Renz, P. 2024. Biochemical soil health indicator scores based on a multivariate soybean yield prediction model. , .

Soils are a non-renewable resource, which is the foundation of all ecosystems. Mismanagement of soil particularly in agro-ecosystems has degraded soil. To guide management of soils, to remediate soils, and enable optimal agricultural production, soil health indicators are needed. The objective of this dissertation was to determine the potential of biological and other soil properties to predict soybean yields. The central approach was based on soil samples from farmers’ fields instead of long-term experimental sites (LTES). Farmers’ fields in this study represented diverse management practices that exist in the agricultural sector. Soil Health (SH) measurements that are calibrated and that can consistently detect land management are lacking which was shown in Roper’s et al. (2017) 2017 publication that found existing SH tests (CASH, Haney) had limited ability to identify agronomic land management practices at a North Caroline LTES. And that they were poorly correlated with crop yields. This means that the quote by the Soil Health Institute “There is no standardized measurement for Soil Health in the United States” is still true. Unlike most previous research on SH, which was based on data from long term experimental sites (LTES), this investigation utilized analyses of soil samples from farmers’ fields. Farmers were surveyed to collect historical management information on each field after which LIDAR data, and soil type information from the Soil Survey website was obtained. For the 2019, 2020, 2021 growing seasons Ohio sampling sites were visited during the spring season and a composite soil sample at a depth of 0-15 cm was collected. In 2021 soil samples were collected at three LTES in Ohio and one in Michigan. Furthermore, soils were sampled at two virgin and two restored prairie sites in Ohio. Soil samples were analyzed for microbial community composition, enzyme activities, total carbon (TC), soil organic carbon (SOC), total nitrogen (TN), pH and texture. Microbial communities were profiled using the Ester-Linked Fatty Acid Methyl Ester (EL-FAME) analysis. The enzyme activity of β-glucosidase (NAG), N-acetyl glutamate synthase (NAG), and arylsulfatase (AS) were chosen because previous research has shown these measurements have been shown to be sensitive in detecting soil/crop management effects. The results of this dissertation showed that no individual soil property variable or other variable had a strong relationship with soybean yield. The multi-variate regression analysis on the other hand resulted in a correlation of determination value (R2) of 0.84. In this analysis a statistical machine learning algorithm (Elastic Net) was used with the help of the glmnet R package. The optimized model was then used to develop the biochemical Soil Heath Index (SHI) by extracting the individual regression coefficients of all biological variables (e.g. enzyme and EL-FAME variables) and the usage of a mathematical algorithem. The most common SH indicators in this study and the computed SH scores were analyzed for their ability to detect soil management at four LTES by running the Tukey's Honest Significant Difference test in combination with a sensitivity scoring algorithm. The sensitivity scores were used to identify the most sensitive SH indicators. In the final analysis 512 soil variables were scored for their ability to detect agricultural land management practices (e.g. crop rotation, cover cropping, soil amendments, tillage practices), restored prairies, and virgin soil in Ohio. Additionally at the agricultural scale each variable was tested for its temporal sensitivity. The most sensitive SH indicators were identified with the help of a sensitivity scoring algorithm and their relationship to soil organic carbon was determined. The remaining SH indicators were used to determine beneficial and detrimental agricultural land management practices.

Associated Treatment Areas:

• MCSE Main Cropping Systems Experiment

Download citation to endnote bibtex