Published online 8 June 2007
Published in Soil Sci Soc Am J 71:1251-1256 (2007)
DOI: 10.2136/sssaj2006.0235
© 2007 Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
NUTRIENT MANAGEMENT & SOIL & PLANT ANALYSIS
Relationships between Soybean Yield, Soil pH, and Soil Carbonate Concentration
Natalia P. Rogovska,
Alfred M. Blackmer and
Antonio P. Mallarino*
Dep. of Agronomy, Iowa State Univ., Ames, IA 50011
* Corresponding author (apmallar{at}iastate.edu).
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ABSTRACT
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Soybean [Glycine max (L.) Merrill] often shows symptoms of iron deficiency chlorosis (IDC) on high pH, calcareous soils of the U.S. Midwest. The objective of this study was to assess the variation in soybean yield that could be explained by soil pH and carbonate concentration in Iowa fields. Color aerial images of soybean canopy taken from 2000 to 2002 from 12 fields having acid to calcareous soils were used to select 10 to 28 sampling areas 10 to 25 m2 in size to encompass significant variability in early soybean growth and IDC symptoms in each field. Representative areas 0.93 m2 in size were identified through field observations to collect soil samples and measure grain yield. Soil pH measured to a 15-cm depth across fields ranged from 5.6 to 8.2 and calcium carbonate equivalent (CCE) ranged from 0 to 30%. Soil CCE varied from 2.5 to 30% as pH ranged from 7.7 to 8.2. Grain yield decreased with increasing pH and CCE in 9 and 11 fields, respectively. Soil pH and CCE explained 30 and 41% of the variability in relative yield across sites, respectively. An alkalinity stress index (ASI) that combined both measurements (pH + 0.14CCE) was developed based on the relative effects of each measurement on yield and explained 45% of the yield variability across sites. The index developed was a better predictor of soybean yield in fields with high-pH soils than each measurement alone.
Abbreviations: ASI, alkalinity stress index • CCE, calcium carbonate equivalent • IDC, iron deficiency chlorosis • SCN, soybean cyst nematode
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INTRODUCTION
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Soybean is extensively grown in areas of the U.S. Midwest where fields often have areas of acid to calcareous soils intermingled in complex spatial patterns. Calcareous soils are defined as soils containing sufficient CaCO3 and MgCO3 to effervesce visibly when treated with a strong acid (Soil Science Society of America, 1997). Carbonate concentration usually is expressed as CCE. Soil pH is highly buffered by carbonates and bicarbonates in calcareous soils, and measured pH values usually range from 7.5 to 8.3, depending on the concentration of CO2 and other factors (Loeppert, 1986; Bloom, 2000).
Soybean tends to have yellow leaves and grow poorly on the calcareous soils, and these symptoms are commonly described as IDC. Iron is abundant in most soils, so Fe deficiencies are caused by interactions of several factors that control its solubility in the soil solution and plant sap (Brown et al., 1959a, 1959b; Walter and Aldrich, 1970; Coulombe et al., 1984; Fleming et al., 1984; Loeppert, 1986; Marschner et al., 1986; Camp et al., 1987; Mengel, 1994; Nikolic and Romheld, 1999). The solubility of Fe decreases with increases in pH and bicarbonate concentration, which are interrelated through pH buffering by equilibria among H2CO3, HCO3–, and CO3–2 (Loeppert, 1986; Bloom, 2000). Soil water content can influence concentrations of Fe in solution by controlling soil aeration because CO2 concentrations influence soil pH and HCO3– concentrations and O2 concentration influences soil redox potential and the ratio of relatively soluble Fe+2 to less soluble Fe+3 (Chen and Barak, 1982; Inskeep and Bloom, 1984). Therefore, the intensity of IDC symptoms within a given area and both the size and shape of chlorotic areas vary from year to year due to variation in soil moisture regimes (Inskeep and Bloom, 1986; Bloom and Inskeep, 1986). Identification of the causes of soybean IDC is complicated by the effects of other factors, such as high concentration of NaCl in some soils and infestation with soybean cyst nematodes (SCN; Heterodera glycines Ichinohe), which often produce plant symptoms similar to IDC (Levitt, 1980; Niblack and Norton, 1992; Tylka, 1995, 2001; Niblack, 1999). Another complication is that soybean cultivars differ in sensitivity to IDC (Cianzio et al., 1979; Froehlich and Fehr, 1981; Al-Showk et al., 1986; Charlson et al., 2003).
