Greenhouse gas emissions and N2o mitigation in beef cattle production on tropical pasture

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Quantifying N2O and CO2 emissions from subtropical pasture

Greenhouse gas emissions from a well established, unfertilized tropical grass-legume pasture were monitored over two consecutive years using high resolution automatic sampling. Nitrous oxide emissions were highest during the summer months and were highly episodic, related more to the size and distribution of rain events than WFPS alone. Mean annual emissions were significantly higher during 2008 (5.7 ± 1.0 g N2O-N/ha/day) than 2007 (3.9 ± 0.4 and g N2O-N/ha/day) despite receiving nearly 500 mm less rain. Mean CO2 (28.2 ± 1.5 kg CO2 C/ha/day) was not significantly different (P < 0.01) between measurement years, emissions being highly dependent on temperature. A negative correlation between CO2 and WFPS at >70% indicated a threshold for soil conditions favouring denitrification. The use of automatic chambers for high resolution greenhouse gas sampling can greatly reduce emission estimation errors associated with temperature and WFPS changes.

N2O emissions from agricultural lands: a synthesis of simulation approaches

Nitrous oxide (N2O) is primarily produced by the microbially-mediated nitrification and denitrification processes in soils. It is influenced by a suite of climate (i.e. temperature and rainfall) and soil (physical and chemical) variables, interacting soil and plant nitrogen (N) transformations (either competing or supplying substrates) as well as land management practices. It is not surprising that N2O emissions are highly variable both spatially and temporally. Computer simulation models, which can integrate all of these variables, are required for the complex task of providing quantitative determinations of N2O emissions. Numerous simulation models have been developed to predict N2O production. Each model has its own philosophy in constructing simulation components as well as performance strengths. The models range from those that attempt to comprehensively simulate all soil processes to more empirical approaches requiring minimal input data. These N2O simulation models can be classified into three categories: laboratory, field and regional/global levels. Process-based field-scale N2O simulation models, which simulate whole agroecosystems and can be used to develop N2O mitigation measures, are the most widely used. The current challenge is how to scale up the relatively more robust field-scale model to catchment, regional and national scales. This paper reviews the development history, main construction components, strengths, limitations and applications of N2O emissions models, which have been published in the literature. The three scale levels are considered and the current knowledge gaps and challenges in modelling N2O emissions from soils are discussed.

Nitrogen fertilizer management for nitrous oxide (N2O) mitigation in intensive corn (Maize) production: an emissions reduction protocol for US Midwest agriculture

Nitrous oxide (N2O) is a major greenhouse gas (GHG) product of intensive agriculture. Fertilizer nitrogen (N) rate is the best single predictor of N2O emissions in row-crop agriculture in the US Midwest. We use this relationship to propose a transparent, scientifically robust protocol that can be utilized by developers of agricultural offset projects for generating fungible GHG emission reduction credits for the emerging US carbon cap and trade market. By coupling predicted N2O flux with the recently developed maximum return to N (MRTN) approach for determining economically profitable N input rates for optimized crop yield, we provide the basis for incentivizing N2O reductions without affecting yields. The protocol, if widely adopted, could reduce N2O from fertilized row-crop agriculture by more than 50%. Although other management and environmental factors can influence N2O emissions, fertilizer N rate can be viewed as a single unambiguous proxy—a transparent, tangible, and readily manageable commodity. Our protocol addresses baseline establishment, additionality, permanence, variability, and leakage, and provides for producers and other stakeholders the economic and environmental incentives necessary for adoption of agricultural N2O reduction offset projects.

