| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Dep. of Agron., Iowa State Univ., Ames, IA 50011-1010
* Corresponding author (John.A.Guretzky{at}erdc.usace.army.mil).
Received for publication May 7, 2003.
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Abbreviations: AUM, animal unit month • DEM, digital elevation model • ECa, apparent electrical conductivity • GIS, geographic information system • GPS, global positioning system
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Landscape position in pastures affects the distribution and abundance of legumes in southeastern Iowa pastures. Establishment, diversity, and persistence of legumes are greater on backslope landscape positions than summit or toeslope positions (Harmoney et al., 2001). As a proportion of total dry matter, legume production was 161 g kg–1 on backslopes, 62 g kg–1 on summits, and 7 g kg–1 on toeslopes. Competition from grasses limits legume establishment and persistence on summit and toeslope positions (Guretzky et al., 2004; Harmoney et al., 2001).
Visual delineation of where one landscape position ends and another begins in pastures can be difficult. Spatial information about fields or pastures are displayed, stored, and analyzed more effectively with a geographic information system (GIS). A GIS allows users to create a DEM for fields or pastures of interest that displays spatial data three dimensionally and enables users to calculate topographic derivatives such as slope, aspect, flow accumulation, and wetness index (Burrough and McDonnell, 1998). Digital elevation models also allow maps of plant, soil, and environmental attributes to be overlayed to improve and aid visual or statistical comparisons.
Scientists have examined the relationship of DEM-derived attributes such as slope, flow accumulation, and wetness index with grain yields of corn (Zea mays L.) and soybean [Glycine max (L.) Merr.], soil properties, and distribution of drainage classes in fields. Kravchenko and Bullock (2000) used a DEM to examine the relationship between topographic attributes such as slope and flow accumulation with soil properties and corn and soybean grain yield. Moore et al. (1993) used a DEM to examine the relationship between topographic attributes and several soil properties.
Field-scale mapping of soil ECa also may be used to characterize the spatial variation of soil properties correlated with site productivity (Johnson et al., 2001; Kitchen et al., 1999, 2003). Soil ECa is a field-based measure of the electrical conductivity of bulk soil and is primarily a function of soil salinity, saturation percentage, water content, and bulk density (Corwin and Lesch, 2003). It is determined by sensors that use either electrical resistivity or electromagnetic induction (Corwin and Lesch, 2003; Sudduth et al., 2003). The Veris 3100 (Veris Technol., Salina, KS) and the EM-38 (Geonics Limited, Mississauga, ON, Canada) are commercial sensors that use electrical resistivity and electromagnetic induction, respectively, to measure ECa (Corwin and Lesch, 2003; Sudduth et al., 2003). Mapping of ECa is advantageous because it minimizes the number of soil samples required to describe overall field conditions (Johnson et al., 2001). In Illinois, soil ECa was least in well- and moderately well-drained soils and greatest in poorly and somewhat poorly drained soils (Kravchenko et al., 2002). In eastern Colorado, greatest ECa values were characteristic of eroded surfaces and lower crop yields (Johnson et al., 2001).
Digital elevation models and soil ECa mapping have not been used for site-specific management of pastures. The objective in our study was to measure and characterize the abundance and distribution of legumes in pastures along gradients of DEM-derived slope and soil ECa. We expected slope and soil ECa to affect the distribution of legumes indirectly through their relationship with soil properties correlated with productivity of grasses in pastures. We determined plant species composition and collected elevation and ECa data across four replicated pastures, each of which were divided into plots assigned one of three different stocking systems. This information was integrated into a GIS where we created a DEM and quantified the relationship among the percentage of legumes, slope, and soil ECa.
|
MATERIALS AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Each pasture replicate was divided into 0.46-ha plots, with each plot being assigned one of three stocking treatments: continuous, rotational, and nongrazed. Each plot was similar in that they each contained five landscape positions: a summit, backslope, toeslope, opposite backslope, and opposite summit. Two of the replicates (six plots) contained backslopes with north–south-facing aspects, and two replicates (six plots) had backslopes with east–west-facing aspects. Aspect did not affect legume production in these pastures (Harmoney et al., 2001). The effects of aspect or landscape position were not examined in this study.
