Complementary land use in the Richmond River catchment: Evaluating economic and environmental benefits

Land Use Policy 87 (2019) 104070

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Complementary land use in the Richmond River catchment: Evaluating economic and environmental benefits

T

Leslie Beardmorea, , Elizabeth Heagneyb, Caroline A. Sullivanc ⁎

a

School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia Economic & Strategic Analysis Branch, NSW Office of Environment and Heritage, Sydney, New South Wales, Australia c National Centre for Flood Research, Southern Cross University, Lismore, New South Wales, Australia b

ARTICLE INFO

ABSTRACT

Keywords: Public benefits Private benefits Environmental benefits Land use change Economic modelling

Agricultural land uses can contribute to land degradation, water quality decline, and loss of ecosystem function and biodiversity in the surrounding catchment. Trees can assist in catchment management, and re-afforestation strategies have been implemented in an effort to mitigate agricultural impacts and improve degraded land and waterways worldwide. Re-afforestation strategies often target private land, and their success relies on landholder participation. Landholders’ decisions about land-use allocation are driven primarily by the private financial costs and benefits associated with different farming strategies. This research assesses the private on-farm financial impact and the public environmental benefit of land use transition from beef grazing to a mixed beef grazingforestry system in the Richmond River catchment on the east coast of Australia. GIS analysis identified more than 30% of the catchment as beef grazing land potentially available for re-afforestation, across a variety of soil types and geomorphic characteristics. We used a farm-scale financial model to assess the costs and benefits associated with transition from grazing to a variety of cattle-forestry mixtures that were determined on the basis of their suitability to soil type and slope in different parts of a catchment. We also used a multi-criteria approach to assess the environmental outcomes associated with each transition. The results demonstrate that diversification to a mixed beef grazing-forestry system consistently provides environmental benefit, but the financial impact on landholders varies depending on soil type. Landholders on ferrosol and vertosol soils in this catchment have favourable options that can simultaneously deliver private and public benefits, whereas landholders on kurosol and dermosol soils are more restricted, with environmental improvements possible only as a trade-off with farm financial performance. Based on these results, we suggest that different policy mechanisms are required to encourage graziers in different parts of the catchment to shift towards mixed cattle-forestry systems.

1. Introduction 1.1. Background Agricultural land uses can give rise to land degradation, greenhouse gas emissions, water quality decline and loss of ecosystem function and biodiversity (Tong and Chen, 2002; Foley et al., 2005; Townsend et al., 2012; Finn et al., 2014). At a catchment-scale, the removal of native vegetation to facilitate agriculture can lead to increased run-off velocity causing erosion and transport of sediments, increasing the sediment load of waterways within a catchment (Allan et al., 1997; Foley et al., 2005). The nutrient enrichment of receiving waters negatively impacts water quality threatening the healthy function of aquatic ecosystems (Foley et al., 2005; Davis and Koop, 2006). Rivers across the world have

experienced a deterioration in water quality caused by suspended solids and other diffuse pollutants, often from fertilisers associated with agricultural land use (Foley et al., 2005; Vorosmarty et al., 2010). In addition to the clearing of land and associated erosion, agriculture, and grazing in particular, has a number of impacts on water quality. Grazing alters the physical properties of soil through compaction from trampling that increases bulk density and reduces porosity, contributing to the increased run-off and erosion that depletes soil fertility (Bird et al., 1992; Udawatta et al., 2010). Sediment-laden runoff introduces nutrients and faecal coliforms to waterways, as well as increasing the turbidity of waterways (Udawatta et al., 2010). Cattle with access to waterways also adversely affect biodiversity by altering native riparian vegetation through physical damage to plants which changes the structure and composition of habitat for aquatic life

Corresponding author at: 7 Osprey Place, East Ballina, New South Wales, Australia. E-mail addresses: [email protected] (L. Beardmore), [email protected] (E. Heagney), [email protected] (C.A. Sullivan). ⁎

https://doi.org/10.1016/j.landusepol.2019.104070 Received 21 October 2018; Received in revised form 22 June 2019; Accepted 23 June 2019 Available online 01 July 2019 0264-8377/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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(Robertson, 1998 as cited in Jansen and Robertson, 2001; Agouridis et al., 2005). Biodiversity loss puts regulating and supporting ecosystem services (i.e. those ecosystem services that regulate fundamental ecological processes and those essential to the production of other services) in jeopardy, raising questions about the long-term sustainability of agriculture in damaged catchment systems (Sandhu et al., 2012). Minimising the loss of such ecosystem services is vital to ensure the continued provision of other ecosystem services such as food and fibre from agricultural production (Dale and Polasky, 2007; Sandhu et al., 2012). Whilst farmers often acknowledge the importance of ecosystem services (Smith and Sullivan, 2014) they are usually motivated to address onfarm issues that directly impact private benefits such as productivity and income rather than issues that affect public benefits at the broader landscape-scale (Seymour and Ridley, 2005). Land management actions that return public benefits but lack private benefits may require incentives to promote widespread adoption as participation is most strongly influenced by private benefits (Pannell, 2008; Cary and Roberts, 2011; Januchowski-Hartley et al., 2012). Specific management practices at a farm-scale can maintain or improve biodiversity and enhance ecosystem services on- and off-farm. The exclusion of stock from the vicinity of waterways can reduce the potential impairment of water quality (Hughes and Quinn, 2014). Revegetation can provide public environmental benefits as trees increase infiltration and reduce run-off which minimises the loss of soil, nutrients and pollutants to waterways, improving water quality as well as possibly reducing flood impacts (Swinton et al., 2007). Tree litter reduces raindrop impact on the soil surface and increases soil organic matter while roots bind the soil also lessening soil erosion (Bird et al., 1992). Trees also store carbon that would otherwise be released as CO2 providing climate change mitigation as well as improving biodiversity through increased habitat (Swinton et al., 2007). Planting trees can provide private on-farm environmental benefits. Shelter from trees acting as windbreaks reduces wind damage in horticultural crops and trees also provide shade for livestock minimising productivity losses (Bird et al., 1992; Kingwell et al., 2013). Recent research has also revealed that tree cover cools the surface of the land in contrast to land devoid of trees (Ellison et al., 2017). Moreover, trees planted for timber production can diversify farming activities, and have the potential to provide additional income (Prinsley, 1992; Cubbage et al., 2012). Based on this knowledge we could expect farmers to be motivated to improve their management practices for the benefit of the environment; however, factors such as costs of transition, and lack of time and/or money to implement new practices can act as barriers to change (Jellinek et al., 2013). Consequently, the management of agricultural land for water quality purposes in many catchments, remains inadequate (Food and Agriculture Organization of the United Nations, 2017). Payment for ecosystem services (PES) schemes, which seek to compensate landholders for activities that deliver a public (or thirdparty) benefit, as well as potentially improving landholders’ livelihoods (Farley and Costanza, 2010; Higgins et al., 2014), are increasingly common in Australia (Lindenmayer et al., 2012; Ansell et al., 2016). A Commonwealth Government Emissions Reduction Fund sought to improve management practices by providing incentives to undertake various activities such as farm forestry in an effort to curb Australia’s greenhouse gas emissions (Department of the Environment and Energy, 2017). Local area initiatives are also being developed. For example, the Lismore Local Government Area has developed a Biodiversity Management Strategy (2015–2035), which provides small grants to landholders through its Rural Landholder Initiative, to promote biodiversity on private land. This is funded by a rates levy for the duration of the strategy (Lismore City Council, 2016). PES schemes have had mixed uptake worldwide. Low rates of participation suggest financial or other barriers remain. Moreover, there are also questions around cost-effectiveness. Are we compensating landholders for making real trade-offs

between production and the environment? Or are we paying for actions that would deliver a net private benefit to landholders even in the absence of PES? These questions obviously have major implications for optimising outcomes from a limited environmental budget. 1.2. The problem As governments in Australia and around the globe seek to improve environmental outcomes associated with agricultural landscapes the issue of cost-effective policy design comes to the fore. It is clear that converting intensive grazing land to mixed farm-forestry systems can deliver environmental benefits to society at large, but assessing the merit of government investment, in terms of its capacity to deliver a net positive benefit (where benefits to society outweigh government expenditure) requires a more detailed economic analysis. Questions around the type of policy interventions that can achieve both landholder participation and environmental additionality (i.e. outcomes that would not have been achieved in the absence of government policy) also require a detailed knowledge of on-farm finances and the implications of transition for private landholders. Numerous economic models have been developed to evaluate alternative land uses and their associated environmental and/or economic benefits at large spatial scales. Global models include GIAM, an integrated assessment model incorporating climate change impacts on factors that affect land use for instance crop, livestock and oil prices (Bryan et al., 2014; Grundy et al., 2016). National models such as LUTO, a land use trade-off model, consider land use and sustainability and subsequent impacts on ecosystem services (Bryan et al., 2016). The MedAction Policy Support System uses a bottom-up process for agricultural land use focusing on sustainable land management practices and interaction with water resources and agricultural output (van Delden et al., 2011). At a catchment-scale, SWAT, a soil and water assessment model, is used to quantify water-related effects of land management practices using hydrology, erosion and nutrient losses from the landscape (Ghebremichael et al., 2013; Salmoral et al., 2017). At a local-scale, many whole-farm models (WFMs) and bio-economic farm models (BEFMs) have also been developed, as well as models for specific industries such as the GRAZPLAN models for optimal use of farm resources or APSIM, the Agricultural Production Systems Simulator that assesses greenhouse gas emissions of different cropping practices (Donnelly et al., 2002; Janssen and van Ittersum, 2007; Robertson et al., 2012; Dumbrell et al., 2015). There is however, a lack of modelling that can be applied at a very fine spatial scale, to individual land parcels within a catchment where land use change is proposed. Modelling of this nature is likely to be more useful in highlighting financial impacts on individual landholders to inform their decisions about land-use allocation, and especially their participation in on-farm actions that provide catchment-wide public benefits (Pannell, 2008). 1.3. Aim and objectives This study aims to investigate the feasibility of using small-scale farm forestry to address agricultural impacts associated with grazing activities at the individual farm scale. We assess the merits of land-use transition from a government policy perspective assessing the public and private costs and benefits that accrue as a result of transition to mixed grazing-forestry systems. We aim to a) determine whether society is better off in the long run (30 years) if we make these types of land use transitions now, and b) if land-use transitions are desirable, where are they so, and how are they best achieved? We have undertaken a case study analysis, assessing the public and private benefits of increased tree cover in the Richmond River catchment, located on the east coast of northern New South Wales. It is proposed that forestry could complement existing grazing operations in this region, but the costs and benefits of transition to a mixed cattle2

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forestry system will vary significantly at the farm level, depending on a range of factors, like soil type and land slope. We analyse a range of different transition scenarios to determine the potential financial and environmental outcomes from land use change in different parts of the catchment based on these underlying biophysical constraints. We discuss the policy implications for targeted and cost-effective land-use transition and use of the Pannell Public Private Benefits Framework to identify optimal policy based on the mix of public and private benefits being delivered from transition in different parts of the catchment.

association with dairy grazing. Second, land with existing forest cover was excluded. Forest was defined as crown cover of over-storey strata equal to or greater than 20% (Australian Bureau of Agricultural and Resource Economics and Sciences, 2015). Third, we selected only those areas with slope less than 20%; land with slope greater than 20% was determined unsuitable for ground-based forestry machinery and excluded (Forests NSW, 2005). Finally, the frost zone (< 10 m elevation) was excluded for soil types whose chosen tree species were frost sensitive. These criteria were applied to arrive at the approximate area of beef grazing land within the Richmond River catchment potentially available for re-afforestation (Table 1). Data for the analysis were obtained from a number of government agencies (Appendix A - Table A1) and included base GIS layers for catchment boundaries; catchment hydrology; local government areas; catchment multi-attributes (land use, tree canopy cover, soil type, slope); digital elevation model and rainfall. The projection of all data was converted to GDA 1994 Zone 56 relevant for north-east New South Wales in which the study area is located. The mapped area of beef grazing land within the catchment potentially available for re-afforestation was calculated at ˜ 218,687 ha (Table 1, Fig. 2). Within this area, there are a variety of soil types, of which the kurosols, ferrosols, dermosols and vertosols are dominant and cover approximately 78% (170,138 ha) of the beef grazing land area potentially available for re-afforestation.

