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Precision Agriculture

, Volume 13, Issue 3, pp 351–369 | Cite as

In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture

  • R. R. Pullanagari
  • I. J. YuleEmail author
  • M. P. Tuohy
  • M. J. Hedley
  • R. A. Dynes
  • W. M. King
Article

Abstract

A study was conducted to explore the potential use of a hand-held (proximal) hyperspectral sensor equipped with a canopy pasture probe to assess a number of pasture quality parameters: crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation–anion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) during the autumn season 2009. Partial least squares regression was used to develop a relationship between each of these pasture quality parameters and spectral reflectance acquired in the 500–2 400 nm range. Overall, satisfactory results were produced with high coefficients of determination (R 2), Nash–Sutcliffe efficiency (NSE) and ratio prediction to deviation (RPD). High accuracy (low root mean square error-RMSE values) for pasture quality parameters such as CP, ADF, NDF, ash, DCAD, lignin, ME and OMD was achieved; although lipid was poorly predicted. These results suggest that in situ canopy reflectance can be used to predict the pasture quality in a timely fashion so as to assist farmers in their decision making.

Keywords

Hyperspectral proximal sensing Pasture quality Partial least squares regression (PLSR) 

Notes

Acknowledgments

The authors are grateful for the technical support received from: Michael Killick, Massey University, Palmerston North, New Zealand; Grant Rennie, AgResearch, Ruakura, Hamilton, New Zealand; Brian DeVantier, AgResearch Grasslands, Palmerston North, New Zealand; Staff from AgResearch, Lincoln, New Zealand and comments on the manuscript from Carolyn Hedley and Cathe Goulter.

