Exploring the predictability of soil texture and ... - Wiley Online Library

European Journal of Soil Science, July 2015, 66, 631–638

doi: 10.1111/ejss.12228

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool J . W e t t e r l i n d, K . P i i k k i, B . S t e n b e r g & M . S ö d e r s t r ö m Precision Agriculture and Pedometrics, Department of Soil and Environment, SLU, Swedish University of Agricultural Science, PO Box 234, SE-532 23 Skara, Sweden

Summary In soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (ECa ), reflectance in the visible and near-infrared (vis-NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5-cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm-specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis-NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross-validation (RMSECV) of 0.3–0.5% for SOM, about 6% for clay and silt and 10–11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including ECa reduced the RMSECV on average for all particle size fractions by 5–10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis-NIR sensor measurements with measurements of ECa and or IF.

Introduction Improved nutrient use efficiency is crucial to secure sufficient and sustainable food production. Enhanced precision in crop production, such as site-specific fertilizer management, is regarded as one of the key instruments for success (Sutton et al., 2013). Thus, there is a large demand for detailed information on soil properties at the field and farm scale that can be met only by efficient, simple techniques that enable fast and spatially dense sampling at smaller costs than with conventional laboratory analysis. Proximal soil sensors are frequently proposed as such a technical solution (Viscarra Rossel et al., 2011). Although proximal soil sensors offer great advantages in terms of efficiency, no single soil sensor can provide information on all soil properties that may be of interest. Moreover, most proximal soil sensors do not measure these soil properties directly and/or the measurements are affected by several soil properties simultaneously. Correspondence: J. Wetterlind. E-mail: [email protected] Received 11 December 2014; revised version accepted 11 December 2014

This is why combining information from conceptually different soil sensors has the potential to enhance the accuracy of prediction and robustness (Viscarra Rossel et al., 2011). For example, when delineating fields into management zones, Wong et al. (2010) and Castrignanò et al. (2012) used a combination of electromagnetic induction (EMI) and gamma-ray sensors to overcome shortcomings in the individual sensors when differentiating between certain soil properties. Sensors to measure soil strength can be used when mapping spatial variation in soil compaction, but they are affected by several other soil properties such as moisture, organic matter (SOM) content and texture. Because more information than measurements of soil strength is needed to understand soil compaction, much of the literature on combining proximal soil sensors relates to soil compaction measurements (Lui et al., 1996; Hummel et al., 2004; Mouazen & Ramon, 2006; Hemmat & Adamchuk, 2008; Kweon et al., 2009; Naderi-Boldaji et al., 2013). Studies have been published on predicting soil carbon or SOM (Kweon et al., 2009; Knadel et al., 2011; Kweon, 2012; Mahmood et al., 2012), pH and macronutrients (Taylor et al., 2010; Mahmood

© 2015 The Authors. European Journal of Soil Science published by John Wiley & Sons Ltd on behalf of British Society of Soil Science. 631 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

