Integrated shrub management in semi-arid ... - South West NRM

AGRICULTURAL SYSTEMS Agricultural Systems 88 (2006) 332–359 www.elsevier.com/locate/agsy

Integrated shrub management in semi-arid woodlands of eastern Australia: A systems-based decision support model James C. Noble *, Paul Walker CSIRO Sustainable Ecosystems, GPO Box 284, Canberra, ACT 2601, Australia Received 8 October 2004; received in revised form 2 May 2005; accepted 13 June 2005

Abstract What is causing the increasing densities of native shrubs, or so-called Ôwoody weedsÕ, in some semi-arid pastoral lands and how might they be most effectively managed? This question has been on the rangeland policy agenda in Australia for more than one hundred years. This paper describes a fresh examination of this question using a systems approach. A key component of the approach involved Ômapping the problemÕ. Using a systems-based approach, landholders developed four system diagrams broadly describing the ecology of woody weed re-occurrence, control options, property economics and management constraints with diagrams identifying how different factors related to, or influenced, each other. Agency personnel also constructed a system diagram describing institutional and regulatory constraints, and their interactions. Later, all these system diagrams formed the basis for an adaptive management model with capabilities for developing and quantitatively evaluating alternative management strategies relating to woody weeds. This model is called the Woody Weed Planner. The Woody Weed Planner contains mathematical relationships developed through field experimentation over the last 25–50 years covering the ecology of woody weeds, control options and control economics. These relationships enable the user to generate mathematical responses as a result of changing model parameters. A key component of the model is the ability to simulate the effects of alternative management responses given different rainfall scenarios. To enable this to occur, the Planner allows the user to replay historical rainfall patterns

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Corresponding author. Tel.: +61 2 6242 1643; fax: +61 2 6242 1705. E-mail address: [email protected] (J.C. Noble).

0308-521X/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2005.06.018

J.C. Noble, P. Walker / Agricultural Systems 88 (2006) 332–359

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and ask the question ‘‘what impact will these have on woody weeds, stocking rates and economic performance on my property?’’ Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Semi-arid woodlands; Integrated shrub management; Woody weed planner

1. Introduction The cumulative effects of woody weed proliferation throughout much of the semi-arid woodlands of eastern Australia over the past century have led to a progressive decline in pastoral productivity (Harrington et al., 1984; Wilson and MacLeod, 1991; Noble, 1997). In the 1980s, aggregate income opportunity loss was estimated to be as high as A$80 million per annum for Queensland and New South Wales combined (Hodgkinson and Beeston, 1982; MacLeod, 1993). With average property sizes in western New South Wales then in the order of 15,000–20,000 ha, such valuations represented capital losses in extreme cases of around A$45,000–$60,000 annually. At a property level, gross margins for sheep enterprises in shrub-infested country were calculated at A$10–$15 per dry sheep equivalent (DSE), virtually half the values (A$20–$25 per DSE) derived for open country (Burgess and Murphy, 1989). Several management options are currently available for controlling shrubs, although their use over extensive paddocks, typical of Australian rangelands, is heavily constrained by cost factors (MacLeod and Johnston, 1990). Furthermore, detailed research over the past decade has clearly shown that single treatment options for shrub management are generally unsuccessful in providing long-term solutions (Noble et al., 1991). One approach that has shown considerable promise for treating large areas of shrub-infested range is prescribed fire (Hodgkinson and Harrington, 1985). Whilst fire generally kills topgrowth of all shrub species, budda or false sandalwood (Eremophila mitchellii), turpentine (E. sturtii) and some punty bush (Cassia nemophila syn.Senna nemophila), can regenerate rapidly after fire through coppicing. Small-scale experiments using artificial fuel have shown, however, that the risk of mortality of such species increased significantly if a second fire was imposed one year later, particularly in the autumn (Hodgkinson, 1986; Noble et al., 1986). Such annual burning on a larger scale, however, is operationally impractical in semi-arid areas because of insufficient time for adequate herbage fuel loads to accumulate. Accordingly, instead of waiting for infrequent seasonal events, e.g., two years in succession characterised by above-average precipitation, to generate sufficient stocks of grass fuel to carry a prescribed fire, it has been suggested that chemical defoliants might be used on a paddock scale to mimic the defoliation pattern of successive experimental fires applied in the autumn (Noble et al., 1991). If effective at reduced concentrations, then such defoliation treatments would not only be cost-effective for broadscale shrub control, but would also minimise any negative

