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Australian Journal of Experimental Agriculture, 2008, 48, 109–113
Predicting livestock productivity and methane emissions in northern Australia: development of a bio-economic modelling approach E. CharmleyA,B , M. L. StephensA and P. M. KennedyA A B
CSIRO Livestock Industries, JM Rendel Laboratory, PO Box 5545, Rockhampton, Qld 4702, Australia. Corresponding author. Email:
[email protected]
Abstract. Enteric fermentation from livestock is a large source of methane, which has a global warming potential 23 times that of carbon dioxide. In Australia, enteric emissions from the livestock sector contribute 10% of Australia’s greenhouse gases. The northern Australian beef industry of 16 million animals is a major contributor to these emissions. However, relative to temperate systems, comparatively little research has been conducted on enteric methane emissions from tropical feeding systems. This paper describes a modelling approach that estimates cattle methane emissions for various bioregions of northern Australia. The approach incorporates a metabolisable energy based model of animal production linked to a property herd economic model. This provides a flexible tool to evaluate animal and property herd dynamics on regional methane yields and liveweight productivity, as well as to assess financial impacts. The model predicts that an important determinant of methane output per unit of product is reduced days to market. Reduced days to market may be achieved through a range of energy supplementation and marketing strategies.
Introduction In Australia, agriculture is the second largest contributor to greenhouse gases, accounting for 16% of emissions (Australian Greenhouse Office 2007). Enteric fermentation in ruminants contributes a large proportion of these greenhouse gases in the form of methane; a compound having 23 times the global warming potential of CO2 (Australian Greenhouse Office 2007). The beef industry in northern Australia is typified by large pastoral properties supporting about 16 million beef cattle (ABARE 2006). Historically, cattle were grown and finished under pastoral conditions or exported as live cattle, but this is gradually changing with an increasing proportion of cattle finished through feedlots (ABARE 2006). Pasture production in northern Australia is highly seasonal with pasture growth occurring in the wet season (November to April), followed by a senescent period through the dry season. This produces a marked seasonal pattern of pasture availability and quality (Tothill and Gillies 1992). In addition, the dominant pasture species are C4 grasses, which generally have lower nutritional value than temperate grasses. Pasture management practices have adapted to these conditions, but stocking rates and individual animal production are generally lower than in temperate regions. Low animal productivity is associated with high methane output per unit of product and low pasture quality is associated with high methane output per unit of dry matter (DM) intake (Johnson and Johnson 1995). Consequently, methane emissions from the northern Australian beef herd are considered to be higher than for more intensive systems. However, relative to temperate feeding systems, comparatively little research has been conducted on enteric methane emissions from tropical forages. Thus, the factors affecting methane production are poorly understood for © CSIRO 2008
northern beef production systems. Furthermore, the changing composition (e.g. breed, turn-off age) and size of the northern beef herd, and the changing climatic conditions, mean that methane emissions are likely to be highly variable and difficult to predict. This paper describes a modelling approach that estimates methane emissions from the northern Australian beef herd under typical conditions found in the bioregions described in Fig. 1. Details of the modelling approach are published in an Accessory Publication. The purpose of the work was to refine the broad accounting methods used by the Intergovernmental Panel on Climate Change (1996) and to explore the dynamics of methane emissions by beef cattle in northern Australia. The approach allows for rapid assessment of the impact of management and environmental changes not only on methane emissions but also financial profitability at a property level. Material and methods A spreadsheet (Microsoft Excel) based model – the Northern Australia Beef Cattle Energetics and Methane Simulator (NABCEMS) – was developed to estimate methane emissions from cattle under northern Australian conditions (for details, see Accessory Publication). The model encompasses three key components: (i) animal; (ii) pasture; and (iii) property herd or bioregion. The NABCEMS model also links with a separate economic herd model to represent herd dynamics and profit maximisation behaviour (Fig. 2). The animal component of the model was based on the ARC (1980) metabolisable energy (ME) system for estimating nutrient requirements of cattle which was developed in the United Kingdom for temperate conditions. This system
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Fig. 1. Australian Bureau of Agricultural and Resource Economics (ABARE) broadacre zones and regions in Australia.
