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Published in Agron. J. 96:181-194 (2004).
© American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA

SPATIAL VARIABILITY

The Spatial Distribution of Soybean Cyst Nematode in Relation to Soil Texture and Soil Map Unit

Felicitas Avendaño*,a, Francis J. Pierceb, Oliver Schabenbergerc and Haddish Melakeberhand

a Dep. of Plant Pathol., 351 Bessey Hall, Iowa State Univ., Ames, IA 50011
b Cent. for Precision Agric. Syst., Washington State Univ., 24106 North Bunn Rd., Prosser, WA 99350
c SAS Inst., Cary, NC 27513
d Dep. of Entomol., 243 Nat. Sci. Bldg., Michigan State Univ., East Lansing, MI 48824

* Corresponding author (avendano{at}iastate.edu).

Received for publication November 21, 2002.

   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Evidence suggests that variation in soil texture may be key to explain the variability of soybean cyst nematode (Heterodera glycines Ichinohe) population density within infested fields and may be important to the delineation of soybean cyst nematode (SCN) management zones. The purpose of this work was to assess the spatial structure of soil texture in two fields of known SCN population density and its relationship to published soil survey maps and to quantify the relationship between soil texture and SCN population density variability across fields and over time. Cysts were extracted by elutriation from single-core soil samples collected in a geostatistical sampling design. Soil texture analysis was performed using a modified hydrometer method. Classical and geostatistical tools were employed to characterize and map soil texture and correlate sand, silt, and clay with SCN population. Cyst population density was consistently higher in loamy sand than in sandy clay loam. Sand, clay, and silt in the soil were spatially structured and strongly correlated with SCN population density consistently over time. The number of eggs per cyst was not related to soil type or texture. This study demonstrates the value of soil survey maps as indicators of where SCN can be expected in an infested field and how the addition of site-specific texture data can improve the spatial prediction of SCN. This study provides the basis for future experimentation to define soil texture tolerance limits for SCN and lays out foundations for new and integrated approaches to site-specific management of SCN.

Abbreviations: SCN, soybean cyst nematode


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
SOYBEAN CYST NEMATODE is a major economic pest in soybean [Glycine max (L.) Merr.] with wide geographic distribution in the major soybean-growing areas of the USA (Wrather et al., 2001). The resilience of SCN makes management and not eradication the most viable option for minimizing its impacts on soybean production (Bridge, 1996). Site-specific management of SCN is of interest because population densities vary spatially within fields (Donald et al., 1999; Avendaño et al., 2003), and with variable management in response to SCN population density, soybean growers might increase the efficacy and reduce the costs of SCN management practices. To be of value, site-specific management requires that the spatial variability be highly structured to ensure that spatial prediction and corresponding management maps are accurate (Pierce and Nowak, 1999). However, based on geostatistical sampling of SCN populations, Avendaño et al. (2003) found that the spatial variability of SCN in two Michigan fields was poorly structured, leading them to conclude that the success of site-specific management of SCN in these fields is unlikely, particularly given the high cost of sampling nematodes. In areas within these same fields, however, there were repeated occurrences of nondetectable or low densities of SCN, and the authors report correspondence between SCN infestation and remote-sensed imagery and yield maps. This observation suggests that other criteria might be available to delineate management zones for site-specific management of SCN in these fields (Avendaño et al., 2003). This notion is supported by evidence that environmental conditions created by the interaction of weather, soil, landscape, and plant factors assist in the dispersion of eggs, determine SCN survivability, or limit its growth potential and thereby regulate the spatial dynamics of SCN (Lehman, 1994; Koenning and Sipes, 1998; Donald et al., 1999, 2001; Workneh et al., 1999).

The determination of spatio-temporal dynamics of yield-limiting factors and the identification of cause-and-effect relationships among limiting factors are also critical components of site-specific management of nematodes (Evans et al., 2002; Melakeberhan, 2002; Wyse-Pester et al., 2002). Within the two fields studied by Avendaño et al. (2003), SCN population densities appeared to vary by soil mapping unit, and the soil mapping units were differentiated primarily on soil texture differences. This would suggest that soil texture or some combination of individual soil separates (sand, silt, and clay) are related directly or through the soil properties and processes they influence to SCN population densities and may be useful in delineating SCN management zones.

