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Published in Soil Sci. Soc. Am. J. 67:1823-1830 (2003).
© 2003 Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA

DIVISION S-5—PEDOLOGY

Organic Carbon, Texture, and Quantitative Color Measurement Relationships for Cultivated Soils in North Central Iowa

M. E. Konen*,a, C. L. Burrasb and J. A. Sandorb

a Dep. of Geography, Northern Illinois Univ., DeKalb, IL 60115
b Dep. of Agronomy, Iowa State Univ., Ames, IA 50011

* Corresponding author (konen{at}geog.niu.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
The quantification of soil organic C (SOC) concentrations is becoming increasingly more desirable because of environmental and economic concerns regarding the reactivity of SOC with pesticides, fertilizers, and waste materials. The objectives of this study were to quantify soil color organic C relationships and to quantify soil particle-size organic C relationships for Ap horizons in north central Iowa. All of the 130 soils examined developed in glacigenic diamicton or local hillslope sediment derived from glacigenic diamicton. A Minolta CR-310 chroma meter was used to quantify the percentage of reflectance, and Munsell value and chroma for both air-dry and moist soils. Organic C concentration of the sample set ranged from 4.4 to 70.8 g kg-1. Significant relationships were observed between organic C concentration and percentage of reflectance (r2 = 0.77 moist, r2 = 0.74 air-dry), Munsell value (r2 = 0.77 moist, r2 = 0.74 air-dry), Munsell chroma (r2 = 0.68 moist, r2 = 0.77 air-dry), the percentage of sand (r2 = 0.74), the percentage of clay (r2 = 0.71), and geometric mean particle diameter (GMPD) (r2 = 0.74). Logarithmic relationships existed for reflectance, Munsell value and chroma, and GMPD while linear relationships were observed for sand and clay contents. Chroma meter soil color measurements and particle-size data are useful predictors of organic C concentrations for Ap horizons in north central Iowa. Evidence from this study and the literature suggest that unique relationships exist for different soil landscapes.

Abbreviations: DML, Des Moines Lobe • GMPD, geometric mean particle diameter • SOC, soil organic C • SOM, soil organic matter


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
THE GENETIC and morphologic significance of soil color has been widely recognized by soil scientists (Simonson, 1993). Quantification of SOC concentrations is becoming increasingly more desirable because of environmental and economic concerns regarding the reactivity of SOC with pesticides, fertilizers, and waste materials. Many herbicide labels note the need to vary application rates according to SOC concentrations. Rapid information correlating soil color parameters to organic C concentration should prove to be desirable with the increased utilization of precision agriculture techniques. The potential exists for development of subsurface optical color sensors for use in precision agriculture. Such an instrument could attach to a tillage implement shank and run several centimeters below the surface in moist Ap horizons, avoiding plant residue on the surface. Soil color properties could then be determined on the go, giving detailed spatial information across management units (Hummel et al. 1996). This information could be used in a geographic information system to predict SOC concentrations and adjust pesticide applications, seeding rates, etc. accordingly, utilizing variable rate technologies. To accomplish this, quantitative soil color parameter-SOC predictive equations will need to be developed, most likely for specific soil-geographic regions or local soil landscapes.

Numerous studies have attempted to relate soil color parameters to either SOC or soil organic matter (SOM) concentrations by utilizing human color chip matching or spectrophotometers rather than chroma meter technology. Brown and O'Neal (1923) performed early work focusing on soil color–organic matter relationships in Iowa. Their work led in part to the adoption of the Munsell color system to quantify soil color. Alexander (1971) developed a field color chart for estimating the organic matter concentrations of silty textured cultivated mineral soils in Illinois. Alexander's chart was developed using the Munsell color system and correlated ranges of organic matter concentrations with color chips. Steinhardt and Franzmeier (1979) presented a semi-quantitative technique to estimate SOM for cultivated silt loam textured surface soils in Indiana. Their method also utilized field soil scientist determined Munsell soil color parameters and grouped soils according to Munsell parameters and ranges of SOM concentrations. Franzmeier (1988) discussed the relationships between organic matter concentration, soil color, and soil texture for Indiana soils. Franzmeier presented several equations correlating organic matter concentrations to Munsell value and chroma, all with r2 < 0.48. Fernandez and Schulze (1987) noted that visual matching of soil colors is not adequate for developing relationships between color and the amount of organic matter present in a soil. Kelly and Judd (1976) reported that individuals have the ability to interpolate accurately and consistently between color chips. Still, soil coloring with the human eye is subjective and variability between soil scientists exists as demonstrated by Post et al. (1993).

