Current Projects

Identifying drivers of Crop yield stability in the Upper Midwest.

—————————————————————————————————————————

A number of recent studies have shown that soil organic matter may confer increased spatiotemporal yield stability to cropping systems (See Kane et al., 2021, or Pan et al., 2009 as examples). However, the mechanisms for this decrease in yield variability are not well defined, especially at the field and subfield scale. Working with researchers from Michigan State and private industry, we’re using a combination of physical soil organic matter analysis techniques and machine learning algorithms to starts teasing apart the drivers of yield stability in these systems, as well as thinking about the formation and function of soil organic matter in areas with unstable yields.

Regional soil organic matter controls on agronomic outcomes.

—————————————————————————————————————————

Physical soil organic matter fractions give us increased insight into soil organic matter formation and function, but is there a place for them on a soil health or soil fertility report? That is the question that we aim to answer with this project. Using a robust dataset that spans across both cropping systems and regions, we’re using physical fractions and advanced statistical modeling techniques to understand the value of soil organic matter fractions to farmers and ranchers, and what properties (e.g., yield and yield stability, nutrient use efficiency, soil health parameters) these fractions may be able to act as an indicator for.

Improving physical Soil organic matter fractionation methodologies.

—————————————————————————————————————————

One of the best ways to understand soil organic matter is through physical fractions, such as particulate and mineral associated organic matter (POM and MAOM, respectively). Part of my current work investigates the various methods by which these fractions can be separated (e.g., by particle size, or particle density) and uses a variety of analytical tools to understand how these procedural fractions map on to their conceptual counterparts. Improving the exchangability of fraction data between methods will improve data synthesis and meta-analytical studies, as well provide more robust datasets for the next generation of soil organic matter models.

Understanding long-term litter decomposition dynamics.

—————————————————————————————————————————

The mechanisms by which plant litter decomposes and enters the soil organic matter pool represent a set of well-studied phenomena. However, these studies tend to be conducted on relatively short time-horizons, representing processes that happen on sub-decadal scales. This work uses isotopic tools to look at the long term (> 10 years) fate of litter in grassland ecosystems to understand the eventual fate and distribution of litter carbon across the various soil organic matter fractions. By combining isotopic enrichment and soil organic matter fractionation, we are able to make novel and exciting observations about how carbon and nitrogen move through and persist in grassland ecosystems.

Characterizing different forms of mineral associated organic matter.

—————————————————————————————————————————

The mineral associated fraction of the soil organic matter can persist in soils for a considerable amount of time. However, recent work has shown that there is likely a dynamic portion of it as well, that has the potential to provide nutrients and energy to soil microbes as well as crops. Using a combination of laboratory experiments, isotopic tools, and spectroscopic analyses, we’re investigating the means by which we can start to identify this fast cycling mineral-associated pool such that we can better characterize and understand it. Whereas the mineral-associated fraction tends to have a lower C:N ratio (and thus is relatively nitrogen rich) compared to the rest of the soil organic matter, this work could represent an important step forward in cropland nitrogen management.