Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution

Environmental Modelling & Software 69 (2015) 141e154

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Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft

Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution Jeffery D. Connor a, *, Brett A. Bryan a, Martin Nolan a, Florian Stock b, Lei Gao a, Simon Dunstall b, Paul Graham c, Andreas Ernst b, David Newth d, Mike Grundy e, Steve Hatfield-Dodds f a

CSIRO Land and Water, Waite Campus, SA 5064, Australia CSIRO Digital Productivity, Clayton, VIC 3186, Australia CSIRO Energy, Newcastle, NSW 2300, Australia d CSIRO Oceans and Atmosphere, Black Mountain, ACT 2601, Australia e CSIRO Agriculture, Dutton Park, QLD, Australia f CSIRO Land and Water, Black Mountain, ACT, Australia b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 December 2014 Received in revised form 22 March 2015 Accepted 23 March 2015 Available online

In a globalised world, land use change outlooks are influenced by both locally heterogeneous land attributes and world markets. We demonstrate the importance of high resolution land heterogeneity representation in understanding local impacts of future global scenarios with carbon markets and land competition influencing food prices. A methodologically unique Australian continental model is presented with bottom-up parcel scale granularity in land use change, food, carbon, water, and biodiversity ecosystem service supply determination, and partial equilibrium food price impacts of land competition. We show that food price feedbacks produce modest aggregate national land use and ecosystem service supply changes. However, high resolution results show amplified land use change and ecosystem service impact in some places and muted impacts in other areas relative to national averages. We conclude that fine granularity modelling of geographic diversity produces local land use change and ecosystem service impact insights not discernible with other approaches. Crown Copyright © 2015 Published by Elsevier Ltd. All rights reserved.

Keywords: Land use change Economics Scenarios Partial equilibrium Model Australia Ecosystem services

1. Introduction With growing economic globalisation, local wellbeing in agricultural regions is increasingly influenced by national, continental and global markets and policies (Meyfroidt et al., 2013). Globalisation can provide great advantages such as better access to food and lower food prices. However, it can also create adverse local economic and environmental impacts. For example, biofuel or carbon sequestration subsidy policy in the USA or Europe can reduce land available for agriculture and this can create incentive to deforest and place land under agriculture in South America and Asia with adverse local water quality, erosion, and global climate regulation impacts (Meyfroidt et al., 2010). Reducing local negatives with minimal loss of macro-scale benefits requires understanding

* Corresponding author. Tel.: þ61 8 8303 8784. E-mail address: [email protected] (J.D. Connor). http://dx.doi.org/10.1016/j.envsoft.2015.03.015 1364-8152/Crown Copyright © 2015 Published by Elsevier Ltd. All rights reserved.

of local environmental, land use and regional development impacts and policies to intervene in national and global context (Renwick et al., 2013). Price feedbacks are a key mechanism through which global land and market policies influence local land use. For example, significant competing demand for agricultural land arising through bioenergy policy has resulted in land competition, reducing food supply and driving up food prices (Wright, 2012). Consequently, there is a growing need for detailed understanding of the multiple global to local scale processes influencing land use change and related ecosystem services and it is essential that such modelling accounts for price feedbacks (Lambin and Meyfroidt, 2011; Rounsevell et al., 2012). One response has been a proliferation of regional systems models at a spatial resolution commensurate with the heterogeneous factors influencing land use change (LUC) and ecosystem service supply (ES). For example, these models have been used to assess the impacts of prices and policy drivers on land use change and ecosystem services including food, fiber, carbon sequestration,

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soil health, biodiversity, water resources, and bioenergy (Polasky et al., 2011; Wu et al., 2004; Polglase et al., 2013; Goldstein et al., 2012; Nelson et al., 2009; Bryan et al., 2010, Bryan et al., 2008; Antle et al., 2003; Paterson and Bryan, 2012; Harper et al., 2007). These models typically identify potential land use and ecosystem services changes through bottom-up processes. A range of spatial data and models are typically combined with economic information and behavioural models to quantify land use change and the production of ecosystem services. Global drivers such as climate and market prices are usually considered as exogenous with the justification that change in supply of, say, agricultural commodities, from an individual region is unlikely to affect global market prices (Polasky et al., 2011). In contrast, consideration of supply and demand dynamics and price interactions is de rigeur in national to global scale partial and general equilibrium models, and integrated assessment models. Partial equilibrium modelling represents market supply, demand, and price interactions with a focus on one or a few specific sectors (e.g. agriculture, forestry, energy). An advantage is the opportunity for detailed production technology representation. A national scale example is the US national forest and agriculture sector optimisation model (FASOM) (Alig et al., 2010; Mccarl and Schneider, 2001), and global scale examples include GLOBIOM (Havlik et al., 2011) and NEXUS (Souty et al., 2012). General equilibrium modelling is the other common method for considering market price feedbacks. All economic sectors are typically included in general equilibrium modelling but more coarsely and with aggregated production functions. An example is Gurgel et al. (2011), who used general equilibrium methods to model price feedbacks through land use competition in global carbon cap and trade scenarios allowing land based carbon sequestration. Integrated assessment models extend general equilibrium models by combining global climate models with global economic models including supply, demand, and price interactions for an array of resources which can include food, energy, water, and carbon. Golub et al. (2013) provides a good example of integrated assessment of global greenhouse gas emissions, energy, forest and agricultural land use markets, and carbon accounts. Global to national scale land use change models have evolved recently. One trend has been away from very large regions (e.g. 56 for the entire USA in FASOM) toward higher spatial resolution grid representations of land use such as the 0.5  0.5 grid cells used in GLOBIOM and 16 km  16 km cells in continental Europe LUC modelling by Metzger et al. (2006). Global LUC models such as GLOBIOM, GTAP Agro-Ecological Zone (AEZ) (Golub et al., 2009), and NEXUS (Souty et al., 2012) have also evolved to represent land use as shares within spatial units for categories such as crop, pasture, and forestry, and land use intensity levels have been introduced, differentiated by factors such as predominant altitude, slope, and soil properties (Havlik et al., 2011). The most sophisticated models account for market impacts as well as local land heterogeneity with a two-step, top-down process. First, an aggregate quantity of land use change is estimated with non-spatial or coarse resolution partial or general equilibrium models of agricultural and/or forest commodity production levels. Then, land use, consistent with aggregate production determined in the first step, is allocated at higher spatial resolution using a range of techniques such as statistical models based on past land use change and heuristic algorithms to allocate land consistently considering land use regulations and land suitability. Sohl et al. (2012) applied this approach at regional scale to the Great Plains, USA with land use changes at the local level inferred from bottom-up detailed mapping of current land uses, change rates inferred from top-down national to global scenarios, and expert opinion inputs. Britz and Hertel (2011) used global market modelling and detailed land

