Productivity and Carbon Dioxide Exchange of Leguminous Crops: Estimates from Flux Tower Measurements

Gilmanov, Tagir G.; Baker, John M.; Bernacchi, Carl J.; Billesbach, David P.; Burba, George; Castro, Saulo; Chen, Jiquan; Eugster, Werner; Fischer, M. L.; Gamon, John A.; Gebremedhin, Maheteme; Glenn, Aaron J.; Griffis, Timothy J.; Hatfield, Jerry L.; Heuer, Mark; Howard, Daniel M.; Leclerc, Monique Y.; Loescher, Henry W.; Marloie, Oliver; Meyers, Tilden P.; Olioso, Albert; Phillips, R.; Prueger, John H.; Skinner, R. Howard; Suyker, Andrew E.; Tenuta, Mario; Wylie, Bruce K.

doi:10.2134/agronj2013.0270

Cited by 45 publications

(37 citation statements)

References 36 publications

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“…Additionally, the exact definition and parameterization of the LUE model may itself affect observations regarding light-use efficiency. Many studies define LUE as the flux rate divided by PAR irradiance (incident PPFD) [71,77], causing the efficiency term to be confounded with green canopy structure. In deciduous canopies, this definition of the model terms can lead to large seasonal variations in apparent efficiency that may actually be caused by variations in green canopy structure (the APAR green term of our model), not efficiency as defined in our analysis.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

et al. 2016

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This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012-2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived: (1) a narrow-band NDVI (NDVI 680,800 , derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO 2 flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against f APAR green (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APAR green (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APAR green rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems.

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Section: Discussionmentioning

confidence: 99%

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

et al. 2016

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“…We used NEP at the 30 min time step from the flux towers to quality control for possible instrument related outliers. The quality controlled carbon flux data sets were partitioned into gross primary production (GPP) and ecosystem respiration (Re) components utilizing light response and vapor pressure-based partitioning of carbon flux into GPP and Re components [2][3][4]. Partitioning the fluxes into GPP and Re facilitated a more functional filling of temporary flux tower data gaps [24].…”

Section: Flux Tower Datamentioning

confidence: 99%

“…Regional syntheses of carbon flux tower data [2][3][4] provide geographically referenced estimates from multiple flux towers and offer detailed summarization of carbon flux properties.…”

Section: Introductionmentioning

confidence: 99%

“…Throughout modern history, scientists have been developing models to formulate generalized algorithms to expand geographically and temporally sparse field observations to a much larger landscape means [2][3][4] or to make up-scaled maps of the measured field characteristics [5]. Understanding, mapping, and quantifying regional carbon flux magnitudes and variability of terrestrial non-point carbon sinks (removal of atmospheric carbon) and sources (emission of carbon to the atmosphere) is important in the promotion of ecosystem sustainability.…”

Section: Introductionmentioning

confidence: 99%

“…The primary objective of this study was to combine measurements acquired at flux towers with applicable remote sensing, weather, and other biogeophysical data to develop separate grassland and cropland ecologically-based net ecosystem production (NEP) models and derive mean daily NEP maps at a weekly time step from 2000 to 2008 for the study area of interest, the U.S. Great Plains (Figure 1). Although a flux tower measures only its surrounding vicinity, numerous studies have shown that these recordings can be used in regional or land cover specific synthesis [3,4] or up-scaled to much greater levels through analyses of geospatial data relationships using computer modeling [9,13,18]. Of particular interest is the use of data mining regression tree algorithms for carbon flux mapping [5,10,11,13,14,18].…”

Section: Introductionmentioning

confidence: 99%

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Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis

et al. 2016

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This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000-2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m −2 ·year −1 ) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type.

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Carbon use efficiency of hayed alfalfa and grass pastures in a semiarid environment

2018

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Management, environment, and agroecosystem type are key factors influencing photosynthetic carbon (C) uptake and C use efficiency (CUE), calculated as the ratio of net ecosystem production to gross ecosystem production (NEP:GEP). Current literature has mainly emphasized annual C balance in studies involving multiple years with continuous monitoring of ecosystem C fluxes, yet CUE has not been thoroughly analyzed during the growing season, particularly in paired comparisons of contrasting types of pasture under semiarid conditions. From 2009 through 2013, we used eddy covariance method to determine daily, seasonal, and annual C budgets in rainfed alfalfa (Medicago sativa L.) and grass ecosystems subjected to periodic harvest (haying) near Mandan, North Dakota, USA. We found consistently higher magnitudes of C fluxes (ecosystem respiration [ER], NEP, GEP) and hay production in alfalfa than grassland. Leaf area and canopy nitrogen content per unit land area were key driving factors for daily, seasonal, and annual differences in C fluxes between agroecosystems. Net ecosystem C balance indicated C losses occurred through haying in both ecosystems, though no changes in soil C stocks were detected in either ecosystem over the course of the study. Mean NEP:GEP ratios (±standard error) during periods of steady carbon dioxide (CO2) uptake before and after haying were 0.43 ± 0.01 and 0.26 ± 0.03 for alfalfa and grassland, respectively, implying more efficient C use in the former. Moreover, alfalfa had consistently greater CUE than grassland despite variations in sunlight, temperature, and precipitation within and between growing seasons. Ratios of ER to GEP were also repeatedly lower in alfalfa than grassland in all five growing seasons. Under drought conditions, we infer alfalfa roots accessed water in the soil profile unavailable to more shallow‐rooted grass species. Overall, hayed alfalfa was more efficient and tolerant than grassland in assimilating and using atmospheric CO2 under variable intra‐ and inter‐seasonal conditions. Outcomes from this study suggest the inclusion of alfalfa in unirrigated crop rotations can sustain high CUE, C uptake, and hay production while mitigating C losses in a semiarid environment.

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Productivity and Carbon Dioxide Exchange of Leguminous Crops: Estimates from Flux Tower Measurements

Abstract: All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Cited by 45 publications

References 36 publications

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis

Carbon use efficiency of hayed alfalfa and grass pastures in a semiarid environment

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