Journal of Environmental Management 172 (2016) 49e57

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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman

Review

Carbon farming economics: What have we learned? Kai Tang a, b, *, Marit E. Kragt a, c, Atakelty Hailu a, Chunbo Ma a a

School of Agricultural and Resource Economics, University of Western Australia, Crawley, WA 6009, Australia School of Economics and Trade, Guangdong University of Foreign Studies, Guangzhou, Guangdong 510006, China c Centre of Environmental Economics and Policy, University of Western Australia, Crawley, WA 6009, Australia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 April 2015 Received in revised form 17 January 2016 Accepted 5 February 2016 Available online xxx

This study reviewed 62 economic analyses published between 1995 and 2014 on the economic impacts of policies that incentivise agricultural greenhouse (GHG) mitigation. Typically, biophysical models are used to evaluate the changes in GHG mitigation that result from landholders changing their farm and land management practices. The estimated results of biophysical models are then integrated with economic models to simulate the costs of different policy scenarios to production systems. The cost estimates vary between $3 and $130/t CO2 equivalent in 2012 US dollars, depending on the mitigation strategies, spatial locations, and policy scenarios considered. Most studies assessed the consequences of a single, rather than multiple, mitigation strategies, and few considered the co-benefits of carbon farming. These omissions could challenge the reality and robustness of the studies' results. One of the biggest challenges facing agricultural economists is to assess the full extent of the trade-offs involved in carbon farming. We need to improve our biophysical knowledge about carbon farming co-benefits, predict the economic impacts of employing multiple strategies and policy incentives, and develop the associated integrated models, to estimate the full costs and benefits of agricultural GHG mitigation to farmers and the rest of society. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Agricultural GHG mitigation Economic analyses Biophysical models Cost estimates Policy incentives

Contents 1. 2. 3.

4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.1. Biophysical models in carbon farming economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2. Economic models in carbon farming economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3. Economic incentives and policy measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4. Economic estimates of carbon farming strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.1. Carbon sequestration strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.2. GHG emission mitigation strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Current issues in the economics of carbon farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.1. Incorporating co-benefits of carbon farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.2. Analysis of multiple mitigation strategies in carbon farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Conclusions and research priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

1. Introduction

* Corresponding author. School of Agricultural and Resource Economics, University of Western Australia, Crawley, WA 6009, Australia. E-mail address: [email protected] (K. Tang). http://dx.doi.org/10.1016/j.jenvman.2016.02.008 0301-4797/© 2016 Elsevier Ltd. All rights reserved.

The risk posed by global warming due to anthropogenic greenhouse gas (GHG) emissions will be a major challenge for human beings in the coming decades (IPCC, 2007; World Bank, 2010). The agricultural sector is one of the largest producers of

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K. Tang et al. / Journal of Environmental Management 172 (2016) 49e57

nitrous oxide (N2O) and methane (CH4), which are two GHGs with significant global warming potentials (GWP). The GWP of N2O and CH4 are 310 and 21, respectively e which means that they will trap 310 and 20 times more heat than CO2 over a 100 year time horizon (Povellato et al., 2007; Jackson et al., 2009). Agriculture produces about 6.1 giga tonnes of carbon dioxide equivalents (CO2e) per year, accounting for 10 to 12 percent of global GHG emissions (Bonesmo et al., 2012). There is increasing evidence that agriculture can play an important role in removing GHG from the atmosphere (Lal, 1999; Bustamante et al., 2014). For example, agricultural soils offer the potential to absorb CO2. Lal (2004) suggested that, globally, agricultural soils can offset about 15 percent of global GHG emissions. For Australia, Garnaut (2008) estimated that agriculture has a potential to mitigate 84 million tonnes of CO2e GHG per year, equalling about 15% of the national GHG emissions (Department of Agriculture, Fisheries and Forestry, 2012). In recent years, policies aimed at encouraging GHG mitigation activities have been adopted in different parts of the world. These include the European Union Emissions Trading Scheme, Japan's Voluntary Emission Trading Scheme, New Zealand's Emissions Trading Scheme, and the Emission Reduction Fund in Australia.1 Such policies can provide incentives to encourage the adoption of carbon farming practices by landholders. Carbon farming practices refer to those agricultural activities that can sequester carbon and/ or reduce GHG emissions. Carbon sequestration includes conservation tillage, continuous cropping, and rotational cropping that increases soil carbon, or afforestation on agricultural land which stores carbon in vegetation (Antle et al., 2002a; Capalbo et al., 2004; Antle et al., 2007a; Bosch et al., 2008; Gonz alez-Estrada et al., 2008; Hunt, 2008). GHG emission mitigation strategies include livestock and fertiliser management changes (Khakbazan et al., 2009; Berdanier and Conant, 2012; Bonesmo et al., 2012). From an economic perspective, one expects farmers to only adopt a carbon farming practice if the change in practices is profitable. There have been several studies evaluating the economics of agricultural GHG mitigation. These studies address, for example, the costs and profits of carbon farming for landowners, the efficiencies of various mitigation strategies, and the effectiveness of different policy incentives (e.g. Antle et al., 2001; De Cara et al., 2005; Kragt et al., 2012). Notwithstanding the range of economic case-studies, there exists no systematic review that brings together the body of work on the economics of agricultural GHG mitigation. This study attempts to fill this knowledge gap by conducting a comprehensive review of the literature and to identify key lessons by examining the primary tools used, policy scenarios assessed, and mitigation costs estimated.