allocation downscaling within the EU. Asselen and Verburg (2013) implemented a similar approach in the CLUMONDO model with global market representation and detailed small grid cell land allocation downscaling. In the downscaling process, both studies combined spatial regression and prioritisation rules to allocate land consistently with land policy and opportunity cost of change (Verburg and Overmars, 2009; Rounsevell et al., 2006; Verburg et al., 2006). The goals of this article are to: describe a fine resolution integrated land use modelling approach for Australia; investigate how food price feedbacks from land use competition may impact land use change in aggregate and locally at high resolution; and, to assess the significance of food price feedback relative to other significant land use drivers including agricultural productivity, carbon price changes over time and inertia in landholder decisions to convert from agricultural to alternative land uses. The next section describes the Australian continental Land Use Trade-offs (LUTO) model (Bryan et al., 2014b) which is methodologically unique in modelling high resolution land use and ecosystem service processes interacting with macro level impacts of land use competition influencing agricultural commodity prices. Next, LUTO is applied to evaluation of aggregate national and small region local land use change and ecosystem service supply responses to a set of global future outlooks. This is followed by testing of aggregated national and small region local land use change and ecosystem service supply sensitivity to agricultural commodity price feedback effects of competition for land in novel non-agricultural uses such as carbon sequestration. We also compare the magnitude of price feedback effects to effects of other key influential uncertainties in LUTO including global change, agricultural productivity and investment hurdles to land use change adoption behaviour. Discussion and conclusion sections focus on how simultaneous price feedback and high resolution land heterogeneity accounting provides very different small region estimates of land use change and ecosystem service supply than coarser heterogeneity modelling would. 2. Australian land use change and ecosystem services supply model The Australian Land Use Trade-offs model (LUTO), shown conceptually in Fig. 1, estimates land use change for the 85.3 Mha intensive agricultural area of Australia (Fig. 2) and supply of five land based ecosystem services: food, carbon, water, energy, and biodiversity. Global integrated assessment modelling of three global outlooksdinternally consistent futures for the global climate, population, economy, and greenhouse gas emissionsdgenerated trajectories for climatic conditions, carbon and, energy prices, and food demand as input to LUTO. The starting point is current national mapping of agricultural land use (24 irrigated and rain-fed agricultural commodities) and information on current returns to production, mapped at 0.01 grid cell (~1.1 km) resolution (Australian Bureau of Statistics, 2006, Marinoni et al., 2012). The model is iterated over annual time steps for 38 years from 2013 to 2050. With each annual time step, several parameters are updated including global prices for carbon, energy, global demand for crops and livestock determined by integrated assessment, and domestic agricultural productivity. An optimisation model allocates land in each cell to one of five categories of alternative land use: 1. Agriculture (24 types, rain-fed and irrigated); 2. Carbon plantings (fast-growing Eucalyptus monocultures); 3. Environmental plantings (local native tree and shrub species);

J.D. Connor et al. / Environmental Modelling & Software 69 (2015) 141e154

Annual updates - global prices, - Australian agricultural and f orest yields, - production costs, - f ood demand Annual Economic Returns (AER) recalculated by - year, - land use, - location

Year counter (t =2013,..,2050)

143

Land use. High resolution models of price, cost, and production, f or land uses: - agriculture, - biof uels, - bioenergy, - carbon plantings - environmental plantings

Global outlooks. Long term trajectories of - energy price, - f ood (crop/livestock demand), - carbon price, - climate

Spatio-temporal outputs Australian Land Use Trade-offs Model (LUTO) Converts agricultural land use cells to new land uses. Equilibrates f ood supply, demand through endogenous partial equilibrium price adjustment Biodiversity Sub-model Converts cells to environmental plantings in order of cost-ef fectiveness up to biodiversity payment budget limit.

