Bureau of Resource Sciences, P.O. Box Ell, Queen Victoria Terrace, ... Queensland Department of Primary Industries, P.O. Box 46, Brisbane, Qld, 4000, Australia.
METHODS REDUCE
FOR EXPLORING GREENHOUSE
TROPICAL
GRAZING
MANAGEMENT
GAS EMISSIONS
OPTIONS
TO
FROM
SYSTEMS
S. M A R K H O W D E N and D A V I D H. W H I T E Bureau of Resource Sciences, P.O. Box Ell, Queen Victoria Terrace, Canberra, ACT, 2600, Australia
and GREG M. MCKEON, J O E C. S C A N L A N and J O H N O. C A R T E R Queensland Department of Primary Industries, P.O. Box 46, Brisbane, Qld, 4000, Australia
Abstract. Increasing atmospheric concentrations of 'greenhouse gases' are expected to result in global climatic changes over the next decades. Means of evaluating and reducing greenhouse gas emissions are being sought. In this study an existing simulation model of a tropical savanna woodland grazing system was adapted to account for greenhouse gas emissions. This approach may be able to be used in identifying ways to assess and limit emissions from other rangeland, agricultural and natural ecosystems. GRASSMAN, an agricultural decision-support model, was modified to include sources, sinks and storages of greenhouse gases in the tropical and sub-tropical savanna woodlands of northern Australia. The modified model was then used to predict the changes in emissions and productivity resulting from changes in stock and burning management in a hypothetical grazing system in tropical northeastern Queensland. The sensitivity of these results to different Global Warming Potentials (GWPs) and emission definitions was then tested. Management options to reduce greenhouse gas emissions from the tropical grazing system investigated were highly sensitive to the GWPs used, and to the emission definition adopted. A recommendation to reduce emissions by changing burning management would be to reduce fire frequency if both direct and indirect GWPs of CO2, CH4, N20, CO and NO are used in evaluating emissions, but to increase fire frequency if only direct GWPs of CO2, CH4 and N20 are used. The ability to reduce greenhouse gas emissions from these systems by reducing stocking rates was also sensitive to the GWPs used. In heavily grazed systems, the relatively small reductions in stocking rate needed to reduce emissions significantly should also reduce the degradation of soils and vegetation, thereby improving the sustainability of these enterprises. The simulation studies indicate that it is possible to alter management to maximise beef cattle production per unit greenhouse gases or per unit methane emitted, but that this is also dependent upon the emission definition used. High ratios of liveweight gain per unit net greenhouse gas emission were found in a broadly defined band covering the entire range of stocking rates likely to be used. In contrast, high values of liveweight gain per unit 'anthropogenic' greenhouse gas emission were found only at very low stocking rates that are unlikely to be economically viable. These results suggest that policy initiatives to reduce greenhouse gas emissions from tropical grazing systems should be evaluated cautiously until the GWPs have been further developed and the implications of emission definitions more rigorously determined.
Climatic Change 27: 4%70, 1994. Q 1994 KluwerAcademic Publishers. Printed in the Netherlands.
50
S. MarkHowdenet al.
1. Introduction Global climate is widely expected to change in response to increases in the atmospheric concentration of radiatively-active (or 'greenhouse') gases such as carbon dioxide, methane and nitrous oxide (e.g. Houghton et al., 1990). Methods are being sought to evaluate and reduce greenhouse gas emissions for agricultural and other industries. Existing models of agricultural systems provide a way of estimating greenhouse gas emissions and evaluating options for reducing emissions from agriculture. Such models generally have several advantages: (1) they are developed and validated with extensive agronomic data; (2) they represent the dominant processes in the system; (3) they are supported by continuing research activity and agronomic fieldwork; (4) with existing spatial information they can be scaled up to make regional or national calculations using Geographic Information Systems; and (5) they calculate a range of biological, financial and physical outputs, and thus allow the possible conflicts between short-term financial gain, long-term productivity and other objectives to be evaluated. However, agricultural models also have some disadvantages, one being that their use in addressing agricultural management problems does not necessarily require total flows of carbon to be calculated. Another disadvantage is that ecologically important processes (e.g. termite activities) that do not directly affect agricultural production are rarely included. It follows, therefore, that if existing agricultural models can be modified to include carbon flows and other processes, then greenhouse gas emissions could be calculated and management opportunities to reduce these emissions could be assessed. GRASSMAN (Scanlan and McKeon, 1990) is an example of an agricultural model that evaluates the impact of different management options (e.g. changing stocking rate, clearing trees, changing burning regime) and climate on pasture condition, animal performance and paddock financial returns. It is designed particularly to evaluate the management of the black speargrass (Heteropogon contortus) and eucalypt woodland ecosystems of tropical and sub-tropical eastern Queensland, Australia. The pasture production and animal production models on which GRASSMAN is based have been validated for these situations (McKeon and Rickeft, 1984; McKeon et al., 1990; Scanlan, 1992; Scanlan and McKeon, 1993). In these tropical and sub-tropical grazing systems, greenhouse gases including carbon dioxide, methane, carbon monoxide, nitrous oxide and other oxides of nitrogen can be emitted by ruminants and other herbivores, termites and other detritus feeders, and by burning. However, these systems can act as sinks for greenhouse gases, since carbon can be stored if the biomass of trees, shrubs or grass is increased, and because some soil bacteria consume methane. Simulation models such as GRASSMAN are useful to analyse emissions from these grazing systems, because of the interactions that exist between the system components (Boag et al., 1993). For example, cattle stocking rates affect burning regimes by ClimaticChan~eMay 1994
Options to Reduce Greenhouse Gas Emissions from Tropical Grazing Systems
51
fixed from
I"-
I
I o"- 1 Cattle
Tree litter [ and dead trees]
I Grass litter I
°,,=° I [
CO2 GH4 CO N20
Dung
I
Rumen fermentation
]
]
CO2 CH4 N20
CO2
CH4
CO2 CHd
Fig. 1. Majorflows of carbon through a tropical savanna woodland grazing system which result in the emission of greenhouse gases. Two sources of nitrous oxide are also shown. controlling fire fuel loads, and burning influences the growth of shrubs and trees, which in turn affects grass growth and thus stocking rate. All of these affect the net greenhouse gas emissions from these grazing systems. In this study we added equations to the GRASSMAN model to include the above sources and sinks of greenhouse gases and several natural processes that affect the size of these fluxes. The modified model is then used in an exploratory study to predict the changes in emissions and productivity resulting from changes in stock and burning management in a hypothetical grazing system in tropical north-eastern Queensland. The sensitivity of these results to different Global Warming Potentials and emission definitions is then tested.
2. Modifying a Rangeland Model to Include Sources, Sinks and Storages of Greenhouse Gases A general model for pathways of greenhouse gas emissions within a tropical savanna woodland system is outlined in Figure 1. A range of routines was added to G R A S S M A N in order to allow it to calculate emissions from these pathways. The model does not specifically calculate tree biomass, and hence equations to link tree basal area to the components of tree biomass were required. Whilst Climatic Change May 1994
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s. Mark Howdenetal.
