As global atmospheric CO2 rapidly approaches a key tipping point, there is an urgent need to implement strategies to reverse this pattern. A generally accepted understanding of carbon (C) in agricultural fields includes: (H1) substantial C loss occurs when natural vegetation is converted to crops, (H2) soils typically reach a steady-state C concentration under contemporary practices, and (H3) improved management or crop selection can enhance soil C stocks over time. Significant variability exists, but studies consistently show large C losses from agricultural ecosystems, supporting H1. Although steady-state C levels (H2) are commonly assumed, measuring C gains or losses in mature agroecosystems is challenging. Efforts to increase soil C storage (H3) have limited data due to the diversity of potential practices, compounded by substantial variability in soil C measurements. Here, long-term (7-17 year) ecosystem C flux data from diverse cropping systems revealed that conventionally tilled annual row crops (maize and soybean) act as significant long-term atmospheric C sources, challenging H2. Furthermore, conservation tillage practices reduced C losses compared with conventional tillage but showed minimal evidence for long-term ecosystem C storage, even after 20+ years. This indicates that no-till practices reduce C losses but imply that no soil C is added, challenging H3. By contrast, perennial Miscanthus × giganteus, Panicum virgatum, and restored tallgrass prairie systems store C at the ecosystem scale more effectively than minimally tilled annual row crops. Analysis over multiple years demonstrates significant ecosystem C storage with perennial crops, varying by species, starting in the first year of transition. These findings, although focused on one region, suggest that the assumptions of steady-state C levels and increased storage from conservation practices do not universally apply and that significant changes to agroecosystems are required to increase C storage.

ABSTRACT Bioenergy from biofuels has the potential to slow growing atmospheric carbon dioxide concentrations by reducing fossil fuel use. However, growing bioenergy feedstocks is a land‐intensive process. In the United States, the recent expansion of maize bioethanol has presented some environmental costs, prompting the development of several alternative bioenergy feedstocks. These feedstocks, selected in part for traits associated with ecosystem services, may provide opportunities for environmental benefits beyond fossil fuel displacement. We hypothesized that these bioenergy ecosystems will provide direct climatic cooling through their influence on carbon and radiative energy fluxes (i.e., through albedo). To test this hypothesis, we investigated the potential cooling effect of five current or potential bioenergy feedstocks using multi‐year records from eddy covariance towers. Perennial feedstocks were carbon sinks, with an annual mean net ecosystem carbon balance (NECB) of −2.7 ± 2.1 Mg C ha−1 for miscanthus, −0.8 ± 1.1 Mg C ha−1 for switchgrass, and −1.4 ± 0.7 Mg C ha−1 for prairie. In contrast, annual rotations were generally carbon sources, with an annual mean NECB of 2.6 ± 2.4 Mg C ha−1 for maize‐soy and 3.2 ± 2.1 Mg C ha−1 for sorghum‐soy. Using maize‐soy as a baseline, conversion to alternative feedstocks increased albedo, inducing further cooling. This effect was strongest for miscanthus, with −3.5 ± 2.0 W m−2 of radiative forcing, and weakest for sorghum, with −1.4 ± 1.4 W m−2. When feedstock effects on carbon and albedo were compared using carbon equivalents, carbon fluxes were the stronger ecosystem effect, underscoring the role of perennial species as effective carbon sinks. This work highlights the impact of feedstock choice on ecosystem processes as an element of bioenergy land conversion strategies.