Area

Value

Quantity

Food Carbon Water

Energy Biodiversity

Fig. 1. Australian Land Use Trade-offs Model (LUTO) overview.

4. Biofuels (wheat grain and residue for transport fuel); 5. Bioenergy (short rotation Eucalypt for biomass-based electricity). To ensure an equivalent economic basis for comparison, returns to all land uses were calculated in net present value terms over

100 years at a commercial discount rate of 10% per annum.1 The long time horizon was chosen to represent permanency requirements for carbon sequestration and biodiversity payment program policies. 2.1. General equilibrium carbon and energy prices, partial equilibrium food price impacts of land competition The internally consistent global outlooks that create input to LUTO are modelled with an Australian evolution of the Global Trade and Environment Model (GTEM) (Pant and Fisher, 2007) developed for Australian climate policy assessment (Garnaut, 2011). Emphasis is on climate, energy sector modelling, and greenhouse gas emissions and carbon balance with carbon price determined by emissions growing with population and income-related energy demand, carbon policy and technology (Newth et al., 2015). Agricultural production is represented for three aggregate commodities: crops, livestock, and processed food in 13 national to continental scale regions with nested constant elasticity of substitution production functions involving imperfect substitution of capital, labour, and a fixed supply of land. The model does not represent land availability changes for agri-food production resulting from land competition influencing food price. Given this omission, we applied a second best, partial equilibrium strategy of

Fig. 2. Study area and broad agricultural land use.

1 The relatively high discount rate represents the average cost of credit that Australian farmers faced over the past 30 years and could face again over the long and uncertain time horizon modelled, noting that average rates of interest on farm debt were as high as 15.8% in 1990 and as low as 6.6% in 2013 (ABARES, 2014).

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(Polglase et al., 2008). Biofuel and bioenergy production from feedstock, greenhouse gas benefits, and economic returns to feedstock production, were calculated using the Energy Sector Model for Australia (Graham et al., 2011). Water resource impacts of land use change were estimated at 0.5 degree resolution with a landscape hydrology model called the Australian Water Resources AssessmentdLandscape Model (Van Dijk and Renzullo, 2011) which quantifies expected changes in water run-off (ML ha1 yr1) resulting from the conversion of short-rooted annual crops and pastures to deep-rooted tree-based land uses.

2.3. Land use allocation engine

Fig. 3. Partial equilibrium price dynamics for a hypothetical agricultural commodity.

modelling land use competition impacts on agricultural commodity prices. As shown conceptually in Fig. 3, absent competition for land in other uses, Australia would face the exogenously determined price for food arising from global demand and supply. Without price feedbacks, Australia can be considered a food pricetaker, represented by the flat demand curve Dx in Fig. 3. Given the exogenous world food price Px and the Australian supply curve without land use competition (S0), quantity Q0 is produced. In essence, this is an Australian approximation for how global competition for land in alternate uses impacts agricultural commodity prices.2 Competition for land shifts the food supply curve upward to S1. Without accounting for price feedback effects, quantity Q1x is the predicted supply at price Px. To capture food price feedbacks, a food demand curve Dn is introduced with an elasticity of demand taken from the literature. With land use competition, a decrease in supply due to conversion of agriculture to other land uses creates a price feedback effect driving the price up to Pn. Including price feedback effects results in higher equilibrium price Pn and quantity of food supplied Q1n. This approximate formulation enables us to test the local and national scale impacts of including price feedback effects in bottom-up models of land use change and ecosystem services. 2.2. Ecosystem service modelling overview We based spatial estimates of annual carbon sequestration over time on published 3-PG2 forest stand growth and carbon sequestration modelling that varies across Australia with climate and soil conditions (Polglase et al., 2008). A spatial distribution of biodiversity priority scores was calculated using generalized dissimilarity modelling to value environmental planting for each cell given the amount of nearby intact native vegetation and value of ecological restoration for enhancing plant community representation under climate change (Bryan et al., 2014b). Wheat production (grain and residue) was modelled using the Agricultural Production Systems Simulator, APSIM (Keating et al., 2003; Zhao et al., 2015; Bryan et al., 2014a). Biomass production from woody perennials for processing into renewable electricity was modelled using 3-PG2

The primary objective of the LUTO land use allocation engine is to maximize the returns from land uses including: (a) agricultural commodity production (Equation (1)); (b) carbon plantings, biofuel, and bioenergy feedstock production (Equation (2)). The secondary objective is to maximize biodiversity services (Equation (3)) given an annual budget constraint for environmental planting payments (Equation (7)). In each cell, the choice is whether to continue with current agricultural land use, or to change to a new land use. Maximize:

X j¼AG

Crj xrj

(1)

r2R;j¼AG

X

þ

X

Psj ysj 

1=gdrj xrj

(2)

r2R;j2J 

X 

þ

 drj þ ∅r þ Vr xrj

(3)

r2R;j¼EP

subject to:

X

ysj 

X

ysj  Dsj X

xrj Qrj

c j ¼ AG

(4)

r2R

s2S

c s2S; j ¼ AG

xrj ¼ 1

(5) (6)

j2J

X

∅rf xrj  B

(7)

r2R;j¼EP

Indices: r ¼ grid cells j2J ¼ land use including AG ¼ 23 agricultural commodities, CP ¼ carbon plantings, EP ¼ environmental plantings, BF ¼ biofuels, BE ¼ bioenergy}. J ¼ {AG, CP, EP, BF, BE}, J* ¼ {CP, EP, BF, BE} s2S ¼ segments in discretized food demand functions Variables: xrj ¼ proportion of grid cell r in land use j ysj ¼ quantity of commodity j produced in segment s2S Parameters:

2

The accuracy of the approximation depends on how similar proportionally Australian and global land use changes are in response to changing relative prices of food and carbon.

