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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 � r 2 � 0·70) while ash and lipid are poorly predicted (0·33 � r 2 � 0·40). However, the seasonspecific models improved the prediction accuracy, in autumn (0·73 � r 2 � 0·83) for CP, ADF, NDF and lignin; in spring (0·61 � r 2 � 0·78) for CP and ash; in summer (0·77 � r 2 � 0·80) for CP and ash, indi

Summary This paper reports the development and evaluation of a field technique for in situ measurement of root density using a portable spectroradiometer. The technique was evaluated at two sites in permanent pasture on contrasting soils (an Allophanic and a Fluvial Recent soil) in the Manawatu region, New Zealand. Using a modified soil probe, reflectance spectra (350–2500 nm) were acquired from horizontal surfaces at three depths (15, 30 and 60 mm) of an 80-mm diameter soil core, totalling 108 samples for both soils. After scanning, 3-mm soil slices were taken at each depth for root density measurement and soil carbon (C) and nitrogen (N) analysis. The two soils exhibited a wide range of root densities from 1.53 to 37.03 mg dry root g −1 soil. The average root density in the Fluvial soil (13.21 mg g −1 ) was twice that in the Allophanic soil (6.88 mg g −1 ). Calibration models, developed using partial least squares regression (PLSR) of the first derivative spectra and reference data, were able to predict root density on unknown samples using a leave-one-out cross-validation procedure. The root density predictions were more accurate when the samples from the two soil types were separated (rather than grouped) to give sub-populations (n = 54) of spectral data with more similar attributes. A better prediction of root density was achieved in the Allophanic soil (r 2 = 0.83, ratio prediction to deviation (RPD) = 2.44, root mean square error of cross-validation (RMSECV) = 1.96 mg g −1 ) than in the Fluvial soil (r 2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g −1 ). It is concluded that pasture root density can be predicted from soil reflectance spectra acquired from field soil cores. Improved PLSR models for predicting field root density can be produced by selecting calibration data from field data sources with similar spectral attributes to the validation set. Root density and soil C content can be predicted independently, which could be particularly useful in studies examining potential rates of soil organic matter change.