Quantifying the surface energy fluxes of grazed and ungrazed steppes is essential to understand the roles of grasslands in local and global climate and in land use change. We used paired ...eddy-covariance towers to investigate the effects of grazing on energy balance (EB) components: net radiation (Rn), latent heat (LE), sensible heat (H), and soil heat (G) fluxes adjacent grazed and ungrazed areas in a desert steppe of the Mongolian Plateau for a two-year period (2010-2012). Near 95% of Rn was partitioned as LE and H, whereas the contributions of G and other components of the EB were 5% at an annual scale. H dominated the energy partitioning and shared ~50% of Rn. When comparing the grazed and the ungrazed desert steppe, there was remarkably lower Rn and a lower H, but higher G at the grazed site than at the ungrazed site. Both reduced available energy (Rn - G) and H indicated a "cooling effect" feedback onto the local climate through grazing. Grazing reduced the dry year LE but enhanced the wet year LE. Energy partitioning of LE/Rn was positively correlated with the canopy conductivity, leaf area index, and soil moisture. H/Rn was positively correlated with the vapor pressure deficit but negatively correlated with the soil moisture. Boosted regression tree results showed that LE/Rn was dominated by soil moisture in both years and at both sites, while grazing shifted the H/Rn domination from temperature to soil moisture in the wet year. Grazing not only caused an LE shift between the dry and the wet year, but also triggered a decrease in the H/Rn because of changes in vegetation and soil properties, indicating that the ungrazed area had a greater resistance while the grazed area had a greater sensitivity of EB components to the changing climate.
Over 5 million ha of US Conservation Reserve Program (CRP) grasslands have been converted to annual crops since 2000, driven mainly by demand for corn grain ethanol. Much of the soil carbon ...sequestered under CRP is lost upon conversion, creating a 'carbon debt' that is presumed to be repaid by future greenhouse gas (GHG) savings from ethanol's substitution for petroleum. Model simulations, extrapolations, and national statistics rather than direct measurements have been used thus far to estimate the long-term global warming impact (GWI) of such conversions. Here we report measured GWIs for three 22-year-old CRP grassland fields and three conventionally tilled agricultural (AGR) fields (11-17 ha) converted to either annual no-till corn or perennial cellulosic (switchgrass or restored prairie) bioenergy crops. We assessed GWIs for each field over eight years using whole-system life cycle analysis (LCA) by measuring: (a) GHG fluxes via eddy covariance and static chamber methodologies, (b) farming operations and agronomic inputs, and (c) the fossil fuel offset by ethanol use. Payback times were much longer than those estimated by prior modeling efforts. After 8 years, cumulative GWIs of switchgrass, restored prairie, and corn at the CRP grasslands were, respectively, −2.6 4.0, 6.9 3.6 and 85.2 5.1 Mg CO2-equivalent ha−1. The switchgrass system had repaid its carbon debt by year eight and the restored prairie will have likely repaid by year ten; however, the no-till corn system appears likely to require >300 years. The same bioenergy crops grown on former agricultural lands, with no sequestered carbon lost on conversion, repaid their carbon debts within two years. Results indicate that GWI estimates and carbon debt payback times due to conversion of CRP lands to annual bioenergy crops have been substantially underestimated by current models.
Leaf photosynthesis of perennial grasses usually decreases markedly from early to late summer, even when the canopy remains green and environmental conditions are favorable for photosynthesis. ...Understanding the physiological basis of this photosynthetic decline reveals the potential for yield improvement. We tested the association of seasonal photosynthetic decline in switchgrass (
L.) with water availability by comparing plants experiencing ambient rainfall with plants in a rainfall exclusion experiment in Michigan, USA. For switchgrass exposed to ambient rainfall, daily net CO
assimilation (
) declined from 0.9 mol CO
m
day
in early summer to 0.43 mol CO
m
day
in late summer (53% reduction; P<0.0001). Under rainfall exclusion shelters, soil water content was 73% lower and
was 12% and 26% lower in July and September, respectively, compared to those of the rainfed plants. Despite these differences, the seasonal photosynthetic decline was similar in the season-long rainfall exclusion compared to the rainfed plants;
in switchgrass under the shelters declined from 0.85 mol CO
m
day
in early summer to 0.39 mol CO
m
day
(54% reduction; P<0.0001) in late summer. These results suggest that while water deficit limited
late in the season, abundant late-season rainfalls were not enough to restore
in the rainfed plants to early-summer values suggesting water deficit was not the sole driver of the decline. Alongside change in photosynthesis, starch in the rhizomes increased 4-fold (P<0.0001) and stabilized when leaf photosynthesis reached constant low values. Additionally, water limitation under shelters had no negative effects on the timing of rhizome starch accumulation, and rhizome starch content increased ~ 6-fold. These results showed that rhizomes also affect leaf photosynthesis during the growing season. Towards the end of the growing season, when vegetative growth is completed and rhizome reserves are filled, diminishing rhizome sink activity likely explained the observed photosynthetic declines in plants under both ambient and reduced water availability.