Psj ¼ price of commodity j in segment s2S Qrj ¼ quantity produced per grid cell r by agricultural land uses

J.D. Connor et al. / Environmental Modelling & Software 69 (2015) 141e154

Crj ¼ cost of production of agricultural land uses per grid cell r Dsj ¼ maximum quantity of agricultural commodity j produced in segment s2S drj ¼ net returns per grid cell r by non-agricultural land use j g ¼ the hurdle rate multiplier amount by which novel land use return must exceed current land use return to trigger land use change (explained in more detail in the sensitivity analysis methods description below) Vr ¼ biodiversity priority score for each grid cell r (0  Vr  100) ∅r ¼ the payment (opportunity cost) for conversion to environmental plantings B ¼ total annual biodiversity budget Starting with intensive agricultural land use at the year 2013, the model iterates annually to 2050. In each year, returns to land use are calculated according to food demand, prices for carbon and energy, determined exogenously with global integrated assessment modelling, and change in agricultural productivity. The above linear programming model is solved each year and the impacts of land use change calculated. The model is premised on several assumptions. One is that while prices and thus, returns to relevant production options evolve over time in the model, the decision at each annual time step is based on the prices at the time the decision is made. We also assumed land use change from agriculture to a new land use to be irreversible for tree-based land uses. While this simplification could lead to erroneous results in scenarios that might favour reversion to agriculture after conversion to tree-based land use, this is unlikely in the outlooks we modelled given high costs of reversing investment in long lived forests and long-term commitments likely to feature in carbon market policies, and given the growing carbon price over time. Another key assumption relates to how observed conversion to forest from agriculture requires comparatively high net present value of returns (Richards and Stokes, 2004). We represent this with an adoption hurdle rate where new land uses had to be twice as profitable as agriculture for land use change to occur. The detail of how this is implemented and tested with sensitivity analysis is described below. While the approach provides a coarse aggregate calibration of model response to observation, it remains a simple rational choice approach based on expected utility. Much of the nuance of the complex and context-varying diversity of factors that actually drive temporally evolving land use change decisions is generalised. One of the great challenges in land use change modelling is capturing more realistic and sophisticated adaptation behavioural representations in models such as representation of asymmetric risk valuation, bounded rationality, heuristic-based decisionmaking and resulting bias, pro-environmental and altruistic behavioural motives, and the evolution of decision-making rules in response to evolving environmental and social conditions (Meyfroidt et al., 2013). 2.4. Agricultural sub-model Agricultural returns over time are determined endogenously using a classic agricultural sector partial equilibrium objective of maximising the sum of consumer and producer surplus. The first summation term in Equation (1) represents consumer willingness to pay for agricultural commodities. This is derived beginning with downward sloping aggregated national level demand as a function of price relationships for the 24 modelled agricultural commodities. The demand functions have the exponential functional form shown for the inverse price quantity relationship in equation (8). The term e is the price elasticity of demand and values for this parameter were based on published price

145

elasticities of demand for different food products (Andreyeva et al., 2010). e1

P ¼ ayt

(8)

To overcome the computational challenge of a non-linear objective, the demand curve (Equation (8)) was converted to a stepwise linear function in segments with S representing the set of segments and ysj indicating level of commodity j demanded in segment s2S at price Psj. A constraint (Equation (4)) was used to ensure that supply is greater than or equal to demand for each agricultural commodity. Another constraint (Equation (5)) ensured that the amount of each commodity produced at each price was not greater than the maximum possible segment quantity (Dsj). The combined effect of these terms is that agricultural commodity production is chosen in specific grid cells which maximize the sum of producer and consumer surplus, given production costs and consumer demand responsiveness to prices. Because we defined one price for all levels of demand that are possible within each supply segment, price is not exact for the solution quantity demanded. Using an iterative approach, we ensured convergence between our approximate price and the exact price based on the demand curve for the equilibrium quantity. If divergence was too great, segments were rescaled to smaller intervals in the neighbourhood of the solution, and equilibrium prices and quantities were recalculated. This process was repeated until convergence was achieved. 2.5. Carbon plantings, environmental plantings, biofuel and bioenergy sub-models The carbon and environmental plantings sub-models enable analysis of incentives to offset greenhouse gas emissions through carbon sequestration in biomass from reforestation of cleared agricultural land. The biofuel and bioenergy sub-models assess the establishment of a biomass-based biofuel and renewable electricity industry where farmers can sell feedstock into the energy markets, instead of agricultural commodity markets. These new land uses may out-compete agricultural production under certain environmental and economic conditions. Within the LUTO engine, at each time period t, net returns to land use for carbon plantings, environmental plantings, biofuels, and bioenergy (drj ) are calculated based on spatial process models of yields and costs and exogenously determined carbon and energy prices. Competition between new land uses and agriculture for the fixed land supply is represented in Equations (1)e(3). The optimisation model allocates new land uses to grid cells where they outcompete agriculture. Simultaneously, through elasticity of demand, reduced supply of agricultural commodities increases agricultural commodity prices. The biodiversity payment scheme for environmental plantings (Equation (3)) includes the net economic returns to carbon, drj for j ¼ environmental plantings in each grid cell, ∅rf the payment required for return to environmental planting to equal net returns of the most profitable land use, and the biodiversity priority score Vr (see Bryan et al., 2014b for additional details). The objective function selects grid cells for environmental plantings to maximise biodiversity services under a biodiversity budget constraint set at $125 million per annum in this application (Equation (7)). 2.6. Global outlook description The LUTO model is illustrated with three global outlooks from the Australian National Outlook (ANO)dan integrated assessment

