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Qin Z, Guan K, Zhou W, Peng B, Tang J, Jin Z, Grant R, Hu T, Villamil MB, DeLucia E, Margenot AJ, Umakant M, Chen Z, Coppess J. Assessing long-term impacts of cover crops on soil organic carbon in the central US Midwestern agroecosystems. GLOBAL CHANGE BIOLOGY 2023; 29:2572-2590. [PMID: 36764676 DOI: 10.1111/gcb.16632] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/14/2022] [Accepted: 11/28/2022] [Indexed: 05/31/2023]
Abstract
Cover crops have been reported as one of the most effective practices to increase soil organic carbon (SOC) for agroecosystems. Impacts of cover crops on SOC change vary depending on soil properties, climate, and management practices, but it remains unclear how these control factors affect SOC benefits from cover crops, as well as which management practices can maximize SOC benefits. To address these questions, we used an advanced process-based agroecosystem model, ecosys, to assess the impacts of winter cover cropping on SOC accumulation under different environmental and management conditions. We aimed to answer the following questions: (1) To what extent do cover crops benefit SOC accumulation, and how do SOC benefits from cover crops vary with different factors (i.e., initial soil properties, cover crop types, climate during the cover crop growth period, and cover crop planting and terminating time)? (2) How can we enhance SOC benefits from cover crops under different cover crop management options? Specifically, we first calibrated and validated the ecosys model at two long-term field experiment sites with SOC measurements in Illinois. We then applied the ecosys model to six cover crop field experiment sites spanning across Illinois to assess the impacts of different factors on SOC accumulation. Our modeling results revealed the following findings: (1) Growing cover crops can bring SOC benefits by 0.33 ± 0.06 MgC ha-1 year-1 in six cover crop field experiment sites across Illinois, and the SOC benefits are species specific to legume and non-legume cover crops. (2) Initial SOC stocks and clay contents had overall small influences on SOC benefits from cover crops. During the cover crop growth period (i.e., winter and spring in the US Midwest), high temperature increased SOC benefits from cover crops, while the impacts from larger precipitation on SOC benefits varied field by field. (3) The SOC benefits from cover crops can be maximized by optimizing cover crop management practices (e.g., selecting cover crop types and controlling cover crop growth period) for the US Midwestern maize-soybean rotation system. Finally, we discussed the economic and policy implications of adopting cover crops in the US Midwest, including that current economic incentives to grow cover crops may not be sufficient to cover costs. This study systematically assessed cover crop impacts for SOC change in the US Midwest context, while also demonstrating that the ecosys model, with rigorous validation using field experiment data, can be an effective tool to guide the adaptive management of cover crops and quantify SOC benefits from cover crops. The study thus provides practical tools and insights for practitioners and policy-makers to design cover crop related government agricultural policies and incentive programs for farmers and agri-food related industries.
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Affiliation(s)
- Ziqi Qin
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, Illinois, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Kaiyu Guan
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, Illinois, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Wang Zhou
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, Illinois, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bin Peng
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, Illinois, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jinyun Tang
- Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Zhenong Jin
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Robert Grant
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | - Tongxi Hu
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, Illinois, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - María B Villamil
- Department of Crop Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Evan DeLucia
- Department of Crop Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Andrew J Margenot
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, Illinois, USA
- Department of Crop Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Mishra Umakant
- Sandia National Laboratories California, Computational Biology & Biophysics, California, USA
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, California, USA
| | - Zhangliang Chen
- Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana Champaign, Urbana, Illinois, USA
- Department of Natural Resources and Environmental Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jonathan Coppess
- Department of Agricultural and Consumer Economics, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Illinois, Urbana, USA
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Eddy covariance-based differences in net ecosystem productivity values and spatial patterns between naturally regenerating forests and planted forests in China. Sci Rep 2022; 12:20556. [PMID: 36446935 PMCID: PMC9709070 DOI: 10.1038/s41598-022-25025-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/23/2022] [Indexed: 11/30/2022] Open
Abstract
Net ecosystem productivity (NEP), the difference between gross primary productivity (GPP) and ecosystem respiration (ER), is the basis of forest carbon sinks. Revealing NEP differences between naturally regenerating forests (NF) and planted forests (PF) can benefit for making carbon neutrality strategies. Based on 35 eddy covariance measurements in China, we analyzed NEP differences in values and spatial patterns between NF and PF. The results showed that NF had slightly lower NEP than PF, resulting from the high stand age (SA) and soil fertilizer, while their differences were not significant (p > 0.05). The increasing latitude decreased mean annual air temperature thus decreased GPP both in NF and PF. However, the higher SA and soil fertilizer in NF made most GPP release as ER thus induced no significant NEP spatial variation, while lower SA and soil fertilizer in PF made NEP spatially couple with GPP thus showed a decreasing latitudinal pattern. Therefore, stand characteristics determined the differences in NEP values but indirectly affected the differences in NEP spatial variations through altering GPP allocation. The decreasing latitudinal pattern of NEP in PF indicates a higher sequestration capacity in the PF of South China. Our results provide a basis for improving the forest carbon sequestration.