Stomatal regulation plays a critical role in controlling tree water loss and mediating atmosphere–vegetation–soil water coupling, yet the implications of species-specific differences in stomatal regulation on this coupling remain poorly understood. Drimys species possess primitive leaf anatomy with limited stomatal closure capacity, while Nothofagus exhibits more effective stomatal control. We compared multi-year sap flux data from these two co-occurring Southern Chilean species to evaluate how stomatal traits influence water-coupling dynamics across timescales. Using boosted regression tree modeling and wavelet coherence analysis, we found that while both species showed similar functional responses to environmental drivers, the relative importance of these drivers differed between them. Both Drimys and Nothofagus responded to VPD, but Drimys sap flux was more strongly influenced by soil moisture, particularly during early season wet periods and late-season drought. In contrast, Nothofagus showed greater dependence on light and vapor pressure deficit (VPD), reflecting tighter stomatal regulation. Wavelet coherence analyses further confirmed stronger soil moisture control of sap flux in Drimys, especially at weekly to sub-seasonal timescales, and provided evidence that stomatal regulation can either buffer or amplify late-season soil moisture deficits. These findings suggest Drimys follows a high water use, low-conservation strategy closely tied to soil moisture, whereas Nothofagus demonstrates more conservative water use governed by atmospheric conditions. The strong soil moisture dependence of Drimys may increase its vulnerability to future warming and drying trends, with implications for forest composition and hydrological modeling in a changing climate.

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs.

A generally accepted understanding of carbon in agricultural fields is that (1) a significant amount of carbon was lost from soils when land was converted from natural vegetation to agricultural ecosystems, (2) over time a steady-state soil carbon concentration is reached with standard agronomic practices, and (3) that improved management practices or optimal crop species selection can result in increased carbon storage over time. The extent of the carbon lost from conversion from native to agricultural ecosystems is variable, but multiple studies show that carbon losses were large and consistent (supporting point 1). While an assumption of steady state is generally accepted (point 2), there are significant challenges in measuring whether mature agroecosystems are gaining or losing carbon. Multiple strategies are continually proposed to increase soil carbon storage (point 3), yet data is sparce given the plentitude of potential opportunities being considered. Furthermore, direct analysis of soil carbon is prone to substantial heterogeneity, leading to challenge in detecting signals relative to noise in heterogeneous soil environments. Eddy covariance measurements, while unable to measure pools of carbon in the soil directly, can infer changes in ecosystem carbon storage when measurements are integrated over time. Here, long-term eddy covariance datasets were used to measure carbon and water fluxes over agricultural ecosystems in the Central Midwestern United States over multiple years for different crop and management practices. The measurements show that conventionally tilled Midwestern row crops, maize and soybean, integrated on a rotational basis typical for this region represents a large carbon source to the atmosphere, counter to point 2 above. However, similar measurements over the same agroecosystem but with conservation tillage practices indicate long-term carbon storage, even after 20+ years of conservation tillage with significant climate variability. These results suggest that large-scale adoption of no-till practices can significantly reduce the large-scale losses of carbon associated with conventional tillage and, potentially, lead to small but meaningful increases in soil carbon storage, supporting point 3. Similar measurements were also collected over perennial grass ecosystems with potential for bioenergy production, including Miscanthus giganteus, Panicum virgatum, and high species diversity prairie. While these agricultural ecosystems show promise for offsetting fossil carbon emissions, they are also predicted to be better at storing carbon than annual row crops. Multiple years of analysis from these ecosystems show that perennial bioenergy crops are much more likely to lead to ecosystem carbon storage than minimally-tilled annual row crops as early as the first year of transition, but the amount of storage varies based on which species is planted. While this research is focused on one location, the results suggest that the assumptions of steady-state and increases in storage over time may not hold for all agricultural ecosystems and in all locations.

As we face today's large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model's soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system.

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.

