Root characterization methods are constrained by one or more of the following factors: cost, limited resolution, or time of analysis. Most length-determination methods are based on the line-intersect principle. An alternate method using a flatbed scanner and a microcomputer was developed. Algorithms were developed to estimate root area, length, and diameter (mean root width). Two-dimensional area is estimated by summing the number of pixels representing a single or group of gray scales. Length and width are calculated as a function of the area, the number of edge pixels, and the number of pixels on diagonal edges. Human hair, plastic line, and root samples, with widths from 0.13 to 1.65 mm and lengths near 100 em, were measured manually and with image analysis. Mean lengths of four repeated scans after reorientation were within 0.4 to 1.2% of manually determined lengths. Four repeated scans (118 pixels cm-1 ) of reoriented roots yielded length estimates with C.V.'s from 0.31 to 2.18% on low density samples (

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An imaging method was developed to evaluate crop species differences in root hair morphology using high resolution scanners, and to determine if the method could also detect root hair responses to soil water availability. High resolution (1890 picture elements (pixels) cm −1 ) desktop scanners were buried in containers filled with soil to characterize root hair development under two water availability levels (−63 and −188 kPa) for canola (Brassica napus L. cv Clearwater), camelina (Camelina sativa L. Crantz cv Cheyenne), flax (Linum usitatissimum L. cv CDC Bethune), and lentil (Lens culinaris Medik. cv Brewer). There was notable effect of available moisture on root hair geometry (RHG). At −188 kPa, length from the root tip to the root hair initiation zone decreased and root hair length (RHL) became more variable near the root hair initiation zone as compared to −63 kPa. For the response of primary axial RHL, significant main effects were present for both water availability (P<0.05) and species (P<0.0001); lateral RHL showed a significant main effect for both water availability (P< 0.05) and species (P<0.01) as well. For both primary axial and lateral root hair density (RHD), there was a significant effect of species (P<0.0001), but no significant response to water availability. No water availability x species interaction was present in any case. Low available water reduced RHL in both primary axial and lateral roots. The change in RHL due to water availability was most evident in canola and camelina. Additionally, those with greater RHLÞ in primary axial roots and a similar trend was found in lateral RHL. Both water and species had a significant effect on primary axial root surface area (RSA) (P<0.05) but no significant effect was found for lateral RSA. For primary axial RSA the longest and most dense root hair had the greatest RSA. This novel approach to in situ rhizosphere imaging allowed observation of species differences in root hair development in response to water availability and should be useful in future studies of rhizosphere interactions and crop water and nutrient management. Keywords Rhizosphere . Scanner . Root hair . High resolution imaging . Plant soil interaction . Imaging scan Abbreviations Pixel Picture element RHD Root hair length RHL Root hair length RHG Root hair geometry RSA Root surface area MRI Magnetic resonance imaging USB Universal serial bus ABA Abscisic acid Plant Soil (2011) 339:125-135

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The objective of this study was to investigate the benefits of methods that incorporate terrain attributes as covaria-tes into the prediction of soil depth. Three primary terrain attributes-elevation, slope and aspect-were tested to improve the depth prediction from conventional soil survey dataset. Different methods were compared: 1) ordinary kriging (OK), 2) co-kriging (COK), 3) regression-kriging (REK), and 4) linear regression (RE). The evaluation of predicted results was based on comparison with real validation data. With respect to means, OK and COK provided the best prediction (both 110 cm), RE and REK gave the worst results, their means were significantly lower (79 and 108 cm, respectively) than the mean of real data (111 cm). F-test showed that COK with slope as covariate gave the best result with respect to variances. COK also reproduced best the range of values. The use of auxiliary terrain data improved the prediction of soil depth. However, the improvement was relatively small due to the low correlation of the primary variable with used terrain attributes.