|PHILLIPS, LORI - Agriculture And Agri-Food Canada|
|ACEVEDO, MIGUEL - University Of Florida|
Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/24/2020
Publication Date: 12/7/2020
Citation: Perez-Guzman, L., Phillips, L.A., Acevedo, M.A., Acosta Martinez, V. 2020. Comparing biological methods for soil health assessments: EL-FAME, enzyme activities, and qPCR. Soil Science Society of America Journal. 85(3):636-653. https://doi.org/10.1002/saj2.20211.
Interpretive Summary: A healthy soil is important for sustaining crop production. Currently, there are different methods to assess soil health. Our goal was to compare results from traditional (more commonly used) and new (advanced) methods in their ability to provide good evaluation of soil health. To achieve this, we analyzed agricultural soils from the Texas High Plains region, that have been under continuous cotton production and varied in their sand content (16 to 69%). Our results showed that the methods were sensitive to demonstrate differences between soil types. We found strong relationships of results from the different methods. Our study is important to facilitate comparisons among research laboratories or countries that may use different methods.
Technical Abstract: Soil health initiatives have categorized assays for enzyme activities (EAs) that measure p-nitrophenol and ester-linked fatty acid methyl ester (EL-FAME) as Tier 2 indicators for biological activity and community structure analysis, respectively. Quantitative polymerase chain reaction (qPCR) assays of functional and taxonomic communities are emerging Tier 3 indicators. To facilitate comparisons of soil biological health between research groups that may employ different methods, we applied these current and emerging indicators to semiarid soils from the Texas High Plains sampled in the growing season and postharvest from 2014 through 2018. Microbial groups via EL-FAME markers and EAs were strongly correlated (r > .79) with qPCR assays of equivalent taxonomic and functional genes. To further quantify the predictive power of these relationships, we modeled several genes as a function of EA or EL-FAME markers, combined with other related covariates (e.g., soil texture, pH, irrigation, and soil organic C [SOC]) using a generalized linear model. The latter was trained using data from 2014, which was an average year in terms of temperature and precipitation for the region. Subsequently, the model was tested making predictions for 2015–2018, which represented high variability in climatic conditions, ensuring a thorough assessment of its predictive power. In most cases, soil texture, SOC, and Tier 2 indicators were identified as moderate to strong predictors of the biological responses. Our results suggest that the different approaches for assessing either function or community in these semiarid soils were highly comparable and provided similar information on how microbial communities were responding to both management and climate.