Scenarios for Australian agricultural production and land use to 2050

Scenarios for Australian agricultural production and land use to 2050

Agricultural Systems 142 (2016) 70–83 Contents lists available at ScienceDirect Agricultural Systems journal homepage: ...

2MB Sizes 0 Downloads 46 Views

Agricultural Systems 142 (2016) 70–83

Contents lists available at ScienceDirect

Agricultural Systems journal homepage:

Scenarios for Australian agricultural production and land use to 2050 Michael J. Grundy a, Brett A. Bryan b,⁎, Martin Nolan b, Michael Battaglia c, Steve Hatfield-Dodds d, Jeffery D. Connor b, Brian A. Keating a a

CSIRO, EcoSciences Precinct, Dutton Park, Qld 4102, Australia CSIRO, Waite Campus, Urrbrae, SA 5064, Australia CSIRO, Sandy Bay, Tas 7001, Australia d CSIRO, Black Mountain, Canberra ACT 2601, Australia b c

a r t i c l e

i n f o

Article history: Received 27 May 2015 Received in revised form 4 November 2015 Accepted 17 November 2015 Available online 26 November 2015 Keywords: Futures Australia Intensification Cropping Food security Climate change Global change Carbon sequestration Modelling

a b s t r a c t Australian agricultural land use and production have evolved within an economic and environmental context that may change substantially in terms of productivity rates, resource scarcity and degradation, greenhouse gas abatement policy, climate change, and global demand. We used an integrated systems modelling approach to explore the response of Australian land use and agricultural production to these changes from 2013 to 2050. We found potentially large transitions in spatial and temporal patterns of land use, agricultural production, output rates, and profitability. New land uses such as carbon plantings, biofuels and bioenergy, and environmental plantings competed with food and fibre production, reducing its area. Global outlooks, including the strength of action on climate change and population assumptions, had a strong influence. Capacity constraints and adoption inertia reduced and delayed land use change. Agricultural production and land use were sensitive to productivity assumptions. Despite the competition for land from new land uses, agricultural production increased under most settings, with greatest impact from land use transitions concentrated on livestock production. Agricultural profits also increased under most settings due to higher prices and output rates. Negligible land use change was observed with carbon payments below $50 per tCO2-e, and significant change did not occur before 2030 in any but the unconstrained, high-abatement scenarios. We conclude that transformative land use change is plausible but high levels of food/fibre production can co-exist with non-food land uses motivated by market responses to global change and domestic policy. Thereby, the Australian land sector can continue its significant contribution to global food security while responding to new economic opportunities. Policy settings can influence these outcomes through reducing infrastructure constraints, strategies for enhancing adoption, and research and development in agricultural technology and productivity. Due to the long time frames required to change attitudes and land use and management practices, consideration of the possible impacts of global change on agriculture and potential policy responses is timely. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Ensuring global food security is the defining challenge for agriculture in the 21st century (Foley et al., 2011). Growing human population, rising incomes, and changing patterns of food preferences and consumption will increase global demand for agricultural products (Godfray et al., 2010; Kastner et al., 2012). Land for agricultural expansion is limited (Pardey et al., 2014) and competition from other land uses will impact on the existing land base (Bryan et al., 2013; Harvey and Pilgrim, 2011; Smith et al., 2010). Numerous challenges threaten future agricultural productivity, including climate change and ongoing natural resource degradation (Arrouays et al., 2014; Ausubel et al., 2013; FAO, 2011; Fischer et al., 2014; Sonneveld and Dent, 2009). Rising food demand and mounting production challenges have prompted a ⁎ Corresponding author. E-mail address: [email protected] (B.A. Bryan). 0308-521X/© 2015 Elsevier Ltd. All rights reserved.

reassessment of global agricultural capacity to ensure food security (Kastner et al., 2012; Nelson et al., 2010). Australia is a significant food exporter and understanding the possible impact of these multiscale drivers and risks for Australian food production is essential for informing public debate and policy for the future of agriculture, land use, and its contribution to global food security. The century-long fall in global food prices was replaced with consistent price rises from 2004 (Fischer et al., 2014). Recent food shortages resulting from sharp food-price rises (or shocks) have had global impacts (Lagi et al., 2011) adding to the already large number of foodinsecure people (FAO et al., 2013). While food insecurity has many dimensions, the relationship between food price and food insecurity is swift and direct. Ivanic et al. (2012) estimated that the food price shocks of 2008 and 2011 led to an average global food price rise of 118 and 37%, and a net increase in people living in extreme poverty (Ravallion et al., 2009) of 105 million and 44 million, respectively. A broad set of drivers have contributed to these recent price shocks including extreme

M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

climatic events, an increased tendency for countries to restrict exports and practice other price insulation mechanisms to ensure their own food security (Anderson et al., 2013), and through biofuel mandates to increase energy security (Carter et al., 2011). Food insecurity has been associated with significant social consequences including increased mortality rates, social unrest, and geopolitical upheaval (Lagi et al., 2011). The world's population will continue to grow for at least the next few decades (United Nations, 2013b) and food demand in aggregate is predicted to grow by 50–80% by 2050 (Keating et al., 2014). Recent trends seem likely to continue, not only in increased demand for food (or kilojoules) in general (FAO, 2011), but also in significant increases in the food traded per person globally (Schmitz et al., 2012), and in broadened food preferences (Alexandratos and Bruinsma, 2012; Hajkowicz et al., 2012). Arable land used for agriculture has increased slightly over recent decades, while the area of arable land per person has declined from 0.34 ha/person in 1973 to 0.23 ha/person in 2008 (extracted from FAOSTATS in 2013). Strategies for meeting future food demand include increased productivity on existing lands and some increased land for food production (Fischer et al., 2014; Springer and Duchin, 2014). However, land degradation continues (FAO, 2011), input resources are likely to become scarcer and more expensive (Odegard and van der Voet, 2014), and the impacts of climate change and its mitigation are complex (Challinor et al., 2014; Falloon and Betts, 2010; Gornall et al., 2010). At the same time, increased demands are being made of the world's landscapes for other ecosystem services (Bryan et al., 2013; Law et al., 2014; O'Farrell and Anderson, 2010). Garnaut (2011), in assessing Australia's policy options to respond to global climate change, observed that much of the response in the first few decades could arise from the land sector. Significant impacts on land use patterns could potentially arise both from climate change and its mitigation through biosequestration to reduce atmospheric concentrations of greenhouse gases and other mechanisms such as biofuels. Many other studies have also documented and projected the need for increasing diversity of services from agricultural land (Bryan and Crossman, 2013; DeFries et al., 2004; Gordon et al., 2010). Historically, Australian agricultural production and productivity have risen in response to pressure from declining terms of trade (1.6% per year on average across crops and livestock from 1961 to 2006) (Sheng et al., 2013). This has been driven largely by innovation expressed as continuous adoption of new technologies (new genotypes, changes in land management, increased resource-use efficiency), by increases in land and labour productivity, and by efficiencies achieved via increasing the scale of farm operations (ABARES, 2014). The level of production and the distribution of land use have, in the past, responded to a range of other factors including structural adjustment programs, price support schemes, and subsidies in both domestic and international settings. While domestic support for agriculture has declined substantially over recent decades to approximately 3% of gross farm receipts in 2013 (OECD, 2013), continuing high levels of support internationally impact on Australian production and land use through the resultant distortions in markets and prices (Baffes and Gorter, 2005). In addition, production is influenced by irrigation establishment, infrastructure development, and resource availability. Climatic extremes such as prolonged drought and significant flood events and price volatility affect both the level of production and farmers' land use choices (Gornall et al., 2010). Australia is currently a major exporter of grain and animal protein. About 65% of Australia's agricultural production is exported, and grain ($6.0B, 12% of world exports), beef ($6.8B, 17%), and wool ($2.8B, 67%) are significant in world trade (ABARES, 2012). It has been a consistent policy aim within Australia to maintain this level of excess production and exports, partly as a contribution to satisfying global food demand (Commonwealth of Australia, 2014). This net excess of production over domestic demand has been built on a sustained increase in


