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Accurately predicting the impacts of higher temperatures, different precipitation rates and elevated CO2 concentrations on crop yields and GHG emissions is required in order to develop adaptation strategies. The objectives of this study were to calibrate and evaluate a regionalized denitrification-decomposition (DNDC) model using measured crop yield, soil temperature, moisture and N2O emissions, and to explore the impacts of climate change scenarios (Representative Concentration Pathways (RCP) 4.5 and RCP 8.5) on crop yields and N2O emissions in Southwestern Ontario, Canada. This simulation study was based on a winter wheat-maize-soybean rotation under conventional tillage (CT) and no tillage (NT) practices at Woodslee, Ontario, Canada. The model was calibrated using various statistics including the d index (0.85–0.99), NSE (Nash-Sutcliffe efficiency, NSE > 0) and nRMSE (normalized root mean square error, nRMSE

Carbon dioxide and nitrous oxide are two important greenhouse gases (GHG) released from cropping systems. Their emissions can vary substantially with climate, soil, and crop management. While different methods are available to account for GHG emissions in life cycle assessments (LCA) of crop production, there are no standard procedures. In this study, the objectives were: (i) to compare several methods of estimating CO 2 and N 2 O emissions for a LCA of cropping systems and (ii) to estimate the relative contribution of soil GHG emissions to the overall global warming potential (GWP) using results from a field experiment located in Manitoba, Canada. The methods were: (A) measurements; (B) Tier I and (C) Tier II IPCC (Intergovernmental panel on Climate Change) methodology, (D) a simple carbon model combined with Intergovernmental Panel for Climate Change (IPCC) Tier II methodology for soil N 2 O emissions, and (E) the DNDC (DeNitrification DeComposition) agroecosystem model. The estimated GWPs (−7.2–17 Mg CO 2 eq ha −1 y −1 ; −80 to 600 kg CO 2 eq GJ −1 y −1 ) were similar to previous results in North America and no statistical difference was found between GWP based on methods D and E and GWP based on observations. The five methods gave estimates of soil CO 2 emissions that were not statistically different from each other, whereas for N 2 O emissions only DNDC estimates were similar to observations. Across crop types, all methods gave comparable CO 2 and N 2 O emission estimates for perennial and legume crops, but only DNDC gave similar results with respect to observations for both annual and cereal crops. Whilst the results should be confirmed for other locations, the agroecosystem model and method D can be used, at certainly one selected site, in place of observations for estimating GHGs in agricultural LCA.

Accurate estimates of manure-induced carbon retention coefficients (MCR) in soil are required when assessing carbon (C) storage and the C footprint in agricultural production systems. Eight field studies using various types and rates of manure applications on different crop rotations with durations varying from 10 to 74 years were used to quantify MCRs across diverse climatic conditions in Canada. The rate of solid cattle and swine manure had no impact on MCR which averaged 26%, whereas the MCR for liquid manure, including swine and cattle liquid manures, was much smaller, at only 5%. Under semi-arid conditions, irrigation had no impact on MCR. Compared to stockpiled manures, composted manure had a higher MCR (~36%), due to the additional stabilization of C during the composting process. The MCRs can be effectively stratified based on the type of manure affecting soil organic C differently, and the approach has potential application in regional and national estimates of soil C storage in Canada and elsewhere.

Recent developments in composting technology enable dairy farms to produce their own bedding from composted manure. This management practice alters the fate of carbon and nitrogen; however, there is little data available documenting how gaseous emissions are impacted. This study measured in-situ emissions of methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O), and ammonia (NH3) from an on-farm solid-liquid separation system followed by continuously-turned plug-flow composting over three seasons. Emissions were measured separately from the continuously-turned compost phase, and the compost-storage phase prior to the compost being used for cattle bedding. Active composting had low emissions of N2O and CH4 with most carbon being emitted as CO2-C and most N emitted as NH3-N. Compost storage had higher CH4 and N2O emissions than the active phase, while NH3 was emitted at a lower rate, and CO2 was similar. Overall, combining both the active composting and storage phases, the mean total emissions were 3.9×10-2gCH4kg-1 raw manure (RM), 11.3gCO2kg-1 RM, 2.5×10-4g N2O kg-1 RM, and 0.13g NH3 kg-1 RM. Emissions with solid-separation and composting were compared to calculated emissions for a traditional (unseparated) liquid manure storage tank. The total greenhouse gas emissions (CH4+N2O) from solid separation, composting, compost storage, and separated liquid storage were reduced substantially on a CO2-equivalent basis compared to traditional liquid storage. Solid-liquid separation and well-managed composting could mitigate overall greenhouse gas emissions; however, an environmental trade off was that NH3 was emitted at higher rates from the continuously turned composter than reported values for traditional storage.

