Scenario analysis of fertilizer management practices for N2O mitigation from corn systems in Canada

Exploring adaptation responses of maize to climate change scenarios in southern central Rift Valley of Ethiopia

In this study, we assessed responses of adaptation options to possible climate change scenarios on maize growth and yield by using projections of 20 coupled ensemble climate models under two representative concentration pathways (RCPs) 4.5 and 8.5 by means of a DSSAT model. Growth and yield simulations were made across present and future climate conditions using the hybrid maize variety (Shone). Subsequently, simulated yields were compared with farmer' average and on-farm trial yields. Results showed that on-farm trial yield (5.1-7.3 t ha-1) lay in between farmers' average yield (2.9-5 t ha-1) and water-limited potential yield (6.3-10.6 t ha-1). Maize yields achieved in farmers' fields are projected to decline towards mid-century and further towards the end of the century regardless of the adaptation options compared with baseline in low potential clusters. Results of a combination of adaptation options including February planting, use of 64 kg ha-1 N and conservation tillage provided yield advantage of 5.8% over the 30 cm till under medium GHGs emission scenario during mid-century period at Shamana. Mulching with 5 t ha-1 was projected to produce a 4-5% yield advantage in the Hawassa cluster during the mid-century period regardless of changes in tillage or planting window. Under a high GHGs emission scenario, over 13.4% yield advantage was projected in the Bilate cluster due to conservation tillage and June planting during the mid-century period. In the Dilla cluster, the use of 10 t ha-1 mulch, conservation tillage and early planting (February) would result in a 1.8% yield advantage compared with the control either in medium or high GHGs emission scenarios. Thus, the most promising and least risky practices among simulated strategies were the use of nitrogen and mulching in combination with tillage or planting date adjustment. However, adaptation options remained least promising and highly risky if not integrated with mulching or nitrogen use. Hence, the negative impacts of future climate change and subsequent yield gaps would be reduced by optimizing the application of nitrogen, mulch and their interaction with planting date and tillage in high and low potential areas of maize production.

Partial substitution of manure reduces nitrous oxide emission with maintained yield in a winter wheat crop

Conventional fertilization of agricultural soils results in increased N₂O emissions. As an alternative, the partial substitution of organic fertilizer may help to regulate N₂O emissions. However, studies assessing the effects of partial substitution of organic fertilizer on both N₂O emissions and yield stability are currently limited. We conducted a field experiment from 2017 to 2021 with six fertilizer regimes to examine the effects of partial substitution of manure on N₂O emissions and yield stability. The tested fertilizer regimes, were CK (no fertilizer), CF (chemical fertilizer alone, N 300 kg ha^-1, P₂O₅ 150 kg ha^-1, K₂O 90 kg ha^-1), CF + M (chemical fertilizer + organic manure), CFR (chemical fertilizer reduction, N 225 kg ha^-1, P₂O₅ 135 kg ha^-1, K₂O 75 kg ha^-1), CFR + M (chemical fertilizer reduction + organic manure), and organic manure alone (M). Our results indicate that soil N₂O emissions are primarily regulated by soil mineral N content in arid and semi-arid regions. Compared with CF, N₂O emissions in the CF + M, CFR, CFR + M, and M treatments decreased by 16.8%, 23.9%, 42.0%, and 39.4%, respectively. The highest winter wheat yields were observed in CF, followed by CF + M, CFR, and CFR + M. However, the CFR + M treatment exhibited lower N₂O emissions while maintaining high yield, compared with CF. Four consecutive years of yield data from 2017 to 2021 illustrated that a single application of organic fertilizer resulted in poor yield stability and that partial substitution of organic fertilizer resulted in the greatest yield stability. Overall, partial substitution of manure reduced N₂O emissions while maintaining yield stability compared with the synthetic fertilizer treatment during the wheat growing season. Therefore, partial substitution of manure can be recommended as an optimal N fertilization regime for alleviating N₂O emissions and contributing to food security in arid and semi-arid regions.

