Theme 23: Biodiversity
Description
Research was conducted to assess the potential of using near infrared reflectance spectroscopy (NIRS) to determine botanical composition and measures of species diversity in pasture samples. Samples were collected from six hill country (Ballantrae) and two lowland (Aorangi) pastures in New Zealand. Samples were collected in summer (March) and Autumn (May) and subsamples were dissected to determine botanical composition and species diversity. Measures of diversity included species richness, Shannon’s index, and Simpson’s index. Reflectance data were collected from a second subsample that had been dried and finely ground. Calibrations were developed using modified partial least squares. Acceptable calibration equations were developed for predicting values of all variables evaluated in the study. It appears feasible that with further refinement NIRS could be used routinely for predicting diversity of pasture samples and for reducing the process time when a large number of samples is required.
Citation
Moore, K. J. and Barker, D. J., "Determining Pasture Biodiversity with NIRS" (2021). IGC Proceedings (1993-2023). 19.
https://uknowledge.uky.edu/igc/19/23/19
Included in
Determining Pasture Biodiversity with NIRS
Research was conducted to assess the potential of using near infrared reflectance spectroscopy (NIRS) to determine botanical composition and measures of species diversity in pasture samples. Samples were collected from six hill country (Ballantrae) and two lowland (Aorangi) pastures in New Zealand. Samples were collected in summer (March) and Autumn (May) and subsamples were dissected to determine botanical composition and species diversity. Measures of diversity included species richness, Shannon’s index, and Simpson’s index. Reflectance data were collected from a second subsample that had been dried and finely ground. Calibrations were developed using modified partial least squares. Acceptable calibration equations were developed for predicting values of all variables evaluated in the study. It appears feasible that with further refinement NIRS could be used routinely for predicting diversity of pasture samples and for reducing the process time when a large number of samples is required.
