A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures
The aim of this study was to develop near infrared spectroscopy (NIRS) calibrations to predict quality parameters, dry matter (DM, g kg−1) and crude protein (CP, g kg−1 DM), in fresh un-dried grass. Knowledge of these parameters would enable more precise allocation of quality herbage to grazing live...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Information Processing in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317321000366 |
| _version_ | 1797703120804904960 |
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| author | Darren J. Murphy Bernadette O' Brien Michael O' Donovan Tomas Condon Michael D. Murphy |
| author_facet | Darren J. Murphy Bernadette O' Brien Michael O' Donovan Tomas Condon Michael D. Murphy |
| author_sort | Darren J. Murphy |
| collection | DOAJ |
| description | The aim of this study was to develop near infrared spectroscopy (NIRS) calibrations to predict quality parameters, dry matter (DM, g kg−1) and crude protein (CP, g kg−1 DM), in fresh un-dried grass. Knowledge of these parameters would enable more precise allocation of quality herbage to grazing livestock. Perennial ryegrass samples (n = 1 615) were collected over the 2017 and 2018 grazing seasons at Teagasc Moorepark to develop a NIRS calibration dataset. Additional samples were collected for an independent validation dataset (n = 197) during the 2019 grazing season. Samples were scanned using a FOSS 6500 spectrometer at 2 nm intervals in the range of 1 100 ~ 2 500 nm and absorption was recorded as log 1/Reflectance. Reference wet chemistry analysis was carried out for both parameters and the resultant data were calibrated against spectral data by means of modified partial least squares regression. A range of mathematical spectral treatments were examined for each calibration, which were ranked in order of standard error of prediction (SEP) and ratio of percent deviation (RPD). Best performing calibrations achieved high predictive precision for DM (R2 = 0.86 SEP = 9.46 g kg−1, RPD = 2.60) and moderate precision for CP (R2 = 0.84 SEP = 20.38 g kg−1 DM, RPD = 2.37). These calibrations will aid the optimisation of grassland management and the development of precision agricultural technologies. |
| first_indexed | 2024-03-12T05:00:59Z |
| format | Article |
| id | doaj.art-7fc2857357154814838873064deed1df |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T05:00:59Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-7fc2857357154814838873064deed1df2023-09-03T09:12:27ZengElsevierInformation Processing in Agriculture2214-31732022-06-0192243253A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pasturesDarren J. Murphy0Bernadette O' Brien1Michael O' Donovan2Tomas Condon3Michael D. Murphy4Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland; Department of Process, Energy and Transport Engineering, Munster Technological University, Cork, IrelandTeagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, IrelandTeagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, IrelandTeagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, IrelandDepartment of Process, Energy and Transport Engineering, Munster Technological University, Cork, Ireland; Corresponding author at: Dept of Process, Energy & Transport Engineering, Munster Technological University, Cork T12 P928, Ireland.The aim of this study was to develop near infrared spectroscopy (NIRS) calibrations to predict quality parameters, dry matter (DM, g kg−1) and crude protein (CP, g kg−1 DM), in fresh un-dried grass. Knowledge of these parameters would enable more precise allocation of quality herbage to grazing livestock. Perennial ryegrass samples (n = 1 615) were collected over the 2017 and 2018 grazing seasons at Teagasc Moorepark to develop a NIRS calibration dataset. Additional samples were collected for an independent validation dataset (n = 197) during the 2019 grazing season. Samples were scanned using a FOSS 6500 spectrometer at 2 nm intervals in the range of 1 100 ~ 2 500 nm and absorption was recorded as log 1/Reflectance. Reference wet chemistry analysis was carried out for both parameters and the resultant data were calibrated against spectral data by means of modified partial least squares regression. A range of mathematical spectral treatments were examined for each calibration, which were ranked in order of standard error of prediction (SEP) and ratio of percent deviation (RPD). Best performing calibrations achieved high predictive precision for DM (R2 = 0.86 SEP = 9.46 g kg−1, RPD = 2.60) and moderate precision for CP (R2 = 0.84 SEP = 20.38 g kg−1 DM, RPD = 2.37). These calibrations will aid the optimisation of grassland management and the development of precision agricultural technologies.http://www.sciencedirect.com/science/article/pii/S2214317321000366Near infrared spectroscopyFresh grass analysisGrassland managementGrass qualityPrecision agriculture |
| spellingShingle | Darren J. Murphy Bernadette O' Brien Michael O' Donovan Tomas Condon Michael D. Murphy A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures Information Processing in Agriculture Near infrared spectroscopy Fresh grass analysis Grassland management Grass quality Precision agriculture |
| title | A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures |
| title_full | A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures |
| title_fullStr | A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures |
| title_full_unstemmed | A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures |
| title_short | A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures |
| title_sort | near infrared spectroscopy calibration for the prediction of fresh grass quality on irish pastures |
| topic | Near infrared spectroscopy Fresh grass analysis Grassland management Grass quality Precision agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S2214317321000366 |
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