Grazing treatments began in 1996. From 1996 to 1998, grazing began at the end of May and continued until early to mid-August within the continuously stocked plots. Each of the rotationally stocked plots was grazed for 4 d in mid-May, early July, and late October. Stocking rates were similar among the rotational and continuous stocking treatments: 9.4 animal unit months (AUMs) ha–1 within the rotational system and 10.1 AUMs ha–1 within the continuous system. An AUM is equivalent to the amount of dry forage that a 454-kg cow, dry or with a calf less than 6 mo old, who eats about 12 kg of dry matter per day, will consume in one month (Iowa State Univ. Ext., 1998). Nongrazed plots were not grazed, but dead vegetation was mowed in mid-November (Harmoney et al., 2001).
From 1999 to 2001, continuously stocked paddocks were grazed by two nonlactating beef cows for 28 d in May and June, 21 d in July, and 14 d in October. Rotationally stocked paddocks were grazed with eight to nine cows for 4 d in May, seven to eight cows for 4 d in July, and six to seven cows for 4 d in October. Cows were placed within the continuously and rotationally stocked paddocks on the same date and were removed from the continuously stocked paddocks when residue heights for the majority of the herbage was <13 cm. Cows were removed from the rotationally stocked paddocks after the 4-d period of each grazing event. Stocking density within the rotational stocking method was intended to be heavy enough to reduce selective grazing, remove the majority of forage within a 4-d span, and increase the period of rest between grazing events.
Terrain Analysis and Vegetation Sampling
In 1999, we used a survey grade global position system (GPS) to determine elevation throughout each plot. These data were incorporated into a GIS, ArcView 3.2 (ESRI, Redlands, CA), and used to create a DEM for each pasture. We used spline methods in ArcView to develop a base elevation map for each pasture because these methods appeared to produce the smoothest maps. Parameters were weight = 0.1, number of points = 12, type = regularized, and output grid size = 1 m2 for the spline method. From the base elevation map, we derived slope using the Spatial Analyst extension in ArcView.
Flow accumulation and wetness index were also derived in ArcView using the Hydrologic Modeling extension. These measures are based on how water and sediment flow across a landscape (Burrough and McDonnell, 1998; Moore et al., 1993). Flow accumulation is a measure of the cumulative amount of material that passes through each grid cell and is based on elevation and overland flow direction. Flow accumulation is usually displayed on a log scale, and cells with greater flow accumulation are areas of concentrated flow and may indicate stream channels (Burrough and McDonnell, 1998). Wetness index is an index of moisture retention and is calculated as ln[specific catchment area/tan(slope)] (Burrough and McDonnell, 1998; Moore et al., 1993).
Soil ECa was measured and georeferenced on 4 Sept. 2000 and 28 Aug. 2001 using an EM-38 (Geonics Limited, Mississauga, ON, Canada). The ECa data were determined in each plot and obtained at 
315 points per plot. Inverse distance weighting was used within ArcView to interpolate maps of ECa because it did not require semivariogram modeling (Burrough and McDonnell, 1998). Parameters chosen were nearest neighbors = 12, power = 2, and no barriers.
Species composition of the four 1.4-ha pastures was determined using a percentage cover method (Daubenmire, 1968). In each 0.46-ha plot, we recorded the percentage of aerial cover for each plant species within approximately one hundred 0.2-m2 sample quadrats. We calculated the relative percentage of cover for each species on a 0 to 100% scale because the total percentage of aerial cover for all species could sum to > or < 100% within each quadrat due to overlapping of species or gaps between plant species. Quadrats were randomly distributed and sampled each May and July of 2000 and 2001. Following georeferencing of each sample location with a GPS, we incorporated the vegetation and sample position data into ArcView. In ArcView, the vegetation point data were overlayed with the elevation, slope, wetness index, flow accumulation (log), and ECa maps, and corresponding values from these variables were assigned to each vegetation sample point. This information was then exported from ArcView for statistical analysis.
