A comparison of Tier 1, 2, and 3 methods for quantifying nitrous oxide emissions from soils amended with biosolids.

Municipal biosolids are a nitrogen (N)-rich agricultural fertilizer which may emit nitrous oxide (N2O) after rainfall events. Due to sparse empirical data, there is a lack of biosolids-specific N2O emission factors to determine how land-applied biosolids contribute to the national greenhouse gas inventory. This study estimated N2O emissions from biosolids-amended land in Canada using Tier 1, Tier 2 (Canadian), and Tier 3 (Denitrification and Decomposition model [DNDC]) methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC). Field data was from replicated plots at 8 site-years between 2017 and 2019 in the provinces of Quebec, Nova Scotia and Alberta, Canada, representing three distinct ecozones. Municipal biosolids were the major N source for the crop, applied as mesophilic anaerobically digested biosolids, composted biosolids, or alkaline-stabilized biosolids alone or combined with an equal amount of urea-N fertilizer to meet the crop N requirements. Fluxes of N2O were measured during the growing season with manual chambers and compared to N2O emissions estimated using the IPCC methods. In all site-years, the mean emission of N2O in the growing season was greater with digested biosolids than other biosolids sources or urea fertilizer alone. The emissions of N2O in the growing season were similar with composted or alkaline-stabilized biosolids, and no greater than the unfertilized control. The best estimates of N2O emissions, relative to measured values, were with the Tier 3 > adapted Tier 2 with biosolids-specific correction factors > standard Tier 2 = Tier 1 methods of the IPCC, according to the root mean square error statistic. The Tier 3 IPCC method was the best estimator of N2O emissions in the Canadian ecozones evaluated in this study. These results will be used to improve methods for estimating N2O emissions from agricultural soils amended with biosolids and to generate more accurate GHG inventories.

Perspectives on peak liquid manure temperature with implications for methane emissions.

Methane emissions from liquid manure storage are currently estimated with a methane conversion factor (MCF) based on manure temperature inputs or air temperatures as a substitute in the 2019 IPCC Tier 2 method. However, differences between peak manure temperature and peak air temperature (Tdiff ) in warm seasons are likely to occur and result in poor estimates of MCF and methane emissions. To address this concern, this study aims to investigate the relationship between the Tdiff and ratio of manure surface area to manure volume (Rs:v ) using a mechanistic model and by analyzing farm-scale measurement studies across Canada. Positive correlations between Tdiff and Rs:v were found using a modeling approach and from farm-scale results (r = 0.55, p = 0.06). Tdiff ranged from -2.2 to 2.6°C in farm-scale results mainly collected from eastern Canada. We suggest that manure volume and surface area, in addition to removal frequency, could be used to estimate Tdiff and be part of the criteria for improving manure temperature estimates, which could lead to improved estimates of MCF.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

A gap in nitrous oxide emission reporting complicates long-term climate mitigation.

Nitrous oxide (N2O) is an important greenhouse gas (GHG) that also contributes to depletion of ozone in the stratosphere. Agricultural soils account for about 60% of anthropogenic N2O emissions. Most national GHG reporting to the United Nations Framework Convention on Climate Change assumes nitrogen (N) additions drive emissions during the growing season, but soil freezing and thawing during spring is also an important driver in cold climates. We show that both atmospheric inversions and newly implemented bottom-up modeling approaches exhibit large N2O pulses in the northcentral region of the United States during early spring and this increases annual N2O emissions from croplands and grasslands reported in the national GHG inventory by 6 to 16%. Considering this, emission accounting in cold climate regions is very likely underestimated in most national reporting frameworks. Current commitments related to the Paris Agreement and COP26 emphasize reductions of carbon compounds. Assuming these targets are met, the importance of accurately accounting and mitigating N2O increases once CO2 and CH4 are phased out. Hence, the N2O emission underestimate introduces additional risks into meeting long-term climate goals.

Global evaluation of inhibitor impacts on ammonia and nitrous oxide emissions from agricultural soils: A meta-analysis.