632 J. Wetterlind et al. et al., 2012; Schirrmann et al., 2012), and soil texture and clay content (Taylor et al., 2010; Mahmood et al., 2012; Piikki et al., 2013). Both Piikki et al. (2013) and Mahmood et al. (2012) combined data from sensor readings that were measured independently in the sense that the data were not recorded simultaneously and not always at exactly the same location. In the study by Mahmood et al. (2012), measurements by one of the sensors were made in the laboratory. This introduces, to various degrees, uncertainties related to temporal and geographical differences that can be reduced by using multi-sensor platforms where measurements by different sensors are made simultaneously. Mahmood et al. (2012) combined measurements of ECa with those from visible and near infrared (vis-NIR) light reflectance spectroscopy. Taylor et al. (2010) and Piikki et al. (2013) used ECa measurements in combination with proximal measurements of gamma ray spectrometry, together with elevation (Taylor et al., 2010), other terrain attributes and a panchromatic aerial photograph (Piikki et al., 2013). Electrical conductivity can be measured with or without contact with the soil; it is affected by several soil properties, including texture, SOM content, soil moisture and salinity (Adamchuk et al., 2004). However, ECa measurements are often integrated over large volumes of soil, which can introduce difficulties when they are combined with other sensor measurements made with smaller volumes (Mahmood et al., 2012). Vis-NIR spectroscopy has been studied extensively over the last 20–25 years. Absorption in the vis-NIR range of the electromagnetic spectrum can relate to soil properties because of absorption by molecules related to SOM, water and clay minerals (Stenberg et al., 2010). The vis-NIR spectrophotometers have, for example, been combined with sensors of soil strength to compensate for soil moisture (Mouazen & Ramon, 2006). In our study, we used a multi-sensor probe system equipped with sensors to measure ECa and vis-NIR spectra together with a load cell for measuring the force used to push a probe vertically through the soil. The system enables simultaneous sensor measurements in a soil profile. The ECa measurements represent a volume of soil that is similar to that represented by the other sensor measurements. The system has been used previously to predict soil carbon and bulk density in six fields in Kansas with promising results, but the benefits of combining the three sensor measurements were not obvious and there is a need for further investigation (Kweon et al., 2009). To benefit from proximal sensor measurements rather than using conventional soil sampling, and to make this approach practicable, the number of calibration samples needs to be as small as possible to reduce costs (Wetterlind et al., 2010; Viscarra Rossel et al., 2011). Mapping the soil on farms in many countries is based on one sample per hectare at most, and for soil texture the sample size might be smaller. In addition, a small number of calibration samples allows for the additional costs of the sensor measurements. The aim of the present study was to explore the accuracy of predictions of soil texture (fractions of clay, silt and sand) and SOM content in a practical application using a commercial integrated soil profiling tool with three different soil sensors. A further aim

was to evaluate whether the predictions were improved when the sensors were combined, rather than using the sensors individually. The results from this study were also used as the first step in a three-dimensional soil mapping approach presented by Piikki et al. (2014).

Materials and methods The study was carried out on two farms, Entorp and Brogården, which are about 15 km apart on an agricultural plain in south-west Sweden (Figure 1a). Several adjacent fields of Entorp and Brogården with a total area of 55 and 37 ha, respectively, were sampled. On each farm, 20 sampling locations were selected to cover the spatial variation based on initial scans by two non-invasive proximal sensors that measured ECa (EM-38-MK2; Geonics Ltd, Mississauga, Ontario, Canada) and natural gamma radiation (The Mole, The Soil Company, Groningen, the Netherlands) (Figure 1). These sensor measurements are described in detail in Piikki et al. (2014), where the results from this project were used for the development of three-dimensional soil maps. The Veris P4000 vis-NIR-ECa-IF probe (Veris Technologies Inc., Salina, Kansas, USA) measurements were recorded in situ (Figure 1b,c) at all sites on both farms. The probe was equipped with a cone tip with EC contacts to record dipole ECa data (for technical details on a similar ECa sensor, see Christy et al., 1994). The vis-NIR spectra were recorded through a sapphire glass window about 0.1 m above the cone tip. The wavelength range was 350–2200 nm, with a spectral resolution of 8 nm. The vis-NIR spectra from the instrument were obtained as apparent absorbance (log(1/reflectance)). A load cell measured the insertion force (IF) needed to push the 1-m long probe into the soil. Two profile scans, 0.1–0.8 m apart, were made at each soil sampling location and the sensor data from the three sensors were logged simultaneously approximately every 1.5 cm to a depth of 0.8 m. Data from all three sensors were automatically logged into the same output file, with columns for ECa , IF and apparent absorbance values at about every fifth wavelength in the vis-NIR spectral range. For reference analyses of SOM content and texture, three cores of soil were taken to a depth of 0.8 m at each sampling location within 0.5 m of where the sensor probe was inserted. These cores of soil were divided into three layers (0–0.2, 0.4–0.6 and 0.6–0.8 m) and pooled to form one composite sample per layer and sampling location. No reference soil samples were taken in the 0.2–0.4 m layer to avoid the presumed compaction zone resulting from ploughing, which might have affected the probe measurements and would have been detrimental to the calibrations. The soil layers used in the study included the topsoil layer, which is commonly used in conventional farm soil mapping, and two subsoil layers. The SOM content was analysed by loss on ignition (LOI) at 550∘ C and was corrected for structural water in clay minerals (Ekström, 1927). Soil texture (fractions of clay < 0.002 mm, silt 0.002–0.06 mm and sand 0.06–2 mm) was analysed by a sedimentation method (Gee & Bauder, 1986). Logged data from the three sensors were recalculated to correspond to the three depth intervals in the reference soil samples by

