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A Comparison of Two Models to Evaluate Soil Physical Property Effects on Corn Root Growth
Author(s) -
Benjamin Joseph G.,
Nielsen David C.,
Vigil Merle F.,
Mikha Maysoon M.,
Calderon Francisco J.
Publication year - 2013
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2012.0104
Subject(s) - environmental science , limiting , growing season , agronomy , soil science , range (aeronautics) , biology , mechanical engineering , materials science , composite material , engineering
Two models for evaluating soil physical condition effects on root growth were compared. The first model, called the Jones model, is a submodel for root growth limitations used by several complex soil–plant–atmosphere models. The second model uses soil physical limitations as identified by the least limiting water range (LLWR). Root surface area density ( R sa ) and bulk density ( ρ b ) were determined at the V6, V12, and R1 growth stages of corn in 2004. Water contents ( θ ) throughout the growing season were determined twice per week with a neutron probe. The cumulative predicted relative root growth suitability ( P RGS ) was determined using soil physical limitations to root growth defined by each model. Significant plot‐to‐plot variability was observed in ρ b and θ . The LLWR resulted in a wider range of P RGS for all sampling times and soil depths. Regressions using the LLWR criteria for soil physical limitations resulted in significant correlations between R sa with P RGS at the expanding zone of root exploration, indicating more root surface area with better soil conditions. Regressions using the Jones criteria for soil physical limitations resulted in either a nonsignificant correlation between R sa with P RGS or a linear, negative correlation, indicating less root surface area with better soil conditions. Using limitations of soil physical properties as identified by the LLWR in larger, more comprehensive plant and root growth models may provide a better response of these models to variable soil conditions.