Inhibitors are widely considered an efficient tool for reducing nitrogen (N) loss and improving N use efficiency, but their effectiveness is highly variable across agroecosystems. In this study, we synthesized 182 studies (222 sites) worldwide to evaluate the impacts of inhibitors (urease inhibitors [UI], nitrification inhibitors [NI] and combined inhibitors) on crop yields and gaseous N loss (ammonia [NH3 ] and nitrous oxide [N2 O] emissions) and explored their responses to different management and environmental factors including inhibitor application timing, fertilization regime, cropping system, water management, soil properties and climatic conditions using subgroup meta-analysis, meta-regression and multivariate analyses. The UI were most effective in enhancing crop yields (by 5%) and reducing NH3 volatilization (by 51%), whereas NI were most effective at reducing N2 O emissions (by 49%). The application of UI mitigates NH3 loss and increases crop yields especially in high NH3 -N loss scenarios, whereas NI application would minimize the net N2 O emissions and the resultant environmental impacts especially in low NH3 -N loss scenarios. Alternatively, the combined application of UI and NI enables producers to balance crop production and environmental conservation goals without pollution tradeoffs. The inhibitor efficacy for decreasing gaseous N loss was dependent upon soil and climatic conditions and management practices. Notably, both meta-regression and multivariate analyses suggest that inhibitors provide a greater opportunity for reducing fertilizer N inputs in high-N-surplus systems and presumably favor crop yield enhancement under soil N deficiency situations. The pursuit of an improved understanding of the interactions between plant-soil-climate-management systems and different types of inhibitors should continue to optimize the effectiveness of inhibitors for reducing environmental losses while increasing productivity.

Evaluating the 2019 IPCC refinement for estimating methane conversion factors in Canada.

Methane emissions from liquid manure management in Canada are an important greenhouse gas source. A wide range of seasonal temperatures, distribution of livestock farms, and various management practices in Canada means that regional methane conversion factors (MCF) that account for spatially discrete climate and management should be used. This study explores the impacts of using the 2019 IPCC Refinement methodology on estimates of MCFs across Canada. MCFs were calculated for 3403 locations across Canada using historical weather data, with varying management parameters at each location (emptying efficiency, timing, and frequency of manure removal). Sensitivity to two model parameters was also evaluated (minimum manure temperature, damping factor). Results showed the influence of climate, as average MCF in each ecozone ranged from 0.27 in the Mixedwood Plains to 0.15 in the Taiga. Further climate variation within ecozones was evident. For example, the MCF range within Mixedwood Plains was 0.17 to 0.33. The MCF reduction by improving management was evident as the average MCF in Canada was 0.15 for triannual removal, 0.21 for biannual removal, 0.28 for one-time removal in spring, and 0.32 for one-time removal in fall. Emptying efficiency was found to be critical; for example, the average MCF for triannual removal with 100% efficiency was 0.14 but increased to 0.15 at 95%, 0.17 at 85%, and 0.30 at 50% efficiency. The damping factor had higher sensitivity in terms of model parameters because it influences peak manure temperature in summer before manure removal. Our results suggest that the average MCF in Canada will be similar to the 2006 IPCC value, but that using the 2019 IPCC Refinement provides a greater ability to represent the variations in climate and management regionally across the country. This will improve accuracy and enable inventory practitioners to reflect regional farm management changes in national methane emission estimates.

Greenhouse gas emissions following biosolids application to farmland: Estimates from the DeNitrification and DeComposition model.