A recommended nitrogen application strategy for high crop yield and low environmental pollution at a basin scale

Mitigating environmental pollution and sustaining grain production have been foundational issues in sustainable development, however, ascertaining the optimal balance remains poorly investigated. This study used the Soil and Water Assessment Tool (SWAT) model to simulate crop growth and nitrogen loss, established the mapping relationship between nitrogen input to yield and water quality, and proposed a general method to determine a nitrogen application strategy for high yield and low pollution at a basin scale. Lake Xiaoxingkai basin, which is the primary maize producing area in China as well as an internationally important wetland distribution area, was used as a case study. First, we designed application scenarios for 10 base fertilizers (B1-B10) and 10 topdressing fertilizers (T1-T10) and evaluated their combined effects of maize growth to identify the critical nitrogen fertilizer rates determined under fixed and dynamic base/topdressing ratios. Then, the critical base and topdressing fertilizer rates were determined. Based on the mapping relationship between nitrogen fertilizer rate and nitrogen loss, we then revealed water quality at the basin outlet under the critical base and topdressing fertilizer rates. Finally, we proposed alternative nitrogen application strategies for high yield and low pollution while considering the different preferences of decision-makers for the economy, agriculture, and environment. We found that adjusting the ratio of base to topdressing fertilizer may create a win-win situation for agriculture and the environment, which will provide a scientific basis for sustainable development.

Responses of Low-Cost Input Combinations on the Microbial Structure of the Maize Rhizosphere for Greenhouse Gas Mitigation and Plant Biomass Production

The microbial composition of the rhizosphere and greenhouse gas (GHG) emissions under the most common input combinations in maize (Zea mays L.) cultivated in Brazil have not been characterized yet. In this study, we evaluated the influence of maize stover coverage (S), urea-topdressing fertilization (F), and the microbial inoculant Azospirillum brasilense (I) on soil GHG emissions and rhizosphere microbial communities during maize development. We conducted a greenhouse experiment and measured methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O) fluxes from soil cultivated with maize plants under factorial combinations of the inputs and a control treatment (F, I, S, FI, FS, IS, FIS, and control). Plant biomass was evaluated, and rhizosphere soil samples were collected at V5 and V15 stages and DNA was extracted. The abundance of functional genes (mcrA, pmoA, nifH, and nosZ) was determined by quantitative PCR (qPCR) and the structure of the microbial community was assessed through 16S rRNA amplicon sequencing. Our results corroborate with previous studies which used fewer input combinations and revealed different responses for the following three inputs: F increased N₂O emissions around 1 week after application; I tended to reduce CH₄ and CO₂ emissions, acting as a plant growth stimulator through phytohormones; S showed an increment for CO₂ emissions by increasing carbon-use efficiency. IS and FIS treatments presented significant gains in biomass that could be related to Actinobacteria (19.0%) and Bacilli (10.0%) in IS, and Bacilli (9.7%) in FIS, which are the microbial taxa commonly associated with lignocellulose degradation. Comparing all factors, the IS (inoculant + maize stover) treatment was considered the best option for plant biomass production and GHG mitigation since FIS provides small gains toward the management effort of F application.

Assessing the impacts of diversified crop rotation systems on yields and nitrous oxide emissions in Canada using the DNDC model

Process-based models are effective tools for assessing the sustainability of agricultural productivity and environmental health under various management practices and rotation systems. The objectives of this study were to (1) calibrate and evaluate the DeNitrification-DeComposition (DNDC) model using measurements of yields, nitrogen (N) uptake, soil inorganic N, soil temperature, soil moisture and nitrous oxide (N₂O) emissions under long-term fertilized continuous corn (CC) and corn-oats-alfalfa-alfalfa (COAA) rotation systems in southwest Ontario from 1959 to 2015, Canada, and (2) explore the impacts of four diverse rotation systems (CC, COAA, corn-soybean-corn-soybean (CSCS) and corn-soybean-winter wheat (CSW)) on corn yields and annual N₂O emissions under long-term climate variability. DNDC demonstrated "good" performance in simulating corn, oats and alfalfa yield (normalized root mean square error (nRMSE) < 20%, Nash-Sutcliffe efficiency (NSE) > 0.5 and index of agreement (d) > 0.8). The model provided "fair" to "good" simulations for corn N uptake and soil inorganic N (NSE > 0.2 and d > 0.8), and also daily soil temperature and soil moisture (nRMSE <30% and d > 0.7) for both calibration and validation periods. The model demonstrated "good" performance in estimating daily and cumulative N₂O emissions from both the continuous and rotational corn, whereas it produced "poor" to "good" predictions for N₂O emissions from the rotational oats and alfalfa crops, however, the emissions from these crops were very low and the relative magnitude of these emissions between all crops investigated were well predicted. The lowest N₂O emissions were from COAA followed by CSCS, CSW then CC. The highest corn yields were from COAA, followed by CSW, CSCS, then CC. This study highlights how modelling approaches can help improve the understanding of the impacts of diversified rotations on crop production and greenhouse gas emissions and contribute towards developing policies aimed at improving the sustainability and resiliency of cropping systems.