In the north-central region of the USA, calcareous soils and associated soybean IDC symptoms are commonly found within the Des Moines Lobe landform. This region extends from central Iowa to central Minnesota and into southeast South Dakota. Complex spatial patterns of soil associations formed as the soils developed by weathering of calcareous parent materials deposited during the past 12000 yr (Rabenhorst et al., 1991; Prior, 1997). Long-term soil-forming processes involved are leaching of carbonates from the surface layers on much of the landscape and net upward movement of water and accumulation of carbonates around the edges of temporary water-filled depressions. The CCE in the surface layer of these soils often varies greatly within a distance of a few meters. Calcareous areas in fields are also produced by erosion of acid or neutral surface soil layers from knolls and side slopes that uncovers calcareous subsoil.
Study of spatial patterns in plant size and canopy cover within fields of the Des Moines Lobe as revealed by color aerial images suggested that both plant color and size may be correlated with pH and carbonate concentrations (Blackmer and Rogovska, 2001), and that aerial images can give an indication of the spatial distribution of these soil characteristics. This study showed that spatial patterns of soybean growth often approximately matched boundaries of soil map units classified as calcareous. The growth patterns occurred in much finer scale than soils were mapped, however, and often showed a continuous gradation rather than the distinct boundaries imposed by the categorical classification of soils into map units. These observations suggest that relationships between the intensity of IDC symptoms and soil carbonates should consider a continuous relationship rather than only presence or absence of carbonates and IDC symptoms. Continuous relationships between carbonate concentration and IDC symptoms have been observed by some researchers (Morris et al., 1990; Franzen and Richardson, 2000) but not by others (Anderson, 1982; Clark, 1982; Vose, 1982).
The objective of this study was to assess the proportion of soybean grain yield variability that can be explained by soil pH and carbonate concentration in fields with calcareous soils and to better understand the relative impacts of pH and carbonate concentration on yield. The study was designed to use remote sensing of the soybean canopy to help identify areas of calcareous soils within fields to study these relationships more effectively.
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MATERIALS AND METHODS
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This study was conducted in fields located within the Des Moines Lobe landform in central and north-central Iowa. The landscape of this area is flat to gently rolling, divided into fields (usually 400 by 800 m) for management, and dominated by a corn (Zea mays L.)–soybean cropping system. Aerial imagery of the early soybean canopy being collected by the Iowa Soybean Association from many fields was used to identify 12 fields from 2000 to 2002 with large within-field differences in early plant growth and apparent IDC symptoms in Boone, Greene, and Story counties. Six fields were sampled in 2000, three in 2001, and three in 2002. Once fields were selected, additional photographs of the 12 fields were taken throughout the growing season beginning in late June through early September to monitor changes in spatial patterns of plant growth and color. The images were taken using a 35-mm camera pointed downward through a hole in the bottom of an airplane from a height of 1000 to 1200 m above the ground using Kodak Elite Chrome 200 film. Digitized soil survey maps (Iowa Cooperative Soil Survey, 2001) indicated that the dominant soil series in the fields studied were Clarion (fine-loamy, mixed, superactive, mesic Typic Hapludolls), Nicollet (fine-loamy, mixed, superactive, mesic Aquic Hapludolls), Webster (fine-loamy, mixed, superactive, mesic Typic Endoaquolls), and Canisteo (fine-loamy, mixed, superactive, calcareous, mesic Typic Endoaquolls), while all fields had smaller areas of Harps (fine-loamy, mixed, superactive, mesic Typic Calciaquolls) and Okoboji (fine, smectitic, mesic Cumulic Vertic Endoaquolls) soils. The topsoil (0–15 cm) of the Harps series is calcareous, whereas the topsoil of the Canisteo and Okoboji series can be calcareous, too.