assigns energy requirements for maintenance, activity (including walking and chewing), growth, gestation and lactation of the animal. These requirements are determined by intake and digestibility of feed and characteristics of the animal [maturity, sex and breed type and class (i.e. growing, reproductive,
Pasture quality
E. Charmley et al.
lactating, mature)]. The model incorporates some SCA (1990) relationships reflecting conditions in northern Australia and the fasting metabolism requirement was dependent upon the proportion of Bos indicus genetics in the animal (Vercoe 1970). Typical milk production curves and energy values of milk for Bos indicus × Bos taurus cattle were based on the data of Hunter and Magner (1988). Energy requirements for milk production were estimated using ARC (1980). The model allows for selection of peak milk production between 5.9 and 7.2 kg/day. Minimum liveweight (LW) and LW gain thresholds for conception can be entered by the user as a means of controlling conception rate. The pasture component of the model requires inputs of available biomass and diet quality (digestibility) throughout a season. While nitrogen also plays a role in diet quality, there was insufficient information to include this parameter in the model. The model uses a weekly time step and is driven primarily by the diet digestibility to reflect differences in mean forage quality in different environments. In a data input page of the spreadsheet, two different patterns between diet quality and time of year can be selected, either a rapid or a more gradual decline in digestibility as the dry season advances. For each digestibility pattern, different mean digestibilities can be chosen. The model allows choices between three options for estimating feed intake, based on ARC (1980), SCA (1990), or data from SCA (1990) adjusted for tropical conditions by applying a 20% reduction, based on data from D. B. Coates (pers. comm.). Directly or indirectly, these estimates all relate to the quality of the diet and the weight of the animal. The model runs for three classes in the herd simultaneously; growing animals, reproductive females and mature bulls. These are independently described by sex, frame size and breed,
Pasture utilisation rate
Animal lifetime methane
Methane
Lifespan ARC ME model Digestible DM intake
Reproductive generation
Annual property methane
Production generation
Supplements
Activity Production (foetus, milk, growth)
Daily methane output model Branding rates, mortalities Costs, prices
Animal LW gain
Property herd economic model
Herd cohorts, turn-off
Fig. 2. Conceptual diagram of the modelling approach.
Gross margin
Bioregion inputs
Annual regional methane
Modelling livestock productivity and methane emission
birthweight and maximum mature weight. For a scenario with supplementation, a minimum rate of daily gain can be selected and the model calculates the amount of supplement required when the pasture fails to meet the requirements for the rate of gain selected. The supplement has the feed characteristics of molasses, since this is the prevalent supplement used in northern Australia. Potential pasture intake is modified by grazing pressure and supplementation. Grazing pressure is calculated based on available biomass and stocking rate. Maximum biomass is set by the user for the end of the wet season and is chosen to reflect regional norms based on historical data (Hall et al. 1998). Daily removal of biomass by cattle is estimated from the individual animal’s intake and the stocking rate (animal per hectare) to derive residual biomass. As residual biomass declines, grazing pressure increases and actual intake as a proportion of potential intake declines. This relationship was defined using either the equation of Rayburn (Coleman 2005) or one based on the personal observations of J. O. Carter (pers. comm.), with the choice of equation dependent on bioregion and typical carrying capacity. These equations represent high and low impacts of stocking rate on actual as a proportion of potential intake, respectively. To account for the effects of supplementation on pasture intake, a curvilinear relationship between substitution rate (change in pasture intake per unit supplement intake) and pasture quality was used based on data from the literature. Supplements increased pasture DM intake when pasture energy digestibility was below 50% and reduced pasture intake when this was above 50%. There are few data on factors affecting methane yield in cattle fed tropical forages. One of three options can be selected in NABCEMS. The first is based on stoichiometry and uses a relationship between digestibility and methane (Benchaar et al. 2001). The second uses the relationship of Kurihara et al. (1999) modified by Hunter (2007), currently in the Australian greenhouse gas inventory (Australian Greenhouse Office 2007). The final option is based on Hunter (2007) but modified with recent results from our laboratory. The property and regional component of the model incorporates animal LW gain and methane emissions into a herd structure based on typical trading enterprises. Economic and physical data from the annual ABARE farm surveys database (http://www.abareconomics.com/ame/mla/mla.asp) were used to describe a typical trading enterprise for each bioregion (Fig. 2). Data collected included the total number of properties, property size, animal numbers carried, variable (direct livestock materials) costs per animal and herd composition and performance such as branding rate, death rate and percentage of bulls. These data were used as inputs into a herd economic model to optimise breeding numbers and profitability using partial budgeting techniques (Makeham and Malcolm 1993). In this case, the Breedcow and Dynama herd budgeting software (Holmes 2005) were used to optimise breeding herds and gross margins as a measure of profitability for a given market turn-off (e.g. live export, Japan Ox). Gross margin is defined as the gross income of an enterprise less the variable costs incurred.
