| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
a USDA-ARS, 120 Keim Hall, Lincoln, NE 68583-0934
b Usda-Ars, Aerc-Csu, Ft. Collins, Co 80523-1325
c Univ. of Nebraska, 103 Miller Hall, Lincoln, NE 68583
* Corresponding author (cjohnso2{at}bigred.unl.edu)
 |
ABSTRACT |
|---|
 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
|---|
0- to 30-cm depth). A geo-referenced soil-sampling scheme separated each field into four ECa classes that were sampled (0- to 7.5- and 7.5- to 30-cm depths) in triplicate. Soil physical parameters (bulk density, moisture content, and percentage clay), chemical parameters (total and particulate organic matter [POM], total C and N, extractable P, laboratory-measured electrical conductivity [EC1:1], and pH), biological parameters (microbial biomass C [MBC] and N [MBN], and potentially mineralizable N), and surface residue mass were significantly different among ECa classes (P 
0.06) at one or both depths (0–7.5 and 0–30 cm). Bulk density, percentage clay, EC1:1, and pH were positively correlated with ECa; all other soil parameters and surface residue mass were negatively correlated. Field-scale ECa classification delimits distinct zones of soil condition, providing an effective basis for soil sampling. Potential uses include assessing temporal impacts of management on soil condition and managing spatial variation in soil-condition and yield-potential through precision agriculture and site-specific management.
Abbreviations: EC1:1, laboratory-measured electrical conductivity using a 1:1 soil:water ratio • ECa, field-scale apparent electrical conductivity • MBC, microbial biomass C • MBN, microbial biomass N • POM, particulate organic matter
|
INTRODUCTION |
|---|
|
TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
|---|
Complex inter-relationships exist between physical, chemical, and biological soil properties and their response to land management; these factors are responsible for crop productivity and ecological potential (Bauer and Black, 1994; Gardner and Clancy, 1996; Olson et al., 1996). Soil condition is the combined characteristics of a given soil that define its level of function as a medium for crop production and a contributor to air and water quality. In this paper, we define ecological potential as the complementary interactions between the soil biological community and the soil environment that optimize soil condition and are determined by land management.
Different approaches have been used to detect and map soil condition patterns related to spatial variation in productivity. Lark (1997) used intensive grid sampling (20-m intervals), based upon soil texture and depth, to identify seven map units across a 6-ha field. He found significant differences among map units for several yield-related soil properties including percentage moisture and organic matter, mineral N, and pH at 0- to 20-cm depths (P 0.003). However, this type of intensive grid sampling is both labor intensive and costly, making it impractical at the farm-scale. Francis and Schepers (1997) used selective soil sampling based on soil color, texture, depth, slope, and erosion characteristics to produce fertilizer recommendation zones. These zones effectively partitioned concentrations of the nonmobile nutrients P, K, and Zn. Studies, such as these, underscore a need for cost-effective technology to assess spatial variation in soil condition at the field-scale.
Laboratory measurement of EC1:1 is a useful integrator of soil physical, chemical, and biological factors that regulate soil function (Smith and Doran, 1996). Geo-referenced in situ estimates of ECa are now being made at the field scale using both direct contact sensors to measure resistance and noncontact sensors based upon electromagnetic induction technology (Dolittle et al., 1995; Jaynes et al., 1995; Jaynes, 1996). These two approaches provide highly correlated measures of ECa and both have been shown to correlate with crop productivity at topsoil depths to 90 cm (Fritz et al., 1999; Sudduth et al., 1999).
Measured soil ECa is determined by clay type and percentage, soil moisture (in conjunction with pore size, tortuosity, and water-filled space as they vary with depth), salinity of the soil solution, and temperature (Rhoades et al., 1989; McNeill, 1980). For individual soils, one or more of these factors will dominate measured ECa. Substantial research effort has been directed toward understanding location-specific relationships between ECa and those factors contributing to its measurement, including moisture (Khakural and Robert, 1998; Sheets and Hendrickx, 1995; Kachanoski et al., 1988), salinity (Lesch et al., 1992; Rhoades and Corwin, 1981; Rhoades and Ingvalson, 1971), and salinity and clay content (Williams and Hoey, 1987).
