Project Overview
In recent years, there has been increasing consumer demand for agricultural systems to meet quantitative sustainability metrics, including those pertaining to greenhouse gas (GHG) emissions. At present, organic agricultural systems, which rely upon a greater understanding and management of complex, natural biological and biogeochemical processes, may not be accurately evaluated within existing GHG emission tools, which were developed to monitor emissions from conventional systems.
This study presents a review and synthesis of recent efforts to improve two well-established GHG decision support tools (COMET-Farm and the Cool Farm Tool) to better represent organic management scenarios.
Farmer Takeaways
- At present, many of the tools available for producers to predict/estimate agricultural GHG emissions (such as the Cool Farm Tool and COMET-Farm) are not adequately equipped to address the complexities of diverse, organic management systems.
- While these models may provide a good baseline for estimating agricultural GHG emissions and initiating on-farm GHG emissions reduction strategies, there is room for improvement, particularly within the organic sector. Such improvements can be made by utilizing large datasets (i.e. meta-analyses and regression analyses) to identify important predictor variables, to evaluate model performance, and to adjust both process-based and empirical models.
Project Objectives and Approach
To improve agricultural decision support tools both at the user end (data input requirements) and the underlying process-based and empirical models to improve confidence in tool soil C and GHG estimates
- A meta-analysis of cover crop studies in temperate climates was conducted to quantify the effects of cover crops on soil C stocks at the 0-30cm soil depth and to identify key management and ecological factors that impact variation in this response.
- The meta-analysis synthesized data from 40 different publications covering both organic and conventional farming systems in six countries.
- The size of the soil organic carbon (SOC) effect for each combination of cover crop (treatment) and no cover crop (control) was estimated for each study.
- The dataset developed for the cover crop meta-analysis was applied to inform and improve components of both the COMET-Farm and Cool Farm Tool.
- The DayCent model (a process-based, ecosystem model utilized in the COMET-Farm tool) was parameterized for one new cover crop species not previously included as a cover crop option in COMET-Farm (sunn hemp) and evaluated for performance. Several previously-established cover crops (annual ryegrass, cereal rye, clovers, vetch, and cover crop mixtures) were evaluated as well.
- Linear, multiple mixed regression models were applied to the Cool Farm Tool’s empirical formulas to identify the strongest predictors of cover crop effects on SOC changes.
Key Findings
Findings from the cover crop meta-analysis indicated that, in temperate climates, cover crops can significantly increase soil organic carbon (SOC) stocks over systems without cover crops, with variables including (1) growing window, (2) annual cover crop biomass production, and (3) soil clay content explaining the most variability in soil C effects
- Inclusion of cover crops in annual and perennial cropping systems increased SOC stocks at the 0-30cm depth by 12%, averaging 1.11 Mg C/ha more soil C relative to similarly managed systems without cover crops.
- Cover crops planted as continuous cover or autumn-planted/terminated resulted in 20-30% greater total soil C stocks relative to other cover crop growing windows.
- High annual cover crop biomass production (>7 Mg/ha/yr) resulted in 30% higher total soil C stocks than lower levels of biomass production.
Results from the cover crop model simulations identified areas where the models’ predictive abilities were stronger (i.e., general cover crop-induced changes in soil C stocks), as well as model limitations (i.e., cover crop biomass production, residue quality/C:N ratio, and nitrous oxide emissions)
- The DayCent model (COMET-Farm tool) was generally able to simulate changes in soil C stocks with cover crop adoption, but varied in its efficacy to predict cover crop biomass production (actual vs. predicted values) by cover crop species.
- The DayCent model was limited in its ability to predict cover crop residue quality (C:N ratio) and nitrous oxide (N2O) emissions.
- Of the cover crop-induced SOC change predictor variables evaluated in the Cool Farm Tool, the strongest (and most economic) included the predictor of cover crop biomass, with a cover crop biomass greater than 1 Mg/ha/yr required for a positive change in SOC surface soils.
Resources
Schipanski, M.E., S.C. McClelland, H.M. Hughes, R. Jabbour, D. Malin, J. Hillier, K. Paustian, and E. Reaves. 2024. Improving Decision Support Tools for Quantifying GHG Emissions from Organic Production Systems. Organic Agriculture 14:503-512.
Read MoreLocation
Collaborators
Shelby McClelland, New York University
Helen Hughes, University of Edinburgh
Randa Jabbour, University of Wyoming
Daniella Malin, Sustainable Food Lab
Jonathan Hillier, University of Wyoming
Keith Paustian, Colorado State University
Elizabeth Reaves, Sustainable Food Lab
Region
Midwest, Northeast/Mid-Atlantic, Northwest, Plains, Southeast, West/Southwest
Topic
Climate Solutions, Tools and Technology
Year Published
2024