GRASSMAN calculates the effect of grazing on grass standing dry matter and pasture growth, the important process of grazing on root pools and root growth needs to be explicitly modelled (Howden, 1988). Tree root, grass root and litter pools were likewise required. Litter pools represent the source of emissions by decay and burning, as well as a source for consumption by termites which produce methane and CO2 (Khalil et al., 1990). Furthermore, as GRASSMAN does not model emissions from any pools, emissions and their Global Warming Potentials (GWPs) also need to be accounted for in the revised model. Termites are an important component of the biology of tropical Australian savannas (e.g. Holt and Coventry, 1990). Termite biomass has been calculated to be equivalent to or greater than cattle biomass in some of these ecosystems (Mott et aL, 1985; Holt and Easy, 1993). Termites produce methane from the action of microbes in their guts. They have been estimated to contribute between 4 and 30% of the global methane emissions (Zimmerman et al., 1982; Khalil et al., 1990). The potentially large contribution to methane emissions from grazed savanna woodlands required that they be included in this study. The modifications required to the GRASSMAN model to allow it to calculate greenhouse gas emissions are documented below, following a brief description of the existing model.
2.1. Description o f GRASSMAN The major objective of GRASSMAN is to allow users to investigate the interactions between tree clearing treatments, stock and pasture management and climate. Central to this is the simulation of the effects of tree and pasture management on tree density (expressed as tree basal area; m2/ha) calculated on a six-monthly time step for a period of 15 years. The six-month periods correspond to the 'wet' season (November to April) and 'dry' season (May to October) experienced in these environments. Tree clearing is achieved by a number of alternative physical or chemical treatments and by pasture burning. The rate of change of tree basal area varies with clearing method. Tree growth is simulated by changes in tree basal area and height as a function of maximum obtainable tree basal area (e.g. 15 m2/ha). The effects of pasture burning on tree density are a function of pasture yield and height of trees. Actual pasture growth is a function of potential pasture growth and tree basal area. Potential grass growth in the absence of trees is calculated as a function of soil fertility and rainfall. The effect of tree basal area on grass growth is derived from empirical equations across a range of tree densities (Scanlan and Burrows, 1990). The effects of actual pasture growth on animal liveweight gain are calculated as a function of stocking rate, pasture utilisation and potential liveweight gain (McKeon and Rickert, 1984). Pasture yields are calculated as the net result of pasture growth, removal by grazing, and leaf and stem detachment. The effects of overgrazing on Climatic Change May 1994
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pasture production are simulated by calculating the change of grass basal area as a function of pasture utilisation and seasonal rainfall conditions. GRASSMAN was originally written in FORTRAN, but was subsequently rewritten in PASCAL as a decision support system for graziers and extension officers. It is currently being modified to account for regional differences in biological processes, type of beef enterprise and economics. The following sections describe the modifications made to GRASSMAN to account for greenhouse gases. 2.2. Trees The dry matter production of eucalypt trees is not calculated directly in GRASSMAN, but is derived from the simulation of the changes in tree basal area (TBA; m2/ha) and average height (H; m), calculated as a function of time and management actions (Scanlan and McKeon, 1990). The Total Standing Dry Matter (TSDM) of trees is made up of stems, twigs and leaves. Madgwick et al. (1991 ) presented some relationships of these different tree biomass components. These relationships were further developed using the data of Harrington (1979) to provide the following equations:
Stem weight(t/ha) = 0.216 * T B A • H ,
(1)
Twig weight(t/ha) = 0.45 • T B A .
(2)
If tree basal area is greater than 15 me/ha then:
Leaf weight (t/ha) = 1.25 * v@--B-A.
(3)
If tree basal area is less than 15 m2/ha then leaf weight (t/ha) is calculated as a function of the percentage of leaf weight in terms of total standing dry matter (L): L(%) = exp(3.22 - 0.467 • ln(H + 0.01)),
(4)
Leaf weight (t/ha) = L~ (1 O0 - L) • (stem weight + twig weight).
(5)
The weight of tree roots (t/ha) was calculated as a function of the percentage of root weight when expressed in terms of total tree biomass (R): R(%) = (0.093 + 0.4718 • exp(-0.0106 • T S D M ) ) • 100,
(6)
Root weight(t/ha) = R / ( 1 0 0 - R) * ( T S D M ) .
(7)
These calculations are made at the end of each six-month period. Thus changes in tree basal area and height result in changes in dry matter pools. The calculated changes in total tree dry matter represent net growth, as litter fall and root decay Climatic Change May 1994
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s. Mark Howdenet al.
are assumed to have occurred during each interval simulated. Total tree growth is calculated as the sum of net growth and tree litter fall. Where a management treatment has been applied and resulted in the death of trees, there is a reduction in the basal area and height of the remaining trees, and hence reductions in the leaf, twig, stem and root pools. These reductions are quantified by comparing wet season pools with the pools at the end of the previous dry season. The biomass lost from the leaf and twig pools as a result of the tree clearing treatment is added to the tree litter pool and the biomass lost from the root pool is added to a dead root pool. The biomass lost from the stem pool is added to a standing dead wood pool or a fallen dead wood pool depending on the tree clearing method used. Decay processes operate on these pools of dead material in later seasons. Data on breakdown of this dead material (Bevege, 1978; Frost, 1985) were used to estimate decay rates of 3.55% and 0.75% every six months for the root and other pools respectively. The pool of fallen dead wood is affected by burning whilst the standing pool is not. 2.3. Tree Litter Annual tree litter fall in these environments (e.g. Burrows and Burrows, 1992) was estimated to be 50% of the standing crop of leaf as calculated by the relationships of Madgwick et al. (1991). Burrows and Burrows (1992) found that litter fall (leaf and twigs) occurs mainly at the start of the wet season (November to January) with 75% of litter fall occurring during the October to March period. Litter fall from trees can also occur as a result of tree cleating treatments as discussed above. The leaf litter pool is subject to decay processes with litter taking about two years to decay completely (Bevege, 1978). In the modified model, it is assumed that 60% of the annual breakdown occurs during summer and 40% during winter as described for grass litter later. 2.4. Grasses The net growth of black speargrass (Heteropogon contortus) shoots (i.e. growth detachment) is calculated by GRASSMAN (dependent upon rainfall, animal intake, pasture condition, tree density etc.) for each six-month season and this is added to the existing grass shoot pool. The growth of grass roots of this species is related to shoot growth (S) and the effects of grazing on carbon allocation within the plant (Howden, 1988): -
Grass root growth = S * (1 - 1.46 • U)
(8)
where in wet season, U = current season's proportional utilisation of grass by cattle (calculated in GRASSMAN), -
Climatic Change May 1994
Options to Reduce Greenhouse Gas Emissions from Tropical Grazing Systems
55
in dry season, U = utilisation of grass by cattle in the previous wet season, and (1 - 1.46 • U) _> 0.25. There is little information available on rates of root decay in Australian tropical and subtropical grasslands. Based on the work of Frost (1985) and le Houerou (1989) in similar environments in Africa, in this model a quarter of the roots were assumed to decay during each six-month season. -