References

  1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park: Sage Publications, Inc.Google Scholar
  2. Albayrak, S. (2008). Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in sainfoin pasture. Sensors, 8(11), 7275–7286. doi: 10.3390/s8117275.CrossRefGoogle Scholar
  3. AOAC. (2005). Official methods of analysis of AOAC international (18th ed., Vol. 1). Gaithersburg: Association of Official Analytical Chemists Inc., AOAC International.Google Scholar
  4. Beeri, O., Phillips, R., Hendrickson, J., Frank, A., & Kronberg, S. (2007). Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110(2), 216–225.CrossRefGoogle Scholar
  5. 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(2), 128–144. doi: 10.1007/s11119-008-9078-9.CrossRefGoogle Scholar
  6. Biewer, S., Fricke, T., & Wachendorf, M. (2009b). Development of canopy reflectance models to predict forage quality of legume–grass mixtures. Crop Science, 49(5), 1917–1926. doi: 10.2135/cropsci2008.11.0653.CrossRefGoogle Scholar
  7. Corson, D. C., Waghorn, G. C., Ulyatt, M. J., & Lee, J. (1999). NIRS: Forage analysis and livestock feeding. Proceedings of the New Zealand Grassland Association, 61, 127–132.Google Scholar
  8. Curran, P. J. (1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30(3), 271–278. doi: 10.1016/0034-4257(89)90069-2.CrossRefGoogle Scholar
  9. Davies, A. M. C., & Fearn, T. (2006). Back to basics: Calibration statistics. Spectroscopy Europe, 18(2), 31–32.Google Scholar
  10. Esbensen, K. H., Guyot, D., Westad, F., & Houmoller, L. P. (2009). Multivariate data analysis—in practice: An introduction to multivariate data analysis and experimental design (5th ed.). Oslo, Norway: CAMO.Google Scholar
  11. FAO. (2010). Greenhouse gas emissions from the dairy sector: A life cycle assessment. Rome, Italy: Animal Production and Health Division.Google Scholar
  12. Holmes, C. W., Wilson, G. F., Mackenzie, D. D. S., Flux, D. S., Brookes, I. M., & Davey, A. W. F. (2007). Milk production from pasture. Palmerston North, New Zealand: Massey University.Google Scholar
  13. Kawamura, K., Watanabe, N., Sakanoue, S., & Inoue, Y. (2008). Estimating forage biomass and quality in a mixed sown pasture based on partial least squares regression with waveband selection. Grassland Science, 54(3), 131–145.CrossRefGoogle Scholar
  14. Kokaly, R. F. (2001). Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 75(2), 153–161. doi: 10.1016/S0034-4257(00)00163-2.CrossRefGoogle Scholar
  15. Kokaly, R. F., & Clark, R. N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267–287. doi: 10.1016/S0034-4257(98)00084-4.CrossRefGoogle Scholar
  16. Kusumo, B. H. (2009). Development of field techniques to predict soil carbon, soil nitrogen and root density from soil spectral reflectance. PhD thesis unpubl, Massey University, Palmerston North.Google Scholar
  17. Kusumo, B. H., Hedley, M. J., Hedley, C. B., Arnold, G. C., & Tuohy, M. P. (2009). Predicting pasture root density from soil spectral reflectance: Field measurement. European Journal of Soil Science, 61(1), 1–13. doi: 10.1111/j.1365-2389.2009.01199.x.CrossRefGoogle Scholar
  18. Kusumo, B. H., Hedley, C., Hedley, M., Hueni, A., Tuohy, M., & Arnold, G. (2008). The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils. Australian Journal of Soil Research, 46(6–7), 623–635. doi: 10.1071/SR08118.CrossRefGoogle Scholar
  19. Lebot, V., Champagne, A., Malapa, R., & Shiley, D. (2009). NIR determination of major constituents in tropical root and tuber crop flours. Journal of Agricultural and Food Chemistry, 57(22), 10539–10547. doi: 10.1021/jf902675n.PubMedCrossRefGoogle Scholar
  20. Legates, D., & Jr McCabe, G. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241. doi: 10.1029/1998WR900018.CrossRefGoogle Scholar
  21. Marten, G. C., Halgerson, J. L., & Cherney, J. H. (1983). Quality prediction of small grain forages by near infrared reflectance spectroscopy. Crop Science, 23(1), 94–96.CrossRefGoogle Scholar
  22. Marten, G. C., Shenk, J. S., & Barton, F. E. I. (1985). Near infrared reflectance spectroscopy (NIRS): Analysis of forage quality. In Agriculture handbook: No. 643 (p. 96): United States Dept. of Agriculture, Agricultural Research Service.Google Scholar
  23. Martin, M. E., & Aber, J. D. (1997). High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecological Applications, 7(2), 431–443.CrossRefGoogle Scholar
  24. Miehle, P., Livesley, S., Li, C., Feikema, P., Adams, M., & Arndt, S. (2006). Quantifying uncertainty from large-scale model predictions of forest carbon dynamics. Global Change Biology, 12(8), 1421–1434. doi: 10.1111/j.1365-2486.2006.01176.x.CrossRefGoogle Scholar
  25. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900.Google Scholar
  26. Murray, R. I., & Yule, I. J. (2007). Developing variable rate application technology: economic impact for farm owners and topdressing operators. New Zealand Journal of Agricultural Research, 50(1), 65–72.CrossRefGoogle Scholar
  27. Mutanga, O. (2004). Hyperspectral remote sensing of tropical grass quality and quantity. PhD thesis, Wageningen University, Wageningen, The Netherlands.Google Scholar
