Ram C. Dalal

Nitrous oxide emission from Australian agricultural lands and mitigation options: a review

Increases in the concentrations of greenhouse gases, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and halocarbons in the atmosphere due to human activities are associated with global climate change. The concentration of N2O has increased by 16% since 1750. Although the atmospheric concentration of N2O is much smaller (314 ppb in 1998) than of CO2 (365 ppm), its global warming potential (cumulative radiative forcing) is 296 times that of the latter in a 100-year time horizon. Currently, it contributes about 6% of the overall global warming effect but its contribution from the agricultural sector is about 16%. Of that, almost 80% of N2O is emitted from Australian agricultural lands, originating from N fertilisers (32%), soil disturbance (38%), and animal waste (30%). Nitrous oxide is primarily produced in soil by the activities of microorganisms during nitrification, and denitrification processes. The ratio of N2O to N2 production depends on oxygen supply or water-filled pore space, decomposable organic carbon, N substrate supply, temperature, and pH and salinity. N2O production from soil is sporadic both in time and space, and therefore, it is a challenge to scale up the measurements of N2O emission from a given location and time to regional and national levels. Estimates of N2O emissions from various agricultural systems vary widely. For example, in flooded rice in the Riverina Plains, N2O emissions ranged from 0.02% to 1.4% of fertiliser N applied, whereas in irrigated sugarcane crops, 15.4% of fertiliser was lost over a 4-day period. Nitrous oxide emissions from fertilised dairy pasture soils in Victoria range from 6 to 11 kg N2O-N/ha, whereas in arable cereal cropping, N2O emissions range from <0.01% to 9.9% of N fertiliser applications. Nitrous oxide emissions from soil nitrite and nitrates resulting from residual fertiliser and legumes are rarely studied but probably exceed those from fertilisers, due to frequent wetting and drying cycles over a longer period and larger area. In ley cropping systems, significant N2O losses could occur, from the accumulation of mainly nitrate-N, following mineralisation of organic N from legume-based pastures. Extensive grazed pastures and rangelands contribute annually about 0.2 kg N/ha as N2O (93 kg/ha per year CO2-equivalent). Tropical savannas probably contribute an order of magnitude more, including that from frequent fires. Unfertilised forestry systems may emit less but the fertilised plantations emit more N2O than the extensive grazed pastures. However, currently there are limited data to quantify N2O losses in systems under ley cropping, tropical savannas, and forestry in Australia. Overall, there is a need to examine the emission factors used in estimating national N2O emissions; for example, 1.25% of fertiliser or animal-excreted N appearing as N2O (IPCC 1996). The primary consideration for mitigating N2O emissions from agricultural lands is to match the supply of mineral N (from fertiliser applications, legume-fixed N, organic matter, or manures) to its spatial and temporal needs by crops/pastures/trees. Thus, when appropriate, mineral N supply should be regulated through slow-release (urease and/or nitrification inhibitors, physical coatings, or high C/N ratio materials) or split fertiliser application. Also, N use could be maximised by balancing other nutrient supplies to plants. Moreover, non-legume cover crops could be used to take up residual mineral N following N-fertilised main crops or mineral N accumulated following legume leys. For manure management, the most effective practice is the early application and immediate incorporation of manure into soil to reduce direct N2O emissions as well as secondary emissions from deposition of ammonia volatilised from manure and urine. Current models such as DNDC and DAYCENT can be used to simulate N2O production from soil after parameterisation with the local data, and appropriate modification and verification against the measured N2O emissions under different management practices.

