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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Soil Management and Sugarbeet Research » Research » Publications at this Location » Publication #362154

Research Project: Management Practices for Long Term Productivity of Great Plains Agriculture

Location: Soil Management and Sugarbeet Research

Title: A global meta-analysis of soil organic carbon response to corn stover removal

Author
item XU, HUI - Argonne National Laboratory
item SIEVERDING, HEIDI - South Dakota School Of Mines And Technology
item KWON, HIYOUNG - Argonne National Laboratory
item CLAY, DAVID - South Dakota State University
item Stewart, Catherine
item Johnson, Jane
item QIN, ZHANGCAI - Argonne National Laboratory
item Karlen, Douglas
item WANG, MICHAEL - Argonne National Laboratory

Submitted to: Global Change Biology Bioenergy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/26/2019
Publication Date: 5/26/2019
Citation: Xu, H., Sieverding, H., Kwon, H., Clay, D., Stewart, C.E., Johnson, J.M., Qin, Z., Karlen, D.L., Wang, M. 2019. A global meta-analysis of soil organic carbon response to corn stover removal. Global Change Biology Bioenergy. 00:1-19. https://doi.org/10.1111/gcbb.12631.
DOI: https://doi.org/10.1111/gcbb.12631

Interpretive Summary: Corn (Zea Mays L.) stover is a global resource used as livestock bedding or feed, and as a fuel and bioenergy feedstock. However, excessive stover removal can decrease soil organic C (SOC) stocks, leading to increased soil erosion and decreased soil health. Field studies of stover removal find the site-specific response of SOC stocks and accrual rates, but quantitative synthesis of empirical evidence needed for policy decision-making is lacking. Our objective was to generalize stover removal effects on SOC at global scales using meta-analysis and meta-regression techniques. Four hundred and nine data points were extracted from 76 experimental sites and used for a global meta-analysis to quantify the effects of stover removal rate, tillage, soil texture, and soil sampling depth on SOC change. On average, residue retention had 7% greater SOC stocks compared to removal fields and was dependent on stover removal, soil depth considered, and crop rotation, with relatively little dependence on tillage system. Harvesting corn stover while maintaining SOC will require a nuanced approach that incorporates agricultural management with model validation to provide science-based policy recommendations for carbon management.

Technical Abstract: Corn (Zea Mays L.) stover is a global resource used as livestock bedding or feed, and as a fuel and bioenergy feedstock. However, excessive stover removal can decrease soil organic C (SOC) stocks, leading to increased soil erosion and decreased soil health. Field studies of stover removal find the site-specific response of SOC stocks and accrual rates, but quantitative synthesis of empirical evidence needed for policy decision-making is lacking. Our objective was to generalize stover removal effects on SOC at global scales using meta-analysis and meta-regression techniques. Four hundred and nine data points were extracted from 76 experimental sites and used for a global meta-analysis to quantify the effects of stover removal rate, tillage, soil texture, and soil sampling depth on SOC change. On average, residue retention had 7% greater SOC stocks compared to removal fields and increased SOC over time at a rate of 0.41 Mg C ha-1yr-1 on average. The magnitude of SOC stock reduction depended on the intensity of stover removal, soil depth considered, and crop rotation, with relatively little dependence on tillage system. In comparison to studies that reported on equivalent soil mass, results from fixed-depth measurements overestimated the carbon storage reduction by 7%. Use of meta-regression is necessary to identify influencing variables (e.g., crop rotation, SOC stock, sampling depth) which were not clearly seen in simple heterogeneity tests. Harvesting corn stover while maintaining SOC for sustainable crop production is a critical goal, and these analyses can help determine science-based policy recommendations for carbon management and serve as input for SOC model validation.