© 2015 The Authors. European Journal of Soil Science published by John Wiley & Sons Ltd on behalf of British Society of Soil Science European Journal of Soil Science, 66, 631–638

Sensor fusion for texture and SOM predictions

(a)

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40°0’0"E

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(b)

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ECa

TC 0

200

400 m

(c)

50°0’0"N

TC

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Location of experimental sites

0

200

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Figure 1 (a) Study area with location of the experimental sites, and the sampling locations at (b) Entorp and (c) Brogården. Dark colour indicates large ECa or TC values.

depth-weighted averages using the statistical software environment R (R Development Core Team, 2012). Before depth aggregation, spectra were first inspected visually and non-typical soil spectra were removed. Depth-aggregated absorbance vis-NIR spectra were transformed and smoothed by the first-order Savitzky-Golay derivative (Savitzky & Golay, 1964). The number of smoothing points used in the transformation differed between farms and soil properties (Entorp, 7, 9, 5 and 11 for clay, silt, sand and SOM content, respectively; Brogården, 3 for clay, silt and sand and 5 for SOM content). Initially, a range of smoothing points was tested for each soil property on each farm to select the number used in the final calibration models. The number of smoothing points resulting in the smallest root mean square error of cross-validation (RMSECV; Equation (1)) was selected. Because of excessive noise, bands below 400 nm were excluded from the analysis. Earlier experience with spectroscopic calibration models for soil texture with similar types of soil has shown that excluding the visible part of the spectra sometimes improves performance compared with using the full vis-NIR range. Calibrations were made, therefore, that included both the visible and near infrared wavelength range (vis-NIR, 400–2200 nm), as well as the near infrared range only (NIR, 780–2200 nm). For each farm, the three sensors were calibrated to the soil properties one by one by simple linear regression and in combination by partial least squares regression (PLSR). This resulted in seven calibration models for each soil property: ECa , IF, NIR/vis-NIR, ECa + IF, NIR/vis-NIR + ECa , NIR/vis-NIR + IF

and NIR/vis-NIR + ECa + IF (Figure 2). The calibration models included all three depths and were validated by cross-validation in 20 segments, with one segment per sampling site in which all three depth intervals were kept together. The calibrations were evaluated based on the RMSECV and the modelling efficiency (E; Equation (2)). The value E indicates the proportion of the total variation explained by the model (the 1:1 line between predicted and measured values) and includes both the relationship between measured and predicted values and systematic errors. √ √ n √1 ∑( )2 yi , y − ̂ RMSECV = √ n i=1 i

(1)

n ∑ ( )2 yi yi − ̂

E =1−

i=1 n ∑ ( )2 yi − yi

,

(2)

i=1

where y denotes the measured value, ̂ y the predicted value and n the number of samples. Because the data from the different sensors had different ranges and units, the predictor variables were standardized by mean-centring and scaling by 1/standard deviation before being subjected to PLSR. Preliminary results also showed that predictions were improved in some cases by scaling the wavelengths when the vis-NIR sensor was used on its own. Therefore, when


634 J. Wetterlind et al.

Figure 2 Overview of the experimental design.

the vis-NIR sensor was used on its own, calibrations were made (i) without scaling the wavelengths and (ii) with the wavelengths scaled by 1/standard deviation as when used in combination with the other sensors (Figure 2). The best calibration results in terms of RMSECV and E were then used in the comparisons with the other sensors. When the sensors were combined, IF and ECa were treated in two different ways. Calibrations were made (i) without giving extra weight to IF and ECa and (ii) with extra weight given to IF and ECa data (20 or 50 times greater) to compensate for the larger number of variables in the NIR and vis-NIR spectra than with the single variable from the IF and ECa sensors (Figure 2).