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J.D. Connor et al. / Environmental Modelling & Software 69 (2015) 141e154

Table 1 Dimensions of the three global outlooks assessed in this study. Indicator

Units

Population in 2050 World GDP per capita in 2050 World GDP in 2050 Benchmark Representative Concentration Pathway (RCP) Radiative forcing in 2100

Billion people US$ '000 2010 cap1 US$ trillion

Atmospheric concentration in 2100 Emissions per capita in 2050 Coverage of abatement policy

CO2 ppm tCO2 cap1

Global abatement effort

W m2

Value in 2010

Scenario L1

M3

H3

6.9 8.8 61 e

8.1 20 161.6 RCP3-PD

10.6 18.6 197 RCP4.5

10.6 18.6 197.8 RCP8.5

e

4.5

8.5

e 7 e

Peak at 3.0 then decline to 2.6 445 (declining) 3.1 All sources

1360 (rising) 8.7 No sources

e

Very strong

650 4.3 All, excluding emissions from livestock Strong

No action

of plausible futures for Australia's environment, economy, and society (Hatfield-Dodds et al., 2015) (Table 1). ANO global integrated assessment modelling outputs for oil and carbon price, and crop and livestock demand, are shown in Fig. 4. 2.7. Endogenous price modelling

Fig. 4. GIAM-modelled crop, livestock, oil, and carbon price paths under the three global outlooks.

To explore the sensitivity of land use change and ecosystem service supply to price feedbacks and how the effect may differ at national and local scale, we implemented the LUTO model with exogenously determined vs. endogenously determined agricultural commodity prices. In exogenous price modelling we assumed that the global price was not influenced by Australian supply of agricultural commodities. Australian agricultural commodity demands were treated as perfectly price elastic so that LUTO food prices followed GIAM food demand trajectories. In the endogenous price model formulation, the increasing food price feedback resulting from competing uses for scarce land were treated with the partial equilibrium model formulation described above. Here, competition for land decreases agricultural commodity supply and drives up prices via the elasticity of demand curve Equation (8), thereby attenuating further change. Our prior hypothesis is that inclusion of price feedback effects will produce very different land use change and ecosystem service impacts for small regions than for Australia in aggregate with impact much smaller than national average impacts in some regions and much larger in others. To test this hypothesis we evaluated the area of agriculture, carbon plantings, and environmental plantings estimated with the two price formulations for four diverse regions and for Australia in aggregate.

Fig. 5. Area of agriculture under each global outlook with exogenous versus endogenous food price determination.

J.D. Connor et al. / Environmental Modelling & Software 69 (2015) 141e154

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2.8. Sensitivity analysis Ideally we would like to have validated our model so that patterns of change that it predicts would be consistent with past land use change responses to changing conditions. However, validation is fundamentally problematic when considering long horizon futures with conditions well outside of past experience because there is no observable precedent to validate to. Still, sensitivity analysis is informative. Following best practice in complex model evaluation (Bennett et al., 2013; Robson, 2014), we assessed the sensitivity of results to key influential uncertainties identified with a global sensitivity analysis (GSA). As described in the supporting information, the influence of 50 key input parameters on 24 key model outputs were assessed with the extended Fourier amplitude sensitivity test (eFAST) for the three global outlooks considered in this analysis. The results show variation in the most influential parameters across outputs of interest in global outlook scenarios. Nonetheless, key determinants of land use change and ecosystem service outcomes consistently arise including carbon price, agricultural commodity prices, agricultural productivity, and the adoption hurdle rate parameter. The strong effect of these model parameters is consistent with the findings of other land use change model sensitivity analyses (Ewert et al., 2005; Newell and Stavins, 2000). Full global sensitivity analysis results are provided in the supporting information Tables A3eA5. We also provide a local sensitivity analysis to specifically compare the food price feedback influence to the influence of other key factors driving land use change and ecosystem services. Sensitivity to food price feedback is assessed by running the LUTO model with endogenous and exogenous food price formulations as explained above (Section 2.7). Sensitivity to global context and in particular, price was tested by comparing outcomes across our baseline M3 (moderate carbon price) global outlook versus high carbon price, L1 and no carbon price, H3 global outlooks. We tested sensitivity to agricultural productivity with a base case assumption moderate historical trend growth rate of 1.5% p.a. and lower (0%) and upper (3% p.a.) bound estimates.3 We also tested sensitivity to adoption behaviour assumptions influencing the rate of uptake of land use change modelled with three hurdle rate multipliers (Equation (2)). A hurdle rate multiplier of one is equivalent to the commonly implemented profit function approach assuming that land owner's are willing to change land use if returns to the new land use exceed returns to current agricultural use. However, empirical evidence suggests that higher hurdles are more realistic. Our base case assumption, a multiplier of two, approximates the finding of only about half the carbon supply at any given carbon price from regression estimates in comparison to profit function estimates for the United States (Richards and Stokes, 2004). Our upper bound hurdle rate multiplier of five is consistent with the upper bound of implied discount rates in findings from the southeast USA where land holders actually required annual rates of return ranging from 13% (Murray-Rust et al., 2013) to 18% (Prestemon and Wear, 2000) to switch to long rotation (25 year) forest land use, and central American findings of implicit discount rates of 15e25% required for land use change (Naidoo and Adamowicz, 2006).

