Influence of historic land use change on the biosphere-atmosphere-exchange of C and N trace gases in the humid, subtropical region of Queensland

Increases in atmospheric concentrations of the greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) due to human activities have been linked to climate change. GHG emissions from land use change and agriculture have been identified as significant contributors to both Australia’s and the global GHG budget. This is expected to increase over the coming decades as rates of agriculture intensification and land use change accelerate to support population growth and food production. Limited data exists on CO2, CH4 and N2O trace gas fluxes from subtropical or tropical soils and land uses. To develop effective mitigation strategies a full global warming potential (GWP) accounting methodology is required that includes emissions of the three primary greenhouse gases. Mitigation strategies that focus on one gas only can inadvertently increase emissions of another. For this reason, detailed inventories of GHGs from soils and vegetation under individual land uses are urgently required for subtropical Australia. This study aimed to quantify GHG emissions over two consecutive years from three major land uses; a well-established, unfertilized subtropical grass-legume pasture, a 30 year (lychee) orchard and a remnant subtropical Gallery rainforest, all located near Mooloolah, Queensland. GHG fluxes were measured using a combination of high resolution automated sampling, coarser spatial manual sampling and laboratory incubations. Comparison between the land uses revealed that land use change can have a substantial impact on the GWP on a landscape long after the deforestation event. The conversion of rainforest to agricultural land resulted in as much as a 17 fold increase in GWP, from 251 kg CO2 eq. ha-1 yr-1 in the rainforest to 889 kg CO2 eq. ha-1 yr-1 in the pasture to 2538 kg CO2 eq. ha-1 yr-1 in the lychee plantation. This increase resulted from altered N cycling and a reduction in the aerobic capacity of the soil in the pasture and lychee systems, enhancing denitrification and nitrification events, and reducing atmospheric CH4 uptake in the soil. High infiltration, drainage and subsequent soil aeration under the rainforest limited N2O loss, as well as promoting CH4 uptake of 11.2 g CH4-C ha-1 day-1. This was among the highest reported for rainforest systems, indicating that aerated subtropical rainforests can act as substantial sink of CH4. Interannual climatic variation resulted in significantly higher N2O emission from the pasture during 2008 (5.7 g N2O-N ha day) compared to 2007 (3.9 g N2O-N ha day), despite receiving nearly 500 mm less rainfall. Nitrous oxide emissions from the pasture were highest during the summer months and were highly episodic, related more to the magnitude and distribution of rain events rather than soil moisture alone. Mean N2O emissions from the lychee plantation increased from an average of 4.0 g N2O-N ha-1 day-1, to 19.8 g N2O-N ha-1 day-1 following a split application of N fertilizer (560 kg N ha-1, equivalent to 1 kg N tree-1). The timing of the split application was found to be critical to N2O emissions, with over twice as much lost following an application in spring (emission factor (EF): 1.79%) compared to autumn (EF: 0.91%). This was attributed to the hot and moist climatic conditions and a reduction in plant N uptake during the spring creating conditions conducive to N2O loss. These findings demonstrate that land use change in subtropical Australia can be a significant source of GHGs. Moreover, the study shows that modifying the timing of fertilizer application can be an efficient way of reducing GHG emissions from subtropical horticulture.

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.

Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI).

Varying the spatial distribution of applied nitrogen (N) fertilizer to match demand in crops has been shown to increase profits in Australia. Better matching the timing of N inputs to plant requirements has been shown to improve nitrogen use efficiency and crop yields and could reduce nitrous oxide emissions from broad acre grains. Farmers in the wheat production area of south eastern Australia are increasingly splitting N application with the second timing applied at stem elongation (Zadoks 30). Spectral indices have shown the ability to detect crop canopy N status but a robust method using a consistent calibration that functions across seasons has been lacking. One spectral index, the canopy chlorophyll content index (CCCI) designed to detect canopy N using three wavebands along the "red edge" of the spectrum was combined with the canopy nitrogen index (CNI), which was developed to normalize for crop biomass and correct for the N dilution effect of crop canopies. The CCCI-CNI index approach was applied to a 3-year study to develop a single calibration derived from a wheat crop sown in research plots near Horsham, Victoria, Australia. The index was able to predict canopy N (g m-2) from Zadoks 14-37 with an r2 of 0.97 and RMSE of 0.65 g N m-2 when dry weight biomass by area was also considered. We suggest that measures of N estimated from remote methods use N per unit area as the metric and that reference directly to canopy %N is not an appropriate method for estimating plant concentration without first accounting for the N dilution effect. This approach provides a link to crop development rather than creating a purely numerical relationship. The sole biophysical input, biomass, is challenging to quantify robustly via spectral methods. Combining remote sensing with crop modelling could provide a robust method for estimating biomass and therefore a method to estimate canopy N remotely. Future research will explore this and the use of active and passive sensor technologies for use in precision farming for targeted N management.