Results and discussion Soil texture and SOM content in soil samples Soil texture (clay, silt and sand fractions) on the two farms ranged from coarse to fine-textured (Figure 3). Compared with Brogården, the sand content was larger and the range of soil texture was also greater at Entorp. The samples at Entorp had a bimodal distribution for clay and sand content, with many of the soil samples having either very fine or very coarse texture. As can be seen in Figure 3, the three particle size classes were strongly correlated with each other at Entorp (r = 0.96 for clay and silt, r < −0.98 for sand and clay, and sand and silt) and weakly to strongly correlated at Brogården (r = 0.36 for clay and silt, r = −0.72 and −0.90 for sand and silt and sand and clay, respectively). The variation in SOM content at Entorp was moderate, with a mean SOM content of 0.85 (including the very small SOM content in the subsoil) and a range of 1.4 to 2.9%. At Brogården, three topsoil samples had a larger SOM content (8.4–11.2%) than the rest of the samples (maximum 3.9%). At those sampling locations, the large SOM content in the topsoil was not accompanied by a greater SOM content in the subsoil.

Calibrations using individual sensors For the calibrations using individual sensors, the largest E values and the smallest RMSECV values were obtained with the vis-NIR sensor for all soil properties on both farms (Tables 1–3). Use of the

vis-NIR wavelength range resulted in the best calibration results for SOM content, whereas using the NIR range gave the best results for clay, silt and sand. A few samples with very large values compared with the rest of the samples (outliers) strongly influenced the calibration models, often resulting in over-optimistic validation statistics. Therefore, calibrations of SOM content at Brogården were made both with and without the three soil samples with SOM content greater than 8% (SOM and SOMlow , respectively; Table 3). As could be expected, because of the larger range in SOM content, the calibration models for SOM resulted in larger values of E than those for SOMlow , whereas the latter had the smallest RMSECV values. The smaller RMSECV value for SOMlow was accompanied by a decrease in the standard deviation of almost 50%, which also usually leads to smaller RMSECV values. Nevertheless, regardless of the differences in the range of SOM content in the two datasets at Brogården, the prediction results for the individual sensors and the different sensor combinations were ranked in the same way for both datasets. The soil texture in the three samples with large SOM content was similar to that in the rest of the samples; therefore, they were retained in the calibrations for clay, silt and sand. For SOM content, scaled (1/standard deviation) vis-NIR spectra resulted in better validation statistics than calibrations when the unscaled vis-NIR spectra were used. The opposite was true for soil texture. Consequently, calibration results using scaled (1/standard deviation) vis-NIR spectra were plotted for SOM content (Table 3), whereas calibration results using unscaled NIR spectra are given for clay, silt and sand content (Tables 1, 2). One explanation for the different effects of scaling might be the often weak signal for organic matter in the vis-NIR spectrum, especially with small organic matter content and a large variation in the mineral matrix (Stenberg et al., 2010). The increased effect of weak absorption features in the spectra with scaling might, therefore, enhance the information on organic matter and consequently lead to better calibration models compared with unscaled first-order derivative spectra. For soil texture, especially clay content, calibrations using only the ECa measurements were almost as good as those using NIR (RMSECV 6.5 and 5.5% for ECa and NIR, respectively, and 6.4% for both ECa and NIR for clay content on the two farms). Insertion



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Figure 3 Texture triangles showing clay, silt and sand content in the soil samples at (a) Entorp and (b) Brogården. Table 1 Cross-validated results and weighting for soil texture at Entorp. The best predictions with or without extra weight on apparent electrical conductivity (ECa ) and insertion force (IF) are presented Clay / % SDa = 22.5

ECa IF EC + IF NIR NIR + ECa NIR + IF NIR + ECa + IF

Silt / % SD = 15.8

Sand / % SD = 37.8

E

RMSECV

wb

E

RMSECV

w

E

RMSECV

w

0.92 –c 0.94 0.94 0.94 0.93 0.94

6.5 – 5.6 5.5 5.8 6.1 5.8

– – – – nwd nw nw, nw

0.81 0.04 0.87 0.84 0.88 0.82 0.88

7.1 16.0 5.8 6.6 5.7 6.9 5.8

– – – – 50 20 20, nw

0.89 0.01 0.92 0.91 0.92 0.90 0.93

13.0 38.8 10.7 11.6 10.8 12.6 10.3

– – – – 50 20 50, 20

a Standard

deviation for comparison with root mean square error of cross-validation (RMSECV). a and/or IF measurements were given 20 or 50 times extra weight compared with the spectral measurements to compensate for the large number of wavelengths (= variables) in the spectral data. When both ECa and IF are included, figures before the comma refer to ECa and those after to IF. c Cross-validation resulted in ME < 0. d No extra weight. b Weighted, EC