2. Methods 2.1. Study area Our study was undertaken in the Richmond River catchment, located in northern NSW on Australia’s east coast, an area where rivers face a high level of threat to biodiversity (Department of Environment, Climate Change and Water NSW, 2010; Vorosmarty et al., 2010; Townsend et al., 2012). The Richmond River originates in the NSWQueensland border ranges (one of Australia’s National Biodiversity Hotspots) and flows south-east to Ballina (a major urban centre) where it enters the Pacific Ocean. The catchment drains a land area of more than 700,000 ha (NSW Department of Primary Industries, 2017). The main tributary of the Richmond River is the Wilsons River which passes through the other major urban centre of Lismore and enters the Richmond River on the coastal plain at the town of Coraki (NSW Department of Primary Industries, 2017). Average annual rainfall varies across the catchment, from 1000-1500 mm/year in the inland west, to 1500–2000 mm/year in the coastal east and 2000–3000 mm/ year in the northern upland ranges (Bureau of Meteorology, 2017). The catchment has a wide range of soil types including ferrosols in the northern reaches, kurosols in the west and south and a mix of vertosols and dermosols throughout the mid reaches (NSW Office of Environment and Heritage, 2010) (Fig. 1). More than 11% (80,000 ha) of the catchment is within protected areas such as national parks and reserves and includes Gondwanan rainforests in the higher elevation ranges and extensive wetlands on the lower coastal floodplains (NSW Department of Primary Industries, 2017). Small high conservation value remnants (less than 1% of the original 75,000 ha extent) of the Big Scrub rainforest are scattered across the Alstonville plateau to the north and east of Lismore (Lott and Duggin, 1993). Catchment land use is characterised by agriculture, varying from the dominant beef and dairy grazing of the upper floodplains, to cropping of mostly sugar cane in the coastal floodplains (NSW Department of Primary Industries, 2017). Horticulture (e.g. macadamia nuts, tropical fruits) is found in the middle and upper regions of the catchment (NSW Department of Primary Industries, 2017). Historical drainage of the Richmond River floodplain and extensive land clearing for agriculture have produced negative impacts on water quality and biodiversity within the catchment (Lismore City Council, 2015). A 2015 assessment concluded an overall grade of ‘poor’ for the catchment, where Ryder et al. (2015) found that agriculture has degraded geomorphic and riparian conditions resulting in diminished water quality. Areas of poor geomorphic condition were typified by high bank slopes and serious bank slumping associated with cleared land and/or cattle grazing and accessing waterways (Ryder et al., 2015). The same influences have contributed to the poor condition of the river’s riparian zone (Ryder et al., 2015).

2.3. Forestry species selection We selected 11 tree species for inclusion in our study, on the basis that these are considered good forestry species that match the geomorphic and climate conditions of our study catchment (Lines-Kelly and Currey, 1994; Clarke et al., 2009). The final selection of tree species was carried out in consultation with forestry scientists at Southern Cross University (K. Glencross, personal communication, March 22, 2017; G. Palmer, personal communication, March 20, 2017). Matching primarily occurred on the basis of soil type and rainfall. We selected a mix of timber, nut and essential oil tree species to provide diversification. Three species (Eucalyptus siderophloia, Eucalyptus acmenioides, Corymbia variegata) are applied to kurosol soils, three species (Lophostemon confertus, Elaeocarpus grandis, Macadamia integrifolia) are applied to ferrosol soils, three species (Eucalyptus pilularis, Eucalyptus saligna, Eucalyptus microcorys) are applied to dermosol soils and two species (Eucalyptus tereticornis Melaleuca alternifolia) are applied to vertosol soils (Table 2). The number of seedlings planted per hectare varies from 312/ha for the nut species, 1000/ha for each timber species and 35,000/ha for the essential oil species (Table 2). The spacing and placement of seedlings is expected to differ between sites. Within site planting variability is also anticipated due to slope and aspect. We have considered 12 production scenarios each for the kurosol, ferrosol and dermosol soils and 3 production scenarios for the vertosol soil (Table 3). Each scenario had a minimum of 20% trees to meet the requirements for classification as a forest (Australian Bureau of Agricultural and Resource Economics and Sciences, 2015). The land use scenarios for all soil types include:

• 80% beef grazing/20% trees, • 60% beef grazing/40% trees, and • 40% beef grazing/60% trees.

2.2. Mapping land suitability

For the kurosols, ferrosols and dermosols, the percentage trees of each scenario included either two or three species. Given the vertosols only had two suitable species from the chosen sample, both species are included in each scenario. A total of 40 production scenarios were set up including the base case of 100% grazing of available land, considered the current land use (hereafter ‘the base case’) (Table 3).

We undertook spatial analysis to identify the area of beef grazing land within the catchment that is potentially available for re-afforestation. Four criteria were used. First, we identified areas that are currently used for grazing by locating areas of native pastures and volunteer or naturalised pastures. Improved pasture was excluded due to 3

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Fig. 1. Map of Richmond River catchment. The inset depicts the catchment’s location within Australia. The map shows soil types according to the (Australian Soil Classification (Isbell and National Committee on Soil and Terrain, 2016), major watercourses and the local government areas within the catchment. Table 1 Staged criteria used to identify beef grazing areas considered suitable for re-afforestation. Description

Area (ha)

Criteria

Richmond River catchment Pasture land use Native, volunteer, naturalised pasture (i.e. beef grazing) Beef grazing < 20% tree canopy cover Beef grazing < 20% tree canopy cover and < 20% slope Beef grazing < 20% tree canopy cover, < 20% slope and > 10 m elevation for ferrosol, kurosol and dermosol soils

> 700,000 302,367 278,185 278,091 224,066 218,687

Not applicable Not applicable Improved pasture (24,185 ha) excluded Canopy cover ≥20% (94 ha) excluded Slope ≥20% (54,025 ha) excluded Elevation ≤10 m excluded for ferrosol, kurosol and dermosol soils (5,379 ha)

2.4. Farm-scale financial modelling

We estimated the net present value (NPV) of transition to each of the 39 silvo-agricultural scenarios, taking into account the base case and all marginal costs and benefits associated with each scenario. We considered establishment costs (soil preparation, planting, fertilising, weed spraying), transition costs and ongoing maintenance (pruning, thinning, monitoring) as the operational costs. We also included the likely revenues generated from forestry and grazing. NPV was derived from quantifiable cash flows of costs and benefits over the 30-year project life calculated in real terms and discounted using a discount rate of 7% (New South Wales Treasury, 2007). The impact of price fluctuation was assessed through sensitivity analysis. Discount rates of 4% and 10% were also assessed as part of sensitivity testing for NPV (New South Wales Treasury, 2007).

A simple farm finance model was developed to incorporate data on beef grazing and 11 tree species suitable for re-afforestation on existing beef grazing land. The model was applied to analyse the base case plus 39 hypothetical production scenarios in which small-scale farm forestry replaced a varying proportion of grazing land (Table 3). The concept for the financial model was derived from Sullivan and Heagney (2016) who developed the model framework as an agroforestry planning tool for rehabilitating degraded catchments in Fiji and Vanuatu. Production statistics relating to the chosen tree species was obtained from a variety of reliable entities including university forestry scientists, industry and government (Table 4). Data on beef grazing was similarly sourced (Table 5) with NSW beef industry averages applied to the average-sized grazing enterprise in the catchment. This data formed the base information input into the financial model. A project life of 30 years was determined based on the final harvest (and felling) of timber species. The spatial unit applied in the model was one hectare.

2.5. Assessment of environmental benefits and risk factors Environmental performance was determined using a multi-criteria approach based on six key indicators: three positive indicators (carbon 4

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Fig. 2. Map of beef grazing land within the Richmond River catchment potentially available for re-afforestation. The map also depicts the average annual rainfall (mm) across the catchment and the frost exclusion zone for the ferrosol, kurosol and dermosol soils.

evapotranspiration of the groundcover being either pasture (grass) or forest (Zhang et al., 2001). Each of the environmental indicators were given a score between zero and three with zero being nil, one being low, two being medium and three being high. A high result for any of the negative indicators was inverted so that all indicators could be presented on the same scale. All indicators were equally weighted. Risk factors were determined using four equally weighted indicators for market risk, pest risk, fire tolerance and flood tolerance. The market risk indicators were determined based on a ten-year time series of average industry prices for beef, hardwood timber, macadamia nuts and tea-tree essential oil (Australian Bureau of Agricultural and Resource Economics and Sciences, 2016a, 2016b; Australian Macadamia Society, 2017; T. Larkman of ATTIA Ltd, personal communication, March 21, 2017). As the price structure varied between industries, the indicator score was rated on the standard deviation as a percentage of the average for each industry. The pest risk rating for high risk species was reduced to medium risk in scenarios with more than two species based on the reduction of insect or disease damage related to using species mixtures (Kelty, 2006). The fire and flood tolerance for the base case was determined as low, although it is assumed that a landholder would make every effort to relocate cattle to minimise risk. Each of these risk indicators were given a score between zero and three with zero being nil, one being low, two being medium and three being high. A high result for either the market or pest risk indicators was inverted to normalise it onto the same scale.

