|Sudduth, Kenneth - Ken|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/23/2003
Publication Date: 11/3/2003
Citation: Myers, D.B., Kitchen, N.R., Sudduth, K.A. 2003. Estimation of soybean root length density distribution with direct and sensor based measurements of claypan morphology [abstract][CD-ROM]. ASA-CSSA-SSSA Annual Meeting Abstracts.
Technical Abstract: Hydrologic and morphological properties of claypan landscapes cause variability in soybean root and shoot biomass. This study was conducted to develop predictive models of soybean root length density distribution (RLDd) using direct measurements and sensor based estimators of claypan morphology. A catena of claypan and associated depositional soils was sampled post-harvest near Centralia, MO. Roots were extracted from soil cores using hydro-pneumatic elutriation, scanned, and measured with image analysis techniques. Profile clay-maximum depth (CMD), apparent bulk soil electrical conductivity (ECa), and elevation were used to predict soybean RLDd in 15 cm layers (15'120 cm) and total profile root length density (RLDt). Based on earlier investigations, it is known that claypan depth influences RLDd and RLDt. For this study, a dataset covering a wider range of landscape positions showed useful relationships for the prediction of RLD at a given depth. RLD at 15-60 cm was best predicted, corresponding to the profile depths with the greatest claypan variability. RLDt was influenced by landscape position and shallow ECa. In general, soybean root growth is stimulated in and below the claypan as compared to an ideal soil profile. Both CMD and rapid estimators of claypan morphology such as EC may be successfully implemented to predict RLDd in claypan soils.