TURNER REVIEW No. 18. Greenhouse gas fluxes from natural ecosystems

Besides water vapour, greenhouse gases CO2, CH4, O3 and N2O contribute ~60%, 20%, 10% and 6% to global warming, respectively; minor contribution is made by chlorofluorocarbons and volatile organic compounds (VOC). We present CO2, CH4 and N2O fluxes from natural and relatively unmanaged soil–plant ecosystems (the ecosystems minimally disturbed by direct human or human-induced activities). All natural ecosystems are net sinks for CO2, although tundra and wetlands (including peatlands) are large sources of CH4, whereas significant N2O emissions occur mainly from tropical and temperate forests. Most natural ecosystems decrease net global warming potential (GWP) from –0.03 ± 0.35 t CO2-e ha–1 y–1 (tropical forests) to –0.90 ± 0.42 t CO2-e ha–1 y–1 (temperate forests) and –1.18 ± 0.44 t CO2-e ha–1 y–1 (boreal forests), mostly as CO2 sinks in phytobiomass, microbial biomass and soil C. But net GWP contributions from wetlands are very large, which is primarily due to CH4 emissions. Although the tropical forest system provides a large carbon sink, the negligible capacity of tropical forests to reduce GWP is entirely due to N2O emissions, possibly from rapid N mineralisation under favourable temperature and moisture conditions. It is estimated that the natural ecosystems reduce the net atmospheric greenhouse gas (GHG) emissions by 3.55 ± 0.44 Gt CO2-e y–1 or ~0.5 ppmv CO2-e y–1, hence, the significant role of natural and relatively unmanaged ecosystems in slowing global warming and climate change. However, the impact of increasing N deposition on natural ecosystems is poorly understood, and further understanding is required regarding the use of drainage as a management tool, to reduce CH4 emissions from wetlands and to increase GHG sink from the restoration of degraded lands, including saline and sodic soils. Data on GHG fluxes from natural and relatively unmanaged ecosystems are further compounded by large spatial and temporal heterogeneity, limited sensitivity of current instruments, few and poor global distribution of monitoring sites and limited capacity of models that could integrate GHG fluxes across ecosystems, atmosphere and oceans and include feedbacks from biophysical variables governing these fluxes.

Salinity and sodicity effects on respiration and microbial biomass of soil

An understanding of the effects of salinity and sodicity on soil carbon (C) stocks and fluxes is critical in environmental management, as the areal extents of salinity and sodicity are predicted to increase. The effects of salinity and sodicity on the soil microbial biomass (SMB) and soil respiration were assessed over 12weeks under controlled conditions by subjecting disturbed soil samples from a vegetated soil profile to leaching with one of six salt solutions; a combination of low-salinity (0.5dSm−1), mid-salinity (10dSm−1), or high-salinity (30dSm−1), with either low-sodicity (sodium adsorption ratio, SAR, 1), or high-sodicity (SAR 30) to give six treatments: control (low-salinity low-sodicity); low-salinity high-sodicity; mid-salinity low-sodicity; mid-salinity high-sodicity; high-salinity low-sodicity; and high-salinity high-sodicity. Soil respiration rate was highest (56–80mg CO2-C kg−1 soil) in the low-salinity treatments and lowest (1–5mg CO2-C kg−1 soil) in the mid-salinity treatments, while the SMB was highest in the high-salinity treatments (459–565mg kg−1 soil) and lowest in the low-salinity treatments (158–172mg kg−1 soil). This was attributed to increased substrate availability with high salt concentrations through either increased dispersion of soil aggregates or dissolution or hydrolysis of soil organic matter, which may offset some of the stresses placed on the microbial population from high salt concentrations. The apparent disparity in trends in respiration and the SMB may be due to an induced shift in the microbial population, from one dominated by more active microorganisms to one dominated by less active microorganisms.

