ARS | Publication request: Characterizing root distribution with adaptive neuro-fuzzy analysis

Submitted to: International Agrophysics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 4, 2010
Publication Date: March 1, 2011
Citation: Krueger, E., Prior, S.A., Kurtener, D., Rogers Jr, H.H., Runion, G.B. 2011. Characterizing root distribution with adaptive neuro-fuzzy analysis. International Agrophysics. 25:93-96.

Interpretive Summary: Accurate descriptions of root distribution patterns in a complex soil is often difficult using conventional mathematics. Fuzzy logic provides an alternative formal mathematical structure for analyzing such complex processes. This system employs fuzzy if-then rules to model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. Root simulation results were plausible indicating that the model captured the underling process dynamics. This method may be a viable alternative to more traditional statistical techniques in characterizing root distribution in the complex plant/soil system.

Technical Abstract: Root-soil relationships are pivotal to understanding crop growth and function in a changing environment. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statistics are employed. The Adaptive Neuro-Fuzzy Inference System (ANFIS) has been applied in many agricultural and environmental fields and may represent a viable means for dealing with complexities of root distribution in soil. We applied this method to vertical and horizontal root distribution data collected from a potato (Solanum tuberosum L.) cropping system. Simulations indicated that ANFIS gave plausible results. This indicates that ANFIS may offer a viable alternative to more traditional statistical techniques for evaluation of complex root distribution patterns.