Agronomy Journal Journal of Natural Resources and Life Sciences Education
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (10)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Hansen, N. C.
Right arrow Articles by Strock, J. S.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Hansen, N. C.
Right arrow Articles by Strock, J. S.
Agricola
Right arrow Articles by Hansen, N. C.
Right arrow Articles by Strock, J. S.
Related Collections
Right arrow Crop Growth and Development
Right arrow Other Crop Management
Right arrow Soybean
Right arrow Plant Nutrition
Right arrow Production Agriculture
Published in Agron. J. 95:1595-1601 (2003).
© American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA

SOYBEAN

Iron Deficiency of Soybean in the Upper Midwest and Associated Soil Properties

N. C. Hansen*,a, M. A. Schmittb, J. E. Andersonc and J. S. Strockd

a Univ. of Minnesota, West Central Res. and Outreach Cent., 46352 State Hwy. 329, Morris, MN 56267
b Univ. of Minnesota, Dep. of Soil, Water, and Clim., 1991 Upper Buford Circle, St. Paul, MN 55108
c Univ. of Minnesota–Morris, Div. of Sci. and Mathematics, Morris, MN 56267
d Univ. of Minnesota, Southwest Res. and Outreach Cent., 23669 130th St., Lamberton, MN 56152

* Corresponding author (hansennc{at}mrs.umn.edu).

Received for publication December 12, 2002.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Iron deficiency chlorosis is a common, yield-limiting condition for soybean [Glycine max (L.) Merr.] grown in areas with high-pH, calcareous soils. The objectives of this study were to document the extent of chlorosis in an area of the upper Midwest, to understand producers' perceptions and management practices related to Fe deficiency chlorosis, and to investigate the soil properties associated with it. A survey tool evaluated the perceptions of soybean producers in western Minnesota regarding cause, extent, and management of chlorosis. A detailed field study in western Minnesota compared plant attributes and soil properties in severely chlorotic, moderately chlorotic, and nonchlorotic sites. Soybean producers indicated that Fe chlorosis is responsible for substantial yield loss on 24% of their crop even though the majority of the producers selected chlorosis-resistant varieties. Chlorotic plants had stunted growth and poor nodule development relative to nonchlorotic plants. Compared with nonchlorotic areas, soil in chlorotic areas had greater soil moisture content and concentrations of soluble salts, carbonates, and diethylenetriaminepentaacetic acid (DTPA)-Cr and had lesser concentrations of DTPA extractable Fe, Mn, Ni, and Cd. Discriminant analysis identified soluble salts, DTPA-Fe, DTPA-Cr, and soil moisture content as a set of significant predictors of chlorosis. This set of variables suggests that chlorosis occurs due to multiple stresses and not simply to limited available Fe. For a diagnostic soil test, a combination of DTPA-Fe and soluble salts predicted chlorosis expression. These observations should be considered in soybean variety screening so that variety tolerance can be matched with specific soil properties.

Abbreviations: CCE, calcium carbonate equivalent • CI, cone index • DTPA, diethylenetriaminepentaacetic acid • EC, electrical conductivity • SCN, soybean cyst nematode


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
IRON DEFICIENCY is a common yield-limiting factor for soybean grown on high-pH, calcareous soil and has been reported in the Great Plains (Clark, 1982; Loeppert et al., 1984) and in the North-Central United States (Franzen and Richardson, 2000; Goos and Johnson, 2000; Inskeep and Bloom, 1984). Iron deficiency results in a characteristic interveinal chlorosis in new leaves and can cause substantial yield loss in soybean (Inskeep and Bloom, 1987; Morris et al., 1990). A particular challenge to studying and managing Fe deficiency is a high level of temporal and spatial variability in chlorosis expression. In some years, chlorosis develops during early growth stages and disappears as the plants mature. In more severe cases, chlorosis can persist throughout the entire season. Chlorosis generally occurs in patchy areas of fields and frequently, but not always, in low areas. Franzen and Richardson (2000) showed that chlorotic patches did not occur in a pattern consistent with changes in soil type.