Georeferenced aerial images of soybean canopy, digitized soil survey maps, and global positioning system receivers were used to identify data collection areas to include the widest possible ranges of plant height and color within each field as revealed by differences in color of the images. Ten to 28 areas approximately 10 to 25 m2 in size were identified within each field without prior knowledge of soil pH or carbonate concentrations, although during field visits, limited information was obtained by applying acid to the soil surface to check for carbonate presence. A special effort was made to include some data collection areas where plant height or color was not consistent with the soil series in soil survey maps or the acid test for carbonate concentration. Areas 0.93 m2 in size were defined within each area at the end of the season to measure grain yield and collect soil samples. Grain was harvested by hand from 0.61-m segments of two adjacent rows in fields where soybean was planted using row spacing of 76-cm (all fields except Sites 4 and 12) and from four adjacent rows in fields where row spacing was 38 cm. The plants were cut 2 cm above the ground, placed in a bag, dried at 60°C for at least 48 h, and threshed. Grain yield was expressed as yield per unit area and as relative yield for each field by dividing the yield from each sampling area by the largest yield observed in a field and multiplying the result by 100. Use of relative yield enabled us to pool data from all sites to study relationships between yield and the measured soil properties across sites.
Soil samples were collected immediately after grain harvest from the 0.93-m2 sampling areas with a power auger 15 cm in diameter. Soil from three cores taken to a depth of 15 cm was mixed thoroughly, placed in a cloth bag, and dried at 60°C for at least 48 h. The dry soil samples were crushed to pass a 2-mm sieve and analyzed for pH in water using 1:1 soil/water ratio as described by Bloom (2000). Percentage CCE was measured using a pressure calcimeter method as described by Boellstorff (1978), except that a digital manometer (Model SP27–78, MKS Baratron, Burlington, MA) was used to measure CO2 produced during 7 min following the addition of acid to the soil. Relationships between relative grain yield and soil pH or CCE were studied by correlation and regression analyses using the SAS statistical package (SAS Institute, 2002). An outlier analysis was performed on data from each field using Standardized–Studentized residual values from regression models and the Bonferroni simultaneous inference approach in SAS. One outlier was deleted in Site 1 and one in Site 2.
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RESULTS AND DISCUSSION
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Relationships between Soybean Grain Yield and Soil pH or Calcium Carbonate
Relationships between soil pH and relative yield within individual sites shown in Fig. 1 indicate a negative trend in all sites, although a linear model was statistically significant (P 
0.05) at nine sites. An obvious negative trend existed in Sites 3, 8, and 12 but a linear model was not statistically significant. The r2 values of significant linear models ranged from 0.22 to 0.59. Low r2 values in some fields could be explained by very high yield variability at pH approximately 7.5 or higher, which often encompassed the widest yield range. For most fields, the distribution of points obviously does not fit a continuous model well, being linear or curvilinear. This is the case even for some high r2 values. For example, high r2 (among the highest) at two sites are explained by a tight cluster of three high-yielding points at the most acid pH but no values until pH >7.5 (in Site 1) or a single very high-yielding point at the lowest pH (in Site 7).
Figure 2 shows data pooled across all sites. A linear model indicated that soil pH explained 30% of the yield variability while a quadratic model (not shown) explained only a slightly higher proportion (33%). The yield variability occurring at pH >7.5 weakens the strength of the general relationship across the entire pH range. Mean relative yields for 0.5 pH-unit intervals shown in Fig. 2 clearly indicate, however, that the frequency of low yield was much higher at pH >6.0, although sometimes soybean grown on high-pH areas yielded as much as that grown on slightly acid areas. The fact that soybean yield generally was higher for soil as acid as pH 5.6 is consistent with previous observations in fields within the Des Moines Lobe. Liming research in Iowa (Bianchini and Mallarino, 2002) and Minnesota (Vetsch and Randall, 2004) showed high soybean yield and little or no response to lime application in similarly acidic soils of the region having calcareous subsoil at depths shallower than approximately 90 cm. Iowa liming recommendations (Sawyer et al., 2002) recognize this fact by recommending no lime application for soybean for pH >5.9 in soil associations with calcareous subsoil, compared with pH >6.4 in other associations.