Numerous attempts at quantifying soil color parameters over the past several decades have utilized analytical laboratory equipment and/or remote sensing techniques (Shields et al. 1968; Al-Abbas et al. 1972; Page 1974; Baumgardner et al. 1979; Krishnan et al., 1981; Ruckman et al., 1981; Stoner and Baumgardner 1981; Pitts et al. 1983; Griffis, 1985; Fernandez and Schulze, 1987; Henderson et al. 1989; Henderson et al. 1992; Schulze et al. 1993; Matthias et al. 1999). The theory behind the instrumentation and techniques is discussed and summarized by Torrent and Barron (1993), Madiero Netto (1996), Baumgardner et al. (1985) and Fernandez and Schulze (1987).

Fernandez et al. (1988) utilized reflectance spectra in Indiana to evaluate soil color–SOM relationships. They observed a significant (r2 = 0.94) linear relationship between organic matter and moist Munsell value for 12 Ap, Bt, and Bg samples from two Indiana toposequences. They concluded that organic matter concentrations were more predictable for a given landscape than for a large geographic area. Also in Indiana, Schulze et al. (1993) report a weak relationship (r2 = 0.31) between moist Munsell value and organic C concentrations for 105 Ap horizons located throughout Indiana and varying in textural composition. Stronger relationships were observed when grouping soils by textural class and individual landscapes. Silty textures produced a linear relationship while sandy textures resulted in a logarithmic relationship. Ibarra-F et al. (1995) examined soil color parameters in Mexico with a chroma meter and found SOC–color parameter (hue, value, chroma, and reflectance) relationships with an r2 ranging from 0.01 to 0.53. Lindbo et al. (1998) used a chroma meter to evaluate color, organic C, and hydromorphology relationships for sandy epipedons in Maryland. They reported an r2 = 0.63 for air-dry Munsell value and organic C concentrations.

Soil texture–SOC relationships are not as abundant in the literature as soil color information. Nichols (1984) examined southern Great Plains soils to determine if SOC concentrations could be predicted from several environmental factors. The percentage of clay content was found to be the best predictor of organic C in Nichols' study (r = 0.86). Franzmeier (1988) noted that organic matter concentrations generally increased for Indiana Ap horizon samples with increasing clay content but did not present any quantitative equations for SOM–texture relationships.

A chroma meter like those utilized by Ibarra-F et al. (1995) and Lindbo et al. (1998) has numerous advantages for quantifying soil color properties. The technique is non-destructive and further analyses can be run on the same sample. The instrument is portable and can be taken to the field. Color parameters may be determined on both dry and moist samples in seconds. Sample preparation is minimal and the method is more consistent, accurate, and precise than color quantification by the human eye using Munsell Soil Color Books or color charts developed specifically for estimating SOC or SOM concentrations.

The chroma meter utilized in this study can be used in the field with a global positioning satellite unit and detailed soil color parameter spatial maps can be rapidly produced. Predictive SOC–color parameter equations can then be developed for specific soil-geographic areas based on field or laboratory determined chroma meter measurements and laboratory measured SOC concentrations. This study was conducted to initiate the quantification of soil color parameters and the development of predictive SOC concentration equations in the intensively farmed north central region. The objectives of this study were to quantify soil color–organic C relationships using a chroma meter and to quantify soil particle-size–organic C relationships for Ap horizons in north central Iowa.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Description of the Study Area and Field Sampling
Sampling was performed in the physiographic region in north central Iowa known as the Des Moines Lobe (DML) (Fig. 1) . The DML has been the focus of numerous soil-geomorphic and Quaternary geology studies (Walker, 1966; Burras and Scholtes, 1987; Kemmis, 1991; Steinwand, 1992; and Konen, 1999). The surficial geologic material over the majority of the DML in Iowa is glacigenic diamicton or local hillslope sediments derived from glacigenic diamicton.



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  		Fig. 1. Location of the Des Moines Lobe in Iowa. Letters indicate the location of the three watersheds sampled.