A numerical model was developed to determine the year-round temperature distribution within liquid manure storages, which is relevant for modelling temperature-dependent greenhouse gas and ammonia emissions. The model considers net short and long-wave radiation, heat conduction through walls and floor, surface convective heat transfer, evaporative heat loss, and manure loading. The model was implemented and validated for a circular concrete storage tank (40 m diameter; 2.5 m depth) on a dairy farm in Ontario, Canada, where it is assumed that heat transfer occurs in horizontal and vertical directions, and is symmetric in the angular direction. Annual root mean square error (RMSE), Nash–Sutcliffe model efficiency, coefficient of determination, and average bias between model estimates and measured values were 2.4 °C, 0.91, 0.95, and 0.12 °C, respectively. The model performed best in summer and autumn (lowest RMSE), which are the most important seasons for modelling temperature, as both temperature and manure volume are highest, leading to the greatest opportunity for gaseous emissions. A sensitivity analysis indicated that the most significant parameters were solar absorptivity, manure depth, incoming manure temperature, emissivity, and wind-speed. For every unit increase in depth (m), incoming manure temperature (°C), or wind-speed (m s −1 ), the peak summer temperature changed by −7.3, 0.3, or −2.4 °C, respectively. Parameters with little effect on temperature were the manure solids content and thermal conductivity of soil around the sides and the bottom of the tank. Results show that heat transfer is primarily 1-dimensional, and a simplified 1-D model would be sufficient for future applications.

Process-based models are useful tools for estimating the complex interactions between plant, soil and climate systems, assessments which are necessary for improving nutrient cycling and reducing trace gas emissions. Incorporation of knowledge gained through new research is ongoing, thus there is a need for evaluation of model processes and process interactions. In this study, three sites in eastern Canada (St. Bruno, St. Jean, and Ottawa) planted with spring wheat during the years 1993–2007 were used to evaluate and compare the water and N process simulations of the models DayCent, DNDC, and STICS. The simulated soil moisture by all models was generally well represented with low ARE ( 0.1. The unsaturated flow mechanism included in DayCent further improved soil moisture estimates compared to the other models. When sufficient replicate data was available measurement variability was considered, resulting in soil nitrogen being only slightly underestimated (ARE of −10, −1, and −22%, for DayCent, DNDC, and STICS, respectively). On average across the three sites, considering all statistics, the DNDC model proved to be most accurate for simulating mineral N, followed by DayCent and then STICS. Continued process model development is reliant on measurement datasets that can accurately represent carbon and nitrogen dynamics. Frequently, site specific biases convolute model mechanism evaluation and thus assessments have to be conducted across numerous sites to better benchmark model performance. On this premise a comprehensive multi-site inter-model mechanism evaluation was conducted and future model development needs were identified.

A water footprint considers both the water volumes involved in production processes and the resulting waste water generated. The grey water (GW) footprint represents the volume of fresh water required to assimilate pollutants to acceptable concentrations—a concept proposed by the water footprint network—but it faces several difficulties when applied to agricultural production systems. Crop production cannot be fully controlled and it is weather-dependent, which greatly affects the year-to-year GW calculations. In this study, we examined the effect of time step on the calculation of annual GW footprints by utilizing 30 years of daily average nitrate-nitrogen (NO 3 -N) concentrations in drainage water (both leachate and runoff water derived from a process-based model) from corn and soybean production systems. For each crop year, the volume of water required to assimilate NO 3 -N to an acceptable threshold concentration (i.e. −1 ) was calculated over different time steps (daily, weekly, monthly, seasonally and yearly), and each case was summed to an annual GW value. Daily average NO 3 -N concentrations in the effluent water were generally below the acceptable threshold concentrations, with intermittent exceedances. Thus, the fields often provided their own ‘dilution’ water, and annual average concentrations were only 2.0 mg L −1 and 0.4 mg L −1 for corn and soybean, respectively. The GW footprint varied significantly when calculated for different time steps. The greatest annual footprint occurred when calculated daily (shortest time step). The GW footprint for corn ranged from 2.7 × 10 3 m 3 ha −1 , or 2700 mm of water, when estimated daily to zero for the yearly time step. For soybean it ranged from 0.5 × 10 3 m 3 ha −1 , or 500 mm of water, to zero. The GW footprint results are therefore highly dependent on the time step of calculation. The effect of this issue extends beyond crop production as it is exported and amplified through feed rations to affect the GW footprint from animal production. To be able to reconcile these problems, the GW calculation pathways should be reconsidered and standardized.

