An efficient subsampling method for estimating corn root characteristics with scanner-based image analysis

Abstract

Quantifying root length, surface area, average diameter, and volume of fully-matured corn (Zea mays L.) is labor intensive, time consuming, and costly. Accurate and efficient subsampling techniques are needed to overcome these limitations. In this study, eight corn root systems were grown to maturity in a sand-culture hydroponics system to develop and test root system subsampling techniques for accuracy (uncertainty assessment) and efficiency (time). Each entire root system was separated into coarse and fine roots, which were then composited into 65 subsamples, either visually or by mass, followed by subsample scanning to quantify root characteristics. A bootstrap non-parametric procedure was used to determine the sample size needed to represent the total root system and quantify uncertainty based on the number of subsamples analyzed. When subsamples were composited visually, as many as 60 subsamples (92% of the total root system) were necessary to represent the characteristics of the root system within ±5% of the true mean at a 95% confidence level. In contrast, when subsamples were composited by equal mass, a maximum of 15 subsamples (23% of the total root system) were needed to be representative, requiring 2 h and 15 min per root system. The findings show that separating the entire root system by coarse and fine roots and then weighing into equal mass subsamples before scanning decreased the number of subsamples and time required to accurately estimate corn root characteristics. Thus, this subsampling approach considerably reduced the effort and cost of processing corn root systems.