Decades of research have addressed Fe deficiency in soybean and have been instrumental in understanding the Fe deficiency stress response mechanisms used by soybean (Jolley and Brown, 1994). There is a wide variation in susceptibility to Fe deficiency among soybean varieties, and variety selection is the most important management practice for producers with chlorosis-prone soils. Brown et al. (1967) provides some basis for the importance of variety selection. Private companies and public institutions routinely rank varieties for their susceptibility to Fe deficiency. However, despite extensive research and variety screening efforts, Fe deficiency remains a challenge in large soybean production areas with calcareous soils, including parts of Iowa, Minnesota, North Dakota, and South Dakota. The extent and impact of the chlorosis problem in this area is not well documented. Two objectives for this research were to evaluate the extent of the chlorosis problem in an area of the upper Midwest and to understand producers' perceptions about causes and adoption of management practices related to Fe deficiency.

A third research objective was to investigate the soil and environmental conditions associated with the expression of Fe deficiency across a wide set of soil and management conditions. Soil properties that have been shown to affect the expression and spatial variability of Fe deficiency chlorosis include the concentration and reactivity of soil carbonates (Inskeep and Bloom, 1987; Morris et al., 1990), the concentration and forms of soil Fe (Morris et al., 1990), and the concentration and quality of soil salts (Inskeep and Bloom, 1987; Morris et al., 1990). It is well documented that high levels of HCO-3 will induce Fe deficiency stress (Inskeep and Bloom, 1984; Coulombe et al., 1984). The concentration of HCO-3 in the soil solution is affected by the reactivity of soil carbonates, exchangeable bases, soil moisture content, and the concentration of CO2. High solution ionic strength, measured by soil electrical conductivity (EC), has been associated with increases in chlorosis expression (Franzen and Richardson, 2000; Morris et al., 1990). Understanding the environmental conditions that control the expression of Fe deficiency will aid in identifying management practices to reduce it and may improve the development and selection of varieties that are tolerant to Fe deficiency chlorosis.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Soybean Producer Survey
An area encompassing west-central and southwest Minnesota was identified as a large soybean-producing area with calcareous soils and common occurrence of Fe deficiency chlorosis in soybean. A survey was prepared and mailed to 120 soybean producers in this area. Producers were identified using databases from agribusiness and Extension sources. The survey was designed to characterize soybean production; the extent, severity, and effects of chlorosis; producer perceptions of the factors causing chlorosis; and chlorosis management practices. Producers who participated in the survey were asked if the project team could collect data from a soybean field on their farm that has a history of chlorosis. Producers who agreed provided a map to a field of their choosing for data collection.

Field Survey
Between 17 July and 28 July 2000, 60 field sites, as identified by producers, were evaluated for the field survey. Upon arrival to each field site, three field positions were identified based on a visual assessment of the severity of chlorosis symptoms. The three field positions were (i) the most chlorotic area, (ii) the nearest nonchlorotic area, and (iii) a moderately chlorotic area located between the other two field positions. A set of plant attributes was evaluated for each field position and included visual chlorosis score, relative leaf chlorophyll concentration, nodulation, and plant canopy height. Visual chlorosis scores were made by two observers based on a scale of 1 (green) to 5 (severe chlorosis with necrosis). Relative leaf chlorophyll concentration measurements were obtained using a Minolta SPAD meter (Minolta, Ramsey, NJ) on five leaves from the newest fully developed trifoliate leaf. Visual scores for nodulation were conducted by two observers and ranked for both nodule quantity and nodule size on a scale from 1 (no nodules) to 5 (ample large, active nodules). Plant canopy height was measured with a meter stick.

At each location, a composite soil sample was taken from each field position from 0 to 15 cm. Samples were oven-dried, ground, and analyzed for pH, calcium carbonate equivalent (CCE), concentration of soluble salts measured by EC, and the concentrations of extractable P, K, Fe, Mn, Zn, Cu, Ni, Cd, and Cr. Soil pH and EC were determined on a 1:1 (v/v) soil water mixture. Extractable P was determined using the Olsen method (Frank et al., 1998), and extractable K was determined according to Thomas (1982). Soil CCE was determined by the method of Allison and Moodie (1965). Extractable Fe, Mn, Zn, Cu, Ni, Cd, and Cr were determined by inductively coupled plasma optical emission spectroscopy on a DTPA extract. Nematode egg density was determined by the method described in Chen et al. (2001). Soil moisture percentage ({theta}d) was determined gravimetrically on a separate soil core sampled from 0 to 20 cm below the soil surface.