Clays Can Decrease Gaseous Nutrient Losses from Soil-Applied Livestock Manures

Clays could underpin a viable agricultural greenhouse gas (GHG) abatement technology given their affinity for nitrogen and carbon compounds. We provide the first investigation into the efficacy of clays to decrease agricultural nitrogen GHG emissions (i.e., N2O and NH3). Via laboratory experiments using an automated closed-vessel analysis system, we tested the capacity of two clays (vermiculite and bentonite) to decrease N2O and NH3 emissions and organic carbon losses from livestock manures (beef, pig, poultry, and egg layer) incorporated into an agricultural soil. Clay addition levels varied, with a maximum of 1:1 to manure (dry weight). Cumulative gas emissions were modeled using the biological logistic function, with 15 of 16 treatments successfully fitted (P < 0.05) by this model. When assessing all of the manures together, NH3 emissions were lower (×2) at the highest clay addition level compared with no clay addition, but this difference was not significant (P = 0.17). Nitrous oxide emissions were significantly lower (×3; P < 0.05) at the highest clay addition level compared with no clay addition. When assessing manures individually, we observed generally decreasing trends in NH3 and N2O emissions with increasing clay addition, albeit with widely varying statistical significance between manure types. Most of the treatments also showed strong evidence of increased C retention with increasing clay additions, with up to 10 times more carbon retained in treatments containing clay compared with treatments containing no clay. This preliminary assessment of the efficacy of clays to mitigate agricultural GHG emissions indicates strong promise.

N2O fluxes from the native and grazed semi-arid steppes and their driving factors in Inner Mongolia, China

This paper presents results of 2 years (from January 2005 to December 2006) of measurement of N2O fluxes from the native and grazed Leymus chinensis (LC) steppes in Inner Mongolia, China using the static opaque chamber method. The measurement was at a frequency of twice per month in the growing season and once per month in the non-growing season. In addition, the possible effect of water-heat factors on N2O fluxes was statistically analyzed. The results indicated that there were distinct seasonal patterns in N2O fluxes with large fluxes in spring, summer, and autumn but negative fluxes in winter. The annual net emission of N2O ranging from 0.24 to 0.30 kg N2O-N ha(-1) and from 0.06 to 0.26 kg N2O-N ha(-1) from the native and grazed LC steppe, respectively. Grazing activities suppressed N2O production. In the growing season, soil moisture was the primary driving factor of N2O fluxes. The high seasonal variation of N2O fluxes was regulated by the distribution of effective rainfall, rather than precipitation intensity. Air temperature or soil temperature at 0, 5, and 10 cm depth was the most restricting factor of N2O fluxes in the non-growing season.

Application of biogas slurries from energy crops to arable soils and their impact on carbon and nitrogen dynamics