Table 2 Selected tree species applied in the financial model. Species

Common name

Soil type

Product

Eucalyptus siderophloia Eucalyptus acmenioides Corymbia variegata Lophostemon confertus Macadamia integrifolia Elaeocarpus grandis Eucalyptus pilularis Eucalyptus saligna Eucalyptus microcorys Melaleuca alternifolia Eucalyptus tereticornis

Grey ironbark White mahogany Spotted gum Brush box Macadamia nut Silver quandong Blackbutt Sydney blue gum Tallowwood Tea-tree Forest red gum

Kurosol Kurosol Kurosol Ferrosol Ferrosol Ferrosol Dermosol Dermosol Dermosol Vertosol Vertosol

Timber Timber Timber Timber Nut Timbera Timber Timber Timber Essential oil Timber

The chosen tree species are dictated by the dominant soil types within the area of beef grazing land potentially available for re-afforestation in the Richmond River catchment and are a mix of timber, nut and essential oil species. a Fruit is also a product of Elaeocarpus grandis. This study did not consider the potential of this product.

sequestration potential, soil stabilisation, soil enrichment) and three negative indicators (impact on biodiversity, fertiliser application, water use). Carbon sequestration potential of each species was determined by converting total biomass to carbon and applying an expansion factor (Gifford, 2000; Snowdon et al., 2000; Murphy et al., 2013). The impact on biodiversity rating was higher for scenarios with more than two species because of the association benefit provided by different species characteristics that provide a variety of habitat or resources (Lindenmayer et al., 2003; Kelty, 2006). Water use was calculated based on minimum catchment rainfall of 1000 mm/year and

2.6. Cost-effectiveness analysis The environmental performance score associated with each of our production scenarios (derived from the multi-criteria analysis described in Section 2.5) was combined with information on financial outcomes 5

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Table 3 Hypothetical production scenarios applied in the model. Scenario

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Grazing/tree combination (%) Grazing

Grey ironbark

White mahogany

Spotted gum

Brush box

Macadamia

Silver quandong

Blackbutt

Sydney blue gum

Tallowwood

Tea-tree

Forest red gum

100 80 80 80 80 60 60 60 60 40 40 40 40 80 80 80 80 60 60 60 60 40 40 40 40 80 80 80 80 60 60 60 60 40 40 40 40 80 60 40

– 10 – 10 7 20 – 20 13 30 – 30 20 – – – – – – – – – – – – – – – – – – – – – – – – – – –

– – 10 10 7 – 20 20 13 – 30 30 20 – – – – – – – – – – – – – – – – – – – – – – – – – – –

– 10 10 – 6 20 20 – 14 30 30 – 20 – – – – – – – – – – – – – – – – – – – – – – – – – – –

– – – – – – – – – – – – – 10 – 10 7 20 – 20 13 30 – 30 20 – – – – – – – – – – – – – – –

– – – – – – – – – – – – – – 10 10 7 – 20 20 13 – 30 30 20 – – – – – – – – – – – – – – –

– – – – – – – – – – – – – 10 10 – 6 20 20 – 14 30 30 – 20 – – – – – – – – – – – – – – –

– – – – – – – – – – – – – – – – – – – – – – – – – 10 – 10 7 20 – 20 13 30 – 30 20 – – –

– – – – – – – – – – – – – – – – – – – – – – – – – – 10 10 7 – 20 20 13 – 30 30 20 – – –

– – – – – – – – – – – – – – – – – – – – – – – – – 10 10 – 6 20 20 – 14 30 30 – 20 – – –

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 10 20 30

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 10 20 30

Forty production scenarios included the base case at scenario 1. Twelve scenarios each were considered for the kurosol (scenarios 2–13), ferrosol (scenarios 14–25) and dermosol soils (scenarios 26–37) and 3 scenarios for the vertosol soil (scenarios 38–40). Each scenario had a minimum of 20% trees.

(from Section 2.4) within a cost-effectiveness framework. We estimated the NPV of the marginal private financial costs and benefits that would be incurred by the landholder under each scenario. Used in this context, the NPV is a measure of discounted cash flow analysis that indicates whether a production scenario is worthwhile (Regan et al., 2015) from an individual landholder’s point of view. Our cost-effectiveness analysis used a base case of 100% beef grazing of available land. NPV is assessed over a 30-year horizon at a discount rate of 7%. We used price volatility identified through the risk assessment process described in Section 2.5 as the basis of a sensitivity analysis. We included additional variation to reflect the marginal change in the other types of risk (i.e. pest, fire, flood; see Section 2.5) associated with each production scenario. We estimated marginal change in risk score of 0.022, 0.002, 0.036 and 0.02 for dermosol, ferrosol, kurosol and vertosol soils respectively (see results Section 3.1). Considered as a percentage of overall risk score (maximum of 0.33) this represents an increase in risk of 7%, 1%, 11% and 6% associated with conversion from grazing to mixed grazing-forestry on dermosol, ferrosol, kurosol and vertosol soils respectively. We tested an appropriate percentage above and below NPV to estimate a financial performance range on each of the soil types assessed. We also performed sensitivity testing on discount rate, using alternative discount rates of 4% and 10% to assess outcome on the NPV associated with each scenario.

3. Results 3.1. Assessing environmental performance, risk, and private financial outcomes Multi-criteria analysis was used to estimate the economic performance and risk level associated with each production scenario (Table 6). These are summarised by soil type in Table 7 and Fig. 3. The highest environmental performance is achieved by the dermosols, reaching a top score of 0.21 (Table 7) whilst the base case receives the lowest score for environmental performance with 0.12. Transition from grazing to silvo-pasture achieved an average net environmental gain compared to the base case for all soil types assessed. The highest average environmental performance was observed on dermosol soils; environmental outcomes on vertosols were marginally lower than those observed on other soil types (Fig. 3a). Transition to silvo-pasture was also associated with higher levels of management risk compared with the base case (Fig. 3b), highlighting the importance of undertaking sensitivity analysis when comparing outcomes of different transition scenarios. Kurosol and dermosol soils generally perform better than the ferrosols, vertosols and the base case with regard to risk. The best risk score of 0.15 is achieved by four kurosol scenarios and two dermosol scenarios mostly due to the better 6

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Table 4 Species specific data for selected tree species including the data sources. Data description

Source

Timber: Harvests (year) Volume (m3/ha per annum) No. trees planted per ha Slope; Fertiliser application; Additional water; Enrich soil; Stabilise soil; Impact on biodiversity; Pest risk; Fire tolerance; Flood tolerance Frost zone (< 10 m elevation) Timber price ($/m3) Costs/expenses Nut: First harvest (year) Yield first crop (kg/ha) Subsequent harvests (years) Year of maximum yield Amount maximum yield (kg/ha) No. trees planted per ha Slope; Fertiliser application; Additional water; Enrich soil; Stabilise soil; Impact on biodiversity; Pest risk; Fire tolerance; Flood tolerance Nut price ($/kg) Costs/expenses Essential oil: First harvest (year) Yield first crop (kg/ha) Subsequent harvests (years) Year of maximum yield Amount maximum yield (kg/ha) No. trees planted per ha Slope; Fertiliser application; Additional water; Enrich soil; Stabilise soil; Impact on biodiversity; Pest risk; Fire tolerance; Flood tolerance Essential oil price ($/kg) Costs/expenses Timber/nut/essential oil carbon sequestration: Air dry density (ADD)

K. Glencross, personal communication, March 22, 2017; G. Palmer, personal communication, March 20, 2017. NSW Forestry Corporation, personal communication, March 27, 2017. M. Wright of Super Forest Plantations, personal communication, March 24, 2017.

Australian Macadamia Society. (2017). New growers information booklet. Retrieved from http:// australian-macadamias.org/industry/for-growers/new-growers S. Mulo of QLD Department of Agriculture & Fisheries, personal communication, March 20, 2017.

Rural Industries Research and Development Corporation. (2017). Research programs, plant industries, new and emerging plant industries, tea tree oil. Retrieved from http://www.rirdc.gov. au/research-programs/plant-industries/new-and-emerging-plant-industries/tea-tree-oil T. Larkman of ATTIA Ltd, personal communication, March 21, 2017.

Bootle, K. R. (1983). Wood in Australia: types, properties and uses. Roseville, Australia: McGrawHill Book Company Australia Pty Ltd.

scores received for market risk and fire risk (Table 7). The remainder of the scenarios range from 0.11 to 0.14 with variation in the final score arising from a high degree of variation across all risk factors. These relative risk levels have been incorporated into our cost-effectiveness analysis by informing the appropriate testing levels for sensitivity analysis associated with each of our modelled scenarios. Fig. 4a depicts the contribution of each indicator for environmental performance and risk assessment associated with the base case and each soil type. The variation in the nature of environmental benefits delivered by different silvo-pasture combinations suggests that any policy or incentive scheme seeking to deliver a specific environmental outcome could be targeted to specific soil types within the catchment. Differences in the composition of risk associated with silvo-pasture on different soil types (Fig. 4b) suggest that any recommendation for land-use transition should account for site-specific risk factors and likely implications on associated financial outcomes (see Section 3.2, Table 10). The average net present value (NPV) of farm financial performance associated with each production scenario is provided in Table 8 based

on Equation 1. The base case reported NPV of $603/ha. Thirty-one per cent of scenarios performed better than the base case, indicating a net private financial gain to landholders implementing these strategies. These occurred exclusively on ferrosol and vertosol soils. The maximum benefit is expected for a transition from the base case to a vertosol soil scenario ($10,023/ha) whilst transition to a ferrosol soil scenario shows a positive benefit of $4,012/ha. The remaining 69% delivered a NPV lower than the base case, indicating a net private financial loss to landholders implementing these strategies. These included all scenarios proposed on kurosol or dermosol soils. Equation 1 Calculation and example of Net Present Value (dollars per hectare). Net Present Value = $4,506 =

(% Species mix X Species summary) X (60% Grazing X $603) + (13% Brush box X $182) + (13%Macadamia X $31,942) + $14% Silver quandong X -$227) X

Model summary 1

Table 5 Beef grazing data including the data sources. Data description

Source

NSW Beef Industry: Average total cash costs Average total cash receipts Average farm cash income Average farm size (ha) Average number of cattle/ha Richmond-Tweed Beef Industry: Average head/farm Average farm size (ha)

Commonwealth Department of Agriculture & Water Resources – AGSURF data – based on farm survey data collected by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for period 2001 to 2015 (2016) (http://apps.daff.gov.au/AGSURF/)

NSW Department of Primary Industries 2006 and 2011 Agricultural Census Data (2011) (http://www.dpi.nsw.gov.au/land-and-water/land-use/lup/abs-agricultural-census-data-2006-and-2011/analysis-census-data)

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Table 6 Model output for each production scenario including the base case (labelled as Scenario 1). Model output

Weighting

Environmental performance

Risk factors

Contribution of environmental performance to overall score

Contribution of risk factors to overall score

0.17

0.17

0.16

0.17

0.17

0.16

0.35

0.25

0.25

0.25

0.25

0.3

Scenario

NPV at year 30

Carbon seq. potential

Soil stabilisation

Soil enrichment

Impact on biodiversity

Fertiliser application

Water use

Total environ.