The literature supports two important points in this regard: that nematode population dynamics are related to soil texture (particle size composition), structure (spatial arrangement and continuity of the soil pores between and within the particles), and related soil hydraulic properties and that these soil properties vary spatially and often with strong spatial structure. Soil texture and structure strongly affect crop production and ecosystem health, including the nematode community (Gupta, 1994; Heal and Dighton, 1985; McKeague and Wand, 1982; Topp et al., 1997; Workneh et al., 1999). While there seems to be some variation among nematodes, generally coarse, sandy soils favor nematode population growth by providing more space for nematode movement than poorly structured soils containing finer particles, which cannot form stable compound aggregates and so pack closer and diminish total porosity (Jones et al., 1969). Population densities of root lesion nematodes (Pratylenchus penetrans Cobb), Aglenchus agricola (de Man) Meyl, stylet nematodes (Tylenchorhynchus spp.), clover cyst nematode (Heterodera trifolii Goffart), and Paratylenchus spp. were significantly correlated with sand or silt particle size classes (Wallace et al., 1993). However, this relationship may be reversed for other nematodes. For example, higher densities of southern root knot nematode [Meloidogyne incognita (Kofoid and White) Chitwood] were associated with higher levels of clay in a loamy sand soil (Noe and Barker, 1985). Tillage influences nematode prevalence and population density by increasing the amount of space available for nematode movement even in fine soils rich in clay (Jones et al., 1969; Workneh et al., 1999; Donald et al., 2001). In fields under tillage management, repeated soil disruption during land preparation and cultivation may have alleviated oxygen deficiencies arising from saturation due to high clay content, thus favoring the nematode population (Young, 1987; Workneh et al., 1999). In a field study on silt loam, the response of field-grown soybean to SCN varied depending on the water status of the soil and SCN level (Johnson et al., 1993). In this case, the increase in SCN penetration of the soybean root system corresponded positively to the increase in soil oxygen diffusion rate and corresponding decrease in water potential. Water-holding capacity was found to be the most important soil factor affecting the success of the oat (Avena sativa L.) crop at various levels of cereal cyst nematode (Heterodera avenae Woll.). As water-holding capacity level increased, the number of nematodes tolerated by the oat crop without failure also increased (Fidler and Bevan, 1963). Soybean cyst nematode survival in the soil in the absence of a host was related to soil moisture, being highest at field capacity, followed by dry soil and last by flooded conditions (Slack et al., 1972). However, Barker and Koenning (1989) noticed that numbers of SCN eggs, infective juveniles, and cysts were affected by soil texture but not by soil moisture in the presence of soybean. Soybean plants respond to moisture stress by increasing root biomass, which would favor reproduction of SCN, thus increasing population density under drought conditions (Koenning et al., 1988; Barker and Koenning, 1989; Koenning and Barker, 1995). Koenning and Barker (1995) also found that although SCN can increase to damaging levels in fine-textured soils, the low rate of increase in these soils limits the damage potential of this nematode to soybean, as does the fact that damage is less severe per unit increase in population density. Low reproductive rate in soils with high clay content means the nematodes take longer to attain damaging levels in fine-textured soils. When the soybean roots are damaged early in the season, the number of potential feeding sites for newly hatched J2s is reduced; therefore, SCN population density declines. In sandy soil, the population may increase rapidly during the season, but in soils richer in clay, where the reproductive rate is reduced, the SCN population cannot recover as rapidly; in fact, it may take years to achieve damaging levels again, creating a cyclical pattern (Koenning and Barker, 1995).

Given that SCN population dynamics are regulated at least in part by soil properties and associated processes, the delineation of site-specific SCN management zones would appear feasible if soil properties are spatially structured and if quantitative relationships between SCN and these soil properties occur and are known. Considerable evidence supports the spatial variability of soil properties and that this variability can have spatial structure adequate for site-specific management (Robertson et al., 1993; Pierce and Nowak, 1999; Kravchenko and Bullock, 2000, 2002; Cassel et al., 2000; Basso et al., 2001; Gaston et al., 2001; Mueller et al., 2001). Thus, there is sufficient evidence to suggest a significant relationship between SCN populations and soil texture and that soil texture varies spatially. However, quantitative relationships on the spatial covariance of SCN with soil texture and/or predictive relationships between soil properties and SCN needed for management zone delineation are not available. We therefore hypothesize that the variation of SCN population density between soil mapping units observed in the fields studied by Avendaño et al. (2003) suggests that soil texture may be a key variable to explain the variability of SCN population density within these fields and may be important to the delineation of SCN management zones.