Soil penetration resistance (soil strength) was evaluated with a profile distribution of cone index (CI) values. A Rimik CP20 static load cone penetrometer (Soil Measurement Syst., Tuscon, AZ) was used to measure soil strength. The CP20 had a semi-included cone angle of 30°, a cone base diameter of 12.8 mm, and a shaft diameter of 9.5 mm. Profile CI measurements were recorded at 25-mm depth intervals to a depth of 0.6 m. At each location, one soil strength measurement was taken for each field position. Soil strength measurements were recorded near the same locations as soil samples taken for gravimetric soil moisture determination. Nonlinear regression methods by Busscher et al. (1997) were used to adjust CI values for differences in water content.

Statistical Methods
Summaries of the plant and soil attribute variables were constructed using the means procedure in SAS (SAS Inst., 1999) for each field position. The GLM procedure in SAS was used to compute F statistics and p values to identify plant and soil attributes with significantly different means (p < 0.10) across field positions after adjusting for location block effects.

The LOGISTIC procedure in SAS was used to perform a proportional-odds model analysis of the field position categorical response (chlorotic, moderately chlorotic, or nonchlorotic), with soil attribute predictor variables. Using Box–Cox and graphical methods, transformations were developed to approximate joint normality of the soil attribute predictor variables. Transformations consisted of squaring soil moisture and taking logarithms of soil EC, DTPA-Fe, and soil penetration resistance. Stepwise variable selection was used to select the set of soil attribute variables needed to predict position. Our stepwise selection method included location strata variables to adjust for variation in data collection locations. A stepwise canonical discriminant analysis was used to confirm the findings of the proportional-odds model analysis (Agresti, 1990).

Multiple linear regression models were developed to explain the variation in plant attributes in terms of soil attribute predictor variables. The REGRESSION procedure in SAS and the ARC regression software package were used to develop these models (Cook and Weisberg, 1999).


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Mail Response Survey
The mail response survey of soybean producers had a response rate of 66% (79/120). Of the producers who responded to the survey, 99% indicated chlorosis is a major production issue. On average, the respondents each produce 220 ha of soybean annually, and they estimated that 24% of their soybean crop is affected by Fe chlorosis. Average yield loss associated with chlorotic areas was estimated to be 0.8 Mg ha-1. Yield losses of this magnitude resulting from Fe chlorosis have been reported (Inskeep and Bloom, 1987). These results illustrate that Fe deficiency is a major production issue with a large economic impact.

Thirty three percent of respondents indicated that they have livestock in their farming operation. Among those with livestock, 23% believe that manure added to fields had lessened the degree of chlorosis while 77% believe that manure had not. This result stands in contrast to recommendations that addition of organic matter may correct Fe chlorosis (Tisdale et al., 1985). All of the producers surveyed confirmed that they have observed a pattern in chlorotic soybean fields due to field equipment wheel tracks, with soybean plants in the wheel tracks appearing better (greener) than those outside the wheel tracks. Although the results regarding manure and wheel track patterns do not directly lead to a specific management recommendation, they do provide insight on the nature of the chlorosis problem and what factors play an important role in its development.

Producers in the survey were asked to rank their perception of soil properties that relate to the expression of Fe deficiency chlorosis (1 = most significant, 5 = least significant). Of the factors provided as choices in the survey, soil pH (1.4) and salinity (1.7) were perceived as the most significant factors, with poor drainage (2.6) and poor nodulation (3.2) being less significant. Other factors written in by producers include weather, herbicide use, and infection by soybean cyst nematode [Heterodera glycines] (SCN).