In addition to use as a direct indicator of those soil properties affecting it, it is also possible to use ECa as an indirect measure of other soil properties and productivity (Jaynes, 1996). The effectiveness of ECa mapping for predicting crop yield appears to depend upon the degree to which soil properties affecting yield are correlated with the soil factors affecting ECa. Sudduth et al. (1995) found strong correlations during relatively dry years between both ECa and depth to claypan, and depth to claypan and yield. However, ECa was found to be a poor predictor of yield for claypan soils in a wet year. Strong correlations have also been shown between ECa and soil attributes linked to forest productivity including soil saturated-extract electrical conductivity, exchangeable Ca and Mg, and cation-exchange capacity (McBride et al., 1990).
Most published research applies ECa mapping to the appraisal of one or two specific factors contributing to soil condition and productivity. However, there is little information in the literature regarding the use of ECa sensors to evaluate spatial variation in overall soil condition for arable land; where soil condition encompasses both soil characteristics that affect ECa, and other soil characteristics affecting yield potential with which they may be correlated. The objective of this study was to assess ECa mapping as a basis for soil sampling design and for spatially delineating soil physical, chemical, and biological properties (0- to 7.5- and 0- to 30-cm depths) related to yield and ecological potential. This information is essential for monitoring the impact of management on temporal trends in soil condition and for the successful implementation of site-specific management.
|
MATERIALS AND METHODS |
|---|
|
TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
|---|
250 ha, located 30 km east of Sterling, CO (40.6° N, 103.0° W). Centered within the semiarid Central Great Plains, the site receives highly variable rates of precipitation, ranging between 250 and 680 mm and averaging 420 mm annually. Typically, 80% of precipitation falls during the growing season between April and September. Soils are mapped as a complex of Platner (fine, smectitic, mesic Aridic Paleustolls), Weld (fine, smectitic, mesic Aridic Argiustolls), and Rago loam (fine, smectitic, mesic Pachic Argiustolls) and range in slope from 0 to 5%.
The site was managed for nearly 70 yr as a winter wheat–fallow rotation under conventional-tillage. During most of this time, it was farmed as eight fields of 31 ha, four planted to wheat and four in fallow each year. Beginning in 1999, cropping was intensified to a wheat–corn–millet–fallow rotation using strict no-till management. By retaining the eight-field subdivisions within the section, each phase of the 4-yr rotation is duplicated each year (Fig. 1)
. Fields 1 and 4, 2 and 7, and 3 and 6 are paired (i.e., replicates) with regard to recent management history. Fields 5 and 8 have identical histories except for 1997 when field 8 was planted to conventionally-tilled millet, while field 5 was left fallow.
|
15 m apart. The instrument was calibrated, as per manufacturer instructions, prior to data collection for each field.
The Veris 3100 uses three pairs of coulter-electrodes to determine soil ECa. The coulters penetrate the soil surface to a depth of 6 cm. One pair of electrodes functions to emit an electrical current into the soil, while the other two pairs detect decreases in the emitted current due to its transmission through soil (resistance). The depth of measurement is based upon the spacing of the coulter-electrodes. The center pair, situated closest to the emitting (reference) coulter-electrodes, integrates resistance between depths of 0 and 30 cm, while the outside pair integrates between 0 and 90 cm. Output from the Veris Data Logger reflects the conversion of resistance to conductivity (1/resistance = conductivity). We used only surface data (0–30 cm) in this study since it corresponded most closely to soil sampling depths (described later).
A Trimble AG132 DGPS system (Trimble Navigation Ltd., Sunnyvale, CA) with submeter accuracy was used to geo-reference ECa measurements. The Veris data logger records latitude, longitude, and shallow and deep ECa data (mS m-1) at 1-s intervals in an ASCII text format. For reporting purposes, ECa units were converted to dS m-1 by dividing mS m-1 by 100. Given the average collection speed of 0.44 m sec-1, 250 ECa measurements were taken per hectare.