The purpose of this work was to characterize the relationship between soil texture and the variability observed in SCN population density for the Michigan fields reported by Avendaño et al. (2003). Specific objectives were to assess the spatial structure of texture within fields of known SCN population density in Michigan and its relationship to published soil survey maps, determine the extent to which the spatial variability in SCN cyst population density relates to soil texture, quantify the relationship between soil separates (sand, silt, and clay) and SCN population density, and assess the extent to which this relationship holds between fields with similar soil types but different SCN populations.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Research Site, Sampling Design, and Soil Sample Collection
The experimental design of the initial phase of this study to assess the spatial variability of SCN population was reported by Avendaño et al. (2003). Briefly, the study was conducted in Shiawassee County, MI, in 1999 and 2000 on two fields, (Field A and Field B) maintained by the cooperating farmer. Field A was 24 ha, managed under no-tillage since 1996, and planted to corn (Zea mays L.) in 1998. Field B was 13 ha, conventionally tilled after wheat (Triticum aestivum L.) in 1998, and managed under no-tillage thereafter. In 1999, an SCN-susceptible soybean variety (‘Asgrow 1901’) and in 2000 an SCN-resistant variety (‘Asgrow 2201’), both Roundup-ready, were grown in both fields. Soybean was planted in 19.1-cm rows at a rate of 519000 viable seeds ha–1 in 1999 and 494000 viable seeds ha–1 in 2000. Row orientation was north–south in Field A and east–west in Field B.

Soil series in Field A were Belding sandy loam (coarse-loamy, mixed, frigid Argic Endoaquods), Breckenridge sandy loam (coarse-loamy, mixed, nonacid, frigid Mollic Endoaquepts), Brookston loam (fine-loamy, mixed, superactive, mesic Typic Argiaquolls), Conover loam (fine-loamy, mixed, active, mesic Udollic Endoaqualfs), and Newaygo sandy loam (fine-loamy over sandy or sandy-skeletal, mixed, frigid Alfic Haplorthods). Soil series in Field B were Brookston loam, Newaygo sandy loam, and Berville loam (fine-loamy, mixed, mesic Typic Argiaquolls) (Threlkeld and Feenstra, 1974). Soil series maps were digitized from Threlkeld and Feenstra (1974) (Fig. 1B and 2A).



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  		Fig. 1. (A) Nested design for the collection of soil samples. Samples were collected at the indicated distances in random directions within grid cells of 50 by 50 m. (B) Location of sampled sites and soil series present in Field A. Crosses indicate the location of samples collected for the determination of soybean cyst nematode population density; black circles indicate which of those samples were also used for soil particle size analysis. Soil series are (1) Newaygo sandy loam, (2) Conover loam, (3) Brookston loam, (4) Breckenridge sandy loam, and (5) Belding sandy loam. Soil series map was digitized from Threlkeld and Feenstra (1974). Spatial distribution of (C) sand, (D) clay, and (E) silt percentages in the soil as interpolated by ordinary kriging within the area sampled. The shadings in C, D, and E represent percentage ranges. (F) Soil type delineation determined based on the proportion of sand, silt, and clay. Soil types are (1) loamy sand, (2) sandy loam, (3) loam, and (4) sandy clay loam.

 


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  		Fig. 2. (A) Location of sampled sites and soil series present in Field B. Crosses indicate the location of samples collected for the determination of soybean cyst nematode population density (see Fig. 1A); black circles indicate which of those samples were also used for soil particle size analysis. Soil series are (1) Newaygo sandy loam, (2) Brookston loam, and (3) Berville loam. Soil series map was digitized from Threlkeld and Feenstra (1974). Spatial distribution of (B) sand, (C) clay, and (D) silt percentages in the soil as interpolated by universal kriging within the area sampled. The shadings in B, C, and D represent percentage ranges. (E) Soil type delineation determined based on the proportion of sand, silt, and clay. Soil types are (1) loamy sand, (2) sandy loam, and (3) sandy clay loam.

 
The spatial sampling for SCN population density consisted of a geostatistical sampling design applied within 8 and 5.25 ha in the center of both fields, as shown in Fig. 1A, 1B, and 2A and described by Avendaño et al. (2003). A bucket auger (8-cm diam. by 23-cm depth; Riverside Augers, Eijkelkamp, Giesbeek, the Netherlands) was used to collect 160 single-core soil samples from Field A and 110 from Field B within 1 wk before planting and within 3 d after harvest in 1999 and in 2000. Soil cores were placed in individual plastic bags and, on arrival at the lab, were stored in 37.8-L (10-gallon) Rubbermaid containers at 4°C until they were processed (within 30 d).

