JOURNAL ARTICLE
Balancing Tradeoffs in Climate-Smart Agriculture: Will Selling Carbon Credits Offset Potential Losses in the Net Yield Income of Small-Scale Soybean (Glycine max L.) Producers in the Mid-Southern United States?
Published In: Decision Analysis (INFORMS), 2023, v. 20, n. 4. P. 252 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Contasti, Adrienne L.; Firth, Alexandra G.; Baker, Beth H.; Brooks, John P.; Locke, Martin A.; Morin, Dana J. 3 of 3
Abstract
This article focuses on evaluating tradeoffs between soil organic carbon (SOC) storage, crop yield, and income in soybean production systems in Mississippi when adopting climate-smart (CS) agricultural actions—specifically no-till (NT) and cover crops (cereal rye, *Secale cereale* L., and crimson clover, *Trifolium incarnatum* L.)—versus traditional practices (reduced tillage and no cover crops). Using a structured decision-making framework combined with Bayesian decision networks, the study quantifies how these CS actions affect SOC dynamics, yield probabilities, and net income, including potential offsets from voluntary carbon credit markets. Results indicate that adopting NT can minimize yield income losses if producers already use cover crops, and that income from carbon credits may offset yield losses when switching from traditional to CS practices. The analysis highlights temporal variability in SOC storage risks and underscores the importance of integrating economic incentives with soil health benefits to support producer adoption of sustainable practices.
Additional Information
- Source:Decision Analysis (INFORMS). 2023/12, Vol. 20, Issue 4, p252
- Document Type:Article
- Subject Area:Agriculture and Agribusiness
- Publication Date:2023
- ISSN:1545-8490
- DOI:10.1287/deca.2023.0478
- Accession Number:174178973
- Copyright Statement:Copyright of Decision Analysis (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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