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environmental impacts. Results from preliminary experiments (Noble et al., 2001) are sufficiently encouraging to warrant continuing development of such integrated management systems. These are based on strategies involving two or more treatments aimed at minimising the weaknesses and maximising the strengths of each treatment (Scifres, 1986; Scifres and Hamilton, 1993). Despite this major research effort, there would appear to be several key limitations to the application of current knowledge. These include restrictions on cash flow where individual graziers are loathe to borrow money for shrub control even though 20-year budgets may suggest a positive return in the end (MacLeod and Johnston, 1990). Variable and unpredictable rainfall introduces another major element of uncertainty given that it has a major impact on the timing and sequence of treatments that might be applied, especially prescribed fire (Noble et al., 1986). Other factors inhibiting adoption of research results may include such things as labour shortage and risk management, e.g., future commodity prices, impacts of prescribed fire and chemicals on non-target species or areas, etc. There is now little doubt that integrated management systems for woody weeds can provide significant productivity gains when economic and seasonal conditions permit, as well as enhancing environmental values in the longer term (Noble, 1997; Page et al., 2000). However, these gains will only accrue when strategies are applied systematically. While a decision-support model entitled SHRUBKILL (Ludwig, 1988) has already been constructed to assist graziers in managing woody weeds, it is limited primarily to using prescribed fire only. More comprehensive decision-support systems based on a fully integrated approach to shrub control, such as the Integrated Brush Management System (IBMS) (Scifres et al., 1985), have already been developed for American rangelands (see also Scifres, 1986, 1987) and it is timely that such management tools become available in Australia. The traditional research approach based on examining single treatments or ecological processes in isolation has only had a limited impact on scrub management to date. There is a growing recognition that research has to become an integral part of the pastoral management system, a process now widely known as Ôactive adaptive managementÕ or Ôlearning by doingÕ (Holling, 1978; Walters and Holling, 1990; Bosch et al., 1996; Walters, 1997). For active adaptive management to be effective in a pastoral context, it is essential that landholders, in addition to other relevant stakeholders, collaborate fully right from the beginning. Adaptive management hinges on being able to measure the outcome of particular management strategies, and to quickly and efficiently adapt them where necessary for future management. As a systems-based approach, it aims to design for uncertainty and to benefit from the unexpected while ongoing community dialogue facilitates development of computer-based decision-support systems (DSS). This paper describes a project aimed at developing more effective, computer-based management models for use in the adaptive management of Ôwoody weedsÕ problems in semi-arid eastern Australia in an attempt to bring science and management together. While we would like the Woody Weed Planner to be used by landholders and managers, given the potential complexity of such decision support systems we recognise that it is more likely that it would be used primarily by advisers. However,


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by engaging landholders and managers in the development of the Woody Weed Planner, we would hope that they would more readily accept its use by agency people and other advisers because of their intellectual equity in the model and its assumptions.

2. Methods and materials The principal activities of the project were: (a) define the spatial and temporal resolution required for a decision support system for woody weed management; (b) systematically describe and quantify the relationships between various factors and community sectors so as to obtain an holistic view of the system, i.e. at paddock, property and regional scales; (c) incorporate biological, ecological and economic data from past research, as well as similar knowledge acquired by local landholders over time, into relevant decision support packages for woody weed management; and (d) select case studies of weed impacts to examine management alternatives and cost:benefit analyses. The project employed ÔSystems ThinkingÕ, a conceptual approach described by Senge (1990) as ÔThe Fifth DisciplineÕ, the four other sister disciplines being personal mastery, mental models, shared vision and team learning. This approach is particularly helpful in defining problems, formulating and testing potential solutions, and implementing effective solutions that endure (Goodman et al., 1997). It focuses on patterns of behaviour through time by identifying the underlying causes and impacts and constructing a diagram showing how various factors interact. In the context of this study, adaptive management was seen to hinge on being able to measure the outcome of particular management strategies, and quickly and efficiently to adapt them where necessary for future management. Using well-established systems methodologies (e.g., Senge et al., 1994) and the INFLUENCE software package (Walker, 1996), the behaviour of shrub-dominated ecosystems and the way in which that behaviour is influenced through interactions with human (social, economic and political) systems, could then be systematically portrayed. As in adaptive management studies of other ecosystems (e.g., Walters et al., 2000), the workshop process followed discrete steps. 2.1. Study area The Cobar Pediplain was identified as a suitable subregion providing the requisite degree of spatial resolution within the Western Division of New South Wales (Fig. 1). Key variables and processes of interest were identified using broad-brush data based on results from previous research (Noble, 1997) and qualitative information, e.g., maps. A plan for the first workshop was drawn up and invitations

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Fig. 1. Map showing the general location of the Cobar Pediplain and study site (‘‘Gambolalley’’) in western New South Wales (after Ongley, 1974).

issued to representative stakeholders, primarily those had collaborated in previous research and who were familiar with the woody weed problem in the designated study region. 2.2. Mental modelling workshops The first workshop involved a full day spent at Cobar in June 1998 with 14 landholders attending. The woody weed problem was diagrammed through mental modelling, a process whereby the mental models held by participants could be identified and recorded. This process involved establishing two groups of no more than 6–8 people working independently who were then asked to identify the various factors and processes they felt characterised the woody weed problem. This information


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was recorded on large sheets of white paper with causal links drawn between key variables. This stage of the process enabled all participants to deepen his or her understanding of the dynamics behind the issue by identifying the feedback loops contributing to the problem. Causal links were established to explore the thinking behind various theories identified during the diagramming phase. Data from each group were subsequently entered into a computer and influence diagrams constructed using the INFLUENCE software package (Walker, 1996). This program systematically describes interactions and feedback loops that have a significant effect on how a complex system operates. By using INFLUENCE in this way it was possible to undertake a structural topological analysis and to identify the key determinants and key impacts recorded in the diagrams. A further half-day workshop held at Dubbo in November 1998 involved 12 participants representing five government agencies. A mental modelling approach similar to that used with landholders was followed in order to elucidate relevant legislative and regulatory constraints that needed to be taken into account when developing strategies for woody weed control. 2.3. Developing key relationships The intervening 7–12 month period between the initial 1998 and subsequent 1999 workshops with landholders was used to develop and quantify the various functional relationships and interactions identified in the first workshop. This was a critical phase involving a thorough assessment of relevant research data available from a range of sources, both formal and informal, and then using this information to construct appropriate graphs for key relationships. During the second workshop held at Cobar in June 1999, workshop participants (8 landholders) constructed ÔBehaviour Over TimeÕ graphs portraying how they thought particular variables behaved over time, e.g., what changes in the price of wool were likely to occur in the foreseeable future; what were the likely ramifications of increasing property size, etc.? These were not required to be exact but were designed to show how individuals believed they had changed, and were likely to change, over time. An important objective here was to provide a means for comparing model output with the expectations of the workshop participants. This does not mean that the model behaviour necessarily reflected these expectations as unexpected outcomes could occur in the model, but it did provide an indication of the likely acceptance of model results by the workshop participants. 2.4. Developing a decision-support system Once there had been general consensus reached on causal relationships, the model-building process then proceeded with the development of a computer-based decision-support system. Ventana (1988–95) software was utilised during this phase with other procedures similar to case-based reasoning methodology (Bosch et al., 1996,

