HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Schut, A. G. T. Articles by Meuleman, J. Search for Related Content PubMed Articles by Schut, A. G. T. Articles by Meuleman, J. Agricola Articles by Schut, A. G. T. Articles by Meuleman, J. Related Collections Forage Management Field evaluation techniques Remote Sensing Plant Analysis Grazing Management

Pasture Management

Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality

^a Plant Research International, P.O. Box 16, 6700 AA, Wageningen, the Netherlands
^b Animal Sciences Group, Applied Research, Wageningen Univ. and Research Centre, P.O. Box 65, 8200 AB Lelystad, the Netherlands (A.G.T. Schut, present address: Dep. of Spatial Sciences, Curtin Univ. of Technology, GPO Box U1987, Perth, Australia)

^* Corresponding author (tschut{at}agric.wa.gov.au)

Received for publication August 5, 2005. Grassland management has a large influence on the operating cost and environmental impact of dairy farms and requires accurate, detailed, and timely information about the yield and quality of grass. Our objective was to evaluate imaging spectroscopy as a means to obtain accurate, detailed, and rapid measurements of grass yield and quality. The work consisted of three steps. In the first step, a new mobile measurement system comprising several hyperspectral sensors was constructed and calibrated on measurements collected in six field experiments in the Netherlands in 2 yr. A partial least squares regression model was used to fit parameters derived from hyperspectral images to values of DM (dry matter) yield and quality obtained through destructive sampling. Leave-k-out cross validation showed relative errors of prediction of 8 to 22% (167–477 kg DM ha^–1 absolute) for DM yield, 21% (0.07 absolute) for the fraction of clover in DM, 6 to 12% for nutrient concentration, 15 to 16% for sugar concentration, and 3 to 5% for feeding values. In the second step, the measurement system was used to predict grassland yield and quality on fields from two farms. In the third step, the absence of calibration data for a specific date was simulated with a leave-harvest-out procedure. Predictions of absolute values were strongly biased due to system instability. Prediction of relative values was good, with a mean absolute error of 183 kg ha^–1 for DM yield. The instability of the measurement system requires that duosampling must be used for prediction of absolute values.

Abbreviations: CCD, charge coupled device • DM, dry matter • PLS, partial least squares • RMSEP, root mean squared error of prediction • RMSECV, root mean squared error of cross validation