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Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12213512] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.
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Fu C, Wang G, Bible K, Goulden ML, Saleska SR, Scott RL, Cardon ZG. Hydraulic redistribution affects modeled carbon cycling via soil microbial activity and suppressed fire. GLOBAL CHANGE BIOLOGY 2018; 24:3472-3485. [PMID: 29654607 DOI: 10.1111/gcb.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 02/19/2018] [Indexed: 06/08/2023]
Abstract
Hydraulic redistribution (HR) of water from moist to drier soils, through plant roots, occurs world-wide in seasonally dry ecosystems. Although the influence of HR on landscape hydrology and plant water use has been amply demonstrated, HR's effects on microbe-controlled processes sensitive to soil moisture, including carbon and nutrient cycling at ecosystem scales, remain difficult to observe in the field and have not been integrated into a predictive framework. We incorporated a representation of HR into the Community Land Model (CLM4.5) and found the new model improved predictions of water, energy, and system-scale carbon fluxes observed by eddy covariance at four seasonally dry yet ecologically diverse temperate and tropical AmeriFlux sites. Modeled plant productivity and microbial activities were differentially stimulated by upward HR, resulting at times in increased plant demand outstripping increased nutrient supply. Modeled plant productivity and microbial activities were diminished by downward HR. Overall, inclusion of HR tended to increase modeled annual ecosystem uptake of CO2 (or reduce annual CO2 release to the atmosphere). Moreover, engagement of CLM4.5's ground-truthed fire module indicated that though HR increased modeled fuel load at all four sites, upward HR also moistened surface soil and hydrated vegetation sufficiently to limit the modeled spread of dry season fire and concomitant very large CO2 emissions to the atmosphere. Historically, fire has been a dominant ecological force in many seasonally dry ecosystems, and intensification of soil drought and altered precipitation regimes are expected for seasonally dry ecosystems in the future. HR may play an increasingly important role mitigating development of extreme soil water potential gradients and associated limitations on plant and soil microbial activities, and may inhibit the spread of fire in seasonally dry ecosystems.
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Affiliation(s)
- Congsheng Fu
- Department of Civil & Environmental Engineering, Center for Environmental Science and Engineering, University of Connecticut, Storrs, Connecticut
| | - Guiling Wang
- Department of Civil & Environmental Engineering, Center for Environmental Science and Engineering, University of Connecticut, Storrs, Connecticut
| | - Kenneth Bible
- Forest Service, Pacific Northwest Research Station, Portland, Oregon
| | - Michael L Goulden
- Department of Earth System Science, University of California, Irvine, California
| | - Scott R Saleska
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona
| | - Russell L Scott
- Southwest Watershed Research Center, USDA-Agricultural Research Service, Tucson, Arizona
| | - Zoe G Cardon
- The Ecosystems Center, Marine Biological Laboratory, Woods Hole, Massachusetts
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5
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Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar. FORESTS 2018. [DOI: 10.3390/f9040214] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Liu S, Zhuang Q, Chen M, Gu L. Quantifying spatially and temporally explicit
CO
2
fertilization effects on global terrestrial ecosystem carbon dynamics. Ecosphere 2016. [DOI: 10.1002/ecs2.1391] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Shaoqing Liu
- Department of Earth, Atmospheric, and Planetary Sciences Purdue University West Lafayette Indiana 47907 USA
| | - Qianlai Zhuang
- Department of Earth, Atmospheric, and Planetary Sciences Purdue University West Lafayette Indiana 47907 USA
- Department of Agronomy Purdue University West Lafayette Indiana 47907 USA
| | - Min Chen
- Department of Global Ecology Carnegie Institution for Science Stanford California 94305 USA
| | - Lianhong Gu
- Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA
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Fischer R, Ensslin A, Rutten G, Fischer M, Schellenberger Costa D, Kleyer M, Hemp A, Paulick S, Huth A. Simulating carbon stocks and fluxes of an African tropical montane forest with an individual-based forest model. PLoS One 2015; 10:e0123300. [PMID: 25915854 PMCID: PMC4410999 DOI: 10.1371/journal.pone.0123300] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Accepted: 03/02/2015] [Indexed: 11/18/2022] Open
Abstract
Tropical forests are carbon-dense and highly productive ecosystems. Consequently, they play an important role in the global carbon cycle. In the present study we used an individual-based forest model (FORMIND) to analyze the carbon balances of a tropical forest. The main processes of this model are tree growth, mortality, regeneration, and competition. Model parameters were calibrated using forest inventory data from a tropical forest at Mt. Kilimanjaro. The simulation results showed that the model successfully reproduces important characteristics of tropical forests (aboveground biomass, stem size distribution and leaf area index). The estimated aboveground biomass (385 t/ha) is comparable to biomass values in the Amazon and other tropical forests in Africa. The simulated forest reveals a gross primary production of 24 tcha(-1) yr(-1). Modeling above- and belowground carbon stocks, we analyzed the carbon balance of the investigated tropical forest. The simulated carbon balance of this old-growth forest is zero on average. This study provides an example of how forest models can be used in combination with forest inventory data to investigate forest structure and local carbon balances.