productivity within the major agricultural industries. However, mirroring trends in other industrialised countries (Fuglie and NinPratt, 2013), the rate of increase in total factor productivity of Australian agriculture has slowed since the mid 1990s. The climateadjusted total factor productivity increase declined from 2.15% pa prior to 2000 to 1.06% pa over the following decade for cropping (Hughes et al., 2011). Lower levels of productivity increase, and even some absolute declines, were observed in other agricultural industries. This decline was primarily attributed by Sheng et al. (2011) to less emphasis on agricultural R&D investment. These are complex challenges for agriculture, land use, and food production, and integrated analysis of possible futures for agricultural production, land use, and the contribution to meeting global food demand is urgent for Australia and internationally (Falloon and Betts, 2010; Hibbard and Janetos, 2013). Undertaken as part of the Australian National Outlook (Hatfield-Dodds et al., 2015a), we explored the interactions and implications of three key issues for agriculture: the longterm outlook for food demand, increasing competition for land, and the impact of productivity changes. We analysed interactions and alternatives using a scenario approach from 2013 to 2050. While we do not attempt to predict the future, scenarios can usefully illuminate potential responses to combinations of policy, markets, and actions (Audsley et al., 2015; Herrero et al., 2014; Mancosu et al., 2015; Odegard and van der Voet, 2014; Pardey et al., 2014; Rutten et al., 2014; Santelmann et al., 2004). Our analysis involved a novel integration of multiple systems models, linking global, national, and local scales. We explored scenarios enveloping plausible ranges in climate change and climate change policy, national and global population, and the carbon, energy, and food prices consistent with these settings. Based on these land use drivers, we assessed potential agricultural production and land use competition between 23 existing agricultural land uses and seven new land uses for 72 unique scenario combinations of four global outlooks, three productivity rates, three adoption hurdle rates, and two capacity constraint settings. While many forces will ultimately conspire to constrain and shape actual futures, understanding influential drivers and their interactions through scenarios is essential for informing future policy and investment decisions to sustain Australian agricultural production and land systems. 2. Methods 2.1. Study area The study area covered Australia's intensively managed agricultural lands; an area of approximately 85 million ha stretching from central eastern Queensland to the wheat belt of southern Western Australia. The extent of these lands was defined using the National Land Use Map of Australia Version 4 (ABARES, 2010) and the National Vegetation Information System (ESCAVI, 2003). Significant agricultural production occurs outside the study area, particularly extensive beef cattle production on natural pastures in northern Australia. Isolated pockets of irrigated production (e.g. Ord River scheme) were also omitted where they were not contiguous to the main production areas. The study area includes 33 Mha of beef cattle, 18 Mha of sheep, 3 Mha of dairy and 25 Mha of grain production (Fig. 1) (Marinoni et al., 2012). The much smaller areas of irrigated production of high value crops provide a disproportionately high value of production (e.g. in the Murray–Darling Basin 80% of the profit comes from 5% of the agricultural area) (Bryan et al., 2009). 2.2. Modelling framework We used a set of models to characterise aspects of global and domestic society, economy, and environment (Fig. 2), loosely coupled and interacted such that outcomes in each model may influence other models. At a global level, we explored the dimensions of uncertainty


M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

returns to each land use option for annual time steps at a spatial grid cell resolution of ~ 1.1 km across the study area. For agriculture, LUTO draws on extensive spatial models and data layers for current land use, management practices and inputs, physical characteristics, productivity, price, and costs of production. It then combines these with future food demand and input cost projections as well as productivity and adoption rate assumptions to quantify the spatial distribution of economic returns to 23 irrigated and rain-fed agricultural commodities. LUTO also calculates the economic returns to seven new potential land use alternatives (Table 1). LUTO uses simulation models to quantify biomass accumulation and flow-on products such as carbon sequestration and bioenergy production, agricultural yields, biodiversity services, and the impacts of climate change for land uses over space and time. Given the relative economic profitability of land uses and the assumed response of landholders to these economic signals, LUTO identifies areas of land use change and the impacts on a range of economic and environmental indicators. 2.4. Global outlooks and domestic scenarios Fig. 1. Distribution of broad agricultural land uses in Australia's intensive land use zone. © 2015 CSIRO. All Rights Reserved.

arising from alternative outlooks for global population growth and food demand, greenhouse gas emissions and climate trajectories, and energy prices. Domestically, we modelled consumption patterns, resource efficiency trends, emerging land sector markets, and changes in agricultural productivity. The global analysis models include: • A global trade and environment model (GIAM.GTEM) (Pant, 2007) calibrated to use the latest Global Trade Analysis Project data (Newth et al., 2015). • A multi-region global electricity model (Global and Local Learning Model, GALLM) (Hayward and Graham, 2013) • Three climate change models (incorporated in GIAM.Climate based on the Simple Carbon Climate Model) (Harman and Trudinger, 2014)

Outputs from these global models were used as inputs into a set of domestic models for Australia: • An energy sector model covering electricity and transport (ESM—codeveloped by the CSIRO and the Australian Bureau of Agricultural and Resource Economics in 2006) • A multi-regional economic model (MMRF·H2O — Monash University) (Hanslow, 2010) • A rainfall and runoff analysis model (NIAM.Flow) • A biodiversity model (Ferrier et al., 2007) • A model of resource and material flows (MEFISTO) (Baynes et al., 2014) • A model of land use change and impacts for ecosystem services (LUTO) (Bryan et al., 2014b; Connor et al., 2015).

This paper will concentrate on the outlooks for agricultural land use and production primarily from the LUTO model. 2.3. The LUTO land use model The LUTO model is a partial equilibrium model of land use change and its impacts that has been elsewhere described (Bryan et al., 2014b; Connor et al., 2015), evaluated (Dong et al., 2015; Gao and Bryan, accepted for publication; Gao et al., in review), and applied (Bryan et al., 2015, in review, accepted for publication; Hatfield-Dodds et al., 2015b). Land use change occurs in LUTO based on economic profitability and competition among land uses given assumptions about the adoption behaviour of landholders. LUTO calculates the economic

The global context was set by specifying low, mid and high global population projections and a range of global emissions and climate change settings equivalent to Representative Concentration Pathways 2.6, 4.5, and 8.5 (Moss et al., 2010; van Vuuren et al., 2011). Following a stakeholder engagement process as part of the Australian National Outlook, we selected four global outlooks for analysis: L1 low emissions (RCP 2.6), low population; M2 moderate emissions (RCP 4.5), medium population; M3 moderate emissions (RCP 4.5), high population; and H3 high emissions (RCP 8.5), high population (Hatfield-Dodds et al., 2015a; Newth et al., 2015). Global outlooks were developed through the set of linked models to provide internally-consistent projections of key price, demand, and climate variables for each scenario. Although three General Circulation Models (GCM) were used in the overall National Outlook, the land use results were relatively insensitive to the choice of GCM, and for simplicity we focused this study on the Max Planck Institute Earth System Model (MPI-ESM-LR) (Giorgetta et al., 2013). Global outlooks also specified demand for electricity and transport fuel feedstocks which was calculated by the national Energy Sector Model (ESM) (Hayward and Graham, 2013). The global modelling is described in detail elsewhere (Hatfield-Dodds et al., 2015a; Newth et al., 2015). Characteristics of the global scenarios are detailed in Table 2. Domestic scenarios centred around market policy settings with price signals derived from the GIAM modelling (Fig. 3) including marketbased payments per unit of carbon supplied, and a $125 million annual budget (consistent with recent policy DCCEE, 2011) cost-effectively targeted as a top-up payment based on biodiversity co-benefits achieved through environmental plantings. The GIAM price projections also informed establishment costs. While the biodiversity payment scheme was included in the modelling, the impact of varying this is assessed elsewhere (Bryan et al., 2014b, accepted for publication). We also considered policy that includes new markets for bioenergy feedstocks (renewable electricity) and biofuels (liquid fuel). 2.5. Sensitivity analyses: productivity, adoption, and capacity constraints We undertook sensitivity analyses for two key uncertainties: productivity growth and adoption behaviour (Table 3). We assessed low, medium, and high levels of growth in agricultural and forestry productivity following historical analyses of total factor productivity of Australian agriculture (Nossal and Sheng, 2010) and our experience in the Australian forestry industry. Productivity influenced economic returns by increasing yields for the same levels of input. Adoption behaviour was captured using a hurdle rate intended to explore the tendency for adoption to lag profitability changes. The hurdle rate represented how profitable alternative land uses needed to be before

M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

Fig. 2. The modelling framework used in the Australian National Outlook. © 2015 CSIRO. All Rights Reserved.