2.5. Grass Litter GRASSMAN calculates grass detachment. The detached grass is added to the grass litter pool, which can be reduced by termite consumption. The remaining pool is subject to decay processes. Grass litter decay rates, measured by litter bags, vary with temperature and moisture (Robbins et al., 1989). Seasonal rates of decay of native grass litter have been derived from a study by D. Cooksley (pers. comm.) in the black speargrass zone of Queensland, and from Christie (1979), Prebble and Stirk (1980) and Friedel (1981). On the basis of these studies, the grass litter pool in the model is reduced by 30% during the wet season and a further 20% during the dry season. Following the decay process in winter, the grass litter pool can be reduced to zero if burning occurs. 2.6. Termites Methane emissions from termites are a function of the amount of feed consumed and the proportion of this that is converted into methane. Termites in these ecosystems emit between 1.07% (Holt, 1988)and 1.39% (Khalil et al., 1990)of the carbon they consume in the form of methane, the remainder being emitted as CO2 with very small traces of other gases such as chloroform. Feed consumption of termites can be very high in tropical environments with around 25-30% of the litterfall being eaten (Josens, 1983; Mott et aL, 1985). Feed consumption by termites is a function of the termite biomass and the feed consumption per unit biomass. Holt and Easy (1993) have measured the biomass of one termite species in grasslands and woodlands in tropical north-eastern Queensland. Using these data, measurements of alate (swarming termite) production and estimates of biomass of other termite classes (Holt, 1988) and species including subterranean termites, termite biomass was estimated at up to 96 kg/ha in a grazed ecosystem typical of those referred to here. This is similar to the estimate of up to 100 kg/ha by Mott et aL (1985) for tropical savannas in Australia. The termite biomass in subtropical grazing systems is thought to be lower, but few measurements are available. Wood (1978) found that termites annually consume food at an average rate of seven times their own biomass. The main termite species in the tropical grasslands addressed here feed on grass litter (Holt and Coventry, 1988; Braithwaite et al., 1988). Consumption of dry matter by termites was thus calculated to be 30% of the grass litter pool in each season. An upper limit to consumption of 672 kg dry matter/year Climatic ChangeMay 1994
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S. Mark Howdenet al.
was set, based on the previous biomass estimates and annual feed intake. Of the carbon consumed by termites, 1.23% (average of measured emissions) was emitted in the form of methane, the remainder as CO2. The grass litter pool was reduced by the amount consumed by termites. Nitrous oxide emissions have also been measured from termite mounds in eastern Australia (Khalil et al., 1990). Based on these measurements, emissions of nitrous oxide from termites were estimated to be 0.0125% of the weight of carbon consumed assuming a fixed C : N ratio. 2.7. Cattle Cattle are a major source of methane in Australia (Howden, 1992). Methane emitted from beef cattle is produced by the action of microbes in the rumen and, to a lesser extent, the hind gut. The amount of methane produced is a function of the intake of the animal and the proportion of this intake that is emitted as methane. Small amounts of methane may also be emitted from fermentation processes in the dung. Several studies have drawn complex relationships between the proportion of intake that is emitted as methane by ruminants, the quality and quantity of feed and the physiological state of the animal (e.g. Blaxter and Clapperton, 1965). However, these studies have dealt with temperate animal breeds and temperate feedstuffs. A recent analysis (Howden et al., 1993) of published information on methane emissions from animals bred for Australian conditions and eating feeds more typical of those found in Australia, including some tropical feeds, has found that emissions were linearly related to dry matter intake (kg/day) such that: M e t h a n e (kg/day) = Intake • 0.0188 + 0.00158.
(9)
The feed intake of beef cattle is calculated within GRASSMAN using the relationships of McKeon and Rickert (1984). Methane emissions are calculated directly from this using equation (9). The standing grass pool was reduced by the amount eaten by cattle. Methane is also produced from the organic fraction of the dung of cattle. The amount of organic matter excreted in the dung was calculated as a function of digestibility (D; from GRASSMAN) and the ash content of the feed (A; assumed to be 10%). D u n g (kg) = Intake • (100 - D - A)/100.
(10)
Williams (1993) recently measured methane production from cattle manure under field conditions in temperature Australia and found that only about 1% of the methane production potential was achieved. In tropical and sub-tropical pastures, the actions of dung beetles (scarabs) may also aerate faeces and hence further lower methane generation. However, quantitative information on this effect is not currently available. Climatic Change May 1994
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Based on the data of Williams (1993) and the general approach of Casada and Safley (1990), 9.3 g of methane was estimated to be released from each kilogram of organic matter in the dung. The remainder of the carbon in the dung was emitted a s C O 2.
2.8. Burning Burning is a common management practice in tropical and sub-tropical grasslands. Burning is used to control shrub and tree regrowth that otherwise reduces pasture production (Scanlan and Burrows, 1990) and increases mustering costs. Burning is also used to control the occurrence of undesirable grasses (Orr et al., 1991; Paton and Rickert, 1989), to provide livestock with high quality feed at the end of the dry season, to prevent patch grazing occurring in the same location several years in succession (Andrew, 1986) and for other purposes. However, burning results in substantial emissions of greenhouse gases, including carbon dioxide, methane, carbon monoxide, nitrous oxide and other oxides of nitrogen (Galbally et al., 1992). In GRASSMAN, burning occurs at the end of the dry season in any nominated year provided that there is more than 1000 kg/ha of standing grass dry matter at that time; the fuel load necessary to affect trees and shrubs. When a burn is simulated in the model, the grass litter, tree litter and aboveground grass pools are reset to zero. Of the carbon that was in these pools, 94.1% is released as carbon dioxide, 0.4% is released as methane and 5.5% is released as carbon monoxide (Carras et al., 1993). Sixty percent of the fallen dead wood pool is assumed to be burnt, resulting in emissions as above. Tree and non-tree carbon pools that were burnt were assumed to have nitrogen contents of 0.4% and 0.6%, respectively (Mott et aL, 1985). Of the total nitrogen in the pools that were burnt, 10% is released as nitric oxide (NO) and 0.7% as nitrous oxide (N20) (Carras et al., 1993). 2.9. Greenhouse Gas Emissions The net emissions of carbon from the system during any period form a balance with carbon inputs and changes in carbon storages. Carbon was emitted from the system in the form of carbon dioxide due to decay, respiration and burning, as carbon monoxide from buming and as methane from cattle digestion, dung fermentation, termite consumption and burning (Figure 1). Carbon inputs to the system arose from the growth of grass and trees. Carbon storages changed with the size of tree leaf, twig, stem and root biomass, grass shoot and root biomass, grass and tree litter pools, dead wood pools and dead root pools. Net emissions of carbon dioxide were calculated as the difference between carbon dioxide emissions and the carbon dioxide absorbed from the atmosphere for growth. Net emissions of methane were calculated as the difference between methane emissions and the methane uptake of soil in this environment (3.504 kg Climatic ChangeMay 1994
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s. MarkHowdenetal.