Soybean Cyst Nematode Analysis
Cysts were extracted from 100-cm3 subsamples using a semiautomatic elutriator (Res. Serv. Instrument Shop, The University of Georgia, Athens, GA; Byrd et al., 1976). The system had an extraction efficiency of 60%. Cysts were further separated from soil particles following the sugar flotation–centrifugation method (Dunn, 1969) and then counted under a stereo-microscope. Three cysts per sample were randomly selected and crushed, and eggs and second-stage juveniles were counted, with the average used to determine the eggs per cyst for each sample containing at least one cyst.

Soil Texture Analysis
A subset of the 1999 sampling locations was selected to evaluate the relationship between soil texture and SCN population densities (Fig. 1B and 2A). Sample sites were chosen to include all soil series described for each field (Threlkeld and Feenstra, 1974) and to include areas of high as well as undetectable cyst density. Particle size analysis was conducted in duplicate on a subsample of soil from each of the 25 and 24 selected samples from Field A and Field B, respectively, using a modified hydrometer method (Gee and Bauder, 1986). Oven-dried soil was lightly crushed on a tray using a rolling pin to break up soil structure until the sample passed through a 2-mm aperture sieve (mesh 10). Forty grams of the <2-mm sieved soil was pretreated with 30% (w/v) hydrogen peroxide to oxidize the organic matter. Following chemical and physical dispersion of the samples as described by Gee and Bauder (1986), samples were transferred to 1000-mL sedimentation cylinders. After thorough mixing of the soil suspension, the cylinder was left undisturbed for exactly 8 h; the suspension density was then measured with a hydrometer (ASTM 152H Bouyoucos style) reading the upper edge of the meniscus. The hydrometer reading corrected for a blank cylinder was used to calculate the clay fraction in the sample by dividing the difference between the hydrometer reading of a sample and the blank reading by the initial sample weight and multiplying this number by 100. Next, the contents of the cylinder were poured out through a 45-µm aperture sieve (mesh 325) to retain sand particles. The sand retained in the sieve was oven-dried and weighed. The proportion of sand in each sample was calculated as net sand weight divided by the initial weight of the sample (40 g) multiplied by 100. Silt fraction was calculated by subtracting the percentages of sand and clay from the initial sample weight. Soil type was determined for each sample based on the percentage contributed by each soil fraction as defined in the texture triangle recommended by USDA (Soil Survey Division Staff, 1993).

Statistical Analysis
Descriptive statistics were applied to characterize soil particle size distribution for each field. The soil separates sand and clay were subjected to geostatistical analysis to determine empirical omnidirectional semivariograms (Matheron, 1963). The parameters of theoretical semivariogram models fitted to the empirical semivariograms were estimated by (nonlinear) least squares. The spatial distributions of sand and clay were mapped by predicting values at the nodes of a 1- by 1-m grid with universal or ordinary kriging using the structural properties of the estimated theoretical semivariogram and the sampled values at observed locations. Such a fine grid was used to predict sand and clay percentage values as close to the locations sampled for SCN as possible. The predicted values for the spatial distribution of silt were determined as 100 – (predicted sand value + predicted clay value) at each node of the 1- by 1-m grid.

Cyst population densities and numbers of eggs per cyst at planting and harvest are shown as box plots in Fig. 3. Cyst density and number of eggs per cyst means were compared across sampling times within fields with Fisher's (protected) LSD test ({alpha} = 0.05) using logarithmic-transformed data [log10 (cysts 100 cm–3 soil + 1), log10 (eggs per cyst + 1)] to increase symmetry and to stabilize the variance.



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  		Fig. 3. Box plots of soybean cyst nematode (A) cyst population density and (C) eggs per cyst in Field A and (B) cyst population density and (D) eggs per cyst in Field B. The y axis indicates the sampling time: P '99, planting 1999; H '99, harvest 1999; P '00, planting 2000; and H '00, harvest 2000. The sample mean is indicated with a dashed vertical line and the median with a solid vertical line in each box. Significant differences among the means at different sampling times within each field are indicated with lowercase letters. Mean comparisons were performed on logarithmic-transformed data; original data are shown for clarity. Means with the same letter were not significantly different (protected LSD, 0.05 significance level).

