. D., Di Stefano, V., Pinelli, P., Traversi, M. L. & Orlandini, S. (2008). Optically-assessed preformed flavonoids and susceptibility of grapevine to Plasmopara viticola under different light regimes. Functional Biology 35, 7784.
Barthod, S., Cerovic, Z. & Epron, D. (2007). Can dual chlorophyll fluorescence excitation be used to assess the variation in the content of UV-absorbing phenolic compounds in leaves of temperate tree species along a light gradient? Journal of Experimental Botany 58, 17531760.
Berntsen, J., Thomsen, A., Schelde, K., Hansen, O. M., Knudsen, L., Broge, N., Hougaard, H. & Hørfarter, R. (2006). Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat. Precision Agriculture 7, 6583.
Bryant, J. P., Chapin, F. S. & Klein, D. R. (1983). Carbon/nutrient balance of boreal plants in relation to vertebrate herbivory. Oikos 40, 357368.
Cartelat, A., Cerovic, Z. G., Goulas, Y., Meyer, S., Lelarge, C., Prioul, J.-L., Barbottin, A., Jeuffroy, M.-H., Gate, P., Agati, G. & Moya, I. (2005). Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crops Research 91, 3549.
Cerovic, Z. G., Ounis, A., Cartelat, A., Latouche, G., Goulas, Y., Meyer, S. & Moya, I. (2002). The use of chlorophyll fluorescence excitation spectra for the non-destructive in situ assessment of UV-absorbing compounds in leaves. Plant Cell and Environment 25, 16631676.
Evans, J. R. (1989). Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia 78, 919.
Fritschi, F. B. & Ray, J. D. (2007). Soybean leaf nitrogen, chlorophyll content, and chlorophyll a/b ratios. Photosynthetica 45, 9298.
Gitelson, A. A., Buschmann, C. & Lichtenthaler, H. K. (1999). The chlorophyll fluorescence ratio F735/F700 as an accurate measure of the chlorophyll content in plants. Remote Sensing of Environment 69, 296302.
Ghozlen, N. B., Cerovic, Z. G., Germain, C., Toutain, S. & Latouche, G. (2010). Non-destructive optical monitoring of grape maturation by proximal sensing. Sensors 10, 1004010068.
Goulas, Y., Cerovic, Z. G., Cartelat, A. & Moya, I. (2004). Dualex: a new instrument for field measurements of epidermal UV-absorbance by chlorophyll fluorescence. Applied Optics 43, 44884496.
Hamilton, J. G., Zangerl, A. R., DeLucia, E. H. & Bernebaum, M. R. (2001). The carbon-nutrient balance hypothesis: its rise and fall. Ecology Letters 4, 8695.
Huang, J. L., He, F., Cui, K. H., Buresh, R. J., Xu, B., Gong, W. & Peng, S. (2008). Determination of optimal nitrogen rate for rice varieties using a chlorophyll meter. Field Crops Research 105, 7080.
Jones, C. G. & Hartley, S. E. (1999). A protein competition model for phenolic allocation. Oikos 86, 2744.
Krause, G. H., Gallé, A., Gademann, R. & Winter, K. (2003). Capacity of protection against ultraviolet radiation in sun and shade leaves of tropical forest plants. Functional Plants Biology 30, 533542.
Lejealle, S., Evain, S. & Cerovic, Z. G. (2010). Multiplex: a new diagnostic tool for management of nitrogen fertilization of turfgrass. In 10th International Conference on Precision Agriculture, Denver, Colorado, 18–21 July, 2010, CD-ROM (Ed. Khosla, R.), p. 15. Denver, CO: International Society of Precision Agriculture.
Li, J. W., Yang, J. P., Li, D. S., Fei, P. P., Guo, T. T., Ge, C. S. & Chen, W. Y. (2011). Chlorophyll meter's estimate of weight-based nitrogen concentration in rice leaf is influenced by leaf thickness. Plant Production Science 14, 177183.
Monsi, M. & Saeki, T. (1953). Uber der lichtfaktor in den pflanzengesellschaften und seine bedeutung fur die stoffproduktion. Japanese Journal of Botany 14, 2252.
Peng, S. B., Buresh, R. J., Huang, J. L., Zhong, X. H., Zou, Y. B., Yang, J. C., Wang, G. H., Liu, Y. Y., Hu, R. F., Tang, Q. Y., Cui, K. H., Zhang, F. S. & Dobermann, A. (2011). Improving nitrogen fertilization in rice by site-specific N management. In Sustainable Agriculture vol. 2 (Eds Lichtfouse, E., Hamelin, M., Navarrete, M. & Debaeke, P.), pp. 943952. Dordrecht, The Netherlands: Springer.
Posada, J. M., Lechowicz, M. J. & Kitajima, K. (2009). Optimal photosynthetic use of light by tropical tree crowns achieved by adjustment of individual leaf angles and nitrogen content. Annals of Botany 103, 795805.
Samborski, S. M., Tremblay, N. & Fallon, E. (2009). Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agronomy Journal 101, 800816.
Tremblay, N., Wang, Z. & Bélec, C. (2010). Performance of Dualex in spring wheat for crop nitrogen status assessment, yield prediction and estimation of soil nitrate content. Journal of Plant Nutrition 33, 5770.
Tremblay, N., Wang, Z. & Cerovic, Z. G. (2012). Sensing crop nitrogen status with fluorescence indicators. A review. Agronomy for Sustainable Development 32, 451464.
Tuccio, L., Remorini, D., Pinelli, P., Fierini, E., Tonutti, P., Scalabrelli, G. & Agati, G. (2011). Rapid and non-destructive method to assess in the vineyard grape berry anthocyanins under different seasonal and water conditions. Australian Journal of Grape and Wine Research 17, 181189.
Wolf, B. (1982). A comprehensive system of leaf analyses and its use for diagnosing crop nutrient analysis. Communications in Soil Science and Plant Analysis 13, 10351059.
Zhang, Y. P. & Tremblay, N. (2010). Evaluation of the Multiplex® fluorescence sensor for the assessment of corn nitrogen status. In 10th International Conference on Precision Agriculture, Denver, Colorado, 18–21 July, 2010, CD-ROM (Ed. Khosla, R.), p. 9. Denver, CO: International Society of Precision Agriculture.

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Use of fluorescence-based sensors to determine the nitrogen status of paddy rice

  • J. W. LI (a1) (a2) (a3), J. X. ZHANG (a1) (a2), Z. ZHAO (a1) (a2), X. D. LEI (a1) (a2), X. L. XU (a1) (a2), X. X. LU (a1) (a2), D. L. WENG (a4), Y. GAO (a5) and L. K. CAO (a1) (a2)...


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