CH4/ha/year; Khalil et al., 1990). Emissions of nitrous oxide and nitric oxide also occur from this system but the size of terrestrial sinks is uncertain. Gases vary in their radiative activity and in their atmospheric residence time. To allow for these differences, relative Global Warming Potentials (GWPs) have been calculated for the common greenhouse gases (e.g. Shine et al., 1990). They represent the relative warming effect of a unit mass of the gas when compared with the same mass of carbon dioxide over a specified period. The relative GWPs used in the model were the 100-year integration period values given by IPCC (1992) for carbon dioxide (1), direct effects of methane (11) and nitrous oxide (270). Indirect warming effects may also occur with methane (15), carbon monoxide (3) and nitric oxide (40) (Shine et aL, 1990). However, there is currently debate (IPCC, 1992) as to the magnitude (and even the applicability of the warming potential concept) of these effects. In view of the importance of these three gases in the emission budget of tropical grassland ecosystems (e.g. Galbally et al., 1992), the sensitivity of this budget with regard to the inclusion of these indirect GWPs is investigated in an exploratory study described later. The direct and, where used, indirect warming potential values were multiplied by the respective emissions calculated by the model to give total greenhouse gas emissions in carbon dioxide equivalents. 2.10. Changes in Soil Organic Matter Soil organic matter is an important source or sink for carbon and nitrogen (Gifford et al., 1992). Management changes such as tree clearing, burning and grazing will affect both inputs of soil organic matter and output from the soil organic matter pool by changing the soil environment and thus decomposition rates (Ingram, 1990). Few data are available on the changes in soil organic matter in tropical and sub-tropical Australia (Dowling et al., 1986). In monsoonal grasslands, heavy defoliation which reduced litterfall also reduced carbon contents of the top soil layers by up to 40% over a two year period (Bridge et aL, 1983). In the subtropical Brigalow (Acacia harpophylla) woodlands of central Queensland, clearing resulted in variable changes in soil organic carbon, depending on subsequent landuse (Graham et al., 1981). Pasture sites that had been cleared (for generally about 12 years) had, on average, 23% lower organic carbon content than uncleared sites, a decrease of 4 tonnes of carbon per hectare in the 0-10 cm surface layer. Given the general lack of data on the impacts of management on soil carbon in the ecosystems modelled here, the carbon budget model described above was not modified to include changes in soil organic matter. The processes of litter incorporation, faeces production and root death are likely to contribute to soil organic matter, whilst decomposition of organic matter produces CO2 through respiration of the microbial biomass. These processes have been grouped in the model with the carbon from litter decay, faecal decomposition and root death lost directly as CO2. Thus the possible role of soil organic matter as a source or sink of carbon is not yet simulated. Climatic ChangeMay 1994
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A management change that will reduce litter fall (e.g. clearing trees or increasing stocking rate), will reduce the soil carbon content, releasing CO2 in the process (e.g. Bridge et al., 1983). Hence, the calculations made in the model of the effect of such management changes on greenhouse gas emissions are likely to be conservative. The maximum likely changes in soil carbon over a 15 year period (the timehorizon used in GRASSMAN) are estimated to be: (a) 4-6 t C/ha for clearing and conversion to pasture (Graham et al., 1981; DoMing et al., 1986); and (b) 7 t C/ha for overgrazing (J. Williams, pers. comm.). The simulation of soil organic matter may be achieved by linking routines from one of the soil carbon/nitrogen models developed for other grassland ecosystems such as CENTURY (Metherell et al., 1993), SPUR (Hanson et al., 1988) or DNDC (Li et al., 1992). However, before this could be done, these models would have to be calibrated and validated for these tropical ecosystems and then adapted to run on the same time-step as the GRASSMAN model.
2.11. Validation
Validation of a model such as that described here would be difficult because of (1) the large number of variables, the difficulty of measuring many of them (e.g. tree root pools and growth and decay rates) and the large sample sizes needed due to the substantial spatial heterogeneity found in these ecosystems; (2) the long time-frame needed for sampling due to large seasonal variations in climate and lags in system response to climate and management (see Figure 2); and (3) the large number of treatments needed to cover the range of options needed to be investigated. Given the considerable time and cost that would be involved in establishing such an experimental program, it is unlikely to be supported. An alternative method to obtain validation of some components of the model is to use existing field trials in conjunction with the flux difference approach to measuring trace gas emissions developed by O. T. Denmead (personal communication). This technique effectively integrates net emissions under field conditions over mediumsized plots (about 20 by 20 m) that can include grazing animals. However, this methodology is unsuitable for evaluating the substantial emissions from biomass burning as the gases quickly rise above the height of the sensors. Sampling of pool sizes before and after measurements would still be required to identify emission sources and sinks. The framework for evaluating emissions developed in this paper has not yet been validated as a consequence of the above. However, the relationships on which the GRASSMAN simulations of tree and pasture growth are based have been validated (Scanlan and Burrows, 1990; Scanlan, 1992; Scanlan and McKeon, 1993). As these form the basis for calculating carbon flows, and as the other relationships have been developed where possible using experimental data from the ecosystems addressed here, some confidence can be attributed to the results. In Climatic Change May 1994
S. Mark Howden et al.
60
Net emissions (t CO 2 e q u i v s / h a / y e a r )
1°11 4 2 0 -2 -41
......
.=....n.
...........
•
...........
!
...........
•
...........
i
.........................
-6 L~--l-
Season Fig. 2. Simulated net emissions (kg CO2 equivalents/ha) each six-month season from an unburnt, moderately grazed pasture in the black speargrass zone of north-eastern Australia. Direct Global Warming Potentials only are used in calculating emissions. Dry season burning is indicated by arrows. view of this, but recognising the preliminary nature of the model, an exploratory study was conducted. This is outlined below.
