Denitrification, the anaerobic reduction of nitrogen oxides to nitrogenous gases, is an extremely challenging process to measure and model. Much of this challenge arises from the fact that small ...areas (hotspots) and brief periods (hot moments) frequently account for a high percentage of the denitrification activity that occurs in both terrestrial and aquatic ecosystems. In this paper, we describe the prospects for incorporating hotspot and hot moment phenomena into denitrification models in terrestrial soils, the interface between terrestrial and aquatic ecosystems, and in aquatic ecosystems. Our analysis suggests that while our data needs are strongest for hot moments, the greatest modeling challenges are for hotspots. Given the increasing availability of high temporal frequency climate data, models are promising tools for evaluating the importance of hot moments such as freeze-thaw cycles and drying/rewetting events. Spatial hotspots are less tractable due to our inability to get high resolution spatial approximations of denitrification drivers such as carbon substrate. Investigators need to consider the types of hotspots and hot moments that might be occurring at small, medium, and large spatial scales in the particular ecosystem type they are working in before starting a study or developing a new model. New experimental design and heterogeneity quantification tools can then be applied from the outset and will result in better quantification and more robust and widely applicable denitrification models.
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BFBNIB, DOBA, EMUNI, FZAB, GEOZS, IJS, IMTLJ, IZUM, KILJ, KISLJ, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. ...Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.
Farmers, food supply companies, and policymakers need practical yet scientifically robust methods to quantify how improved nitrogen (N) fertilizer management can reduce nitrous oxide (N2O) emissions. ...To meet this need, we developed an empirical model based on published field data for predicting N2O emission from rainfed maize (Zea mays L.) fields managed with inorganic N fertilizer in the United States and Canada. Nitrous oxide emissions ranged widely on an area basis (0.03–32.9 kg N ha−1 yr−1) and a yield‐scaled basis (0.006–4.8 kg N Mg−1 grain yr−1). We evaluated multiple modeling approaches and variables using three metrics of model fit (Akaike information criteria corrected for small sample sizes AICc, RMSE, and R2). Our model explains 32.8% of the total observed variation and 50% of observed site‐level variation. Soil clay content was very important for predicting N2O emission and predicting the change in N2O emission due to a change in N balance, with the addition of a clay fixed effect explaining 37% of site‐level variation. Sites with higher clay content showed greater reductions in N2O emission for a given reduction in N balance. Therefore, high‐clay sites are particularly important targets for reducing N2O emissions. Our linear mixed model is more suitable for predicting the effect of improved N management on N2O emission in maize fields than other published models because it (a) requires only input data readily available on working farms, (b) is derived from field observations, (c) correctly represents differences among sites using a mixed modeling approach, and (d) includes soil texture because it strongly influences N2O emissions.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•Models are often required to develop environmental projects and verify compliance.•Model selection is project-specific; no single model fits all applications.•Models must be transparent and ...validated for each combination of crop and practice.•We developed a 7-step methodology to avoid pitfalls of model misuse.•We apply this methodology to greenhouse gas emissions from maize fields.
Robust quantification of the environmental performance of agricultural management practices is critical both for ensuring regulatory compliance and for creating accountability in voluntary environmental markets and corporate sustainability commitments. Because environmental impacts cannot be measured under all conditions and on all farms, models are required. However, models must be used appropriately if predictions of environmental performance are to be reliable. To assist policymakers and stakeholders, we define a 7-step process for model selection and use, and present a case study applying this 7-step process to greenhouse gas emissions from corn (Zea mays L.) fields in the USA. Based on this case study and other examples from the literature, we suggest that all models are limited by the data available to validate them for different combinations of cropping systems, management practices, site conditions, and types of environmental performance. Additionally, both statistical and process models are much more reliable for making predictions of environmental performance for multiple fields and years than for predictions of a single location and year. We suggest that using this 7-step process will help improve predictions of environmental performance for regulatory and voluntary purposes at local, state, and national scales.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Farmers, food supply-chain entities, and policymakers need a simple but robust indicator to demonstrate progress toward reducing nitrogen pollution associated with food production. We show that ...nitrogen balance—the difference between nitrogen inputs and nitrogen outputs in an agricultural production system—is a robust measure of nitrogen losses that is simple to calculate, easily understood, and based on readily available farm data. Nitrogen balance provides farmers with a means of demonstrating to an increasingly concerned public that they are succeeding in reducing nitrogen losses while also improving the overall sustainability of their farming operation. Likewise, supply-chain companies and policymakers can use nitrogen balance to track progress toward sustainability goals. We describe the value of nitrogen balance in translating environmental targets into actionable goals for farmers and illustrate the potential roles of science, policy, and agricultural support networks in helping farmers achieve them.
