Distribution of Legumes along Gradients of Slope andSoil Electrical Conductivity in Pastures

limits legume establishment and persistence on summit and toeslope positions (Guretzky et al., 2004; Harmoney Legumes establish and persist on backslope landscape positions et al., 2001). but fail on summits and toeslopes in southeastern Iowa pastures, suggesting that these pastures be managed site specifically. Visual Visual delineation of where one landscape position delineation of landscape positions, however, can be difficult, and charends and another begins in pastures can be difficult. acterization of spatial variability through soil sampling is expensive. Spatial information about fields or pastures are displayed, Creation of digital elevation models (DEM) and apparent soil electristored, and analyzed more effectively with a geographic cal conductivity (ECa) mapping are inexpensive alternatives to deinformation system (GIS). A GIS allows users to create scribing field conditions. Our objective was to examine the relationa DEM for fields or pastures of interest that displays ship of DEM-derived slope, soil ECa, and legume distribution in spatial data three dimensionally and enables users to pastures. We examined these relationships across four 1.4-ha pastures. calculate topographic derivatives such as slope, aspect, Each pasture was divided into 0.46-ha plots that were assigned one flow accumulation, and wetness index (Burrough and of three stocking treatments: continuous, rotational, and nongrazed. McDonnell, 1998). Digital elevation models also allow We found that legumes, as a percentage of pasture cover, were greatest at 15 to 20% slopes and intermediate values of soil ECa. The absolute maps of plant, soil, and environmental attributes to be ECa value at which legumes were maximized varied by plot within overlayed to improve and aid visual or statistical comeach stocking system and year ECa was measured. When ECa was parisons. standardized by pasture and year, however, a nonlinear response curve Scientists have examined the relationship of DEMexplained 23 to 42% of the variation of legume cover across the plots. derived attributes such as slope, flow accumulation, and Grazing reduced competition from smooth brome (Bromus inermis wetness index with grain yields of corn (Zea mays L.) Leyss.) and reed canarygrass (Phalaris arundinacea L). These grasses and soybean [Glycine max (L.) Merr.], soil properties, dominated at 0 to 8% slopes and where ECa was either low or high and distribution of drainage classes in fields. Kravin value. We concluded that slope and soil ECa data are useful in chenko and Bullock (2000) used a DEM to examine identifying sites where legumes are successful in pastures and showed the relationship between topographic attributes such as potential for use in site-specific management of pastures. 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 L improve the quality and production of pasattributes and several soil properties. ture swards. Through a symbiotic relationship with Field-scale mapping of soil ECa also may be used rhizobium bacteria, legumes fix atmospheric N. The deto characterize the spatial variation of soil properties composition of legume leaf residues, roots, and nodules correlated with site productivity (Johnson et al., 2001; increases soil N, and N transfer to coexisting grasses Kitchen et al., 1999, 2003). Soil ECa is a field-based increases sward productivity and quality of forage measure of the electrical conductivity of bulk soil and grasses (Heichel et al., 1985). Legumes are also greater is primarily a function of soil salinity, saturation percentin crude protein than grasses (Van Soest, 1982), and age, water content, and bulk density (Corwin and Lesch, their incorporation with cool-season grasses improves 2003). It is determined by sensors that use either electrithe seasonal distribution of dry matter and nutrients in cal resistivity or electromagnetic induction (Corwin and pastures (Sleugh et al., 2000). Lesch, 2003; Sudduth et al., 2003). The Veris 3100 (Veris Landscape position in pastures affects the distribution Technol., Salina, KS) and the EM-38 (Geonics Limited, and abundance of legumes in southeastern Iowa pasMississauga, ON, Canada) are commercial sensors that tures. Establishment, diversity, and persistence of leuse electrical resistivity and electromagnetic induction, gumes are greater on backslope landscape positions than respectively, to measure ECa (Corwin and Lesch, 2003; summit or toeslope positions (Harmoney et al., 2001). Sudduth et al., 2003). Mapping of ECa is advantageous As a proportion of total dry matter, legume production because it minimizes the number of soil samples rewas 161 g kg 1 on backslopes, 62 g kg 1 on summits, quired to describe overall field conditions (Johnson et and 7 g kg 1 on toeslopes. Competition from grasses al., 2001). In Illinois, soil ECa was least in welland moderately well-drained soils and greatest in poorly J.A. Guretzky, K.J. Moore, C.L. Burras, and E.C. Brummer, Dep. of Agron., Iowa State Univ., Ames, IA 50011-1010. Received 7 May 2003. and somewhat poorly drained soils (Kravchenko et al., *Corresponding author ([email protected]). Abbreviations: AUM, animal unit month; DEM, digital elevation Published in Agron. J. 96:547–555 (2004).  American Society of Agronomy model; ECa, apparent electrical conductivity; GIS, geographic information system; GPS, global positioning system. 677 S. Segoe Rd., Madison, WI 53711 USA