2.12. Exploratory Study An exploratory study was undertaken with the modified G R A S S M A N model o f a grazing system typical of those of the black speargrass zone in tropical northeastern Australia around latitude, 19 ° S. The study was of a pasture with native grass species growing on duplex soils under a medium density, mature woodland of Silverleaf Ironbark (Eucalyptus melanophloia) trees with a shrub understorey. The basal area o f the trees was 6.0 m2/ha (about 7 0 - 9 0 trees per hectare) with average height o f 15 m. Average wet season rainfall was 500 m m and dry season rainfall 200 mm, but seasonal rainfall was varied in the stochastic model runs as described below. Potential liveweight gains per head of cattle were 140 kg during the wet season and 10 kg during the dry season (McCown, 1981). The stocking strategy chosen was to adjust stocking rates on a seasonal basis in response to pasture growth so as to give relatively constant utilisation of summer growth (amount eaten as a proportion of amount grown; M c K e o n et al., 1990). The level of pasture utilisation Climatic Change May 1994
Options to Reduce Greenhouse Gas Emissionsfrom Tropical Grazing Systems
61
was varied as described below. Pasture was burnt every third year if there was adequate material to support a burn. Termite biomass was high (96 kg/ha), as is often found in these ecosystems (Holt, 1988; Holt and Easy, 1993). These are the key inputs needed to characterise the simulation. Most previous attempts to evaluate greenhouse gas emissions from livestock have been based on a 'per head' approach. Hence, the most obvious way to reduce emissions is to substantially reduce animal numbers. In tropical savannas, this translates directly to a reduction in regional productivity as there are usually few alternative land-use options. However, in these grazing systems there are substantial interactions between various components, and hence the relationship between stocking rate and emissions may be non-linear. Furthermore, stocking rate is an important determinant of the production and sustainability of these ecosystems (e.g. Abel, 1992), both of which need to be considered when investigating viable emission reduction options. Biomass burning is another management tool that also has important implications for greenhouse gas emissions, productivity and the sustainable use of tropical and sub-tropical grasslands. Hence, two exploratory studies were completed using the model. The first investigated the effect of changes in stocking rate on emissions. The second investigated the effect of changing fire frequency on emissions. Stocking rate was varied by altering the utilisation rate of the pasture from 0% (ungrazed) to 45% (heavily grazed). A utilisation rate of about 30% will generally provide sustainable grazing (McKeon et al., 1990), although many graziers in the region addressed in this exploratory study have utilisations in the 40-50% range (Burrows et al., 1990) resulting in overstocking and land degradation (Tothill and Gillies, 1992). Burning treatments were: never burnt, burnt every fifth year, every third year and every year. Burning management in the region is very variable with some graziers burning annually where possible and others never setting fire to their rangelands. Pasture utilisation was held constant at 30% for all of these simulations. Climatic variability has profound effects on all of the components of these grazing systems (e.g. Scanlan and McKeon, 1993), on their management and productivity (e.g. McKeon et al., 1990) and, presumably, on their levels of greenhouse gas emissions. Hence, each stocking rate or burning treatment was replicated with a random climatic sequence that had mean values as given above and equal variances. Preliminary studies indicated that five replications were required to give an adequate sample. Results are presented as the mean of these five simulations. The greenhouse gas emissions calculated were: (1) net total emissions (kg CO2 equivalent/ha/year) calculated from CO2, CH4 and N20 and their respective 100-year direct GWPs; (2) net emissions (kg CO2 equivalent/ha/year) from all major greenhouse gases emitted from the system (i.e. CO2, CH4, CO, N20 and NO) and their respective direct and indirect GWPs; and (3) the net emissions of methane (kg/ha/year). These emissions can also be expressed as anthropogenic
Climatic ChangeMay 1994
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(a)
Net em[ss[ons (k9 CO 2 equ[vs/ha/year)
Liveweight gain (kg/ha/year)
//
100 SO-
30
20-
O' 15-50 10-100" -150
5-
I 0.1
I 0.2
Stocking rate (AE/ha)
E 0.3
0.4
0 -120
-80
-40
0
40
80
120
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Fig. 3. Simulated relationships between: (a) net greenhouse gas emissions (kg CO2 equivalents/ha/year) and stocking rate (AE/ha); (b) liveweight gain (kg/ha/year) and net emissions for a black speargrass pasture in north-eastern Australia using direct Global WarmingPotentials only. The mark (*) on Figure 3b indicates a sustainable stocking rate (30% utilisation).
(man-made) emissions where these are the difference between net emissions under any management option and the emissions in the natural, ungrazed state. The index of productivity was animal production per unit area (kg liveweight gain/ha/year) for growing cattle. Stocking rates are expressed throughout as adult equivalents (AE; defined as a 450 kg steer). Several reports on emission reduction options and strategies have emphasised the desirability of increasing the amount of product per unit greenhouse gas emissions (e.g. Leng, 1991; NGRS, 1992). Hence, a 'production efficiency' coefficient is calculated as the liveweight gain per hectare divided by the amount of emissions as defined above. 3. Results Simulated net greenhouse gas emissions showed considerable seasonal variation. During the wet season, this grazing system was a sink for up to 4 t CO2 equivalents/ha (direct GWPs only), whilst in the dry season it was a source of up to 2 t CO2 equivalents/ha with burning increasing this to 8 t (Figure 2). Net emissions were very sensitive to the GWPs used. When direct GWPs only were used, the system was a net source of about 90 kg CO2 equivalents/year when heavily grazed (Figure 3a). This decreased in a slightly non-linear manner to zero emissions at a stocking rate of about 0.17 AE/ha and then decreased further such that when ungrazed, it was a sink of about 100 kg CO2 equivalents/year. In contrast, when both indirect and direct GWPs were used, the grazing system was a net emitter of greenhouse gases at all stocking rates, with emissions increasing from around 270 (ungrazed) to about 570 kg CO2 equivalents/year with increasing stocking rate (Figure 4a). These differences are partly due to the increased GWP values when indirect effects are included. They are also due to these systems becoming a potential sink Climatic Change May 1994
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Options to Reduce Greenhouse Gas Emissions from Tropical Grazing Systems Liveweight Gain (kg/ha/year)
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Fig. 4. Simulatedrelationshipsbetween: (a) net and anthropogenicgreenhousegas emissions (kg CO2 equivalents/ha/year) and stocking rate (AE/ha); (b) liveweight gain (kg/ha/year) and anthropogenic emissions (net emissions can be calculated by adding 270 to x-axis values); (c) net production efficiency (kg LWG/kg net emission CO2 equivalents) and stocking rate; and (d) anthropogenic production efficiency (kg LWG/kg anthropogenicemission CO2 equivalents) and stocking rate for a black speargrass pasture in north-eastern Australia when using both direct and indirect Global WarmingPotentials. The mark(*) on Figure4b indicates a sustainablestockingrate(30% utilisation).
for carbon dioxide because of the assignment of a zero GWP to carbon monoxide. This is because some carbon is being fixed during photosynthesis as carbon dioxide (GWP of 1), but released during burning as carbon monoxide (GWP of 0). Reducing emissions by a given percentage (e.g. 20% reductions in the interim Australian target) will require different levels of management change, and have different impacts on productivity depending on the GWPs and emission definition used. For example, if direct GWPs only are used, 20% reductions in net emissions could be achieved with little or no change in productivity for grazing systems stocked at or near a sustainable stocking rate (Figure 3b). If direct and indirect GWPs are used, similar reductions in anthropogenic emissions would require stocking rate changes that reduced productivity by about 10% (Figure 4b) whilst 20% reductions in net emissions would require changes resulting in approximately a 35% drop in productivity (Figure 4b). The relationship between production efficiency (kg LWG/unit emissions) and stocking rate was also sensitive to the type of emission being evaluated. Optimum efficiency was found at a higher stocking rate for net emissions (0.18 AE/ha; Climatic Change May 1994
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(a) Net methane emissions (kg/ha/year)
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Fig. 5. Relationshipsbetween (a) net methane emissions (kg/ha/year) and stocking rate (AE/ha); and (b) methane productionefficiency (kg LWG/kg CH4) and stocking rate (AE/ha).
(a)
(b) Never burnt
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Fig. 6. Theeffectoffirefrequencyonnetemissionsofgreenhousegases(kgCO2 equivalents/ha/year) from a black speargrass pasturein north-easternAustraliawhen using (a) direct and indirect GWPs; and (b) direct GWPs only. Stocking rate was set seasonally to achieve 30% utilisation of pasture.
