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a Dep. of Crop and Soil Sci., Univ. of Georgia, Athens, GA 30602
b Global Hydrology and Climate Center, NASA, Huntsville, AL 35806
* Corresponding author (dkissel{at}uga.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Abbreviations: ECEC, effective cation exchange capacity • GIS, geographic information system • HIV, hydroxy-interlayered vermiculite • OC, organic carbon • OM, organic matter
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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When a proton is accepted by a functional group, a cation exchange site is eliminated, lowering the effective cation exchange capacity (ECEC). Conversely, the ECEC is increased when deprotonation occurs. As the soil acidifies, titratable acidity increases and ECEC decreases. The change in titratable acidity or ECEC per unit change in pH is a measure of the pH buffering capacity (Bouman et al., 1995). Since measurement of titratable acidity is time consuming, soil pH buffering capacity can often be estimated with sufficient accuracy from other soil properties.
The soil properties related to pH buffering capacity are well documented by correlation studies to be pH and OM (Bailey et al., 1989), OM and aluminum (Bloom et al., 1979; Magdoff et al., 1987), OM and ECEC (Mowbray and Schlesinger, 1988; Ngachie and Smyth, 1989), pH and ECEC (Levine and Ciolkosz, 1988; Hochman et al., 1989), OC, clay and change in CEC (Aitken et al., 1990), and texture, OM, and pH (Curtin and Rostad, 1997).
Aitken et al., (1990) determined that their measure of soil pH buffering capacity of 40 acid Queensland soils, which they called "lime buffering capacity" was related primarily to the OC and clay concentrations of the soils; therefore, they concluded that the pH buffering capacity must be governed principally by the same two variables. Use of multiple linear regression analysis showed that OC was the most significant variable, accounting for 78% of the variance in pH buffering capacity, although clay accounted for 32% of the variance. The bigger effect of OC than clay in affecting pH buffering capacity can sometimes be due to the buffering capacity differences between OC and clay. As reported by Bache (1988), OC can have a buffering capacity 300 times as great as kaolinite.
A measure of soil pH buffering capacity is needed to understand rates of natural soil weathering, soil pH responses to inputs of lime, and rates of soil acidification from acid-forming nitrogen fertilizers, acid rain, and acid mine wastes (Bloom, 2000). Knowledge of the soil pH alone can aid in evaluating the fertility of a soil but it does not indicate the amount of lime needed to neutralize soil acidity. The soil pH buffering capacity is the other soil characteristic needed to estimate the lime requirement of an acid soil. An estimate of the spatial variability of a soil's pH buffering capacity is therefore needed to estimate the spatial variability of the lime requirement.
Knowledge of soil pH buffering capacity would also be needed in estimating the rate of soil acidification due to nitrification of ammoniacal N sources such as N fertilizer applied to the soil. If uniform rates of N fertilizer were applied, then those areas of the field with a lower pH buffering capacity would acidify at a more rapid rate than areas of the field with higher pH buffering capacity if all other factors were equal.
For those agricultural production fields that have relatively small changes in the type of clay, it may be possible to map soil pH buffering capacity from maps of OC and clay. Recent remote sensing procedures to map OC (Chen et al., 2000) and clay (Chen et al., 2004) of surface soil make it possible to map soil pH buffering capacity from a relationship that predicts soil pH buffering capacity from soil OC and clay contents. The objective of this research was to evaluate the feasibility of using remote sensing of bare surface soils to map the soil pH buffering capacity of three fields, each with widely varying OC and clay concentrations.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Sample locations in the fields were determined visually based on the apparent variation in soil OM and surface soil texture. The location of each sample was measured to submeter accuracy with a Model TSC-1 global positioning system (GPS) receiver by Trimble Corporation (Sunnyvale, CA). The locations were real-time differentially corrected with signals from either OmniStar Inc. (Houston, TX) or the U.S. Coast Guard beacon located in Macon, GA, about 70 km distance from the Cabin field. The soil samples collected with a 2-cm-diam. soil probe at each of the 136 locations consisted of nine soil cores taken from the 0- to 15-cm depth within a 2- by 2-m2 area. The samples were composited, mixed thoroughly, air-dried, sieved with a 2-mm sieve, and stored until analyzed.