 
The DML is characterized by low-relief, hummocky glacial topography containing numerous closed depressions. The area is intensively used for row-crop production agriculture. The majority of DML soils were heavily influenced by tallgrass prairie vegetation during their formation. Mollic epipedons are common in soils across the majority of the landscape. Local hillslope sedimentation of organic-rich colloidal sediments occurred in closed depression footslope landscape positions on the DML during the late Pleistocene and throughout the Holocene (Walker, 1966; Burras and Scholtes 1987; and Konen 1999).

The soils examined in this study are representative of the Clarion (fine-loamy, mixed, superactive, mesic Typic Hapludoll)–Nicollet (fine-loamy, mixed, superactive, mesic Aquic Hapludoll)–Webster (fine-loamy, mixed, superactive, mesic Typic Endoaquoll) soil association found on the DML in Iowa. Soils of minor extent, but representing a broader range of organic C concentrations and soil textural classes were also included to evaluate the effectiveness of predictive equations for this soil-geographic region. All soils sampled developed in glacigenic diamicton or local hillslope sediments derived from glacigenic diamicton. Samples were collected from all landscape positions and drainage classes across catenas in closed depression watersheds.

One hundred and thirty Ap horizons from three watersheds on the DML were described and sampled (Fig. 1). Each watershed consisted of a closed depression and the contributing drainage area. Watershed A is located in Dickinson County (43° 26' N lat. and 95° 17' W long.) and is 5.6 ha in size; Watershed B is located in Boone County (42° 12' N lat. and 93° 57' W long.) and is 2.6 ha in size; and Watershed C is located in Story County (42° 01' N lat. and 93° 17' W long.) and is 12 ha in size. Each Ap horizon, ranging from 17 to 30 cm thick, was sampled throughout its entirety and aggregated into one laboratory sample because mixing and homogenization of this zone occurs with each tillage pass. Within each watershed, one hillslope transect comprising the full range in soil color properties was systematically sampled from summit down to closed depression centers to evaluate individual catena trends relative to the combined data set from the DML. All samples were randomly collected with the exception of the transect samples. Watershed A contained 38 samples (12 from transect); Watershed B contained 41 samples (eight from transect); and Watershed C contained 51 samples (13 from transect).

Soil Analysis
Samples were air-dried in the lab, crushed and sieved using a brass sieve with 2-mm openings. The <2-mm fraction was used for all laboratory analyses. Quantitative soil color measurements were made in the laboratory with a tri-stimulus colorimeter utilizing the 400- to 700-nm portion of the electromagnetic spectrum. A Minolta CR-310 chroma meter (Minolta Corp, Ramsey, NJ) fitted with a CR-A33e glass light projection tube attachment was used to quantify percentage of reflectance and Munsell value and chroma. The chroma meter was calibrated with a Minolta standard reference plate at the beginning of each sample run. Color properties of the calibration plate were then remeasured after every 20 soil samples. The procedure involved placement of soil material to a depth of 3 cm in a 450-mL plastic cup. The chroma meter measuring probe was placed firmly in a vertical position on the soil sample to ensure a uniform flat surface for measurement. Triplicate measurements were made and average values reported. An internal standard laboratory reference sample was analyzed every 10 samples. After air-dry measurements were made, soil samples were moistened with deionized water until no further change in color was observed. Care was taken to ensure that soil samples did not glisten. Moist color properties were then measured.

Total C was measured with an automated dry combustion instrument (Model CHN 600, LECO, St. Joseph, MI) as described by Soil Survey Staff (1996). Organic C was presumed to equal total C as no calcareous samples were analyzed in this study. Particle-size analysis was measured using a modified pipette procedure described by Soil Survey Staff (1996). To facilitate the calculation of a GMPD size for each soil horizon, a modified Wentworth size-fractionation scheme was used as described by Walter et al. (1978) and Konen (1999). Geometric mean particle diameter was calculated using the formula presented by Krumbein and Pettijohn (1938). Statistical analyses were conducted using SAS software (SAS Institute, 1999) and are expressed at the 0.05 probability level.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Soil Texture–Organic Carbon Relationships
The range in textural class distribution of the soils is shown in Fig. 2 , and the taxonomic classification of the study soils appears in Table 1. A summary of selected soil properties for the sample set is presented in Table 2. Clay contents for the 130 samples ranged from 7.8 to 36.3% by weight. A high correlation (r2 = 0.71) existed for clay contents versus organic C concentrations (Fig. 3) . Specifically, as clay contents increased a linear increase occurred in organic C concentrations. As expected, this trend is similar to that described by Nichols (1984) for upland soils of the southern Great Plains although the slope and intercept are different. This indicates that no universal equation exists for all soils and that regional differences should be expected due to differences in SOC composition and mineralogy. This suggests quantitative relationships should be investigated for individual soil-geographic regions.



