A conceptual framework for assessing the suitability of novel soil biological health indicators in varying agroecosystems

Tvisha K Martin and Christine Sprunger, School of Environment and Natural Resources, The Ohio State University, Wooster, OH, Prabhjot Singh, Environmental Sciences Graduate Program, The Ohio State University, Columbus, OH

Background/Question/Methods
Respiration (microbial activity), Nematode Indexes, and Enzyme Activities are currently novel soil biological health indicators that have yet to be incorporated into traditional nutrient soil test reports for farmer use. This project aims to quantify and map the relationships between soil biological health indicators across a diverse range of agricultural management practices. This framework will allow farmers to identify which soil biological health indicators best inform their soil management based on their specific field conditions and farm operations. This study collected soil biological health indicators including soil organic matter (SOM), Permanganate Oxidizable Carbon (POXC), Protein, Respiration, Nematode Indexes, and Enzyme Activities from 2 long-term research sites and 44 on-farm sites across the state of Ohio. Soil texture, fertilizer usage, tillage, soil texture, and crop diversity were recorded across each site. Structural Equation Modelling (SEM) was then used to quantify the strength of the relationships between the soil biological health indicators and ecosystem type.
Results/Conclusions
Results indicate that soil biological indicators of enzymes and microbial respiration had a strong relationship to systems that had elevated SOM, POXC, and protein. Additionally, Nematode Indexes had a strong relationship to aggregate stability, indicating that the health of the soil food web is heavily impacted by soil physical structure. Overall, management systems that had increased crop rotational diversity and reduced disturbance for more than 10 years demonstrated positive and significant (p<0.05) relationships with the soil biological indicators. The development of this framework is a model that can aid in determining which soil health indicators may be more informative for a given agroecosystem.