Sustained increase in atmospheric CO 2 is strongly coupled with rising temperature and persistent droughts. While elevated CO 2 promotes photosynthesis and growth of vegetation, drier and warmer climate can potentially negate this benefit, complicating the prediction of future terrestrial carbon dynamics. Manipulative studies such as free air CO 2 enrichment (FACE) experiments have been useful for studying the joint effect of global change factors on vegetation growth; however, their results do not easily transfer to natural ecosystems partly due to their short-duration nature and limited consideration of climatic gradients and potential confounding factors, such as O 3 . Urban environments serve as a useful small-scale analogy of future climate at least in reference to CO 2 and temperature enhancements. Here, we develop a data-driven approach using urban environments as test beds for revealing the joint effect of changing temperature and CO 2 on vegetation response to drought. Using 75 urban-rural paired plots from three climate zones over the conterminous United States (CONUS), we find vegetation in urban areas exhibits a much stronger resistance to drought than in rural areas. Statistical analysis suggests the drought resistance enhancement of urban vegetation across CONUS is attributed to rising temperature (with a partial correlation coefficient of 0.36) and CO 2 (with a partial correlation coefficient of 0.31) and reduced O 3 concentration (with a partial correlation coefficient of −0.12) in cities. The controlling factor(s) responsible for urban-rural differences in drought resistance of vegetation vary across climate regions, such as surface O 3 gradients in the arid climate, and surface CO 2 and O 3 gradients in the temperate and continental climates. Thus, our study provides new observational insights on the impacts of competing factors on vegetation growth at a large scale, and ultimately, helps reduce uncertainties in understanding terrestrial carbon dynamics.

Perennial crops have been the focus of bioenergy research and development for their sustainability benefits associated with high soil carbon (C) and reduced nitrogen (N) requirements. However, perennial crops mature over several years and their sustainability benefits can be negated through land reversion. A photoperiod‐sensitive energy sorghum (Sorghum bicolor) may provide an annual crop alternative more ecologically sustainable than maize (Zea mays) that can more easily integrate into crop rotations than perennials, such as miscanthus (Miscanthus × giganteus). This study presents an ecosystem‐scale comparison of C, N, water and energy fluxes from energy sorghum, maize and miscanthus during a typical growing season in the Midwest United States. Gross primary productivity (GPP) was highest for maize during the peak growing season at 21.83 g C m−2 day−1, followed by energy sorghum (17.04 g C m−2 day−1) and miscanthus (15.57 g C m−2 day−1). Maize also had the highest peak growing season evapotranspiration at 5.39 mm day−1, with energy sorghum and miscanthus at 3.81 and 3.61 mm day−1, respectively. Energy sorghum was the most efficient water user (WUE), while maize and miscanthus were comparatively similar (3.04, 1.75 and 1.89 g C mm−1 H2O, respectively). Maize albedo was lower than energy sorghum and miscanthus (0.19, 0.26 and 0.24, respectively), but energy sorghum had a Bowen ratio closer to maize than miscanthus (0.12, 0.13 and 0.21, respectively). Nitrous oxide (N2O) flux was higher from maize and energy sorghum (8.86 and 12.04 kg N ha−1, respectively) compared with miscanthus (0.51 kg N ha−1), indicative of their different agronomic management. These results are an important first look at how energy sorghum compares to maize and miscanthus grown in the Midwest United States. This quantitative assessment is a critical component for calibrating biogeochemical and ecological models used to forecast bioenergy crop growth, productivity and sustainability.

Climate change has been implicated in the widespread 'greening' of the arctic in recent decades. However, differences in arctic greening patterns among satellite platforms and recent reports of decreased rate of greening or of browning have made attributing arctic greening trends to a warming climate challenging. Here, we compared MODIS greening trends to those predicted by the coupled carbon and nitrogen model (CCaN); a mass balance carbon and nitrogen model that was driven by MODIS surface temperature and climate. CCaN was parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite based data. Wefound that, at long temporal and large spatial scales, MODIS greening trends were consistent with ecological and biogeochemical data from arctic tundra. However, at smaller spatial scales, observations and CCaN greening trends differed in the location, extent, and magnitude of greening. CCaN was unable to capture the high rates of MODIS greening in northern wetlands, and the patchy MODIS browning in the southern portion of the North Slope. This model-data disagreement points to disturbance and its legacy impacts on land cover as an important mechanism for understanding greening trends on the North Slope of Alaska.