Very large yield variability at approximately pH >7.5, where grain yield often ranged from 0 to 100%, can be partly explained by large variation in soil carbonate concentration. The correlation between soil CCE and relative yield within each site were negative and statistically significant (P 0.05) at 11 of the 12 sites (Fig. 3). The r2 of significant linear models ranged from 0.51 to 0.82. Data pooled across all sites (Fig. 4) also show that yield decreased as soil CCE increased, and a linear model explained 41% of variability in relative yield. The large variation in yields across almost the entire CCE range tended to weaken the linear relationship. Mean relative yield for CCE intervals shown in Fig. 4 clearly indicate, however, that yield decreased significantly with increasing CCE.
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Fig. 3. Relationships between soil carbonate concentration and relative soybean yield within 12 fields.
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Fig. 4. Relationship between soil carbonate concentration and relative soybean yield across sampling areas in 12 fields.
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The variability observed in the overall relationship between relative soybean yield and either soil pH or CCE is consistent with the fact that factors other than pH or CCE influence soybean growth and yield within a field, and cannot be identified with the methods used in this study. The relationships indicate, however, that soil pH and CCE accounted for a major proportion of yield variability in these fields. A particularly large yield variation at soil pH higher than approximately 7.5 can be explained by large soil CCE variation because CCE varied from almost the lowest to the highest values in that range (Fig. 5). Aerial images and field observations indicated large variation in leaf chlorosis within that pH range, which suggested different degrees of IDC symptoms; however, factors other than Fe deficiency could be responsible for both chlorosis and high yield variation within such a high pH range. Previous research (Tylka et al., 1998; Leon et al., 2005) showed higher incidence of SCN in high-pH soils with varying CCE levels. Therefore, it is possible that variable incidence of both IDC and SCN reduced soybean yield in high-pH soils and introduced unexplained variability at pH >7.5.
A large variation of relationships between yield and pH or CCE suggests that pH and CCE have interacting effects on soybean yield. Figure 5 shows the relationship found between soil pH and CCE. The data show high pH variation when there were no carbonates and little pH variation when carbonates were present. Calculations using the statistical Cate–Nelson model (Cate and Nelson, 1971) indicated a change point at pH 7.7 and 2.5% CCE (not shown). At levels above these values, pH varied only from 7.7 to 8.2 while CCE varied from 2.5 to 30%. Such a large variation in CCE for pH 
7.7 can explain the almost vertical distribution of relative yield values ranging from 0 to 100% yield at that pH range in Fig. 2. A multiple regression model estimating relative yield as a function of both pH and CCE (not shown) indicated that 47% of soybean yield variability across sites was explained by those two factors simultaneously. Therefore, these results suggest the usefulness of an index to account for both factors at the same time.
Development of an Alkalinity Stress Index
Study of the relationships between relative yield and soil pH across sites showed that class means for relative yield decreased by 22% for each unit increase in soil pH (Fig. 2). Similar study of CCE showed that class means for relative yield tended to decrease by 3% for each unit increase in soil CCE (Fig. 4). The average effect of a CCE unit on soybean yield was 0.14 times the effect of the pH unit. Therefore, we developed an alkalinity stress index (ASI) calculated by weighing the effects of these measurements for their effect on yield. Equation [1] defines the index, in which units of CCE are adjusted to have an average effect on plants equal to the average effect of one pH unit as observed in this study:
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Figure 6 shows that the relationship between ASI and relative soybean yield within each site was continuous (i.e., did not show the dichotomy shown for the relationship between yield and pH) and negative linear models were significant (P < 0.05) at 11 of the 12 sites. The r2 of significant models ranged from 0.47 to 0.89. The lack of significance of the relationship for Site 12 is explained mainly by one observation with high relative yield and ASI values. Figure 7 shows a significant linear relationship across all sites and that ASI explained 45% of variability in relative yield. In contrast to relationships between yield and soil pH or CCE, the unexplained variability observed for the overall relationship between yield and ASI was lower and approximately similar for the entire observation range.
