IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12901-d937658.html
   My bibliography  Save this article

Hedgerows on Crop Field Edges Increase Soil Carbon to a Depth of 1 meter

Author

Listed:
  • Jessica L. Chiartas

    (Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA)

  • Louise E. Jackson

    (Department of Land, Air and Water Resources, University of California Davis, Davis, CA 95616, USA)

  • Rachael F. Long

    (University of California Cooperative Extension, Woodland, CA 95695, USA)

  • Andrew J. Margenot

    (Department of Crop Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
    Agroecosystem Sustainability Center, Institute for Environment, Energy and Sustainability, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA)

  • Anthony T. O'Geen

    (Department of Land, Air and Water Resources, University of California Davis, Davis, CA 95616, USA)

Abstract

Effective incentivization of soil carbon (C) storage as a climate mitigation strategy necessitates an improved understanding of management impacts on working farms. Using a regional survey on intensively managed farms, soil organic carbon (SOC) concentrations and stocks (0–100 cm) were evaluated in a pairwise comparison of long-term (10+ years) woody hedgerow plantings and adjacent crop fields in Yolo County, CA, USA. Twenty-one paired sites were selected to represent four soil types (Yolo silt loam, Brentwood clay loam, Capay silty clay, and Corning loam), with textures ranging from 16% to 51% clay. Soil C was higher in the upper 100 cm under hedgerows (14.4 kg m −2 ) relative to cultivated fields (10.6 kg m −2 ) and at all depths (0–10, 10–20, 20–50, 50–75, and 75–100 cm). The difference in SOC (3.8 kg m −2 ) did not vary by soil type, suggesting a broad potential for hedgerows to increase SOC stocks. Assuming adoption rates of 50 to 80% across California for hypothetical field edges of average-size farms, and an identical SOC sequestration potential across soil types, hedgerows could sequester 10.8 to 17.3 MMT CO 2 e, or 7 to 12% of California’s annual greenhouse gas reduction goals.

Suggested Citation

  • Jessica L. Chiartas & Louise E. Jackson & Rachael F. Long & Andrew J. Margenot & Anthony T. O'Geen, 2022. "Hedgerows on Crop Field Edges Increase Soil Carbon to a Depth of 1 meter," Sustainability, MDPI, vol. 14(19), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12901-:d:937658
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12901/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/19/12901/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fernandez, Romina & Quiroga, Alberto & Noellemeyer, Elke & Funaro, Daniel & Montoya, Jorgelina & Hitzmann, Bernd & Peinemann, Norman, 2008. "A study of the effect of the interaction between site-specific conditions, residue cover and weed control on water storage during fallow," Agricultural Water Management, Elsevier, vol. 95(9), pages 1028-1040, September.
    2. Serita D. Frey & Juhwan Lee & Jerry M. Melillo & Johan Six, 2013. "The temperature response of soil microbial efficiency and its feedback to climate," Nature Climate Change, Nature, vol. 3(4), pages 395-398, April.
    3. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juan Carlos Chávez & Felipe J. Fonseca & Manuel Gómez-Zaldívar, 2017. "Resoluciones de disputas comerciales y desempeño económico regional en México. (Commercial Disputes Resolution and Regional Economic Performance in Mexico)," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 79-93, May.
    2. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 95-109.
    3. Yan Yu Chen & Chun-Cheih Chao & Fu-Chen Liu & Po-Chen Hsu & Hsueh-Fen Chen & Shih-Chi Peng & Yung-Jen Chuang & Chung-Yu Lan & Wen-Ping Hsieh & David Shan Hill Wong, 2013. "Dynamic Transcript Profiling of Candida albicans Infection in Zebrafish: A Pathogen-Host Interaction Study," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    4. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    5. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    6. Simplice A. Asongu & Nicholas M. Odhiambo, 2019. "Governance, capital flight and industrialisation in Africa," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-22, December.
    7. M. J. Aziakpono & S. Kleimeier & H. Sander, 2012. "Banking market integration in the SADC countries: evidence from interest rate analyses," Applied Economics, Taylor & Francis Journals, vol. 44(29), pages 3857-3876, October.
    8. Bianca Maria Colosimo & Luca Pagani & Marco Grasso, 2024. "Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 429-447, January.
    9. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    10. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    11. Ionela Munteanu & Adriana Grigorescu & Elena Condrea & Elena Pelinescu, 2020. "Convergent Insights for Sustainable Development and Ethical Cohesion: An Empirical Study on Corporate Governance in Romanian Public Entities," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    12. Daniel Boss & Annick Hoffmann & Benjamin Rappaz & Christian Depeursinge & Pierre J Magistretti & Dimitri Van de Ville & Pierre Marquet, 2012. "Spatially-Resolved Eigenmode Decomposition of Red Blood Cells Membrane Fluctuations Questions the Role of ATP in Flickering," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    13. Doukas, Haris & Papadopoulou, Alexandra & Savvakis, Nikolaos & Tsoutsos, Theocharis & Psarras, John, 2012. "Assessing energy sustainability of rural communities using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1949-1957.
    14. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    15. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    16. -, 2015. "The effects of climate change on the coasts of Latin America and the Caribbean: Climate variability, dynamics and trends," Documentos de Proyectos 39866, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    17. Dorota Toczydlowska & Gareth W. Peters & Man Chung Fung & Pavel V. Shevchenko, 2017. "Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components," Risks, MDPI, vol. 5(3), pages 1-77, July.
    18. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    19. Joanna Jasnos, 2021. "Hydrogeochemical Characteristics of Geothermal Waters from Mesozoic Formations in the Basement of the Central Part of the Carpathian Foredeep and the Carpathians (Poland) Using Multivariate Statistica," Energies, MDPI, vol. 14(13), pages 1-31, July.
    20. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12901-:d:937658. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.