Electrical Conductivity Class and Soil Sampling Point Determination
The soil sampling design used in this study represents a stratified sampling approach (Cook and Stubbendieck, 1986) with allocation into four geo-referenced ECa ranges. Data were sorted into ECa ranges in the following manner: Veris data were downloaded and saved as an image file using ERDAS Imagine (ERDAS Inc., Atlanta, GA). In this format, unsupervised classification (ERDAS, 1997) was performed to individually recode the eight fields in the study into four classes (ECa ranges). Four was determined to be the number of classes that could be evaluated with a manageable number of soil samples given the large area of land encompassed by this study. Ranges of ECa were assigned to each class (e.g., field #1 in Fig. 2
, bottom) to reflect spectral patterns seen in the original gray-scale ECa maps (Fig. 2, top). In this way, unsupervised classification served to group ECa pixels into naturally occurring clusters. Table 1 shows ECa class ranges for individual fields, as well as ECa class means across all eight fields.
|
|
Soil and Residue Sampling
Given the significant amount of time required for ECa mapping, classification, and sample-site identification, particularly for an experiment of this size, simultaneous mapping and soil sampling were not possible. Soil samples were collected on two different dates based upon crop status. Wheat and fallow fields were sampled in mid-August following wheat harvest. Corn and millet fields were sampled in mid-November after corn harvest. The rationale for two different sampling times is presented in the results and discussion section.
Soil samples were collected at depths of 0 to 7.5 and 7.5 to 30 cm. Seven 4-cm diameter cores were taken at each sampling site, separated by depth, composited, and mixed. Because of high moisture contents, the 7.5- to 30-cm samples were sieved through a 4-mm screen, while the drier surface samples were sieved to 2 mm. A portion of each sample was refrigerated at 4°C, while the remainder was air dried. Deep samples were run through a soil grinder (M.G. Johnston Industries, Lakeville, MN) to pass a 2-mm sieve after air drying. The crushing action of this type of grinder leaves residues intact, and so does not interfere with the measurement of POM.
Soil temperature is known to fluctuate seasonally and to affect the measurement of ECa. For this reason, duplicate soil temperature measurements were taken in surface soils (0–7.5 cm) at each sampling site for both sampling dates.
As an estimate of productivity, surface residue cover was measured in wheat and fallow fields (Fields 1, 4, 5, and 8) at the time of soil sampling in mid-August. A representative area was selected 3 m south of each soil-collection site, and above-surface residues were removed from an 85-cm diameter area. Samples were oven-dried and mass per unit area was calculated (kg ha-1).
Soil Analyses
Physical, chemical, and biological soil attributes were assessed as per the minimum data set proposed by Doran and Parkin (1996). Physical measurements included soil texture (Kettler et al., in review), gravimetric water content, and bulk density. Bulk density was calculated for the composited soil cores collected at each site by dividing oven-dried mass by sample volume. Chemical measurements consisted of whole-soil organic matter and POM (0.053- to 0.5- and 0.5- to 2-mm size fractions) by loss on ignition (Cambardella et al., 2001), pH and EC1:1 using a 1:1 water:soil mixture, 2 M KCl-extracted NO3-N and NH4-N measured on a LACHAT FIA auto-analyzer (Zellweger Analytics, Milwaukee, WI), total C and N analyzed with a Carlo Erba NA 100 (CE Elantech, Lakewood, NJ), and P by the Bray-1 method (Bray and Kurtz, 1945). Biological measurements included MBC and MBN by microwave irradiation (Islam et al., 1998) and anaerobically-incubated potentially-mineralizable N (Waring and Bremmer, 1964; Keeney, 1982). Microbial biomass C, MBN, pH, EC1:1, and anaerobic potentially-mineralizable N analyses were made on fresh soil within 2 wk of collection. All other testing was performed on air-dried soil. Data were expressed on a volumetric basis except for KCl-extracted NO3-N and NH4-N, reported as mg kg-1 soil; and soil moisture, reported as kg kg-1 soil.
Statistical Analyses
While soil laboratory analyses were conducted on 0- to 7.5- and 7.5- to 30-cm depth samples as collected, statistical comparisons were made on 0- to 7.5- and 0- to 30-cm increments. Data from 0- to 7.5- and 7.5- to 30-cm analyses were combined and weighted to calculate 0- to 30-cm depth measurements. The significance of classification by ECa ranges was determined for each of the soil attributes measured using an ANOVA for a randomized complete block strip-split plot design with crop (wheat, corn, millet or fallow) and ECa class as treatment factors. Pearson correlation coefficients were estimated, across replicates and crops, for all pairs of soil variables using both values from all sampling points (n = 96) and ECa class sample means (n = 4). In addition, ANOVA by sampling date were run to compare mean soil gravimetric moisture, water-filled-pore space, and temperature for the two sampling times.