The survey asked producers to identify practices they have implemented specifically to manage Fe deficiency. The percentage of producers implementing management practices was variety selection (70%); seeding management, including planting population, row spacing, or planting date (42%); artificial drainage (33%); tillage practice (16%); fertilizer (11%); and herbicide selection (6%). The majority of producers in the survey are selecting varieties as a management approach for Fe deficiency. This result, together with the producers' indication of the extent and severity of chlorosis, suggests that variety selection alone does not remedy Fe deficiency problems in this region. One difficulty in variety selection is the interaction of genotype with environmental factors for Fe deficiency expression. Varieties identified as Fe efficient in field-screening nurseries may not exhibit the same efficiency in locations with different soil and environmental properties. The field survey in this study was conducted to identify soil conditions that correlate with Fe deficiency symptoms over a large number of locations. This information can be used to improve variety screening to better match varieties with specific environmental conditions.

Field Survey
A visual estimate of the percentage of each surveyed field that was chlorotic averaged 22% (range: 1–90%). This estimate is similar to the estimate made by producers in the mail response survey that an average of 24% of their soybean crop exhibits chlorosis. Plant attributes from all locations were compared among field positions (Table 1). Visual chlorosis score differed among field positions and averaged 4.4 for the most chlorotic field position, indicating the presence of severe chlorosis symptoms at most locations. Visual chlorosis scores in the nonchlorotic and moderately chlorotic sites averaged 1.1 and 3.1, respectively. Distances separating the chlorotic and nonchlorotic areas were generally less than 50 m, illustrating the high spatial variability in the expression of Fe chlorosis. Differences in relative leaf chlorophyll concentration were proportional to the differences for visual chlorosis score. Relative leaf chlorophyll concentration had a significant negative relationship to the visual chlorosis score (r2 = 0.738, p < 0.001), suggesting that these two methods of assessing the degree of chlorosis were similarly effective (Fig. 1) . Canopy height was significantly different among field positions, and there was a negative relationship between visual chlorosis score and canopy height (r2 = 0.808, p < 0.001), suggesting that chlorosis expression corresponds to stunted aboveground growth (Fig. 1). Although the field survey did not compare soybean grain yields, the large differences in aboveground growth lend support to the producers' estimation of yield loss due to Fe chlorosis. Visual nodule scores were also different among the three field positions, averaging 0.7, 1.7, and 3.5 for the chlorotic, moderately chlorotic, and nonchlorotic areas, respectively. Soybean roots in the chlorotic areas had few small nodules that were generally not pink inside, indicating poor activity while roots from the nonchlorotic areas had abundant and active nodules. Laboratory research has shown that active N fixation by symbiotic association of soybean and Bradyrhizobium japonicum aided soybean in Fe acquisition (Burton et al., 1998; Terry et al., 1991). It is not possible in the current study to determine whether poor nodule formation in chlorotic areas is a cause or a symptom of Fe deficiency, but there is clear evidence that the symptoms are correlated. Soybean producers did not identify nodulation as a highly significant cause of chlorosis in the mail response survey.


View this table:
[in this window]
[in a new window]
  		Table 1. Plant attributes related to the expression of Fe chlorosis measured in chlorotic, moderately chlorotic, and nonchlorotic field positions at 60 locations in western Minnesota.

 


View larger version (24K):
[in this window]
[in a new window]
  		Fig. 1. Relative leaf chlorophyll concentration, measured by the SPAD meter, and soybean canopy height plotted against visual chlorosis score (scale: 1 = green to 5 = severe chlorosis).

 
Soil properties were compared among field position for all locations (Table 2). Soil moisture was significantly different among field positions (p < 0.001), with soil being wetter in the chlorotic positions than in the nonchlorotic positions. Chlorosis was frequently, but not always, observed in low areas of fields. Higher soil moisture likely results from a combination of field position and lower evapotranspiration from the stunted soybean in the chlorotic positions. Others have shown in the greenhouse that chlorosis expression is exacerbated with increasing soil moisture in calcareous soils (Inskeep and Bloom, 1986). Soil penetration resistance (CI) was different among field positions (p = 0.074). Greater penetration resistance was observed in the nonchlorotic areas than in the chlorotic areas. However, for all field positions, the CI values are not reflective of severe soil compaction, and the differences between field positions are small. It is not known if the small changes in CI values relate to the producers' observation that soybean growing in wheel tracks exhibits less chlorosis than that growing between wheel tracks.