 
Each SCN observation was associated with a kriging-predicted value for the proportion of sand, clay, and silt, matched by location. A soil type classification was assigned to each SCN observation based on the predicted proportion contributed by each soil separate as defined by the texture triangle recommended by USDA (Soil Survey Division Staff, 1993). Cysts and eggs per cyst were then compared among soil types by sampling time within fields using Fisher's (protected) LSD test for means ({alpha} = 0.05). The effects of each of the soil texture fractions on transformed cysts and eggs per cyst were analyzed with analysis of variance (ANOVA). The values predicted for the percentage of sand and clay were rounded to the nearest unit; consequently, the same value appeared at more than one location within each field. Soybean cyst nematode counts associated with the same sand or clay percentage were averaged, and regression coefficients were determined on means by simple linear regression analysis. Lack-of-fit tests were used to determine the appropriateness of the models fitted. Regression models were compared between fields and between sampling times within fields for parallelism when appropriate ({alpha} = 0.05) (SAS Release 8, SAS Inst., Cary, NC).

The cross-correlogram is a geostatistical tool used to describe the spatial continuity between measurements of different attributes or of the same attribute measured at different times. The cross-correlation function given by Goovaerts (1997) was used here to calculate cross-correlograms for logarithmic-transformed cysts and eggs per cyst with predicted sand and clay percentage in the soil.


   RESULTS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Soil Particle Size Distribution and Spatial Analysis
Soil particle size distribution of the surface layer in Field A and Field B corresponds to an overall surface texture of sandy loam (Table 1). Even though on average soil particle size composition was similar, there were differences between Field A and Field B in the range of variability observed for each fraction, and considerable differences were observed when the data were analyzed spatially. Variation in the sand separate was similar in both fields, but clay varied more in Field B while Field A varied more in the silt fraction. Semivariogram models showed great spatial structure in the distribution of sand and clay (nugget close to zero), with similar ranges of autocorrelation for each separate in Field A and Field B (sand: 66–70 m; clay: 130 m) (Fig. 4). The values predicted by kriging were used to generate contour maps of the levels of sand, clay, and silt (Fig. 1C–1E and 2B–2D). In Field A, the proportion of sand was the highest in the areas delineated for Belding sandy loam and lowest in the area of Brookston loam (Fig. 1B and 1C). The proportion of clay in the soil in Field A was mostly between 10 and 20% except for a few small patches where the proportion reached values slightly higher than 20% in the area of Breckenridge sandy loam (Fig. 1B and 1D). The highest level of silt was located within the area of Conover loam (Fig. 1B and 1E). In Field B, the lowest level of sand and the highest of clay and silt corresponded with the delineation for Brookston loam, whereas the highest level of sand and the lowest of clay and silt were located in the area of Newaygo sandy loam and Berville loam. Intermediate levels were found in the transition zone between Brookston loam and the other two soil types (Fig. 2A–2D). Overlaying the soil separate maps, we mapped the spatial distribution of the resulting soil map units in each field based on the proportion contributed by each separate (Fig. 1F and 2E). Sand content seemed to dictate soil map units delineation in Field A (Fig. 1C and 1F) while clay content dictated soil map units delineation in Field B (Fig. 2C and 2E). Field B graded from sandy clay loam in the south to loamy sand in the north while Field A was predominantly sandy loam and loamy sand with patches of sandy clay loam and loam (Fig. 1F and 2E). The soil maps obtained in this way were useful to locate areas of very distinct soil map units located within the delineations reported by Threlkeld and Feenstra (1974) in each field.


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  		Table 1. Soil texture in Field A and Field B as determined using a modified Bouyoucos hydrometer method (Gee and Bauder, 1986).

 


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  		Fig. 4. Semivariograms of (A) sand and (B) clay percentage in the soil in Field A, and (C) sand and (D) clay percentages in the soil in Field B. Black circles indicate omnidirectional empirical semivariogram, the solid line indicates the theoretical model fitted by means of least squares, and the dashed line is the sample variance.

 
Soybean Cyst Nematode Population Density
The within-fields and between-years variability in preplant cyst and eggs per cyst for 1999 and 2000 have been reported in Avendaño et al. (2003). Briefly, the number of cysts 100 cm–3 soil at planting and at harvest was similar in 1999 and in 2000 in Field A (Fig. 3A); whereas in Field B, there were more cysts in 2000 than in 1999 and more at harvest than at planting (Fig. 3B). A section of Field B containing 26% of the samples was covered with water at harvest in 2000. The localized flooding reduced the number of samples characterized by low cyst density observed in previous samplings, thereby influencing the results obtained for this particular sampling time as evidenced by a sample mean greater than expected (Fig. 3B). Generally, there were more eggs per cyst in Field B than in Field A in both years and sampling times, with the greatest number of eggs per cyst observed at harvest in 1999 (Fig. 3C and 3D).

