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Cereal Research Communications

, Volume 39, Issue 1, pp 147–159 | Cite as

NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions

  • L. Cabrera-Bosquet
  • G. Molero
  • A. M. Stellacci
  • J. Bort
  • S. Nogués
  • J. L. ArausEmail author
Agronomy

Abstract

The application of spectroradiometric index such as the normalized difference vegetation index (NDVI) to assess green biomass or nitrogen (N) content has focused on the plant canopy in precision agriculture or breeding programs. However, little is known about the usefulness of these techniques in isolated plants. The few reports available propose the use of a spectroradiometer in combination with special adaptors that improve signal acquisition from plants, but this makes measurements relatively slow and unsuitable. Here we studied the direct use (i.e. without adaptors) of a commercial cost-effective spectroradiometer, GreenSeeker™ (NTech Industries Ins., Ukiah, California, USA) provided with an active sensor (i.e. equipped with its own source of radiation) for measuring NDVI in four genotypes of durum wheat (Triticum turgidum L. var. durum) grown in pots under a range of water and N regimes. Strong correlations were observed between NDVI measurements and dry aboveground biomass (AB), total green area (TGA), green area without spikes (GA) and aboveground N content (AN). To prove the predictive ability of NDVI measured under potted conditions, linear regression models for each growth trait and for plant N content were built with the data of two genotypes. The models accurately predicted growth traits and N content, confirming the direct relationship between total plant biomass and spectroradiometric readings.

