359-1 A Farmer-Friendly Tool for Predicting Soil Organic Matter.

Poster Number 1309

See more from this Division: ASA Section: Environmental Quality
See more from this Session: Long-Term Studies On Soil Carbon and Greenhouse Gas Emissions

Wednesday, November 6, 2013
Tampa Convention Center, East Exhibit Hall

Vinayak Shedekar, Food, Agricultural and Biological Engineering, The Ohio State University Research Foundation, Columbus, OH, Khandakar R. Islam, Soil, Water and Bioenergy Resources, Ohio State University, Piketon, OH, Randall C Reeder, Food, Agricultural and Biological Engineering, Ohio State University, Columbus, OH and Jerry Grigar Jr., NRCS, USDA-NRCS, East Lansing, MI
Poster Presentation
  • Poster 79834 at 2013 ASA Meeting by Shedekar etal..pdf (1.2 MB)
  • Abstract:
    Removal of crop residue for biofuels and other purposes is becoming a common practice in the Midwest. However, removal of crop residue adversely impacts the stability of soil organic matter (SOM) stock in the soil. Therefore, there is a growing interest among farmers, crop consultants and bio-energy related industry for a tool that can help them optimize their management practices and crop residue removal rates, while maintaining a good soil health in the long term.

    Several models have been developed to predict soil organic matter (SOM) dynamics in agricultural soils. Some of the examples include CENTURY, CANDY, DAISY, RothC. However, most of these models are data intensive and require a certain level of skill-set to operate. A simple, easy-to-use and less data intensive tool to predict SOM may be very useful for farmers to test different short and long-term scenarios with respect to removal of residue and/or management practices.

    Professor R.E. Lucas, at the Michigan State University, developed a simple two-pool exponential decay model that uses decomposition constants based on various management criteria. The model considers annual loading rates of fresh plant residue and simulates decomposition of organic matter into ‘active’ and ‘passive’ fractions as affected by management practices. This model was tested by Lucas himself and, recently by Jerry Grigar (NRCS, Michigan), using data from 14 different long-term trials in Michigan. Overall, the model showed promising accuracy in predicting the percent SOM (R2 > 0.90, unpublished data) on annual basis. Considering the simplistic nature and minimal data requirements of Lucas’s model, a spreadsheet tool has been developed in order to assist farmers as a “SOM Calculator”.

    The SOM Calculator consists of a user-friendly interface, with options to select crop rotation, management practices (conservation tillage, drainage, manure application etc.), and residue removal rates per year. Based on these inputs, the calculator uses first order decay functions as primary basis for calculating annual changes in SOM over short- and long-term. The calculator has been further expanded to predict active and passive fractions of SOM, total active nitrogen and overall soil quality. The calculator is also capable of calculating the revenue generated from residue removal, percent change in SOM and corresponding amount of soil carbon sequestered or lost. Data from long-term trials in Ohio and Michigan will be used to broaden the spatial validity of the calculator.

    See more from this Division: ASA Section: Environmental Quality
    See more from this Session: Long-Term Studies On Soil Carbon and Greenhouse Gas Emissions

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