Skip to main content

Advertisement

SpringerLink
Log in

Off-nadir hyperspectral measurements in maize to predict dry matter yield, protein content and metabolisable energy in total biomass

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Sensor-based methods of analysis to assess dry matter yield and quality constituents of crops are time- and labour-saving, and can facilitate site-specific management. Nevertheless, standard nadir measurements of maize (Zea mays cv. Ambrosius), based on top-of-canopy reflectance, are difficult due to plant heights of more than three metres. This study was conducted to explore the potential of off-nadir field spectral measurements for the non-destructive prediction of dry matter yield (DM), metabolisable energy (ME) and crude protein (CP) in total biomass in a maize canopy. Plants were measured at five different heights (0–50, 50–100, 100–50, 150–200 and 200–250 cm above the soil) at three zenith view angles (60°, 75° and 90°, respectively). Modified partial least squares regression was used for analysis of the hyperspectral data (355–2300 nm and 620–1000 nm). Optimum combinations of angle and height as well as an optimum one-sensor-strategy were determined for DM yield, CP and ME in total biomass. Coefficients of determination for off-nadir measurements were compared to nadir measurements; the results showed improved prediction accuracies for DM yield and ME using off-nadir measurements, but not for CP for which nadir measurements were better.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Aparicio, N., Villegas, D., Royo, C., Casadesus, J., & Araus, J. L. (2004). Effect of sensor view angle on the assessment of agronomic traits by groundlevel hyperspectral refelctance measurements in durum wheat under contrasting Mediterranean conditions. International Journal of Remote Sensing, 25, 1131–1152.

    Article  Google Scholar 

  • Bausch, W. C., Halvorson, A. D., & Cipra, J. (2008). Quickbird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots. Biosystems Engineering, 101, 306–315.

    Article  Google Scholar 

  • Biewer, S., Erasmi, S., Fricke, T., & Wachendorf, M. (2009a). Prediction of yield and the contribution of legumes in legume-grass mixtures using field spectrometry. Precision Agriculture, 10, 128–144.

    Article  Google Scholar 

  • Biewer, S., Fricke, T., & Wachendorf, M. (2009b). Determination of dry matter yield from legume-grass swards by field spectroscopy. Crop Science, 49, 1927–1936.

    Article  Google Scholar 

  • Biewer, S., Fricke, T., & Wachendorf, M. (2009c). Determination of forage quality in legume-grass mixtures using field spectroscopy. Crop Science, 49, 1917–1926.

    Article  Google Scholar 

  • Castrillo, C., Baucells, M., Vicente, F., Muñoz, F., & Andueza, D. (2005). Energy evaluation of extruded compound foods for dogs by near-infrared spectroscopy. Journal of Animal Physiology and Animal Nutrition, 89, 194–198.

    Article  PubMed  CAS  Google Scholar 

  • Charles-Edwards, D. A., Stutzel, H., Ferraris, R., & Beech, D. F. (1987). An analysis of spatial variation in the nitrogen content of leaves from different horizons within a canopy. Annals of Botany, 60, 421–426.

    Google Scholar 

  • Cho, M. A., Skidmore, A., Corsi, F., van Wieren, S. E., & Sobhan, I. (2007). Estimation of green/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation, 9, 414–424.

    Article  Google Scholar 

  • Cozzolino, D., Fassio, A., Fernández, E., Restaino, E., & La Manna, A. (2006). Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy. Animal Feed Science and Technology, 129, 329–336.

    Article  CAS  Google Scholar 

  • Cozzolino, D., & Moron, A. (2004). Exploring the use of near infrared reflectance spectroscopy (NIRS) to predict trace minerals in legumes. Animal Feed Science and Technology, 111, 161–173.

    Article  CAS  Google Scholar 

  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E., & McMurtrey, J. E., I. I. I. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239.

    Article  Google Scholar 

  • DeBoever, J. L., Cottyn, B. G., De Brabander, D. L., Vanacker, J. M., & Boucqué, Ch. V. (1997a). Prediction of the feeding value of maize silages by chemical parameters, in vitro digestibility and NIRS. Animal Feed Science Technology, 66, 211–222.

    Article  Google Scholar 

  • DeBoever, J. L., Cottyn, B. G., Vanacker, J. M., & Boucqué, Ch. V. (1995). The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle. Animal Feed Science Technology, 51, 243–253.

    Article  CAS  Google Scholar 

  • DeBoever, J. L., Cottyn, B. G., De Brabander, D. L., Vanacker, J. M., & Boucqué, Ch. V. (1997b). Prediction of the feeding value of maize silages by chemical parameters, in vitro digestibility and NIRS. Animal Feed Science Technology, 66, 211–222.

    Article  Google Scholar 

  • Diker, K., & Bausch, W. C. (2003). Radiometric field measurements of maize for estimating soil and plant nitrogen. Biosystems Engineering, 86, 411–420.

