Predicting the Stability of Organic Matter Originating from Different Waste Treatment Procedures

Area of Science:

• Agricultural Science
• Environmental Science
• Soil Science

Background:

• Recycling organic wastes into farmland presents a challenge in balancing soil carbon sequestration with mitigating carbon dioxide (CO2) emissions.
• Predicting the stability of organic matter (OM) in organic wastes and their treated products is crucial for addressing this challenge.

Purpose of the Study:

• To develop a predictive model for the stability of organic matter (OM) in various treated organic wastes.
• To assess the impact of different waste treatment methods (anaerobic digestion, composting) on OM stability and subsequent soil CO2 emissions.

Main Methods:

• Characterization of 31 organic wastes and their treatment products using sequential extraction and 3D fluorescence spectroscopy.
• Development of a partial least squares (PLS) regression model integrating OM characterization data.
• Soil incubation experiments to measure CO2 emissions from treated waste application.

Main Results:

• Organic matter became less accessible and biodegradable after treatment, particularly composting, leading to reduced soil CO2 emissions.
• The PLS model accurately predicted the soil stability of solid digestate and compost using data from untreated wastes.
• Treatment methods significantly influence OM stability and potential for soil carbon sequestration.

Conclusions:

• A predictive modeling approach can effectively estimate the stability of organic matter in treated organic wastes.
• This approach aids in selecting optimal and cost-effective treatment methods to stabilize organic carbon and minimize soil CO2 emissions.
• The findings support sustainable agricultural practices by enhancing soil carbon storage and reducing greenhouse gas emissions from organic waste recycling.