Relationship between Soil Map Units and Soybean Cyst Nematode Population Density
Cyst population density varied by soil map unit. In Field A, cyst density was consistently higher in loamy sand and sandy loam than in the other two soil map units although differences in cyst density among soil map units were statistically significant only at harvest in 1999 and planting in 2000 (Fig. 5A–5D). In Field B, the number of cysts 100 cm–3 soil was the highest in loamy sand and lowest in sandy clay loam for both years and sampling times (Fig. 5A–5D). The number of eggs per cyst was not significantly different among soil map units in either field or sampling time (Table 2).



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  		Fig. 5. Soybean cyst nematode mean cyst population density [log10 (cysts 100 cm–3 of soil + 1)] ± standard error by soil type in Field A (open circles) and in Field B (closed circles) at (A) planting 1999, (B) harvest 1999, (C) planting 2000, and (D) harvest 2000. Soil types are sandy clay loam (SCL), sandy loam (SL), loamy sand (LS), and loam (L). Means with the same letter were not significantly different (protected LSD, 0.05 significance level).

 

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  		Table 2. Mean soybean cyst nematode (SCN) eggs per cyst by soil type in Field A and Field B in 1999 and in 2000.

 
Relationship between Soil Particle Size and Soybean Cyst Nematode Population Density
Soybean cyst nematode population density was related to the proportion of sand, clay, and silt in the soil (Fig. 6; Table 3). The relationship with sand was characterized by a positive slope, not significantly different across sampling times in Field B (Table 3; Fig. 6A–6D). The slope and intercept for the regression model at harvest 1999 in Field A were not significantly different ({alpha} = 0.05) from the model for Field B at harvest 1999 and at planting in 2000 (Fig. 6B and 6C). Cyst density decreased with increasing clay proportion in the soil at all sampling times in Field B and at harvest both years in Field A, with similar slopes across sampling times and between fields ({alpha} = 0.05) (Table 3; Fig. 6E–6H). Silt proportion in the soil and SCN were also negatively related, with cyst population density decreasing linearly with increasing percentage of silt at all sampling times in Field B (Table 3; Fig. 6I–6L). Regression models were also parallel for silt across sampling times. The relationship between silt and cysts was not significant in Field A.



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  		Fig. 6. Relationship between soybean cyst nematode cyst population density [log10 (cysts 100 cm–3 of soil + 1)] and the proportion of each soil fraction in the sample. Means ± standard error for Field A (open circles) and for Field B (closed circles) are indicated. The left column (A, B, C, and D) corresponds to the percentage of sand, the central column (E, F, G, and H) corresponds to clay percentage, and the right column (I, J, K, and L) corresponds to silt percentage at (A, E, and I) planting 1999, (B, F, and J) harvest 1999, (C, G, and K) planting 2000, and (D, H, and L) harvest 2000. Regression curves fitted are indicated as solid lines (Field B) and dashed lines (Field A) where significant (0.05 significance level). Regression coefficients are shown in Table 3.

 

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  		Table 3. Regression models and coefficients of determination for the relationship between cyst population density and sand, clay, or silt proportion in the soil in Field A and Field B in 1999 and in 2000.

 
Unlike the cyst population, the relationship between eggs per cyst and soil particle size was highly variable (Fig. 7). In Field A, the proportion of sand in the soil and the number of eggs per cyst were positively related at planting (y = 1.85 – 0.019x, r2 = 0.27) and at harvest (y = –1.06 + 0.032x, r2 = 0.20) in 1999 (Fig. 7A and 7B). The number of eggs per cyst was negatively related to clay percentage in Field A at harvest in 2000 (y = 2.54 – 0.095x, r2 = 0.53) (Fig. 7H). The number of eggs per cyst was only related to silt percentage in Field B at planting in 1999 (y = 2.86 – 0.051x, r2 = 0.32). Otherwise, relationships between eggs per cyst and sand, clay, or silt were not significant.



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  		Fig. 7. Relationship between soybean cyst nematode eggs per cyst [log10 (eggs per cyst 100 cm–3 of soil + 1)] and the proportion of each soil fraction in the sample. Means ± standard error in Field A (open circles) and in Field B (closed circles) are indicated. The left column (A, B, C, and D) corresponds to sand percentage, the central column (E, F, G, and H) corresponds to clay percentage, and the right column (I, J, K, and L) corresponds to silt percentage at (A, E, and I) planting 1999, (B, F, and J) harvest 1999, (C, G, and K) planting 2000, and (D, H, and L) harvest 2000. Regression curves fitted are indicated as solid lines (Field B) and dashed lines (Field A) where significant (0.05 significance level).