3

Many studies specify productivity endogenously as increasing production intensity in response to rising prices. We prefer the flexibility of our exogenous approach as it allows representation of more diverse future circumstances such as the possibility of severe climate change limiting possibilities to increase yields in Australia despite price signals that would encourage this.

Fig. 6. Price paths for selected agricultural commodities with exogenous versus endogenous price determination under the L1 (top) and M3 (bottom) global outlooks. Units of production are tonnes for crops and head for livestock.

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commodities, followed by broadacre crops. Further, because these lower value commodities, especially livestock, occur in locations with significant carbon sequestration potential, land competition is strong in these areas. The identical endogenous and exogenous price trajectories for intensive citrus, grape, stone fruit, rice, sugar and cotton crops in scenarios M3 and L1 result because these land uses are so profitable that new land uses cannot compete economically even at the highest 2050 carbon prices considered in our outlooks.

3. Results 3.1. Aggregate land use impact of land use competition food price effects The area of land under agricultural production is greater with endogenous food price determination, especially under the global outlooks with stronger emissions abatement action (Fig. 5). While some endogenous price feedback impacts are evident in the M3 outlook above a threshold carbon price of about 50 $ tCO1 2 , on aggregate this effect is moderate until later years of the L1 global scenario where carbon prices begin to exceed 100 $ tCO1 2 . Under L1, price endogeneity culminates in 21% greater land area in agricultural production by 2050. No effect of price endogeniety is evident in the H3 outlook involving no carbon price.

3.3. Aggregate impact on ecosystem services Estimated aggregate land use changes are shown in Fig. 7 (“Australia average” column), and aggregated ecosystem service estimates are shown in Fig. 8 (“Australia average” column). Greater agricultural production and less land use change out of agriculture were estimated with the partial equilibrium formulation due to commodity price feedbacks for moderate (M3) and high (L1) carbon price global outlooks. Water interception was less under endogenous commodity prices with less land conversion to treebased uses, leading to less interception and evapotranspiration. Endogenous pricing also led to less provision of biodiversity benefit and carbon sequestration due to reduced environmental and carbon plantings. These effects were stronger in L1 than M3 following carbon price and other economic characteristics of these global outlooks that increase the competitiveness of new land uses relative to agriculture. An interesting characteristic of these results is that the production value impact is proportionately less than the area impact because the first increments of land to change out of agriculture have very low economic production value. In contrast, supply of

3.2. Agricultural commodity price dynamics Agricultural commodity price dynamics for the two price determination models are shown in Fig. 6. Absent a carbon price creating competition for land in H3, the endogenous and exogenous price models produced identical commodity price trajectories (not shown in Fig. 6). Scenario L1, with the strongest mitigation effort and carbon price trajectory growth, resulted in significant endogenous and exogenous price path divergence for grazing-based livestock commodities (sheep and beef) as well as winter cereal, legume, and oilseed crops (Australia's most significant broadacre crops), while in M3 with intermediate carbon price growth, only livestock price trajectories diverge. This price divergence pattern arises because extensive livestock production systems have the lowest return per hectare of all agricultural

100

Percent of total area

80

60

40

20

0 Environmental plantings Darling Downs

Carbon plantings

Agriculture Endogenous - L1

Liverpool Plains

Upper Yorke and Lower North

WA Wheatbelt

Australia average

Ag

CP

EP

Ag

CP

EP

Ag

CP

EP

Ag

CP

EP

Ag

CP

EP

39.3

59.9

0.8

45.8

53.2

1.0

96.6

3.4

0.1

89.8

8.9

1.2

52.5

45.2

2.3

Exogenous - L1

30.9

68.0

1.1

18.2

80.8

1.0

94.6

5.3

0.1

64.3

34.1

1.6

41.1

56.0

2.9

Endogenous - M3

66.8

33.0

0.1

94.1

5.8

0.1

99.6

0.4

0.0

98.7

0.4

0.9

70.7

27.6

1.8

Exogenous - M3

52.7

46.6

0.7

81.1

18.7

0.2

99.5

0.5

0.0

98.2

0.9

0.9

66.8

31.3

2.0

Fig. 7. Land use in 2050 for M3 and L1 with endogenous and exogenous price determination.