    Article  Google Scholar 

  • Dos Santos Simões, M., Vieira Rocha, J., & Camargo Lamparelli, R. A. (2005). Spectral variables, growth analysis and yield of sugarcane. Scientia Agricola, 62, 199–207.

    Google Scholar 

  • Elwadie, M. E., Pierce, F. J., & Qi, J. (2005). Remote sensing of canopy dynamics and biophysical variables estimation of corn in Michigan. Agronomy Journal, 97, 99–105.

    Article  Google Scholar 

  • Erasmi, S., & Dobras, E. S. (2004). Potential and limitations of spectral reflectance measurements for the estimation of the site specific variability in crops. In M. Owe & G. D’Urso (Eds.), Remote sensing for agriculture, ecosystems and hydrology V, Proceedings of the Society of Photo-optical Instrumentation Engineers (Vol. 5232, pp. 42–51). Bellingham, WA, USA: SPIE—The International Society for Optical Engineering.

  • Freeman, K. W., Girma, K., Arnall, D. B., Mullen, R. W., Martin, K. L., Teal, R. K., et al. (2007). By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agronomy Journal, 99, 530–536.

    Article  CAS  Google Scholar 

  • Gastal, F., & Lemaire, G. (2002). N uptake and distribution in crops: An agronomical and ecophysiological perspective. Journal of Experimental Botany, 53, 789–799.

    Article  PubMed  CAS  Google Scholar 

  • Gianelle, D., & Guastella, F. (2007). Nadir and off-nadir hyperspectral field data: Strengths and limitations in estimating grassland biophysical characteristics. International Journal of Remote Sensing, 28, 1547–1560.

    Article  Google Scholar 

  • Goel, P. K., Prasher, S. O., Landry, J. A., Patel, R. M., Bonnell, R. B., Viau, A. A., et al. (2003). Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture, 38, 99–124.

    Article  Google Scholar 

  • González-Martín, I., Hernández-Hierro, J. M., & González-Cabrera, J. M. (2007). Use of NIRS technology with a remote reflec-tance fibreoptic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa. Analytical and Bioanalytical Chemistry, 387, 2199–2205.

    Article  PubMed  Google Scholar 

  • Greenwood, D. J., Lemaire, G., Gosse, G., Cruz, P., Draycott, A., & Neetson, J. J. (1990). Decline in percentage N of C3 and C4 crops with increasing plant mass. Annals of Botany, 66, 425–436.

    CAS  Google Scholar 

  • Halgerson, J. L., Sheaffer, C. C., Martin, N. P., Peterson, P. R., & Weston, S. J. (2004). Near-infrared reflectance spectroscopy prediction of leaf and mineral concentrations in Alfalfa. Agronomy Journal, 96, 344–351.

    Article  CAS  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.

    Article  Google Scholar 

  • Heege, H. J., Reusch, S., & Thiessen, E. (2008). Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany. Precision Agriculture, 9, 115–131.

    Article  Google Scholar 

  • Jackson, R. D., & Pinter, P. J. (1986). Spectral response of architecturally different wheat canopies. Remote Sensing of Environment, 20, 43–56.

    Article  Google Scholar 

  • Lee, K.-S., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K., & Gower, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91, 508–520.

    Article  Google Scholar 

  • Lemaire, G., & Gastal, A. (1997). N uptake and distribution in plant canopies. In G. Lemaire (Ed.), Diagnosis of the nitrogen status in crops (pp. 3–43). Berlin: Springer.

    Google Scholar 

  • Longe, O. G., & Ogedegbe, N. (1989). Influence of fiber on metabolizable energy of diet and performance of growing pullets in the tropics. British Poultry Science, 30, 193–196.

    Article  PubMed  CAS  Google Scholar 

  • Los, S. O., North, P. R. J., Grey, W. M. F., & Barnsley, M. J. (2005). A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry. Remote Sensing of Environment, 99, 400–411.

    Article  Google Scholar 

  • Maleki, M. R., Mouazen, A. M., De Ketelaere, B., Ramon, H., & De Baerdemaeker, J. (2008). On-the-go variable-rate phosphorus fertilisation based on a visible and near-infrared soil sensor. Biosystems Engineering, 99, 35–46.

    Article  Google Scholar 

  • Martens, H., & Naes, T. (1989). Multivariate Calibration. Chichester: Wiley.

    Google Scholar 

  • Meier, U. (2001). Growth stages of mono- and dicotyledonous plants. BBCH monograph (2nd ed.). Braunschweig, Germany: Federal Biological Research Centre for Agriculture and Forestry.

    Google Scholar 

  • Miao, Y., Mulla, D. J., Randall, G. W., Vetsch, J. A., & 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, 45–62.

    Article  Google Scholar 

  • Mistele, B., & Schmidhalter, U. (2008). Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. European Journal of Agronomy, 29, 184–190.