M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

3.1. Timing and extent of land use change

Table 1 Existing agricultural land uses and new land uses considered in the LUTO model. © 2015 CSIRO. All Rights Reserved. Agriculture, food/fibre land uses Winter cereals, Summer cereals, Rice, Winter legumes, Summer legumes, Winter oilseeds, Summer oilseeds, Sugarcane, Pastures and crops for hay, Cotton, Other non-cereal crops, Vegetables, Citrus, Apples, Pears and other pome fruit, Stone fruit excluding tropical, Tropical stone fruit, Nuts, Plantation fruit, Grapes, Dairy, Beef, Sheep New land uses Carbon plantings (Eucalyptus monocultures), Environmental plantings (mixed local native species), Wheat biofuels (grain and crop residue for biofuels), Wheat food/biofuels (grain for food, crop residue for biofuels), Wheat food/bioenergy (grain for food, crop residue for renewable electricity), Woody perennials bioenergy (short-rotation eucalypts for renewable electricity, Woody perennials biofuels (short-rotation eucalypts for biofuels)

they would be adopted by landholders (i.e. change would occur as soon as a new land use became more profitable, or when profitability was twice, or five times that of the existing agricultural land use). The rates used were based on the range observed in that literature (Bullard et al., 2002; Dumortier, 2013; Murray-Rust et al., 2013; Prestemon and Wear, 2000; Regan et al., 2015; Schröter et al., 2005). Finally, we assessed the impacts of several practical constraints to the rate of land use change associated with the capacity of the agricultural sector to change to substantially new land uses. This capacity is likely to be limited by the availability of labour and resources, and the time required to develop supporting infrastructure. To capture this, we specified a set of capacity constraints including a maximum rate of reforestation (carbon plantings plus environmental plantings) of 100,000 ha pa initially based on observed planting rates achieved in past agribusiness managed investment schemes. After the first year that 100,000 ha had been planted, the maximum rate grew by 7% pa for the first 10 years then by 10% pa thereafter. The maximum capacity to process biofuels was initiated at 400 ML pa in 2013, increasing by 50– 100 ML pa to 2020, then increasing by 400 ML pa thereafter. Similarly, the maximum capacity to process bioenergy started at 0.2 PJ in 2013 and increased by 2.5 PJ pa from 2015. 3. Results The full set of scenarios creates a multi-dimensional set of possible combinations. Here we outline results that assess plausible future changes across space and time from 2013 to 2050 in the land use patterns of agriculture, on the amount of agricultural production, on the output rates (i.e. yields) achieved, and the level of economic returns. These are explored under different global outlooks, productivity and adoption rates, and capacity constraint settings. In the text, we include illustrative examples for the central scenario—medium productivity, 2× hurdle rate.

The area of land used for cropping and livestock production changed under all scenarios of modest to very strong global action on carbon (i.e. M2, M3, L1; Fig. 4). Capacity constraints had a significant effect on the cumulative amount and the timing of land use change. Where there was no global action on carbon (i.e. H3), minimal change occurred. Under the M2 global outlook, and for all constrained scenarios, minimal change was observed before 2030. The land lost from agricultural production was converted to alternative land uses depending on the settings and incentives (Fig. 5). The extent and spatial pattern of this land use change varied across scenarios (Figs. S1–S8). Change into either carbon or environmental plantings was economically advantageous in Australia's grazing lands, particularly in the north-east, and on the margins of existing cropland—dynamics that are described in detail below. Under the H3 global outlook, there was a relatively small (5.4 Mha, 6.9%) decrease in agricultural area which mostly transitioned to biofuel cropping and was only observed without capacity constraints. Most of the change to food/biofuel cropping (i.e. the use of stubble for biofuels and grain sold into the food market) occurred in the western Australian wheat belt, whereas biofuel cropping (i.e. biofuels from both stubble and grain) was viable in parts of the wheat-sheep zone of south-eastern Australia (Figs. S1–S8). In the presence of infrastructure capacity constraints, and under the M2 global outlook (with its relatively low carbon payment, Fig. 3), minor conversion of agricultural land occurred (5.4 Mha, 6.8%), chiefly through an increased use of land for environmental plantings supported by a biodiversity payment. When constraints were relaxed, larger areas were converted (25.1 Mha, 31.8%), mostly to wheat cropping for food/ biofuel and biofuel only. In the latter case, the spatial patterns were similar to those under the high emission scenarios (H3) (Figs. S1–S8). Under the M3 global outlook, with carbon payments rising to $120 in 2050, significant land use change resulted, with more complex mixtures of land use. The presence of capacity constraints dampened and altered the types of land use change. For example, under the central scenario, agricultural area declined by 37.0 Mha (47.0%) unconstrained versus 14.2 Mha (18.0%) when constrained. Most of this land converted to carbon plantings (Figs. 5, S1–S8). The L1 global outlook, based on strong global action to curb greenhouse gas emissions, saw a substantial increase in carbon plantings. Unconstrained and under central settings, 46.0 Mha (58.3%) of agricultural land was converted. Least change (23.0 Mha, 29.2%) occurred with capacity constraints imposed and assuming a high adoption hurdle rate (i.e. 5 ×) and low productivity growth (Fig. 4). Greatest change (62.7 Mha, 80.0%) occurred without capacity constraints, and assuming a low adoption hurdle rate (i.e. 1×) and high productivity growth. Much of this change occurred in Queensland and New South Wales, especially in beef grazing areas (Figs. 5, S1–S8). In the grain-growing areas of the

Table 2 Characteristics of the global outlooks. © 2015 CSIRO. All Rights Reserved. 2010 Units Climate outlook Representative Concentration Pathway Cumulative emissionsa Temperature increase in 2100b Population outlook Populationc Abatement effort Emissions per person a b c

Uncertain Gt CO2 o C Billion people Tpc CO2e

Uncertain Uncertain 6.9 Varied 7.0

Emissions for relevant RCPs (van Vuuren et al., 2011). 66% range for 2100 relative to pre-industrial conditions from Rogelj et al. (2012). Based on United Nations (2013a).

Global Scenarios, 2050 L1




L 2.6 1437 1.3–1.9 1 8.1 Very strong 2.2

M 4.5 2091 2.0–3.0 3 10.6 Strong 4.7

M 4.5 2091 2.0–3.0 2 9.3 Modest 5.4

H 8.5 2823 4.0–6.1 3 10.6 No action 8.7

M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83


hurdle rates (i.e. 5 ×), sheep and beef production increases were similar to those achieved in cropping. It is notable that crop and sheep production can be doubled with medium or high productivity settings under the M2 and M3 scenarios as most land use conversion occurred in beef grazing areas. 3.3. Output rates

south and west, high productivity and/or low hurdles favoured increased use of stubble for biofuels.

Agricultural output per unit area was determined both by the effects of model parameters on production, and by land use change. Output rates tended to increase steadily over time and this trend was fairly consistent across global outlooks and capacity constraint settings (Fig. 7). The most notable response of the output rates was the marked increase for cattle (265.4% in M3) compared with sheep (118.4%) and crops (73.1%). Change to non-food land uses was first observed on more marginal beef grazing land—the more productive and more profitable land has higher per unit carrying capacity. This effect was general across agricultural land uses but was much less obvious in sheep and cropping land, and as the land with lower output rates per hectare tended to transition first, contributing to the increase in average output rates over time. Apart from cattle output rates, these trends were relatively insensitive to capacity constraints. The impact of productivity and adoption hurdle settings compounded the above effect in that the output rate was greater at higher productivity and under lower adoption hurdle rates which precipitated more land use change. Productivity assumptions had a strong effect on output rates for all types of agriculture with M3 cropping output rates increasing 5.9% under low productivity compared to 143.6% under high productivity growth. Adoption hurdle rates were less influential on the output rates of crops and sheep but had a more notable impact on cattle. For example, M3 cattle output rates increased 312.0% under 1× hurdle compared to 205.0% under 5× hurdle.

3.2. Agricultural production

3.4. Value of agricultural outputs

Agricultural production was most sensitive to the presence of capacity constraints and productivity assumptions (Fig. 6). Global outlook and adoption hurdle rates had a clear but less pronounced effect. Australian production of crops, cattle, and sheep outputs continued to increase, assuming moderate productivity increases and a 2× adoption hurdle rate. Under the L1 scenario with no capacity constraints, growth in production was flat in sheep and cattle, and subdued in crops, especially after 2040. When capacity constraints were imposed, food crop production growth was maintained beyond 2050. In general, crop production was more resilient than livestock to change across most scenario combinations. Agricultural production was influenced by productivity and by the conversion of agricultural land use to non-food purposes. Almost no production increases were achieved by 2050 under assumptions of low productivity rate increases, while production increased substantially under high-productivity assumptions (e.g. M3 central crop production increased 60.8%). The highest production increases were only observed at high productivity growth rates or at high adoption hurdle rates, particularly in crops. Despite increased productivity in sheep and beef, they could not compete with non-food land uses after 2030 in many scenarios. In these cases, the change in land use drove the production outcome. With high adoption

Value of agricultural outputs varied both in aggregate and in spatial distribution across global outlooks, productivity and hurdle rate assumptions, and capacity constraint settings. However, agricultural value across the landscape, as measured by the aggregate real value of food and fibre (2010 dollars), increased over time across all scenarios and sensitivity settings. With the exception of the more modest increases seen in M2, the patterns show a consistently high increase in the value of agricultural outputs—for example, with the value of sheep production increasing 157.4% under M3 (Fig. 8). It is a notable feature that very strong abatement is consistent with high value outcomes. Crops, sheep, and cattle showed similar increases in output value and there was a relatively minor impact of capacity constraints (e.g. crop value increased 249.8% unconstrained versus 276.25% constrained) (Fig. 8). Agricultural value was sensitive to productivity settings (e.g. value of sheep in M3 increased 96.2% under low productivity versus 192.3% under high) and adoption hurdle rate (e.g. value of sheep in M3 increased 104.8% under 1 × hurdle versus 224.5% under 5 ×) (Fig. 8). Value of outputs were notably less from cattle and sheep where more land use change occurred (e.g. under low hurdle rates) and the value of outputs was significantly higher for cropping under high productivity rates.