a

Ratio of endogenous to exogenous

J.D. Connor et al. / Environmental Modelling & Software 69 (2015) 141e154

160

120 80 40

0 Darling Downs

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Water intercepted

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Fig. 8. Ecosystem service supply in 2050 for L1 (a) and M3 (b) endogenous price estimate as percentage of exogenous price estimates for agricultural value, carbon sequestration, biodiversity and water interception.

carbon sequestration, biodiversity, and water services varied more proportionally to the change in land area. 3.4. Sensitivity analysis: is food price feedback important in aggregate? The results show that price feedback effects (labelled endogenous price in Fig. 9) only have a modest influence on land use change and ecosystem services at the aggregate national level. Variation across global outlooks (labelled H3 and L1) had the greatest influence on water, carbon, and agricultural area, followed by hurdle rate assumptions (labelled as high and low investment hurdle), agricultural productivity growth (labelled as high and low productivity growth), and finally, price feedback impacts. For biodiversity benefit, investment hurdle assumptions have even greater influence than global outlook, followed by price feedback effects, and finally agricultural productivity growth assumptions. Growth in agricultural productivity is by far the most influential determinant of agricultural production value outcomes with progressively less influence on outcome from

global outlook, investment hurdle rate and price feedback assumptions. On balance, for Australia in aggregate, these results suggest that excluding agricultural price feedbacks is likely to produce relatively minor differences in land use change and ecosystem service supply estimates compared to what can be expected from varying other assumptions. In what follows, we test whether these relatively minor distortions in land use change and ecosystem service estimates hold in high resolution localised assessments. 3.5. Local impacts of endogenous price determination Casual inspection of the national land use change maps (Fig. 10) shows that land use in specific regions differs between exogenous and endogenous model formulations. The local small region land use changes estimated with endogenous and exogenous food price modelling are shown in Figs. 11 and 12. Fig. 7 summarises the area of agriculture, carbon plantings, and environmental plantings estimated with the two price formulations and Fig. 8 presents

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Fig. 9. Ecosystem service and land use sensitivity to varying agricultural price determination treatment (labelled “endogenous price” in the figure) and other parameters considered in the sensitivity analysis. For comparability all values are expressed as percentage changes from “baseline” results generated with the M3 (moderate carbon price) global context scenario, moderate agricultural productivity growth, endogenous agricultural price determination, and a 2 tree based land use conversion hurdle multiplier.

Fig. 10. Differences in 2050 land use for exogenous versus endogenous price determination for L1 (top) and M3 (bottom)..

endogenous food price modelling ecosystem service estimates as percentages of exogenous price estimates for each of the regions and for Australia in aggregate. A number of systematic differences are evident across outlooks and regions. For example, in the M3 outlook in the WA Wheatbelt and Upper Yorke and Lower North regions impacts of price feedbacks are hardly discernible with estimated increases in agricultural area of less than 1% for each region which is only a fraction of the national average impact (Fig. 7) and estimated agricultural value increase are only a fraction of the already small national average effect (Fig. 8). In contrast for both the Liverpool Plains and Darling Downs estimated price feedback impacts on agricultural area are about four times greater than the national average effect (Fig. 7). Further, accounting for price feedback increases agricultural value for the study area as whole by just 1%, but by 4% and 7% for these regions. In the M3 outlook, in the WA Wheatbelt and Upper Yorke and Lower North areas, the land area impact of price feedback was dampened relative to national average and amplified in the Liverpool Plains and Darling Downs. The same spatial pattern of dampened and amplified influence is also evident in environmental planting area impacts (Fig. 7), and in biodiversity benefit (Fig. 8) for M3, while systematic variation across regions in price feedback impacts on carbon and water benefits in outlook M3 are less evident (Fig. 8). Variations across regions in the L1 outlook are more complicated. The muted land conversion relative to the national average rate in the Upper Yorke and Lower North region persists in scenario L1. Despite very high carbon prices in the scenario, comparatively high wheat yields and poor carbon sequestration potential leads to limited land use change (Fig. 7). The M3 effects continue in the Liverpool Plains where differences in land area conversion are much greater in exogenous versus endogenous price determination models. These amplified land use changes are also reflected in greater than average relative changes in agricultural value as a

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Fig. 11. Land use in 2050 for L1 with endogenous (left) and exogenous (right) price determination.

result of endogenous price modelling in the region (Fig. 8). While land use change (Fig. 7) and carbon impacts (Fig. 8) attributable to price feedback were estimated to be small compared to national impacts for the WA Wheatbelt in M3, they are estimated to be relatively large in L1. 4. Discussion We have addressed the growing need to understand how local land use change and ecosystem service outcomes are influenced by global market forces. The novelty of the analysis is the partial equilibrium modelling with bottom up land use change economics and ecosystem service supply. Previous land use models that account for market price feedbacks and fine resolution heterogeneity (Van Delden et al., 2010, Verburg et al., 2008; Britz et al., 2011; Asselen and Verburg, 2013) involve two steps. First, regionally differentiated market equilibrium models determine demand and price feedbacks. Then, high resolution spatially

differentiated local land use change patterns within regions are mapped with a down scaling approach. Our approach provides substantially more detail than previous models in accounting for within-region, parcel-scale spatial differences in land attributes influencing economic returns and ecosystem service productivity diversity. On aggregate, at the national scale, not much seems to be gained from the high resolution bottom-up modelling of spatially heterogeneous local environmental and geographic land attributes interacting with food price feedbacks from land competition. For Australia as a whole, agricultural price feedback impacts on land use change and ecosystem service supply were small in comparison to the impacts of uncertainties such as global outlooks which capture variation in carbon price, energy price, and food demand. Impacts of price feedback were also small in comparison to impacts of agricultural productivity and land use change behaviour assumptions. At first glance, this result seems to suggest that it would be reasonable to ignore price feedbacks due to their relatively small

