Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy Farming
This study focuses on calibrating and validating models for hyperspectral canopy reflectance data that are useful to predict the nutritive value of ryegrass-white clover mixed herbage available to the grazing cow. Hyperspectral measurements and herbage cuts were collected from 286 sampling plots fro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/10/11/1826 |
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| author | Federico N. Duranovich Ian J. Yule Nicolas Lopez-Villalobos Nicola M. Shadbolt Ina Draganova Stephen T. Morris |
| author_facet | Federico N. Duranovich Ian J. Yule Nicolas Lopez-Villalobos Nicola M. Shadbolt Ina Draganova Stephen T. Morris |
| author_sort | Federico N. Duranovich |
| collection | DOAJ |
| description | This study focuses on calibrating and validating models for hyperspectral canopy reflectance data that are useful to predict the nutritive value of ryegrass-white clover mixed herbage available to the grazing cow. Hyperspectral measurements and herbage cuts were collected from 286 sampling plots from a dairy farm from July 2017 to May 2018. Hyperspectral data were pre-treated by applying a Savitzky-Golay filter followed by a Gap-segment derivative algorithm. Herbage samples were analyzed for determination of herbage nutritive value traits, digestible organic matter in dry matter (DOMD), metabolizable energy (ME), crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF). Partial least squares regression was performed to calibrate the spectra against the five nutritive value traits. Results indicate that accuracy was moderately high for the CP model (R<sup>2</sup> = 0.78) and moderate for the DOMD, ME, NDF and ADF models (0.54 < R<sup>2</sup> < 0.67). The possibility of being able to use proximal sensing for the estimation of herbage nutritive value in the field could potentially contribute to more efficient grazing management with potential economic benefits for the farm business. |
| first_indexed | 2024-03-10T14:41:50Z |
| format | Article |
| id | doaj.art-477c0bed1dfc4c4297d2223c9967f998 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T14:41:50Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-477c0bed1dfc4c4297d2223c9967f9982023-11-20T21:43:25ZengMDPI AGAgronomy2073-43952020-11-011011182610.3390/agronomy10111826Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy FarmingFederico N. Duranovich0Ian J. Yule1Nicolas Lopez-Villalobos2Nicola M. Shadbolt3Ina Draganova4Stephen T. Morris5School of Agriculture and Environment, College of Sciences, Massey University, Private Bag 11-222, Palmerston North 4442, New ZealandMassey AgriFood Digital Lab, School of Food and Advanced Technology, College of Sciences, Massey University, Private Bag 11-222, Palmerston North 4442, New ZealandSchool of Agriculture and Environment, College of Sciences, Massey University, Private Bag 11-222, Palmerston North 4442, New ZealandSchool of Agriculture and Environment, College of Sciences, Massey University, Private Bag 11-222, Palmerston North 4442, New ZealandSchool of Agriculture and Environment, College of Sciences, Massey University, Private Bag 11-222, Palmerston North 4442, New ZealandSchool of Agriculture and Environment, College of Sciences, Massey University, Private Bag 11-222, Palmerston North 4442, New ZealandThis study focuses on calibrating and validating models for hyperspectral canopy reflectance data that are useful to predict the nutritive value of ryegrass-white clover mixed herbage available to the grazing cow. Hyperspectral measurements and herbage cuts were collected from 286 sampling plots from a dairy farm from July 2017 to May 2018. Hyperspectral data were pre-treated by applying a Savitzky-Golay filter followed by a Gap-segment derivative algorithm. Herbage samples were analyzed for determination of herbage nutritive value traits, digestible organic matter in dry matter (DOMD), metabolizable energy (ME), crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF). Partial least squares regression was performed to calibrate the spectra against the five nutritive value traits. Results indicate that accuracy was moderately high for the CP model (R<sup>2</sup> = 0.78) and moderate for the DOMD, ME, NDF and ADF models (0.54 < R<sup>2</sup> < 0.67). The possibility of being able to use proximal sensing for the estimation of herbage nutritive value in the field could potentially contribute to more efficient grazing management with potential economic benefits for the farm business.https://www.mdpi.com/2073-4395/10/11/1826proximal hyperspectral sensingherbage nutritive value measurementgrazing managementpartial-least squares regression |
| spellingShingle | Federico N. Duranovich Ian J. Yule Nicolas Lopez-Villalobos Nicola M. Shadbolt Ina Draganova Stephen T. Morris Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy Farming Agronomy proximal hyperspectral sensing herbage nutritive value measurement grazing management partial-least squares regression |
| title | Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy Farming |
| title_full | Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy Farming |
| title_fullStr | Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy Farming |
| title_full_unstemmed | Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy Farming |
| title_short | Using Proximal Hyperspectral Sensing to Predict Herbage Nutritive Value for Dairy Farming |
| title_sort | using proximal hyperspectral sensing to predict herbage nutritive value for dairy farming |
| topic | proximal hyperspectral sensing herbage nutritive value measurement grazing management partial-least squares regression |
| url | https://www.mdpi.com/2073-4395/10/11/1826 |
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