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Estimation of wheat nitrogen status under drip irrigation with canopy spectral indices

Published online by Cambridge University Press:  02 October 2014

X. L. JIN ,
W. Y. DIAO ,
C. H. XIAO ,
F. Y. WANG ,
B. CHEN ,
K. R. WANG  and
S.-K. LI
X. L. JIN
Affiliation:
Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
W. Y. DIAO
Affiliation:
Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
C. H. XIAO
Affiliation:
Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
F. Y. WANG
Affiliation:
Institute of Cotton, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, China
B. CHEN
Affiliation:
Institute of Cotton, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, China
K. R. WANG*
Affiliation:
Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
S.-K. LI*
Affiliation:
Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China
*
*To whom all correspondence should be addressed. Email: [email protected] and [email protected]
*To whom all correspondence should be addressed. Email: [email protected] and [email protected]
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Summary

Crop nitrogen (N) status is an important indicator of crop health and predictor of subsequent crop yield. The present study was conducted to analyse the relationships between nitrogen nutrition index (NNI), nitrogen biomass difference (ΔNB) and spectral indices in wheat, and then attempt to improve field N management. Spectral indices and concurrent sample N and biomass parameters were obtained from the Shihezi University experimental site in Xinjiang, China during 2009 and 2010. The results showed that all spectral indices were significantly correlated with NNI. Regression functions with the highest determination coefficient (R2) and the lowest root mean square error (RMSE) were used to improve prediction of NNI, and then the selected spectral index was used to estimate NNI and ΔNB. The strongest relationships were observed for the products of modified normalized difference 705 × biomass dry weight (BND705) and the enhanced vegetation index 2 (EVI2) for estimating NNI. There were also strong relationships between the NNI and the normalized NNI (ΔNNI) as well as between ΔNNI and ΔNB, with a linear relationship between ΔNB and the spectral index BND705 and a linear relationship between ΔNB and the spectral index EVI2. These results indicated that BND705 and EVI2 can be used to improve the accuracy of NNI estimation, and the correlations of ΔNB and NNI with BND705 and EVI2 can be used to further improve field N management in wheat.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 7 , September 2015 , pp. 1281 - 1291
Copyright
Copyright © Cambridge University Press 2014 