The significance of ECa classification to surface residue cover was determined for wheat and fallow fields only with the ANOVA run by crop in order to separate cropped and noncropped effects. Differences were declared significant at the 0.05 level, unless stated otherwise. Correlations between ECa and residue were analyzed across replicates by crop, using all sampling points (n = 24) and ECa class sample means (n = 4). All statistical analyses were performed using SAS (SAS Institute, 1997).
|
RESULTS AND DISCUSSION |
|---|
|
TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
|---|
0.05) were found for EC1:1 and pH at the surface (0–7.5 cm) and for NO3-N at both surface and 0–30 cm depths. It is reasonable that NO3-N would differ among cropping treatments given variation in recommended application rates for the different crops. Cornfields received the highest N rates and showed the highest levels of NO3-N following harvest probably due to drought stress and the inability of the plants to fully utilize available N. Both EC1:1 and pH are affected by NO3-N levels in soil, causing them to show corresponding differences among cropping treatments for surface soils.
Crop x ECa class interactions (P 0.05) were found for only pH and large-fraction POM (0.5–2 mm) at the 0–30 cm depth. Levels of POM were highest in the millet and corn treatments as compared with wheat and fallow. This can be attributed to greater residue production by these crops, as well as to the fact that both were preceded by wheat in 1998 (Fig. 1). Conversely, the wheat and fallow treatments produce less (or no) residue and had been cropped during only one of the 1998 and 1999 growing seasons.
With the exception of KCl-extracted NO3-N and NH4-N, all measured soil physical, biological, and chemical parameters were significantly different among ECa classes (P 0.06) at one or both sampling depths (Table 2). In general, the greatest differences were shown for soil chemical properties, probably because of greater across-site variation. Chemical parameters associated with residue inputs, whole soil organic matter, large-fraction POM (0.5–2 mm), and total C and N, were significantly different among ECa classes at both soil depths. These and other measured parameters, including percentage silt, water content, extractable P, MBC, MBN, and anaerobic potentially-mineralizable NH4, were negatively correlated with ECa at one or both soil depths, suggesting an negative relationship between ECa and yield (Table 3).
|
|
Potassium chloride-extracted NO3-N and NH4-N were not significantly partitioned by ECa classification and exhibited a narrow range of variability across the experimental site. Nitrogen transformations in soil are controlled by soil water content, texture, biological activity, cropping, and the composition and quantity of organic matter (Stevenson, 1982). These soil characteristics impact the discordant processes of volatilization, nitrification, immobilization, and leaching (losses) or mineralization (gains) that define levels of soil inorganic N (Jansson and Persson, 1982; Stevenson, 1982). Our analyses indicate that, at the time of sample collection, available N levels were not related to variability in soil condition. In this study it should also be noted that, prior to soil sampling, millet and corn crops were severely drought stressed at critical growth stages during July and August. Since adequate moisture is essential for effective crop uptake of N (Olson, 1984), erratic across-field crop demand for available N may have altered its spatial variation. It is also possible that for this location, factors other than inorganic N dominate measured ECa.
Physical soil attributes of texture and bulk density, although less effectively partitioned than chemical and biological attributes, were still different among the ECa classes (Table 2). The one exception was percentage sand at the 0- to 30-cm depth, which was uniform across sampling sites. As per other reports (Kachanoski et al., 1988; Khakural and Robert, 1998), positive correlations were found between ECa and clay content (Table 3), with clay content ranging between 12.2 and 38.8 % in sampled soils.
All measured biological parameters, MBC, MBN, and anaerobic potentially-mineralizable NH4, were significantly different among ECa classes in the 0- to 7.5-cm depth only (Table 2). Correlations between ECa (30-cm depth) and soil surface measurements (0–7.5 cm) of moisture and biological activity were stronger than those between ECa and the same measurements taken at 0- to 30-cm (Table 3). This may suggest some ECa bias toward soil surface conditions.
It is interesting to note that when correlation analyses were conducted using ECa class means (n = 4) for soil parameters, as opposed to using values from all sampling points (n = 24), the relationships between ECa and those parameters improved dramatically (data not shown). All measured parameters, except percentage sand and NO3-N, were highly correlated with ECa (r 
0.80) at one or both sampling depths. We know that measured ECa synthesizes the effects of certain static and dynamic soil characteristics; these characteristics are, in turn, correlated with other soil properties that underlie overall soil condition and productivity. Highly variable levels of a specific soil parameter can be associated with a single ECa value due to the buffering effect of corresponding variations in opposing soil parameters affecting ECa. Consequently, for this experimental site strong correlations do not exist between ECa and individual soil parameters at point sources. Field-scale apparent electrical conductivity appears to be a tool most useful for the delineation of overall soil condition.