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1997) employed to record selected case histories documenting local experience with woody weed control. A final full-day workshop held in Canberra in July 2000 evaluated the decision-support system by developing possible management interventions or scenarios and running the model. This workshop involved a small group of six extension personnel representing the major government agency (then the Department of Land and Water Conservation) responsible for land administration and extension services in the Western Division. Each participant had a personal computer containing the Woody Weed Planner software. The aim of this phase was to run small, self-contained experiments and use them as learning opportunities. Instead of attempting to ÔsolveÕ the problem, the focus was on gaining a deeper understanding of the structures producing the problem. The workshop was used to develop a set of questions to ask the management model relative to various woody weed communities and their responses to different treatment options under various seasonal sequences, i.e., drought versus non-drought conditions, using individual case studies. To make the Planner operational at paddock level, a test paddock(s) was required where contrasting climatic scenarios, e.g., ÔwetÕ versus ÔdryÕ rainfall sequences, could be used to validate the model. Subsequently detailed rainfall data were obtained for ‘‘Gambolalley’’, a property situated c. 120 km west of Cobar (see Fig. 1), where a proportion of a large paddock of 2784 ha (since subdivided into three smaller paddocks) had been burnt in February 1998. Rainfall data were supplemented with additional paddock level data (e.g., area, species composition) to enable appropriate simulation procedures to be applied.

3. Results 3.1. Mental mapping with landholders The workshop enabled a wide range of landholder viewpoints to be captured in a very short time. Further, these views were structured, each representing a set of influencing linkages and feedback loops that could be analytically examined. Workshop sessions were conducted with two groups of landholders thereby enabling valuable replication while encouraging full participation. Both groups were able to develop four diagrams broadly describing the ecology of woody weed proliferation, control options, property economics and institutional (regulatory) constraints, clearly showing the key factors influencing the area affected by woody weeds and related management decisions. These duplicate diagrams were later combined and used to construct four System diagrams (Fig. 2). By the end of the workshop, these views had been stored in computer-readable format and could be used to demonstrate the power and efficiencies of the approach for integrating different viewpoints while also providing a tangible product at the conclusion of the workshop. In addition, graphs showing direct and indirect drivers could be generated.


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Fig. 2. Four INFLUENCE models constructed by stakeholders at the first workshop.

3.2. Mental mapping workshop with agency personnel This half-day workshop successfully developed a comprehensive model identifying important causal relationships, and their interactions, in the context of potential regulatory constraints (Fig. 3). As at the earlier landholder workshop, this information was entered into the computer and preliminary System diagrams constructed. As with the landholder diagrams, these diagrams are incomplete, but provide a good initial framework for linking institutional and regulatory frameworks into the control, ecology and economic models. Three aims had been satisfied by the end of these initial workshops. Firstly, the problem had been clearly defined together with management actions, key variables, spatial extent, time horizons, etc. Secondly, the dependencies between management outcomes and key interventions were identified and finally, the possible impacts associated with any major changes within the study region. 3.3. Workshop to review model assumptions The second landholder workshop aimed principally at reviewing most of the key assumptions and preliminary relationships already developed. A primary task was to determine whether or not landholders were satisfied that the various completed

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Fig. 3. System model of regulatory constraints constructed by agency personnel at the second workshop.

sub-models were behaving in a realistic and logical manner. Central to the operation of each model were the key assumptions derived from the System diagrams. These were individually presented, discussed, and where necessary, revised.


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Examples of the assumptions which we asked workshop attendees to review and comment on included: As native herbivore grazing pressure increases, C3 (palatable) grasses are preferred over C4 (generally unpalatable) grasses and woody weeds and 70% are lost at maximum grazing pressure, 50% of C4 grasses die while there is no mortality of woody weeds. This assumption was revised as a result of the workshop. Before prescribed fire can be used, a threshold for grass (fuel) biomass must be exceeded. The species composition and the threshold were discussed by the workshop attendees. Fire mortality of woody weeds varies significantly according to species (sprouters versus seeders) and also plant age (all species are vulnerable to fire under 1 year). The maximum number of goats that are sustainable is a function of the area being grazed and the number of goats per unit area where all grasses are killed. Goats kill grass proportionally to the maximum sustainable density. If the amount of edible biomass is greater than the minimum amount required for goats, then the number of additional goats that can be added is proportional to the extra forage biomass available (expressed as a percentage of the minimum required). Considerable discussion centred on the use of goats and assumptions used in the grazing/browsing sub-model. As a result, it was decided to make some of these assumptions user-selectable in the model such that, for example, the user could turn an assumption on or off. The reason for doing this was that there were inconsistencies between the views held by different landholders and scientists of the importance or relevance of some of these assumptions, for example, the assumption that goats would only browse shrubs once palatable perennial grasses had been eliminated. 3.4. Constructing the decision-support model The current Woody Weed Planner comprises the following sub-models:

ecological, grazing, fire, goat, mechanical control, and economics of control sub-models.