Statistical Analysis
The mean, maximum, minimum, standard deviation, and skewness were determined for elevation, slope, wetness index, flow accumulation (log), and ECa variables associated with the vegetation sampling points using the Univariate procedure in SAS (Statistical Analysis Software, Version 8.2, SAS Inst., Cary, NC). Vegetation points with an elevation, slope, or ECa value that was greater than 2.5 standard deviations from the mean for each of these variables were removed from the data set. Approximately 300 outliers were removed from the data set; outliers were not removed based on extreme vegetative characteristics. Following removal of elevation, slope, and soil ECa outliers, these variables were normally distributed (Table 1).
|
4500 remaining samples. The large number of vegetation samples encouraged us to divide the range of slope and ECa values into 6 and 10 classes and limit our regression analyses to just slope and soil ECa variables. Slope was divided into six classes so that a high number of observations occurred at the low and high ends of the slope range. Soil ECa was divided into a greater number of classes because more classes better demonstrated the vegetation–ECa relationship and improved the fit of nonlinear regression curves. The average percentage of cover of legumes, smooth brome, and reed canarygrass was calculated from the vegetation samples for each slope and ECa class within each stocking method and pasture replicate. Smooth brome and reed canarygrass were the dominant grasses competing with legumes in these pastures. The percentage of legume cover was calculated by summing the percentage of cover of all legume species within each slope and ECa class. Linear regressions were computed between legume cover and slope across the four plots of each stocking system. Separate regressions were performed by plot within each stocking system between the percentage of legume cover and ECa measured in 2000 and 2001 because regression analyses performed across all plots within each stocking system were not significant (data not shown). When the relationship between the percentage of legume cover and ECa was nonlinear, we log-transformed the legume cover data. When a response curve is fit to the logarithm of original abundance data, a Gaussian response curve is actually fit to the original abundance data (ter Braak and Looman, 1995).
The Gaussian response curve had the formula:
 |
Log transformation of the original abundance data eliminated negative values in the predicted response curve and enabled the derivation of the optimum ECa value that gave the maximum percentage of legume cover, u = –b1/2b2; the maximum legume cover at the optimum, c = exp(b0 + b1u + b2u2); and its tolerance, t = 1/
(–2b2) (ter Braak and Looman, 1995).
We also fit a Gaussian response curve to the relationship between log-transformed legume cover data and soil ECa standardized within each pasture. This curve was fit across all plots within each stocking system. We standardized ECa by ranking each of the original 10 ECa classes within each pasture replicate from 0 to 9. The data were not log-transformed when linear regressions were computed between soil ECa and the percentage of legume, smooth brome, and reed canarygrass cover. All regression analyses were performed using the REG procedure in SAS (Statistical Analysis Software, Version 8.2, SAS Inst., Cary, NC). We concluded that the relationship between legume cover and soil ECa was nonlinear if the Guassian response curve improved the fit of the regression over that of the linear response function and was significant at P < 0.10.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
|
|
|
A nonlinear response curve fit the relationship between the percentage of legume cover and soil ECa across the plots within each stocking system when soil ECa was standardized within each pasture replicate in 2000 and 2001 (Fig. 2) . The nonlinear response curve, fit across the mean of each 2000 soil ECa class of the four plots, explained 23 to 38% of legume cover variation. The optimum soil ECa in 2000, of which legume cover was maximized, was 5.9 within the continuous stocking system, 4.1 in the nongrazed system, and 3.8 in the rotational stocking system. The maximum legume cover at the optima was 17, 13, and 9% within the rotational, continuous, and nongrazed systems, respectively (Fig. 2).
|
Apparent Soil Electrical Conductivity and Dominant Grasses
The dominant grasses in the pastures also showed relationships to soil ECa. The percentage of smooth brome cover in the pastures decreased as soil ECa measured in 2000 and 2001 increased. The decline was linear within the rotational and continuous stocking systems and nonlinear in the nongrazed system (Fig. 3)
. An opposite response occurred for reed canarygrass as its cover increased as ECa measured in 2000 and 2001 increased (Fig. 4)
. The slope of the relationship between reed canarygrass cover and soil ECa was greater within the nongrazed system than within the rotational and continuous stocking systems (Fig. 4).
|
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
Legumes establish and persist on landscape positions with 15 to 20% slopes but fail at positions with lesser slopes because competition from grasses limits seedling survival of legumes on positions with lesser slopes (Guretzky et al., 2004). Backslope positions, which averaged 15% slope, produced 0.5 and 2.4 Mg ha–1 less grass dry matter than summit and toeslope positions, respectively (Harmoney et al., 2001). Soil organic matter and A-horizon thickness tends to be proportional to slope. Soils on steep slopes tend to be thin and have low organic matter in the A-horizon (Birkeland, 1999). Slope was negatively correlated with organic matter content and corn and soybean grain yields in Illinois and Indiana (Kravchenko and Bullock, 2000). Along a toposequence in Colorado, DEM-derived slope was negatively correlated with A-horizon thickness, extractable P, and organic matter (Moore et al., 1993).