Fig. 3. Prices output from GIAM and ESM (electricity price) modelling for the four global outlooks used as input into land use modelling (Hatfield-Dodds et al., 2015a; Newth et al., 2015). Note that the M2 outlook assumes higher global agricultural productivity to provide a wider range of export price outlooks for the national analysis. © 2015 CSIRO. All Rights Reserved.

4. Discussion Table 3 Dimensions of the domestic sensitivity analyses. © 2015 CSIRO. All Rights Reserved. Component


Productivity (% simple increase pa.) Yield (agriculture) Tree growth (carbon plantings) Adoption behaviour (profitability hurdle rate multiplier)

Low 0% 0% 1×

Medium 1.5% 0.5% 2×

High 3.0% 1.0% 5×

We have explored plausible dimensions of the potential change in Australian agricultural land use and production to 2050. Consistent with recent findings of agricultural model intercomparisons (Nelson et al., 2014; Popp et al., 2014; Schmitz et al., 2014; von Lampe et al., 2014), we found widely varying trajectories for land use and the agricultural production system. At one end of the spectrum, with little global action on climate change (i.e. H3), the mix of land uses across the


M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

Fig. 4. Area of agricultural production of food/fibre (not including wheat food/biofuel or wheat food/bioenergy land uses where grain is sold into the food market) under the four global outlooks, two capacity constraint settings, three adoption hurdle rate assumptions, and three productivity rate assumptions. © 2015 CSIRO. All Rights Reserved.

landscape was similar to the present arrangements (Figs. 4, 5), depending on the relative commodity prices and costs of production and their capacity to respond to climate change including the increased frequency of extreme events (Rosenzweig et al., 2014). At the other end, under

strong global action (i.e. M3, L1), a wider mix of enterprises is possible with income streams resulting in large areas of existing agriculture converted to new land uses including carbon plantings, biofuels, or environmental plantings (Erb et al., 2012). A change to non-food and fibre land

M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83


Fig. 5. Potential land use change over time under the four global outlooks and two capacity constraint settings for medium productivity and 2× hurdle rate. © 2015 CSIRO. All Rights Reserved.

uses is more likely on grazing land, especially cattle grazing. Substantially less change was observed on cropping lands across most scenarios. Notwithstanding significant shifts in land use, total agricultural production was resilient partly because output rates rose on the more productive lands which are more likely to stay in food and fibre production. In many scenarios, it was possible to maintain the excess of production over demand historically achieved in Australian agriculture (assuming that demand is correlated with domestic population increase). With strong global action against climate change, differences emerged between agricultural industries following the disproportionate impact of competition on different land uses. The value of production grew in all scenarios, and agricultural land use was projected to be part of an overall increase in profitability across the land sector as a whole (Bryan et al., in review). Thus, we found that strong environmental policy settings did not necessarily involve trade-offs with economic performance (Hatfield-Dodds et al., 2015b). Although difficult to compare due to differing scenario assumptions and model parameterisations (von Lampe et al., 2014), our findings concur with some recent global model outputs, most of which found decreasing cropland area for the region (Kraxner et al., 2013; Schmitz et al., 2014) (noting that under the current parameterisation, cropland area cannot increase in LUTO, only decrease with increasing competition from alternative land uses). However, our results diverge from other recent modelling which suggests an ~ 78% increase in cropland area for Australia from 2000 to 2040 (Van Asselen and Verburg, 2013). Sensitivity to carbon price has also been commonly found in land use change projections (Benitez et al., 2007; Bryan et al., 2014b; Humpenoeder et al., 2014; Nielsen et al., 2014; Schneider et al., 2007) with higher carbon prices underpinning increased competition for land from a diversified set of land uses (Bryan et al., 2015; Funk et al., 2014; Haim et al., 2014; Longmire et al., 2015; Paul et al., 2013; Polglase et al., 2013; Rokityanskiy et al., 2007). Disproportionate impacts of land use competition on grazing lands have also been found in other studies (Funk et al., 2014; Winsten et al., 2011). 4.1. Influence of global and domestic drivers The results suggest that choices made globally and within Australia about climate change mitigation may strongly influence Australia's agricultural landscapes and shape agricultural industries (see also Erb et al.,

2012; Reilly et al., 2012; Sohl et al., 2012). Beyond broadening the range of economically profitable land use choices available to landholders, in accord with Humpenoeder et al. (2014), sensitivity to carbon payment also shaped the timing of change—an aspect which few studies have considered. Significant change was observed prior to 2030 only under very strong and immediate global action on climate (i.e. L1 — limiting increases in temperature to around 2 °C), whereas potential land use change tended to occur after 2030 under more gradual, modest to strong global action on climate (i.e. M2, M3). The complex interaction between productivity and the rate and timing of land use change is an innovative feature of this analysis. The spatial pattern of land use change was strongly influenced by productivity assumptions—especially the tendency for the least productive agricultural land to change first—which increased output rates and helped maintain aggregate production increases in the most productive land. High productivity synchronously increased total agricultural production and rates of land use change. Increased production and supply led to lower prices which reduced the profitability and competitiveness of agriculture. High productivity increases also influenced the output from non-food land uses. As wheat yields rose, for example, more stubble was available for biofuel production and, towards 2050 and with increasing fuel prices, highly productive crop land was used to produce both food from grain and biofuels from crop residue. With tree productivity increases, carbon plantings also became more competitive, reducing agricultural area. If long-term agricultural productivity trends are able to be maintained, however, most scenarios suggest the capacity to increase food production on a smaller proportion of the land base. We found average production increases of over 100% in some scenarios—consistent with the level of increase needed to maintain Australia's contribution to global food security and the level needed globally to match current need and future dietary demand. Output rates (yield) varied significantly over time and space, and between agricultural industries. Most notable was the increase in cattle output rates as more marginal (i.e. least productive and least profitable) lands were used for various forms of non-food land use. For most settings, the value of output also increased as output prices rose and as land use responded to global and national economic signals. We have also shown that capacity constraints had a significant effect on the spatial extent, timing, and location of land use change. Similarly, the modelled lags in adoption and change through a hurdle rate had


M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

Fig. 6. Production of food/fibre under the four global outlooks, two capacity constraint settings, three adoption hurdle rate assumptions, and three productivity rate assumptions. © 2015 CSIRO. All Rights Reserved.

similar but qualitatively different constraining effects on change. The influence of these constraint settings illustrates that management of the supply environment will impact land sector patterns and outputs as significantly as the demand generated through the global change outlooks. 4.2. Implications for policy: productivity and infrastructure The most striking result of this study is the potential for large scale land conversion to carbon plantations under global outlooks with strong to very strong global emissions abatement action. These pervasive effects of competition for land have generated ongoing debate in Australia over the past decade with the design of emission trading schemes, the Carbon Farming Initiative, and the rapid expansion of coal seam gas and wind power generation. The policy implications of competition for land from non-food uses are complex and important and are dealt with elsewhere (Bryan et al., 2014b, 2015, in review, accepted for publication). There are also significant implications for water policy which are also explored

elsewhere (Bryan et al., 2015; Connor et al., in review). Here, we focus on the implications of the results for policy for maintaining and increasing the productivity and competitiveness of Australian agriculture—a long term policy focus of the Australian Government. Maintaining Australia's contribution to global food security given projected domestic population growth will require a sustained productivity increase similar to historical levels and higher than that observed in recent years. Productivity is a key element of the maintenance and growth of Australian agricultural production given increasing competition for land. Sustaining agricultural productivity increases requires simultaneous improvements in a range of system elements. Both within the paddock and across the landscape, it requires making and finding synergies in genetic resources, land management practices, labour utilisation, broader resource use, land allocation and land use choice, production timing and patterns, and environmental response and impacts. The management and ‘ownership’ of these components of productivity are also complex, and distributed across multiple stakeholders in any agricultural system. Responsibility and effectiveness is

M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83


Fig. 7. Output rates (cattle and sheep head/ha; crop production t/ha) of food/fibre production from 2010 volumes under the four global outlooks, two capacity constraint settings, three adoption hurdle rate assumptions, and three productivity rate assumptions. © 2015 CSIRO. All Rights Reserved.