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Affiliation(s)
- Rico Fischer
- Department of Ecological Modelling, Helmholtz Centre for Environmental Research–UFZ, Permoserstr. 15, 04318, Leipzig, Germany
- * E-mail:
| | - Andreas Ensslin
- Institute of Plant Sciences, University of Bern, Altenbergrain 21, CH-3013, Bern, Switzerland
| | - Gemma Rutten
- Institute of Plant Sciences, University of Bern, Altenbergrain 21, CH-3013, Bern, Switzerland
| | - Markus Fischer
- Institute of Plant Sciences, University of Bern, Altenbergrain 21, CH-3013, Bern, Switzerland
| | - David Schellenberger Costa
- Department of Biology and Environmental Sciences, University of Oldenburg, Carl-von-Ossietzky–Strasse 9–11, 26111, Oldenburg, Germany
| | - Michael Kleyer
- Department of Biology and Environmental Sciences, University of Oldenburg, Carl-von-Ossietzky–Strasse 9–11, 26111, Oldenburg, Germany
| | - Andreas Hemp
- Dept. of Plant Systematics, University of Bayreuth, Universitaetsstr. 30-31, 95440, Bayreuth, Germany
| | - Sebastian Paulick
- Department of Ecological Modelling, Helmholtz Centre for Environmental Research–UFZ, Permoserstr. 15, 04318, Leipzig, Germany
| | - Andreas Huth
- Department of Ecological Modelling, Helmholtz Centre for Environmental Research–UFZ, Permoserstr. 15, 04318, Leipzig, Germany
- Institute of Environmental Systems Research, University of Osnabrueck, Barbarastraße 12, 49076, Osnabrueck, Germany
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Rowland L, Hill TC, Stahl C, Siebicke L, Burban B, Zaragoza-Castells J, Ponton S, Bonal D, Meir P, Williams M. Evidence for strong seasonality in the carbon storage and carbon use efficiency of an Amazonian forest. GLOBAL CHANGE BIOLOGY 2014; 20:979-91. [PMID: 23996917 PMCID: PMC4298765 DOI: 10.1111/gcb.12375] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 08/14/2013] [Indexed: 05/25/2023]
Abstract
The relative contribution of gross primary production and ecosystem respiration to seasonal changes in the net carbon flux of tropical forests remains poorly quantified by both modelling and field studies. We use data assimilation to combine nine ecological time series from an eastern Amazonian forest, with mass balance constraints from an ecosystem carbon cycle model. The resulting analysis quantifies, with uncertainty estimates, the seasonal changes in the net carbon flux of a tropical rainforest which experiences a pronounced dry season. We show that the carbon accumulation in this forest was four times greater in the dry season than in the wet season and that this was accompanied by a 5% increase in the carbon use efficiency. This seasonal response was caused by a dry season increase in gross primary productivity, in response to radiation and a similar magnitude decrease in heterotrophic respiration, in response to drying soils. The analysis also predicts increased carbon allocation to leaves and wood in the wet season, and greater allocation to fine roots in the dry season. This study demonstrates implementation of seasonal variations in parameters better enables models to simulate observed patterns in data. In particular, we highlight the necessity to simulate the seasonal patterns of heterotrophic respiration to accurately simulate the net carbon flux seasonal tropical forest.
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Affiliation(s)
- Lucy Rowland
- School of Geosciences, University of EdinburghEdinburgh, EH9 3JN, UK
| | | | | | | | | | | | - Stephane Ponton
- INRA, UMR 1137 Ecologie et Ecophysiologie ForestièresChampenoux, 54280, France
| | - Damien Bonal
- INRA, UMR 1137 Ecologie et Ecophysiologie ForestièresChampenoux, 54280, France
| | - Patrick Meir
- School of Geosciences, University of EdinburghEdinburgh, EH9 3JN, UK
- Research School of Biology, Division of Plant Sciences, Australian National UniversityCanberra, ACT, 0200, Australia
| | - Mathew Williams
- School of Geosciences, University of EdinburghEdinburgh, EH9 3JN, UK
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Miguez-Macho G, Fan Y. The role of groundwater in the Amazon water cycle: 2. Influence on seasonal soil moisture and evapotranspiration. ACTA ACUST UNITED AC 2012. [DOI: 10.1029/2012jd017540] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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10
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Verbeeck H, Peylin P, Bacour C, Bonal D, Steppe K, Ciais P. Seasonal patterns of CO2fluxes in Amazon forests: Fusion of eddy covariance data and the ORCHIDEE model. ACTA ACUST UNITED AC 2011. [DOI: 10.1029/2010jg001544] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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