Keywords

spectroradiometer NDVI active sensors biomass nitrogen content green area wheat 

Abbreviations

TGA

total green area per plant

GA

green area without spikes

NDVI

normalized difference vegetation index

RMSE

root mean square error

RE

relative error

AB

aboveground biomass

AN

aboveground nitrogen content

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References

  1. Alvaro, F., García del Moral, L.F., Royo, C. 2007. Usefulness of remote sensing for the assessment of growth traits in individual cereal plants grown in the field. Int. J. Remote Sensing 28:2497–2512.CrossRefGoogle Scholar
  2. Aparicio, N., Villegas, D., Araus, J.L., Casadesús J., Royo, C. 2002. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci. 42:1547–1555.CrossRefGoogle Scholar
  3. Aparicio, N., Villegas, D., Casadesús, J., Araus, J.L., Royo, C. 2000. Spectral vegetation indices as a non-destructive tools for determining durum wheat yield. Agron. J. 92:83–91.CrossRefGoogle Scholar
  4. Aparicio, N., Villegas, D., Royo, C., CasadesÚs, J., Araus, J.L. 2004. Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions. Int. J. Remote Sensing 25:1131–1152.CrossRefGoogle Scholar
  5. Araus, J.L. 1996. Integrative physiological criteria associated with yield potential. In: Reynolds, M.P., Rajaram, S., McNab, A. (eds), Increasing Yield Potential in Wheat: Breaking Barriers. CIMMYT, Mexico D.F., pp. 150–166.Google Scholar
  6. Araus, J.L., Casadesús, J., Bort, J. 2001. Recent tools for the screening of physiological traits determining yield. In: Reynolds, M.P., Ortiz-Monasterio, J.I., McNab, A. (eds), Application of Physiol. in Wheat Breeding. CIMMYT, Mexico D.F., pp. 59–77.Google Scholar
  7. Babar, M.A., Reynolds, M.P., van Ginkel, M., Klatt, A.R., Raun, W.R., Stone, M.L. 2006. Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 46:1046–1057.CrossRefGoogle Scholar
  8. Bellairs, S.M., Turner, N.C., Hick, P.T., Smith, R.C.G. 1996. Plant and soil influences on estimating biomass of wheat in plant breeding plots using field spectral radiometers. Aust. J. Agricult. Res. 47:1017–1034.CrossRefGoogle Scholar
  9. Borel, C., Simonneau, T., This, D., Tardieu, F. 1997. Stomatal conductance and ABA concentration in the xylem sap of barley lines of contrasting genetic origins. Aust. J. Plant Physiol. 24:607–615.Google Scholar
  10. Bort, J., Casadesús, J., Araus, J.L., Grando, S., Ceccarelli, S. 2002. Spectral vegetation indices as nondestructive indicators of barley yield in Mediterranean rain-fed conditions. In: Slafer, G.A., Molina-Cano, J.L., Savin, R., Araus, J.L., Romagosa, I. (eds), Barley Science Recent Advances from Molecular Biology to Agronomy of Yield and Quality. Food Products Press, The Haworth Press, Inc., New York, USA.Google Scholar
  11. Cabrera-Bosquet, L., Molero, G., Bort, J., Nogués, S., Araus, J.L. 2007. The combined effect of constant water deficit and nitrogen supply on WUE, NUE and Δ 13C in durum wheat potted plants. Ann. Appl. Biol. 151:277–289.CrossRefGoogle Scholar
  12. Cabrera-Bosquet, L., Molero, G., Nogués, S., Araus, J.L. 2009. Water and nitrogen conditions affect the relationships of D 13C and D 18O with gas exchange and growth in durum wheat. J. Exp. Bot. 60:1633–1644.CrossRefGoogle Scholar
  13. Casadesús, J., Tambussi, E., Royo, C., Araus, J.L. 2000. Growth assessment of individual plants by an adapted remote sensing technique. In: Royo, C., Nachit, M.M., Di Fonzo, N., Araus, J.L. (eds), Durum Wheat Improvement in the Mediterranean Region: New Challenges. CIHEAM-IAMZ, Zaragoza, pp. 129–132.Google Scholar
  14. Chen, D., Brutsaert, W. 1998. Satellite-sensed distribution and spatial patterns of vegetation parameters over a tallgrass prairie. J. Atm. Sci. 55:1225–1238.CrossRefGoogle Scholar
  15. Filella, I., Serrano, L., Serra, J., Peñuelas, J. 1995. Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci. 35:1400–1405.CrossRefGoogle Scholar
  16. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Penuelas, J., Valentini, R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Applic. 5:28–41.CrossRefGoogle Scholar
  17. Hoagland, D.R., Arnon, D.I. 1950. The water-culture method for growing plants without soil. California Agricultural Experiment Station Circular 347:1–32.Google Scholar
  18. Jamieson, P.D., Porter, J.R. Wilson, D.R. 1991. A test of the computer simulation model ARC-WHEAT1 on wheat crops grown in New Zealand. Field Crops Res. 27:337–350.CrossRefGoogle Scholar
  19. Large, E.C. 1954. Growth stages in cereals. Plant Pathol. 3:128–129.CrossRefGoogle Scholar
  20. Li, F., Gnyp, M.L., Jia, L., Miao, Y., Yu, Z., Koppe, W., Bareth, G., Cen, X., Zhang, F. 2008. Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Res. 106:77–85.CrossRefGoogle Scholar
  21. Martí J., Bort J., Slafer G.A., Araus J.L. 2007. Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index? Annals of Appl. Biol. 150:253–257.CrossRefGoogle Scholar
  22. Richards, R.A. 2002. Seedling vigour in wheat — sources of variation for genetic and agronomic improvement. Aust. J. Agric. Res. 53:41–50.CrossRefGoogle Scholar
  23. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1973. Monitoring vegetation systems in the great plains with ERTS. In: Third ERTS Symposium, NASA SP-351. NASA, Washington, D.C., USA, Vol. 1, pp. 309–317.Google Scholar
  24. Tremblay, N., Wang, Z., Ma, B.L., Belec, C., Vigneault, P. 2009. A comparison of crop data measured by two comercial sensors for variable-rate nitrogen application. Precision Agric. 10:145–161.CrossRefGoogle Scholar
  25. Verhulst, N., Govaerts, B. 2010. The Normalized Difference Vegetation Index (NDVI) GreenSeeker™ Handheld Sensor: Toward the Integrated Evaluation of Crop Management. Part A: Concepts and Case Studies. CIMMYT, Mexico D.F.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2011

Authors and Affiliations

  • L. Cabrera-Bosquet
    • 1
  • G. Molero
    • 1
  • A. M. Stellacci
    • 2
  • J. Bort
    • 1
  • S. Nogués
    • 1
  • J. L. Araus
    • 1
    • 3
    Email author
  1. 1.Unitat de Fisiologia Vegetal, Facultat de BiologiaUniversitat de BarcelonaBarcelonaSpain
  2. 2.Dipartimento di Scienze delle Produzioni VegetaliUniversità degli Studi di BariBariItaly
  3. 3.International Maize and Wheat Improvement Center (CIMMYT)El BatánMexico

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