Modelling adaptation strategies to reduce adverse impacts of climate change on maize cropping system in Northeast China

Maize (Zea mays L.) production in Northeast China is vulnerable to climate change. Thus, exploring future adaptation measures for maize is crucial to developing sustainable agriculture to ensure food security. The current study was undertaken to evaluate the impacts of climate change on maize yield and partial factor productivity of nitrogen (PFPN) and explore potential adaptation strategies in Northeast China. The Decision Support System for Agrotechnology Transfer (DSSAT) model was calibrated and validated using the measurements from nine maize experiments. DSSAT performed well in simulating maize yield, biomass and N uptake for both calibration and validation periods (normalized root mean square error (nRMSE) < 10%, -5% < normalized average relative error (nARE) < 5% and index of agreement (d) > 0.8). Compared to the baseline (1980-2010), the average maize yields and PFPN would decrease by 7.6-32.1% and 3.6-14.0 kg N kg^-1 respectively under future climate scenarios (2041-2070 and 2071-2100) without adaptation. Optimizing N application rate and timing, establishing rotation system with legumes, adjusting planting dates and breeding long-season cultivars could be effective adaptation strategies to climate change. This study demonstrated that optimizing agronomic crop management practices would assist to make policy development on mitigating the negative impacts of future climate change on maize production.

Mapping Conservation Management Practices and Outcomes in the Corn Belt Using the Operational Tillage Information System (OpTIS) and the Denitrification–Decomposition (DNDC) Model

Identifying and quantifying conservation-practice adoption in U.S. cropland is key to accurately monitoring trends in soil health regionally and nationally and informing climate change mitigation efforts. We present the results of an automated system used across 645 counties in the United States Corn Belt from 2005 to 2018, mapped at field-scale and summarized for distribution at aggregated scales. Large-scale mapping by OpTIS (Operational Tillage Information System), a software tool that analyzes remotely sensed data of agricultural land, provides trends of conservation tillage (defined as >30% residue cover), cover cropping, and crop rotations, while modeling by DNDC (Denitrification–Decomposition), a process-based model of carbon and biogeochemistry in soil, provides estimates of the ecosystem outcomes associated with the changes in management practices mapped by OpTIS. Ground-truthing data acquired via OpTIS mobile, a roadside field-surveying app, were used for verification in 30 counties. OpTIS results for the Corn Belt show adoption of cover crops after planting corn and soy increased from 1% to 3% of the mapped area when comparing 2006 to 2018. Comparison of trends for conservation tillage use from 2006 to 2018 shows a slight decrease in conservation tillage adoption, from 46% to 44%. Results from DNDC show these soils sequestered soil organic carbon (SOC) at an area-weighted mean change in SOC (dSOC) rate of 161 kgC/ha/year. Comparatively, in a scenario modeled without the adoption of soil health management practices, the same soils would have lost SOC at an area-weighted rate of −65 kgC/ha/year. As many factors affect changes to SOC, including climate and initial SOC in soils, modeling counterfactual scenarios at the field scale demonstrates outcomes of current soil health management in comparison to regional management practices and best management practices, with respect to SOC sequestration. Regional trends in adoption rates of conservation agriculture and resulting soil health implications are of great use for a wide range of stakeholders. We demonstrate the capability of OpTIS remote sensing to deliver robust, large-scale, multi-sensor, ground-verified monitoring data of current and historical adoption of conservation practices, and of DNDC process-based modeling to provide assessments of the associated environmental outcomes across regions in U.S. cropland.

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).

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 the effects of manure application rate and timing on nitrous oxide emissions from managed grasslands under contrasting climate in Canada

