Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory‐based carbon accounting

West, Tristram O.; Brandt, Craig C.; Baskaran, Latha; Hellwinckel, Chad; Mueller, Rick; Bernacchi, Carl J.; Bandaru, Varaprasad; Yang, Bai; Wilson, Bradly; Marland, Gregg; Nelson, Richard; Ugarte, Daniel de la Torre; Post, Wilfred M.

doi:10.1890/08-2352.1

Cited by 94 publications

(60 citation statements)

References 33 publications

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“…Schulze et al [2009] suggested that croplands are a net source of greenhouse gasses to the atmosphere in Europe. In contrast, croplands were identified as a sink in the United States [West et al, 2010]. Smith et al [2008] proposed that croplands could have a large potential in greenhouse gas mitigation through specific GHG-management practices, and different local management practices may be one of the reasons why croplands sometimes appear as a source and sometimes as a sink in different regions of the world.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe

Chen

Werf²,

Dolman³

et al. 2011

Geophys. Res. Lett.

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[1] Croplands cover 12% of the ice-free land surface and play an important role in the global carbon cycle. Light use efficiency (LUE) models have often been employed to estimate the exchange of CO 2 between croplands and the atmosphere. A key parameter in these models is the maximum light use efficiency ("*), but estimates of "* vary by at least a factor 2. Here we used 12 agricultural eddyflux measurement sites in North America and Europe to constrain LUE models in general and "* in particular. We found that LUE models could explain on average about 70% of the variability in net ecosystem exchange (NEE) when we increased the "* from 0.5 to 0.65-2.0g C per MJ Photosynthetic Active Radiation (PAR). Our results imply that croplands are more important in the global carbon budget than often thought. In addition, inverse modeling approaches that utilize LUE model outputs as a-priori input may have to be revisited in areas where croplands are an important contributor to regional carbon fluxes. Citation: Chen, T., G. R. van der Werf, A. J. Dolman, and M. Groenendijk (2011), Evaluation of cropland maximum light use efficiency using eddy flux measurements in North

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Section: Introductionmentioning

confidence: 99%

Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe

Chen

Werf²,

Dolman³

et al. 2011

Geophys. Res. Lett.

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“…McBride et al [19] and West et al [23] do not include CO 2 flux from soil respiration or photosynthesis in determining the GHG flux as this carbon is typically accounted for by determining soil carbon stocks and estimates of standing biomass. CO 2 and N 2 O flux can be measured directly in the field or estimated from the literature.…”

Section: Ghg Emissionsmentioning

confidence: 99%

Sustainability Indicators of Biomass Production in Agroforestry Systems

Thevathasan¹,

Gordon²,

Simpson³

et al. 2014

TOASJ

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Production of biomass for bioenergy will depend on the sustainability of the production resource-base: the soil, water, air and the diversity of the ecosystem as a whole. For soil, potential sustainability indicators, including soil nitrogen and phosphorus, total organic matter, components associated with soil erosion and bulk density are discussed. Indicators related to the water resource water quality, water availability index, nitrate levels in water and biological oxygen demand are also discussed. Greenhouse gases and their sequestration potentials are discussed for maintaining atmospheric air quality. For biodiversity, a biodiversity index and soil biota index were selected as potential indicators. In addition to the production resource-base, the paper also discusses the importance of economic and social sustainability indices. To summarize, we are suggesting that a common method of visualizing the above indicated indices is to generate amoeba diagrams. This paper also provides a review on the term 'sustainability' and specific indicators, with metrics where possible, are described as candidates for inclusion as potential indicators appropriate for agroforestry based bioenergy systems in Canada. Three agroforestry based bioenergy systems are described and specific indicators are discussed within the bounds of these systems.

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“…The Soil Plant Atmosphere (SPA) model (Williams et al, 1996(Williams et al, , 2001) is a process-based model that simulates ecosystem photosynthesis and water balance at fine temporal and spatial scales (30 min time step, up to 10 canopy and 20 soil layers). SPA employs some well-tested theoretical representations of ecophysiological processes, such as for the calculation of photosynthesis (the Farquhar model, Farquhar and von Caemmerer, 1982) and leaf-level transpiration (PenmanMonteith equation, Jones, 1992).…”

Section: Crop Model: Structure Parameterization and Initial Conditionsmentioning

confidence: 99%

“…SPA employs some well-tested theoretical representations of ecophysiological processes, such as for the calculation of photosynthesis (the Farquhar model, Farquhar and von Caemmerer, 1982) and leaf-level transpiration (PenmanMonteith equation, Jones, 1992). These two processes are linked by a model of stomatal conductance, which optimises the daily gain of C per unit of leaf nitrogen within the limits of canopy water storage and soil to canopy water transport (Williams et al, 1996). A C mass balance model as described in Williams et al (2005) has been added to SPA, and a C 4 photosynthesis model based on Collatz et al (1992) was integrated.…”

Section: Crop Model: Structure Parameterization and Initial Conditionsmentioning

confidence: 99%

“…However, no study so far has attempted to assimilate RS time series of various field patches into a crop model simulating agroecosystem C exchange. In a recent study (West et al, 2010), cropland inventory data were combined with the MODIS land cover product to calculate spatially resolved estimates of C budgeting for the conterminous USA. However, as no mechanistic crop model has been applied and consequently no MODIS data were assimilated, the authors could not provide estimates of the seasonality of upscaled C fluxes.…”

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confidence: 99%

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A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS

et al. 2013

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Agroecosystem models are strongly dependent on information on land management patterns for regional applications. Land management practices play a major role in determining global yield variability, and add an anthropogenic signal to the observed seasonality of atmospheric CO₂ concentrations. However, there is still little knowledge on spatial and temporal variability of important farmland activities such as crop sowing dates, and thus these remain rather crudely approximated within carbon cycle studies. In this study, we present a framework allowing for spatio-temporally resolved simulation of cropland carbon fluxes under observational constraints on land management and canopy greenness. We apply data assimilation methodology in order to explicitly account for information on sowing dates and model leaf area index. MODIS 250 m vegetation index data were assimilated both in batch-calibration for sowing date estimation and sequentially for improved model state estimation, using the ensemble Kalman filter (EnKF), into a crop carbon mass balance model (SPAc). In doing so, we are able to quantify the multiannual (2000–2006) regional carbon flux and biometry seasonality of maize–soybean crop rotations surrounding the Bondville Ameriflux eddy covariance site, averaged over 104 pixel locations within the wider area. (1) Validation at the Bondville site shows that growing season C cycling is simulated accurately with MODIS-derived sowing dates, and we expect that this framework allows for accurate simulations of C cycling at locations for which ground-truth data are not available. Thus, this framework enables modellers to simulate current (i.e. last 10 yr) carbon cycling of major agricultural regions. Averaged over the 104 field patches analysed, relative spatial variability for biometry and net ecosystem exchange ranges from ∼7% to ∼18%. The annual sign of net biome productivity is not significantly different from carbon neutrality. (2) Moreover, observing carbon cycling at one single field with its individual sowing pattern is not sufficient to constrain large-scale agroecosystem carbon flux seasonality. Study area average growing season length is 20 days longer than observed at Bondville, primarily because of an earlier estimated start of season. (3) For carbon budgeting, additional information on cropland soil management and belowground carbon cycling has to be considered, as such constraints are not provided by MODIS

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Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory‐based carbon accounting

Cited by 94 publications

References 33 publications

Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe

Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe

Sustainability Indicators of Biomass Production in Agroforestry Systems

A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS

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