Sample Collection for pH Buffering Capacity Map Validation
For verification of the models to map soil pH buffer capacity, 18 additional samples were collected from those fields, as described above, with sampling locations based on a visual examination of the OC maps to cover a wide range in soil pH buffering capacity. A GPS location was measured for the samples. The samples were composited, mixed thoroughly, air-dried, sieved with a 2-mm sieve, and stored until analyzed.
Sample Analysis
A Carlo-Erba (Milan, Italy) NA 1500 Analyzer for Carbon and Nitrogen (Kirsten, 1983) was used to determine the total surface soil OC concentration of each sample by dry micro-Dumas combustion and particle-size distribution was determined with the pipette method described by Kilmer and Alexander (1949). Soil pH was measured (1:1 soil to solution) in water (Thomas, 1996). Buffer curves were established for each of the soil samples by titrating 25 g of soil suspended in water (1:10) with 1-mL inputs of saturated 0.022 M Ca(OH)2, and the change in pH was measured. The soils were allowed to equilibrate, and a reading was taken following each 1-mL addition only after there was no change in pH for 10 s. For each soil, a linear regression function was fitted to the relationship between Ca(OH)2 added (abscissa) and the soil pH (ordinate).
Long-Term Incubation
Three additional soil samples were collected from each of the three study fields by the same method described above. The nine samples were air-dried, crushed, sieved, and stored until incubation. An initial pH reading was taken of each sample. The soil pH was measured (1:1 soil to solution) in water while being stirred (Thomas, 1996) with an Orion Model 710A meter and an Orion 9170 Triode electrode (Thermo Electron Corp., Beverly, MA). Each sample was titrated per the method described above, and from the titration analysis the equivalent amount of laboratory grade calcium carbonate (CaCO3) was determined that would be sufficient to raise the pH of the sample from the initial pH to pH 6.0 and pH 6.5. The pH values of 6.0 and 6.5 were chosen because they are typical target pH values for crops grown in the southeast Coastal Plain region.
The appropriate amount of CaCO3 (100% passing 100-mesh) needed to raise the initial pH value to 6.0 or 6.5 was thoroughly mixed with a 0.500-kg sample of each soil for a total of 18 samples (Table 1). To each sample, enough water was added to bring the soil sample to approximately field capacity. The samples were incubated in 946-mL low-density polyethylene containers with snap-on lids. Six 3-mm openings were drilled through each lid for air exchange. A glass stir-rod was inserted through one opening of each container for mixing the soil.
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X-Ray Diffraction Analysis
Since soil pH buffering capacity is affected by both the concentration and the type of clay mineral, soil samples were taken to determine the relative uniformity of clay mineral type in the three fields. Two samples were chosen from each field set to represent the range in percentage OC and percentage clay. These samples ranged from 0.16 to 2.31% OC and from 1.4 to 32.6% clay. The samples were prepared according to the method described by Whittig and Allardice (1986). Clay saturation and heat treatments were Mg2+–saturated, air-dried; Mg2+–saturated, ethylene glycol solvated; K+–saturated, air-dried; and K+–saturated, heated to 100, 300, and 550°C. X-ray diffraction patterns were made from 3 to 32° 2
by Cu K
radiation and a curved-crystal monochromator.
Mapping Procedures for Surface Organic Carbon and Clay
The OC concentrations of the surface soil in the three study fields were mapped using the procedures outlined by Chen et al. (2000), which were designed to map OC concentrations at the field scale. In their study, Chen et al. (2000) used an aerial color slide of bare surface soil of the 115-ha field (Cabin field) used in the present study. Soil samples were collected at 28 locations throughout the field, and the OC concentrations were determined according to the dry micro-Dumas combustion method described above. The image intensity values in the red, green, and blue bands and surface OC concentrations were fit to a logarithmic linear equation. To predict the surface OC concentrations, they applied the equation to each pixel to calculate its OC value, and then determined the spatial distribution of OC across the field. The same procedure was applied to the 45-ha Adkins field, where 59 samples were collected, and to the 23-ha field (Bowling field) in neighboring Dooly County. In the Bowling field, 26 samples were collected.
