A Sampling Method for Measurement of Large Root Systems with Scanner‐Based Image Analysis

Abstract

Measurement of relatively small (<100 m total length, <6 g fresh wt.) root systems has been simplified by image analysis, but measuring larger root systems remains time‐consuming and inaccurate. Reliability of root estimation can be improved through identification of effective sampling methods. We devised a system for the collection of homogeneous root subsamples by air‐stirring in water. We optimized the subsampling technique and used a scanner‐based image analysis system to measure total root length, mean root diameter, and root surface area of three maize (Zea mays L.) genotypes with contrasting root morphologies: leafy reduced stature (LRS), leafy normal stature (LNS), and Pioneer 3905 (P3905), a commercial hybrid. Root length was determined for 957 subsamples. Confidence intervals were generated by software using the bootstrap resampling approach for optimizing sample size. Confidence intervals for mean estimates of each sample size were defined by ordering the evaluation function values from the smallest to the largest in a set of 5000 iterations. The lower and upper bounds of confidence intervals were also calculated using the standard procedure. This system allowed collection of homogeneous subsamples. Calculations showed that ∼10% of total root volume should be analyzed for estimation of the entire root system to be accurate within 10%. Although unreplicated, these data suggest that maize genotypes with the leafy trait have greater root lengths (1.75 km for LRS, 2.37 km for LNS, and 0.49 km for conventional commercial hybrid P3905) and a greater proportion of fine roots than the nonleafy type.