US climate policy yields water quality cobenefits in the Mississippi Basin and Gulf of Mexico.

We utilize a coupled economy-agroecology-hydrology modeling framework to capture the cascading impacts of climate change mitigation policy on agriculture and the resulting water quality cobenefits. We analyze a policy that assigns a range of United States government's social cost of carbon estimates ($51, $76, and $152/ton of CO2-equivalents) to fossil fuel-based CO2 emissions. This policy raises energy costs and, importantly for agriculture, boosts the price of nitrogen fertilizer production. At the highest carbon price, US carbon emissions are reduced by about 50%, and nitrogen fertilizer prices rise by about 90%, leading to an approximate 15% reduction in fertilizer applications for corn production across the Mississippi River Basin. Corn and soybean production declines by about 7%, increasing crop prices by 6%, while nitrate leaching declines by about 10%. Simulated nitrate export to the Gulf of Mexico decreases by 8%, ultimately shrinking the average midsummer area of the Gulf of Mexico hypoxic area by 3% and hypoxic volume by 4%. We also consider the additional benefits of restored wetlands to mitigate nitrogen loading to reduce hypoxia in the Gulf of Mexico and find a targeted wetland restoration scenario approximately doubles the effect of a low to moderate social cost of carbon. Wetland restoration alone exhibited spillover effects that increased nitrate leaching in other parts of the basin which were mitigated with the inclusion of the carbon policy. We conclude that a national climate policy aimed at reducing greenhouse gas emissions in the United States would have important water quality cobenefits.

Long-range dependence and extreme values of precipitation, phosphorus load, and Cyanobacteria.

Extreme daily values of precipitation (1939-2021), discharge (1991-2021), phosphorus (P) load (1994-2021), and phycocyanin, a pigment of Cyanobacteria (June 1-September 15 of 2008-2021) are clustered as multi-day events for Lake Mendota, Wisconsin. Long-range dependence, or memory, is the shortest for precipitation and the longest for phycocyanin. Extremes are clustered for all variates and those of P load and phycocyanin are most strongly clustered. Extremes of P load are predictable from extremes of precipitation, and precipitation and P load are correlated with later concentrations of phycocyanin. However, time delays from 1 to 60 d were found between P load extremes and the next extreme phycocyanin event within the same year of observation. Although most of the lake's P enters in extreme events, blooms of Cyanobacteria may be sustained by recycling and food web processes.

Environmental outcomes of the US Renewable Fuel Standard.

The Renewable Fuel Standard (RFS) specifies the use of biofuels in the United States and thereby guides nearly half of all global biofuel production, yet outcomes of this keystone climate and environmental regulation remain unclear. Here we combine econometric analyses, land use observations, and biophysical models to estimate the realized effects of the RFS in aggregate and down to the scale of individual agricultural fields across the United States. We find that the RFS increased corn prices by 30% and the prices of other crops by 20%, which, in turn, expanded US corn cultivation by 2.8 Mha (8.7%) and total cropland by 2.1 Mha (2.4%) in the years following policy enactment (2008 to 2016). These changes increased annual nationwide fertilizer use by 3 to 8%, increased water quality degradants by 3 to 5%, and caused enough domestic land use change emissions such that the carbon intensity of corn ethanol produced under the RFS is no less than gasoline and likely at least 24% higher. These tradeoffs must be weighed alongside the benefits of biofuels as decision-makers consider the future of renewable energy policies and the potential for fuels like corn ethanol to meet climate mitigation goals.

Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer.

As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10-, 30-, 60-, and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (∼7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0-100%, each) in a midsized city in the Upper Midwest United States. Variability in daytime air temperature within the urban landscape averaged 3.5 °C (range, 1.1-5.7 °C). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1 °C (range, 1.2-3.0 °C), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.

Fine-Scale Analysis of the Energy-Land-Water Nexus: Nitrate Leaching Implications of Biomass Cofiring in the Midwestern United States.

