[Show abstract][Hide abstract] ABSTRACT: Significant climate risks are associated with a positive carbon–temperature feedback in northern latitude carbon-rich ecosystems, making an accurate analysis of human impacts on the net greenhouse gas balance of wetlands a priority. Here, we provide a coherent assessment of the climate footprint of a network of wetland sites based on simultaneous and quasi-continuous ecosystem observations of CO2 and CH4 fluxes. Experimental areas are located both in natural and in managed wetlands and cover a wide range of climatic regions, ecosystem types, and management practices. Based on direct observations we predict that sustained CH4 emissions in natural ecosystems are in the long term (i.e., several centuries) typically offset by CO2 uptake, although with large spatiotemporal variability. Using a space-for-time analogy across ecological and climatic gradients, we represent the chronosequence from natural to managed conditions to quantify the “cost” of CH4 emissions for the benefit of net carbon sequestration. With a sustained pulse–response radiative forcing model, we found a significant increase in atmospheric forcing due to land management, in particular for wetland converted to cropland. Our results quantify the role of human activities on the climate footprint of northern wetlands and call for development of active mitigation strategies for managed wetlands and new guidelines of the Intergovernmental Panel on Climate Change (IPCC) accounting for both sustained CH4 emissions and cumulative CO2 exchange.
Proceedings of the National Academy of Sciences 03/2015; 112(15). DOI:10.1073/pnas.1416267112 · 9.67 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Photosynthesis (PSN) is a pigment level process in which antenna pigments (predominately chlorophylls) in chloroplasts absorb photosynthetically active radiation (PAR) for the photochemical process. PAR absorbed by foliar non-photosynthetic components is not used for PSN. The fraction of PAR absorbed (fAPAR) by a canopy/vegetation (i.e., fAPARcanopy) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images, referred to as MOD15A2 FPAR, has been used to compute absorbed PAR (APAR) for PSN (APARPSN) which is utilized to produce the standard MODIS gross primary production (GPP) product, referred to as MOD17A2 GPP. In this study, the fraction of PAR absorbed by chlorophyll throughout the canopy (fAPARchl) was retrieved from MODIS images for three AmeriFlux crop fields in Nebraska. There are few studies in the literature that compare the performance of MOD15A2 FPAR versus fAPARchl in GPP estimation. In our study MOD15A2 FPAR and the retrieved fAPARchl were compared with field fAPARcanopy and the fraction of PAR absorbed by green leaves of the vegetation (fAPARgreen). MOD15A2 FPAR overestimated field fAPARcanopy in spring and in fall, and underestimated field fAPARcanopy in midsummer whereas fAPARchl correctly captured the seasonal phenology. The retrieved fAPARchl agreed well with field fAPARgreen at early crop growth stage in June, and was less than field fAPARgreen in late July, August and September. GPP estimates with fAPARchl and with MOD15A2 FPAR were compared to tower flux GPP. GPP simulated with fAPARchl was corroborated with tower flux GPP. Improvements in crop GPP estimation were achieved by replacing MOD15A2 FPAR with fAPARchl which also reduced uncertainties of crop GPP estimates by 1.12–2.37 g C m− 2 d− 1.
[Show abstract][Hide abstract] ABSTRACT: There is a continued need for models to improve consistency and agreement with observations [Friedlingstein et al., 2006], both overall and under more frequent extreme climatic events related to global environmental change such as drought [Trenberth et al., 2007]. Past validation studies of terrestrial biosphere models have focused only on few models and sites, typically in close proximity and primarily in forested biomes [e.g., Amthor et al., 2001; Delpierre et al., 2009; Grant et al., 2005; Hanson et al., 2004; Granier et al., 2007; Ichii et al., 2009; Ito, 2008; Siqueira et al., 2006; Zhou et al., 2008]. Furthermore, assessing model‐data agreement relative to drought requires, in addition to high‐quality observedCO2 exchange data, a reliable drought metric as well as a natural experiment across sites and drought conditions.
Journal of Geophysical Research Atmospheres 02/2013; 115. DOI:10.1029/2009JG001229 · 3.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Precipitation is one of the most important climate factors that can affect the gross ecosystem production (GEP) of terrestrial ecosystems. Positive impacts of precipitation on annual GEP have been reported for vegetated areas worldwide. However, little is known about the influence of precipitation intensity on GEP, especially at the monthly to seasonal temporal scale. Here we show that monthly GEP is insensitive to the sum of monthly total precipitation (Ps, mm), but positively correlated to precipitation intensity (Pa, mm), defined as the average precipitation per event from half-hourly measurements over a month. Different plant functional types (PFTs) exhibit substantial differences in the sensitivity of monthly GEP to Pa. PFTs of water-limited regions responded more intensely than those in mesic environments, as demonstrated by a negative correlation between the slope of the GEP-Pa regression line and average Pa. Furthermore, this slope increases with latitude, indicating higher sensitivity of GEP to Pa for boreal ecosystems than for temperate regions. Therefore, we anticipate increased intensity of storms, as projected by some climate models, may impart a previously overlooked positive impact on precipitation intensity on GEP.