Each sub-model has been diagrammed using Vensim Simulation Language (Ventana, 1988-95). Behind each diagram is a mathematical representation of a dynamic process with equations referring to species, age-cohorts and control options as outlined below. The woody weed planner was designed to operate at a paddock level, since this is basically the management unit.

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The Woody Weed Planner operates on a monthly time step over ten years. It calculates all the variables for each species and for all the models previously described each month and uses these results in subsequent monthly calculations. Every three years the model stops calculating. This provides an opportunity to review the impacts of the controls on woody weeds and other variables included in the model. After reviewing the impact of current policy levers, the user can change the management regime if required. In this way, adaptive management can be explored. 3.4.1. Ecological sub-model Modelling of the plant population dynamics of both individual shrub species, as well as key herbage species such as C3 and C4 perennial grasses, is a critical function of the ecological sub-model. The relationships portrayed in Fig. 4 are based on the following processes and relationships: plant composition for each landscape unit (initially 4 shrub species and 3 functional groups of grasses), plants generate seeds when they mature (i.e., in the case of shrubs they are at least 5 years old), seeds either accumulate in the soil seedbank or else they decline according to management, new plants are recruited when rainfall conditions are appropriate, plant composition is used to characterise the landscape unit in terms of biomass, when plants reach maximum biomass (or numbers reach carrying capacity for that landscape unit), plant competition affects seed production and seedling recruitment (see Harrington, 1991), Change rainfall data Raindata

min rainfall for Speargrass generation

impact of TGP control on grasses

min rainfall for seed generation

Rainfall seed to germinating plant lookup

seed generation rate2 seed generation rate

quantity of seed generated seed to germinating plant rate total plant death

grass plants

seeds

plant death by chaining

total plants

species composition seedbank

Germinating Plants

Plants by Age

Rainfall threshold for Speargrass growth

death Population Shift Control seed mortality

reduction in Rainfall threshold after controls

Annual Plant death Current threshold for Speargrass growth

seed death rate

Grass death by competition

Grass Death by competition lookup

Total Mature Plants

initial seedbank Initial Plants initial plant seedbank ratio

Total Mature Weeds TotalWeeds to startgrass death

Time since mechanical options

Rainfall requirement ratio

Time since fire Time since chaining options occurred Time since blading occurred

Fig. 4. Seed and plant growth model. Variable names enclosed in brackets signify variables from other sub-models.


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grazing affects different species, and also plants of different maturity – this, in turn, affects seed production and seedling recruitment, some species are more susceptible to grazing pressure than others, grazing responds to rainfall, grass biomass is a critical factor affecting fire probability, rainfall, humidity and temperature also influence fire probability and effectiveness, and different species respond differently to fire, as do plants of different maturity. This is operationalised for four species of woody weeds and three functional groups of grasses. Each shrub species is represented by a full age-cohort plant growth model tracing plants from seed production through germination, seedling establishment and finally to plant maturity. Plants of different age categories are also differentially affected by inter- and intra-specific competition. 3.4.2. Grazing sub-model The grazing pressure model shown in Fig. 5 consists of two components: domestic livestock, and non-domestic herbivores (i.e. kangaroos and feral goats). This model looks at how total grazing pressure, and livestock grazing pressure on its own, both influence survival of perennial grasses of contrasting palatability (C3 versus C4) and age (e.g., seedling versus adult). It also predicts the amount of forage required to ensure livestock survival at different stocking rates.

native herbivore grazing pressure

grazing pressure lookup

Mortality grass at max TGP

Rainfall effect native herbivores on plants Raindata plant death by native herbivores

disable TGP threshold when foraging causes mortality livestock mortality percent mortality by livestock

total grass mortality Stock on paddock grass plants

forage required

plants per stock

plant death by livestock

forage per stock

Stock numbers Percent forage from c3c4

Fig. 5. Grazing pressure model.