Figure 4c) than for anthropogenic emissions (0.03 AE/ha; Figure 4d). Net methane emissions were intermediate (0.12 AE/ha; Figure 5b). The shape of the relationship was also different, with net emissions having a broad range of stocking rates which had near-optimal efficiency (Figure 4c) whilst this range was considerably more limited for both net methane (Figure 5b) and, particularly, anthropogenic emissions (Figure 4d). The trends in net emissions with burning frequency were also very sensitive to the GWPs used. When direct GWPs only were used, increasing burning frequency decreased net emissions such that the system was a sink for about 10 kg CO2 equivalents/year when burnt every year (Figure 6b). In contrast, when both indirect and direct GWPs were used, increasing burning frequency increased net emissions (Figure 6a). Climatic Change May 1994
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4. Discussion A simulation model of the fluxes and storages of greenhouse gases in a tropical grazing system was constructed by adapting an existing agricultural systems model, GRASSMAN. The use of an existing model which had been validated for plant growth and animal production provided a coherent framework which greatly aided model development. In exploratory analyses conducted with the modified model, it became apparent that management options to reduce greenhouse gas emissions from the tropical grazing system investigated were highly sensitive to the Global Warming Potentials used and to the emission definition adopted. For example, the recommendation to reduce emissions through changes in burning management would be to reduce fire frequency if both direct and indirect GWPs are used in evaluating emissions, but to increase fire frequency if only direct GWPs are used. These results occur largely because some of the carbon fixed during photosynthesis as carbon dioxide (GWP of 1) is being emitted during burning as carbon monoxide (GWP of 0), resulting in a carbon dioxide sink. Carbon monoxide emissions, and hence the size of this sink, increase with fire frequency. The ability to reduce greenhouse gas emissions from these systems by reducing stocking rates is also sensitive to the GWPs used and to the emission definition adopted. For example, if direct GWPs only are used, 20% reductions in net emissions (or even a net sink) could be achieved with little or no change in productivity for grazing systems stocked at or near a sustainable stocking rate. In contrast, if both indirect and direct GWPs are used, similar reductions in net emissions will require changes in stocking rate that will reduce productivity by about 35%. For anthropogenic emissions the drop in productivity would be about 10%. The implications of these reductions in productivity on the farm and regional economies need to be investigated with a whole-farm model and macro-economic model respectively. However, in all cases where stocking rates were increased over sustainable levels, there was an increase in net emissions coupled with a decrease in productivity. Studies by Burrows et al. (1990) and Tothill and Gillies (1992) suggest that many producers in the speargrass zone may be using such stocking rates, with 15-20% of the area being degraded beyond economic recovery and a further 35-60% in a deteriorating state. In these heavily grazed systems, the relatively small reductions in stocking rate that are needed to reduce emissions significantly may also have the effect of reducing soil and vegetation degradation, thereby improving the sustainability of these enterprises. Implementation of these stocking rate recommendations to reduce greenhouse gas emissions could be achieved as part of overall sustainable farming. The simulation studies indicate that it is possible to alter management to maximise beef cattle production per unit greenhouse gases or per unit methane emitted. However, the optimum production efficiency with regard to emissions is again dependent upon the emission definition used. High ratios of liveweight gain per Climatic Change May 1994
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unit net greenhouse gas or methane emission were found in a broadly defined band covering the entire range of stocking rates likely to be used. In contrast, high values of liveweight gain per unit anthropogenic emission were found only at very low stocking rates that are unlikely to be economically viable. The results presented above with regard to changes in stocking rate, burning management and optimisation of production per unit emissions suggest that policy initiatives regarding the reduction of greenhouse gas emissions from tropical grazing systems should be approached cautiously until the GWPs have been further developed and the implications of emission definitions more rigorously evaluated. The modified GRASSMAN model does not have routines that deal with soil organic matter. Increases in stocking rates may result in reductions in the size of this carbon pool by reducing litter inputs (e.g. Bridge et aL, 1983), resulting in net emissions above those estimated here. The converse situation could occur if stocking rates are reduced. Small proportional changes in the size of this large pool could have a substantial impact on the emissions budget (McKeon et al., 1992) and therefore the emission trends with stocking rate noted here are likely to be conservative. Pasture improvement by fertilisation and the establishment of legumes and more productive grasses may result in increases in soil organic matter in conjunction with increases in stocking rate; a result diametrically opposed to that above. However, these improvements are largely restricted to areas with higher and more reliable rainfall than those addressed here. Furthermore, increases in methane emissions from cattle would probably more than offset carbon sequestration. Opportunities exist to improve the simulation of soil carbon and also nitrogen fluxes by integrating GRASSMAN with components of models such as SPUR (Hanson et al., 1988), CENTURY (Metherell et al., 1993) and DNDC (Li et al., 1992) and by an experimental program to validate these components. The extrapolation of the emission calculations performed with the modified GRASSMAN model to the whole of the northern Australian beef industry represents a difficult problem, as it requires the classification of the speargrass zone (23 million ha) into areas that have been cleared and maintained as cleared pastures, areas that have been cleared for the first time (estimated as 150 000 ha/yr; Burrows, 1990), and areas that have been cleared but are currently undergoing regrowth due to the lack of burning and past management practises to prevent regrowth. Whilst all of these scenarios can be simulated by the current GRASSMAN model, the calculation of the areas involved remains a major limitation to correctly estimating emissions from the black speargrass zone. Methodologies for estimating these areas would include monitoring the sale of arboricides, land description as previously demonstrated by Weston et al. (1981), and using remote sensing for vegetation mapping (Danaher et al., 1992). A similar problem exists in estimating the results of methane emissions from termites and methane uptake by the soil. The values and relationships used in this model are derived from relatively few measurements, often taken in experimental plots of very limited area. The use of these measurements as the basis of seasonal Climatic ChangeMay 1994
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estimates is also difficult due to short-term variation in gas emissions related to mound temperature and moisture content (Holt, 1988), and the intermittent nature of most of the emission measurements taken to date. Models such as GRASSMAN are frequently being tested and upgraded and, where appropriate, modified to account for regional differences in the underlying physical and biological processes. They therefore provide a valuable and feasible framework for identifying ways to assess and limit emissions from other rangeland, agricultural and natural ecosystems.