thus shared by these stakeholders including individual farmers and growers, owners and managers of infrastructure, marketers, research organisations, policy-makers, and a host of others. The productivity of Australian agriculture is also profoundly affected by the international context and changes and in the performance of other sectors of the economy. Carberry et al. (2013) concluded that, within a sample of crop yields in Australia, farmers are operating close to technical efficiency in terms of water and nitrogen (Hochman et al., 2012). There is evidence that the rate of total factor productivity growth has slowed both in Australia (Hughes et al., 2011) and globally (Alston et al., 2009), attributed primarily to a decline in investment in research and development which feeds technical innovations, and to climate variability and change (Sheng et al., 2011). Productivity increases are not easily gained and will depend on reducing inefficiencies where they exist (other nutrients, soil biology, soil physical health, water use), finding genetic gains, implementing changes to the agronomic system (precision

farming, better risk management), new fertiliser technologies, and improved integration of farm enterprises (Bryan et al., 2014a; Kirkegaard and Hunt, 2010; Monjardino et al., 2015). Investment in research and development, and wider support for innovation, has an important contribution across all areas of potential productivity improvements (Alston et al., 2009; Pardey et al., 2013). Overall levels of food production depend on the land available and the extent to which potential productivity increases are adopted to increase production. Productivity improvements may also be used to lower economic risk allowing reduced inputs for similar or reduced yields, especially as climate variability increases. Increasing production from Australian agriculture will require a concentration on productivity and risk management improvement in cropping and grazing lands where both the return on investment and environmental potential are relatively high. As various forms of carbon farming gain growing importance in the landscape, agricultural research programs will need to sharpen focus on those lands where food and fibre production increases


M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

Fig. 8. Change (%) in real value of food/fibre production under the four global outlooks, two capacity constraint settings, three adoption hurdle rate assumptions, and three productivity rate assumptions. © 2015 CSIRO. All Rights Reserved.

are most likely. Global action to limit the pace and extent of climate change is crucial in the long term. Land use change in Australia has historically been influenced by land and water quality, land availability through tenure and related constraints, resource availability and, increasingly, competition from mining, urban, and conservation uses. In our study, the various factors influencing the capacity for land use change had different effects spatially and temporally. Policy options are required to build capacity, ease bottlenecks, and facilitate desirable land use change. We will need policy mechanisms such as programs to provide new or enhanced infrastructure (e.g. irrigation for agriculture, nurseries for plantations, biofuel plants), economic incentives such as payments and disincentives such as taxes and levies (Bryan et al., accepted for publication), and new forms of information, extension, and advisory systems. Our results suggest that such policy settings can be highly influential in the development of Australia's land use and the enhancement of agricultural productivity.

4.3. Limitations We found only a minor negative impact on production (i.e. crop yields, tree growth) due to climate change in this study (Bryan et al., 2014b). This is partly because the relatively small change in temperature and climate predicted had a small aggregate effect (on average influencing production by b10% and in spatially variable ways to 2050). This also reflected our modelling of smooth trend changes. Thus, the modelling provides little insight into the potential impacts of extreme events or increased climate variability (e.g. droughts, storms, heat waves) that significantly impact agriculture. Prior to 2050 however, increased frequency and intensity of climate shocks are expected and are already occurring (Donat et al., 2013), but are not simulated here. Additionally, the simulation does not yet capture the impact of thresholds in agricultural systems, such as the relationship between night temperatures and flowering (Mohammed and Tarpley, 2011; Shi et al., 2013). These climate shocks and key thresholds are likely to be a notable feature in agricultural

M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83


production and regional livelihoods (Challinor et al., 2014), as they have been in recent times. Depending on the severity and duration of climate shocks, the short term impact on productivity and production may well exceed the longer term trends modelled in this study. We did not model Australian land use change outside the existing intensive land use footprint. Most beef cattle production occurs outside of the intensive use zone where the likelihood of change has not been assessed. Hence, extrapolating the beef results from this study for Australia as a whole is inappropriate at this stage and these lands will be brought into the next phase of model development. While there is substantial interest in agricultural development in northern Australia, plausible levels of change would not affect the trends found in this study. Neither did we model the indirect effects of changes in Australian land use and agricultural production on global land use patterns. Via global trade linkages, changes in supply of Australian agricultural exports can affect global prices and create incentives for land use change elsewhere (Lambin and Meyfroidt, 2011), often incurring a carbon debt from land clearance (Fargione et al., 2008). We assumed that with the changes in global environmental and economic drivers assessed here, the competition for land experienced in Australia would also occur globally, potentially resulting in a production gap (Sakschewski et al., 2014). The study was similarly limited by not considering social and community responses to land use change and the desirability of this change. For example, many scenarios describe an economic environment where non-food land uses compete more effectively with food production over large areas of Australia; during those decades where food security seems likely to grow in urgency. An emphasis on productivity is clearly essential; other policies and actions would focus on reducing food waste and halting and ameliorating the decline in the productive base of agriculture (Keating et al., 2014). Policies favouring food security will emphasise the infrastructure needed to favour food and fibre production as land use changes. So it is probable that various forms of intervention will modify the trends in some scenarios; we consider these forms of discussion and exploration of options a desirable outcome of the research.

the degree and scope of change in Australian agricultural land use and production. Better knowledge of the efficacy of infrastructure deployment, economic and market incentives and mechanisms, and the role of information, extension, and advisory systems can inform policy settings. The near term (one to five years) may be reasonably predictable — in the absence of shocks. Beyond then and out to 2050, confident predictions become more difficult. In addition, it is possible that the many external elements that have historically impacted on Australian agriculture and land use (for example, global social unrest, market collapse, trade agreements and trade restrictions) are themselves increasingly volatile and unpredictable—and that these fluctuations are significantly larger than have been experienced previously. Shocks, and the ability to cope with these, may be more important than general trends. Transformative change to the Australian landscape begins towards 2030 if there is concerted global action on climate change, but later where more moderate mitigation is pursued. This scale of change will arise from the increase in global food demand, and the scarcity and increased demand for energy and other inputs. While direct effects of climate change will occur, our analysis suggests that climate change mitigation policy settings (e.g. carbon price) may have a considerably stronger impact over the period to 2050. Food production, in most scenarios, will occupy a smaller land use footprint in Australia but this area will produce more. Due to the long lead times typically associated with transformational change in land systems, the time for a national conversation on these potential changes and the policy instruments to manage their effects is now. Supplementary data to this article can be found online at http://dx.

5. Conclusions


Plausible settings of external drivers acting through to 2050 modelled in this study suggest a range of impacts for Australian land use and agricultural production. Under most scenarios, there is more non-food land use (i.e. carbon plantings, environmental plantings, and biofuels) across the landscape; the nature, spatial distribution and the timing are highly dependent on the drivers and the resultant trajectory of change. Nonetheless, the value of agricultural production increases across all scenarios; even before accounting for new land uses. Sustained agricultural production and farm income is predicated on continued productivity improvement over the coming decades; the current evidence of decline is therefore disturbing. Increased levels of food production can be achieved, but this sensitivity to productivity and the reduction in land area devoted to food production revealed in the modelling suggests the need for urgency in growing the measures needed to enhance and sustain agricultural productivity. The study provides insights into the framework in which policy must play within Australia as forces of population growth and climate change and its mitigation come to bear. Increase in food production is not necessarily incompatible with the broader range of non-food land uses emerging across these scenarios. Beyond these land use choices, there are many aspects that constrain the future for agriculture and our response to it, including improving yield and closing yield gaps, opportunities to achieve productivity changes, and the social and cultural settings implied in the modelled change. While these interventions are not modelled, they provide the means through which the response to the various scenarios explored here can be managed. Land use pattern and agricultural outputs were sensitive to both adoption rates and to constraints on infrastructure and capacity. Policy and investment in reducing these barriers will be important in influencing

ABARES, 2010. Land Use of Australia, Version 4, 2005-06 Dataset. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, Australia. ABARES, 2012. Agricultural Commodity Statistics 2012. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, Australia. ABARES, 2014. Australian Agricultural Productivity Growth: Past Reforms and Future Opportunities. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, Australia. Alexandratos, N., Bruinsma, J., 2012. World agriculture towards 2030/2050: the 2012 revision. ESA Working Paper No. 12–03. FAO, Rome. Alston, J.M., Beddow, J.M., Pardey, P.G., 2009. Agricultural research, productivity, and food prices in the long run. Science 325, 1209–1210. Anderson, K., Ivanic, M., Martin, W., 2013. Food Price Spikes, Price Insulation and Poverty. National Bureau of Economic Research Working Paper Series No. 19530. Arrouays, D., Grundy, M.G., Hartemink, A.E., Hempel, J.W., Heuvelink, G.B.M., Hong, S.Y., Lagacherie, P., Lelyk, G., McBratney, A.B., McKenzie, N.J., Mendonca-Santos, M.d.L., Minasny, B., Montanarella, L., Odeh, I.O.A., Sanchez, P.A., Thompson, J.A., Zhang, G.L., 2014. GlobalSoilMap: toward a fine-resolution global grid of soil properties. Adv. Agron. 125, 93–134. Audsley, E., Trnka, M., Sabate, S., Maspons, J., Sanchez, A., Sandars, D., Balek, J., Pearn, K., 2015. Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation. Clim. Chang. 128, 215–227. Ausubel, J.H., Wernick, I.K., Waggoner, P.E., 2013. Peak farmland and the prospect for land sparing. Popul. Dev. Rev. 38, 221–242. Baffes, J., Gorter, H.D., 2005. Disciplining agricultural support through decoupling. Policy Research Working Paper 3533. The World Bank, Washington DC. Baynes, T., West, J., Schandl, H., McFallan, S., 2014. MEFISTO: material and energy flows integrated with stocks. Gordon Research Conference Industrial Ecology, Lucca, Italy. Benitez, P.C., McCallum, I., Obersteiner, M., Yamagata, Y., 2007. Global potential for carbon sequestration: geographical distribution, country risk and policy implications. Ecol. Econ. 60, 572–583. Bryan, B.A., Crossman, N.D., 2013. Impact of multiple interacting financial incentives on land use change and the supply of ecosystem services. Ecosyst. Serv. 4, 60–72. 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., 2015a. Land use and sustainability under intersecting global change and domestic policy scenarios: trajectories for Australia to 2050. Glob. Environ. Chang (in review).