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The following relationships underpin this model: as native herbivore grazing pressure increases, C3 (palatable) grasses are preferred over C4 grasses and woody weeds; as rainfall increases to a maximum, the proportion of net primary production consumed by native herbivory declines but not to zero; grass mortality occurs when livestock grazing reaches a threshold percentage of maximum; if the amount of C3 grass is greater than forage required then there is no plant death whereas if the amount of C3 grass is less than the forage required, then all age classes are killed; if the amount of C3 grass is less than the forage required, the balance of forage required is obtained from C4 grasses in proportion to the age classes (this assumption was also revised as a result of the workshop); there is no death of woody weeds through domestic livestock grazing; and if total grass biomass is less than the forage required, then livestock numbers are reduced in proportion to the forage deficit. This model has a dynamic interaction with the ecological model. For example, rainfall events will trigger changes in the species composition and hence affect the grazing functions. The feedback impact of native grazing and domestic stock on species composition is also dynamically calculated. 3.4.3. Fire sub-model The successful re-introduction of fire as a management tool is not simple. Interactions between total grazing, vegetation change and fire frequency are important - the greater the total grazing pressure, the greater the amount of herbage and therefore potential fuel, consumed. Survival of some species, e.g., hopbush, is consistently low across a wide range of size (age) classes whereas mortality of fire-tolerant species, e.g., turpentine, decreases rapidly as plant size increases. Considerably more rainfall is required to produce sufficient fuel in areas with dense woody weeds. Furthermore, a single fire will provide little more than short-term benefits, especially where sprouters such as turpentine predominate. This model examines the amount of herbage generated by particular rainfall sequences before predicting whether there is sufficient fuel available to utilise prescribed fire for controlling woody weeds (Fig. 6). Effectiveness of such fire treatment will be dependent on both woody weed species and age. Before prescribed fire can be used, a threshold for grass (fuel) biomass must be exceeded. Fire mortality of woody weeds varies significantly according to species (sprouters versus seeders) and also plant age (all species are vulnerable to fire under 1 year). The fire model is relatively simple. Basically it first checks with the ecological model to determine if grass biomass is sufficient for a fire and then determines a fire


Rainfall threshold for fire reduction in Rainfall threshold after controls

grass plants

Speargrass fuel

threshold for grass biomass to start fire

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Rainfall limit

Fire fuel load decision to use fire as a control

Time since fire options death by fire fire plausible but not used Time since use of fire Mortality due to fire

Fig. 6. Fire model.

hazard rating based on climatic factors. The decision to burn or not to burn, given a higher fire hazard rating, is a policy decision which the user working with the Planner makes at model simulation run-time. 3.4.4. Goat sub-model Goats are essentially herbivores (grazers) rather than folivores (browsers) and therefore actively compete with sheep for whatever herbage is available (Fig. 7). Generally, they only start to browse shrubs once herbage becomes limiting. Past experience indicate that some woody weeds including hopbush, punty bush and emubush were browsed to varying degrees, others such as budda and turpentine being virtually unpalatable. The foliage of many woody plants may have relatively high nitrogen contents although little of this nitrogen is available as digestible protein. However, despite their relatively low forage value, some landholders have successfully used goats for controlling some species of woody weeds. This model looks at the effect of varying goat numbers on the death of both grass and woody weed species of contrasting age. The number of goats that can be sustained is determined by the amount of edible biomass (shrub + grass) available. The key assumptions are: the maximum number of goats that are sustainable is a function of the area being grazed and the number of goats per unit area required to kill all grasses; goats kill grass proportionally to the maximum sustainable density; if the amount of edible biomass is greater than the minimum amount required for goats, then the number of additional goats that can be added is proportional to the extra forage biomass available (expressed as a percentage of the minimum required);

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Fig. 7. Goat model.

goat-induced mortality of woody weeds will vary significantly according to species and age; edible weed biomass is the sum of available punty bush plus hopbush; and if grass biomass is less than the minimum required for goats, then the effective death of woody weeds is proportional to the numbers of goats required to achieve maximum woody weed death (expressed as a percentage). Considerable discussion centred on the use of goats. Because there was considerable divergence of opinion in relation to some of the assumptions, for example, the assumption that goats will only browse shrubs once palatable perennial grasses have been eliminated, it was decided to make some of these user-selectable enabling an assumption to be turned on or off. The reason for doing this is that there are inconsistencies between the views held by different landholders and scientists of the importance or relevance of some of these assumptions. 3.4.5. Mechanical control model Mechanical chaining and blade ploughing primarily affect mature plants. The model (Fig. 8) takes coefficients from the input data on the effects of these control methods on each species and each age cohort. The effects of one-way and two-way chaining are also built into the model. The model assumes that chaining occurs in the winter and wetter months (June–August). 3.4.6. Economics of control model Practical experience and past research have clearly indicated the limited effectiveness of single control treatments. However, not only must multiple treatment strategies be biologically effective, they must also be cost-effective on a paddock


Percent paddock treated with mechanical control Decision to blade plough

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Time since chaining occurred

Chaining decision

Plants by Age Activate Chaining

Activate blading Time since blading occurred

possible plant death by chaining

plant death by blading

effect twoway chaining on weeds

plant death by chaining

effect blading on weeds blade death

effect oneway chaining on weeds

Chain death

Fig. 8. Mechanical control model.

scale. Integrated management systems based on two or more treatments generally aim to minimise the weaknesses and amplify the strengths of each treatment (Scifres, 1986). The most expensive treatments when applied singly to control shrubs are blade ploughing and chemical arboricides. Because of their high costs, such treatments are generally confined to high-value areas. Goats have been employed to browse shrubs regenerating after mechanical treatment. While shrub control may only be partially successful, it is significantly better than either goats or chemical treatment alone since young budda foliage is almost free of essential oils. It is important to realise that the economic model in the Planner (see Fig. 9) is not a full Property Financial Planning model. It focuses solely on the cost-effectiveness of the control options, and combinations of these. The key assumptions in this model are: The benefits derived from reducing native herbivore grazing on grasses are proportional to the costs involved. The cost of purchasing additional stock is a function of the area involved multiplied by the stocking rate multiplied by the cost per head of livestock. The cost of fire control is dependent on the unit cost of applying fire multiplied by the area involved and dependent on the decision to use fire as a control. Total costs involved are the sum of the control-option costs.