Influence of biochars on flux of N2O and CO2 from Ferrosol

Biochars produced by slow pyrolysis of greenwaste (GW), poultry litter (PL), papermill waste (PS), and biosolids (BS) were shown to reduce N₂O emissions from an acidic Ferrosol. Similar reductions were observed for the untreated GW feedstock. Soil was amended with biochar or feedstock giving application rates of 1 and 5%. Following an initial incubation, nitrogen (N) was added at 165 kg/ha as urea. Microcosms were again incubated before being brought to 100% water-filled porosity and held at this water content for a further 47 days. The flooding phase accounted for the majority (<80%) of total N₂O emissions. The control soil released 3165 mg N₂O-N/m², or 15.1% of the available N as N₂O. Amendment with 1 and 5% GW feedstock significantly reduced emissions to 1470 and 636 mg N₂O-N/m², respectively. This was equivalent to 8.6 and 3.8% of applied N. The GW biochar produced at 350°C was least effective in reducing emissions, resulting in 1625 and 1705 mg N₂O-N/m² for 1 and 5% amendments. Amendment with BS biochar at 5% had the greatest impact, reducing emissions to 518 mg N₂O-N/m², or 2.2% of the applied N over the incubation period. Metabolic activity as measured by CO₂ production could not explain the differences in N₂O emissions between controls and amendments, nor could NH₄⁺ or NO₃^– concentrations in biochar-amended soils. A decrease in NH₄⁺ and NO₃^– following GW feedstock application is likely to have been responsible for reducing N₂O emissions from this amendment. Reduction in N₂O emissions from the biochar-amended soils was attributed to increased adsorption of NO₃^–. Small reductions are possible due to improved aeration and porosity leading to lower levels of denitrification and N₂O emissions. Alternatively, increased pH was observed, which can drive denitrification through to dinitrogen during soil flooding.

Environmental factors controlling temporal and spatial variability in the soil-atmosphere exchange of CO2, CH4 and N2O from an Australian subtropical rainforest

The temporal variations in CO2, CH4 and N2O fluxes were measured over two consecutive years from February 2007 to March 2009 from a subtropical rainforest in south-eastern Queensland, Australia, using an automated sampling system. A concurrent study using an additional 30 manual chambers examined the spatial variability of emissions distributed across three nearby remnant rainforest sites with similar vegetation and climatic conditions. Interannual variation in fluxes of all gases over the 2 years was minimal, despite large discrepancies in rainfall, whereas a pronounced seasonal variation could only be observed for CO2 fluxes. High infiltration, drainage and subsequent high soil aeration under the rainforest limited N2O loss while promoting substantial CH4 uptake. The average annual N2O loss of 0.5 ± 0.1 kg N2O-N ha−1 over the 2-year measurement period was at the lower end of reported fluxes from rainforest soils. The rainforest soil functioned as a sink for atmospheric CH4 throughout the entire 2-year period, despite periods of substantial rainfall. A clear linear correlation between soil moisture and CH4 uptake was found. Rates of uptake ranged from greater than 15 g CH4-C ha−1 day−1 during extended dry periods to less than 2–5 g CH4-C ha−1 day−1 when soil water content was high. The calculated annual CH4 uptake at the site was 3.65 kg CH4-C ha−1 yr−1. This is amongst the highest reported for rainforest systems, reiterating the ability of aerated subtropical rainforests to act as substantial sinks of CH4. The spatial study showed N2O fluxes almost eight times higher, and CH4 uptake reduced by over one-third, as clay content of the rainforest soil increased from 12% to more than 23%. This demonstrates that for some rainforest ecosystems, soil texture and related water infiltration and drainage capacity constraints may play a more important role in controlling fluxes than either vegetation or seasonal variability

Soil N2O and CO2 emissions from cotton in Australia under varying irrigation management

Irrigation is known to stimulate soil microbial carbon and nitrogen turnover and potentially the emissions of nitrous oxide (N2O) and carbon dioxide (CO2). We conducted a study to evaluate the effect of three different irrigation intensities on soil N2O and CO2 fluxes and to determine if irrigation management can be used to mitigate N2O emissions from irrigated cotton on black vertisols in South-Eastern Queensland, Australia. Fluxes were measured over the entire 2009/2010 cotton growing season with a fully automated chamber system that measured emissions on a sub-daily basis. Irrigation intensity had a significant effect on CO2 emission. More frequent irrigation stimulated soil respiration and seasonal CO2 fluxes ranged from 2.7 to 4.1 Mg-C ha−1 for the treatments with the lowest and highest irrigation frequency, respectively. N2O emission happened episodic with highest emissions when heavy rainfall or irrigation coincided with elevated soil mineral N levels and seasonal emissions ranged from 0.80 to 1.07 kg N2O-N ha−1 for the different treatments. Emission factors (EF = proportion of N fertilizer emitted as N2O) over the cotton cropping season, uncorrected for background emissions, ranged from 0.40 to 0.53 % of total N applied for the different treatments. There was no significant effect of the different irrigation treatments on soil N2O fluxes because highest emission happened in all treatments following heavy rainfall caused by a series of summer thunderstorms which overrode the effect of the irrigation treatment. However, higher irrigation intensity increased the cotton yield and therefore reduced the N2O intensity (N2O emission per lint yield) of this cropping system. Our data suggest that there is only limited scope to reduce absolute N2O emissions by different irrigation intensities in irrigated cotton systems with summer dominated rainfall. However, the significant impact of the irrigation treatments on the N2O intensity clearly shows that irrigation can easily be used to optimize the N2O intensity of such a system.