Acknowledgements This research was supported by CSIRO's Australian National Outlook initiative and CSIRO Agriculture.


M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83

Bryan, B.A., Runting, R., Capon, T., Cunningham, S., Perring, M.P., Kragt, M., Nolan, M., Law, E.A., Renwick, A., Eber, S., Christian, R., Wilson, K.A., 2015b. Designer policy for carbon and biodiversity co-benefits under global change. Nat. Clim. Chang. 10.1038/NCLIMATE2874 (in press). Bryan, B.A., Crossman, N.D., Nolan, M., Li, J., Navarro, J., Connor, J.D., 2015c. Land use efficiency: anticipating future demand for land-sector greenhouse gas emissions abatement and managing trade-offs with agriculture, water, and biodiversity. Glob. Chang. Biol. 21, 4098–4114. Bryan, B.A., Hajkowicz, S., Marvanek, S., Young, M.D., 2009. Mapping economic returns to agriculture for informing environmental policy in the Murray–Darling Basin, Australia. Environ. Model. Assess. 14, 375–390. Bryan, B.A., King, D., Zhao, G., 2014a. Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps. Environ. Res. Lett. 9, 044005. Bryan, B.A., Meyer, W.S., Campbell, C.A., Harris, G.P., Lefroy, T., Lyle, G., Martin, P., McLean, J., Montagu, K., Rickards, L.A., Summers, D.M., Thackway, R., Wells, S., Young, M., 2013. The second industrial transformation of Australian landscapes. Curr. Opin. Environ. Sustain. 5, 278–287. 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., Hatfield-Dodds, S., 2014b. Supply of carbon sequestration and biodiversity services from Australia's agricultural land under global change. Glob. Environ. Chang. 28, 166–181. Bullard, S.H., Gunter, J.E., Doolittle, M.L., Arano, K.G., 2002. Discount rates for nonindustrial private forest landowners in Mississippi: how high a hurdle? South. J. Appl. For. 26, 26–31. Carberry, P.S., Liang, W.-l., Twomlow, S., Holzworth, D.P., Dimes, J.P., McClelland, T., Huth, N.I., Chen, F., Hochman, Z., Keating, B.A., 2013. Scope for improved eco-efficiency varies among diverse cropping systems. Proc. Natl. Acad. Sci. 110, 8381–8386. Carter, C.A., Rausser, G.C., Smith, A., 2011. Commodity booms and busts. Ann. Rev. Resour. Econ. 3, 87–118. Challinor, A.J., Watson, J., Lobell, D.B., Howden, S.M., Smith, D.R., Chhetri, N., 2014. A metaanalysis of crop yield under climate change and adaptation. Nat. Clim. Chang. 4, 287–291. Commonwealth of Australia, 2014. Agricultural Competitiveness Issues Paper (Canberra). Connor, J.D., Bryan, B.A., Nolan, M., Grundy, M., 2015a. Competition for water: addressing water impacts of forest carbon sequestration incentive policy with water markets. Clim. Chang. (in review). Connor, J.D., Bryan, B.A., Nolan, M., Stock, F., Dunstall, S., Graham, P., Ernst, A., Newth, D., Grundy, M.J., Hatfield-Dodds, S., 2015b. Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution. Environ. Model. Softw. 69, 141–154. DCCEE, 2011. Securing a Clean Energy Future: The Australian Government's Climate Change Plan. Department of Climate Change and Energy Efficiency, Commonwealth of Australia, Canberra, Australia. DeFries, R.S., Foley, J.A., Asner, G.P., 2004. Land-use choices: balancing human needs and ecosystem function. Front. Ecol. Environ. 2, 249–257. Donat, M.G., Alexander, L.V., Yang, H., Durre, I., Vose, R., Dunn, R.J.H., Willett, K.M., Aguilar, E., Brunet, M., Caesar, J., Hewitson, B., Jack, C., Klein Tank, A.M.G., Kruger, A.C., Marengo, J., Peterson, T.C., Renom, M., Oria Rojas, C., Rusticucci, M., Salinger, J., Elrayah, A.S., Sekele, S.S., Srivastava, A.K., Trewin, B., Villarroel, C., Vincent, L.A., Zhai, P., Zhang, X., Kitching, S., 2013. Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J. Geophys. Res. Atmos. 118, 2098–2118. Dong, M., Bryan, B.A., Connor, J.D., Nolan, M., Gao, L., 2015. Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling. Ecosyst. Serv. 15, 63–74. Dumortier, J., 2013. The effects of uncertainty under a cap-and-trade policy on afforestation in the United States. Environ. Res. Lett. 8, 044020. Erb, K.-H., Haberl, H., Plutzar, C., 2012. Dependency of global primary bioenergy crop potentials in 2050 on food systems, yields, biodiversity conservation and political stability. Energy Policy 47, 260–269. ESCAVI, 2003. Australian Vegetation Attribute Manual: National Vegetation Information System, Version 6.0. Executive Steering Committee for Australian Vegetation Information, Department of the Environment and Heritage, Canberra, Australia. Falloon, P., Betts, R., 2010. Climate impacts on European agriculture and water management in the context of adaptation and mitigation—the importance of an integrated approach. Sci. Total Environ. 408, 5667–5687. FAO, 2011. The State of the World's Land and Water Resources for Food and Agriculture (SOLAW) — Managing Systems at Risk. Food and Agriculture Organization of the United Nations, Earthscan, Rome, London. FAO, IFAD, WFP, 2013. The State of Food Insecurity in the World 2013. The Multiple Dimensions of Food Security. FAO, Rome. Fargione, J., Hill, J., Tilman, D., Polasky, S., Hawthorne, P., 2008. Land clearing and the biofuel carbon debt. Science 319, 1235–1238. Ferrier, S., Manion, G., Elith, J., Richardson, K., 2007. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264. Fischer, R.A., Byerlee, D., Edmeades, G.O., 2014. Crop yields and global food security: will yield increase continue to feed the world? ACIAR Monograph No. 158. Australian Centre for International Agricultural Research, Canberra Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., O’Connell, C., Ray, D.K., West, P.C., Balzer, C., Bennett, E.M., Carpenter, S.R., Hill, J., Monfreda, C., Polasky, S., Rockstrom, J., Sheehan, J., Siebert, S., Tilman, D., Zaks, D.P.M., 2011. Solutions for a cultivated planet. Nature 478, 337–342.

