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Use of X‐ray fluorescence (XRF) spectrometry for estimation of soil texture, pH, and cation exchange capacity (CEC) is desirable given the time‐consuming nature of traditional methods. Recent studies have shown promising results; however, further investigation is required to determine the effects of sample preparation and data evaluation techniques on accuracy. Our objective was to compare (I) a simple but well‐founded approach, combining measurement of powder samples and modeling with elemental contents as predictors in stepwise multiple linear regressions (MLR), with alternative approaches including (II) use of partial least squares regression (PLSR), (III) sample preparation as a pressed pellet, and (IV) spectral intensities as predictors (20 kV, 40 kV, and concatenated 20 + 40 kV). A total of 395 loess soils from three arable fields were used with a fivefold random training‐testing approach and a hold‐one‐site‐out training‐testing approach. With random partitioning, clay, silt, and sand accuracy with approach I was excellent (ratio of performance to interquartile distance in validation (RPIQv) = 8.5–12.9), while pH and CEC estimations were satisfactory to excellent (RPIQv = 2.0–2.5 and 2.2–3.3, respectively). Differences between MLR and PLSR were negligible. Increases in accuracy with pellet samples were 1%–13% of RPIQv for 20 kV intensities, but effects were inconsistent for other predictors. The optimal predictor varied by property, and differences ranged from 3% to 13% of RPIQv. Improvements to accuracy from Approach I to the best alternative were largest for texture (10%–15%) but may be superfluous given the excellent accuracy across all approaches. Although the leave‐one‐site‐out training resulted in variable performance, inclusion of soils from the target site in training assured reliable accuracy. With random partitioning into training and test sets, XRF estimation of soil texture, pH, and cation exchange capacity was successful.Effects of preparation (powder vs. pellet), predictor (elements vs. spectra), and algorithm (multiple linear regression vs. partial least squares regression) were minor.Accuracy improvements with optimized approaches were largest for texture prediction but may be superfluous.Inclusion of soils from the target site in training improved the reliability of models.

The RMSE of validation (RMSEV) and ratio of the interquartile range to RMSEV(RPIQV) are key quality parameters in diffuse reflectance infrared (IR) spectroscopy studies, but the effects of different factors on these parameters are often not sufficiently considered. Our objectives were to reveal the effects of range of contents, sample size, data pretreatment, wavenumber region selection, and algorithms on the evaluation of IR spectra in the wavenumber range from 1,000 to 7,000 cm−1(mid‐ and long‐wave near IR) estimations. Contents of soil organic C (SOC), N, clay, and sand and pH values were determined for surface soils of an arable field in India, and IR spectra were recorded for four samples consisting of 71–263 soils. For each of the four samples, five random partitions into calibration and validation datasets were carried out, and partial least squares regression (PLSR) or support vector machine regression was performed. A plot of the RMSEVvalues against the interquartile ranges of measured values for the validation samples (IQRV) indicated that the IQRVwas a key parameter for all soil properties: a sufficiently high IQRV—which is affected by sample size and random partitioning—resulted in generally good estimation accuracies (RPIQV≥ 2.70). Optimized data pretreatment and wavenumber region selection improved estimation accuracy for SOC and pH. Support vector machine regression was superior to PLSR for the estimation of SOC, clay, and sand, but worse for pH. Overall, this study indicates that multiple partitioning of the data is essential in IR studies and suggests that RPIQVand RMSEVneed to be interpreted in the context of the respective IQRVvalues. Multiple partitioning of the data is essential in infrared studies.The interquartile range (IQR) of measured data was a key parameter affecting the evaluation.A sufficiently high IQR resulted in generally good estimation accuracies in this field study.SVMR was slightly superior to PLSR for the estimation of SOC, clay, and sand.Optimum pretreatments and wavenumber region selection were useful for SOC and pH estimations.

The robustness of soil organic carbon (SOC) and total nitrogen (TN) content prediction accuracy by visible near‐infrared spectroscopy (visNIRS) and mid‐infrared spectroscopy (MIRS) models after a change in the quantity or quality of crop residues requires investigation. Arable soils (0–20 cm) from 20 locations across Germany were collected, and 0, 2, 4, or 8 g C kg soil−1of wheat straw (C/N ratio, 54) or clover (C/N ratio, 13) were added. Before and after a 56‐d incubation, dried and ground samples were measured for SOC and TN content and by visNIRS and MIRS. The complete dataset (n= 280) was subdivided into calibration and validation datasets to test the robustness of partial least squares regression models to changes in crop residue quantity and quality in soil. Noise‐reducing data pretreatments included region selection, moving averages, resampling every second data point, and the Savitzky‐Golay algorithm. The MIRS estimates for SOC (7.4–33 g kg−1) had lower root mean squared error of validation (RMSEV = 0.9–2.9 g kg−1) compared with visNIRS (RMSEV = 1.6–7.1 g kg−1). Total N estimates (0.7–2.8 g kg−1) were more comparable for MIRS (RMSEV = 0.1–0.3 g kg−1) and visNIRS (RMSEV = 0.1–1.0 g kg−1). Loadings of partial least squares regression components suggested the predictive mechanisms for SOC and TN were more similar for visNIRS than for MIRS. Differing crop residue quantity or quality in calibration versus validation resulted in biased SOC and TN estimates by visNIRS and MIRS models. However, calibration with a global residue model containing all soils and crop residue quantities and qualities lowered RMSEVfor SOC and TN prediction with visNIRS and MIRS, demonstrating the usefulness of this approach.