In research designed to determine if the clay concentration of surface soil could be predicted from remotely sensed imagery, Chen et al. (2004) used the same 115-ha field (Cabin field) as for OC mapping. A total of 32 surface soil samples from the field were collected and the clay concentration was determined using the pipette method described by Kilmer and Alexander (1949).
The spatial distribution of electrical conductivity in the Cabin field was determined with 21653 soil electrical conductivity measurements for the 0- to 0.25-m depths taken across the field with the Veris model 3100 sensor of soil conductivity (Veris Corp., Salina, KS) along with submeter GPS locations. A soil conductivity measurement was recorded every second, resulting in a 2- to 2.5-m spacing depending on the tractor speed. The measurements were made along the crop row direction and approximate 30-m spacing in the cross-row direction. On the basis of the measured data, the spatial distribution of the electrical conductivity of surface soil was then derived by spline interpolation with ArcView 3.2 (ESRI, Redlands, CA). A linear relationship between electrical conductivity and clay concentration of surface soil was developed with the 32 samples from the Cabin field by regression analysis. This was done by purposely driving a tight circle around each of the 32 sample points and averaging the conductivity values for that point. The percentage clay values of the 32 soils were then regressed on each of the averaged conductivity values for that soil sample. The result was the relationship percentage clay = 5.67 EC with r2 = 0.78. Finally, the soil clay concentration map was developed by applying this relationship to the electrical conductivity value for each pixel as described by Chen et al. (2004). The same method was also used for the Bowling field, but by developing a new linear relationship with samples from that field. The final equation for the Bowling field was percentage clay = 7.29 EC with r2 = 0.72.
For the Adkins field, the data source for mapping percentage clay was reflected radiation measured aboard a NASA Stennis Lear jet. The NASA Advanced Thermal and Land Application Sensor (ATLAS) was used to obtain data from multispectral radiation bands across thermal, near-infrared, and visible spectrums. Eight bands were used for analysis, and six of the eight bands in the visible range were best fit with an exponential function, and an inverse square function was used to fit the other two bands. Stepwise regression analysis was used to determine the best band combination for predicting soil clay concentration. Bands 3, 4, 5, 6, and 8 accounted for 72% of the variation (Chen et al., 2004).
Statistical Analysis and Mapping Procedures for Soil pH Buffer Slope
Statistical Analysis Software (SAS Institute, 1985) (SAS Institute Cary, NC) was used in regression analysis of the titration data. From visual inspection of the titration data, it appeared that the data of pH vs. Ca(OH)2 added expressed as kg CaCO3 could be fit to the linear equation
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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A t test analysis (SAS Institute, 1985) showed the 30-d pH values and the target pH values were not significantly different (p < 0.001) at the 95% confidence interval. The results indicate that the CaCO3 applications calculated from the Ca(OH)2 titrations raised the soil pH values to the estimated values from the titration curves (Table 1). Apparently, because the liming material applied was finely ground CaCO3 (100% passing 100-mesh), and the sample moisture was kept near field capacity, the liming reaction was nearly complete by Day 30, since the Day 60 and Day 90 values were not significantly different from the Day 30 readings (p < 0.01) at the 95% confidence interval. The slightly lower numerical values at Days 60 and 90 are more likely due to further nitrification reactions from the additional incubation time than they are due to unreacted calcium carbonate.
Effect of Organic Carbon and Clay Concentrations on Soil pH Buffer Capacity
The range in OC and clay concentrations of the three fields was all relatively large. On the Cabin field, the OC concentrations of the 60 samples ranged from 0.28 to 1.89% and the clay varied from 1.4 to 32.6%. In the Adkins field where 59 samples were collected, the percentage OC ranged from 0.12 to 1.74% and the percentage clay ranged from 0.10 to 25.7%. The Bowling field with 26 samples had percentage OC values from 0.36 to 2.3% and percentage clay values from 4.5 to 33.8%.