3.4.7. Testing the decision-support model At the final landholder workshop, the prototype model of the Planner was used to see how well the output generated for different rainfall scenarios matched their recollections of the various herbage and shrub population responses. In particular, there was generally unanimous agreement amongst landholders that the model output in regard to speargrass fuel availability following high-rainfall sequences generally mirrored their recollections of these periods.

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total biomass consumption land value

cost of reducing TGP impact on grasses Yearly biomass control Effect of control

impact of TGP control on grasses

base valuation

control of TGP per unit area max 10

Stock numbers

area of unit

decision to use fire as a control cost of managing fire use of fire

cost of fire control

cost per goat TGP control cost

total goats

cost per domestic stock

cost of goats

cost of domestic livestock

total costs

Fig. 9. Economic model.

For the purposes of the final workshop, the Woody Weed Planner was employed at a paddock level by running it for contrasting climate scenarios using data selected from three adjoining paddocks (originally one paddock called ‘‘Bucks’’) on ‘‘Gambolalley’’ Station (Fig. 10). The aim was to see how the model would behave if similar conditions occurred there in the future. A more detailed description of the Planner, as well as the steps followed in regard to inputs and modification of coefficients when required, are provided in Appendix 1. Examples of the types of output that can be generated by the Planner are shown in the following section. It is important, however, to recognize that these graphs are not designed to provide absolute values but rather to present response patterns aimed at exploring the implications of different management strategies. With each graph, the vertical Y-axis simply provides a measure of ÔabundanceÕ, either in terms of biomass or density, while the horizontal X-axis indicates the number of months since the start of the scenario. Because of the way the model runs, it was possible for various historical patterns to be replayed to see how the model would behave if similar conditions occurred in the future. Two contrasting rainfall scenarios were selected, both extending over a decade in order to compare the outputs generated by the Planner. The 1950–60 sequence was selected to represent a ÔwetÕ decade as it included the exceptional rainfall recorded in 1956, still regarded by many long-term residents of the Cobar district as the best season experienced during the 20th Century (Fig. 11). The 1974–84 decade provided an alternative scenario with two good periods of rainfall in the mid 1970s and 1980s interspersed with some very dry conditions. Where there were no controlling factors limiting recruitment and/or growth, turpentine, hopbush and punty bush all increased in terms of both density and biomass in response to the high rainfall pulses in the autumn, especially in the mid-1970s (Fig. 12).


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Fig. 10. ‘‘Gambolalley’’ paddock map.

These above-average pulses in the mid-1970s did not produce sufficient growth of speargrass (Stipa spp.) to enable prescribed fire to be utilised as a woody weed control option, mainly because rainfall did not occur at the appropriate time (i.e., early autumn) to promote widespread germination. The good autumn rains (February–April) received in 1956 and 1957 were sufficient to generate considerably more than adequate speargrass fuel loads (i.e., exceeding a fuel threshold of 800 kg/ha) (Fig. 13). Only once did speargrass exceed this critical fuel threshold in the presence of native herbivores and once fire had been applied, fuel biomass then declined significantly. The relatively high rainfall seasons experienced during the 1950–1960 decade resulted in a steady increase in the abundance of both punty bush and turpentine, despite no Ôprescribed fireÕ and the presence of native herbivores. If, however, goats were introduced as a control option, both hopbush and punty bush declined to

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Fig. 11. Monthly rainfall (mm) recorded at ‘‘Gambolalley’’ during 1950–60 (broken line) and 1974–84 (solid line).

Fig. 12. Woody weed growth during 1950–60 compared with growth during 1974–84 on ‘‘Gambolalley’’ with no controls imposed. The numbers shown on the Y-axis represent indices of relative plant population size. Legend: turpentine 1950 (solid line); turpentine 1974 (dotted line); punty bush 1950 (dashed line); punty bush 1974 (alternate dots and dashes line).

insignificant levels (Fig. 14). Because of its low palatability, turpentine still continued to increase but not as much as it did in the absence of goats. Overall, there was general consensus amongst participants involved in both the final workshops that the output generated by the Planner for contrasting rainfall and shrub control scenarios provided a highly acceptable representation of expected systems behaviour.


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Fig. 13. Comparison of speargrass production (kg dry matter/ha) during 1950–60 (solid line) and 1974–84 (dotted line) on ‘‘Gambolalley’’.

Fig. 14. Impact of prescribed fire, browsing by goats and grazing by native herbivores on turpentine (solid line), hopbush (dotted line) and punty bush (dashed line) growth during 1950–1960 on ‘‘Gambolalley’’. The numbers shown on the Y-axis represent indices of relative plant population size.