Market

Pest

Fire

Flood

Total risk

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

603 −7 −66 −51 −42 −618 −735 −704 −685 −1229 −1405 −1358 −1331 478 3654 3695 2718 353 6705 6787 4506 228 9756 9879 6621 457 308 328 361 311 12 53 129 165 −283 −223 −113 5736 10144 16000

0.00 0.13 0.13 0.13 0.13 0.27 0.27 0.27 0.27 0.40 0.40 0.40 0.40 0.17 0.13 0.17 0.16 0.33 0.27 0.33 0.31 0.50 0.40 0.50 0.47 0.20 0.20 0.20 0.20 0.40 0.40 0.40 0.40 0.60 0.60 0.60 0.60 0.13 0.27 0.40

0.00 0.20 0.20 0.20 0.20 0.40 0.40 0.40 0.40 0.60 0.60 0.60 0.60 0.20 0.13 0.13 0.15 0.40 0.27 0.27 0.31 0.60 0.40 0.40 0.47 0.20 0.20 0.20 0.20 0.40 0.40 0.40 0.40 0.60 0.60 0.60 0.60 0.13 0.27 0.40

0.67 0.60 0.60 0.60 0.60 0.53 0.53 0.53 0.53 0.47 0.47 0.47 0.47 0.73 0.67 0.67 0.69 0.80 0.67 0.67 0.71 0.87 0.67 0.67 0.73 0.73 0.73 0.73 0.73 0.80 0.80 0.80 0.80 0.87 0.87 0.87 0.87 0.67 0.67 0.67

0.00 0.07 0.07 0.07 0.13 0.13 0.13 0.13 0.27 0.20 0.20 0.20 0.40 0.07 0.07 0.07 0.13 0.13 0.13 0.13 0.27 0.20 0.20 0.20 0.40 0.07 0.07 0.07 0.13 0.13 0.13 0.13 0.27 0.20 0.20 0.20 0.40 0.07 0.13 0.20

1.00 0.93 0.93 0.93 0.93 0.87 0.87 0.87 0.87 0.80 0.80 0.80 0.80 0.93 0.87 0.87 0.89 0.87 0.73 0.73 0.78 0.80 0.60 0.60 0.67 0.93 0.93 0.93 0.93 0.87 0.87 0.87 0.87 0.80 0.80 0.80 0.80 0.87 0.73 0.60

0.40 0.36 0.36 0.36 0.36 0.32 0.32 0.32 0.32 0.28 0.28 0.28 0.28 0.36 0.36 0.36 0.36 0.32 0.32 0.32 0.32 0.28 0.28 0.28 0.28 0.36 0.36 0.36 0.36 0.32 0.32 0.32 0.32 0.28 0.28 0.28 0.28 0.36 0.32 0.28

0.12 0.13 0.13 0.13 0.14 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.17 0.14 0.13 0.13 0.14 0.17 0.14 0.14 0.16 0.19 0.15 0.15 0.18 0.14 0.14 0.14 0.15 0.17 0.17 0.17 0.18 0.20 0.20 0.20 0.21 0.13 0.14 0.15

0.33 0.40 0.40 0.40 0.40 0.47 0.47 0.47 0.47 0.53 0.53 0.53 0.53 0.40 0.33 0.33 0.35 0.47 0.33 0.33 0.38 0.53 0.33 0.33 0.40 0.40 0.40 0.40 0.40 0.47 0.47 0.47 0.47 0.53 0.53 0.53 0.53 0.33 0.33 0.33

0.33 0.40 0.40 0.40 0.40 0.47 0.47 0.47 0.47 0.53 0.53 0.53 0.53 0.40 0.33 0.33 0.38 0.47 0.33 0.33 0.42 0.53 0.33 0.33 0.47 0.33 0.27 0.33 0.36 0.33 0.20 0.33 0.38 0.33 0.13 0.33 0.40 0.27 0.20 0.13

0.33 0.43 0.40 0.43 0.42 0.53 0.47 0.53 0.51 0.63 0.53 0.63 0.60 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.47 0.43 0.43 0.44 0.60 0.53 0.53 0.56 0.73 0.63 0.63 0.67 0.40 0.47 0.53

0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.47 0.60 0.73

0.10 0.12 0.12 0.12 0.12 0.14 0.13 0.14 0.13 0.15 0.15 0.15 0.15 0.11 0.10 0.10 0.10 0.12 0.10 0.10 0.11 0.13 0.10 0.10 0.12 0.12 0.11 0.11 0.12 0.13 0.12 0.13 0.13 0.15 0.12 0.14 0.15 0.11 0.12 0.13

Table depicts the indicator scores for environmental performance and risk factors; scales are relative only. Bold values signifies total.

Percentage species mix is determined by the relevant production scenario in the model. Species summary is the project balance at year 30 ($) for that species based on accumulated net cash flow including costs and revenues for each year. Model summary is the area considered in the model (one hectare), An example calculation for production scenario 21 (Ferrosol soil type, 60% beef grazing, 40% trees) is provided.

performance from $5,736 to $16,000/ha. Ferrosols also showed a trend towards improved profitability with increasing allocation of land to forestry, with environmental performance improving from 0.13 to 0.19, but financial outcomes on this soil appeared to vary with some cattleforestry options improving financial performance by close to $10,000 per ha, but others providing marginal financial outcomes. Subsequent investigation showed these divergent outcomes relate to the selected species mix (especially nut vs. timber species) and highlight the importance of careful species selection. The remaining two soil types (kurosols and dermosols) displayed diminishing financial returns with improving environmental performance (Fig. 6), indicating that any environmental gains from moving towards cattle-forestry mixtures come at a marginal financial cost to the private landholder. Dermosols displayed the greatest potential for environmental improvement, with a marginal improvement in environmental performance of 0.8. This was associated with a loss of approximately $900 per ha. Kurosols delivered a somewhat smaller environmental gain (˜0.5) at even greater financial cost (˜$2,000 per ha).

3.2. Assessing synergies and trade-offs Comparing the environmental performance against the NPV of each production scenario (Fig. 5) reveals very different outcomes across the soil types investigated. However, when assessed separately, the extent of the trade-off (or synergy) between environmental and financial performance is different for each soil type. Two soil types (vertosols and ferrosols) showed a positive correlation between environmental performance and NPV (Fig. 6). For vertosols, conversion from cattle to cattle-forestry mixtures shows increasing environmental performance from a relative score of 0.13 to 0.15 and increases NPV of farm financial 8

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(0.11) (0.13)

(0.12)

0.15 0.17

0.14 40 (0.13) Vertosol

0.15

22 37 (0.13) (0.15) 0.19 0.21 Ferrosol Dermosol

(0.15) 0.17 Kurosol

For each soil type, the highest environmental score, the associated risk score, the model scenario and a description of the production scenario is provided, together with the average environmental score and average risk score.

(0.13) 0.15

40% 20% 40% 40% 20% 40% 13

Beef grazing Grey ironbark, 20% White mahogany, 20% Spotted gum Beef grazing, 30% Brush box, 30% Silver quandong Beef grazing Blackbutt, 20% Sydney blue gum, 20% Tallowwood Beef grazing, 30% Tea-tree, 30% Forest red gum

Scenario description Model scenario Associated risk score Highest environmental score Soil type

Table 7 Model output for the highest environmental performance scores of each soil type.

Average environmental score

Average risk score

L. Beardmore, et al.

Fig. 3. Model output depicting a summary of average environmental performance and risk assessment scores. Graph depicts a summary of average environmental performance ( ± standard error) (3A) and average risk assessment scores ( ± standard error) (3B) for all soil types. The base case is 100% grazing.

We estimated the average cost effectiveness of land-use conversion under each soil type by comparing the financial outcomes per unit of environmental gain (Table 9). We also tested an appropriate percentage above and below NPV to estimate a financial performance range on each of the soil types assessed. Additional sensitivity testing was performed using discount rates of 4% and 10%. Vertosols were the only soils to deliver consistently positive financial outcomes from transition to silvo-pasture based on our sensitivity analysis assumptions (Table 10). Outcomes for the remaining three soil types (kurosols, ferrosols and dermosols) varied under different sensitivity analysis assumptions. This suggests that any silvo-pasture transition on these three soil types would require consideration of market and other site-based risks to determine likely financial outcomes at a specific site or point in time. 4. Discussion 4.1. Overview This research has used hypothetical production scenarios to estimate the environmental and financial outcomes from integrating trees into a beef grazing enterprise. The diversified land practice is assessed in terms of public and private benefits delivered by different cattleforestry mixtures on different soil types found within the Richmond River catchment. The results suggest that the success of diversification opportunities available to landholders are strongly influenced by soil type. As a consequence of this biophysical constraint, landholders in areas of kurosol, ferrosol, dermosol or vertosol soil types within the Richmond River catchment are likely to require different management strategies and incentives to facilitate the transition to a mixed grazingforestry system. 4.2. Spatial targeting for environmental outcomes The on-farm management practice of integrating trees with grazing, often referred to as silvo-pasture (a type of agroforestry), has been shown to have potential to provide benefits at both farm- and 9

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Fig. 4. Model output depicting percentage relative contribution by indicators for environmental performance and risk assessment scores. Graph depicts the relative contribution (%) for each environmental performance indicator (water use, fertiliser application, impact on biodiversity, soil enrichment, soil stabilisation, carbon sequestration potential) and each risk indicator (flood tolerance, fire tolerance, pest risk, market risk) for all soil types and the base case.

Table 8 Benefit of transition from base case to new production scenario. Soil type

NPV ($) of base case (per ha)

Average NPV ($) of soil type scenarios (per ha)

Transition benefit ($) (per ha)

Kurosol Ferrosol Dermosol Vertosol

603 603 603 603

−686 4,615 126 10,627

−1,289 4,012 −478 10,023

The NPV of the base case is subtracted from the average NPV for each soil type to determine the transition benefit on a per hectare basis.

catchment-scale (Udawatta et al., 2010; Wilson and Lovell, 2016; Jose et al., 2017). The results of our study are consistent with this. As we expected, the base case is environmentally out-performed by all tree and beef grazing scenarios, demonstrating the environmental importance of trees in the landscape. But we note that there is a large degree of variation in both environmental and financial outcomes, depending on underlying biophysical constraint relating to soil type, and associated issues around forestry species selection. The area of beef grazing land within the catchment identified as potentially available for re-afforestation is 218,687 ha. This area represents approximately 31% of the catchment where increased forest cover may be possible. Mapping of the beef grazing land potentially available for re-afforestation has shown that much of the identified area is located within the vicinity of major watercourses in the catchment. This highlights the importance of effective management of beef grazing to minimise any further negative land use impacts on water quality in an already degraded catchment (Townsend et al., 2012). However, the quality of environmental outcomes at the catchment scale is likely to differ based on the relative proportion, and relative distribution of soil types across the catchment. Our study estimates that greatest

Fig. 5. Model output depicting Net Present Value and environmental performance for all production scenarios.

environmental gains from conversion from cattle to mixed cattle-forestry systems are likely to occur on dermosol soils, and for selected forestry species planted on ferrosols. These soils cover 10% and 25% of the area considered suitable for re-afforestation. Moderate environmental gains are expected on kurosols, which account for 27% of land available for re-afforestation, and smaller improvements are expected on the remaining 38% of land (16%vertosols and a variety of other less dominant soils). The variation in environmental outcome delivered by land-use change on farms with different soil types highlights the need for spatially targeted programs that account for the biophysical constraints of individual farms. The potential for environmental payment programs aimed at effecting land-use change to provide environmental ‘additionality’ has been a key concern raised in the literature relating to payments for ecosystem services and other incentive schemes (Wunder, 2007; Bennett, 2010; García-Amado et al., 2011). In the context of converting 10

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Fig. 6. Model output depicting the relationship between Net Present Value and environmental performance for all production scenarios displayed by soil type.