    Article  CAS  Google Scholar 

  • Oppelt, N. M. (2008). Vertical profiling of chlorophyll within wheat canopies using multi-angular remote sensing data. Canadian Journal of Remote Sensing, 34, S314–S325.

    Google Scholar 

  • Park, R. S., Gordon, F. J., Agnew, E., Barnes, J., & Steen, R. W. J. (1997). The use of near infrared reflectance spectroscopy on dried samples to predict biological parameters of grass silage. Animal Feed Science Technology, 68, 235–246.

    Article  CAS  Google Scholar 

  • Petisco, C., García-Criado, B., Vázquez de Aldana, B. R., Zabalgogeazcoa, I., Mediavilla, S., & García-Ciudad, A. (2005). Use of near-infrared reflectance spectroscopy in predicting nitrogen, phosphorus and calcium contents in heterogeneous woody plant species. Analytical and Bioanalytical Chemistry, 382, 458–465.

    Article  PubMed  CAS  Google Scholar 

  • Plénet, D., & Lemaire, G. (1999). Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops. Determination of critical N concentration. Plant and Soil, 216, 65–82.

    Google Scholar 

  • Pordesimo, L. O., Hames, B. R., Sokhansanj, S., & Edens, W. C. (2005). Variation in corn stover composition and energy content with crop maturity. Biomass and Bioenergy, 28, 366–374.

    Article  CAS  Google Scholar 

  • Salazar, L., Kogan, F., & Roytman, L. (2008). Using vegetation health indices and partial least squares method for estimation of corn yield. International Journal of Remote Sensing, 29, 175–189.

    Article  Google Scholar 

  • Sandmeier, St., Müller, Ch., Hosgood, B., & Andreoli, G. (1998). Physical mechanisms in hyperspectral BRDF data of grass and watercress. Remote Sensing of Environment, 66, 222–233.

    Article  Google Scholar 

  • SAS Institute (2002–2008). SAS Version 9.2. Cary, NC, USA: SAS Institute.

  • Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36, 1627–1639.

    Article  CAS  Google Scholar 

  • Schmidt, J. P., Dellinger, A. E., & Beegle, D. B. (2009). Nitrogen recommendations for corn: An on-the-go sensor compared with current recommendation methods. Agronomy Journal, 101, 914–924.

    Article  Google Scholar 

  • Senay, G. B., Lyon, J. G., Ward, A. D., & Nokes, S. E. (2000). Using high spatial resolution multispectral data to classify corn and soybean crops. Photogrammetric Engineering & Remote Sensing, 66, 319–327.

    Google Scholar 

  • Starks, P. J., Zhao, D., & Brown, M. A. (2008). Estimation of nitrogen concentration and in vitro dry matter digestibility of herbage of warm-season grass pastures from canopy hyperspectral reflectance measurements. Grass and Forage Science, 63, 168–178.

    Article  CAS  Google Scholar 

  • Therhoeven-Urselmans, T., Michel, K., Helfrich, M., Flessa, H., & Ludwig, B. (2006). Near-infrared spectroscopy can predict the composition of organic matter in soil and litter. Journal of Plant Nutrition and Soil Science, 169, 168–174.

    Article  Google Scholar 

  • Thorp, K. R., Steward, B. L., Kaleita, A. L., & Batchelor, W. D. (2008). Using aerial hyperspectral remote sensing imagery to estimate corn plant stand density. Transactions of the ASABE, 51, 311–320.

    Google Scholar 

  • Tillmann, P. (2001). Kalibrationsentwicklung für NIRS-Geräte—Eine Einführung. (Calibration development for NIRS decices—An introduction). Göttingen: Cuvillier Verlag.

    Google Scholar 

  • Tucker, C. J. (1977). Spectral estimation of grass canopy variables. Remote Sensing of Environment, 6, 11–26.

    Article  Google Scholar 

  • Volkers, K. C., Wachendorf, M., Loges, R., Jovanovic, N. J., & Taube, F. (2003). Prediction of the quality of forage maize by near-infrared reflectance spectroscopy. Animal Feed Science and Technology, 109, 183–194.

    Article  Google Scholar 

Download references

Acknowledgment

We would like to thank Dr. Michael Groves for his linguistic improvements of this article.

Author information

Authors and Affiliations

  1. Department of Grassland Science and Renewable Plant Resources, University of Kassel, Steinstr. 19, 37213, Witzenhausen, Germany

    Daniela Perbandt, Thomas Fricke & Michael Wachendorf

Authors
  1. Daniela Perbandt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Fricke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Wachendorf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Wachendorf.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Perbandt, D., Fricke, T. & Wachendorf, M. Off-nadir hyperspectral measurements in maize to predict dry matter yield, protein content and metabolisable energy in total biomass. Precision Agric 12, 249–265 (2011). https://doi.org/10.1007/s11119-010-9175-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-010-9175-4

Keywords

Search

Navigation