View this table:
[in this window]
[in a new window]
  		Table 2. Means and p values of soil properties measured in chlorotic, moderately chlorotic, and nonchlorotic field positions at 60 locations in western Minnesota. Soil properties compared are moisture content ({theta}d); penetration resistance [cone index (CI)]; pH; Olsen extractable P; K; calcium carbonate equivalent (CCE); soluble salts [electrical conductivity (EC)]; diethylenetriaminepentaacetic acid (DTPA) extractable Fe, Mn, Zn, Cu, Ni, Cd, and Cr; and soybean cyst nematode (SCN) egg density.

 
Soil pH averaged 8.0 (range: 7.0–8.3) for all fields and was not different among field positions (p = 0.4774). This result is in direct contrast to the producers' perception that soil pH is the most significant factor causing Fe chlorosis. Neither Olsen P nor extractable K levels were different among field positions. The soil CCE was high for all fields and field positions and is reflective of the calcareous soils where Fe chlorosis in soybean is common. The soil CCE was significantly different among field positions, with highest values observed in the chlorotic areas. Some studies have also documented that higher solid-phase carbonate concentration measured as CCE relates to chlorosis expression (Franzen and Richardson, 2000; Inskeep and Bloom, 1987). Other studies found that soil CCE did not differ with different degrees of chlorosis but that the reactivity of carbonates (Morris et al., 1990) or the clay-sized fraction of carbonates did (Inskeep and Bloom, 1986). Soluble salt concentration (EC) was different among field positions, with higher EC observed for the chlorotic areas than for the nonchlorotic areas (Table 2). Similar observations have been made elsewhere (Franzen and Richardson, 2000; Inskeep and Bloom, 1987; Loeppert et al., 1994; Morris et al., 1990). Soybean producers in the mail response survey also identified soil salts as one of the more important factors perceived as causing chlorosis.

The concentration of DTPA-Fe was significantly different among field positions, with higher Fe concentrations in the nonchlorotic field position than in the chlorotic field position. The separation of chlorotic areas based on soil DTPA-Fe has had mixed results. Inskeep and Bloom (1987) concluded that there was no difference in soil DTPA-Fe levels when comparing chlorotic and nonchlorotic areas of individual fields. Franzen and Richardson (2000) found differences in DTPA-Fe between chlorotic and nonchlorotic areas for some sites but not for others. McKeague and Day (1966) and Morris et al. (1990) showed a positive correlation between leaf chlorophyll concentration and the concentration of amorphous iron oxide in calcareous soils. The current study concludes that soil DTPA-Fe is important relative to the expression of chlorosis across a large number of locations even though differences are small and average concentrations are higher than those often classified as critical levels (Lindsay and Norvell, 1978). Significant differences were also observed for other DTPA extractable metals, including Mn, Ni, Cd, and Cr. Average DTPA-Mn concentration was higher in the nonchlorotic areas than in the chlorotic areas (Table 2). Iron chlorosis symptoms can be induced when high Mn concentrations interfere with the uptake of Fe (Roomizadeh and Karimian, 1996). Because Mn concentrations are higher in the nonchlorotic areas, no such interaction is suspected here. Concentrations of Ni and Cd were also higher in the nonchlorotic areas than in the chlorotic areas while concentrations of Cr were highest in the chlorotic areas and lowest in the nonchlorotic areas. More research is needed to determine if the differences in these metal concentrations are directly involved in the expression of chlorosis or if the significant correlations observed are coincidental. There was no significant difference in soil DTPA-Zn or DTPA-Cu concentrations.

