|MYERS, DAVID - UNIV OF MO
|Sudduth, Kenneth - Ken
|FRAISSE, CLYDE - WA STATE UNIV
Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 7/17/2002
Publication Date: 12/1/2002
Citation: MYERS, D.B., KITCHEN, N.R., SUDDUTH, K.A., FRAISSE, C.W. LANDFORM, SOIL MORPHOLOGY, AND TILLAGE EFFECTS ON SOYBEAN ROOT DISTRIBUTION FOR CLAYPAN SOILS. PROCEEDINGS 6TH INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE. 2002. CD-ROM (UNPAGINATED). AMERICAN SOCIETY OF AGRONOMY, MADISON, WI.
Technical Abstract: Claypans are soil morphological features that limit crop root growth. These extreme argillic horizons alter root development due to physical impedance, altered hydrology, and reduced plant available water capacity. More detailed spatial information about root development is needed for calibration and validation of crop growth models for application in precision agriculture. The objective of this research was to determine the effects of depth to the claypan (DTC) on soybean root length density (RLD). Soil cores were taken from conservation-till and no-till plots near Centralia, MO. Soil cores 1.2 meters long were obtained using a hydraulic sampler 7 to 21 days after harvest of drilled soybeans. Summit and backslope positions in each plot were chosen for contrasting DTC and degree of erosion. The core segments were soaked overnight in saturated sodium hexametaphosphate solution and separated from the soil with a hydropneumatic elutriator. Roots were stained, rinsed, and floated in culture plates. The plates were imaged with a desktop scanner at 300 dpi. Automated image analysis techniques were employed to enhance the images, remove non-root debris, and measure the roots. Tillage did not affect RLD. The backslope landscape position exhibited about 25% more RLD than did the summit position. We attribute this to greater water stress on the backslope and the crop partitioning more carbon to root development in response to water need. Measuring and mapping morphological features such as the claypan in order to improve crop modeling for site-specific management will become more feasible when sensor information (e.g., soil EC, remote sensing) can reliably be used to predict these soil features.