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BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Declining soil fertility and limited farmer access to inorganic fertilizer frequently cause sub-optimal grain yields throughout sub-Saharan Africa. Farm productivity is also at risk from extreme ...weather and future climate change. Significant uncertainty remains in predicting climate in Africa, increasing the challenge of planning for climate change adaptation. Sorghum is adapted to African climate patterns and is predicted to maintain widespread suitability across different African climatic zones under climate change. Sorghum’s drought tolerance and ability to withstand water logging make it an important crop for maintaining productive agroecosystems under a changing climate. Due to its status as a staple grain, improved sorghum management can provide smallholder farmers with stability in their household nutritional needs. We reviewed sorghum (
Sorghum bicolor
) yield trends across nutrient management scenarios using meta-analysis. We compared yield across eight nutrient management practices: (i) N-only, (ii) P-only, (iii) N and P, (iv) N and P microdose, (v) legume management, (vi) manure addition, (vii) organic matter (OM) amendment, and (viii) mixed amendment. Our review demonstrated (1) yield improvement considering all scenarios averaged 66 % relative to no nutrient inputs, (2) yield under chemical fertilizer amendment increased by 47–98 % of control yield, (3) yield under organic nutrient amendment increased by 43–87 % of control yield, and (4) the profitability of a management scenario was not solely determined by the magnitude of yield increase. For example, due to the high cost of fertilizer, addition of nitrogen (N) and phosphorus (P) generated the largest yield increase, but the lowest profit, in two of three countries analyzed. In contrast, an edible legume in rotation averaged 43 % yield improvement relative to no nutrient inputs and a net profit of US $146 to $263 per hectare. Facilitating access to both fertilizer inputs and diversified rotations has the greatest potential to increase grain yield in Africa.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in ...simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low‐temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf‐to‐canopy scaling and better values of model parameters that control the maximum potential GPP, such asεmax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).
Key Points
Gross primary productivity (GPP) from 26 models tested at 39 flux tower sites
Simulated light use efficiency controls model performance
Models overpredict GPP under dry conditions
The importance of lotic systems as sinks for nitrogen inputs is well recognized. A fraction of nitrogen in streamflow is removed to the atmosphere via denitrification with the remainder exported in ...streamflow as nitrogen loads. At the watershed scale, there is a keen interest in understanding the factors that control the fate of nitrogen throughout the stream channel network, with particular attention to the processes that deliver large nitrogen loads to sensitive coastal ecosystems. We use a dynamic stream transport model to assess biogeochemical (nitrate loadings, concentration, temperature) and hydrological (discharge, depth, velocity) effects on reach-scale denitrification and nitrate removal in the river networks of two watersheds having widely differing levels of nitrate enrichment but nearly identical discharges. Stream denitrification is estimated by regression as a nonlinear function of nitrate concentration, streamflow, and temperature, using more than 300 published measurements from a variety of US streams. These relations are used in the stream transport model to characterize nitrate dynamics related to denitrification at a monthly time scale in the stream reaches of the two watersheds. Results indicate that the nitrate removal efficiency of streams, as measured by the percentage of the stream nitrate flux removed via denitrification per unit length of channel, is appreciably reduced during months with high discharge and nitrate flux and increases during months of low-discharge and flux. Biogeochemical factors, including land use, nitrate inputs, and stream concentrations, are a major control on reach-scale denitrification, evidenced by the disproportionately lower nitrate removal efficiency in streams of the highly nitrate-enriched watershed as compared with that in similarly sized streams in the less nitrate-enriched watershed. Sensitivity analyses reveal that these important biogeochemical factors and physical hydrological factors contribute nearly equally to seasonal and stream-size related variations in the percentage of the stream nitrate flux removed in each watershed.
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BFBNIB, DOBA, EMUNI, FZAB, GEOZS, IJS, IMTLJ, IZUM, KILJ, KISLJ, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
Key Points
Twenty‐one ecosystem models were tested in the frequency domain at nine flux towers
Model error is greatest at the annual and growing‐season diurnal timescales
There are large event‐driven errors and model differences at the synoptic scale
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