Soybean grown in soil infested with SCN can exhibit chlorosis symptoms and stunted growth that appears similar to Fe deficiency chlorosis. For soybean, visual symptoms are generally not apparent unless SCN egg densities in the soil exceed 500 eggs 100 g-1 soil. Severe chlorosis symptoms are expected when egg densities are greater than 2500 eggs 100 g-1 soil (S.Y. Chen, Univ. of Minnesota, personal communication, 2002). We performed a soil analysis for SCN egg density to identify sites where chlorosis symptoms may be occurring due to infection by SCN rather than, or in addition to, Fe deficiency. There was a positive detection of SCN eggs at a total of 30 field sites, but concentrations of eggs were very low, averaging 150 eggs 100 g-1 soil. Only three field sites had SCN egg densities >2500 eggs 100 g-1 soil. There was a significant difference in SCN egg density among field positions when including the three fields with high egg density (p = 0.069) but not if those three sites were excluded (p = 0.440). Therefore, for the majority of the sites surveyed, infection with SCN was not the cause of the visual chlorosis symptoms.

Multivariate Analysis
A discriminant analysis was performed to determine which soil attributes were most useful for predicting chlorosis. The primary method of analysis was the proportional-odds model (Agresti, 1990), using backward elimination analysis with all soil attributes as potential predictor variables transformed to approximate joint normality. The procedure yielded a model with soil moisture, EC, DTPA-Fe, and DTPA-Cr as significant predictors (Table 3). The odds of a more chlorotic field position increase for increases in soil moisture, EC, and DTPA-Cr and decrease with an increase in DTPA-Fe. The same predictor variables were confirmed with a canonical discriminant analysis method.


View this table:
[in this window]
[in a new window]
  		Table 3. Soil properties identified by the proportional-odds model as significant predictors of chlorosis expression. Transformations of the soil variables were used to approximate joint normality.

 
A multivariate regression analysis was performed for soil attributes against visual chlorosis score, relative chlorophyll concentration, and canopy height. Box–Cox and graphical methods identified that a square root transformation of each of the response variables stabilized error variance and improved the fit of the estimated models. To include variation across data collection sites, we used indicator variables for location in our regression models. The regression model for each of the response variables contained the same set of significant predictors: soil percentage moisture, EC, DTPA-Fe, and DTPA-Cr (Table 4). Increases in EC, soil moisture, and DTPA-Cr corresponded to increased severity of chlorosis and stunting. Increases in DTPA-Fe correspond to a decrease in the severity of chlorosis. These results confirmed the results of the proportional-odds model.


View this table:
[in this window]
[in a new window]
  		Table 4. Results of multiple-regression analysis of soil properties identified as significant predictors of chlorosis. Separate analyses were performed for the dependent variables visual chlorosis score, relative chlorophyll concentration (SPAD), and soybean canopy height. Also included in the multiple-regression models, but not shown, were location indicator variables that account for variation across data collection locations.

 
The discriminant analysis is useful in understanding the soil physical and environmental conditions that are associated with the expression of chlorosis. However, there is some interest in developing a diagnostic soil test that would indicate the risk of chlorosis development. Percentage soil moisture is not useful for commercial application of a diagnostic soil test. Among the set of significant predictor variables identified, most commercial soil-testing laboratories are equipped to analyze for EC and DTPA-Fe. We used a logistic regression model to determine the probability of chlorosis based on these two predictors. The results of this logistic regression model illustrate that there is a dynamic interaction among soil DTPA-Fe, soil EC, and the expression of chlorosis (Fig. 2) . As soil EC increases, so does the likelihood of chlorosis development. However, higher concentrations of DTPA-Fe appear to partially mitigate the stress induced by salts. For example, for the average soil EC value of 0.71 dS m-1, DTPA-Fe concentrations of 5.8 and 19 mg L-1 correspond to 75 and 25% probabilities of chlorosis development, respectively. Similarly, the average DTPA-Fe concentration in this study was 8.6 mg L-1. At this level of DTPA-Fe, EC concentrations of 1.1 and 0.39 dS m-1 correspond to a 75 and 25% estimated probability of chlorosis, respectively. Interpretation of these results should be useful in developing a diagnostic soil test to assess risk of chlorosis occurrence. The utility of DTPA-Fe and EC as predictors of chlorosis should not be taken out of the context of high-pH, calcareous soils. Soil pH and CCE were not established as significant predictors in this study, but all of the soils evaluated in the study were high in pH and carbonate levels relative to acidic or noncalcareous soils.