Municipal wastewater sludge may be processed into biosolids and applied to farmland for crop production, rather than be disposed of in landfills. Biosolids supply plant nutrients and increase soil organic carbon but also contribute to the production of greenhouse gases (GHGs). Computational models must therefore be refined to estimate the contribution of these gases to national GHG inventories. The DeNitrification and DeComposition (DNDC) model was evaluated for processes regulating crop growth, GHGs and soil C&N dynamics to determine its suitability for informing policy decision-making and advancing Canada's GHG inventory. Three years (2017-2019) of data were collected from replicated corn (Zea mays L.) plots in Quebec, Canada. The plots received 120 kg of available N ha-1 y-1 in mesophilic anaerobically digested biosolids, composted biosolids, alkaline-stabilized biosolids, urea, or combinations of these, while control plots were left unfertilized. Treatments receiving digested biosolids emitted more nitrous oxide (N2O) during the growing season than other treatments, while carbon dioxide (CO2) emissions were similar between treatments. After calibration, DNDC estimates were within the 95% confidence interval of the measured variables. Correlation coefficients (r) indicated discrepancies in trends between the estimated and measured values for daily CO2 and N2O emissions. These emissions were underestimated in the early and mid-growing season of 2018. They were more variable from plots fertilized with composted or alkaline-stabilized biosolids than from those with digested biosolids. Annual N2O emissions (r = 0.8), crop yields (r = 0.5), and soil organic carbon (r = 0.4) were modelled with higher accuracy than cumulative CO2 emissions (r = 0.3) and total soil N (r = 0.1). These findings suggest that DNDC is suitable for estimating field-scale N2O emissions following biosolids application, but estimates of CO2 emissions could be improved, perhaps by disaggregating the biosolids from the soil organic matter pools in the decomposition subroutines.

Effectiveness of using representative subsets of global climate models in future crop yield projections.

Representative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that were employed to select 5 GCMs from a 20-member ensemble of GCMs from the CMIP5 ensemble for projecting canola and spring wheat yields across Canada under RCP 4.5 and 8.5 emission scenarios in the periods 2040-2069 and 2070-2099, based on crop simulation models. Averages and spreads of the simulated crop yields using the 5-GCM subsets selected by T&P and KKZ approaches were compared with the full 20-GCM ensemble. Our results showed that the 5-GCM subsets selected by the two approaches could produce full-ensemble means with a relative absolute error of 2.9-4.7% for canola and 1.5-2.2% for spring wheat, and covers 61.8-91.1% and 66.1-80.8% of the full-ensemble spread for canola and spring wheat, respectively. Our results also demonstrated that both approaches were very likely to outperform a subset of randomly selected 5 GCMs in terms of a smaller error and a larger range.

Economic and environmental nitrate leaching consequences of 4R nitrogen management practices including use of inhibitors for corn production in Ontario.

Nitrate (NO3-) leaching has negative human and environmental health consequences that can be attributed to and mitigated by agricultural decision making. The purpose of this study is to examine the economic and environmental nitrogen (N) leaching reduction from 4R (Right Rate, Right Source, Right Time, Right Placement) agricultural management practices, including application methods, timing and rates, and the use of nitrification and urease inhibitors, for Ontario corn production. This study employed an integrated biophysical and economic GIS-based simulation model considering corn yields, prices, and production costs, and environmental losses, under historical weather scenarios, with NO3- leaching constraints. Reducing N application from historical to model optimized agronomic rates sharply lowered corn NO3- leaching from 75.3 to 24.9 kt N per year. Increasing model restrictions on corn NO3- leaching increased the use of broadcast and sidedress application methods compared to injection and lower overall production. They also increased the use of nitrification and urease inhibitors, which increased N use efficiency, because they allowed lower leaching from corn production, for a price. Leaching decreases from restrictions trade-off with ammonia (NH3) volatilization increases, but there was no trade-off with nitrous oxide (N2O) emissions. This highlighted the importance of considering net N losses and production trade-offs by policy decision-makers when developing N loss reduction strategies.

Exploring management strategies to improve yield and mitigate nitrate leaching in a typical radish field in northern China.