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Fig. 7. Relationship between an alkalinity stress index and relative soybean yield across sampling areas in 12 fields.
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Figure 8 describes how soil pH and CCE are expressed in different ranges of ASI values and how pH or CCE can correlate with the intensity of stress symptoms (by recognizing that ASI correlates with yield) but the effect of each becomes clear only when the soils are studied separately based on CCE. The Cate–Nelson model (not shown) identified a change point for the relationship between pH and ASI at pH 7.7 (Fig. 8A), above which CCE was the major factor affecting ASI. These results are consistent with the well-known buffering effect of carbonate on pH, i.e., soil pH does not increase with an increase in carbonate concentration after concentrations are high enough to saturate the soil solution. The main advantage of using ASI rather than a model considering both pH and CCE, therefore, is that it expresses a wide range of pH and CCE values as a single index with common units. Relatively large variation for points around the ascending line in Fig. 8A must be attributed to errors in determinations of CCE or to the presence of carbonates partly occluded within relatively large particles. Calculation of a change point for the relationship in Fig. 8B indicated that soil pH is the major factor affecting ASI below 2.5% CCE.
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Fig. 8. Partitioning of the alkalinity stress index into its components (A) soil pH and (B) soil carbonate concentration.
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Use of remote sensing imagery to identify fields with significant variability in plant size and color enabled us to focus on fields and parts of fields encompassing a wide range of soil characteristics and plant stress symptoms. The proportion of soybean yield variability explained by soil pH, carbonate concentration, and ASI were surprisingly good if it is recognized that yield variability can be attributed to variation in many other growth factors (i.e., soil moisture, nutrients, weeds, insects, diseases, etc.). The technique was particularly useful to account for very high variability in plant stress within a few square meters and with highly irregular patterns. The underlying problem avoided by using this technique and small areas to measure yield and relevant soil properties is that, as noted by Cline (1944), a soil sample composed of cores collected within a highly variable area is not representative of the area. Therefore, an important byproduct of this study is that it demonstrated the value of remote sensing of the soybean canopy to guide soil sampling in fields with complex patterns of acid and calcareous soil that are not reflected on commonly available soil survey maps. The value of this technique may be less for regions of crops where variation in soil pH and calcareous concentration has a lesser impact on crop growth and stress symptoms.
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CONCLUSIONS
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Use of remote sensing imagery proved useful to select small areas in 12 soybean fields with complex and irregular patterns of calcareous and noncalcareous soils to study relationships between grain yield and soil pH or carbonate concentration. Minimum topsoil pH (15-cm depth) across sampling areas within each field ranged from 5.6 to 6.8 while maximum pH ranged from 7.8 to 8.2. Soil CCE ranged from 0% to a maximum of 10.8 to 30.2%. At pH values ranging from 7.7 to 8.2, soil CCE varied from 2.5 to 30% across all sites. Grain yield decreased with increasing pH and CCE across within-field sampling areas in 9 and 11 fields, respectively. Study of relationships across all sampling areas and fields showed that yield decreased with increasing pH and CCE, that each measurement explained 30 and 41% of the yield variability, respectively, and that no reasonable pH or CCE independent critical value could be determined. The alkalinity stress index (ASI = pH + 0.14CCE) developed based on relationships between pH and CCE with yield was continuously and linearly correlated with yield across the entire range of observations and explained 45% of the yield variability across fields. This index was shown to be a useful tool because both soil pH and carbonate concentration need to be considered when trying to quantify the degree of potential plant stress in fields with calcareous and noncalcareous soils in the areas studied.
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NOTES
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All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.
Received for publication June 20, 2006.
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TOP
ABSTRACT
INTRODUCTION