References Abel, N. O. J.: 1992, 'What's in a Number? The Carrying Capacity Controversy on the Communal Rangelands of Southern Africa', Ph.D. thesis, University of East Anglia. Andrew, M. H.: 1986, 'The Use of Fire for Spelling Monsoon Tallgrass Pasture Grazed by Cattle', Tropic. Grassl. 20, 69-78. Bevege, D. I.: 1978, Biomass and Nutrient Distribution in Indigenous Forest Ecosystems, Queensland Department of Forestry, Technical Paper 6. Blaxter, K. L. and Clapperton, J. L.: 1965, 'Prediction of the Amount of Methane Produced by Ruminants', Brit. J. Nutrit. 19, 511-522. Boag, S., White, D. H., and Howden, S. M.: 1993, 'Monitoring and Reducing Greenhouse Gas Emissions from Agricultural, Forestry and Other Human Activities - Towards a Systems Approach', Clim. Change 27, 5-11 (this issue). Bowman, E J., Wysel, D. A., Fowler, D. G., and White, D. H." 1989, 'Evaluation of a New Technology when Applied to Sheep Production Systems: Part I - Model Description', Agricult. Syst. 29, 3547. Braithwaite, R. W., Miller, L., and Wood, J. T.: 1988, 'The Structure of Termite Communities in the Australian Tropics', Austral J. Ecol, 13, 375-392. Bridge, B. J., Mott, J. J., and Hartigan, R. J.: 1983, 'The Formation of Degraded Areas in the Dry Savanna Woodlands of Northern Australia'. Burrows, W. H.: 1990, 'Prospects for Increased Production in the North-East Australian Beef Industry through Pasture Development and Management', Agricult. Sci. 3, 19-24. Burrows, D. M. and Burrows, W. H.: 1992, 'Seed Production and Litterfall in Some Eucalypt Communities in Central Queensland', Austral J. Botany 40, 389-403. Burrows, W. H., Carter, J. O., Scanlan, J. C., and Anderson, E. R.: 1990, 'Management of Savannas for Livestock Production in North-Eastern Australia: Contrasts across the Tree-Grass Continuum', J. Biogeogr. 17, 503-512. Carras, J. N., Fraser, P. J., Griffith, D. W. T., Hurst, D. E, and Williams, D. J.: 1993, 'Trace Gas Emissions from Australian Savannah Fires during the 1990 Dry Season', J. Atmos. Chem., in press. Casada, M. E. and Safley, L. M.: 1990, 'Global Methane Emissions from Livestock and Poultry Manure', Report to the Global Change Division, U.S. Environment Protection Authority, Washington, D.C. Christie, E. K.: 1979, 'Ecosystem Processes in Semi-Arid Grasslands. II. Litter Production, Decomposition and Nutrient Dynamics', Austral J. Agricult. Res. 30, 29-42. Danaher, T., Carter, J. O., Brook, K. D., and Dudgeon, G.: 1992, 'Broadscale Vegetation Mapping Using NOAA AVHRR Imagery', Proceedings of the Sixth Australasian Remote Sensing Conference, 2-6 November, 1992, Wellington, New Zealand 3, 128-137. Dowling, A. J., Webb, A. A., and Scanlan, J. C.: 1986, 'Surface Soil Chemical and Physical Patterns in a Brigalow-Dawson Gum Forest, Central Queensland', Austral J. Ecol. 11, 155-162. Friedel, M. H.: 1981, 'Studies of Central Australian Semidesert Rangelands. I. Range Condition and the Biomass Dynamics of the Herbage Layer and Litter', Austral. J. Botany 29, 219-231. Climatic Change May 1994
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Frost, R G. H.: 1985, 'Organic Matter and Nutrient Dynamics in a Broadleafed African Savanna', in Tothill, J. C. and Mott, J. J. (eds.), Ecology and Management of the World's Savannas, Australian Academy of Science, Canberra, pp. 200-206. Galbally, I. E., Fraser, P. J., Meyer, C. P., and Griffith, D. W. T.: 1992, 'Biosphere/Atmosphere Exchange of Trace Gases over Australia', in Gifford, R. M., and Barson, M. M. (eds.), Australia's Renewable Resources: Sustainability and Global Change, Bureau of Rural Resources Proceedings No. 14, Australia, pp. 117-149. Gifford, R. M., Cheney, N. P., Noble, J. C., Russell, J. S., Wellington, A. B., and Zammit, C.: 1992, 'Australian Landuse, Primary Production of Vegetation and Carbon Pools in Relation to Atmospheric Carbon Dioxide Concentration', in Gifford, R. M. and Barson, M. M. (eds.), Australia's Renewable Resources: Sustainability and Global Change, Bureau of Rural Resources Proceedings No. 14, Australia, pp. 151-187. Graham, T. W. G., Webb, A. A., and Waring, S. A.: 1981, 'Soil Nitrogen Status and Pasture Productivity after Clearing of Brigalow (Acacia harpophylla), Austral. J. Experim. Agricult. Animal Husb. 21, 109-118. Hanson, J. D., Skiles, J. W., and Patton, W. J.: 1988, 'A Multi-Species Model for Rangeland Plant Communities', Ecol. Model. 44, 89-123. Harlan, J. R.: 1958, 'Generalized Curves for Gain per Head and Gain per Acre in Rates of Grazing Studies', J. Range Manag. 11, 140-147. Harrington, G. N.: 1979, 'Estimation of Above-Ground Biomass of Trees and Shrubs in a Eucalyptus populnea E Muell. Woodland by Regression of Mass on Tree Trunk Diameter and Plant Height', Austral. J. Botany 27, 135-143. Holt, J. A.: 1988, 'Carbon Mineralization in Semi-Arid Tropical Australia: The Role of Mound Building Termites', PhD thesis, University of Queensland. Holt, J. A. and Coventry, R. J.: 1988, 'The Effects of Tree Clearing and Pasture Establishment on a Population of Mound-Building Termites (Isoptera) in North Queensland', Austral. J. Ecol. 13, 321-325. Holt, J. A. and Coventry, R. J.: 1990, 'Nutrient Cycling in Australian Savannas', Z Biogeogr. 17, 427-432. Holt, J. A. and Easy, J. F.: 1993, 'Numbers and Biomass of Mound-Building Termites (Isoptera) in a Semi-Arid Tropical Woodland near Charters Towers, Queensland', Sociobiology 21, 281-286. le Houerou, H. N.: 1989, The Grazing Land Ecosystems of the African Sahel, Springer-Verlag, Berlin. Houghton, J. G., Jenkins, G. J., and Ephraums, J. J.: 1990, Climate Change: The 1PCC Scientific Assessment, Cambridge University Press, Cambridge. Howden, S. M.: 1988, 'Some Aspects of the Ecology of Four Tropical Grasses with Special Emphasis on BothriochIoa pertusa', PhD thesis, Griffith University. Howden, S. M.: 1992, 'Methane Emissions from Australian Livestock; 1990-91', Report to the IPCC attached to the Australian Greenhouse Gas Emission Inventory, Climate Change Scientific/Technical IDC. Howden, S. M. and Munro, R. K.: 1993, 'Methane Emissions from Australian Domestic Livestock', Bur. Resource Sci. Work. Pap., in press. Howden, S. M., McKeon, G. M., and Scanlan, J. C.: 1993, 'Changing Stocking Rates and Burning Management to Reduce Greenhouse Gas Emissions from Southern Queensland Grasslands', Proceedings of the XVllth International Grassland Congress, Palmerston North, New Zealand, in press. Ingrain, J.: 1990, 'The Role of Trees in Maintaining and Improving Soil Productivity: A Review of Literature', in Prinsley, R. T. (ed.), Agroforestryfor Sustainable Production: Economic Implications, Commonwealth Secretariat, London, pp. 243-304. IPCC: 1992, 'IPCC Supplement: Scientific Assessment of Climate Change', Submission from Working Group I. Intergovernmental Panel on Climate Change, 24 pp. Josens, G.: 1983, 'The Soil Fauna of Tropical Savannas. III. The Termites, in Bourliere, E (ed.), Ecosystems of the World: Tropical Savannas, Elsevier, Amsterdam, pp. 505-524. Khalil, M. A. K., Rasmussen, R. A., French, J. R. J., and Holt, J. A.: 1990, 'The Influence of Termites on Atmospheric Trace Gases: CH4, CO2, CHC13, N20, CO, H2, and Light Hydrocarbons', J. Geophys. Res. 95, 3619-3634.