force, on the other hand, could not explain on its own the variation in soil texture. For SOM content, using only ECa or IF gave similar results, but the RMSECV was about twice as large as when using vis-NIR only. All three soil sensors are influenced by several soil properties. In the ECa and IF measurements this is incorporated into one single output variable, whereas the vis-NIR and NIR spectra consist of several wavelengths with more or less specific reflectance patterns depending on soil constituents, which enables the effects of different soil properties to be separated (Adamchuk et al., 2004; Mahmood et al., 2012). This advantage can probably explain why vis-NIR proved to be the best individual sensor for both texture and SOM. With the moderate SOM content on the two farms, soil texture and soil moisture are most likely to be

the main soil properties influencing sensor measurements. This is probably the reason why ECa predicted clay, and at Entorp also silt and sand, almost as well as the vis-NIR sensor. The poor accuracy of prediction for clay, silt and sand using IF is probably because of the effect of soil moisture on the measurements, as soil moisture affects penetration resistance differently depending on soil texture (Dexter et al., 2007). The interactive effect of soil moisture and soil attributes on sensor measurements is often regarded as a problem in in-field measurements, but it might help prediction because of co-variation with, for example, soil texture. This would mainly be the case for geographically local calibrations, where the measurements could be made during a short time and under similar conditions.


636 J. Wetterlind et al. Table 2 Cross-validated results and weighting for soil texture at Brogården. The best predictions with or without extra weight on apparent electrical conductivity (ECa) and insertion force (IF) are presented. Clay / % SDa = 12.6

ECa IF EC + IF NIR NIR + ECa NIR + IF NIR + ECa + IF

Silt / % SD = 7.5

Sand / % SD = 16.7

E

RMSECV

wb

E

RMSECV

w

E

RMSECV

w

0.75 –c 0.75 0.76 0.81 0.74 0.81

6.4 – 6.5 6.4 5.6 6.6 5.6

– – – – 20 20 20, nw

0.01 – – 0.37 0.40 0.30 0.37

7.7 – – 6.2 6.0 6.5 6.1

– – – – 50 nwd 20, nw

0.53 – 0.49 0.64 0.73 0.62 0.72

11.9 – 12.4 10.4 8.9 10.7 9.1

– – – – 50 – 20, nw

a Standard

deviation for comparison with root mean square error of cross-validation (REMSECV). ECa and/or IF measurements were given 20 or 50 times extra weight compared with the spectral measurements to compensate for the high number of wavelengths (=variables) in the spectral data. When both ECa and IF are included, figures before the comma refer to ECa and those after to IF. c Cross-validation resulted in ME < 0. d No extra weight. b Weighted

Table 3 Cross-validated results for soil organic matter (SOM) content on the two farms

ECa IF ECa + IF vis-NIR vis-NIR + ECa vis-NIR + IF vis-NIR + ECa + IF a SOM

Entorp

Brogården

SOM / % SDb = 0.98

SOM / % SD = 2.2

SOMlow a / % SD = 1.2

E

RMSECV

E

RMSECV

E

RMSECV

0.29 0.54 0.83 0.90 0.90 0.92 0.93

0.82 0.66 0.40 0.31 0.31 0.28 0.26

0.23 0.20 0.40 0.89 0.88 0.93 0.93

1.94 1.97 1.71 0.74 0.76 0.60 0.60

0.40 0.26 0.60 0.82 0.81 0.83 0.83

0.93 1.02 0.73 0.51 0.52 0.49 0.49

content with the removal of three topsoil samples with very large SOM content. deviation for comparison with root mean square error of cross-validation (RMSECV).