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  		Fig. 2. Textural class distribution of the 130 Ap horizons on the Des Moines Lobe, Iowa.

 

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  		Table 1. Taxonomic classification of soil profiles at the subgroup level{dagger}.

 

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  		Table 2. Range of selected laboratory characterization properties for the soil samples.

 


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  		Fig. 3. Organic C and particle-size relationships for the sample set. * Significant at the 0.05 probability level.

 
Sand contents, which ranged from 6.5 to 75.3%, exhibited a strong negative correlation (r2 = 0.74) with organic C concentration; a linear relationship exhibited decreasing organic C concentrations with increasing sand contents (Fig. 3). Soils containing high sand contents are typically better drained than finer textured soils on the DML (Walker, 1966; Burras and Scholtes, 1987; Steinwand, 1992; and Konen, 1999). As a result, one would expect decreased accumulation of organic C with increasing sand contents.

Geometric mean particle diameter of the Ap horizons ranged from 6 to 113 µm. Geometric mean particle diameter size and organic C concentrations also exhibited a strong correlation (r2 = 0.74) (Fig. 3). The relationship was logarithmic with organic C decreasing with increasing GMPD. Based on the relationships previously presented regarding sand and clay contents it is not surprising that GMPD also demonstrated a strong correlation.

Several studies of representative hillslopes on the DML have shown a systematic downslope fining of sediment throughout the solum, an increase in organic C concentrations in A horizons downslope, as well as poorer soil drainage conditions downslope (Walker 1966; Burras and Scholtes 1987; Steinwand, 1992; and Konen 1999). All of the previously mentioned studies were conducted on the DML in closed drainage basins containing soils developed in glacigenic diamicton or postglacial hillslope sediments derived from glacigenic diamicton. Utilizing the GMPD allows for the incorporation of landscape position, soil drainage class, and textural composition without actually quantifying these parameters. For example, on a typical closed depression hillslope on the DML, a soil in the footslope position would likely be more poorly drained, have finer textures, have a thicker A horizon, and contain more organic C than a soil located upslope (Fig. 4) . Geometric mean particle diameter appears to be well suited as a predictor of organic C concentrations in this soil landscape because of the systematic erosion-sedimentation relationships in closed basins and the broad range of particle sizes found in the glacigenic diamicton. We would not expect the GMPD to work as well in open drainage landscapes or in geologic material like loess where a narrower range of particle sizes are present and hillslope sorting of sediments may not be as systematic.



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  		Fig. 4. Typical topographic and pedologic relationships for a closed depression hillslope on the Des Moines Lobe. Lowest elevation in transect set at 10.0 m for presentation purposes. Selected characterization data is shown above sampling points in the transect. Data from watershed C (SOC, soil organic C; GMPD, geometric mean particle diameter).

 
Chroma Meter Color Parameter–Organic Carbon Relationships
This study is among the first reports of chroma meter quantification of soil color parameters in the region. Because of this, we have included summary data for the internal laboratory reference sample to demonstrate the precision and reproducibility of the chroma meter instrument and methodology (Table 3). Air-dry reflectance values of the 130 Ap horizons ranged from 7.16 to 18.47%, while moist reflectance ranged from 4.10 to 8.52%. The percentage of reflectance was a strong predictor of organic C concentrations for the soils examined in this study. Soils with increasing organic C concentrations exhibited logarithmically decreasing percentage of reflectance for both dry and moist samples (Fig. 5) . Both air-dry (r2 = 0.74) and moist (r2 = 0.77) reflectance exhibited strong coefficients of determination. Thus the percentage of reflectance can be used as a predictor of organic C concentrations for this group of soils. Munsell chroma also appears to be a good predictor of SOC concentrations (Fig. 6) . Dry Munsell chroma for the sample set ranged from 0.3 to 2.1 while moist Munsell chroma ranged from 0.0 to 1.4. Dry Munsell chroma was a slightly better predictor than moist Munsell chroma (r2 = 0.77 vs. r2 = .68). Munsell chroma logarithmically decreased with increasing organic C concentrations.