 
Linear correlation coefficients for cysts with sand were very low in Field A (Fig. 8A–8D). The low correlation decreased even more with increasing separation distance between samples. Cysts were better correlated with clay at harvest than at planting, and more so in 2000 (Fig. 8B and 8D). The separation distance at which the cross-correlation cysts-sand or cysts-clay reached zero was the same (approximately 110 m) at harvest in 2000 (Fig. 8D).



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  		Fig. 8. Cross-correlograms of soybean cyst nematode cyst population density and percentage sand (solid line) or clay (dashed line) in the soil in (A–D) Field A and (E–H) Field B. (A and E) planting 1999, (B and F) harvest 1999, (C and G) planting 2000, and (D and H) harvest 2000. Linear correlation coefficients for cyst density with sand and clay are indicated.

 
Cyst population density was highly correlated with sand and clay at all times in Field B, and the correlation decreased in absolute value at the same rate for both separates, reaching zero at the same separation distance between samples (Fig. 8E–8H). The distance at which cross-correlations were zero increased from approximately 115 to 130 m from planting in 1999 to harvest in 2000. The symmetry in the cross-correlograms indicated that the effect of sand on cyst population was equal in magnitude to the effect of clay but with opposite sign.

The number of eggs per cyst was only correlated with clay at harvest in 2000 in Field A; otherwise, correlations were very low as indicated by the linear correlation coefficients (Fig. 9D). Cross-correlograms showed fluctuations in correlation between eggs per cyst and sand or clay as the separation distance between samples increased. In Field B, the symmetry between sand and clay cross-correlograms observed for cysts was also evident for eggs per cyst (Fig. 9E–9H).



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  		Fig. 9. Cross-correlograms of soybean cyst nematode eggs per cyst and percentage sand (solid line) or clay (dashed line) in the soil in (A–D) Field A and (E–H) Field B. (A and E) planting 1999, (B and F) harvest 1999, (C and G) planting 2000, and (D and H) harvest 2000. Linear correlation coefficients for eggs per cyst with sand and clay are indicated.

 
Linear correlation coefficients squared for cysts (Fig. 7) and eggs per cyst (Fig. 8) were somewhat smaller than the R squares for cysts (Tables 3) and for eggs per cyst reported because cross-correlograms were computed using all predicted values, whereas regression analyses were performed on means.


   DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
This study was conducted as part of a project designed to investigate the potential application of site-specific management of SCN in Michigan soybean production systems (Avendaño et al., 2003). The work presented here provides further insights to assess the potential of site-specific management for SCN by describing detailed relationships between SCN population density and soil texture.

While it is not known when SCN was introduced into the fields and what undetermined factors may have contributed to the difference in SCN population density, Field A and Field B presented us with the opportunity to study the relationship between SCN population and soil texture under two different conditions frequently encountered by soybean growers. Cyst population density was high in Field B, and it increased during the study, whereas in Field A, cyst population density was much lower and remained at low levels. While soil survey maps suggested that Field A and Field B had very similar soil types, geostatistical sampling and analysis provided a different perspective into the relationship between soil texture and SCN population dynamics. The spatial distribution of the soil separates was highly structured in both fields, allowing the construction of reliable maps for the distribution of sand, silt, and clay in each field. The arrangement of soil map units obtained from superimposing these maps corresponded in general terms with the soil survey maps reported by Threlkeld and Feenstra (1974).

The relationship between soil map units and SCN population density was consistent between the two fields although differences were attenuated in Field A where cyst population remained low at all times. Cyst population density was consistently higher in loamy sand than in the other soils and was the lowest in sandy clay loam (Fig. 6). While this observation is consistent with previous studies, where SCN was found more abundantly in coarser soils than in finer soils (Dropkin et al., 1976; Koenning and Barker, 1995; Donald et al., 1999), the relationship between soil types and ranges of textural composition needs careful consideration. Koenning and Barker (1995) reported highest SCN egg densities in Fuquay sand (91% sand:6%silt:3% clay), Norfolk sandy loam (84:12:4), and Portsmouth loamy sand (72:18:10) when compared with Cecil sandy clay loam (53:18:29) and Cecil sandy clay (48:13:39). The lowest nematode densities were found in soils with more than 25% clay. It seems reasonable to propose that SCN can sustain high population density levels (above 20 cysts 100 cm–3 soil) only in soils composed of more than 60% sand, less than 20% silt, and less than 20% clay. However, the soil textures defined within this composition correspond to sandy loam, loamy sand and sand. The difference in texture between sandy loam and sandy clay loam in our research fields was the result of reduced sand content ({approx}60%) combined with increased clay (>20%). It is important to note that only a portion of the area defined for sandy loam in the texture triangle is favorable for SCN. Sandy loam with more than 20% silt was associated with low levels of cysts in our study. The particle size composition of sandy clay loam and loam are beyond the 60:20:20 limits, and in accordance with our proposition, these soil types had significantly fewer cysts than loamy sand (Fig. 5). Therefore, it might be best that references to any relationship between SCN population and soil include soil texture in addition to soil classification.

