Proximal sensing of the seasonal variability of pasture nutritive value using multispectral radiometry |
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| Authors: | R R Pullanagari I J Yule M P Tuohy M J Hedley R A Dynes W M King |
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| Institution: | 1. New Zealand Centre for Precision Agriculture (NZCPA), Institute of Natural Resources, Massey University, , Palmerston North, New Zealand;2. Institute of Natural Resources, Massey University, , Palmerston North, New Zealand;3. AgResearch, Lincoln Research Centre, , Christchurch, New Zealand;4. AgResearch, Ruakura Research Centre, , Hamilton, New Zealand |
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| Abstract: | The nutritive value of pasture is an important determinant of the performance of grazing livestock. Proximal sensing of in situ pasture is a potential technique for rapid prediction of nutritive value. In this study, multispectral radiometry was used to obtain pasture spectral reflectance during different seasons (autumn, spring and summer) in 2009–2010 from commercial farms throughout New Zealand. The analytical data set (n = 420) was analysed to develop season‐specific and combined models for predicting pasture nutritive‐value parameters. The predicted parameters included crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, lignin, lipid, metabolizable energy (ME) and organic matter digestibility (OMD) using a partial least squares regression analysis. The calibration models were tested by internal and external validation. The results suggested that the global models can predict the pasture nutritive value parameters (CP, ADF, NDF, lignin, ME and OMD) with moderate accuracy (0·64 ≤ r2 ≤ 0·70) while ash and lipid are poorly predicted (0·33 ≤ r2 ≤ 0·40). However, the season‐specific models improved the prediction accuracy, in autumn (0·73 ≤ r2 ≤ 0·83) for CP, ADF, NDF and lignin; in spring (0·61 ≤ r2 ≤ 0·78) for CP and ash; in summer (0·77 ≤ r2 ≤ 0·80) for CP and ash, indicating a seasonal impact on spectral response. |
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| Keywords: | pasture nutritive value proximal sensing multispectral radiometry partial least squares regression |
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