Global and Planetary Change 01/2013; 100:204–214. DOI:10.1016/j.gloplacha.2012.10.019 · 2.77 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The objective of this study is to examine interannual variability of carbon dioxide exchange and relevant controlling factors in irrigated and rainfed maize–soybean agroecosystems. The mean annual gross primary production (GPP) of irrigated and rainfed maize was 1796 ± 92 g C m−2 y−1 (±standard deviation) and 1536 ± 74 g C m−2 y−1, respectively. Mean annual GPP of soybean (average of irrigated and rainfed crops) was about 56% that of maize. Light use efficiency of maize and soybean during clear sky conditions were 1.96 ± 0.10 and 1.37 ± 0.06 g C MJ−1, respectively. A light use efficiency model, incorporating sensitivity to diffuse light, provided a reasonable simulation of daily GPP of maize and soybean (r2 = 0.89–0.98 and 0.85–0.97, respectively). Simulated growing season GPP totals were within about 10% of the measured values. The green leaf area index (LAI) played a dominant role in explaining interannual variability of GPP in maize. For soybean, both LAI and PAR contributed to the interannual variability. Mean growing season ecosystem respiration (Re) totals were 1029 ± 46 g C m−2 for irrigated maize and 872 ± 29 g C m−2 for rainfed maize. The growing season Re total of soybean (average of irrigated and rainfed crops) was about 78% that of maize. A relationship, based on a reference soil respiration (Re20), air temperature (Ta), and LAI, simulated daily growing season Re reasonably well for maize and soybean (r2 = 0.77–0.91 and 0.51–0.94, respectively). Modeled Re totals during the growing season were generally within 10% of the measured values. Variations in the LAI and Re20 explained the majority of the interannual variability in growing season Re for maize. In addition to LAI and Re20, Ta also contributed to the soybean Re variability. Non growing season Re contributed 10–20% and 17–24% of annual Re in maize and soybean, respectively and was primarily controlled by air temperature and residue biomass (r2 ∼ 81%). About 70% of maize GPP was lost in Re, resulting in the mean annual net ecosystem CO2 production (NEP) of 552 ± 73 g C m−2 y−1 for irrigated maize and 471 ± 52 g C m−2 y−1 for rainfed maize. For soybean, however, most of the annual GPP was lost in Re resulting in a mean annual NEP of −57 ± 43 and 10 ± 52 g C m−2 y−1 for irrigated and rainfed soybean, respectively. In general, as compared to Re, GPP contributed more to explaining the departures (ΔNEP) of NEP from the 4-year mean for maize. Both GPP and Re contributed to the ΔNEP for soybean. Results on the net biome production (NBP) indicated that the irrigated maize–soybean rotation was initially a moderate source of carbon; however, the system appears to be approaching near C neutral recently. The rainfed maize–soybean rotation is approximately C neutral.
[Show abstract][Hide abstract] ABSTRACT: Efforts for increasing soil organic matter (SOM) content under agricultural systems have primarily focused on management practices that reduce exposure of SOM to decomposition via minimum tillage. We assess an alternative approach, termed "fall conservation deep tillage" (FCDT), to SOM stabilization through fall incorporation of crop residues into the soil profile together with N fertilizer. In an eastern Nebraska field under irrigated maize (Zea mays L.), we measured total soil C and N stocks on an equal soil mass basis (0- to 30-cm depth) and the composition of four SOM fractions after 14 yr of previous no-till management and 1, 2, and 3 yr after conversion to FCDT. After 3 yr of FCDT, redistribution of soil C, N, and soil organic matter fractions occurred within the soil profile; however, total soil C and N stocks remained unchanged. An increase in the soil-crop residue contact surface through FCDT increased free light fraction by 170% in deeper strata in the soil profile. Change from no-till to FCDT led to initial reduction of C and N stocks as mobile and calcium bound humic acid fractions after 1 yr of tillage, suggesting enhanced decomposition and/or condensation. By the second and third years of FCDT, stabilization of crop residues into these humified fractions was significant. The trends observed after 3 yr of FCDT at field-scale match prior plot-scale experiments from the same region and suggest that a positive balance of soil C and N accrual and loss may be achieved by enhancing the soil-residue contact surface in these soils.