species. Likewise, the combination of timber species for the dermosols, some high value and some low value, results in varied profitability but impressive environmental performance because all species are timber. The fluctuation in revenue shown in the sensitivity analysis on cash income confirmed that the base case achieves the most constant income but gives up potential income generated from diversifying to a new management system. The income variability surrounding diversification could mean a mixed grazing-forestry system is more prone to uncertainty with substantial income fluctuations during the project period tied to periods of product harvesting. With this in mind, the lower market risk exposure of timber compared to grazing, nut and essential oil promotes more certainty for landholders on kurosol and dermosol soils compared to all other scenarios including the base case. We consider the financial benefits arising from land-use change estimated in our study to be relatively conservative. We have focussed only on direct income and expenditure streams. However, some other expenditures associated with plantation establishment, such as specialised planting equipment, new fencing (if required) and associated labour have not been considered due to unique circumstances of each farming enterprise; these items will be requirements at only a subset of the farms in the study region. However, in addition to the benefits to landholders suggested by this analysis, there are a number of other potentially positive financial benefits for landholders which have not been considered here. A number of synergies between cattle and forestry systems have been shown to contribute a number of on-farm ecosystem services that can assist or enhance production. For example, increased tree cover in pastoral areas is likely to result in higher income from improved productivity and decreased water consumption of beef cattle with access to shade (Kingwell et al., 2013). Decreased weed and fire hazard reduction costs can also be achieved with limited grazing of

Table 9 Cost effectiveness of land-use conversion. Soil type

Marginal NPV ($Au) (average)

Marginal environmental performance (average)

Financial outcome ($Au’000) per unit of environmental gain

Kurosol Ferrosol Dermosol Vertosol

−1,289 4,012 −478 10,023

0.03 0.03 0.05 0.02

−43.41 126.44 −9.09 516.80

The financial outcomes per unit of environmental gain is calculated on the average marginal environmental performance as a proportion of average marginal NPV for each soil type.

grazing land to mixed cattle-forestry systems achieving additionality requires the delivery of an environmental benefit (as discussed in the preceding paragraph in this Section) and that the process of land-use conversion should be unlikely to occur in the absence of an incentive payment. This latter element of additionality requires an understanding of the private costs and benefits incurred by landholders as a result of land-use transition. This is discussed further in the following sections. 4.3. Farm financial performance The results from this work demonstrate that landholders on ferrosol and vertosol soils in the Richmond River catchment have favourable options that simultaneously improve both environmental and private financial outcomes. Landholders on kurosol and dermosol soils, on the other hand, are likely to incur financial losses in association with converting to cattle-forestry-mixtures. The low profitability of the kurosols can be partly explained by the low timber yield in the timber Table 10 Financial performance range for each soil type based on NPV. Soil type

Estimated risk level

Range (low-high) NPV ($ per ha) (4%)

Range (low-high) NPV ($ per ha) (7%)

Range (low-high) NPV ($ per ha) (10%)

Kurosol Ferrosol Dermosol Vertosol

± 11% ± 1% ± 7% ± 6%

−147 to 619 1,634–21,292 1,272 –3,128 8,672–24,369

−1,405 to −7 228–9,879 −283 to 457 5,736–16,000

−1,968 to −313 −1,153–3631 −1,403 to −82 3,974–10,985

The financial performance range for each soil type was estimated with sensitivity testing using discount rates of 4% and 10%.

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In contrast to outcomes on ferrosol and vertosol soils, scenarios for kurosol and dermosol soils show positive net public benefits but negative net private benefits, indicating landholders will require positive incentives to change their land management practices. Positive incentives to encourage change for environmental benefit in these contexts include ‘beneficiary-pays’ mechanisms that offer inducements to private individuals in order to promote maximum participation (Schneider and Ingram, 1990; Pannell, 2008). These are usually funded by the public, for the benefit of the public. The difficulty with beneficiary-pays mechanisms is calculating the amount of incentive required to prompt landholders to adopt the land use change (Schaaf and Broussard, 2006; Pannell, 2008). Typically, financial barriers to adoption such as capital constraints or start-up costs, are included in the amount of incentives (Pannell, 2008). Applicable to this study are the substantial establishment and transition costs whether transitioning to timber, nut or essential oil. Similar to extension, it is recommended that the model be utilised to include individual data to determine the extent of incentive payments. Output from the model regarding establishment costs and predicted environmental performance (for landholders on kurosol and dermosol soils) could be used to assess the level of incentives necessary to promote change by landholders. We have modelled financial outcomes of land-use change in our farm systems, and this is likely to be a key consideration in landholders’ decision-making processes (Pannell, 2008; Cary and Roberts, 2011; Januchowski-Hartley et al., 2012), but the adoption by landholders of a new grazing-forestry system relies on an assortment of personal, social, cultural and economic elements together with features of the new system (Pannell et al., 2006). A decision by a landholder to adopt or not, can be based on their perceptions or expectations of a new practice including its relative advantage over the existing land use practice (Pannell et al., 2006). Relative advantage can be partly demonstrated through economic costs and benefits, level of risk and environmental integrity using a farm-scale financial model such as that used here. This can be applied to individual land parcels and/or farms to inform land management decision making and promote land use change. In addition to these considerations, some landholders may also receive non-pecuniary benefits from making the transition to a mixed beef-grazing system that also have not been captured in the model. These might include perceived benefits from nature conservation such as improved landscape aesthetics, connectedness to nature, recreation opportunities (e.g. bird watching, bushwalking) and improved health and well-being (Sullivan et al., 2004; Strong and Jacobson, 2006; Gosling and Williams, 2010). Similarly, perceived public benefits associated with preservation include improved water quality and landscape aesthetics, more productive habitats for biodiversity, and better recreation and tourism opportunities (Januchowski-Hartley et al., 2012). These latter may be particularly relevant in areas where agritourism and ecotourism are likely to be attractive to landholders. The public private benefit framework illustrated in this Section highlights the policy tools necessary to promote adoption and facilitate change (Pannell, 2008). Within the Richmond River catchment, the type of policy mechanism required to incentivise landholders to make the transition from 100% grazing to a mixed grazing-forestry system is most likely to be prescribed by soil type. In consideration of the biophysical constraints within and/or between properties, as well as the different circumstances of landholders, policy mechanisms must be adapted to individual situations. The process of enabling change using needs-based extension methods provides for this by using individual data to design relevant products and services that are targeted to the individual landholder (Fulton and Vanclay, 2011). It is recommended that the model developed in this study be used as a tool to guide needsbased extension by utilising individual data in the analysis of

Fig. 7. Public private benefit framework after Pannell’s Public Private Benefits Framework (2008). Public and private net benefits are averaged for each soil type based on environmental performance and Net Present Value for each production scenario in the model. Environmental and financial outcomes are shown relative to the base case.

cattle in areas of greater tree cover. In the event of high rainfall, soil organic matter is enriched, improving nutrient cycling and thus productivity and potential income from increased tree cover (Bird et al., 1992; West, 2014). Furthermore, where plantings are carried out on previously unproductive grazing land any income generated from that land is an additional benefit. 4.4. Implications for policy Our study provides a range of policy-relevant insights into publicprivate trade-offs associated with transitioning grazing properties in the Richmond River catchment to cattle-grazing mixtures. Variation in financial outcomes has implications for cost effectiveness – degree to which $ spent (opportunity cost) provides a unit of environmental benefit, for each soil type (Table 9). Whilst this is important for spatial targeting (as discussed in Section 4.2), it also has much broader policy implications for how public policy should be designed to effect land-use change. In order to ascertain the public and private net benefits by soil type, the net environmental and financial outcomes of each scenario were averaged for each soil type and plotted on a Public Private Benefits Framework based on Pannell (2008) (Fig. 7). The framework indicates that production scenarios for ferrosol and vertosol soils have positive net public and private benefits. Under Pannell’s framework, landholder extension activities should be adequate for effecting land-use change on these soil types. Extension services are capacity building and traditionally include technology and knowledge transfer through the diffusion of accessible information (Pannell, 2008; Lapple and Hennessy, 2015). For extension to be successful, a number of social principles that regard adoption as change and extension as the process of learning, must be considered (Vanclay, 2011). Acknowledging that all farm properties are different and all landholders are different is one of those social principles, and when applied it means that extension is best delivered using multiple methods to account for such differences (Vanclay, 2011). Positive incentives are generally financial or regulatory mechanisms to promote adoption of land use change and these can be used to help offset private costs to promote change (Pannell, 2008; Januchowski-Hartley et al., 2012).

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outcomes associated with different transition outcomes. This is the appropriate metric given that our primary aim was to assess potential for investment from a government perspective. We note, however, that landholders’ decisions to participate in farm-forestry transition will be influenced by a range of other financial and non-financial factors. From a financial perspective, timber farm forestry is associated with a longer time-lag from planting to market (compared to agricultural or horticultural crops). Capital costs and other expenditures associated with planting and maintaining trees are incurred upfront, but the timber species included in our farm transition model do not provide any financial return to landholders until 20–30 years after planting (Fig. 8a). This can leave landholders in a position where they are required to carry project deficits of up to $4,681 per hectare over a lengthy period (Fig. 8b). Many landholders may lack the capacity, or the will, to incur the cashflow deficit associated with farm forestry projects, particularly if they are already carrying high levels of farm debt (Cacho et al., 2001). There is some research to show that the cash flow deficits arising from implementing a mixed grazing-forestry system may be eased through market forces, as the increased on-farm asset base can increase property values and enhance landholders’ equity and associated borrowing capacity (Stewart et al., 2011). However, where market forces are insufficient to overcome the cashflow deficit, achieving high levels of landholder participation may require government policies that provide transition payments or some other means of ‘bridging the gap’, between farm forestry establishment costs and subsequent (delayed) returns. Even where market policies or government interventions can improve the short-term financial outcomes from a transition to farm-forestry, barriers to landholder adoption are likely to remain. Many of the documented barriers to adoption of farm forestry or other on-farm environmental initiatives are non-financial. They include quality and access to information (Strong and Jacobson, 2006; Baumgart-Getz et al., 2008; Valdivia et al., 2012), time available to manage the plantation (Valdivia et al., 2012; Jellinek et al., 2013; Wilson and Lovell, 2016), and lack of skills/experience with trees (Kiyani et al., 2017). Some of these barriers may be insurmountable, others may be overcome with education or engagement, or careful scheme design to address landholder concerns.

Fig. 8. Average revenue and average cumulative project balance by soil type. A) shows that on average for each soil type, all production scenarios that include timber have a peak in income during years 20 and 30 associated with timber harvest. Income for vertosol soils from year one onwards is attributed to tea-tree production. Income peaks for ferrosol soils at year five and again at year 15 are attributed to macadamia production. B) shows the variation in average cumulative project balance by soil type noting that ferrosol soils experience the highest project deficit at year four of $4,681 per hectare.

production scenarios applied to individual properties. Such tailor-made consultation is more likely to trigger change compared to non-specific advice.