It is currently uncertain whether process-based models are capable of assessing crop yield and nitrogen (N) losses while helping to investigate best management practices from vegetable cropping systems. The objectives of this study were to (1) calibrate and evaluate the Denitrification-Decomposition (DNDC) model in simulating crop growth and nitrate leaching in a typical field radish system; (2) optimize management practices to improve radish yield and mitigate nitrate leaching under 20-year climate variability. A five-season in-situ field experiment of spring and autumn radish in northern China was established in the autumn of 2017 and measurements of radish yield, N uptake, soil temperature, soil moisture, drainage, and nitrate leaching were obtained under different N usage. DNDC overall demonstrated "good" to "excellent" performance in simulating radish yield, total biomass, N uptake, and soil temperature across all treatments (6.4% ≤ normalized root mean square error (nRMSE) ≤ 15.5%; 0.12 ≤ Nash-Sutcliffe efficiency (NSE) ≤ 0.88; 0.80 ≤ index of agreement (d) ≤ 0.97). DNDC generally exhibited "fair" performance in estimating soil moisture and drainage (10.9% ≤ nRMSE ≤ 27.2%; -0.18 ≤ NSE ≤ 0.37; 0.69 ≤ d ≤ 0.82) and "good" performance when predicting nitrate leaching (12.4% ≤ nRMSE ≤ 26.7%; -0.59 ≤ NSE ≤ 0.51; 0.68 ≤ d ≤ 0.90). Sensitivity analyses demonstrated that optimized management practices (planting dates, irrigation amount, fertilization rate and timing) could substantially reduce N usage by 40%-50%, irrigation amount by 33%-50%, and nitrate leaching by 86%-95% compared to farmers' practice in radish planting system. This study indicated that a modelling method is helpful for evaluating the biogeochemical effects of management alternatives and identifying optimal management practices in radish production systems of China.

Regional climate influences manure temperature and methane emissions - A pan-Canadian modelling assessment.

This study explores the variation of liquid manure temperature (Tm) and CH4 emissions associated with contrasting regional climates, inter-annual weather variation, and manure storage emptying. As a case-study, six regions across Canada were used, spanning 11°32' latitude and 58°30' longitude. Annual average air temperatures ranged from 3.9 °C (prairie climate) to 10.5 °C (maritime climate), with an overall average of 6.6 °C. A model predicted Tm over 30 years, using daily weather (1971-2000), and over one "normal" year (30-year average weather). Modelled Tm was then used in Manure-DNDC to model daily CH4 emissions. Two manure storage emptying scenarios were simulated: (i) early spring and autumn, or (ii) late spring and autumn. Regional differences were evident as average Tm ranged from 8.9 °C to 14.6 °C across the six locations. Early removal of stored manure led to warmer Tm in all regions, and the most warming occurred in colder regions. Regional climate had a large effect on CH4 emissions (e.g. 1.8× greater in the pacific maritime and great lakes regions than the prairie region). Inter-annual weather variability led to substantial variation in inter-annual CH4 emissions, with coefficient of variation being as high as 20%. The large inter-annual range suggests that field measurements of CH4 emissions need to compare the weather during measurements to historical normals. Early manure storage emptying reduced CH4 emissions (vs late removal) in some regions but had little effect or the opposite effect in other regions. Overall, the results from this modelling study suggest: i) Tm differs substantially from air temperature at all locations, ii) accurate estimates of manure storage CH4 emissions require region-specific calculations using Tm (e.g. in emission inventories), iii) field measurements of CH4 emissions need to consider weather conditions relative to climate normal, and iv) emission mitigation practices will require region-specific measurements to determine impacts.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Global Research Alliance N2 O chamber methodology guidelines: Summary of modeling approaches.

Measurements of nitrous oxide (N2 O) emissions from agriculture are essential for understanding the complex soil-crop-climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N2 O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process-based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high-level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models, common modeling techniques such as calibration and validation, assessing model fit, sensitivity analysis, and uncertainty assessment. We also review examples of N2 O modeling for different purposes and describe three commonly used process-based N2 O models (APSIM, DayCent, and DNDC).

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Towards an improved methodology for modelling climate change impacts on cropping systems in cool climates.