View larger version (14K):
[in this window]
[in a new window]
  		Fig. 2. Estimated probabilities (75% and 25%) of a site expressing Fe chlorosis as a function of DTPA-Fe and soluble salt concentration [electrical conductivity (EC)]. Probabilities are based on results of a logistic regression model.

 
Results from this study should be useful for improving future soybean variety screening and for developing resistance to chlorosis. In current field methods used for screening soybean for resistance to chlorosis, it is not customary to characterize soil EC levels. Screening in an environment where chlorosis occurs due to low available Fe without attention to soil EC or other soil parameters involved in chlorosis development may be partially responsible for the large degree of chlorosis occurring in the upper Midwest and especially in areas where EC is relatively high. More research is needed to identify the role of soil salts in chlorosis development and to identify what ions or ion combinations are most critical.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
Soybean producers in a large area of western Minnesota identified chlorosis as an important production issue, resulting in substantial yield loss on 24% of the soybean crop. The problem exists even though the majority of producers are selecting chlorosis-resistant varieties. There is a need to improve soybean variety development and selection approaches when considering Fe chlorosis in areas of calcareous soils. Field surveys confirmed the presence of severe chlorosis and supported the survey result that 24% of the soybean crop is affected. Soybean in chlorotic areas had lower leaf chlorophyll concentrations, stunted growth, and poor nodule development relative to nonchlorotic plants. Differences in soil properties were identified between chlorotic and nonchlorotic areas. Discriminant analysis identified soluble salts, DTPA-Fe, DTPA-Cr, and soil moisture content as a set of significant predictors of chlorosis. For a diagnostic soil test, a combination of DTPA-Fe and soluble salts can predict the risk of chlorosis development for high-pH, calcareous soils. The combination of these two variables should also be considered in soybean variety screening and selection so that variety tolerance will be more closely matched with the soil conditions controlling chlorosis expression.


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




This article has been cited by other articles:


Home page
 Soil Sci.Home page
A. de Santiago, I. Diaz, M. d. C. del Campillo, J. Torrent, and A. Delgado
Predicting the Incidence of Iron Deficiency Chlorosis from Hydroxylamine-Extractable Iron in Soil
Soil Sci. Soc. Am. J., August 20, 2008; 72(5): 1493 - 1499.
[Abstract] [Full Text] [PDF]

Home page
 Agron. J.Home page
S. L. Naeve
Iron Deficiency Chlorosis in Soybean: Soybean Seeding Rate and Companion Crop Effects
Agron. J., October 3, 2006; 98(6): 1575 - 1581.
[Abstract] [Full Text] [PDF]

Home page
 Agron. J.Home page
S. L. Naeve and G. W. Rehm
Genotype x Environment Interactions within Iron Deficiency Chlorosis-Tolerant Soybean Genotypes
Agron. J., May 3, 2006; 98(3): 808 - 814.
[Abstract] [Full Text] [PDF]

Home page
 Agron. J.Home page
J. V. Wiersma
High Rates of Fe-EDDHA and Seed Iron Concentration Suggest Partial Solutions to Iron Deficiency in Soybean
Agron. J., May 13, 2005; 97(3): 924 - 934.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (10)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Hansen, N. C.
Right arrow Articles by Strock, J. S.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Hansen, N. C.
Right arrow Articles by Strock, J. S.
Agricola
Right arrow Articles by Hansen, N. C.
Right arrow Articles by Strock, J. S.
Related Collections
Right arrow Crop Growth and Development
Right arrow Other Crop Management
Right arrow Soybean
Right arrow Plant Nutrition
Right arrow Production Agriculture

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Crop Science Vadose Zone Journal
Journal of Natural Resources
and Life Sciences Education		 Soil Science Society of America Journal
Journal of Plant Registrations Journal of
Environmental Quality		 The Plant Genome