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Leng, R. A.: 1991, 'Improving Ruminant Production and Reducing Methane Emissions from Ruminants by Strategic Supplementation', United States Environmental Protection Agency, EPA/400/1-91/004. Li, C. S., Frolking, S., and Frolking, T. A.: 1992, 'A Model of Nitrous Oxide Evolution from Soil Driven by Rainfall Event. 1. Model Structure and Sensitivity', J. Geogr. Res. 97, 9759-9776. Madgwick, H. A. I., Frederick, D. J., and Thompson Tew, D.: 1991, 'Biomass Relationships in Stands of Eucalyptus Species', Bioresource Technol. 37, 85-91. McCown, R. L.: 1981, 'The Climatic Potential for Beef Cattle Production in Tropical Australia. Part 1. Simulating the Annual Cycle of Liveweight Change', Agricult. Syst. 6, 303-317. Metherell, A. K., Cole, C. V., and Patton, W. J.: 1993, 'Dynamics and Interactions of Carbon, Nitrogen, Phosphorus and Sulphur Cycling in Grazed Pasture', Proceedings of the XVllth International Grassland Congress, Palmerston North, New Zealand, in press. McKeon, G. M. and Rickert, K. G.: 1984, 'A Computer Model of the Integration of Forage Options for Beef Production', Proc. Austral. Soc. Anim. Prod., Armidale, pp. 15-19. McKeon, G. M., Day, K. A., Howden, S. M., Mott, J. J., Orr, D. M., Scattini, W. J., and Weston, E. J.: 1990, 'Northern Australian Savannas: Management for Pastoral Production', J. Biageogr. 17, 355-372. McKeon, G. M., Rickert, K. G., and Scattini, W. J.: 1986, 'Tropical Pastures in the Farming System: Case Studies of Modelling Integration through Simulation', Proceedings 3rd Australian Conference on Tropical Pastures, Tropical Grassland Society, Brisbane, 92-100. McKeon, G. M., Howden, S. M., and Stafford-Smith, M. D.: 1992, 'The Management of Extensive Agriculture: Greenhouse Gas Emissions and Climate Change', in Assessing Technologies and Management Systems for Agriculture and Forestry in Relation to Global Climate Change, Proceedings of IPCC Working Group III Workshop, Canberra, Australia, pp. 42-47. McKeon, G. M., Howden, S. M., Abel, N. O. J., and King, J. M.: 1993, 'Climate Change: Adapting Tropical and Sub-Tropical Grasslands', Proceedings of the XVllth International Grassland Congress, Palmerston North, New Zealand, in press. Minson, D. J. and McDonald, C. K.: 1987, 'Estimating Forage Intake from the Growth of Beef Cattle', Tropic. Grassl. 21, 116-122. Mott, J. J., Williams, J., Andrew, M. H., and Gillison, A. N.: 1985, 'Australian Savanna Ecosystems', in Tothill, J. C. and Mott, J. J. (eds.), Ecology and Management of the World's Savannas, Australian Academy of Science, Canberra, pp. 56-82. NGRS: 1992, 'National Greenhouse Response Strategy', Commonwealth of Australia, December, 1992, Australian Government Publishing Service, Canberra, 114 pp. Orr, D. M., McKeon, G. M., and Day, K. A.: 1991, 'Burning and Exclosure Can Rehabilitate Degraded Black Speargrass (Heteropogon contortus) Pastures', Tropic. Grassl. 25, 333-336. Paton, C. J. and Rickert, K. G.: 1989, 'Burning, then Resting, Reduces Wiregrass (Aristida spp.) in Black Speargrass Pastures', Tropic. Grassl. 23, 2ll-216. Prebble, R. E. and Stirk, G. B.: 1980, 'Throughfall and Stemflow on Silverleaf Ironbark (Eucalyptus melanophloia) Trees', Austral. J. Ecol. 5, 419-427. Robbins, G. G., Bushell, J. J., and McKeon, G. M.: 1989, 'Nitrogen Immobilization in Decomposing Litter Contributes to Productivity Decline in Ageing Pastures of Green Panic', J. Agricult. Sci. (Cambridge) 113, 401-406. Ryan, T.: 1988, 'Cattle Costs and Returns in Central Queensland', Queensland Department of Primary Industries, RQR 88020. Scanlan, J. C.: 1992, 'A Model of Woody Herbaceous Biomass Relationships in Eucalypt and Mesquite Communities', J. Range Manag. 45, 75-80. Scanlan, J. C. and Burrows, W. H.: 1990, 'Woody Overstorey Impact on Herbaceous Understorey in Eucalyptus spp. Communities in Central Queensland', Austral. J. Ecol. 15, 191-197. Scanlan, J. C. and McKeon, G. M.: 1990, GRASSMAN, Queensland Department of Primary Industries. Scanlan, J. C. and McKeon, G. M.: 1993, 'Competitive Effects of Trees on Pasture Are a Function of Rainfall Distribution and Soil Depth', Proceedings of the XVllth International Grassland Congress, Palmerston North, New Zealand (in press). Shine, K. P., Derwent, R. G., Wuebbles, D. J., and Moncrette, J-J.: 1990, 'Radiative Forcing of Climate', in Houghton, J. G., Jenkins, G. J. and Ephraums, J. J. (eds.), Climate Change: The
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IPCC Scientific Assessment, Cambridge University Press, Cambridge, pp. 46--68. Tothill, J. C. and Gillies, C.: 1992, 'The Pasture Lands of Northern Australia', Tropical Grassland Society of Australia, Occasional Publication No. 5, 106 pp. Weston, E. J., Harbison, J., Leslie, J. K., Rosenthal, K. M., and Mayer, R. J.: 1981, Assessment of the Agricultural and Pastoral Potential of Queensland, Agriculture Branch Technical Report 27, Queensland Department of Primary Industries. White, D. H.: 1987, 'Stocking Rate', in Snaydon, R. W. (ed.), Managed Grasslands. B. Analytical Studies, Elsevier, Amsterdam, pp. 227-238. Williams, D. J.: 1993, 'Methane Emissions from the Manure of Free-Range Dairy Cows', Chemosphere 26, 179-187. Wood, T. G.: 1978, 'Food and Feed Habits of Termites', in Brian, M. V. (ed.), Production Ecology of Ants and Termites, IBP 13, Cambridge Press, London, pp. 55-80. Zimmerman, P. R., Greenberg, J. P., Wandiga, S. O., and Crutzen, P. J.: 1982, 'Termites: A Potentially Large Source of Atmospheric Methane, Carbon Dioxide and Molecular Hydrogen', Science 224, 563-565. (Received 16 June, 1993; in revised form 15 December, 1993)
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