Under the conditions of this study, primary factors contributing to measured ECa can be separated into static (clay percentage) and dynamic components (soil moisture and salinity). Soil clay content (0–30 cm) was negatively correlated with parameters associated with productivity including P, POM, total C and N, MBN, and potentially-mineralizable N, all of which are strongly auto-correlated (Table 3). On the other hand, clay content was positively correlated with pH and bulk density. These relationships are likely due to the calcareous nature of soils in northeastern Colorado, where the erosion of topsoil exposes underlying soil horizons that are characterized by increased clay content and CaCO3 and associated elevation of bulk density and pH.
Other potential contributors to ECa, soil moisture content, and NO3-N are correlated with both P and POM components of the soil and with each other. The lack of significant correlation between NO3-N and ECa suggests that it had minimal impact on ECa for these sampling times. While soil moisture generally increases with increasing clay content, there is no correlation between the two for the soils under study. For this site, increases in soil water-holding capacity due to the presence of clay are probably offset by concomitant decreases in soil organic matter components and soil depth. Furthermore, in semiarid environments, plant-available water and crop yields are less defined by the ability of soil to store water than by precipitation inputs.
Residue Analyses
As harvest index relationships between wheat grain mass and aboveground biomass are well accepted, it is likely that the measured across-field variation in residue mass mirrors that of crop yields. Surface residue cover measured at each of the wheat and fallow soil-sampling sites was significantly related to ECa classification and negatively correlated with ECa (Fig. 3) . As would be expected for a collection date shortly after wheat harvest, residue quantity in wheat fields (1375–8459 kg ha-1) was approximately twice that in fallow fields (582–5005 kg ha-1). Residue mass in fallow fields was slightly less correlated with ECa (r = -0.91) than that of wheat fields (r = -0.95), probably because of differential decomposition and redistribution by wind and water. Tremendous variability in residue quantity was found within ECa classes, particularly in low ECa (high residue) zones. Accordingly, correlations between surface residue and ECa declined (r = -0.54 and -0.62, respectively, for wheat and fallow fields) when analyzed using data from all sampling points (n = 24).
|
Soil temperatures (0–7.5 cm) were fairly uniform across the study site for each sampling date, but were significantly different among dates (Table 4). Gravimetric water content and water-filled pore space were quite similar, albeit significantly different, among sampling dates. Although both soil temperature and moisture content affect measured ECa, the design of this experiment is based upon the assumption that the relevance of established ECa zones does not change over time with fluctuations in dynamic soil properties. It has been demonstrated that, while the magnitude of temporal ECa measurements varies with soil moisture and temperature, spatial patterns in ECa remain constant (Sudduth et al., 2000; Veris Technologies, 2001). This finding is essential to the use of ECa mapping as a basis for identifying soil-sampling zones.
|
|
CONCLUSIONS |
|---|
|
TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
|---|
For this study site, soil parameters associated with erosion phase including percentage clay, bulk density, pH, and EC1:1 were positively correlated with ECa measurements integrated over a soil depth of 0 to 30 cm. Other properties related to crop productivity, notably soil moisture, total and POM, total C and N, MBC and MBN, and surface-residue content, were negatively correlated. These results imply a negative relationship between ECa and productivity. The application of spatial techniques to ECa and yield maps from the experimental site will provide verification of the statistical associations between ECa and yield; these analyses will be the focus of a future publication.
In view of the large number of soil samples required for representative estimates of overall-field condition, traditional grid sampling is both expensive and labor intensive. One of the best ways to reduce these costs is to minimize the number of samples required through zone sampling. Zone sampling, based on a combination of soil color, texture, depth, slope, and erosion characteristics, has been shown to be an effective alternative to grid sampling (Francis and Schepers, 1997). In fact, the ECa–classed soil-sampling scheme used in this study appears to integrate these and other soil characteristics. We found that ECa classification effectively delimits distinct zones of soil condition, making it an excellent basis for soil sampling to reflect spatial heterogeneity.
Chen et al. (2000) used soil color, quantified through remotely-sensed imaging, to predict C levels as an indicator of soil condition. Like ECa, remote sensing was found to be a cost-effective basis for delineating soil spatial variability. However, while remotely sensed imagery is typically applied to bare (tilled) soil, ECa has the advantage of effectiveness for cropped land where no-till management is practiced.