4. Discussion Because of its inherent complexity, the woody weed problem typifies what is commonly described as a complex adaptive system (see Abel et al., 2000; Walker

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and Abel, 2002). Such complex systems not only tend to be self-stabilising, i.e., they seem to stubbornly recur and resist change despite peopleÕs best efforts, but they also appear to be purposeful, or to have a mind of their own. In general, complex systems have the following characteristics: 1. The problem has been around long enough to have a history and yet has not been satisfactorily addressed. 2. People involved have multiple, and possibly contrasting, theories about the cause, or causes, of the problem that inhibit discussion. 3. The problem exhibits dynamic complexity. The usual scientific response to threats such as woody weed proliferation has been to narrow the research by focussing on the one process or symptom exclusively. While this may be successful in reducing variability of the ecological target, there have usually been slow synchronous changes in the ecosystem. Spatial homogenising of ecosystems (Ludwig et al., 1997) has, in turn, often led to situations where systems change into persistent degraded states, e.g., chaining of gidgee (Acacia cambagei) woodland in western Queensland has led to domination by subordinate shrubs such as budda, green turkey-bush (Eremophila gilesii) and ellangowan poison bush (Myoporum deserti) (Noble, 1997). Such a negative sequence following management intervention is only broken when the issue is seen as a strategic one of adaptive management, of science at the appropriate scales, and of understanding system behaviour, not one of merely developing better technology (Holling, 1995). Given the degree to which landscapes have now been transformed over the past 130 years or so by shrub proliferation, vegetation management goals are likely to include partial control of scrub in more productive land while remaining reconciled to living with the problem in other, less productive parts (Noble, 1997). No longer is the goal necessarily one of attempting to see landscapes restored to what might have been perceived as their original pre-settlement condition. Rather, the challenge is to be able to be recognise ways in which different landscapes function in terms of rainfall and nutrient redistribution so that management intervention can be directed towards those areas within a paddock that have the potential to benefit from any reduction in shrub density. Furthermore, the need for more effective management systems in the rangelands has also been highlighted by recent enquiries (see Kerin, 2001) and as well as policy developments relating to the introduction of Regional Vegetation Development Management Plans throughout New South Wales (Department of Land and Water Conservation, 1997). For a paddock area, for example, as shown earlier in Fig. 10, the required input is a description of the area of the paddock and a description of the species of woody weeds and guilds of perennial grasses (i.e., C3 and/or C4 grasses) present, their spatial patterning and their maturity. The Woody Weed Planner can currently simulate all the developed models (described earlier) using property and climatic data. The Planner focuses on the dynamic interactions between factors and how these change through time. The user interacts with the model via a Ôcontrol panelÕ consisting of a series of Ôpolicy leversÕ. In this way the Planner is similar in concept to computer


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games or flight simulators. By operating these levers, the user specifies the control options in response to a particular rainfall sequence. The Planner has the capability to graph key indicators of property performance given these levers and rainfall patterns. However, in a scientific sense, it does not predict Ôthe futureÕ. It does, however, enable the operator to explore the future by asking a range of ‘‘what if?’’ questions. Output from the model is seen as indicative of response patterns rather than absolute prediction enabling landholders and advisers to realistically assess the benefits and costs of the complex management decisions they will regularly face under various future scenarios. Because of the way the model runs, it is possible for various historical patterns to be replayed. The user of the Planner can select a specific climatic sequence, for example, a well-known drought period such as the 1937–1947 decade and ask the question ‘‘how would my management strategies cope with such a sequence if it occurred again?’’

5. Conclusions The economics of undertaking such large, paddock-scale shrub control exercises are always going to be a matter of some debate and the importance of looking at the overall management context has been discussed more fully elsewhere (MacLeod et al., 1997). An active adaptive management approach offers a logical, cost-effective approach to managing these complex ecological systems. Alternative strategies involving two or more consecutive treatments for shrub management can be monitored on individual properties at an operational scale before refining, if necessary, the Woody Weed Planner. As Walters et al. (2000) have pointed out, the ability to determine accurately whether or not changes in management strategies have been effective will be heavily dependent on the prior establishment of reliable monitoring systems. While such computer-based management tools can be used by computer-literate landholders and managers, it is envisaged that following appropriate training, the Planner would be used primarily as an advisory tool by extension personnel and rural consultants to test various ideas or to compare contrasting management policies and/or climatic scenarios (Donnelly et al., 2002).

Acknowledgements The authors acknowledge the critical support furnished by those landholders and agency personnel who participated in the various workshops and related activities undertaken during the course of this project. The support given by Tom and Faye Mitchell during the more detailed case studies undertaken on ‘‘Gambolalley’’ was greatly appreciated. Trizia Ojansuu and Gil Pfitzner (both CSIRO Sustainable Ecosystems) provided expert assistance with the production of diagrams while Barney Foran, Neil MacLeod, Margaret Friedel, the editor and two anonymous referees commented constructively on previous drafts of the paper. This project was funded by the WEST 2000 Rural Partnership Program.

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Appendix 1 Using the Woody Weed Planner.1 There are four stages to using the Woody Weed Planner: 1. 2. 3. 4.

preparing rainfall data; preparing vegetation descriptions for the paddock; preparing Woody Weed Planner property inputs; and modifying the model coefficients (optional).