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.

Participation in nursing journal clubs has been correlated with increased familiarity among nurses with research processes and outcomes, as well as improved ability to critique research studies and apply evidence from the literature to practice. For nurses who are direct care providers, time and accessibility are barriers to participation in journal clubs. Providing a journal club using an online, asynchronous format increased participation. This article describes the inception of a nursing journal club and the steps taken to alter the format to increase participation.

Denitrification is a key but poorly quantified component of the N cycle. Because it is difficult to measure the gaseous (NOx, N2O, N2) and soluble (NO3) components of denitrification with sufficient intensity, models of varying scope and complexity have been developed and applied to estimate how vegetation cover, land management and environmental factors such as soil type and weather interact to control these variables. In this paper we assess the strengths and limitations of different modeling approaches, highlight major uncertainties, and suggest how different observational methods and process-based understanding can be combined to better quantify N cycling. Representation of how biogeochemical (e.g. org. C., pH) and physical (e.g. soil structure) factors influence denitrification rates and product ratios combined with ensemble approaches may increase accuracy without requiring additional site level model inputs.

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.

Effective management of dairy manure is important to minimize N losses from cropping systems, maximize profitability, and enhance environmental sustainability. The objectives of this study were (i) to calibrate and validate the DeNitrification-DeComposition (DNDC) model using measurements of silage corn ( L.) biomass, N uptake, soil temperature, tile drain flow, NO leaching, NO emissions, and soil mineral N in eastern Canada, and (ii) to investigate the long-term impacts of manure management under climate variability. The treatments investigated included a zero-fertilizer control, inorganic fertilizer, and dairy manure amendments (raw and digested). The DNDC model overall demonstrated statistically "good" performance when simulating silage corn yield and N uptake based on normalized RMSE (nRMSE)10%, index of agreement ()0.9, and Nash-Sutcliffe efficiency (NSE)0.5. In addition, DNDC, with its inclusion of a tile drainage mechanism, demonstrated "good" predictions for cumulative drainage (nRMSE20%,0.8, and NSE0.5). The model did, however, underestimate daily drainage flux during spring thaw for both organic and inorganic amendments. This was attributed to an underestimation of soil temperature and soil water under frequent soil freezing and thawing during the 2013-2014 overwinter period. Long-term simulations under climate variability indicated that spring applied manure resulted in less NO leaching and NO emissions than fall application when manure rates were managed based on crop N requirements. Overall, this study helped highlight the challenges in discerning the short-term climate interactions on fertilizer-induced N losses compared with the long-term dynamics under climate variability.

Measurements of nitrous oxide (N

The challenges of accurately measuring in situ ammonia (NH3) losses from agricultural systems are well known. Using an open path laser coupled with a backward Lagrangian stochastic dispersion model is a promising approach for quantifying both point- and area-sources, however, this approach requires the open path laser to detect low NH3 concentrations and small concentration differences. In this study, we compared the new GasFinder3 open path laser (Boreal laser Inc., Edmonton, Canada) with the GasFinder2 sensor, the previous version. The study took place at two locations: an outdoor open-air manure compost site, and a field of wheat stubble which was fertilized with urea ammonium nitrate. Results showed the two lasers reported similar concentrations during three days of measurements at the compost site, but differed at the field site, where concentrations were close to the minimum detection limit. The GasFinder3 had a lower standard deviation under all conditions, especially with low wind speed and high relative humidity.