A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission; and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Pyrolysing poultry litter reduces N2O and CO2 fluxes

Application of poultry litter (PL) to soil can lead to substantial nitrous oxide (N2O) emissions due to the co-application of labile carbon (C) and nitrogen (N). Slow pyrolysis of PL to produce biochar may mitigate N2O emissions from this source, whilst still providing agronomic benefits. In a corn crop on ferrosol with similarly matched available N inputs of ca. 116 kg N/ha, PL-biochar plus urea emitted significantly less N2O (1.5 kg N2O-N/ha) compared to raw PL at 4.9 kg N2O-N/ha. Urea amendment without the PL-biochar emitted 1.2 kg N2O-N/ha, and the PL-biochar alone emitted only 0.35 kg N2O-N/ha. Both PL and PL-biochar resulted in similar corn yields and total N uptake which was significantly greater than for urea alone. Using stable isotope methodology, the majority (~ 80%) of N2O emissions were shown to be from non-urea sources. Amendment with raw PL significantly increased C mineralisation and the quantity of permanganate oxidisable organic C. The low molar H/C (0.49) and O/C (0.16) ratios of the PL-biochar suggest its higher stability in soil than raw PL. The PL-biochar also had higher P and K fertiliser value than raw PL. This study suggests that PL-biochar is a valuable soil amendment with the potential to significantly reduce emissions of soil greenhouse gases compared to the raw product. Contrary to other studies, PL-biochar incorporated to 100 mm did not reduce N2O emissions from surface applied urea, which suggests that further field evaluation of biochar impacts, and methods of application of both biochar and fertiliser, are needed.

Improving the statistical preparation for measuring soil N2O flux by closed chamber

Nitrous oxide emissions from soil are known to be spatially and temporally volatile. Reliable estimation of emissions over a given time and space depends on measuring with sufficient intensity but deciding on the number of measuring stations and the frequency of observation can be vexing. The question of low frequency manual observations providing comparable results to high frequency automated sampling also arises. Data collected from a replicated field experiment was intensively studied with the intention to give some statistically robust guidance on these issues. The experiment had nitrous oxide soil to air flux monitored within 10 m by 2.5 m plots by automated closed chambers under a 3 h average sampling interval and by manual static chambers under a three day average sampling interval over sixty days. Observed trends in flux over time by the static chambers were mostly within the auto chamber bounds of experimental error. Cumulated nitrous oxide emissions as measured by each system were also within error bounds. Under the temporal response pattern in this experiment, no significant loss of information was observed after culling the data to simulate results under various low frequency scenarios. Within the confines of this experiment observations from the manual chambers were not spatially correlated above distances of 1 m. Statistical power was therefore found to improve due to increased replicates per treatment or chambers per replicate. Careful after action review of experimental data can deliver savings for future work.