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Fig. 12. Land use in 2050 for M3 with endogenous (left) and exogenous (right) price determination.

aggregate impacts, or to downscale them uniformly in local assessment of land use change or ecosystem service supply as has been common in previous studies (Asselen and Verburg, 2013; Britz and Hertel, 2011; Sohl et al., 2012). However, locally-unique, highly spatially variable, and scenario-dependent impacts of agricultural commodity price feedback from land competition were observed at high resolution that would not be discernible with coarse spatial resolution approaches typical of sectoral models. Near negligible effects were observed in some areas and outlooks and very concentrated impacts were observed in other areas depending on site-specific within-region, spatially-varying distributions of environmental and economic characteristics. An example is the Liverpool Plains in New South Wales where returns to the currently predominant agricultural land use (beef production) are quite marginal yet there are parts of the region with potential for high carbon sequestration from reforestation. The endogenous price of beef rising in response to land competition

combined with a rising carbon price resulted in very much greater proportional land use change in this region than is estimated in aggregate nationally. Ignoring agricultural commodity feedbacks for this region would lead to a serious overestimation of concentrated areas of land use conversion from beef grazing to carbon sequestration. The Upper Yorke and Lower North region of South Australia provides a contrasting example. For our moderate carbon price global outlook much less than national aggregate land use change sensitivity to food price feedbacks were estimated. The systematic trend of muted impacts in some regions and amplified impacts in other areas, especially in the M3 outlook, can be explained by differing predominance of agriculture types between regions and endogenous price impacts by commodity. Specifically, we observe minor divergence in the price of wheat, the most significant product in the WA Wheatbelt and Upper Yorke and Lower North, but much more significant divergence in the price of beef, the most significant product in the Liverpool Plains and Darling Downs in the M3 scenario (Fig. 5).

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Spatial patterns of muted versus amplified impacts don't always hold for regions across global outlooks. For example, reversals across scenarios in the impacts of price feedback were observed for the WA Wheatbelt with land use change very muted compared to the national average for the moderate carbon price M3 global outlook, yet greater than the national average rate in the high carbon price L1 global outlook. Similarly, in the Darling Downs area, changes from price feedbacks were amplified relative to the national average in the M3 outlook and similar to national outcomes in the L1 scenario. These reversals relate to the specific heterogeneity in biophysical conditions determining relative returns and ecosystem service productivity within the region. Such regionspecific conditions create price thresholds consistent with other findings (Bryan et al., 2014b; Paterson and Bryan, 2012) above which significant change occurs and below which very little change results or the reverse. The particular findings illustrate a more general point: detailed modelling of spatial variability in land use economics and ecosystem service productivity can interact idiosyncratically with changes in macro conditions like global carbon, energy, or food price trajectories across global outlooks. There are limitations to, and opportunities for extension of our modelling framework. A more general formulation could also incorporate energy price feedback from the influence of bioenergy and biofuel production on energy supply. General equilibrium representation of global and local land and food demand supply linkages would also allow two-way feedbacks from the world to Australia and vice-versa. Another limitation in our current formulation is the lack of representation of opportunity to expand agricultural area which could lead to overestimation of land competition impacts on food prices.

5. Conclusion From a land use change modelling perspective, we have illustrated an important general point: it is possible to combine traditional market dynamics with fine resolution economic and environmental heterogeneity in modelling land use change and this is necessary to accurately understand local implications of global change for ecosystem services. For our specific case study, highly differentiated local effects suggest that not including price feedbacks could lead to very flawed local estimates in many cases. A more general implication is that globally-determined land use drivers can be idiosyncratically related to the specific local environmental and economic conditions which determine the relative productivities and returns to a range of land-related ecosystem services. While the bottom-up land allocation from the parcelscale land use decision approach used in this study is computationally intensive, it provides insights into how within-region, land-attribute distributional characteristics can influence local land use change and ecosystem service supply that would simply not be discernible with more aggregate representation of regional average conditions. A key policy implication is that small aggregate impacts at national or a larger scale can be highly concentrated in local areas. Failure to appreciate such local effects may threaten the success of popular polycentric governance approaches such as communitybased natural resource management. Objections and resistance from such disaffected local areas may hinder realisation of considerable large-scale policy benefits. Approaches outlined in this article point the way toward a nested process of evaluation to assess both aggregate large-scale impacts and to focus in on particularly impacted regions were localised assessment of mitigation and adaptation options are likely to be particularly important.

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Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.envsoft.2015.03.015.

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