Variability in soil texture and elevation can result in areas of different soil moisture through out a field. Since SCN population dynamics have been associated with soil water potential (Koenning et al., 1988; Barker and Koenning, 1989; Johnson et al., 1993; Koenning and Barker, 1995), elevation could be an important factor affecting SCN population density in addition to soil texture. Measurements of soil moisture, elevation, or both will be included in further analyses as they can contribute significantly to the understanding of SCN spatial distribution.

The number of eggs per cyst was not related to soil map units or texture in our study, with a few exceptions. The data suggest that soil texture affects SCN population at the mobile stages during root finding and penetration, and perhaps development in the roots, rather than the reproductive potential, fecundity, or hatching. This phenomenon was previously reported by Todd and Pearson (1988) when they recovered more SCN females and cysts from newly infested roots in sandy loam (60:30:10) than in silty loams (30:46:24 and 14:60:26). Nevertheless, Young and Heatherly (1990) attributed the lower rate of SCN reproduction to soil type in a study where the number of eggs per cyst and the total number of cysts were lower in Sharkey clay (8.5:34:57.5) than in Dubbs silt loam (23:60.5:16.5). This indicates that a high proportion of clay and very low sand content are necessary to interfere with SCN reproductive potential. However, the effect of this kind of soil on root development should also be taken into consideration (Russell, 1977) since the effect of soil texture on cyst fecundity may be strongly influenced by plant conditions (Koenning and Barker, 1995).

It is difficult to discriminate which of the separates—sand, silt or clay—has the greatest influence on cyst density. From the analysis of cyst density by soil separate, we observed that sand had the opposite effect of clay or silt. These observations indicate that the combination of the separates, that is, the resulting soil texture, has a greater influence on SCN population than a specific separate by itself. This is the first report to document consistency in the relationship between SCN and soil texture across fields and over time. Wyse-Pester et al. (2002) explored the possibility of using correlation between soil attributes (soil texture) and nematode density to reduce the cost of sampling in an effort to map nematode distribution for site-specific management. Although the spatial dependency indicated a potential for mapping true spiral nematode (Helicotylenchus spp.), stylet nematode (Tylenchorhynchus capitatus Allan), and root lesion nematode (Pratylenchus neglectus Rensch) infestations, the small variation in soil texture in their research fields resulted in inconsistent and weak correlations with nematode density.

Site-specific management of soybean yield-limiting factors requires an understanding of the spatio-temporal dynamics of the prevailing conditions. In addition to providing the basis for future experimentation to define soil texture tolerance limits for SCN, this study lays out foundations for new and integrated approaches to site-specific management of SCN and other yield-limiting factors. The covariation of SCN with other factors in soils is helpful to assess spatial variability of SCN populations within fields if site-specific management is to be a plausible strategy to manage SCN in soybean production. We can conclude that soil survey maps can be useful tools to predict expected levels of SCN in an already infested field, but they should be used with caution. Soil map unit delineations based on the texture triangle may not be sufficient when referring to SCN population, and the proportion of each soil fraction should be used in addition. We have shown in this study the benefit of texture-based analysis over soil type–based analysis. Therefore, a soil survey map can be used to identify high-risk sectors, and then a texture analysis of soil from these zones may help in delineating soil map units of expected high cyst density.


   ACKNOWLEDGMENTS
 
The project is part of a Ph.D. dissertation of the first author. We thank Jeannette Makries and Mohamed Elwadie for technical assistance with soil particle size analysis and Chad Holovach, Nathan Nye, Megan Kierzek, Carolyn Stein, Nathan Cottrell, Bindiya Shah, Dan Armstrong, Marisol Soto, Carolina Holteuer, Katrina Blakely, and Teresa Raslich for assistance in collecting and processing samples. We also thank Dave Eickholt and Charles Eickholt (farmers) and Neil R. Miller from ABC-Agribusiness Consultant for providing and maintaining the sites.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Financial support for the project was provided by USDA Hatch Project through the Michigan Agricultural Experimental Station and grants from the Michigan Soybean Promotion Committee, North Central Soybean Research Program, and United Soybean Board to the last author.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 





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