Abstract
Climate benefit assessments of bioenergy crops often focus on biogeochemical impacts, paying little if any attention to biogeophysical impacts. However, land conversions required for ...large-scale bioenergy crop production are substantial and may directly affect the climate by altering surface energy balance. In the US, such land conversions are likely to be met in part by converting Conservation Reserve Program (CRP) grassland to bioenergy crops. Here, we converted three 22 year old CRP smooth brome grass fields into no-till corn, switchgrass, or restored prairie bioenergy crops. We assessed the biogeophysical climate impact of the conversions using albedo changes relative to unconverted reference CRP grassland. The corn and perennial fields had higher annual albedo than the grassland they replaced—causing cooling of the local climate. The cooling of the corn field occurred solely during the non-growing season—especially when surfaces were snow-covered, whereas the cooling of the perennial fields was more prominent during the growing season. Compared to biogeochemical impacts with fossil fuel offsets for the same land conversions over eight years, the annual albedo-induced climate benefits add ∼35% and ∼78% to the annual biogeochemical benefits provided from the switchgrass and restored prairie fields, respectively, and offset ∼3.3% of the annual greenhouse gas (GHG) emissions from the corn field. We conclude that albedo-induced climate mitigation from conversion of CRP lands to perennial but not annual bioenergy crops can be substantial, and future climate impact assessments of bioenergy crops should include albedo changes in addition to GHG balances in order to better inform climate policies.
Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface‐atmosphere coupling. However, methods to estimate ...ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum‐flux data. At 69 forest sites, annual ha robustly predicted site‐to‐site and year‐to‐year differences in canopy heights (R2 = 0.88, 111 site‐years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2 > 0.70 in 74 site‐years). Our results demonstrate the potential of flux‐derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large‐scale and time‐varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.
Plain Language Summary
Vegetation canopy height is a key descriptor of the Earth surface and is in use by many modeling and conservation applications. However, large‐scale and time‐varying data of canopy heights are often unavailable. This synthesis evaluates the applicability and robustness of the calculation of canopy heights from the momentum flux data measured at eddy covariance flux tower sites (i.e., meteorological observation towers with high frequency measurements of wind speed and surface fluxes). We show that the aerodynamic estimation of annual canopy heights robustly predicts the site‐to‐site and year‐to‐year differences in canopy heights across a wide variety of forests. The weekly aerodynamic canopy heights successfully capture the dynamics of vegetation canopies over growing seasons at cropland and grassland sites. Our results demonstrate the potential of aerodynamic canopy heights for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. Given the amount of data collected and the diversity of vegetation covered by the global networks of eddy covariance flux tower sites, the flux‐derived canopy height has great potential for providing a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.