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  		Table 3. Quantitative soil color chroma meter data for the laboratory reference sample.

 


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  		Fig. 5. Organic C and chroma meter reflectance relationships for the sample set. * Significant at the 0.05 probability level.

 


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  		Fig. 6. Organic C and chroma meter Munsell chroma relationships for the sample set. * Significant at the 0.05 probability level.

 
Air-dry Munsell values of the soils ranged from 3.1 to 4.9 while moist Munsell value ranged from 2.3 to 3.4. A strong correlation existed with organic C for both dry (r2 = 0.74) and moist (r2 = 0.77) Munsell value (Fig. 7) . A logarithmic trend existed for both dry and moist Munsell value. Both decreased with increasing organic C concentrations. This trend is consistent with previous studies. Fernandez et al. (1988) noted a strong linear trend (r2 = 0.94, moist Munsell value and SOM) for the two toposequences in Indiana. The Ap samples they examined had an organic C range of approximately 3.3 to 24.8 g kg-1 compared with the soils examined in this study, which had an organic C range of 4.4 to 70.8 g kg-1. The shapes of the graphs in Fig. 7 appear linear if only the 4.4 to 25 g kg-1 SOC values are plotted. Once the higher SOC values are plotted the relationship becomes logarithmic. Fernandez et al. (1988) most likely observed a linear instead of a logarithmic relationship because of the relatively narrow range of organic C concentrations in their data set.



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  		Fig. 7. Organic C and chroma meter Munsell value relationships for the sample set. * Significant at the 0.05 probability level.

 
Stronger relationships existed between moist Munsell value and SOC for the whole data set (r2 = 0.77) than for all but one of the three hillslope transects examined (Fig. 8) . This is in contrast to the findings of Fernandez et al. (1988) who concluded that stronger color–organic matter relationships existed for soils comprising Indiana toposequences than did data sets representing larger geographic areas. The variability in the individual transects examined in this study may be due to the relatively small number of samples relative to the entire data set but seems unlikely since the strongest relationship observed in the entire study is that of the transect from Watershed B (r2 = 0.80). Before the development and application of predictive equations is implemented for precision agriculture decision-making, we suggest further work be done to determine the appropriate spatial scale for quantifying SOC–soil color relationships. At present there is conflicting evidence as to whether individual hillslopes or larger soil geographic areas will provide the strongest predictive relationships.



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  		Fig. 8. Organic C and moist Munsell value relationships for three closed depression hillslope transects on the Des Moines Lobe. Samples were collected from depression centers up to summit drainage divides in each transect. * Significant at the 0.05 probability level.

 

   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
The chroma meter instrument utilized in this study is a rapid and useful tool in quantifying soil color parameters and developing SOC predictive equations. Significant quantitative soil color-organic C and soil texture-organic C relationships existed for soils occurring in glacigenic diamicton on the DML in Iowa. As organic C concentration increases, samples generally exhibit a lower reflectance, lower Munsell value and chroma. Organic C concentrations generally increases with decreasing sand content, increasing clay content, and decreasing GMPD.

Significant relationships were observed between organic C concentration and the percentage of reflectance, Munsell value and chroma, the percentage of sand, the percentage of clay, and geometric mean particle diameter. Coefficient of determination values ranged from 0.68 to 0.77. Logarithmic relationships existed for reflectance, Munsell value, and GMPD while linear relationships were observed for sand and clay contents. The potential for GMPD to be used as a predictor of SOC concentrations in other soil landscapes should be investigated. General trends were similar to other soil color–organic C studies reported in the literature but the predictive equations were different indicating that no universal equation exists for all soils. Evidence from this study and the literature suggest that unique relationships exist for different soil landscapes as local mineralogical, texture, and organic C composition likely cause differences in soil color parameters.

Received for publication August 2, 2002.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 




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