Soil moisture also tends to be greater on less-sloping positions. Across agricultural landscapes in Illinois, somewhat poorly drained and poorly drained soils were dominant at the lowest slopes, and as slope increased, the occurrence of well-drained and moderately well-drained soils increased (Kravchenko et al., 2002). Spatial variation in soil water availability, related to soil depth and drainage patterns, was the primary mechanism causing topographic differences in plant community composition and aboveground productivity in tallgrass prairie in Kansas (Knapp et al., 1993). Greater soil moisture enhances productivity and competitiveness of existing grasses and limits the availability of light to legume seedlings in pastures (Groya and Sheaffer, 1981).
Reports on the influence of topographic position and slope on N availability across landscapes have been inconsistent. Nitrogen mineralization was up to five times greater on upland sites than lowland sites and was inversely related to aboveground biomass yields of tallgrass prairie in Kansas (Turner et al., 1997). In contrast, N mineralization and total N content of biomass followed similar patterns as biomass itself, increasing from upper-slope positions to lower-slope positions in the shortgrass steppe of Colorado (Schimel et al., 1985). Mineralization, expressed as a percentage of total N, however, was greater on summits than toeslope positions, suggesting that despite greater N availability on toeslope positions, turnover of N is greater on summit and backslope positions (Schimel et al., 1985).
Forage producers also can use soil ECa mapping to identify sites where legumes will be successful in pastures. We found that the optimum soil ECa at which legumes were maximized (c) and the maximum itself (u) varied among plots within each stocking system. The relationship between legume content and soil ECa, however, tended to be nonlinear (Table 3), and when ECa was standardized within each pasture, a nonlinear response curve explained 23 to 42% of the percentage of legume cover across the plots (Fig. 2). In most plots, legumes tolerated a range of ECa and were greatest at intermediate ECa values. These patterns were consistent regardless of the year soil ECa was measured (Fig. 2).
Apparent soil electrical conductivity is a field-based measure of the electrical conductivity of bulk soil and is primarily a function of soil salinity, saturation percentage, water content, and bulk density (Corwin and Lesch, 2003). Soil ECa data are easily obtained by sensors that use either electrical resistivity or electromagnetic induction (Corwin and Lesch, 2003; Sudduth et al., 2003). Patterns of soil ECa across a field reflect soil properties that are often correlated with crop productivity and other ecological properties (Johnson et al., 2001; Kitchen et al., 1999), and these patterns are stable across time (Mueller et al., 2003). Mapping of ECa may also minimize expenses by reducing the number of soil samples required to characterize soil property variation throughout a field (Johnson et al., 2001; Sudduth et al., 2003).
We selected soil ECa as a determining factor for legume distribution because it is closely associated with soil properties that affect the productivity of grasses in pastures. Legumes do not respond directly to soil ECa. Their distribution in these pastures is affected by competition from cool-season grasses (Guretzky et al., 2004; Harmoney et al., 2001). In eastern Colorado, high soil ECa indicated areas of erosion; was positively correlated with bulk density, percentage clay, and pH; and was negatively correlated with crop productivity (Johnson et al., 2001). An opposite response occurred in Kentucky fields as soil ECa decreased as factors that limit rooting depth and crop productivity, such as depth to clay increase, depth to bedrock, and depth to fragipan, increased (Mueller et al., 2003). We did not attempt to calibrate ECa to soil properties because earlier attempts at characterizing the relationship of landscape positions with soil properties in these pastures were largely unsuccessful; summit, backslope, and toeslope positions were similar for several properties, including soil P and K, pH, organic matter, and texture in these pastures (Harmoney, 1999).
A review of studies of gently rolling landscapes in Iowa and other areas of the Midwest has shown that high ECa values often are positively correlated with higher soil water contents and lower elevations. Along hillslopes in eastern South Dakota, lower elevations consistently had greater ECa values and soil water contents than higher elevations (Nugteren et al., 2000). Apparent soil electrical conductivity was negatively correlated with well-drained and moderately well-drained soils and positively correlated with somewhat poorly drained and poorly drained soils in Illinois crop fields (Kravchenko et al., 2002). In north-central Iowa, ECa was greatest on toeslope positions where poorly drained soils predominated and lowest on summits and shoulders where well-drained soils were most common (Jaynes et al., 2003).