Assessment of the impact of climate change on agricultural sustainability requires a robust full system estimation of the interdependent soil-plant-atmospheric processes coupled with dynamic farm management. The simplification or exclusion of major feedback mechanisms in modelling approaches can significantly affect model outcomes. Using a biogeochemical model, DNDCv.CAN, at three case-study locations in Canada, we quantified the impact of using commonly employed simplified modelling approaches on model estimates of crop yields, soil organic carbon (SOC) change and nitrogen (N) losses across 4 time periods (1981-2010, 2011-2040, 2041-2070, and 2071-2100). These approaches included using climate with only temperature and precipitation data, annual re-initialization of soil status, fixed fertilizer application rates, and fixed planting dates. These simplified approaches were compared to a more comprehensive reference approach that used detailed climate drivers, dynamic planting dates, dynamic fertilizer rates, and had a continuous estimation of SOC, N and water budgets. Alternative cultivars and rotational impacts were also investigated. At the semi-arid location, the fixed fertilizer, fixed planting date, and soil re-initialization approaches reduced spring wheat (Triticum aestivum L.) yield estimates by 40%, 25%, and 29%, respectively, in the 2071-2100 period relative to the comprehensive reference approach. At both sub-humid locations, the re-initialization of soil status significantly altered SOC levels, N leaching and N runoff in all three time periods from 2011 to 2100. At all locations, SOC levels were impacted when using simplified approaches relative to the reference approach, except for the fixed fertilizer approach at the sub-humid locations. Results indicate that simplified approaches often lack the necessary characterization of the feedbacks between climate, soil, crop and management that are critical for accurately assessing crop system behavior under future climate. We recommend that modellers improve their capabilities of simulating expected changes in agronomy over time and employ tools that consider robust soil-plant-atmospheric processes.

Modifying fertilizer rate and application method reduces environmental nitrogen losses and increases corn yield in Ontario.

Nitrogen (N) use in corn production is an important driver of nitrous oxide (N2O) emissions and 4R (Right source, Right rate, Right time and Right place) fertilizer practices have been proposed to mitigate emissions. However, combined 4R practices have not been assessed for their potential to reduce N2O emissions at the provincial-scale while also considering trade-offs with other N losses such as leaching or ammonia (NH3) volatilization. The objectives of this study were to develop, validate, and apply a Denitrification-Decomposition model framework at 270 distinct soil-climate regions in Ontario to simulate corn yield and N2O emissions across eleven fertilizer management scenarios during 1986-2015. The results show that broadcasting fertilizer at the surface without incorporation had the highest environmental N loss which was primarily caused by NH3 volatilization. When injected at planting or at sidedress, the NH3 loss was reduced considerably. However, because more N was left in the soil, injection and sidedressing induced more losses by nitrate leaching and N2O emissions. Reduction of N rate as proposed by the DNDC model did not affect crop yield but decreased leaching and N2O emissions. Addition of inhibitors promoted a further reduction in N2O emission (11-16%) although lesser than the reduction in N rate. Overall, our results emphasize that N rate adjustment following improvements in placement, use of inhibitors, and application timings can mitigate N2O emissions by 42-57% and result in 3-4% greater yields compared to baseline scenario in Ontario corn production.

Assessing climate change impacts on greenhouse gas emissions, N losses in drainage and crop production in a subsurface drained field.

Future climate change-driven alterations in precipitation patterns, increases in temperature, and rises in atmospheric carbon dioxide concentration ([CO2]atm) are expected to alter agricultural productivity and environmental quality, while high latitude countries like Canada are likely to face more challenges from global climate change. However, potential climate change impact on GHG emissions from tile-drained fields is poorly documented. Accordingly, climate change impacts on GHG emissions, N losses to drainage and crop production in a subsurface-drained field in Southern Quebec, Canada were assessed using calibrated and validated RZWQM2 model. The RZWQM2 model was run for a historical period (1971-2000) and for a future period (2038 to 2070) using data generated from 11 different GCM-RCMs (global climate models coupled with regional climate models). Under the projected warmer and higher rainfall conditions mean drainage flow was predicted to increase by 17%, and the N losses through subsurface drains increase by 47%. Despite the negative effect of warming temperature on crop yield, soybean yield was predicted to increase by 31% due to increased photosynthesis rates and improved crop water use efficiency (WUE) under elevated [CO2]atm, while corn yield was reduced by 7% even with elevated [CO2]atm because of a shorter life cycle from seedling to maturity resulted from higher temperature. The N2O emissions would be enhanced by 21% due to greater denitrification and mineralization, while CO2 emissions would increase by 16% because of more crop biomass accumulation, higher crop residue decomposition, and greater soil microbial activities. Soil organic carbon storage was predicted to decrease 22% faster in the future, which would result in higher global warming potential in turn. This study demonstrates the potential of exacerbating GHG emissions and water quality problems and reduced corn yield under climate change impact in subsurface drained fields in southern Quebec.

