peer-reviewedLime is a crucial soil conditioner to bring agricultural soils to optimum pH values for nutrient availability. Lime
recommendations are typically determined in laboratory extractions, the most common being the “Shoemaker-
McLean and Pratt” (SMP) buffer method, that requires carcinogenic reagents soon to be abolished under the EU
legislation. As an alternative to wet chemistry, mid-infrared (MIR) spectroscopy has shown to be a cost-and time
effective method at predicting soil properties. The capability and feasibility of diffuse reflectance infrared
spectroscopy (DRIFTS) to predict lime requirement (LR) in tillage fields is examined. Samples from 41 cereal
tillage fields (n = 655) are used to build a calibration for DRIFTS using partial least squares regression (PLSR).
The samples were split into calibration set (31 fields, n=495) and validation set (10 fields, n= 160). After preprocessing
with trim, smoothing and standard normal variate, a calibration model using 6 latent variables,
provided R2 of 0.89 and root mean square error of cross-validation (RMSECV) of 1.56 t/ha. Prediction of all
fields from the validation set resulted in R2 of 0.76 and root mean square error of prediction (RMSEP) of 1.68 t/
ha. The predictions of the single fields ranged from R2 values of 0.41 to 0.72, RMSEP of 0.48 to 4.2 t/ha and
ratios of performance to inter-quartile distance (RPIQ) of 0.45 to 3.56. It was shown that the signals of soil
constituents having an influence on the LR were picked up in the spectra and were identified in the loading
weights of the PLSR. While the error is too high to predict the variability of LR within the field, MIR prediction
using field averages provided a viable alternative to current laboratory methods for blanket spreading of lime on
tillage fields.Teagas