It is uncertain whether process-based models are currently capable of simulating the complex soil, plant, climate, manure management interactions that influence soil nitrous oxide (N₂O) emissions from perennial cropping systems. The objectives of this study were (1) to calibrate and evaluate the DeNitrification DeComposition (DNDC) model using multi-year datasets of measured nitrous oxide (N₂O) fluxes, soil moisture, soil inorganic nitrogen, biomass and soil temperature from managed grasslands applied with manure slurry in contrasting climates of Canada, and (2) to simulate the impact of different manure management practices on N₂O emissions including slurry application i) rates (for both single vs. split); and ii) timing (e.g., early vs. late spring). DNDC showed "fair" to "excellent" performance in simulating biomass (4.7% ≤ normalized root mean square error (NRMSE) ≤ 29.8%; -9.5% ≤ normalized average relative error (NARE) ≤ 16.1%) and "good" performance in simulating soil temperature (13.2% ≤ NRMSE ≤ 18.1%; -0.7% ≤ NARE ≤ 10.8%) across all treatments and sites. However, the model only showed "acceptable" performances in estimating soil water and inorganic N contents which was partially attributed to the limitation of a cascade water sub-model and inaccuracies in simulating root development/uptake. Although, the DNDC model only demonstrated "fair" performance in simulating daily N₂O fluxes, it generally captured the impact of the timing and rate of slurry application and soil texture (loam vs. sandy loam) on total N₂O emissions. The DNDC model simulated N₂O emissions from spring better than split manure application (fall and spring) at the Manitoba site partially due to the overestimation of available substrates for microbial denitrification from fall application during the wet spring periods. Although DNDC performed adequately for simulating most of the manure management impacts considered in this study we recommend improvements in the simulation of soil freeze-thaw cycles, manure decomposition dynamics, soil water storage, rainfall canopy interception, and microbial denitrification and nitrification activities in grasslands.

Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting

Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.

Controls and Adaptive Management of Nitrification in Agricultural Soils

Agriculture is responsible for over half of the input of reactive nitrogen (N) to terrestrial systems; however improving N availability remains the primary management technique to increase crop yields in most regions. In the majority of agricultural soils, ammonium is rapidly converted to nitrate by nitrification, which increases the mobility of N through the soil matrix, strongly influencing N retention in the system. Decreasing nitrification through management is desirable to decrease N losses and increase N fertilizer use efficiency. We review the controlling factors on the rate and extent of nitrification in agricultural soils from temperate regions including substrate supply, environmental conditions, abundance and diversity of nitrifiers and plant and microbial interactions with nitrifiers. Approaches to the management of nitrification include those that control ammonium substrate availability and those that inhibit nitrifiers directly. Strategies for controlling ammonium substrate availability include timing of fertilization to coincide with rapid plant update, formulation of fertilizers for slow release or with inhibitors, keeping plant growing continuously to assimilate N, and intensify internal N cycling (immobilization). Another effective strategy is to inhibit nitrifiers directly with either synthetic or biological nitrification inhibitors. Commercial nitrification inhibitors are effective but their use is complicated by a changing climate and by organic management requirements. The interactions of the nitrifying organisms with plants or microbes producing biological nitrification inhibitors is a promising approach but just beginning to be critically examined. Climate smart agriculture will need to carefully consider optimized seasonal timing for these strategies to remain effective management tools.

Understanding the Fertilizer Management Impacts on Water and Nitrogen Dynamics for a Corn Silage Tile-Drained System in Canada

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 (nRMSE < 20%, > 0.8, and NSE > 0.5). 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.

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.

Water table management and fertilizer application impacts on CO2, N2O and CH4 fluxes in a corn agro-ecosystem

Water table management with controlled drainage and subsurface-irrigation (SI) has been identified as a Beneficial Management Practice (BMP) to reduce nitrate leaching in drainage water. It has also been shown to increase crop yields during dry periods of the growing season, by providing water to the crop root zone, via upward flux or capillary rise. However, by retaining nitrates in anoxic conditions within the soil profile, SI could potentially increase greenhouse gas (GHG) fluxes, particularly N₂O through denitrification. This process may be further exacerbated by high precipitation and mineral N-fertilizer applications very early in the growing season. In order to investigate the effects of water table management (WTM) with nitrogen fertilization on GHG fluxes from corn (Zea mays) agro-ecosystems, we conducted a research study on a commercial farm in south-western Quebec, Canada. Water table management treatments were: free drainage (FD) and controlled drainage with subsurface-irrigation. GHG samples were taken using field-deployed, vented non-steady state gas chambers to quantify soil CO₂, N₂O and CH₄ fluxes weekly. Our results indicate that fertilizer application timing coinciding with intense (≥24 mm) precipitation events and high temperatures (>25 °C) triggered pulses of N₂O fluxes, accounting for up to 60% of cumulative N₂O fluxes. Our results also suggest that splitting bulk fertilizer applications may be an effective mitigation strategy, reducing N₂O fluxes by 50% in our study. In both seasons, pulse GHG fluxes mostly occurred in the early vegetative stages of the corn, prior to activation of the subsurface-irrigation. Our results suggest that proper timing of WTM mindful of seasonal climatic conditions has the potential to reduce GHG emissions.