Fuglie, K., Nin-Pratt, A., 2013. Agricultural productivity: a changing global harvest. In: IFPRI (Ed.), 2012 Global Food Policy Report. International Food Policy Research Institute, Washington, DC, pp. 14–25. Funk, J.M., Field, C.B., Kerr, S., Daigneault, A., 2014. Modeling the impact of carbon farming on land use in a New Zealand landscape. Environ. Sci. Pol. 37, 1–10. Gao, L., Bryan, B.A., 2015. Incorporating deep uncertainty into the elementary effects method for robust global sensitivity analysis. Ecol. Model. 1016/j.ecolmodel.2015.10.016 (in press). Gao, L., Bryan, B.A., Nolan, M., Connor, J.D., Song, X.D., Zhao, G., 2015. Robust global sensitivity analysis under deep uncertainty via scenario analysis. Environ. Model. Softw. (in press). Garnaut, R., 2011. The Garnaut Review 2011: Australia in the Global Response to Climate Change. Commonwealth of Australia, Cambridge University Press, Canberra, Australia. Giorgetta, M.A., Jungclaus, J., Reick, C.H., Legutke, S., Bader, J., Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K., Gayler, V., Haak, H., Hollweg, H.-D., Ilyina, T., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan, F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R., Segschneider, J., Six, K.D., Stockhause, M., Timmreck, C., Wegner, J., Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., Stevens, B., 2013. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the coupled model intercomparison project phase 5. J. Adv. Model. Earth Syst. 5, 572–597. Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M., Toulmin, C., 2010. Food security: the challenge of feeding 9 billion people. Science 327, 812–818. Gordon, L.J., Finlayson, C.M., Falkenmark, M., 2010. Managing water in agriculture for food production and other ecosystem services. Agric. Water Manag. 97, 512–519. Gornall, J., Betts, R., Burke, E., Clark, R., Camp, J., Willett, K., Wiltshire, A., 2010. Implications of climate change for agricultural productivity in the early twenty-first century. Philos. Trans. R. Soc. B Biol. Sci. 365, 2973–2989. Haim, D., White, E.M., Alig, R.J., 2014. Permanence of agricultural afforestation for carbon sequestration under stylized carbon markets in the US. For. Policy Econ. 41, 12–21. Hajkowicz, S., Negra, C., Barnett, P., Clark, M., Harch, B., Keating, B., 2012. Food price volatility and hunger alleviation — can cannes work? Agric. Food Secur. 1, 8. Hanslow, K., 2010. NIAM: National Integrated Assessment Model — proof-of-concept development and application. General Paper No. G-210. Centre of Policy Studies, Monash University, Clayton, Victoria. Harman, I.N., Trudinger, C.M., 2014. The Simple Carbon-Climate Model : SCCM7 Technical Documentation. Centre For Australian Weather and Climate Research (CAWCR), CSIRO Marine and Atmospheric Research, Canberra, Australia. Harvey, M., Pilgrim, S., 2011. The new competition for land: food, energy, and climate change. Food Policy 36 (Suppl. 1), S40–S51. Hatfield-Dodds, S., McKellar, L., Brinsmead, T.S., Bryan, B.A., Graham, P., Grundy, M., Harwood, T., Newth, D., Schandl, H., Wonhas, A., Adams, P., Cai, Y., Ferrier, S., Finnigan, J., Hanslow, K., McCallum, R., Nolan, M., Prosser, I., Smith, M.S., Baynes, T., Chiew, F., Connor, J., Geschke, A., Grigg, N., Harman, I., Hayward, J., Keating, B., King, D., Lenzen, M., Lonsdale, M., McCrae, R., Garcia, J.N., Owen, A., Raison, J., Reedman, L., Smith, M.H., Summers, D., Whetton, P., 2015a. CSIRO Australian National Outlook 2015 Technical Report: Economic Activity, Resource use, Environmental Performance and Living Standards, 1970–2050. CSIRO, Canberra, Australia. Hatfield-Dodds, S., Schandl, H., Adams, P., Baynes, T., Brinsmead, T., Bryan, B.A., Chiew, F., Graham, P., Grundy, M., Harwood, T., McCallum, R., McCrea, R., McKellar, L., Newth, D., Nolan, M., Prosser, I., Wonhas, A., 2015b. Australia is ‘free to choose’ economic growth and falling environmental pressures. Nature 527, 49–53. nature16065. Hayward, J.A., Graham, P.W., 2013. A global and local endogenous experience curve model for projecting future uptake and cost of electricity generation technologies. Energy Econ. 40, 537–548. Herrero, M., Thornton, P.K., Bernues, A., Baltenweck, I., Vervoort, J., de Steeg, J.v., Makokha, S., van Wijk, M.T., Karanja, S., Rufino, M.C., Staal, S.J., 2014. Exploring future changes in smallholder farming systems by linking socio-economic scenarios with regional and household models. Glob. Environ. Chang. 24, 165–182. Hibbard, K.A., Janetos, A.C., 2013. The regional nature of global challenges: a need and strategy for integrated regional modeling. Clim. Chang. 118, 565–577. Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., van Rees, H., Marinoni, O., Garcia, J.N., Horan, H., 2012. Quantifying yield gaps in rainfed cropping systems: a case study of wheat in Australia. Field Crop Res. 136, 85–96. Hughes, N., Lawson, K., Davidson, A., Jackson, T., Sheng, Y., 2011. Productivity Pathways: Climate Adjusted Production Frontiers for the Australian Broadacre Cropping Industry. Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra. Humpenoeder, F., Popp, A., Dietrich, J.P., Klein, D., Lotze-Campen, H., Bonsch, M., Bodirsky, B.L., Weindl, I., Stevanovic, M., Mueller, C., 2014. Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environ. Res. Lett. 9. Ivanic, M., Martin, W., Zaman, H., 2012. Estimating the short-run poverty impacts of the 2010–11 surge in food prices. World Dev. 40, 2302–2317. Kastner, T., Rivas, M.J.I., Koch, W., Nonhebel, S., 2012. Global changes in diets and the consequences for land requirements for food. Proc. Natl. Acad. Sci. U. S. A. 109, 6868–6872. Keating, B.A., Herrero, M., Carberry, P.S., Gardner, J., Cole, M.B., 2014. Food wedges: framing the global food demand and supply challenge towards 2050. Glob. Food Secur. 3, 125–132. Kirkegaard, J.A., Hunt, J.R., 2010. Increasing productivity by matching farming system management and genotype in water-limited environments. J. Exp. Bot. 61, 4129–4143. Kraxner, F., Nordstrom, E.-M., Havlik, P., Gusti, M., Mosnier, A., Frank, S., Valin, H., Fritz, S., Fuss, S., Kindermann, G., McCallum, I., Khabarov, N., Boettcher, H., See, L., Aoki, K.,

M.J. Grundy et al. / Agricultural Systems 142 (2016) 70–83 Schmid, E., Mathe, L., Obersteiner, M., 2013. Global bioenergy scenarios — future forest development, land-use implications, and trade-offs. Biomass Bioenergy 57, 86–96. Lagi, M., Bertrand, K., Bar-Yam, Y., 2011. The Food Crises and Political Instability in North Africa and the Middle East (arXiv:1108.2455 [physics.soc-ph]). Lambin, E.F., Meyfroidt, P., 2011. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl. Acad. Sci. U. S. A. 108, 3465–3472. von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S., Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Lotze-Campen, H., d'Croz, D.M., Nelson, G.C., Sands, R.D., Schmitz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., van Meijl, H., 2014. Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison. Agric. Econ. 45, 3–20. Law, E.A., Bryan, B.A., Meijaard, E., Mallawaarachchi, T., Struebig, M., Wilson, K.A., 2014. Ecosystem services from a degraded peatland of central Kalimantan: implications for policy, planning, and management. Ecol. Appl. 25, 70–87. Longmire, A., Taylor, C., Pearson, C.J., 2015. An open-access method for targeting revegetation based on potential for emissions reduction, carbon sequestration and opportunity cost. Land Use Policy 42, 578–585. Mancosu, E., Gago-Silva, A., Barbosa, A., de Bono, A., Ivanov, E., Lehmann, A., Fons, J., 2015. Future land-use change scenarios for the Black Sea catchment. Environ. Sci. Pol. 46, 26–36. Marinoni, O., Garcia, J.N., Marvanek, S., Prestwidge, D., Clifford, D., Laredo, L.A., 2012. Development of a system to produce maps of agricultural profit on a continental scale: an example for Australia. Agric. Syst. 105, 33–45. Mohammed, A.R., Tarpley, L., 2011. Effects of high night temperature on crop physiology and productivity: plant growth regulators provide a management option. In: Casalegno, D.S. (Ed.), Global Warming Impacts - Case Studies on the Economy, Human Health, and on Urban and Natural Environments. InTech. Monjardino, M., McBeath, T., Ouzman, J., Llewellyn, R., Jones, B., 2015. Farmer riskaversion limits closure of yield and profit gaps: a study of nitrogen management in the southern Australian wheatbelt. Agric. Syst. 137, 108–118. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756. Murray-Rust, D., Rieser, V., Robinson, D.T., Miličič, V., Rounsevell, M., 2013. Agent-based modelling of land use dynamics and residential quality of life for future scenarios. Environ. Model. Softw. 46, 75–89. Nelson, G.C., Rosegrant, M.W., Palazzo, A., Gray, I., Ingersoll, C., Robertson, R., Tokgoz, S., Zhu, T., Sulser, T.B., Ringler, C., Msangi, S., You, L., 2010. Food security, farming, and climate change to 2050: scenarios, results, policy options. IFPRI research monograph. International Food Policy Research Institute, Washington DC. Nelson, G.C., Valin, H., Sands, R.D., Havlik, P., Ahammad, H., Deryng, D., Elliott, J., Fujimori, S., Hasegawa, T., Heyhoe, E., Kyle, P., Von Lampe, M., Lotze-Campen, H., d'Croz, D.M., van Meijl, H., van der Mensbrugghe, D., Mueller, C., Popp, A., Robertson, R., Robinson, S., Schmid, E., Schmitz, C., Tabeau, A., Willenbockel, D., 2014. Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl. Acad. Sci. U. S. A. 111, 3274–3279. Newth, D., Cai, Y., Finnigan, J., Harman, I., Grigg, N., 2015. The shrinking space for climate mitigation. CSIRO Australian National Outlook Working Paper, Canberra, Australia. Nielsen, A.S.E., Plantinga, A.J., Alig, R.J., 2014. Mitigating climate change through afforestation: new cost estimates for the United States. Resour. Energy Econ. 36, 83–98. Nossal, K., Sheng, Y., 2010. Productivity growth: trends, drivers and opportunities for broadacre and dairy industries. Aust. Commodities 17, 216–230. Odegard, I.Y.R., van der Voet, E., 2014. The future of food — scenarios and the effect on natural resource use in agriculture in 2050. Ecol. Econ. 97, 51–59. OECD, 2013. Agricultural Policy Monitoring and Evaluation 2013: OECD Countries and Emerging Economies. OECD Publishing. O'Farrell, P.J., Anderson, P.M.L., 2010. Sustainable multifunctional landscapes: a review to implementation. Curr. Opin. Environ. Sustain. 2, 59–65. Pant, H.M., 2007. Global Trade and Environment Model (GTEM). Australian Bureau of Agricultural and Resource Economics and Sciences Canberra, Australia. Pardey, P.G., Alston, J.M., Chan-Kang, C., 2013. Public agricultural R&D over the past half century: an emerging new world order. Agric. Econ. 44, 103–113. Pardey, P.G., Beddow, J.M., Hurley, T.M., Beatty, T.K.M., Eidman, V.R., 2014. A bounds analysis of world food futures: global agriculture through to 2050. Aust. J. Agric. Resour. Econ. 58, 571–589. Paul, K.I., Reeson, A., Polglase, P., Crossman, N., Freudenberger, D., Hawkins, C., 2013. Economic and employment implications of a carbon market for integrated farm forestry and biodiverse environmental plantings. Land Use Policy 30, 496–506. Polglase, P.J., Reeson, A., Hawkins, C.S., Paul, K.I., Siggins, A.W., Turner, J., Crawford, D.F., Jovanovic, T., Hobbs, T.J., Opie, K., Carwardine, J., Almeida, A., 2013. Potential for forest carbon plantings to offset greenhouse emissions in Australia: economics and constraints to implementation. Clim. Chang. 121, 161–175. Popp, A., Rose, S.K., Calvin, K., Van Vuuren, D.P., Dietrich, J.P., Wise, M., Stehfest, E., Humpenoeder, F., Kyle, P., Van Vliet, J., Bauer, N., Lotze-Campen, H., Klein, D., Kriegler, E., 2014. Land-use transition for bioenergy and climate stabilization: model comparison of drivers, impacts and interactions with other land use based mitigation options. Clim. Chang. 123, 495–509. Prestemon, J.P., Wear, D.N., 2000. Linking harvest choices to timber supply. For. Sci. 46, 377–389.


