Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning.
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| Title: | Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning. |
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| Authors: | Yi, Koong1,2 (AUTHOR) koongyi@gmail.com, Benson, Michael C.1,3 (AUTHOR), Pederson, Taylor L.1,4 (AUTHOR), Bernacchi, Carl J.1,3,4 (AUTHOR) bernacch@illinois.edu |
| Source: | GCB Bioenergy. Jul2026, Vol. 18 Issue 7, p1-14. 14p. |
| Subject Terms: | *Evapotranspiration, *Ecosystem dynamics, *Machine learning, *Agricultural ecology, *Water use, *Photosynthetic rates |
| Abstract: | Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site‐years of high‐frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem‐scale carbon, water, and energy fluxes. Using an interpretable machine‐learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water‐use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short‐term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem‐scale carbon–water–energy coupling. By integrating long‐term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data‐driven basis for improving crop and Earth‐system models and for guiding bioenergy landscape design under future climate scenarios. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194920176 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yi%2C+Koong%22">Yi, Koong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> koongyi@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Benson%2C+Michael+C%2E%22">Benson, Michael C.</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pederson%2C+Taylor+L%2E%22">Pederson, Taylor L.</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bernacchi%2C+Carl+J%2E%22">Bernacchi, Carl J.</searchLink><relatesTo>1,3,4</relatesTo> (AUTHOR)<i> bernacch@illinois.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22GCB+Bioenergy%22">GCB Bioenergy</searchLink>. Jul2026, Vol. 18 Issue 7, p1-14. 14p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Evapotranspiration%22">Evapotranspiration</searchLink><br />*<searchLink fieldCode="DE" term="%22Ecosystem+dynamics%22">Ecosystem dynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Agricultural+ecology%22">Agricultural ecology</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+use%22">Water use</searchLink><br />*<searchLink fieldCode="DE" term="%22Photosynthetic+rates%22">Photosynthetic rates</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site‐years of high‐frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem‐scale carbon, water, and energy fluxes. Using an interpretable machine‐learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water‐use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short‐term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem‐scale carbon–water–energy coupling. By integrating long‐term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data‐driven basis for improving crop and Earth‐system models and for guiding bioenergy landscape design under future climate scenarios. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194920176 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/gcbb.70156 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Evapotranspiration Type: general – SubjectFull: Ecosystem dynamics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Agricultural ecology Type: general – SubjectFull: Water use Type: general – SubjectFull: Photosynthetic rates Type: general Titles: – TitleFull: Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yi, Koong – PersonEntity: Name: NameFull: Benson, Michael C. – PersonEntity: Name: NameFull: Pederson, Taylor L. – PersonEntity: Name: NameFull: Bernacchi, Carl J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 17571693 Numbering: – Type: volume Value: 18 – Type: issue Value: 7 Titles: – TitleFull: GCB Bioenergy Type: main |
| ResultId | 1 |