Prediction of N2O emissions under different field management practices and climate conditions

Due to the contributions of nitrous oxide (N₂O) to global climate change and stratospheric ozone destruction, it is important to understand how climate and agricultural management affect N₂O emissions. Although the process-based Denitrification Decomposition (DNDC) model is often used for quantifying emissions of N₂O, the accuracy of these predictions remains in question, and it is not clear which input variables, environmental or field management, have the greatest effect on model performance. In this study, DNDC was evaluated for prediction of N₂O fluxes from two climatically-different corn-field sites in the United States (a Colorado irrigated field and a Minnesota rainfed field). Besides climate, these sites offer the additional advantage that measurements are available for multiple field management practices, including fertilizer application, tillage, and crop rotation. This evaluation found that DNDC did not consistently, correctly predict daily-scale N₂O fluxes. Cumulative growing season N₂O fluxes were significantly under-predicted in Colorado and were both under- and over-predicted in Minnesota. Model calibration of four soil input parameters did not significantly improve N₂O emission predictions at either site or time scale. Modeled and measured N₂O fluxes and model error were all strongly correlated with precipitation. Over-predictions of N₂O fluxes were associated with heavy precipitation and high modeled denitrification. Based on our results, model improvements to decrease model error for corn cropping systems in temperate climate zones should focus on better accounting for the effects of precipitation on denitrification. Despite discrepancies in daily and cumulative growing season N₂O fluxes, DNDC correctly identified the only field management (fertilizer application rate) that significantly influenced the measured N₂O fluxes.

Filling two needs with one deed: Potentials to simultaneously improve phosphorus and nitrogen management in Austria as an example for coupled resource management systems

The tremendous increase in resource consumption over the past century and the environmental challenges it entails has spurred discussions for a shift from a linear to a circular resource use. However, to date most resource studies are restricted to one material or a single sector or process. In this work, a coupled material flow analysis taking the national phosphorus (P) and nitrogen (N) system of Austria as an example for two closely connected resource systems is conducted. Effects of different measures aimed at reducing P and/or N-demand, increasing recycling or reducing emissions to air and water are compared to a reference state (representing the actual situation in 2015). Changes in the mineral fertilizer demand of the system, P and N losses in the waste sector, water emissions of P and N, P soil accumulation and atmospheric N emissions are analyzed. Overall positive feedbacks between measures and between different goals of one measure always outweigh negative ones, which is why the highest efficiency gains (57±4%) can be achieved by a combination of all the 16 measures studied. Potentials for the reduction of mineral fertilizer demand are larger than for emission reduction though, confirming the past priority of environmental protection over resource protection. Although coupling significantly raises model complexity it can be shown that material flows of more than one substance can be simultaneously analyzed in a rather complex system. This may reveal interrelations, co-benefits and trade-offs between different resources that might have been omitted in a mono-substance analysis and thus improve judgment of sustainability and viability of different management strategies.

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.

Incorporating denitrification-decomposition method to estimate field emissions for Life Cycle Assessment

This study focuses on a detailed Life Cycle Assessment (LCA) for flax cultivation in Northern France. Nitrogen related field emissions are derived both from a process-oriented DeNitrification-DeComposition (DNDC) method and the generic Intergovernmental Panel on Climate Change (IPCC) method. Since the IPCC method is synthesised from field measurements at sites with various soil types, climate conditions, and crops, it contains significant uncertainties. In contrast, the outputs from the DNDC method are considered as more site specific as it is built according to complex models of soil science. As it is demonstrated in this paper the emission factors from the DNDC method and the recommended values from the IPCC method exhibit significant variations for the case of flax cultivation. The DNDC based emission factor for direct N₂O emission, which is a strong greenhouse gas, is 0.25-0.5%, significantly lower than the recommend 1% level derived from the IPCC method. The DNDC method leads to a reduction of 17% in the impact category of climate change per kg retted flax straw production from the level obtained from the IPCC method. Much higher reductions are recorded for particulate matter formation, terrestrial acidification, and marine eutrophication impact categories. Meanwhile, based on the DNDC and IPCC methods, a comparative LCA per kg flax straw is presented. For both methods sensitivity analysis as well as comparison of uncertainties parameterisation of the N₂O estimates via Monte-Carlo analysis are performed. The DNDC method incorporates more relevant field emissions from the agricultural life cycle phase, which can also improve the quality of the Life Cycle Inventory as well as allow more precise uncertainty calibration in the LCA inventory.