Currently, most farmers in the Central Great Plains apply management practices uniformly across a field. In this approach, management decisions are based upon measured soil attributes expressed as whole-field averages. The software used to assign ECa classes can also generate class areas (ha class-1) within fields. Thus, whole-field means for specific soil analyses can be easily calculated. ECa class sample means are simply weighted, relative to class area within the whole field, summed, and divided by the number of classes. This approach is superior to traditional random sampling because it accounts for spatial heterogeneity of soil attributes in the sampling design; moreover, whole field means based on stratified sampling have smaller standard errors than are possible with random sampling.
Soil classification using ECa provides an effective basis for delineating interrelated physical, chemical, and biological soil attributes that are expressed as soil condition, crop productivity, and ecological potential. It offers a useful framework for soil sampling to reflect spatial heterogeneity and can be potentially applied to assess temporal impacts of management on soil condition. Furthermore, as variable rate planters, sprayers, and applicators become more refined and cost effective, classification based on ECa can provide spatial data regarding soil condition and yield potential that will serve as an essential link between this new technology and effective management.
|
ACKNOWLEDGMENTS |
|---|
|
NOTES |
|---|
|
TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
|---|
1 Mention of a trademark, proprietary product or vendor does not constitute a guarantee of or warranty of the product by USDA nor imply its approval to the exclusion of other products that may be suitable. 
Received for publication September 5, 2000.
|
REFERENCES |
|---|
|
TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  D. L. Shaner, R. Khosla, M. K. Brodahl, G. W. Buchleiter, and H. J. Farahani How Well Does Zone Sampling Based on Soil Electrical Conductivity Maps Represent Soil Variability? Agron. J., September 8, 2008; 100(5): 1472 - 1480. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. S. Grigera, R. A. Drijber, K. M. Eskridge, and B. J. Wienhold Soil Microbial Biomass Relationships with Organic Matter Fractions in a Nebraska Corn Field Mapped using Apparent Electrical Conductivity Soil Sci. Soc. Am. J., August 3, 2006; 70(5): 1480 - 1488. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. K. Jung, N. R. Kitchen, K. A. Sudduth, and S. H. Anderson Spatial Characteristics of Claypan Soil Properties in an Agricultural Field Soil Sci. Soc. Am. J., June 21, 2006; 70(4): 1387 - 1397. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Hornung, R. Khosla, R. Reich, D. Inman, and D. G. Westfall Comparison of Site-Specific Management Zones: Soil-Color-Based and Yield-Based Agron. J., March 2, 2006; 98(2): 407 - 415. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. K. Jung, N. R. Kitchen, K. A. Sudduth, R. J. Kremer, and P. P. Motavalli Relationship of Apparent Soil Electrical Conductivity to Claypan Soil Properties Soil Sci. Soc. Am. J., May 6, 2005; 69(3): 883 - 892. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. L. Fleming, D. F. Heermann, and D. G. Westfall Evaluating Soil Color with Farmer Input and Apparent Soil Electrical Conductivity for Management Zone Delineation Agron. J., November 1, 2004; 96(6): 1581 - 1587. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. A. Guretzky, K. J. Moore, C. L. Burras, and E. C. Brummer Distribution of Legumes along Gradients of Slope and Soil Electrical Conductivity in Pastures Agron. J., March 1, 2004; 96(2): 547 - 555. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. N. Kravchenko, K. D. Thelen, D. G. Bullock, and N. R. Miller Relationship among Crop Grain Yield, Topography, and Soil Electrical Conductivity Studied with Cross-Correlograms Agron. J., September 1, 2003; 95(5): 1132 - 1139. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. K. Johnson, K. M. Eskridge, B. J. Wienhold, J. W. Doran, G. A. Peterson, and G. W. Buchleiter Using Electrical Conductivity Classification and Within-Field Variability to Design Field-Scale Research Agron. J., May 1, 2003; 95(3): 602 - 613. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. K. Johnson, D. A. Mortensen, B. J. Wienhold, J. F. Shanahan, and J. W. Doran Site-Specific Management Zones Based on Soil Electrical Conductivity in a Semiarid Cropping System Agron. J., March 1, 2003; 95(2): 303 - 315. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS |
" symbols represent selected soil sampling sites.