4.6. Model constraints and other considerations In addition to fixed biophysical constraints, local climate and weather are important considerations for undertaking a mixed silvopastoral system. Whilst planting is best carried out following rain to promote growth, this should be determined by the time of year when rainfall is most likely (West, 2014). Similarly, although the frost zone was excluded for those soil types with frost sensitive species, the time of year and likelihood of frost should be considered to maximise seedling survival (West, 2014). For example, although one of the selected species for the vertosols, Eucalyptus tereticornis (Forest red gum) is considered frost tolerant, it is susceptible to frost damage as a seedling. As this work is a preliminary application of this model, a number of additional caveats need to be considered, and caution should be exercised with understanding the results. An assumption in the model is that all timber planting would occur following rain with the nut and essential oil species requiring irrigation after planting unless rain is timely. Financial outcomes are also linked to climate in other ways. For example, better growth rates (higher yield) and therefore higher income is expected with above average rainfall for most timber species (West, 2014). The exception is Eucalyptus pilularis (Blackbutt) as it may experience a heightened pest risk from root fungus associated with high rainfall (Clarke et al., 2009). Such yield changes and their implications may result if anticipated changes in climate do occur, especially along

4.5. Achieving landholder adoption In order to achieve uptake, incentives to engage landholders in onfarm activities need to outweigh impediments to participation, such as transition costs, and other time and monetary costs (Jellinek et al., 2013). Given the variation in farming operations, soil types and land capability that exists across a landscape, it is unlikely that landholder payment schemes will gain sufficient landholder participation, nor achieve cost-effectiveness, unless they are customised to match the range of different landholder experiences within the target landscape (Smith et al., 2012; Comerford, 2014; Greiner, 2015). Our study shows that economic modelling can be a useful tool in this regard. Our modelling can help decision-makers to identify transition scenarios that deliver greatest environmental benefit at the lowest cost. It can also identify situations where land-use transition is likely to deliver positive financial outcomes to private landholders. The Pannell Public Private Benefits Framework would suggest that, in such situations, it may be possible to achieve landholder participation even in the absence of incentive payments (i.e. in response to extension and engagement activities alone). Our study has focussed on NPV to determine private financial

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the eastern coast of Australia. Localised impacts of climate change should be given careful consideration prior to implementation of recommendations from this study. The financial model used in this current study did not account for a number of elements such as plantation design (spacing of seedlings, slope and aspect position). The spacing and placement of seedlings will vary from one site to the next but is considered essential for the growth and development of a plantation (West, 2014). For example, Macadamia integrifolia (Macadamia nut) may be susceptible to wind damage, therefore, plantation design needs to include windbreaks (NSW Department of Primary Industries, 2005). Lophostemon confertus (Brush box) and Elaeocarpus grandis (Silver quandong) would be suitable for use as windbreaks to protect M. integrifolia on ferrosol soils based on the species combination used in the model scenario. Although establishment costs such as soil preparation, planting seedlings, fertilising and weed spraying have been included in the model, some other set up costs that may be incurred by some landholders in association with silvo-pasture transition are not included (e.g. specialised machinery and new fencing). Costs such as these may pose varying degrees of financial constraint for landholders because farm resources, economic conditions and personal situations of landholders are unique to each property (Pannell and Vanclay, 2011). Likewise, the contribution of labour has not been explicitly accounted for in the model. Labour may vary considerably between farms and impact cash flow in different ways. Whilst some landholders may have the capacity to carry out new plantation activities without impacting the day-to-day tasks associated with existing beef grazing, other landholders may need to employ additional farm workers or input extra hours to create a viable business unit. Within the study area we estimate fencing construction costs would add approximately $150/ha and manual planting labour $250/ha, excluding specialised machinery. The establishment costs that were excluded from the original analysis constitute only 10% of total establishment cost. We also compare the missing costs (on a per hectare basis) with the benefits expected from transition and find that they do not impact on the conclusions drawn from our analysis - i.e. they do not affect the distribution of public versus private benefit for any of the combinations tested. Landholders on kurosol and dermosol soils already experience a net loss from transition, for landholders on ferrosol or vertosol soils experience only a small reduction in the NPV of land transition (maximum reduction of 10% and 4% respectively). However, capital costs incurred with plantation establishment are an important policy consideration in a broader context as they influence the level of financial incentive (see Section 4.4) that landholders might require to make the land use transition. Time to positive cashflow and maximum net shortfall are key considerations to determine which landholders require payments and when. Incentives such as bridging or transition payments will likely be borne by the government to facilitate transition to achieve maximum public environmental benefit. The exclusion of harvest and transport costs for timber and essential oil species in the model was based on those services being contracted out. In the case of timber, the stumpage rate paid to the landholder by the contractor secures the right to harvest and remove the timber offsite. This means that no additional harvesting or transport costs are incurred by the landholder. For the essential oil species, the contractor’s costs to harvest and transport are not yet accounted for in the cash flow and are dependent on the farm’s location, size, yield, site and distillery accessibility (Australian Tea Tree Industry Association, 2017). Application of fertiliser upon planting, although an expense and potentially environmentally degrading helps to expedite tree growth and canopy closure to minimise weeds that compete for water and nutrients thereby increasing the productivity of the plantation (West, 2014). Despite these model constraints, this investigative groundwork suggests further

development of this model would be meaningful if relevant and reliable data is forthcoming as there is much scope to broaden this work to account for these additional differences amongst farm types. 5. Conclusion This is the first study to use a farm-scale financial model to assess the potential public and private benefits of changing land use from beef grazing to a silvo-pastoral system at the farm-scale within a catchment. The results identify a number of possible production scenarios available to landholders in the Richmond River catchment that provide both public environmental and private financial benefit, but both environmental and financial outcomes are strongly influenced by the underlying soil type. It follows that policy designed to effect land use change at a catchment scale should be conscious of the biophysical constraints on different properties within the catchment. Given that more than 30% of the Richmond River catchment is beef grazing land potentially available for increased tree cover, there is likely to be some merit in promoting land use change in land use amongst local landholders. It is recommended that the model developed in this research can be used as a tool to direct spatially-targeted policy mechanisms such as the provision of extension support and positive monetary and non-monetary incentive schemes across the catchment area. Many of our research findings are generalizable to other management contexts. Diversified land use at farm level has been shown to achieve development and conservation outcomes essential for sustainable agriculture across a range of agricultural systems and locations (Abson et al., 2013; Sayer et al., 2013; Torralba et al., 2016). Our research has shown how incentive schemes can be better targeted to deliver outcomes within the biophysical constraints of the landscape. We conclude that a mixture of strategies is likely to be required to facilitate transition by landholders to new land management systems. Extension and positive incentives are complementary strategies that may help to foster land use change at a catchment scale, but each strategy needs to be carefully targeted to specific locations within a catchment. Policy targeting should be undertaken with reference to the financial implications of the biophysical constraints experienced by individual landholders in order to ensure that land use transition policy objectives are achieved in a cost-effective manner. To overcome the limited nature of this preliminary research, future research could include a broadening of the model to include a wider variety of beef grazing-forestry production scenarios across a broader range of locations. Regardless of the limitations of this research however, the results demonstrate how an interdisciplinary approach to data collection and analysis can deliver meaningful information to support a more targeted approach of environmental management policies and tools. Conflicts of interest None. Acknowledgements The authors wish to acknowledge the helpful assistance of staff at the Forestry Corporation of New South Wales, the Commonwealth Department of Agriculture and Water Resources, Queensland Department of Agriculture and Fisheries, the Australian Tea Tree Industry Association, Super Forest Plantations, and the Forestry Research Centre at Southern Cross University. It was carried out under the auspices of the Australian Research Council Linkage Grant number LP130100498. We are also grateful for the comments provided by the anonymous reviewer/s which improved the manuscript.

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Appendix A

Table A1 Summary of government data utilised in the spatial analysis. Data description

Data model

Publisher

NSW Local Government Areas PSMA administrative boundaries

Vector data model Vector data model Vector data model Raster data model Vector data model Vector data model

Australian Government (2014) (https://data.gov.au/dataset/ nsw-local-government-areas) NSW Office of Environment and Heritage (2010) (http://data. environment.nsw.gov.au/) NSW Office of Environment and Heritage (2010) (http://data. environment.nsw.gov.au/) Australian Government – Geoscience Australia (2017) (http:// www.ga.gov.au/elvis/) Australian Government - Bureau of Meteorology (2017) – subject to licence agreement. Australian Government – Geoscience Australia (2015) (http:// www.ga.gov.au)

Catchment boundaries of NSW Multi attribute data – Richmond River Catchment – landform and condition dataset (land use; tree canopy cover; slope; soil type) Digital elevation model (DEM) 1 Second DEM Esri ASCII Grid data Average annual rainfall Surface hydrology lines regional

Comerford, E., 2014. Understanding why landholders choose to participate or withdraw from conservation programs: a case study from a Queensland conservation auction. J. Environ. Manag. 141, 169–176. https://doi.org/10.1016/j.jenvman.2014.01.049. Cubbage, F., Balmelli, G., Bussoni, A., Noellemeyer, E., Pachas, A.N., Fassola, H., Colcombet, L., Rossner, B., Frey, G., Dube, F., de Silva, M.L., Stevenson, H., Hamilton, J., Hubbard, W., 2012. Comparing silvopastoral systems and prospects in eight regions of the world. Agrofor. Syst. 86, 303–314. https://doi.org/10.1007/s10457-0129482-z. Dale, V.H., Polasky, S., 2007. Measures of the effects of agricultural practices on ecosystem services. Ecol. Econ. 64 (2), 286–296. https://doi.org/10.1016/j.ecolecon. 2007.05.009. Davis, J.R., Koop, K., 2006. Eutrophication in Australian rivers, reservoirs and estuaries – a southern hemisphere perspective on the science and its implications. Hydrobiologia 559 (1), 23–76. https://doi.org/10.1007/s10750-005-4429-2. Department of Environment, Climate Change and Water NSW, 2010. State of the Catchments 2010: Overview Northern Rivers Region. Retrieved from. . http://www. environment.nsw.gov.au/soc/stateofthecatchmentsreport.htm. Department of the Environment and Energy, 2017. Climate change, Emissions Reduction Fund. Retrieved from. http://www.environment.gov.au/climate-change/emissionsreduction-fund. Donnelly, J.R., Freer, M., Salmon, L., Moore, A.D., Simpson, R.J., Dove, H., Bolger, T.P., 2002. Evolution of the GRAZPLAN decision support tools and adoption by the grazing industry in temperate Australia. Agric. Syst. 74 (1), 115–139. https://doi.org/10. 1016/S0308-521X(02)00024-0. Dumbrell, N.P., Kragt, M.E., Biggs, J., Meier, E., Thorburn, P., 2015. Climate Change Abatement and Farm Profitability Analyses across Agricultural Environments (Working Paper 1515, School of Agricultural and Resource Economics, the University of Western Australia). Retrieved from. http://ageconsearch.tind.io/record/225674. Ellison, D., Morris, C.E., Locatelli, B., Sheil, D., Cohen, J., Murdiyarso, D., Gutierrez, V., van Noordwijk, M., Creed, I.F., Pokorny, J., Gaveau, D., Spracklen, D.V., Tobella, A.B., Ilstedt, U., Teuling, A.J., Gebrehiwot, S.G., Sands, D.C., Muys, B., Verbist, B., Springgay, E., Sugandi, Y., Sullivan, C.A., 2017. Trees, forests and water: cool insights for a hot world. Glob. Environ. Change 43, 51–61. https://doi.org/10.1016/j. gloenvcha.2017.01.002. Food and Agriculture Organization of the United Nations (FAO), 2017. Water Pollution from Agriculture: a Global Review. Retrieved from. http://www.fao.org/3/ai7754e.pdf. Farley, J., Costanza, R., 2010. Payments for ecosystem services: from local to global. Ecol. Econ. 69, 2060–2068. https://doi.org/10.1016/j.ecolecon.2010.06.010. Finn, D., Dalal, R., Klieve, A., 2014. Methane in Australian agriculture: current emissions, sources and sinks, and potential mitigation strategies. Crop Pasture Sci. 66 (1), 1–22. https://doi.org/10.1071/CP14116. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, C., Ramankutty, N., Snyder, P.K., 2005. Global consequences of land use. Science 309 (5734), 570–574. Retrieved from. http://search.proquest.com.ezproxy.scu.edu.au/. Forests NSW, 2005. Timber Harvesting in Forests NSW Plantations: Forest Practices Code Part 1. Retrieved from. http://www.forestrycorporation.com.au/about/pubs. Fulton, A., Vanclay, F., 2011. Enabling change in family farm businesses. In: Pannell, D., Vanclay, F. (Eds.), Changing Land Management: Adoption of New Practices by Rural Landholders. CSIRO Publishing, Collingwood, Australia, pp. 95–106. García-Amado, L.R., Pérez, M.R., Escutia, F.R., García, S.B., Mejía, E.C., 2011. Efficiency of payments for environmental services: equity and additionality in a case study from a biosphere reserve in Chiapas, Mexico. Ecol. Econ. 70 (12), 2361–2368. https://doi. org/10.1016/j.ecolecon.2011.07.016. Ghebremichael, L.T., Veith, T.L., Hamlett, J.M., 2013. Integrated watershed- and farmscale modelling framework for targeting critical source areas while maintaining farm economic viability. J. Environ. Manag. 114, 381–394. https://doi.org/10.1016/j. jenvman.2012.10.034. Gifford, R.M., 2000. National Carbon Accounting System Technical Report no. 22: Carbon Contents of Above-Ground Tissues of Forest and Woodland Trees. Retrieved from. . http://www.fullcam.com/FullCAMServer/Help/reps/Index.htm.