A review of sampling designs for the measurement of soil organic carbon in Australian grazing lands

The accurate measurement of the soil organic carbon (SOC) stock in Australian grazing lands is important due to the major role that SOC plays in soil productivity and the potential influence of soil C cycling on Australia’s greenhouse gas emissions. However, the current sampling methodologies for SOC stock are varied and potentially conflicting. It was the objective of this paper to review the nature of, and reasons for, SOC variability; the sampling methodologies commonly used; and to identify knowledge gaps for SOC measurement in grazing lands. Soil C consists of a range of biological materials, in various SOC pools such as dissolved organic C, micro- and meso-fauna (microbial biomass), fungal hyphae and fresh plant residues in or on the soil (particulate organic C, light-fraction C), the products of decomposition (humus, slow pool C) and complexed organic C, and char and phytoliths (inert, passive or resistant C); and soil inorganic C (carbonates and bicarbonates). Microbial biomass and particulate or light-fraction organic C are most sensitive to management or land-use change; resistant organic C and soil carbonates are least sensitive. The SOC present at any location is influenced by a series of complex interactions between plant growth, climate, soil type or parent material, topography and site management. Because of this, SOC stock and SOC pools are highly variable on both spatial and temporal scales. This creates a challenge for efficient sampling. Sampling methods are predominantly based on design-based (classical) statistical techniques, crucial to which is a randomised sampling pattern that negates bias. Alternatively a model-based (geostatistical) analysis can be used, which does not require randomisation. Each approach is equally valid to characterise SOC in the rangelands. However, given that SOC reporting in the rangelands will almost certainly rely on average values for some aggregated scale (such as a paddock or property), we contend that the design-based approach might be preferred. We also challenge soil surveyors and their sponsors to realise that: (i) paired sites are the most efficient way of detecting a temporal change in SOC stock, but destructive sampling and cumulative measurement errors decrease our ability to detect change; (ii) due to (i), an efficient sampling scheme to estimate baseline status is not likely to be an efficient sampling scheme to estimate temporal change; (iii) samples should be collected as widely as possible within the area of interest; (iv) replicate of laboratory analyses is a critical step in being able to characterise temporal change. Sampling requirements for SOC stock in Australian grazing lands are yet to be explicitly quantified and an examination of a range of these ecosystems is required in order to assess the sampling densities and techniques necessary to detect specified changes in SOC stock and SOC pools. An examination of techniques that can help reduce sampling requirements (such as measurement of the SOC fractions that are most sensitive to management changes and/or measurement at specific times of the year – preferably before rapid plant growth – to decrease temporal variability), and new technologies for in situ SOC measurement is also required.

Total soil organic matter and its labile pools following mulga (Acacia aneura) clearing for pasture development and cropping 1. Total and labile carbon

Mulga (Acacia aneura) woodlands and open forests occupy about 150 Mha in Australia, and originally occupied 11.2 Mha in Queensland. Substantial areas (1.3 Mha) of the mulga vegetation have been cleared in Queensland, mostly for pasture production, but some areas are also used for cereal cropping. Twenty years after mulga clearing we found a significant loss of total soil organic C (28–35% from the 0–0.05 m depth) and light fraction C (>50% from the 0–1 m depth) from soil under pasture and cropping at a site in southern Queensland. We report here the changes in soil N and labile N pools in a paired-site study following conversion of mulga to buffel pasture (Cenchrus ciliaris) and cereal (mostly wheat) cropping for more than 20 years. Conversion from mulga forest to pasture and cultivation resulted in greater losses of soil N than organic C in the top 0.1 m depths. As a result, C/N ratios in soil under both pasture and cropping were higher than soil under mulga, indicating a decline in soil organic matter quality after mulga clearing. Although land-use change had no significant effect on 15N natural abundance (δ15N) values of total soil N down to a depth of 1 m, δ15N values of wheat tops and roots indicated that the primary source of N under cropping was soil organic N, while that of buffel pasture was a mixed source of soil N and decomposed litter and root N. Light fraction N (<1.6 Mg/m3) declined by 60–70% throughout the 1 m soil profile under pasture and cropping, but it was 15N-enriched in these 2 land-use systems. The δ15N values of mulga phyllodes, twigs, and fine roots, indicated an input of atmospheric fixed N2 that was estimated to be about 25 kg N/ha.year. However, the source and magnitude of this N resource needs to be confirmed. Soil N losses were estimated to be 12 kg N/ha.year under pasture and 17 kg N/ha.year under cropping over a 20-year period. These findings raise the issue of the long-term sustainable use of cleared mulga areas for pasture and/or cropping. The labile C and N pools and N mineralised also declined, which would have an immediate adverse effect on soil fertility and plant productivity of cleared Mulga Lands, as well as reducing their potential as a soil sink for greenhouse gases.