Preparing rainfall data Rainfall input data consist of monthly total rainfall records for each year. The Planner assumes that it will read the rainfall data from an Excel 4.0 Worksheet with the structure outlined below. The first record of the spreadsheet will contain the TIME variable, The 2nd–6th records will contain summary records (e.g., means, min, max, etc.). Record 7 will contain data for 1920, and record 86 will contain data for 1999. The intermediate records will contain data for the period 1921–1989. Each rainfall record consists of: (i) a variable name, and (ii) 120 monthly rainfall (in mm) records covering a 10-year period. The format of the spreadsheet is critical. The Woody Weed Planner imports the rainfall data and is expecting a time-based data set corresponding to named variables in the Planner. For instance, the variable name for 1973 data would be Raindata [Year 1973] while the variable name for 1921 is Raindata [Year 1921]. Preparing vegetation descriptions for the paddock Vegetation data are required for each paddock to be analysed using the Woody Weed Planner. For the purposes of this study, the vegetation on three adjoining paddocks on ‘‘Gambolalley’’ was mapped (Fig. 10). For such paddocks or areas to be coded, a grid needs to be prepared using tracing paper of clear film. The grid can be any shape but should not exceed 16 cells. Individual cells should have the same width and height and should be numbered from 1 to 16. When overlaying the grid, care should be exercised to ensure that it covers the entire paddock map before estimating the area occupied by each shrub species and each age cohort as shown. The Woody Weed Planner currently considers seven plant categories: turpentine (Eremophila sturtii), budda (E. mitchellii), punty bush (Cassia nemophila; syn. Senna nemophila), hopbush (Dodonaea viscosa ssp. angustissima), C3 perennial grasses, C4 perennial grasses, and speargrass (Stipa spp.). The various age cohorts used in the Planner are (i) seedlings (age under 1), (ii) one-year-old,

1

Because of contractual arrangements established with the Department of Infrastructure, Planning and Natural resources (DIPNR), the authors are not in a position to release the Woody Weed Planner on-line.


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(iii) two-year-old, (iv) three-year-old, (v) four-year-old, and (vi) mature plants (five years and older). These data are estimates only with the model designed to simply indicate the range of impacts on the various plant categories. Conversion values then enable biomass (kg per hectare) to be calculated as shown in Table 1. Data from this spreadsheet are the entered into a file called weed2000d.cin. This file contains the value for the paddock descriptions, in addition to a range of model coefficients and is read directly by the Planner. The Planner contains a range of coefficients that parameterise the model. These coefficients apply to the following factors and can be changed by the user if necessary:

shrub only biomass required per goat, grass only biomass required per goat, forage per stock, cost of managing fire, cost per domestic stock, cost per goat, threshold for grass biomass to start fire, threshold when foraging causes mortality, min rainfall for seed generation,

Table 1 Property data for paddocks selected on ‘‘Gambolalley’’ Woody weed planner property data input ‘‘Bucks’’ Initial plants in paddock Age under1

Age 1

Age 2

Age 3

Age 4

Age over 5

0 0 0 0 0 0 0

0 0 0 0 0 0 656.1468

861 0 670 0 218,716 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0

1939 603 3388 0 0 0 0

Turpentine Hopbush Punty Bush Budda C3 grass C4 grass Speargrass

Area of paddock (ha)

Area in cells

Modelled

1192 Base value of land per unit (e.g., $ per ha) 6

14.15

10.9

Computed biomass Plants Plants Plants Plants Plants Plants

– – – – – –

grass turpentine hopbush punty bush budda speargrass

Area to biomass conversion 87,486 984 219 766 547 110

Grass kg per ha Turpentine per unit area Hopbush per unit area Punty bush per unit area Budda per unit area Speargrass per unit area

800 9 2 7 5 1

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min rainfall for speargrass generation, seed death rate, percent forage from C3 and C4 grasses, effect of blading on weeds, effect one-way chaining on weeds, effect two-way chaining on weeds, ÔTotalWeedsÕ to start grass death, mortality due to fire on species, mortality grass at max herbivore pressure on species, and effect of goats on weeds max density goats. These data are also stored in a weed2000d.cin file.

The main menu The Main Menu for the Planner has a range of buttons: Woody Weed Planner – this button shows the various models within the Planner. Analyse model structure – this button initiates causal tracking. Causal tracking allows the user of the Planner to see all the linkages between all the models. For instance, by focusing on ÔRainfallÕ, causal tracking shows all the variables in all the models that are directly or indirectly impacted on by rainfall. Alternatively causal tracking can be used to show all variables that have an impact on ÔFire fuel loadÕ. ÔWhat if?Õ simulation – this button present a series of screen to design and evaluate scenarios. Graph simulation results – this button presents a screen containing a series of simulation results . Compare simulation runs – this button enables two scenarios that are ÔloadedÕ (i.e. Run within one continuous Planner session to be compared in terms of the control options). View Study Property – this button shows the property map (Fig. 10) and also photos of key species. View workshop models – this button enables the results of stakeholder workshops to be viewed. Exit the model. Designing scenarios There are a number of decisions that the user of the Woody Weed Planner must make in designing a scenario. These decisions need to be entered into the appropriate boxes on the Scenario Design Menu. These include: setting the scenario name, setting the rainfall scenario to be evaluated (e.g., 1920–1999 or one of five derived rainfall sequence including best, worst and running averages),


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deciding the stocking levels for domestic stock and for goats, deciding on the control options – To use fire or not. – To use blade ploughing or not. – To use chaining or not, and decision to disable the effect of native herbivore pressure. In addition there are several response functions that can be altered if required. There functions are:

the seeding recruitment response to rainfall events, the effect of plant density on plant mortality, the effect of grass growth on weed mortality, the effect of native herbivore pressure in relation to rainfall, and the cost of controlling native herbivore pressure in relation to herbivore numbers.

The Scenario Design Menu has three key buttons: (i) Graphs of output – this button enables a set of variables to be reviewed as graphs; (ii) Continue Forward – this button runs the simulation for a further three years; and (iii) New Simulation – this concludes the current simulation and saves the current results only until the time the simulation was stopped. Scenarios can be evaluated either during a simulation (i.e., every three years) or alternatively, at the end of a simulation.

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