Linear regression analysis of all the titration data indicated that the relationship between the amount of Ca(OH)2 added and pH could be fit well with the linear equation since r2 values ranged from 0.9499 to 0.9998. Even the soil with the lowest pH of 3.8 gave an excellent fit to the linear equation with an r2 of 0.9994 apparently because, as will be shown later, OC is the major source of titratable acidity. Titration curves of pH vs. Ca(OH)2 added (expressed as the chemically equivalent amount of CaCO3 and as kg ha–1) are shown for two contrasting soils in Fig. 1 . Both soils had a similar initial pH of about 5.4, but differed in the slopes of the titrations. The percentage OC and percentage clay values were 0.56 and 1.16, and 4.5 and 11.8, respectively, for the lower and higher buffered soils, respectively. The linear relationship was typical of all soil samples up to pH 6.5.
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Pearson's correlation was applied to the variables and it was shown that b was more strongly correlated with percentage OC–1 than percentage OC and although the correlation with percentage clay was not as strong, it was significant. On the basis of the correlation results, the variables percentage OC–1 and percentage clay and interactions of the variables were regressed with stepwise regression in SAS (SAS Institute Cary, NC). The model that best predicted the value of b for the 136 samples collected from the three fields was
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X-Ray Diffraction Analysis
An x-ray diffractogram of a representative sample from the Cabin field is shown in Fig. 3
. The dominant minerals were kaolinite and quartz. However, minor amounts of both HIV and gibbsite were found in the samples. Both kaolinite and gibbsite have low pH-dependent charge and have a small contribution to the pH buffering capacity. The HIV would provide a greater contribution to a soil's pH buffering capacity, depending on the degree of Al interlayering, but the amount of HIV in these soils is quite low, as shown in Fig. 3. Five additional samples from the three fields were analyzed by x-ray diffraction, all with very similar results, which are consistent with the very small contribution of clay to the pH buffering capacity across all three fields.
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A total of 18 samples from the three fields that were different from the samples for model development were used to check the accuracy of the pH buffering capacity maps. The measured values for the 18 samples varied approximately tenfold in the pH buffering capacity values from the lowest to the highest. The mapped values were determined as the mean of the pH buffering capacity of the four nearest pixels to the pixel in which the sample was collected. A paired t test analysis of the titrated b values with the mapped b values showed no significant difference at the 95% confidence level. In Fig. 6 , the regression equation is given for the relationship between the measured (titrated) and the predicted (mapped) values of pH buffering capacity (b). The slope of 1.04 was not significantly different from 1.0, and r2 = 0.88, together indicating a high degree of confidence in the generated pH buffering capacity maps.
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Although soil scientists have depended largely on the soil sampling of fields for determining soil pH and the lime requirement, the high degree of variability makes it cost prohibitive to accurately portray the spatial variability of lime requirement for many fields, resulting in a trade-off between cost and accuracy. Remote sensing of soil properties such as OC and clay, on the other hand, can be done with a high degree of spatial accuracy, as noted above, and for reduced cost. More cost-effective ways to estimate soil pH buffering capacity in the laboratory will also benefit the process demonstrated in this paper.
Maps of soil pH buffering capacity can be useful for modeling rates of change in pH with known inputs of N fertilizer and N mineralized from soil OM and crop residues. Although the amount of lime needed to maintain soil pH may be somewhat greater than estimated from nitrification of the applied N fertilizer alone (Gascho and Parker, 2001), long-term prediction of soil pH changes will depend on a better understanding of these soil reactions as well as the ability to map soil pH buffering capacity. Knowledge of the spatial variation in both biological reactions of N and the soil pH buffering capacity will be needed to fully understand spatial changes in pH across the landscape.
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ACKNOWLEDGMENTS |
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Received for publication February 11, 2003.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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