Spatial prediction of N2O emissions in pasture : a Bayesian model averaging analysis

Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2) field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

Influence of different nitrogen rates and DMPP nitrification inhibitor on annual N2O emissions from a subtropical wheat–maize cropping system

Global cereal production will need to increase by 50% to 70% to feed a world population of about 9 billion by 2050. This intensification is forecast to occur mostly in subtropical regions, where warm and humid conditions can promote high N2O losses from cropped soils. To secure high crop production without exacerbating N2O emissions, new nitrogen (N) fertiliser management strategies are necessary. This one-year study evaluated the efficacy of a nitrification inhibitor (3,4-dimethylpyrazole phosphate—DMPP) and different N fertiliser rates to reduce N2O emissions in a wheat–maize rotation in subtropical Australia. Annual N2O emissions were monitored using a fully automated greenhouse gas measuring system. Four treatments were fertilized with different rates of urea, including a control (40 kg-N ha−1 year−1), a conventional N fertiliser rate adjusted on estimated residual soil N (120 kg-N ha−1 year−1), a conventional N fertiliser rate (240 kg-N ha−1 year−1) and a conventional N fertiliser rate (240 kg-N ha−1 year−1) with nitrification inhibitor (DMPP) applied at top dressing. The maize season was by far the main contributor to annual N2O emissions due to the high soil moisture and temperature conditions, as well as the elevated N rates applied. Annual N2O emissions in the four treatments amounted to 0.49, 0.84, 2.02 and 0.74 kg N2O–N ha−1 year−1, respectively, and corresponded to emission factors of 0.29%, 0.39%, 0.69% and 0.16% of total N applied. Halving the annual conventional N fertiliser rate in the adjusted N treatment led to N2O emissions comparable to the DMPP treatment but extensively penalised maize yield. The application of DMPP produced a significant reduction in N2O emissions only in the maize season. The use of DMPP with urea at the conventional N rate reduced annual N2O emissions by more than 60% but did not affect crop yields. The results of this study indicate that: (i) future strategies aimed at securing subtropical cereal production without increasing N2O emissions should focus on the fertilisation of the summer crop; (ii) adjusting conventional N fertiliser rates on estimated residual soil N is an effective practice to reduce N2O emissions but can lead to substantial yield losses if the residual soil N is not assessed correctly; (iii) the application of DMPP is a feasible strategy to reduce annual N2O emissions from sub-tropical wheat–maize rotations. However, at the N rates tested in this study DMPP urea did not increase crop yields, making it impossible to recoup extra costs associated with this fertiliser. The findings of this study will support farmers and policy makers to define effective fertilisation strategies to reduce N2O emissions from subtropical cereal cropping systems while maintaining high crop productivity. More research is needed to assess the use of DMPP urea in terms of reducing conventional N fertiliser rates and subsequently enable a decrease of fertilisation costs and a further abatement of fertiliser-induced N2O emissions.

The relationship between N2O, NO, and N2 fluxes from fertilized and irrigated dryland soils of the Aral Sea Basin, Uzbekistan

Microbial respiratory reduction of nitrous oxide (N2O) to dinitrogen (N2) via denitrification plays a key role within the global N-cycle since it is the most important process for converting reactive nitrogen back into inert molecular N2. However, due to methodological constraints, we still lack a comprehensive, quantitative understanding of denitrification rates and controlling factors across various ecosystems. We investigated N2, N2O and NO emissions from irrigated cotton fields within the Aral Sera Basin using the He/O2 atmosphere gas flow soil core technique and an incubation assay. NH4NO3 fertilizer, equivalent to 75 kg ha−1 and irrigation water, adjusting the water holding capacity to 70, 100 and 130% were applied to the incubation vessels to assess its influence on gaseous N emissions. Under soil conditions as they are naturally found after concomitant irrigation and fertilization, denitrification was the dominant process and N2 the main end product of denitrification. The mean ratios of N2/N2O emissions increased with increasing soil moisture content. N2 emissions exceeded N2O emissions by a factor of 5 ± 2 at 70% soil water holding capacity (WHC) and a factor of 55 ± 27 at 130% WHC. The mean ratios of N2O/NO emissions varied between 1.5 ± 0.4 (70% WHC) and 644 ± 108 (130% WHC). The magnitude of N2 emissions for irrigated cotton was estimated to be in the range of 24 ± 9 to 175 ± 65 kg-N ha−1season−1, while emissions of NO were only of minor importance (between 0.1 to 0.7 kg-N ha−1 season−1). The findings demonstrate that for irrigated dryland soils in the Aral Sera Basin, denitrification is a major pathway of N-loss and that substantial amounts of N-fertilizer are lost as N2 to the atmosphere for irrigated dryland soils.