As scientists seek to better understand the linkages between energy, water, and land systems, they confront a critical question of scale for their analysis. Many studies exploring this nexus restrict themselves to a small area in order to capture fine-scale processes, whereas other studies focus on interactions between energy, water, and land over broader domains but apply coarse resolution methods. Detailed studies of a narrow domain can be misleading if the policy intervention considered is broad-based and has impacts on energy, land, and agricultural markets. Regional studies with aggregate low-resolution representations may miss critical feedbacks driven by the dynamic interactions between subsystems. This study applies a novel, gridded energy-land-water modeling system to analyze the local environmental impacts of biomass cofiring of coal power plants across the upper MISO region. We use this framework to examine the impacts of a hypothetical biomass cofiring technology mandate of coal-fired power plants using corn residues. We find that this scenario has a significant impact on land allocation, fertilizer applications, and nitrogen leaching. The effects also impact regions not involved in cofiring through agricultural markets. Further, some MISO coal-fired plants would cease generation because the competition for biomass increases the cost of this feedstock and because the higher operating costs of cofiring renders them uncompetitive with other generation sources. These factors are not captured by analyses undertaken at the level of an individual power plant. We also show that a region-wide analysis of this cofiring mandate would have registered only a modest increase in nitrate leaching (just +5% across the upper MISO region). Such aggregate analyses would have obscured the extremely large increases in leaching at particular locations, as much as +60%. Many of these locations are already pollution hotspots. Fine-scale analysis, nested within a broader framework, is necessary to capture these critical environmental interactions within the energy, land, and water nexus.

Direct human influence on atmospheric CO2 seasonality from increased cropland productivity.

Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years. This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation. However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear. Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.

Observation of irrigation-induced climate change in the Midwest United States.

Irrigated agriculture alters near-surface temperature and humidity, which may mask global climate change at the regional scale. However, observational studies of irrigation-induced climate change are lacking in temperate, humid regions throughout North America and Europe. Despite unknown climate impacts, irrigated agriculture is expanding in the Midwest United States, where unconfined aquifers provide groundwater to support crop production on coarse soils. This is the first study in the Midwest United States to observe and quantify differences in regional climate associated with irrigated agricultural conversion from forests and rainfed agriculture. To this end, we established a 60 km transect consisting of 28 stations across varying land uses and monitored surface air temperature and relative humidity for 31 months in the Wisconsin Central Sands region. We used a novel approach to quantify irrigated land use in both space and time with a database containing monthly groundwater withdrawal estimates by parcel for the state of Wisconsin. Irrigated agriculture decreased maximum temperatures and increased minimum temperatures, thus shrinking the diurnal temperature range (DTR) by an average of 3°C. Irrigated agriculture also decreased the vapor pressure deficit (VPD) by an average of 0.10 kPa. Irrigated agriculture significantly decreased evaporative demand for 25% and 66% of study days compared to rainfed agriculture and forest, respectively. Differences in VPD across the land-use gradient were highest (0.21 kPa) during the peak of the growing season, while differences in DTR were comparable year-round. Interannual variability in temperature had greater impacts on differences in DTR and VPD across the land-use gradient than interannual variability in precipitation. These regional climate changes must be considered together with increased greenhouse gas emissions, changes to groundwater quality, and surface water degradation when evaluating the costs and benefits of groundwater-sourced irrigation expansion in the Midwest United States and similar regions around the world.

Scenarios reveal pathways to sustain future ecosystem services in an agricultural landscape.

Sustaining food production, water quality, soil retention, flood, and climate regulation in agricultural landscapes is a pressing global challenge given accelerating environmental changes. Scenarios are stories about plausible futures, and scenarios can be integrated with biophysical simulation models to explore quantitatively how the future might unfold. However, few studies have incorporated a wide range of drivers (e.g., climate, land-use, management, population, human diet) in spatially explicit, process-based models to investigate spatial-temporal dynamics and relationships of a portfolio of ecosystem services. Here, we simulated nine ecosystem services (three provisioning and six regulating services) at 220 × 220 m from 2010 to 2070 under four contrasting scenarios in the 1,345-km2 Yahara Watershed (Wisconsin, USA) using Agro-IBIS, a dynamic model of terrestrial ecosystem processes, biogeochemistry, water, and energy balance. We asked (1) How does ecosystem service supply vary among alternative future scenarios? (2) Where on the landscape is the provision of ecosystem services most susceptible to future social-ecological changes? (3) Among alternative future scenarios, are relationships (i.e., trade-offs, synergies) among food production, water, and biogeochemical services consistent over time? Our results showed that food production varied substantially with future land-use choices and management, and its trade-offs with water quality and soil retention persisted under most scenarios. However, pathways to mitigate or even reverse such trade-offs through technological advances and sustainable agricultural practices were apparent. Consistent relationships among regulating services were identified across scenarios (e.g., trade-offs of freshwater supply vs. flood and climate regulation, and synergies among water quality, soil retention, and climate regulation), suggesting opportunities and challenges to sustaining these services. In particular, proactive land-use changes and management may buffer water quality against undesirable future climate changes, but changing climate may overwhelm management efforts to sustain freshwater supply and flood regulation. Spatially, changes in ecosystem services were heterogeneous across the landscape, underscoring the power of local actions and fine-scale management. Our research highlights the value of embracing spatial and temporal perspectives in managing ecosystem services and their complex interactions, and provides a system-level understanding for achieving sustainability of the food-water-climate nexus in agricultural landscapes.