The use of renewable primary products as co-substrate or single substrate for biogas production has increased consistently over the last few years. Maize silage is the preferential energy crop used for fermentation due to its high methane (CH4) yield per hectare. Equally, the by-product, namely biogas slurry (BS), is used with increasing frequency as organic fertilizer to return nutrients to the soil and to maintain or increase the organic matter stocks and soil fertility. Studies concerning the application of energy crop-derived BS on the carbon (C) and nitrogen (N) mineralization dynamics are scarce. Thus, this thesis focused on the following objectives: I) The determination of the effects caused by rainfall patterns on the C and N dynamics from two contrasting organic fertilizers, namely BS from maize silage and composted cattle manure (CM), by monitoring emissions of nitrous oxide (N2O), carbon dioxide (CO2) and CH4 as well as leaching losses of C and N. II) The investigation of the impact of differences in soil moisture content after the application of BS and temperature on gaseous emissions (CO2, N2O and CH4) and leaching of C and N compounds. III) A comparison of BS properties obtained from biogas plants with different substrate inputs and operating parameters and their effect on C and N dynamics after application to differently textured soils with varying application rates and water contents. For the objectives I) and II) two experiments (experiment I and II) using undisturbed soil cores of a Haplic Luvisol were carried out. Objective III) was studied on a third experiment (experiment III) with disturbed soil samples. During experiment I three rainfall patterns were implemented including constant irrigation, continuous irrigation with periodic heavy rainfall events, and partial drying with rewetting periods. Biogas slurry and CM were applied at a rate of 100 kg N ha-1. During experiment II constant irrigation and an irrigation pattern with partial drying with rewetting periods were carried out at 13.5°C and 23.5°C. The application of BS took place either directly before a rewetting period or one week after the rewetting period stopped. Experiment III included two soils of different texture which were mixed with ten BS’s originating from ten different biogas plants. Treatments included low, medium and high BS-N application rates and water contents ranging from 50% to 100% of water holding capacity (WHC). Experiment I and II showed that after the application of BS cumulative N2O emissions were 4 times (162 mg N2O-N m-2) higher compared to the application of CM caused by a higher content of mineral N (Nmin) in the form of ammonium (NH4+) in the BS. The cumulative emissions of CO2, however, were on the same level for both fertilizers indicating similar amounts of readily available C after composting and fermentation of organic material. Leaching losses occurred predominantly in the mineral form of nitrate (NO3-) and were higher in BS amended soils (9 mg NO3--N m-2) compared to CM amended soils (5 mg NO3--N m-2). The rainfall pattern in experiment I and II merely affected the temporal production of C and N emissions resulting in reduced CO2 and enhanced N2O emissions during stronger irrigation events, but showed no effect on the cumulative emissions. Overall, a significant increase of CH4 consumption under inconstant irrigation was found. The time of fertilization had no effect on the overall C and N dynamics. Increasing temperature from 13.5°C to 23.5°C enhanced the CO2 and N2O emissions by a factor of 1.7 and 3.7, respectively. Due to the increased microbial activity with increasing temperature soil respiration was enhanced. This led to decreasing oxygen (O2) contents which in turn promoted denitrification in soil due to the extension of anaerobic microsites. Leaching losses of NO3- were also significantly affected by increasing temperature whereas the consumption of CH4 was not affected. The third experiment showed that the input materials of biogas plants affected the properties of the resulting BS. In particular the contents of DM and NH4+ were determined by the amount of added plant biomass and excrement-based biomass, respectively. Correlations between BS properties and CO2 or N2O emissions were not detected. Solely the ammonia (NH3) emissions showed a positive correlation with NH4+ content in BS as well as a negative correlation with the total C (Ct) content. The BS-N application rates affected the relative CO2 emissions (% of C supplied with BS) when applied to silty soil as well as the relative N2O emissions (% of N supplied with BS) when applied to sandy soil. The impacts on the C and N dynamics induced by BS application were exceeded by the differences induced by soil texture. Presumably, due to the higher clay content in silty soils, organic matter was stabilized by organo-mineral interactions and NH4+ was adsorbed at the cation exchange sites. Different water contents induced highest CO2 emissions and therefore optimal conditions for microbial activity at 75% of WHC in both soils. Cumulative nitrification was also highest at 75% and 50% of WHC whereas the relative N2O emissions increased with water content and showed higher N2O losses in sandy soils. In summary it can be stated that the findings of the present thesis confirmed the high fertilizer value of BS’s, caused by high concentrations of NH4+ and labile organic compounds such as readily available carbon. These attributes of BS’s are to a great extent independent of the input materials of biogas plants. However, considerably gaseous and leaching losses of N may occur especially at high moisture contents. The emissions of N2O after field application corresponded with those of animal slurries.