References Abson, D.J., Fraser, E.D.G., Benton, T.G., 2013. Landscape diversity and the resilience of agricultural returns: a portfolio analysis of land-use patterns and economic returns from lowland agriculture. Agric. Food Security 2 (1), 1–15. https://doi.org/10.1186/ 2048-7010-2-2. Agouridis, C.T., Workman, S.R., Warner, R.C., Jennings, G.D., 2005. Livestock grazing management impacts on stream water quality: a review. J. Am. Water Resour. Assoc. 41 (3), 591–606. Retrieved from. http://www.proquest.com. Allan, J.D., Erickson, D.L., Fay, J., 1997. The influence of catchment land use on stream integrity across multiple spatial scales. Freshw. Biol. 37 (1), 149–161. https://doi. org/10.1046/j.1365-2427.1997.d01-546.x. Ansell, D., Gibson, F., Salt, D. (Eds.), 2016. Learning from Agri-Environment Schemes in Australia, Retrieved from http://www.oapen.org/search?identifier=610749. Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), 2015. Australia’s Forests at a Glance 2015: With Data to 2013–14. Retrieved from. http:// www.agriculture.gov.au/abares/publications/. Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), 2016a. Agricultural Commodity Statistics 2016. Retrieved from. http://www. agriculture.gov.au/abares/publications/. Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), 2016b. Australian Forest and Wood Product Statistics: March and June Quarters 2016, Appendix A. Retrieved from. http://www.agriculture.gov.au/abares/ publications/. Australian Macadamia Society, 2017. Statistics: Australian Production and Indicative Prices 1987–2012 and 2013 Onwards. Retrieved from. http://australianmacadamias.org/industry/about-aussie-macadamias/stats?lang=en. Australian Tea Tree Industry Association, 2017. About Australian Tea Tree Oil. Retrieved from. http://www.teatree.org.au/teatree_about.php. Baumgart-Getz, A., Prokopy, L.S., Floress, K., 2008. Why farmers adopt best management practice in the United States: a meta-analysis of the adoption literature. J. Environ. Manag. 96, 17–25. https://doi.org/10.1016/j.jenvman.2011.10.006. Bennett, K., 2010. Additionality: the next step for ecosystem service markets. Duke Environ. Law Policy Forum. 20 (2), 417–438. Retrieved from. http://go.galegroup. com.ezproxy.scu.edu.au. Bird, P.R., Bicknell, D., Bulman, P.A., Burke, S.J.A., Leys, J.F., Parker, J.N., et al., 1992. The role of shelter in Australia for protecting soils, plants and livestock. Prinsley, R.T. (Ed.), The Role of Trees in Sustainable Agriculture: Review Papers Presented at the Australian Conference, the Role of Trees in Sustainable Agriculture 59–86 Dordrecht, The Netherlands: Kluwer Academic Publishers. Bryan, B.A., Nolan, M., Harwood, T.D., Connor, J.D., Navarro-Garcia, J., King, D., Summers, D.M., Newth, D., Cai, Y., Grigg, N., Harman, I., Crossman, N.D., Grundy, M.J., Finnigan, J.J., Ferrier, S., Williams, K.J., Wilson, K.A., Law, E.A., HatfieldDodds, S., 2014. Supply of carbon sequestration and biodiversity services from Australia’s agricultural land under global change. Glob. Environ. Change 28, 166–181. https://doi.org/10.1016/j.gloenvcha.2014.06.013. Bryan, B.A., Nolan, M., McKellar, L., Connor, J.D., Newth, D., Harwood, T., King, D., Navarro, J., Cai, Y., Gao, L., Grundy, M., Graham, P., Ernst, A., Dunstall, S., Stock, F., Brinsmead, T., Harman, I., Grigg, N.J., Battaglia, M., Keating, B., Wonhas, A., Hatfield-Dodds, S., 2016. Land-use and sustainability under intersecting global change and domestic policy scenarios: trajectories for Australia to 2050. Glob. Environ. Change 38, 130–152. https://doi.org/10.1016/j.gloenvcha.2016.03.002. Bureau of Meteorology, 2017. Climate, Climate Data Online, Average Annual Rainfall, New South Wales. Retrieved from. http://www.bom.gov.au/jsp/ncc/climate_ averages/rainfall/index.jsp?period=an&area=ns#maps. Cacho, O., Greiner, R., Fulloon, L., 2001. An economic analysis of farm forestry as a means of controlling dryland salinity. Aust. J. Agric. Resour. Econ. 45 (2), 233–256. Cary, J., Roberts, A., 2011. The limitations of environmental management systems in Australian agriculture. J. Environ. Manag. 92 (3), 878–885. https://doi.org/10.1016/ j.jenvman.2010.10.055. Clarke, B., McLeod, I., Vercoe, T. (Eds.), 2009. Trees for Farm Forestry: 22 Promising Species, Retrieved from http://www.rirdc.gov.au/publications.

15

Land Use Policy 87 (2019) 104070

L. Beardmore, et al. Gosling, E., Williams, K.J.H., 2010. Connectedness to nature, place attachment and conservation behaviour: testing connectedness theory among farmers. J. Environ. Psychol. 30 (3), 298–304. https://doi.org/10.1016/j.jenvp.2010.01.005. Greiner, R., 2015. Motivations and attitudes influence farmers’ willingness to participate in biodiversity conservation contracts. Agric. Syst. 137, 154–165. https://doi.org/10. 1016/j.agsy.2015.04.005. Grundy, M.J., Bryan, B.A., Nolan, M., Battaglia, M., Hatfield-Dodds, S., Connor, J.D., Keating, B.A., 2016. Scenarios for Australian agricultural production and land use to 2050. Agric. Syst. 142, 70–83. https://doi.org/10.1016/j.agsy.2015.11.008. Higgins, V., Dibden, J., Potter, C., Moon, K., Cocklin, C., 2014. Payments for ecosystem services, neoliberalisation, and the hybrid governance of land management in Australia. J. Rural Stud. 36, 463–474. https://doi.org/10.1016/j.jrurstud.2014.10. 003. Hughes, A.O., Quinn, J.M., 2014. Before and after integrated catchment management in a headwater catchment: changes in water quality. Environ. Manag. 54 (6), 1288–1305. https://doi.org/10.1007/s00267-014-0369-9. Isbell, R.F., National Committee on Soil and Terrain, 2016. The Australian Soil Classification. Retrieved from. 2nd ed. . http://www.clw.csiro.au/aclep/asc_re_on_ line_V2/soilhome.htm. Jansen, A., Robertson, A.I., 2001. Relationships between livestock management and the ecological condition of riparian habitats along an Australian floodplain river. J. Appl. Ecol. 38 (1), 63–75. https://doi.org/10.1046/j.1365-2664.2001.00557.x. Janssen, S., van Ittersum, M.K., 2007. Assessing farm innovations and responses to policies: a review of bio-economic farm models. Agric. Syst. 94 (3), 622–636. https:// doi.org/10.1016/j.agsy.2007.03.001. Januchowski-Hartley, S.R., Moon, K., Stoeckl, N., Gray, S., 2012. Social factors and private benefits influence landholders’ riverine restoration priorities in tropical Australia. J. Environ. Manag. 110, 20–26. https://doi.org/10.1016/j.jenvman.2012. 05.011. Jellinek, S., Parris, K.M., Driscoll, D.A., Dwyer, P.D., 2013. Are incentive programs working? Landowner attitudes to ecological restoration of agricultural landscapes. J. Environ. Manag. 127, 69–76. https://doi.org/10.1016/j.jenvman.2013.04.034. Jose, S., Walter, D., Kumar, B.M., 2017. Ecological considerations in sustainable silvopasture design and management. Agrofor. Syst. 1–15. https://doi.org/10.1007/ s10457-016-0065-2. Kelty, M.J., 2006. The role of species mixtures in plantation forestry. For. Ecol. Manag. 233 (2), 195–204. https://doi.org/10.1016/j.foreco.2006.05.011. Kingwell, R., Anderton, L., Islam, N., Xayavong, V., Wardell-Johnson, A., Feldman, D., Speijers, J., 2013. Broadacre Farmers Adapting to a Changing Climate: Final Report. Retrieved from. . https://www.nccarf.edu.au/biblio. Kiyani, P., Andoh, J., Lee, Y., Lee, D.K., 2017. Benefits and challenges of agroforestry adoption: a case of Musebeya sector, Nyamagabe District in southern province of Rwanda. For. Sci. Technol. 13 (4), 174–180. https://doi.org/10.1080/21580103. 2017.1392367. Lapple, D., Hennessy, T., 2015. Exploring the role of incentives in agricultural extension programs. Appl. Econ. Perspect. Policy 37 (3), 403–417. https://doi.org/10.1093/ aepp/ppu037. Lindenmayer, D.B., Hobbs, R.J., Salt, D., 2003. Plantation forests and biodiversity conservation. Aust. For. 66 (1), 62–66. https://doi.org/10.1080/00049158.2003. 10674891. Lindenmayer, D., Wood, J., Montague-Drake, R., Michael, D., Crane, M., Okada, S., Macgregor, C., Gibbons, P., 2012. Is biodiversity management effective? Cross-sectional relationships between management, bird response and vegetation attributes in an Australian agri-environment scheme. Biol. Conserv. 152, 62–73. https://doi.org/ 10.1016/j.biocon.2012.02.026. Lines-Kelly, R., Currey, A. (Eds.), 1994. Trees for the NSW North Coast: a Guide to Growing Trees in the Wet Subtropics, 2nd ed. NSW Agriculture, Wollongbar, Australia. Lismore City Council, 2015. Biodiversity Management Strategy for the Lismore Local Government Area 2015-2035. Retrieved from. https://www.lismore.nsw.gov.au/ cp_themes/default/page.asp?p=DOC-WHG-36-21-02. Lismore City Council, 2016. Environment and Sustainability, Rural Landholder Initiative. Retrieved from. https://www.lismore.nsw.gov.au/cp_themes/default/page.asp?p= DOC-ZQJ-37-22-03. Lott, R.H., Duggin, J.A., 1993. Conservation Significance and Long Term Viability of Subtropical Rainforest Remnants of the Big Scrub, North-Eastern New South Wales. Report prepared for the Australian Heritage Commission and NSW Department of Planning. University of New England, Armidale, NSW. Murphy, T., Jones, G., Vanclay, J., Glencross, K., 2013. Preliminary carbon sequestration modelling for the Australian macadamia industry. Agrofor. Syst. 87 (3), 689–698. https://doi.org/10.1007/s10457-012-9589-2. New South Wales Treasury, 2007. Economic Appraisal Principles and Procedures Simplified – Office of Financial Management, Policy and Guidelines Paper – tpp07-6. Retrieved from. http://www.treasury.nsw.gov.au/Treasury_Policy_Papers_Index_ Page. NSW Department of Primary Industries, 2005. Prime Fact 5: Macadamia Culture in NSW. Retrieved from. http://www.dpi.nsw.gov.au/agriculture/horticulture/nuts/ growing-guides/macadamia-culture-nsw. NSW Department of Primary Industries, 2017. Water Management, Basins and Catchments, Richmond Catchment. Retrieved from. http://www.water.nsw.gov.au/ water-management/catchments/richmond-catchment. NSW Office of Environment & Heritage, 2010. Multi-attribute Data – Richmond River Catchment – Landform and Condition Dataset. Retrieved from. http://data. environment.nsw.gov.au. Pannell, D.J., 2008. Public benefits, private benefits, and policy mechanism choice for land-use change for environmental benefits. Land Econ. 84 (2), 225–240. Retrieved from. http://web.a.ebscohost.com.ezproxy.scu.edu.au/. Pannell, D.J., Vanclay, F.M., 2011. Changing land management: multiple perspectives on a multi-faceted issue. In: Pannell, D., Vanclay, F. (Eds.), Changing Land Management: Adoption of New Practices by Rural Landholders. CSIRO Publishing, Collingwood,