From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability.

Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance-based risk maps using multi-year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid (SBA), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models (GAMs). Our models showed good to excellent performance in predicting SBA occurrence and abundance (TSS = 0.70, AUC = 0.92; R2  = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm ("Zonation") to produce an abundance-based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance-based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available.

Explicit modeling of abiotic and landscape factors reveals precipitation and forests associated with aphid abundance.

Increases in natural or noncrop habitat surrounding agricultural fields have been shown to be correlated with declines in insect crop pests. However, these patterns are highly variable across studies suggesting other important factors, such as abiotic drivers, which are rarely included in landscape models, may also contribute to variability in insect population abundance. The objective of this study was to explicitly account for the contribution of temperature and precipitation, in addition to landscape composition, on the abundance of a widespread insect crop pest, the soybean aphid (Aphis glycines Matsumura), in Wisconsin soybean fields. We hypothesized that higher soybean aphid abundance would be associated with higher heat accumulation (e.g., growing degree days) and increasing noncrop habitat in the surrounding landscape, due to the presence of the overwintering primary hosts of soybean aphid. To evaluate these hypotheses, we used an ecoinformatics approach that relied on a large dataset collected across Wisconsin over a 9-year period (2003-2011), for an average of 235 sites per year (n = 2,110 fields total). We determined surrounding landscape composition (1.5-km radius) using publicly available satellite-derived land cover imagery and interpolated daily temperature and precipitation information from the National Weather Service COOP weather station network. We constructed linear mixed models for soybean aphid abundance based on abiotic and landscape explanatory variables and applied model averaging for prediction using an information theoretic framework. Over this broad spatial and temporal extent in Wisconsin, we found that variation in growing season precipitation was positively related to soybean aphid abundance, while higher precipitation during the nongrowing season had a negative effect on aphid populations. Additionally, we found that aphid populations were higher in areas with proportionally more forest but were lower in areas where minor crops, such as small grains, were more prevalent. Thus, our findings support our hypothesis that including abiotic drivers increases our understanding of crop pest abundance and distribution. Moreover, by explicitly modeling abiotic factors, we may be able to explore how variable climate in tandem with land cover patterns may affect current and future insect populations, with potentially critical implications for crop yields and agricultural food webs.

Simulated Effects of Soil Texture on Nitrous Oxide Emission Factors from Corn and Soybean Agroecosystems in Wisconsin.

Soil texture is known to have an influence on the physical and biological processes that produce NO emissions in agricultural fields, yet comparisons across soil textural types are limited by considerations of time and practicality. We used the DayCent biogeochemical model to assess the effects of soil texture on NO emissions from agriculturally productive soils from four counties in Wisconsin. We validated the DayCent model using field data from 2 yr of a long-term (approximately 20-yr) cropping systems trial and then simulated yield and NO emissions from continuous corn ( L.) and corn-soybean ( L.) cropping systems across 35 Wisconsin soil series classified as either silt loam, sandy loam, or loamy sand. Silt loam soils had the highest NO emissions of all soil types, exhibiting 80 to 158% greater mean emissions and 100 to 282% greater emission factors compared with loamy sand and sandy loam soils, respectively. The model predicts that for these soils under these cropping systems, denitrification constituted the majority of the NO flux only in the silt loam soils. However, across all soil textures, locations, and years, denitrification explained the most variation (74-98%) in total NO emissions. Our results suggest that soil texture is an important factor in determining a range of NO emission characteristics and is critical for estimating future NO emissions from agricultural fields.

Seasonal nitrous oxide and methane fluxes from grain- and forage-based production systems in wisconsin, USA.

