Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality
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...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
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American Society of Agronomy
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/17082 |
| _version_ | 1848749361659379712 |
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| author | Schut, Antonius Van Der Heijden, G. Hoving, I. Stienezen, M. van Evert, A. Meuleman, J. |
| author_facet | Schut, Antonius Van Der Heijden, G. Hoving, I. Stienezen, M. van Evert, A. Meuleman, J. |
| author_sort | Schut, Antonius |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:19:43Z |
| format | Journal Article |
| id | curtin-20.500.11937-17082 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:19:43Z |
| publishDate | 2006 |
| publisher | American Society of Agronomy |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-170822017-09-13T15:44:33Z Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality Schut, Antonius Van Der Heijden, G. Hoving, I. Stienezen, M. van Evert, A. Meuleman, J. CCD - charge coupled device PLS - partial least squares RMSECV - root mean squared error of cross validation RMSEP - root mean squared error of prediction DM -dry matter 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. 2006 Journal Article http://hdl.handle.net/20.500.11937/17082 10.2134/agronj2005.0225 American Society of Agronomy unknown |
| spellingShingle | CCD - charge coupled device PLS - partial least squares RMSECV - root mean squared error of cross validation RMSEP - root mean squared error of prediction DM -dry matter Schut, Antonius Van Der Heijden, G. Hoving, I. Stienezen, M. van Evert, A. Meuleman, J. Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality |
| title | Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality |
| title_full | Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality |
| title_fullStr | Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality |
| title_full_unstemmed | Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality |
| title_short | Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality |
| title_sort | imaging spectroscopy for on-farm measurement of grassland yield and quality |
| topic | CCD - charge coupled device PLS - partial least squares RMSECV - root mean squared error of cross validation RMSEP - root mean squared error of prediction DM -dry matter |
| url | http://hdl.handle.net/20.500.11937/17082 |