We conclude that moisture availability likely drove landscape patterns in soil ECa and grass productivity in these pastures. Across the pastures, greater soil ECa was associated with lower elevations (Table 2). Elevation is lowest on toeslope positions, albeit where grass productivity and soil ECa are greatest. Soil ECa was also positively correlated with the wetness index and flow accumulation, hydrologic indicators of where water and sediment accumulates over landscapes (Burrough and McDonnell, 1998; Moore et al., 1993). The relationship of ECa with elevation, slope, flow accumulation, and wetness index was similar regardless of the year ECa was measured (Table 2).
Landscape patterns in moisture availability contribute to a dominance of grasses on summits and toeslopes, consequently where soil ECa is low and high, respectively. We found that soil ECa successfully explained the distribution of the dominant grasses in these pastures: smooth brome and reed canarygrass. Where soil moisture is greatest, reed canarygrass dominates the vegetation. In areas where moisture is lowest, smooth brome dominates.
Grazing increases legumes, as a percentage of pasture cover, at 15 to 20% slopes and intermediate soil ECa values. At these slopes and ECa values, rotationally and continuously stocked pastures had a greater percentage of legumes than nongrazed pastures. Legume cover was similar among stocking systems when slopes were <10% and ECa values were either low or high. Large grazing animals create heterogeneity in plant communities through patchy grazing (Adler et al., 2001; Cid and Brizuela, 1998), trampling of vegetation and soils (Hartnett et al., 1996), and the excretion of manure and urine (Steinauer and Collins, 1995). In the absence of such disturbances, competitive interactions may favor the dominant grasses and decrease species diversity in grasslands (Collins, 1987).
Rotational stocking achieved the greatest percentage of legume cover in the pastures. Stocking rates were similar among the continuously and rotationally stocked systems; however, rest periods were longer in the rotationally stocked system. Longer rest periods allowed the legumes to recover from defoliation, flower, and set seed and may have encouraged legume recruitment from the seedbank. In northeastern U.S. pastures, legumes consisted of a greater proportion of total dry matter production in pastures when rest periods were longer and grazing heights taller because upright-growing legumes such as alfalfa and red clover were favored (Carlassarre and Karsten, 2002). Continuous stocking creates and maintains patches in vegetation that differ in degrees of utilization (Cid and Brizuela, 1998). White clover, a prostrate-growing, grazing-tolerant legume, is adapted to highly utilized patches but contributes less to total dry matter production than taller legumes in pasture swards (Carlassarre and Karsten, 2002).
The benefits of sowing legumes on sites with 15 to 20% slopes or intermediate ECa values are numerous. Establishment of legumes on these sites has the potential to improve dry matter yields compared with grass-only pastures (Harmoney et al., 2001). Through a symbiotic relationship with rhizobium bacteria, legumes fix atmospheric N2, allowing them to be virtually self-sufficient for N (Heichel, 1985). The decomposition of legume leaf residues, roots, and nodules also increases soil N and reduces N requirements of grasses (Heichel et al., 1985). Legumes are also greater in crude protein than grasses, and their incorporation with grasses improves herbage quality of forage mixtures (Harmoney, 1999; Sleugh et al., 2000).
In conclusion, we characterized the topographic variability of several southeastern Iowa pastures using soil ECa mapping and DEMs. The distribution of legumes in these pastures was closely associated with gradients of slope and ECa. We recommend that legumes be seeded where soil ECa values are intermediate or slopes are 15 to 20%. Rotational stocking systems will encourage the persistence and production of legumes at these sites by reducing competition from smooth brome and reed canarygrass. If the spatial tools are unavailable or landscape positions are easily differentiated, legumes should be seeded on backslope landscape positions. On backslopes, where slopes are greatest and soil ECa intermediate, legumes will increase soil N availability, improve herbage yields and quality, and optimize the seasonal distribution of dry matter and nutrients in pastures.
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 | |||
points, within the rotational stocking system of year 2001, were removed before analysis.