Assessing the Impacts of Climate Variability on Fertilizer Management Decisions for Reducing Nitrogen Losses from Corn Silage Production.

There is an incentive for dairy farmers to maximize crop production while minimizing costs and environmental impacts. In cold climates, farmers have limited opportunity to balance field activities and manure storage requirements while limiting nutrient losses. A revised DeNitrification DeComposition (DNDC) model for simulating tile drainage was used to investigate fertilizer scenarios when applying dairy slurry or urea on silage corn ( L.) to examine N losses over a multidecadal horizon at locations in eastern Canada and the US Midwest. Management scenarios included timing (spring, fall, split, and sidedress) and method of application (injected [10 cm], incorporated [5 cm], and broadcast). Reactive N losses (NO from drainage and runoff, NO, and NH) were greatest from broadcast, followed by incorporated and then injected applications. Among the fertilizer timing scenarios, fall manure application resulted in the greatest N loss, primarily due to increased N leaching in non-growing-season periods, with 58% more N loss per metric ton of silage than spring application. Split and sidedress mineral fertilizer had the lowest N losses, with average reductions of 9.5 and 4.9%, respectively, relative to a single application. Split application mitigated losses more so than sidedress by reducing the soil pH shift due to urea hydrolysis and NH volatilization during the warmer June period. This assessment helps to distinguish which fertilizer practices are more effective in reducing N loss over a long-term time horizon. Reactive N loss is ranked across 18 fertilizer management practices, which could assist farmers in weighing the tradeoffs between field trafficability, manure storage capacity, and expected N loss.

Long-term Trends in Corn Yields and Soil Carbon under Diversified Crop Rotations.

Agricultural practices such as including perennial alfalfa ( L.), winter wheat ( L.), or red clover ( L.) in corn ( L.) rotations can provide higher crop yields and increase soil organic C (SOC) over time. How well process-based biogeochemical models such as DeNitrification-DeComposition (DNDC) capture the beneficial effects of diversified cropping systems is unclear. To calibrate and validate DNDC for simulation of observed trends in corn yield and SOC, we used long-term trials: continuous corn (CC) and corn-oats ( L.)-alfalfa-alfalfa (COAA) for Woodslee, ON, 1959 to 2015; and CC, corn-corn-soybean [ (L.) Merr.]-soybean (CCSS), corn-corn-soybean-winter wheat (CCSW), corn-corn-soybean-winter wheat + red clover (CCSW+Rc), and corn-corn-alfalfa-alfalfa (CCAA) for Elora, ON, 1981 to 2015. Yield and SOC under 21st century conditions were projected under future climate scenarios from 2016 to 2100. The DNDC model was calibrated to improve crop N stress and was revised to estimate changes in water availability as a function of soil properties. This improved yield estimates for diversified rotations at Elora (mean absolute prediction error [MAPE] decreased from 13.4-15.5 to 10.9-14.6%) with lower errors for the three most diverse rotations. Significant improvements in yield estimates were also simulated at Woodslee for COAA, with MAPE decreasing from 24.0 to 16.6%. Predicted and observed SOC were in agreement for simpler rotations (CC or CCSS) at both sites (53.8 and 53.3 Mg C ha for Elora, 52.0 and 51.4 Mg C ha for Woodslee). Predicted SOC increased due to rotation diversification and was close to observed values (58.4 and 59 Mg C ha for Elora, 63 and 61.1 Mg C ha for Woodslee). Under future climate scenarios the diversified rotations mitigated crop water stress resulting in trends of higher yields and SOC content in comparison to simpler rotations.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.