To evaluate the ability of field remote sensing for predicting pasture macronutrients, hyperspectral reflectance data between 350 and 2500 nm were acquired from a number of dairy and sheep pasture canopies in New Zealand. Reflectance factor, absorbance, derivatives, and continuum-removal
data were regressed against pasture nitrogen (N), phosphorus (P), and potassium (K) concentrations using partial least squares regression (PLSR). Overall, more accurate predictions were achieved using the first derivative data. The accuracy of the PLSR calibration models to predict pasture
N, P, and K concentrations increased with the separation of the pasture samples by season. Predictions with reasonable accuracy (coefficient of determination, R
2 > 0.74, and the ratio of standard deviation (SD) of the nutrients measured to the root mean square
error of cross-validation (RMSECV) ≥ 2.0) were obtained for N during winter (RMSECV ≤ 0.23%), autumn (RMSECV ≤ 0.36%), and summer (RMSECV ≤ 0.43%) seasons; P during autumn (RMSECV = 0.05%); and K during summer (RMSECV = 0.33%).
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Document Type: Research Article
Institute of Natural Resources (INR),Massey University, PO 11222Palmerston North,4442, New Zealand
AgResearch Grassland Research Centre, PO 11008Palmerston North,4442, New Zealand
Publication date: January 10, 2013
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