b Standard

Calibrations using multiple sensors/sensor fusion Combining sensors slightly improved the calibration results for all soil properties, except for clay at Entorp (Tables 1–3). The NIR range in combination with ECa resulted in some of the largest values of E and smallest values of RMSECV for clay, silt and sand at Entorp, although the combination of ECa and IF was almost as good as the combination of ECa and NIR. This compares well with the results of Mahmood et al. (2012), who also reported better predictions for clay, silt and sand when vis-NIR spectroscopy was combined with ECa measurements than when using the individual sensors. Although IF by itself could not predict clay, silt and sand content on either of the farms in this study, it improved predictions of the particle size fractions at Entorp when it was combined with ECa compared with ECa alone or with NIR. The best predictions for SOM were achieved with vis-NIR combined with IF. Thus, for SOM content, IF seemed to contribute additional information compared with vis-NIR alone. The ECa values did not provide any useful additional information and

performed poorly on their own for SOM (Table 3). This contradicts findings by Knadel et al. (2011), who obtained better predictions of soil organic carbon (SOC) with vis-NIR combined with ECa than with vis-NIR alone. However, the field used in their study had considerable spatial variation in SOC and included areas with organic carbon content > 40%. In such more or less organic soils, organic matter would be the most influential property. Mahmood et al. (2012), in some cases, obtained better predictions for total organic carbon and C:N ratio by combining vis-NIR and ECa , despite weak correlations between those soil properties and ECa . By giving extra weight to ECa and IF, there were, in general, better predictions for clay, sand and silt (Tables 1, 2), but not for SOM. Additional weight for ECa improved predictions of all calibrations except for one (clay at Entorp), whereas extra weight for IF improved predictions in some cases but mainly when IF was combined with only NIR. It appeared that adding extra weight to a variable improved the predictions if that variable added better information than when using only NIR as a predictor, as ECa did for most of the calibrations of sand, silt and clay. However, the results



were not entirely straightforward. In some cases extra weight for ECa or IF seemed only to amplify noise and resulted in less accurate calibrations (data not shown). The results for the two farms were quite consistent in terms of rank between the individual sensors and the sensor combinations. For SOM content, the best predictions using individual sensors and a combination of sensors resulted in equally large values of E on the two farms. However, the RMSECV values were smaller at Entorp, corresponding to the smaller standard deviation in SOM content on that farm. The large values of E and small RMSECV values for sand and silt at Entorp are possibly related to the strong correlation with clay content for both sand and silt. For silt, in particular, the correlation with clay content was weak at Brogården. Predicting the soil properties on one farm with the calibration from the other farm did not result in any reliable figures (results not shown), indicating the site-specific nature of the calibrations. This might result from interactions, for example with soil moisture, but this does not necessarily restrict the calibrations to one farm if the conditions are similar over a larger area. However, this issue was beyond the scope of the present study and needs further investigation.

Conclusions The number of samples used in this study was limited, 20 sampling locations (60 soil samples) per farm. Nevertheless, satisfactory predictions were possible for clay, sand and SOM in both fields. The Veris P4000 vis-NIR-ECa -IF probe predicted soil texture and SOM content with prediction errors of around 6% for clay and silt, 10–11% for sand and 0.3–0.5% for SOM content at the two farms studied. Combining conceptually different soil sensors for the prediction of soil texture and SOM content resulted in improved calibrations in all cases except for one, although the differences were not always very large (Tables 1–3). In general, ECa brought additional information to the vis-NIR sensor measurements for predictions of soil particle size fractions, whereas IF improved the prediction results for SOM content. However, additional studies are needed to validate the results from this study and to investigate further the underlying causes. The vis-NIR was the best individual sensor for all soil properties tested on both farms (NIR-range for clay, silt and sand and vis-NIR-range for SOM content). Although a combination of ECa and IF gave equally good or even better predictions than the vis-NIR sensor on its own on one of the farms studied, the same was not true for the other farm. With the moderate SOM content observed in this study, vis-NIR measurements were superior to ECa and IF for predicting its content. These amounts of SOM are representative for the majority of Swedish farms, so the vis-NIR sensor appeared to be necessary if the aim is to replace laboratory soil texture and SOM analyses with sensor measurements. With a sensor system such as that used in this study, where the sensors are already integrated and measurements from the three sensors are obtained simultaneously, even small improvements can justify the extra effort of combining the sensors in the calibrations.

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However, the results do not provide strong support for combining vis-NIR measurements with ECa and or IF measurements, especially if the different sensor measurements are made one-by-one.

Acknowledgements This project was funded by the Swedish Farmers’ Foundation for Agricultural Research and the Swedish Research Council Formas.

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