Agriculture in the midwestern United States is a major anthropogenic source of nitrous oxide (NO) and is both a source and sink for methane (CH), but the degree to which cropping systems differ in emissions of these gases is not well understood. Our objectives were to determine if fluxes of NO and CH varied among cropping systems and among crop phases within a cropping system. We compare NO and CH fluxes over the 2010 and 2011 growing seasons from the six cropping systems at the Wisconsin Integrated Cropping Systems Trial (WICST), a 20-yr-old cropping systems experiment. The study is composed of three grain and three forage cropping systems spanning a spectrum of crop diversity and perenniality that model a wide range of realistic cropping systems that differ in management, crop rotation, and fertilizer regimes. Among the grain systems, cumulative growing season NO emissions were greater for continuous corn ( L.) (3.7 kg NO-N ha) than corn-soybean [ (L.) Merr.] (2.0 kg NO-N ha) or organic corn-soybean-wheat ( L.) (1.7 kg NO-N ha). Among the forage systems, cumulative growing-season NO emissions were greater for organic corn-alfalfa ( L.)-alfalfa (2.9 kg NO-N ha) and conventional corn-alfalfa-alfalfa-alfalfa (2.5 kg NO-N ha), and lower for rotational pasture (1.9 kg NO-N ha). Application of mineral or organic N fertilizer was associated with elevated NO emissions. Yield-scaled emissions (kg NO-N Mg) did not differ by cropping system. Methane fluxes were highly variable and no effect of cropping system was observed. These results suggest that extended and diversified cropping systems could reduce area-scaled NO emissions from agriculture, but none of the systems studied significantly reduced yield-scaled NO emissions.

Existing wetland conservation programs miss nutrient reduction targets.

Restoring wetlands will reduce nitrogen contamination from excess fertilization but estimates of the efficacy of the strategy vary widely. The intervention is often described as effective for reducing nitrogen export from watersheds to mediate bottom-level hypoxia threatening marine ecosystems. Other research points to the necessity of applying a suite of interventions, including wetland restoration to mitigate meaningful quantities of nitrogen export. Here, we use process-based physical modeling to evaluate the effects of two hypothetical, but plausible large-scale wetland restoration programs intended to reduce nutrient export to the Gulf of Mexico. We show that full adoption of the two programs currently in place can meet as little as 10% to as much as 60% of nutrient reduction targets to reduce the Gulf of Mexico dead zone. These reductions are lower than prior estimates for three reasons. First, net storage of leachate in the subsurface precludes interception and thereby dampens the percent decline in nitrogen export caused by the policy. Unlike previous studies, we first constrained riverine fluxes to match observed fluxes throughout the basin. Second, the locations of many restorable lands are geographically disconnected from heavily fertilized croplands, limiting interception of runoff. Third, daily resolution of the model simulations captured the seasonal and stormflow dynamics that inhibit wetland nutrient removal because peak wetland effectiveness does not coincide with the timing of nutrient inputs. To improve the health of the Gulf of Mexico efforts to eliminate excess nutrient, loading should be implemented beyond the field-margin wetland strategies investigated here.

Quantifying the spatiotemporal variability of nitrate in irrigation water across the Wisconsin Central Sands.

The Wisconsin Central Sands is home to large scale vegetable production on sandy soils and managed with frequent irrigation, fertigation, and widespread nitrogen fertilizer application, all of which make the region highly susceptible to nitrate loss to groundwater. While the groundwater is used as the primary source of drinking water for many communities and rural residences across the region, it is also used for irrigation. Considering the high levels of nitrate found in the groundwater, it has been proposed that growers more accurately account for the nitrate in their irrigation water as part of nitrogen management plans. Our objectives were to 1) determine the magnitude of nitrate in irrigation water, 2) quantify the spatiotemporal variability of nitrate, and 3) determine key predictors of nitrate concentration in the region. We sampled irrigation water from 38 fields across six farms from 2018 to 2020. Across the 3 years of our study, nitrate concentration varied more across space than time. On average, our samples were tested at 19.0 mg L-1 nitrate-nitrogen, or nearly two times the U.S. Environmental Protection Agency (EPA) threshold for safe drinking water, equivalent to 48.1 kg ha-1 of applied nitrate-nitrogen with 25.4 cm (or 10 in.) of irrigation. To better understand the spatiotemporal variability in nitrate levels, week of sampling, year, well depth, well casing, and nitrogen application rate were analyzed for their role as predictor variables. Based on our linear mixed effects model, nitrogen application rate was the greatest predictor of the nitrate concentration of irrigation water (p < 0.05).

Data inaccessibility at sub-county scale limits implementation of manuresheds.