Australia, pp. 1–10. Pannell, D.J., Marshall, G.R., Barr, N., Curtis, A., Vanclay, F., Wilkinson, R., 2006. Understanding and promoting adoption of conservation practices by rural landholders. Aust. J. Exp. Agric. 46 (11), 1407–1424. https://doi.org/10.1071/EA05037. Prinsley, R.T., 1992. The role of trees in sustainable agriculture: an overview. In: Prinsley, R.T. (Ed.), The Role of Trees in Sustainable Agriculture: Review Papers Presented at the Australian Conference, The Role of Trees in Sustainable Agriculture. Albury, Victoria, Australia, October 1991. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 59–86. Regan, C.M., Bryan, B.A., Connor, J.D., Meyer, W.S., Ostendorf, B., Zhu, Z., Bao, C., 2015. Real options analysis for land use management: methods, application, and implications for policy. J. Environ. Manag. 161, 144–152. https://doi.org/10.1016/j. jenvman.2015.07.004. Robertson, M.J., Pannell, D.J., Chalak, M., 2012. Whole-farm models: a review of recent approaches. AFBM J. 9 (2), 13–26. Retrieved from. http://science.csu.edu.au/afbm/ archive/afbm-journal/volume-9-no-2. Ryder, D., Mika, S., Richardson, M., Schmidt, J., Fitzgibbon, B., 2015. Richmond Ecohealth Project 2014-assessment of River and Estuarine Condition-Final Technical Report June 2015. Retrieved from. . https://www.rous.nsw.gov.au/cp_themes/ default/page.asp?p=DOC-VBZ-86-71-42. Salmoral, G., Willaarts, B.A., Garrido, A., Guse, B., 2017. Fostering integrated land and water management approaches: evaluating the water footprint of a Mediterranean basin under different agricultural land use scenarios. Land Use Policy 61, 24–39. https://doi.org/10.1016/j.landusepol.2016.09.027. Sandhu, H.S., Crossman, N.D., Smith, F.P., 2012. Ecosystem services and Australian agricultural enterprises. Ecol. Econ. 74, 19–26. https://doi.org/10.1016/j.ecolecon. 2011.12.001. Sayer, J., Sunderland, T., Ghazoul, J., Pfund, J., Sheil, D., Meijaard, E., Venter, M., Boedhihartono, A.K., Day, M., Garcia, C., van Oosten, C., Buck, L.E., 2013. Ten principles for a landscape approach to reconciling agriculture, conservation and other competing land uses. Proc. Natl. Acad. Sci. 110 (21), 8349–8356. https://doi.org/10. 1073/pnas.1210595110. Schaaf, K.A., Broussard, S.R., 2006. Private forest policy tools: a national survey exploring the American public’s perceptions and support. For. Policy Econ. 9 (4), 316–334. https://doi.org/10.1016/j.forpol.2005.10.001. Schneider, A., Ingram, H., 1990. Behavioural assumptions of policy tools. The J. Polit. 52 (2), 510–529. Retrieved from. http://www.jstor.org/stable/2131904. Seymour, E.J., Ridley, A.M., 2005. Toward Environmental Management Systems in Australian agriculture to achieve better environmental outcomes at the catchment scale. Environ. Manag. 35 (3), 311–329. https://doi.org/10.1007/s00267-0040107-9. Smith, F.P., Gorddard, R., House, A.P.N., McIntyre, S., Prober, S.M., 2012. Biodiversity and agriculture: production frontiers as a framework for exploring trade-offs and evaluating policy. Environ. Sci. Policy 23, 85–94. https://doi.org/10.1016/j.envsci. 2012.07.013. Smith, H.F., Sullivan, C.A., 2014. Ecosystem services within agricultural landscapes – farmers’ perceptions. Ecol. Econ. 98, 72–80. https://doi.org/10.1016/j.ecolecon. 2013.12.008. Snowdon, P., Eamus, D., Gibbons, P., Khanna, P., Keith, H., Raison, J., Kirschbaum, M., 2000. National Carbon Accounting System Technical Report no. 17: Synthesis of Allometrics, Review of Root Biomass and Design of Future Woody Biomass Sampling Strategies. Retrieved from. . http://www.fullcam.com/FullCAMServer/Help/reps/ Index.htm. Stewart, H.T.L., Race, D.H., Curtis, A.L., Stewart, A.J.K., 2011. A case study of socioeconomic returns from farm forestry and agriculture in south-east Australia during 1993-2007. For. Policy Econ. 13, 390–395. https://doi.org/10.1016/j.forpol.2011. 03.004. Strong, N., Jacobson, M.G., 2006. A case for consumer-driven extension programming: agroforestry adoption potential in Pennsylvania. Agrofor. Syst. 68 (1), 43–52. https://doi.org/10.1007/s10457-006-0002-x. Sullivan, C.A., Heagney, E., 2016. An Agroforestry Planning Tool: Options for Fiji and Vanuatu. An Output from ACIAR Project ADP/2014/012. Southern Cross University, Lismore, Australia. Sullivan, W.C., Anderson, O.M., Lovell, S.T., 2004. Agricultural buffers at the rural-urban fringe: an examination of approval by farmers, residents, and academics in the Midwestern United States. Landsc. Urban Plan. 69 (2), 299–313. https://doi.org/10. 1016/j.landurbplan.2003.10.036. Swinton, S.M., Lupi, F., Robertson, G.P., Hamilton, S.K., 2007. Ecosystem services and agriculture: cultivating agricultural ecosystems for diverse benefits. Ecol. Econ. 64 (2), 245–252. https://doi.org/10.1016/j.ecolecon.2007.09.020. Tong, S.T.Y., Chen, W., 2002. Modeling the relationship between land use and surface water quality. J. Environ. Manag. 66 (4), 377–393. https://doi.org/10.1006/jema. 2002.0593. Torralba, M., Fagerholm, N., Burgess, P.J., Moreno, G., Plieninger, T., 2016. Do European agroforestry systems enhance biodiversity and ecosystem services? A meta-analysis. Agric. Ecosyst. Environ. 230, 150–161. https://doi.org/10.1016/j.agee.2016.06.002. Townsend, P.V., Harper, R.J., Brennan, P.D., Dean, C., Wu, S., Smettem, K.R.J., Cook, S.E., 2012. Multiple environmental services as an opportunity for watershed restoration. For. Policy Econ. 17, 45–58. https://doi.org/10.1016/j.forpol.2011.06. 008. Udawatta, R.P., Garrett, H.E., Kallenbach, R.L., 2010. Agroforestry and grass buffer effects on water quality in grazed pastures. Agrofor. Syst. 79 (1), 81–87. https://doi. org/10.1007/s10457-010-9288-9. Valdivia, C., Barbieri, C., Gold, M.A., 2012. Between forestry and farming: policy and environmental implications of the barriers to agroforestry adoption. Can. J. Agric. Econ. 60, 155–175. https://doi.org/10.1111/j.1744-7976.2012.01248.x. Vanclay, F., 2011. Social principles for agricultural extension in facilitating the adoption of new practices. In: Pannell, D., Vanclay, F. (Eds.), Changing Land Management: Adoption of New Practices by Rural Landholders. CSIRO Publishing, Collingwood, Australia, pp. 51–68.

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Land Use Policy 87 (2019) 104070

L. Beardmore, et al. van Delden, H., van Vliet, J., Rutledge, D.T., Kirkby, M.J., 2011. Comparison of scale and scaling issues in integrated land-use models for policy support. Agric. Ecosyst. Environ. 142 (1), 18–28. https://doi.org/10.1016/j.agee.2011.03.005. Vorosmarty, C.J., McIntyre, P.B., Gessner, M.O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S.E., Sullivan, C.A., Liermann, C.R., Davies, P.M., 2010. Global threats to human water security and river biodiversity. Nature 467 (7315), 555–561. https://doi.org/10.1038/nature09440. West, P.M., 2014. Growing Plantation Forests, 2nd ed. Springer International Publishing Switzerland, Cham, Switzerland.

Wilson, M.H., Lovell, S.T., 2016. Agroforestry – the next step in sustainable and resilient agriculture. Sustainability 8 (6), 574–589. https://doi.org/10.3390/su8060574. Wunder, S., 2007. The efficiency of payments for environmental services in tropical conservation. Conserv. Biol. 21 (1), 48–58. https://doi.org/10.1111/j.1523-1739. 2006.00559.x. Zhang, L., Dawes, W.R., Walker, G.R., 2001. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37 (3), 701–708. Retrieved from. http://s3.amazonaws.com/academia.edu.documents/.

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