Soil Science Society of America Journal 11/2012; 76(6):2154. DOI:10.2136/sssaj2012.0121 · 1.72 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Estimating seasonal evapotranspiration (ET) has many applications in water resources planning and management, including hydrological and ecological modeling. Availability of satellite remote sensing images is limited due to repeat cycle of satellite or cloud cover. This study was conducted to determine the suitability of different methods namely cubic spline, fixed, and linear for estimating seasonal ET from temporal remotely sensed images. Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model in conjunction with the wet METRIC (wMETRIC), a modified version of the METRIC model, was used to estimate ET on the days of satellite overpass using eight Landsat images during the 2001 crop growing season in Midwest USA. The model-estimated daily ET was in good agreement (R
2 = 0.91) with the eddy covariance tower-measured daily ET. The standard error of daily ET was 0.6 mm (20%) at three validation sites in Nebraska, USA. There was no statistically significant difference (P > 0.05) among the cubic spline, fixed, and linear methods for computing seasonal (July–December) ET from temporal ET estimates. Overall, the cubic spline resulted in the lowest standard error of 6 mm (1.67%) for seasonal ET. However, further testing of this method for multiple years is necessary to determine its suitability.
[Show abstract][Hide abstract] ABSTRACT: In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists
of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate
green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was
installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research
and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and
two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera.
The other vegetation index called Night-time Relative Brightness Index in NIR (NRBINIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was
attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field.
The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately
estimate green LAI (R2=0.99) and green leaf biomass (R2=0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate
total LAI (R2=0.97). The NRBINIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R2=0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical
parameters at low cost.
–Night-time flash image–Dry biomass–Crop phenology
[Show abstract][Hide abstract] ABSTRACT: Crop physiological and phenological status is an important factor that characterizes crop yield as well as carbon exchange between the atmosphere and the terrestrial biosphere in agroecosystems. It is difficult to establish high frequency observations of crop status in multiple locations using conventional approaches such as agronomical sampling and also remote sensing techniques that use spectral radiometers because of the labor intensive work required for field surveys and the high cost of radiometers designed for scientific use. This study explored the potential utility of an inexpensive camera observation system called crop phenology recording system (CPRS) as an alternative approach for the observation of seasonal change in crop growth. The CPRS consisting of two compact digital cameras was used to capture visible and near infrared (NIR) images of maize in 2009 and soybean in 2010 for every hour both day and night continuously. In addition, a four channel sensor SKYE measured crop reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were acquired over crop fields. The six different camera- radiometer- and MODIS-derived vegetation indices (VIs) were calculated and compared with the ground-measured crop biophysical parameters. In addition to VIs that use digital numbers, we proposed to use daytime exposure value-adjusted VIs. The camera-derived VIs were compared with the VIs calculated from spectral reflectance observations taken by SKYE and MODIS. It was found that new camera-derived VIs using daytime exposure values are closely related to VIs calculated using SKYE and MODIS reflectance and good proxies of crop biophysical parameters. Camera-derived green chlorophyll index, simple ratio and NDVI were found to be able to estimate the total leaf area index (LAI) of maize and soybean with high accuracy and were better than the widely used 2g-r-b. However, camera-derived 2g-r-b showed the best accuracy in estimating daily fAPAR in vegetative and reproductive stages of both crops. Visible atmospherically resistant vegetation index showed the highest accuracy in the estimation of the green LAI of maize. A unique VI, calculated from nighttime flash NIR images called the nighttime relative brightness index of NIR, showed a strong relationship with total aboveground biomass for both crops. The study concludes that the CPRS is a practical and cost-effective approach for monitoring temporal changes in crop growth, and it also provides an alternative source of ground truth data to validate time-series VIs derived from MODIS and other satellite systems.
[Show abstract][Hide abstract] ABSTRACT: Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20-120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2-20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.
[Show abstract][Hide abstract] ABSTRACT: Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security.
[Show abstract][Hide abstract] ABSTRACT: More accurate projections of future carbon dioxide concentrations in the atmosphere and associated climate change depend on improved scientific understanding of the terrestrial carbon cycle. Despite the consensus that U.S. terrestrial ecosystems provide a carbon sink, the size, distribution, and interannual variability of this sink remain uncertain. Here we report a terrestrial carbon sink in the conterminous U.S. at 0.63 pg C yr−1 with the majority of the sink in regions dominated by evergreen and deciduous forests and savannas. This estimate is based on our continuous estimates of net ecosystem carbon exchange (NEE) with high spatial (1 km) and temporal (8-day) resolutions derived from NEE measurements from eddy covariance flux towers and wall-to-wall satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS). We find that the U.S. terrestrial ecosystems could offset a maximum of 40% of the fossil-fuel carbon emissions. Our results show that the U.S. terrestrial carbon sink varied between 0.51 and 0.70 pg C yr−1 over the period 2001–2006. The dominant sources of interannual variation of the carbon sink included extreme climate events and disturbances. Droughts in 2002 and 2006 reduced the U.S. carbon sink by ∼20% relative to a normal year. Disturbances including wildfires and hurricanes reduced carbon uptake or resulted in carbon release at regional scales. Our results provide an alternative, independent, and novel constraint to the U.S. terrestrial carbon sink.Research highlights▶ We report a terrestrial carbon sink in the conterminous U.S. at 0.63 pg C yr−1. ▶ U.S. carbon sink varied between 0.51 and 0.70 pg C yr−1 over the period 2001–2006. ▶ The severe droughts in 2002 and 2006 substantially reduced the U.S. carbon sink. ▶ Disturbances reduced carbon uptake or resulted in carbon release at regional scales. ▶ Our results provide an alternative and novel constraint to the U.S. carbon sink.