Evaluating nitrogen utilization efficiency of nonpregnant dry cows offered solely fresh cut grass at maintenance levels

The present study aimed to identify key parameters influencing N utilization and develop prediction equations for manure N output (MN), feces N output (FN), and urine N output (UN). Data were obtained under a series of digestibility trials with nonpregnant dry cows fed fresh grass at maintenance level. Grass was cut from 8 different ryegrass swards measured from early to late maturity in 2007 and 2008 (2 primary growth, 3 first regrowth, and 3 second regrowth) and from 2 primary growth early maturity swards in 2009. Each grass was offered to a group of 4 cows and 2 groups were used in each of the 8 swards in 2007 and 2008 for daily measurements over 6 wk; the first group (first 3 wk) and the second group (last 3 wk) assessed early and late maturity grass, respectively. Average values of continuous 3-d data of N intake (NI) and output for individual cows ( = 464) and grass nutrient contents ( = 116) were used in the statistical analysis. Grass N content was positively related to GE and ME contents but negatively related to grass water-soluble carbohydrates (WSC), NDF, and ADF contents ( < 0.01), indicating that accounting for nutrient interrelations is a crucial aspect of N mitigation. Significantly greater ratios of UN:FN, UN:MN, and UN:NI were found with increased grass WSC contents and ratios of N:WSC, N:digestible OM in total DM (DOMD), and N:ME ( < 0.01). Greater NI, animal BW, and grass N contents and lower grass WSC, NDF, ADF, DOMD, and ME concentrations were significantly associated with greater MN, FN, and UN ( < 0.05). The present study highlighted that using grass lower in N and greater in fermentable energy in animals fed solely fresh grass at maintenance level can improve N utilization, reduce N outputs, and shift part of N excretion toward feces rather than urine. These outcomes are highly desirable in mitigation strategies to reduce nitrous oxide emissions from livestock. Equations predicting N output from BW and grass N content explained a similar amount of variability as using NI and grass chemical composition (excluding DOMD and ME), implying that parameters easily measurable in practice could be used for estimating N outputs. In a research environment, where grass DOMD and ME are likely to be available, their use to predict N outputs is highly recommended because they strongly improved of the equations in the current study.

MAGGnet: an international network to foster mitigation of agricultural greenhouse gases.

Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA). With involvement from 46 alliance member countries, MAGGnet seeks to provide a platform for the inventory and analysis of agricultural GHG mitigation research throughout the world. To date, metadata from 315 experimental studies in 20 countries have been compiled using a standardized spreadsheet. Most studies were completed (74%) and conducted within a 1-3-year duration (68%). Soil carbon and nitrous oxide emissions were measured in over 80% of the studies. Among plant variables, grain yield was assessed across studies most frequently (56%), followed by stover (35%) and root (9%) biomass. MAGGnet has contributed to modeling efforts and has spurred other research groups in the GRA to collect experimental site metadata using an adapted spreadsheet. With continued growth and investment, MAGGnet will leverage limited-resource investments by any one country to produce an inclusive, globally shared meta-database focused on the science of GHG mitigation.

Abundance of Natural Riparian Forests and Tree Plantations in the Amudarya Delta of Uzbekistan and their impact on emissions of soil-borne greenhouse gases

Through a forest inventory in parts of the Amudarya river delta, Central Asia, we assessed the impact of ongoing forest degradation on the emissions of greenhouse gases (GHG) from soils. Interpretation of aerial photographs from 2001, combined with data on forest inventory in 1990 and field survey in 2003 provided comprehensive information about the extent and changes of the natural tugai riparian forests and tree plantations in the delta. The findings show an average annual deforestation rate of almost 1.3% and an even higher rate of land use change from tugai forests to land with only sparse tree cover. These annual rates of deforestation and forest degradation are higher than the global annual forest loss. By 2003, the tugai forest area had drastically decreased to about 60% compared to an inventory in 1990. Significant differences in soil GHG emissions between forest and agricultural land use underscore the impact of the ongoing land use change on the emission of soil-borne GHGs. The conversion of tugai forests into irrigated croplands will release 2.5 t CO2 equivalents per hectare per year due to elevated emissions of N2O and CH4. This demonstrates that the ongoing transformation of tugai forests into agricultural land-use systems did not only lead to a loss of biodiversity and of a unique ecosystem, but substantially impacts the biosphere-atmosphere exchange of GHG and soil C and N turnover processes.

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.

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.