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The calculation of property herd and regional methane emissions required several iterative steps between the NABCEMS and Breedcow models. First, the predicted liveweight gains from the animal component of NABCEMS were used to derive animal sale prices by age class (i.e. weaners, steers, heifers, and culled cows and bulls). The NABCEMS model allows the user to enter relevant weight for age specifications and market prices. For northern industry production scenarios, saleyard prices from the MLA National Livestock Reporting Service were used for relevant markets, including Japan Ox; US manufacturing grade, live exports and export stores (for feedlot finishing). The NABCEMS model calculates net saleyard prices after deducting transport, marketing commission and yarding costs. Second, net animal prices were manually entered into the herd economic model to calculate gross margin for a given marketing option (e.g. Japan Ox). The corresponding steadystate herd outputs (i.e. animal age class cohorts) were then used as inputs in NABCEMS to calculate property liveweight gain and methane emissions. Regional level statistics are also generated based on the total number of properties within each region. Results and discussion To illustrate the capabilities of the model at the animal-only level, we have chosen a scenario which is typified by low pasture productivity and extensive grazing conditions. In this comparison, reproductive cattle are either unsupplemented or provided a supplement to prevent loss of bodyweight in the dry season. Over 6 years, supplementation increased the number of calves born from 2 to 4, reduced methane emissions from the cow per live calf output by 45%, but increased total emissions by 11% (Table 1). Whilst knowing the methanogenic yield of different feeds is important, it is much less important than the capacity of different feeds to promote growth and reproduction as the following sensitivity analysis demonstrates (Table 2). In this scenario, methane as a percentage of GE is reduced by 5%, as might
Table 1. Animal performance and methane emissions for a Brahman cow (mean over 6 years) in the Kimberley region, Australia, without or with molasses supplementation to prevent weight loss Attribute measured
Dry matter intake (kg/day) Metabolisable energy (ME) intake (MJ/day) Proportion of ME (%) Maintenance + activityB Growth Gestation and lactation No. of calves born to 6 years Methane emissions (g/day) (kg/calf) A Supplement B Walking
Animal performance No molasses With molasses supplement supplementA 13.7 111
17.3 151
86 4 10 2
72 16 8 4
405 202
452 113
fed to maintain no daily liveweight loss. 8 km/day on level ground.
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Table 2.
E. Charmley et al.
The direct and indirect impact of reducing methane production by 5% of gross energy (GE)
GE digestibility (%) Methane (% of GE) Methane (MJ/day) Liveweight gain (kg/day) Age at turnoff (550 kg) Lifetime methane emissions (kg)
Baseline data
Reduce methane by 5% only
Reduce methane by 5% plus concomitant changes in energy utilisation
60 12.04 253 0.45 3.15 290
60 11.41 240 0.45 3.15 275
61 11.41 237 0.54 2.63 226
Liveweight gain (tonnes/year)
be expected after selection for low residual feed intake over 10 years (Alford et al. 2006). Applying this reduction in methane emissions without altering other parameters results in a saving of ∼14 kg of methane over the lifetime of the animal. However, if that improvement in methane emissions is integrated with the concomitant increase in ME resulting from reduced energy loss in methane, quite a different response is observed. Increased diet quality translates into higher ME intake per day and the animal reaches slaughter weight in 2.63 years as opposed to 3.15 years. Thus, although daily methane emissions increase, total lifetime methane emissions are reduced from 290 to 226 kg. This example serves to illustrate the relative importance of simply reducing methane emissions v. taking into account the impact of reduced methane emissions on overall energy balance in the animal. In addition to diet quality effects on the individual animal, changes to herd structure as a result of management and marketing decisions are key drivers of production and methane emissions at the property and regional levels. Figure 3 shows the results of a supplementation scenario to maintain a LW gain of 0.5 kg/day for steers in the northern speargrass region. Supplementation reduces turn-off age for the Japanese Ox market from 4 years (526 kg LW) to 2.3 years (650 kg LW). This in turn allows for increased numbers of breeders in the herd to produce more young steers and thus maximise gross
margins. However, the increased methane produced from more breeders is accompanied by increased LW gains from younger steers (Fig. 3). The emissions rate of methane per kg of product is greatly improved from 787 g/kg to 592 g/kg herd LW gain. The methane ‘tail’ of 3–4-year-old steers for the pasture based system generates large amounts of methane relative to the LW gains produced, hence resulting in a higher emission rate for the herd. Supplementation was also shown to improve property gross margin by almost 9% at $150 per animal equivalent. Property level outcomes can also be interpreted in a regional context. If it is assumed, for example, that half of the 482 properties in the region produce 4-year-old steers on a pasture-only based system, a supplementation strategy could lead to an annual emissions saving of over 500 kt (in CO2 equivalent terms) with younger turn-off and a total LW gain of 115 kt per year. However, this would require a substantial amount of supplement of up to 70 kt per annum for the region. These simulations illustrate some of the individual animal and herd dynamics of methane production in northern Australia. The modelling framework can be applied to a wide range of production, management and marketing scenarios to generate information on possible changes in methane emissions and financial gross margins. While these changes can be quantified, the output should be considered in light of the data deficiencies. Three primary areas require refinement and relate to a better
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Fig. 3. Comparison of property herd methane emissions (, methane from pasture; ×, methane from supplement) and liveweight gains by age class from a pasture based (open bars) and supplement (solid black bars) based system in the northern speargrass region (herd size = 3000 adult equivalents).