High subsoil chloride concentrations reduce soil water extraction and crop yield on Vertosols in north-eastern Australia

Salinity, sodicity, acidity, and phytotoxic levels of chloride (Cl) in subsoils are major constraints to crop production in many soils of north-eastern Australia because they reduce the ability of crop roots to extract water and nutrients from the soil. The complex interactions and correlations among soil properties result in multi-colinearity between soil properties and crop yield that makes it difficult to determine which constraint is the major limitation. We used ridge-regression analysis to overcome colinearity to evaluate the contribution of soil factors and water supply to the variation in the yields of 5 winter crops on soils with various levels and combinations of subsoil constraints in the region. Subsoil constraints measured were soil Cl, electrical conductivity of the saturation extract (ECse), and exchangeable sodium percentage (ESP). The ridge regression procedure selected several of the variables used in a descriptive model, which included in-crop rainfall, plant-available soil water at sowing in the 0.90-1.10 m soil layer, and soil Cl in the 0.90-1.10 m soil layer, and accounted for 77-85% of the variation in the grain yields of the 5 winter crops. Inclusion of ESP of the top soil (0.0-0.10 m soil layer) marginally increased the descriptive capability of the models for bread wheat, barley and durum wheat. Subsoil Cl concentration was found to be an effective substitute for subsoil water extraction. The estimates of the critical levels of subsoil Cl for a 10% reduction in the grain yield were 492 mg cl/kg for chickpea, 662 mg Cl/kg for durum wheat, 854 mg Cl/kg for bread wheat, 980 mg Cl/kg for canola, and 1012 mg Cl/kg for barley, thus suggesting that chickpea and durum wheat were more sensitive to subsoil Cl than bread wheat, barley, and canola.

What determines soil organic carbon stocks in the grazing lands of north-eastern Australia?

This study aimed to unravel the effects of climate, topography, soil, and grazing management on soil organic carbon (SOC) stocks in the grazing lands of north-eastern Australia. We sampled for SOC stocks at 98 sites from 18 grazing properties across Queensland, Australia. These samples covered four nominal grazing management classes (Continuous, Rotational, Cell, and Exclosure), eight broad soil types, and a strong tropical to subtropical climatic gradient. Temperature and vapour-pressure deficit explained >80% of the variability of SOC stocks at cumulative equivalent mineral masses nominally representing 0-0.1 and 0-0.3m depths. Once detrended of climatic effects, SOC stocks were strongly influenced by total standing dry matter, soil type, and the dominant grass species. At 0-0.3m depth only, there was a weak negative association between stocking rate and climate-detrended SOC stocks, and Cell grazing was associated with smaller SOC stocks than Continuous grazing and Exclosure. In future, collection of quantitative information on stocking intensity, frequency, and duration may help to improve understanding of the effect of grazing management on SOC stocks. Further exploration of the links between grazing management and above- and below-ground biomass, perhaps inferred through remote sensing and/or simulation modelling, may assist large-area mapping of SOC stocks in northern Australia.