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References

Bausch, W. C. & Duke, H. R. (1996). Remote sensing of plant nitrogen status in corn. Transactions of the American Society of Agricultural and Biological Engineers 39, 1869–1875.Google Scholar
Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyper-spectral approaches. Remote Sensing of Environment 66, 273–285.Google Scholar
Bronson, K. F., Chua, T. T., Booker, J. D., Keeling, J. W. & Lascano, R. J. (2003). In-season nitrogen status sensing in irrigated cotton. II. Leaf nitrogen and biomass. Soil Science Society of America Journal 67, 1439–1448.Google Scholar
Chen, P. F., Haboudane, D., Tremblay, N., Wang, J. H., Vigneault, P. & Li, B. G. (2010). New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment 114, 1987–1997.CrossRefGoogle Scholar
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, 579–587.Google Scholar
Devienne-Barret, F., Justes, E., Machet, J. M. & Mary, B. (2000). Integrated control of nitrate uptake by crop growth rate and soil nitrate availability under field conditions. Annals of Botany 86, 995–1005.CrossRefGoogle Scholar
Erdle, K., Mistele, B. & Schmidhalter, U. (2011). Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research 124, 74–84.Google Scholar
Feng, W., Yao, X., Zhu, Y., Tian, Y. C. & Cao, W. X. (2008). Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European of Journal Agronomy 28, 394–404.Google Scholar
Filella, I., Serrano, L., Serra, J. & Peñuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science 35, 1400–1405.Google Scholar
Gislum, R., Micklander, E. & Nielsen, J. P. (2004). Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics. Field Crops Research 88, 269–277.Google Scholar
Gitelson, A. A., Viña, A., Ciganda, V., Rundquist, D. C. & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Journal of Geophysical Research Letters 32, L08403.Google Scholar
Greenwood, D. J., Gastal, F., Lemaire, G., Draycott, A., Millard, P. & Neeteson, J. J. (1991). Growth rate and % N of field grown crops: theory and experiments. Annals of Botany 67, 181–190.CrossRefGoogle Scholar
Guyot, G., Baret, F. & Major, D. J. (1988). High spectral resolution: determination of spectral shifts between the red and the near infrared. The International Archives of the Photogrammetry and Remote Sensing 11, 750–760.Google Scholar
Hansen, P. M. & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86, 542–553.Google Scholar
He, P., Li, S. T., Jin, J. Y., Wang, H. T., Li, C. J., Wang, Y. L. & Cui, R. Z. (2009). Performance of an optimized nutrient management system for double-cropped wheat-maize rotations in north-central China. Agronomy Journal 101, 1489–1496.Google Scholar
Houlès, V., Guérif, M. & Mary, B. (2007). Elaboration of a nitrogen nutrition indicator for winter wheat based on leaf area index and chlorophyll content for making nitrogen recommendations. European of Journal Agronomy 27, 1–11.CrossRefGoogle Scholar
Jiang, Z., Huete, A. R., Didan, K. & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112, 3833–3845.CrossRefGoogle Scholar
Jin, X. L., Wang, K. R, Xiao, C. H., Diao, W. Y., Wang, F. Y., Chen, B. & Li, S. K. (2012). Comparison of two methods for estimation of leaf total chlorophyll content using remote sensing in wheat. Field Crops Research 135, 24–29.Google Scholar
Jin, X. L., Diao, W. Y., Xiao, C. H., Wang, F. Y., Chen, B., Wang, K. R. & Li, S. K. (2013). Estimation of wheat agronomic parameters using new spectral indices. PLoS ONE 8, e72736.Google Scholar
Ju, X. T., Xing, G. X., Chen, X. P., Zhang, S. L., Zhang, L. J., Liu, X. J., Cui, Z. L., Yin, B., Christie, P., Zhu, Z. L. & Zhang, F. S. (2009). Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proceedings of the National Academy of Sciences of the United States of America 106, 3041–3046.Google Scholar
Justes, E., Jeuffroy, M. H. & Mary, B. (1997). Wheat, barley, and durum wheat. In Diagnosis of the Nitrogen Status in Crops (Ed. Lemaire, G.), pp. 73–91. Berlin: Springer-Verlag.Google Scholar
Kruse, J. K., Christians, N. E. & Chaplin, M. H. (2006). Remote sensing of nitrogen stress in creeping bentgrass. Agronomy Journal 98, 1640–1645.Google Scholar
Lemaire, G. & Gastal, F. (1997). N uptake and distribution in plant canopies. In Diagnosis of the Nitrogen Status in Crops (Ed. Lemaire, G.), pp. 3–43. Berlin: Springer-Verlag.Google Scholar
Lemaire, G., Khaity, M., Onillon, B., Allirand, J. M., Chartier, M. & Gosse, G. (1992). Dynamics of accumulation and partitioning of N in leaves, stems and roots of lucerne (Medicago sativa L.) in a dense canopy. Annals of Botany 70, 429–435.Google Scholar
Lemaire, G., Avice, J. C., Kim, T. H. & Ourry, A. (2005). Developmental changes in shoot N dynamics of lucerne (Medicago sativa L.) in relation to leaf growth dynamics as a function of plant density and hierarchical position within the canopy. Journal of Experimental Botany 56, 935–943.CrossRefGoogle ScholarPubMed
Li, F., Mistele, B., Hu, Y. C., Yue, X. L., Yue, S. C., Miao, Y. X., Chen, X. P., Cui, Z. L., Meng, Q. F. & Schmidhalter, U. (2012). Remotely estimating aerial N status of phenologically differing winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany. Field Crops Research 138, 21–32.Google Scholar
Liang, H. P. & Liu, X. G. (2010). Model for calculating corn nitrogen nutrition index using hyper-spectral data. Transactions of the Chinese Society of Agricultural Engineering 26, 250–255. (in Chinese)Google Scholar
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B. & Rakitin, V. Y. U. (1999). Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum 106, 135–141.Google Scholar
Mistele, B. & Schmidhalter, U. (2008). Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. European Journal of Agronomy 29, 184–190.Google Scholar
Moran, J. A., Mitchell, A. K., Goodmanson, G. & Stockburger, K. A. (2000). Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiology 20, 1113–1120.Google Scholar
Peñuelas, J., Baret, F. & Filella, I. (1995). Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica 31, 221–230.Google Scholar
Rondeaux, G., Steven, M. & Baret, F. (1996). Optimization of soil adjusted vegetation indices. Remote Sensing of Environment 55, 95–107.Google Scholar
Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium- Volume I: Technical Presentations (Eds Freden, S. C., Mercanti, E. P. & Becker, M. A.), pp. 309–317. Washington, D.C.: NASA.Google Scholar
Schepers, J. S., Francis, D. D. & Thompson, M. T. (1989). Simultaneous determination of total C, total N and 15N on soil and plant material. Communications in Soil Science and Plant Analysis 20, 949–959.Google Scholar
Sims, D. A. & Gamon, J. A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range species, leaf structures and development stages. Remote Sensing of Environment 81, 337–354.Google Scholar
Tarpley, L., Reddy, K. R. & Sassenrath-Cole, G. F. (2000). Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Science 40, 1814–1819.Google Scholar
Thenkabail, P. S., Smith, R. B. & Pauw, E. D. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71, 158–182.Google Scholar
Wang, R. D. (2012). Drip Irrigation of Wheat Cultivation. Beijing: China Agriculture Press. (In Chinese)Google Scholar
Xue, L. H., Cao, W. X., Luo, W. H., Dai, T. B. & Zhu, Y. (2004). Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agronomy Journal 96, 135–142.Google Scholar
Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, 415–421.Google Scholar
Zhang, F. S. (2008). Strategy of Chinese Fertilizer Industry and Scientific Application. Beijing: Chinese Agriculture University Press. (In Chinese)Google Scholar
Zhang, J. H., Wang, K., Bailey, J. S. & Wang, R. C. (2006). Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere 16, 108–117.CrossRefGoogle Scholar
Zhang, W. L., Tian, Z. X., Zhang, N. & Li, X. Q. (1996). Nitrate pollution of groundwater in northern China. Agriculture, Ecosystems and Environment 59, 223–231.Google Scholar
Zhu, Y., Li, Y., Feng, W., Tian, Y., Yao, X. & Cao, W. (2006). Monitoring leaf nitrogen in wheat using canopy reflectance spectra. Canadian Journal of Plant Science 86, 1037–1046.Google Scholar