  28. Mutanga, O., & Skidmore, A. (2003). Continuum-removed absorption features estimate tropical savanna grass quality in situ. In Proceedings of the 3rd EARSeL workshop on imaging spectroscopy (Vol. 3, pp. 543–558). Herrsching, Germany.Google Scholar
  29. Mutanga, O., Skidmore, A. K., Kumar, L., & Ferwerda, J. (2005). Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. International Journal of Remote Sensing, 26(6), 1093–1108.CrossRefGoogle Scholar
  30. NASA (1994). Accelerated canopy chemistry program. Washington, DC.Google Scholar
  31. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282–290. doi: 10.1016/0022-1694(70)90255-6.CrossRefGoogle Scholar
  32. Nguyen, H., Kim, J., Nguyen, A., Nguyen, L., Shin, J., & Lee, B.-W. (2006). Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of rice. Precision Agriculture, 7(4), 249–264. doi: 10.1007/s11119-006-9010-0.CrossRefGoogle Scholar
  33. Ozaki, Y., McClure, W., & Christy, A. (2005). Spectral analysis. In: Y. Ozaki, W. McClure, & A. Christy (Ed.), Near infrared spectroscopy in food science and technology. New Jersey: Wiley-Interscience, Wiley: Hoboken.Google Scholar
  34. Prieto, N., Andrés, S., Giráldez, F. J., Mantecón, A. R., & Lavín, P. (2006). Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. Meat Science, 74(3), 487–496. doi: 10.1016/j.meatsci.2006.04.030.PubMedCrossRefGoogle Scholar
  35. Pullanagari, R. R., Yule, I., King, W., Dalley, D., & Dynes, R. (2011). The use of optical sensors to estimate pasture quality. International Journal on Smart Sensing and Intelligent Systems, 4(1), 125–137.Google Scholar
  36. Sanches, I. D. (2009). Hyperspectral proximal sensing of the botanical composition and nutrient content of New Zealand pastures. PhD thesis unpubl. Massey University, Palmerston North, New Zealand.Google Scholar
  37. Sanches, I. D., Tuohy, M. P., Hedley, M. J., & Bretherton, M. R. (2009). Large, durable and low-cost reflectance standard for field remote sensing applications. International Jpournal of Remote Sensing, 30(9), 2309–2319. doi: 10.1080/01431160802549377.CrossRefGoogle Scholar
  38. Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639. doi: 10.1021/ac60214a047.CrossRefGoogle Scholar
  39. Schellberg, J., Hill, M. J., Gerhards, R., Rothmund, M., & Braun, M. (2008). Precision agriculture on grassland: Applications, perspectives and constraints. European Journal of Agronomy, 29(2–3), 59–71. doi: 10.1016/j.eja.2008.05.005.CrossRefGoogle Scholar
  40. Schut, A. G. T., van der Heijden, G. W. A. M., Hoving, I., Stienezen, M. W. J., van Evert, F. K., & Meuleman, J. (2006). Imaging spectroscopy for on-farm measurement of grassland yield and quality. Agronomy Journal, 98(5), 1318–1325. doi: 10.2134/agronj2005.0225.CrossRefGoogle Scholar
  41. Stubbs, T. L., Kennedy, A. C., & Fortuna, A.-M. (2009). Using NIRS to predict fiber and nutrient content of dryland cereal cultivars. Journal of Agricultural and Food Chemistry, 58(1), 398–403. doi: 10.1021/jf9025844.CrossRefGoogle Scholar
  42. Tsai, F., & Philpot, W. (1998). Derivative analysis of hyperspectral data. Remote Sensing of Environment, 66(1), 41–51. doi: 10.1016/S0034-4257(98)00032-7.CrossRefGoogle Scholar
  43. Viscarra Rossel, R. A. (2008). Parles: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems, 90(1), 72–83. doi: 10.1016/j.chemolab.2007.06.006.CrossRefGoogle Scholar
  44. 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(1–4), 183–194. doi: 10.1016/s0377-8401(03)00173-1.CrossRefGoogle Scholar
  45. Williams, P., & Norris, K. (1987). Near-infrared technology in the agricultural and food industries. American Association of Cereal Chemists, Inc., MN 55121, USA.Google Scholar
  46. Wold, S., Sjöström, M., & Eriksson, L. (2001). Pls-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. doi: 10.1016/S0169-7439(01)00155-1.CrossRefGoogle Scholar
  47. Zarco-Tejada, P. (2000). Hyperspectral remote sensing of closed forest canopies: Estimation of chlorophyll fluorescence and pigment content. PhD thesis. York University, Toronto, Ontario, Canada.Google Scholar
  48. Zhao, D. H., Li, J. L., & Qi, J. G. (2005). Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage. Computers and Electronics in Agriculture, 48(2), 155–169.CrossRefGoogle Scholar
  49. Zhao, D., Starks, P. J., Brown, M. A., Phillips, W. A., & Coleman, S. W. (2007). Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassland Science, 53(1), 39–49.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • R. R. Pullanagari
    • 1
  • I. J. Yule
    • 1
    Email author
  • M. P. Tuohy
    • 1
  • M. J. Hedley
    • 2
  • R. A. Dynes
    • 3
  • W. M. King
    • 4
  1. 1.New Zealand Centre for Precision Agriculture (NZCPA), Institute of Natural ResourcesMassey UniversityPalmerston NorthNew Zealand
  2. 2.Institute of Natural ResourcesMassey UniversityPalmerston NorthNew Zealand
  3. 3.AgResearch, Lincoln Research CentreChristchurchNew Zealand
  4. 4.AgResearch, Ruakura Research CentreHamiltonNew Zealand

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