[Show abstract][Hide abstract] ABSTRACT: The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO2) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.
[Show abstract][Hide abstract] ABSTRACT: There is a growing interest in the estimation of gross primary productivity (GPP) in crops due to its importance in regional and global studies of carbon balance. We have found that crop GPP was closely related to its total chlorophyll content, and thus chlorophyll can be used as a proxy of GPP in crops. In this study, we tested the performance of various vegetation indices for estimating GPP. The indices were derived from spectral data collected remotely but at close-range over a period of eight years, from 2001 through 2008. The results show that chlorophyll indices, based on near infrared and either the green or red-edge regions of the spectrum, are capable of accurately predicting widely variable GPP in maize under both rainfed and irrigated conditions.
[Show abstract][Hide abstract] ABSTRACT: The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.
[Show abstract][Hide abstract] ABSTRACT: Continuous measurements of CO2 and water vapor exchanges made in three cropping systems (irrigated continuous maize, irrigated maize–soybean rotation, and rainfed maize–soybean rotation) in eastern Nebraska, USA during 6 years are discussed. Close coupling between seasonal distributions of gross primary production (GPP) and evapotranspiration (ET) were observed in each growing season. Mean growing season totals of GPP in irrigated maize and soybean were 1738 ± 114 and 996 ± 69 g C m−2, respectively (±standard deviation). Corresponding mean values of growing season ET totals were 545 ± 27 and 454 ± 23 mm, respectively. Irrigation affected GPP and ET similarly, both growing season totals were about 10% higher than those of corresponding rainfed crops. Maize, under both irrigated and rainfed conditions, fixed 74% more carbon than soybean while using only 12–20% more water. The green leaf area index (LAI) explained substantial portions (91% for maize and 90% for soybean) of the variability in GPPPAR (GPP over a narrow range of incident photosynthetically active radiation) and in ET/ETo (71% for maize and 75% for soybean, ETo is the reference evapotranspiration). Water productivity (WP or water use efficiency) is defined here as the ratio of cumulative GPP or above-ground biomass and ET (photosynthetic water productivity = ∑GPP/∑ET and biomass water productivity = above-ground biomass/∑ET). When normalized by ETo, the photosynthetic water productivity (WPETo) was 18.4 ± 1.5 g C m−2 for maize and 12.0 ± 1.0 g C m−2 for soybean. When normalized by ETo, the biomass water productivity (WPETo) was 27.5 ± 2.3 g DM m−2 for maize and 14.1 ± 3.1 g DM m−2 for soybean. Comparisons of these results, among different years of measurement and management practices (continuous vs rotation cropping, irrigated vs rainfed) in this study and those from other locations, indicated the conservative nature of normalized water productivity, as also pointed out by previous investigators.
[Show abstract][Hide abstract] ABSTRACT: Carbon dioxide fluxes are being measured in three maize-based agroecosystems in eastern Nebraska in an effort to better understand the potential for these systems to sequester carbon in the soil. Landscape-level fluxes of carbon, water and energy were measured using tower eddy covariance systems. In order to better understand the landscape-level results, measurements at smaller scales, using techniques promoted by John Norman, were made and scaled up to the landscape-level. Single leaf gas exchange properties (CO2 assimilation rate and stomatal conductance) and optical properties, direct and diffuse radiation incident on the canopy, and photosynthetically active radiation (PAR) reflected and transmitted by the canopy were measured at regular intervals throughout the growing season. In addition, soil surface CO2 fluxes were measured using chamber techniques. From leaf measurements, the responses of net CO2 assimilation rate to relevant biophysical controlling factors were quantified. Single leaf gas exchange data were scaled up to the canopy level using a simple radiative model that considers direct beam and diffuse PAR penetration into the canopy. Canopy level photosynthesis was estimated, coupled with the soil surface CO2 fluxes, and compared to measured net ecosystem CO2 exchange (NEE) values from the eddy covariance approach. Estimated values of canopy level absorbed PAR was also compared to measured values. The agreement between estimated and observed values increases our confidence in the measured carbon pools and fluxes in these agroecosystems and enhances our understanding of biophysical controls on carbon sequestration.