Performing Department
(N/A)
Non Technical Summary
Cover cropping has been largely considered a major conservation approach to improve ecosystem services for sustainable agriculture. With current adoption rates low across US farmlands, extensive investments from government and private sectors have strongly encouraged farmers to employ cover crops. However, the rapid momentum from these investments is not well supported by a sufficient foundational knowledge base of cover cropping such that we are ready to scale up its adoption with maximum effectiveness. A major barrier is the limited understanding of cover crop root system traits and their empirical effects on soil and cash crops, especially across the spectrum of cover crop species diversity. In this project we will conduct multi-year trials of twelvecover crop species that integrate with corn production, and use root phenomics, cutting-edge sensing technologies, and machine-learning enabled agroecosystem modeling to gain an improved understanding of the variation for root traits that exists among diverse cover crop species and their influence on soiland cash crops. The societal benefit of this study will be to utilize our enhanced understanding of these relationships to provide more informed species selection that maximizes both yield and ecosystem benefits and thereby supports widespread adoption of cover crop management practices in the US.
Animal Health Component
0%
Research Effort Categories
Basic
70%
Applied
(N/A)
Developmental
30%
Goals / Objectives
The major goal of the project is to fill key knowledge gaps in the foundational knowledge base of cover crop plant species that currently hinder their efficacy and farmer adoption. First, there is a limited understanding of cover crop root system phenotypes and their empirical effects on soils and cash crops. Second, the species diversity of cover crop above- and belowground traits are largely underexplored. Three main objectives support this effort:OBJ 1: Characterize aboveground and belowground phenotypic variation across diverse cover crop species.OBJ 2: Empirically assess the impacts of cover crop root phenotypes on soil properties and corn production.OBJ 3: Improve agroecosystem modeling for simulating cover crop traits and their impacts on soil dynamics and corn production, with implications to prototype decision-support tools for cover crop selection and management.
Project Methods
The project will be conducted at the Danforth Center's field research site across three years. Standard approaches to collect cover crop shoot (cross-sectional grids and hand harvesting) and root (1-2m soil cores) samples will be employed. Both tissues will be ground and analyzed for total nitrogen and carbon content to estimate N:C ratios above and belowground, and to estimate contributions of total N and C to the plot and subsequent corn crop. Statistically significant differences in shoot and root phenotypes among functional groups and species will contribute to a foundational knowledge base for rational design of improved cover crops. To measure corn root growth dynamics we are employing a novel root growth sensing tool, RootTrackers, as well as the "Root Front Velocity" methods employed thus far in just a few published studies. Corn nitrogen and yield will be measured with conventional means. Differences in root growth, nitrogen uptake, and yield will be assessed as an outcome of the diverse cover crops they were paired with. All of these parameters will be assessed over time as the same species will be planted in the same plots year-on-year. Remote hyperspectral imaging will be conducted by manned aircraft and paired with the aforementioned measurements to develop ground truth relationships that allow modeling over a large scale of cover crop and corn growth and nitrogen capture. Model prediction efficacy (ex. total plant nitrogen, total biomass) will be evaluated with manual ground truthing methods. The measurements will also be used to parameterize the ecosys model of diverse cover crop species to enable simulations of the effects of management on corn production, and to prototype a decision support tool that producers can use to maximize both profit and ecosystem service benefits.