Key Points
Aerodynamic canopy height can be calculated robustly and routinely from the eddy covariance momentum flux data
Our estimates match well with in situ measurements of canopy heights across a wide variety of vegetation and ecosystem types
Aerodynamic canopy height can be used to track the dynamics of vegetation canopies, including plant growth, harvest, and disturbance
Reaction temperature and space velocity for methanol conversion to light olefins over SAPO-34 catalyst were experimentally studied in a bench-scale fixed-bed reactor. Temperature was found to be the ...strongest factor that affects product C
2
:C
3
ratio. Different C
2
:C
3
ratios can be obtained by adjusting reaction temperature or controlling the deactivation state of the catalyst. Higher temperature or partially deactivated catalyst produced higher C
2
:C
3
ratios. Over SAPO-34, when the feed was composed of 20
mol% methanol and 80
mol% water the optimum reaction temperature in terms of methanol conversion, selectivity to C
2
–C
4
and catalyst deactivation rate was approximately 400
°C. Higher temperatures led to faster catalyst deactivation due to coke formation and lower selectivities to propylene and butenes; while lower temperatures led to higher selectivities to CO+CO
2 and CH
4, lower methanol conversion, and faster catalyst deactivation due to oligomer blockage of SAPO-34 pores. For the above feed, the optimum methanol weight hourly space velocity in terms of methanol conversion, selectivity to C
2
–C
4
and catalyst deactivation rate, was in the range of 2.6–3.6
h
−1. Lower methanol space velocities led to lower selectivity to C
2
–C
4
and faster catalyst deactivation; while higher methanol space velocities led to lower methanol conversion. At the optimum reaction condition, approximately 20
g of methanol can be processed on 1
g of the catalyst with C
2
–C
4
selectivity of about 90% before methanol conversion drops below 100%. By-products, CO, CO
2, and CH
4, were produced from the decomposition of methanol and DME. This decomposition could be catalyzed on basic sites on SAPO-34. The formation of the by-products depends on the competition between the basic sites (on which methanol and DME decompose to carbon oxides and methane) and acidic sites (on which methanol and DME convert into hydrocarbons).
Introduction:
Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake ...patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.
Methods:
In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (
Zea mays L
.) and soybean (
Glycine max L
.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.
Results and discussion:
The coefficient of determination (
r
2
) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated
r
2
value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops.
Land surface albedo is a significant regulator of climate. Changes in land use worldwide have greatly reshaped landscapes in the recent decades. Deforestation, agricultural development, and urban ...expansion alter land surface albedo, each with unique influences on shortwave radiative forcing and global warming impact (GWI). Here, we characterize the changes in landscape albedo-induced GWI (GWIΔα) at multiple temporal scales, with a special focus on the seasonal and monthly GWIΔα over a 19-year period for different land cover types in five ecoregions within a watershed in the upper Midwest USA. The results show that land cover changes from the original forest exhibited a net cooling effect, with contributions of annual GWIΔα varying by cover type and ecoregion. Seasonal and monthly variations of the GWIΔα showed unique trends over the 19-year period and contributed differently to the total GWIΔα. Cropland contributed most to cooling the local climate, with seasonal and monthly offsets of 18% and 83%, respectively, of the annual greenhouse gas emissions of maize fields in the same area. Urban areas exhibited both cooling and warming effects. Cropland and urban areas showed significantly different seasonal GWIΔα at some ecoregions. The landscape composition of the five ecoregions could cause different net landscape GWIΔα.
Nitrous oxide (N
2
O) is a potent greenhouse gas (GHG) contributing to global warming, with the agriculture sector as the major source of anthropogenic N
2
O emissions due to excessive fertilizer ...use. There is an urgent need to enhance regional-/watershed-scale models, such as Soil and Water Assessment Tool (SWAT), to credibly simulate N
2
O emissions to improve assessment of environmental impacts of cropping practices. Here, we integrated the DayCent model's N
2
O emission algorithms with the existing widely tested crop growth, hydrology, and nitrogen cycling algorithms in SWAT and evaluated this new tool for simulating N
2
O emissions in three agricultural systems (i.e., a continuous corn site, a switchgrass site, and a smooth brome grass site which was used as a reference site) located at the Great Lakes Bioenergy Research Center (GLBRC) scale-up fields in southwestern Michigan. These three systems represent different levels of management intensity, with corn, switchgrass, and smooth brome grass (reference site) receiving high, medium, and zero fertilizer application, respectively. Results indicate that the enhanced SWAT model with default parameterization reproduced well the relative magnitudes of N
2
O emissions across the three sites, indicating the usefulness of the new tool (SWAT-N
2
O) to estimate long-term N
2
O emissions of diverse cropping systems. Notably, parameter calibration can significantly improve model simulations of seasonality of N
2
O fluxes, and explained up to 22.5%-49.7% of the variability in field observations. Further sensitivity analysis indicates that climate change (e.g., changes in precipitation and temperature) influences N
2
O emissions, highlighting the importance of optimizing crop management under a changing climate in order to achieve agricultural sustainability goals.
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