The manureshed concept aims to rebalance surplus manure nutrients produced at animal feeding operations (sources) and the demands from nutrient-deficient croplands (sinks) to reduce negative environmental impacts and utilize nutrients more efficiently. Due to water quality implications, studies focused on this rebalancing have typically created domain boundaries that match a particular watershed. However, a majority of agricultural datasets that are used to inform these analyses-specifically, livestock populations-are only available at the county scale, which generally does not match watershed boundaries. The common method used to address this mismatch is to weight the county statistics based on the proportion of watershed area within the county. However, these straightforward assumptions imply that animal density is uniform across a county, which can be highly problematic, especially in an era of increasing concentration of livestock production on a smaller land area. We present a case study of the Lake Mendota watershed in south-central Wisconsin using both a typical county-based downscaled dataset as well as a more spatially explicit dataset of livestock counts from the Census of Agriculture that aggregates a set of zip codes that best matches the watershed boundary. This comparison reveals a substantial difference in estimated livestock numbers and their associated manure production that is due to a concentration of dairy operations within the watershed compared with the rest of the county. We argue that sub-county scale data need to become more available and integrated into nutrient and water quality management efforts so that manuresheds can be more effectively delineated and implemented.

Contribution of anaerobic digesters to emissions mitigation and electricity generation under U.S. climate policy.

Livestock husbandry in the U.S. significantly contributes to many environmental problems, including the release of methane, a potent greenhouse gas (GHG). Anaerobic digesters (ADs) break down organic wastes using bacteria that produce methane, which can be collected and combusted to generate electricity. ADs also reduce odors and pathogens that are common with manure storage and the digested manure can be used as a fertilizer. There are relatively few ADs in the U.S., mainly due to their high capital costs. We use the MIT Emissions Prediction and Policy Analysis (EPPA) model to test the effects of a representative U.S. climate stabilization policy on the adoption of ADs which sell electricity and generate methane mitigation credits. Under such policy, ADs become competitive at producing electricity in 2025, when they receive methane reduction credits and electricity from fossil fuels becomes more expensive. We find that ADs have the potential to generate 5.5% of U.S. electricity.

Corn-based ethanol production compromises goal of reducing nitrogen export by the Mississippi River.

Corn cultivation in the United States is expected to increase to meet demand for ethanol. Nitrogen leaching from fertilized corn fields to the Mississippi-Atchafalaya River system is a primary cause of the bottom-water hypoxia that develops on the continental shelf of the northern Gulf of Mexico each summer. In this study, we combine agricultural land use scenarios with physically based models of terrestrial and aquatic nitrogen to examine the effect of present and future expansion of corn-based ethanol production on nitrogen export by the Mississippi and Atchafalaya Rivers to the Gulf of Mexico. The results show that the increase in corn cultivation required to meet the goal of 15-36 billion gallons of renewable fuels by the year 2022 suggested by a recent U.S. Senate energy policy would increase the annual average flux of dissolved inorganic nitrogen (DIN) export by the Mississippi and Atchafalaya Rivers by 10-34%. Generating 15 billion gallons of corn-based ethanol by the year 2022 will increase the odds that annual DIN export exceeds the target set for reducing hypoxia in the Gulf of Mexico to >95%. Examination of extreme mitigation options shows that expanding corn-based ethanol production would make the already difficult challenges of reducing nitrogen export to the Gulf of Mexico and the extent of hypoxia practically impossible without large shifts in food production and agricultural management.

Prairie restoration and carbon sequestration: difficulties quantifying C sources and sinks using a biometric approach.

We investigated carbon cycling and ecosystem characteristics among two prairie restoration treatments established in 1987 and adjacent cropland, all part of the Conservation Reserve Program in southwestern Wisconsin, USA. We hypothesized that different plant functional groups (cool-season C3 vs. warm-season C4 grasses) between the two prairie restoration treatments would lead to differences in soil and vegetation characteristics and amount of sequestered carbon, compared to the crop system. We found significant (P < 0.05) differences between the two prairie restoration treatments in soil CO2 respiration and above- and belowground productivity, but no significant differences in long-term (approximately 16-year) carbon sequestration. We used a biometric approach aggregating short-term observations of above- and belowground productivity and CO2 respiration to estimate total net primary production (NPP) and net ecosystem production (NEP) using varied methods suggested in the literature. Net ecosystem production is important because it represents the ecosystem carbon sequestration, which is of interest to land managers and policymakers seeking or regulating credits for ecosystem carbon storage. Such a biometric approach would be attractive because it might offer the ability to rapidly assess the carbon source/sink status of an ecosystem. We concluded that large uncertainties in (1) estimating aboveground NPP, (2) determining belowground NPP, and (3) partitioning soil respiration into microbial and plant components strongly affect the magnitude, and even the sign, of NEP estimates made from aggregating its components. A comparison of these estimates across treatments could not distinguish differences in NEP, nor the absolute sign of the overall carbon balance. Longer-term quantification of carbon stocks in the soil, periodically linked to measurements of individual processes, may offer a more reliable measure of the carbon balance in grassland systems, suitable for assigning credits.