Organic carbon stocks in cropping soils of Queensland, Australia, as affected by tillage management, climate, and soil characteristics

Research both nationally and internationally has indicated that no-till (NT) management used in combination with stubble retention has the potential to increase soil organic carbon (SOC) stocks in cropping soils relative to conventional tillage (CT). However, rates of SOC increase can vary depending on cropping system, climate, and soil type, making the quantification of carbon change difficult on a regional level. Various long-term trials and commercial sites throughout Queensland were used to compare rates of SOC change under CT and NT management in cropping soils, and to determine how climate and soil type interact to influence rates of change. It was observed that NT management was not capable of increasing SOC stocks under the crop–fallow rotation systems practised throughout Queensland, and was unlikely even to hold SOC stocks steady under current management practices. However, SOC losses under NT systems did appear to be slower than under CT, indicating that NT may slow SOC loss following a period of organic carbon input, for example, from a pasture ley. On a regional scale, biomass production (estimated through remote sensing), climate (specifically the vapour pressure deficit), and soil sand content could be used to adequately predict SOC stocks on commercial sites, indicating the importance of considering these factors when assessing SOC stocks following management change across the region.

Comparison of legume-based cropping systems at Warra, Queensland. I. Soil nitrogen and organic carbon accretion and potentially mineralisable nitrogen

Effects on soil nitrogen accretion and potentially mineralisable nitrogen were studied as part of a long-term field experiment established in 1986 to test alternative legume-based systems for restoring fertility in a Vertisol. Organic C accretion was also measured to ascertain the changes in organic matter content. The systems, which were studied only during 1989 and 1990, were a grass+legume ley (purple pigeon grass, Rhodes grass, lucerne, annual medics) of 4 years duration followed by wheat; a 2-year rotation of wheat (lucerne undersown) and lucerne; a 2-year rotation of wheat (medic undersown) and medic; a 2-year rotation of chickpea and wheat; and continuous wheat as control. Soil total N and organic C significantly increased in the 0–10 cm soil layer only under the grass+legume ley. There was no significant change in the soil C/N ratio. Plant residues contained from 52 to 104 kg N/ha in 1990 at the end of the legume phase, with high values for root N in the grass+legume ley. A comparison of N accretion versus fixation at the end of the legume-based systems in 1990 showed that net accumulation of N exceeded fixation in soil under lucerne and grass+legume leys; in the latter, net accumulation of 779 kg N/ha over 3.75 years was measured compared with 384 kg N/ha for N2 fixation. Part of the accumulation of N may have been due to uptake of NH4-N from the deep subsoil. Although values for soil mineral N (0–120 cm) were low at the end of all the legume-based systems, a deep subsoil (120–300 cm) accumulation of NH4-N was found in all treatments. The nitrogen mineralisation potentials (No) for 0–10 cm depth samples taken at the end of the legume phase in 1989 were higher in all the legume-based systems (105–182 mg N/kg) than the wheat control (57 mg N/kg). The rapid biological tests of N availability, both waterlogged and aerobic incubation, were more sensitive to treatment differences than No, in the surface and subsoil (range 12–78 mg N/kg for 0–10 cm soil for the waterlogged procedure). The rapid chemical tests, hot KCl extraction and the autoclave index, showed small treatment effects and did not appear to be useful availability indices. The pasture management (graced v. mown and removed) had no significant effect on total N, organic C and N availability indices in this alkaline Vertisol during the study period.

Soil Health Indicators Under Climate Change: A Review of Current Knowledge

Soil health indicators are a composite set of measurable physical, chemical and biological attributes which relate to functional soil processes and can be used to evaluate soil health status, as affected by management and climate change. Major soil health indicators discussed in the context of climate change are: aggregate stability, water infiltration, and bulk density, soil organic matter content, carbon and nitrogen cycling, microbial biomass and activity, and microbial diversity. In this chapter, we highlight that the selection of a suite of soil health indicators within a minimum data set depends on their sensitivity to management changes, and capacity to integrate and relate to important soil functions. These soil health indicators must also be sensitive to drivers of global change (increasing temperatures, elevated atmospheric carbon dioxide and atmospheric nitrogen deposition, increasing variability in amount, intensity, and distribution of precipitation, extreme climatic events, and their interactions) and should be able to indicate the mitigation and adaptive capability of soil and its resilience to climate change, and provide early warning for the need of implementing climate adaptive management strategies.