Modelling livestock productivity and methane emission
understanding of the forage base that makes up the major component of the diet in northern Australia. They include: • estimation of diet quality under selective grazing conditions; • estimation of dry matter intake under heterogeneous grazing conditions; and • precision of predicting methane yield from cattle grazing tropical forages. Further research will address these limitations through direct measurement of livestock methane emissions from a range of tropical forages and through the integration of forage inputs from regionally specific climatic pasture growth models for northern Australia such as Aussie GRASS (McKeon et al. 1990) into the NABCEMS model. These steps will greatly increase the data on methane emissions from tropical forages fed to cattle and will better account for the high variability and seasonality in forage growth across the bioregions of northern Australia. References ABARE (2006) ‘Australian beef 06.1.’ (Australian Bureau of Agricultural and Resource Economics: Canberra) Alford AR, Hegarty RS, Parnell PF, Cacho OJ, Herd RM, Griffith GR (2006) The impact of breeding to reduce residual feed intake on enteric methane emissions from the Australian beef industry. Australian Journal of Experimental Agriculture 46, 813–820. doi: 10.1071/EA05300 ARC (1980) ‘The nutrient requirements of ruminant livestock.’ Australian Research Council. (CAB International: Wallingford UK) Australian Greenhouse Office (2007) ‘National greenhouse gas inventory 2005.’ (Commonwealth of Australia: Canberra) Benchaar C, Pomar C, Chiquette J (2001) Evaluation of dietary strategies to reduce methane production in ruminants: a modelling approach. Canadian Journal of Animal Science 81, 563–574. Coleman SW (2005) Predicting forage intake by grazing ruminants. In ‘Proceedings of 2005 ruminant nutrition symposium, Florida’. pp. 72–90. (United States Department of Agriculture) Hall WB, McKeon GM, Carter JO, Day KA, Howden SM, Scanlan JC, Johnston PW, Burrows WH (1998) Climate change in Queensland’s grazing lands: II. An assessment of the impact of animal production from native pastures. The Rangeland Journal 20, 177–205. doi: 10.1071/RJ9980177
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Holmes WE (2005) ‘Breedcow and Dynama Herd Budgeting Software Package, Version 5.05 for Windows. Training Series QE99002.’ (Queensland Department of Primary Industries and Fisheries: Townsville) Hunter RA (2007) Methane production by cattle in the tropics. The British Journal of Nutrition 98, 657. doi: 10.1017/S0007114507727460 Hunter RA, Magner T (1988) The effect of supplements of formaldehydetreated casein on the partitioning of nutrients between cow and calf in lactating Bos indicus × Bos taurus heifers fed a roughage diet. Australian Journal of Agricultural Research 39, 1151–1162. doi: 10.1071/AR9881151 Intergovernmental Panel on Climate Change (1996) ‘IPCC guidelines for national greenhouse gas inventories. Greenhouse gas inventory reference manual. Vol. 3.’ (IPCC WG1 technical support unit, Hadley Centre, Meteorological Office: Bracknell, UK) Johnson KA, Johnson DE (1995) Methane emissions from cattle. Journal of Animal Science 73, 2483–2492. Kurihara M, Magner T, Hunter RA, McCrabb GJ (1999) Methane production and energy partition of cattle in the tropics. The British Journal of Nutrition 81, 227–234. Makeham JP, Malcolm LR (1993) ‘The farming game now.’ (Cambridge University Press: Cambridge) McKeon GM, Day KA, Howden SM, Mott JJ, Orr DM, Scattini WJ, Weston EJ (1990) Northern Australia savannas: management for pastoral production. Journal of Biogeography 17, 355–372. doi: 10.2307/2845365 SCA (1990) ‘Feeding standards for Australian livestock, ruminants.’ Standing Committee on Agriculture. (CSIRO Publications: Melbourne) Tothill JC, Gillies C (1992) ‘The pasture lands of northern Australia: their condition, productivity and sustainability.’ Occasional Publication No. 5. (Tropical Grassland Society of Australia: Brisbane) Vercoe JE (1970). Fasting metabolism and heat increment of feeding in Brahman × British and British cross cattle. In ‘Energy metabolism of farm animals. Proceedings of the 5th symposium on energy metabolism in farm animals’. Publication no. 13. pp. 85–88. (European Association of Animal Production)
Manuscript received 9 August 2007, accepted 1 November 2007
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