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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Soil, Water & Air Resources Research » Research » Publications at this Location » Publication #351542

Research Project: Utilization of the G x E x M Framework to Develop Climate Adaptation Strategies for Temperate Agricultural Systems

Location: Soil, Water & Air Resources Research

Title: A solution for sampling position errors in maize and soybean root mass and length estimates

Author
item ORDONEZ, RAZIEL - Iowa State University
item CASTELLANO, MICHAEL - Iowa State University
item Hatfield, Jerry
item LICHT, MARK - Iowa State University
item WRIGHT, EMILY - Iowa State University
item ARCHONTOULIS, SOTIRIOS - Iowa State University

Submitted to: European Journal of Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/4/2018
Publication Date: 6/1/2018
Citation: Ordonez, R.A., Castellano, M.J., Hatfield, J.L., Licht, M.A., Wright, E.E., Archontoulis, S.V. 2018. A solution for sampling position errors in maize and soybean root mass and length estimates. European Journal of Agronomy. 96/156-162. https://doi.org/10.1016/j.eja.2018.04.002.
DOI: https://doi.org/10.1016/j.eja.2018.04.002

Interpretive Summary: Roots are a vital part of plants and it is difficult to measure their characteristics in the soil because of the difficulty in obtaining root samples. To collect roots requires a process of carefully removing the roots from the soil or measuring their distribution with cameras capable of viewing how the roots grow into the soil. This only provides a small view of the root system. We evaluated different ways of characterizing the length and weight of roots from corn and soybean plants in order to be able to determine how single measurements could represent the root distribution within the soil. We found that we could use a simple formula that used distance from the row and either the length or mass of the roots to estimate the values at any position. These relationships were compared to values from the literature along with the field observations. These findings are of value to the research community in being able to have a simple method of determining root length and weight in corn and soybean crops.

Technical Abstract: Root mass and length attributes are difficult to obtain in the field and currently there is uniformity among studies in the literature estimating the effect of sampling position error. With the objectives of 1) quantifying the sampling position error in calculating weighted average root values per unit area and 2) developing an algorithm to minimize root position sampling error so that existing data in the literature can be used in future studies, we collected and analyzed root mass and length data across four sampling positions (0, 12, 24 and 36 cm distance from the plant row; row-to-row spacing 76 cm) from two maize and two soybean fields in central Iowa, USA. In-row sampling position (i.e., 0 cm from the plant row) over-estimated root mass and length by 66% and 46% for maize and soybean, while cores taken in the middle of plant rows (i.e., 36 cm from the plant row) under-estimated root mass and length by 34% and 23% for maize and soybean. As sampling distance from the plant row increased from 0 to 36 cm, maize root mass declined four times faster than soybean, while root length declined at almost the same rate between crops. Sampling 10 cm from the plant row provided the closest estimate to the weighted average value in both crops. We developed a new algorithm that predicts weighted average root attribute values with an R2 of 0.93 for mass and an R2 of 0.70 for length. The algorithm requires two user inputs (the measured root attribute value and the distance from the plant row). The new algorithm was tested across diverse environments, cultivars, and management practices and proven accurate for subsequent use (R2 = 0.70 and R2 = 0.87 for mass and length). This study provides guidance to strategically sample roots in future row crop research and an algorithm to eliminate sampling position bias in existing data.