Seasonal prediction of in situ pasture macronutrients in New Zealand pastoral systems using hyperspectral data

  • Sanches I
  • Tuohy M
  • Hedley M
 et al. 
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Abstract

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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Authors

  • I. D. Sanches

  • M. P. Tuohy

  • M. J. Hedley

  • A. D. Mackay

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