Ravallion, M., Chen, S., Sangraula, P., 2009. Dollar a day revisited. World Bank Econ. Rev. 23, 163–184. 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. Reilly, J., Melillo, J., Cai, Y., Kicklighter, D., Gurgel, A., Paltsev, S., Cronin, T., Sokolov, A., Schlosser, A., 2012. Using land to mitigate climate change: hitting the target, recognizing the trade-offs. Environ. Sci. Technol. 46, 5672–5679. Rogelj, J., Meinshausen, M., Knutti, R., 2012. Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat. Clim. Chang. 2, 248–253. Rokityanskiy, D., Benitez, P.C., Kraxner, F., McCallum, I., Obersteiner, M., Rametsteiner, E., Yamagata, Y., 2007. Geographically explicit global modeling of land-use change, carbon sequestration, and biomass supply. Technol. Forecast. Soc. Chang. 74, 1057–1082. Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A.C., Müller, C., Arneth, A., Boote, K.J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T.A.M., Schmid, E., Stehfest, E., Yang, H., Jones, J.W., 2014. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. U. S. A. 111, 3268–3273. Rutten, M., van Dijk, M., van Rooij, W., Hilderink, H., 2014. Land use dynamics, climate change, and food security in Vietnam: a global-to-local modeling approach. World Dev. 59, 29–46. Sakschewski, B., von Bloh, W., Huber, V., Müller, C., Bondeau, A., 2014. Feeding 10 billion people under climate change: how large is the production gap of current agricultural systems? Ecol. Model. 288, 103–111. Santelmann, M.V., White, D., Freemark, K., Nassauer, J.I., Eilers, J.M., Vache, K.B., Danielson, B.J., Corry, R.C., Clark, M.E., Polasky, S., Cruse, R.M., Sifneos, J., Rustigian, H., Coiner, C., Wu, J., Debinski, D., 2004. Assessing alternative futures for agriculture in Iowa, USA. Landsc. Ecol. 19, 357–374. Schmitz, C., Biewald, A., Lotze-Campen, H., Popp, A., Dietrich, J.P., Bodirsky, B., Krause, M., Weindl, I., 2012. Trading more food: implications for land use, greenhouse gas emissions, and the food system. Glob. Environ. Chang. Hum. Policy Dimens. 22, 189–209. Schmitz, C., van Meijl, H., Kyle, P., Nelson, G.C., Fujimori, S., Gurgel, A., Havlik, P., Heyhoe, E., d'Croz, D.M., Popp, A., Sands, R., Tabeau, A., van der Mensbrugghe, D., von Lampe, M., Wise, M., Blanc, E., Hasegawa, T., Kavallari, A., Valin, H., 2014. Land- use change trajectories up to 2050: insights from a global agro-economic model comparison. Agric. Econ. 45, 69–84. Schneider, U.A., McCarl, B.A., Schmid, E., 2007. Agricultural sector analysis on greenhouse gas mitigation in US agriculture and forestry. Agric. Syst. 94, 128–140. Schröter, D., Cramer, W., Leemans, R., Prentice, I.C., Araújo, M.B., Arnell, N.W., Bondeau, A., Bugmann, H., Carter, T.R., Gracia, C.A., De La Vega-Leinert, A.C., Erhard, M., Ewert, F., Glendining, M., House, J.I., Kankaanpää, S., Klein, R.J.T., Lavorel, S., Lindner, M., Metzger, M.J., Meyer, J., Mitchell, T.D., Reginster, I., Rounsevell, M., Sabaté, S., Sitch, S., Smith, B., Smith, J., Smith, P., Sykes, M.T., Thonicke, K., Thuiller, W., Tuck, G., Zaehle, S., Zierl, B., 2005. Ecology: ecosystem service supply and vulnerability to global change in europe. Science 310, 1333–1337. Sheng, Y., Mullen, J.D., Zhao, S., 2011. A Turning Point in Agricultural Productivity: Consideration of the Causes. Australian Bureau of Agricultural Resource Economics and Sciences, Canberra, Australia. Sheng, Y., Nossal, K., Ball, E., 2013. Comparing agricultural total factor productivity between Australia, Canada and the United States. 57th AARES Annual Conference, Sydney, Australia. Shi, W., Muthurajan, R., Rahman, H., Selvam, J., Peng, S., Zou, Y., Jagadish, K.S.V., 2013. Source–sink dynamics and proteomic reprogramming under elevated night temperature and their impact on rice yield and grain quality. New Phytol. 197, 825–837. Smith, P., Gregory, P.J., van Vuuren, D., Obersteiner, M., Havlík, P., Rounsevell, M., Woods, J., Stehfest, E., Bellarby, J., 2010. Competition for land. Philos. Trans. R. Soc. B Biol. Sci. 365, 2941–2957. Sohl, T.L., Sleeter, B.M., Sayler, K.L., Bouchard, M.A., Reker, R.R., Bennett, S.L., Sleeter, R.R., Kanengieter, R.L., Zhu, Z., 2012. Spatially explicit land-use and land-cover scenarios for the great plains of the United States. Agric. Ecosyst. Environ. 153, 1–15. Sonneveld, B.G.J.S., Dent, D.L., 2009. How good is GLASOD? J. Environ. Manag. 90, 274–283. Springer, N.P., Duchin, F., 2014. Feeding nine billion people sustainably: conserving land and water through shifting diets and changes in technologies. Environ. Sci. Technol. 48, 4444–4451. United Nations, 2013a. World population prospects. The 2012 revision. Volume I: comprehensive tables. United Nations, Newy York. United Nations, 2013b. world population prospects: the 2012 revision, key findings and advance tables. Working Paper No. ESA/P/WP.227. United Nations, New York, USA. Van Asselen, S., Verburg, P.H., 2013. Land cover change or land-use intensification: simulating land system change with a global-scale land change model. Glob. Chang. Biol. 19, 3648–3667. van Vuuren, D., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S., Rose, S., 2011. The representative concentration pathways: an overview. Clim. Chang. 109, 5–31. Winsten, J., Walker, S., Brown, S., Grimland, S., 2011. Estimating carbon supply curves from afforestation of agricultural land in the northeastern US. Mitig. Adapt. Strateg. Glob. Chang. 16, 925–942.