economics of GHG mitigation in non-agricultural sectors. We therefore ran a search paring the above key words with search terms to reflect specific types of carbon farming practices: conservation agricultural practices, conservation tillage, no-till, minimum-till, continuous cropping, rotational cropping, afforestation, crop residue retention, farming land conversion, fertiliser management, and rotational grazing. All search terms were typed without quotation marks. This search initially yielded 139 papers published in peerreviewed journals. The full text of these 139 papers was checked. Only studies that included empirical analysis of the economics of agricultural GHG mitigation were retained. After this process, 62 studies were identified as relevant. For each of these papers, the following were recorded: the studied region, the type of farming system, the types of GHGs covered, biophysical models used, economic models used, policy incentives studied, and research findings. 3. Results and discussion Many studies have integrated biophysical and economic models to examine the feasibility of GHGs mitigation in agriculture. The results of biophysical models, such as estimated on-farm GHG emissions under different carbon farming practices, are necessary inputs for economic models. Economic models are then used to estimate the expected farm revenues and costs associated with those carbon farming strategies. 3.1. Biophysical models in carbon farming economics Biophysical models typically incorporate information on soil types, climate (e.g. rainfall, temperature), initial or historical land use records, plant types, and livestock structure. These models estimate, amongst other things, crop- and livestock yields, vegetation growth, GHG emission levels, and soil carbon levels. Biophysical models that have been used include the following (Table 1): i) CENTURY, a generalised-biogeochemical ecosystem model simulating nutrient dynamics (Parton et al., 1988); ii) APSIM (Agricultural Production Systems Simulator), a process-based model on a paddock scale (Keating et al., 2003); iii) NCAT (National Carbon Accounting Toolbox), an Australian predictive model for carbon flows in forest and agricultural systems (Australian Greenhouse Office, 2006); iv) EPIC (Environmental Policy Integrated Climate), a model that operates on a daily time step and simulates crop production, soil carbon and nitrogen (Sharpley and Williams, 1990)2; and v) CALM (Carbon Accounting for Land Managers), an online calculator that can be used to estimate GHG emissions on farm scale (Lloyd, 2008).

2. Method We reviewed economic analyses of carbon farming published in peer-reviewed journals between 1995 and 2014. A search for relevant publications was conducted in Google Scholar, Wiley Online Library, Web of Science, Science Direct, and EconLit. We used the following search terms: carbon farming economics, greenhouse gas agricultural economics, climate change agricultural economics, soil carbon, farmland GHG emission, cropland GHG emission, methane reduction, agricultural carbon tax, agricultural carbon credit, agroforestry, REDD, and GHG voluntary market. The returned literature included many studies that focused on the

1 http://www.climatechange.govt.nz/emissions-trading-scheme/about/ international-examples.html.

These models share some commonalities. They all provide estimates of the changes in soil carbon caused by varying carbon farming practices. Except for NCAT, the models also consider nitrogen emissions in agricultural systems. Typically, the models are capable of simulating multiple carbon farming practices, such as crop rotation, fertilisation, and tillage (Table 1). There are also some notable differences among the models. CENTURY, APSIM, and EPIC contain sub-modules for soil GHG

2

Earlier versions of EPIC were called Erosion Productivity Impact Calculator.

K. Tang et al. / Journal of Environmental Management 172 (2016) 49e57

51

Table 1 Biophysical models used in the literature on carbon farming economics. Model

Required input

Output

Farm system studied

CENTURY Climate data, soil data, atmospheric nitrogen, farm management data APSIM Climate data, soil data, farm management data NCATa Climate data, soil data, afforestation activities data b EPIC Climate data, soil data, farm management data

Crop yield; soil Dryland grain carbon; nitrogen production system; Savannah agricultural system Dryland grain Crop yield; soil production system carbon and erosion; Tropical Plant growth; afforestation carbon sequestration Crop yield; soil Humid temperate carbon; nitrogen continental agriculture

CALM

Crop yield; livestock yield; soil carbon; nitrogen; methane

Climate data, soil data, farm management data

Region studied

GHG mitigation strategies studied

Central US; Conservation tillage, rotational cropping, fertiliser management North Ghana

Primary studies Antle et al., 2001; Antle et al., 2002b; Antle et al., 2003; Capalbo et al., 2004; Mooney lezet al., 2004; Antle et al., 2007a; Gonza Estrada et al., 2008 Kragt et al., 2012

Western Australia

Conservation tillage, continuous cropping, rotational cropping

Northeast Australia

Afforestation

Hunt, 2008

Conversion of cropland to perennial grasses

Plantinga and Wu, 2003; Feng and Kling, 2005

Upper Mississippi River basin USA North Temperate Suffolk, maritime agricultural system England UK

Franks and Hadingham, 2012 Conservation tillage, continuous cropping, rotational cropping, afforestation, fertiliser management, livestock management

Notes: aNCAT is a derivative of the National Carbon Accounting System (NCAS). bThe soil organic matter model in EPIC follows the approach used in CENTURY; EPIC involves components for the estimate of co-benefits (soil erosion and nitrogen runoff and leaching).

emissions, crop growth and yield. On the other hand, NCAT and CALM are designed to estimate GHG emissions only. Another difference is that CALM incorporates emissions from crop and livestock on farm level, while CENTURY, EPIC, and NACT do not consider livestock emissions. Most of the biophysical models we reviewed are complex, process-based models that are parameterised for specific soil types, climatic conditions, and farm management strategies. In practice, it is a challenge to apply these models in other farming systems with different biophysical conditions since the new application will require re-parametrisation of a large number of variables. In addition, because of their complexity, these biophysical models require specialist knowledge to support their application. It is difficult to configure and use these models when model-specific expertise is not available. These drawbacks limit the ease of applying these biophysical models. 3.2. Economic models in carbon farming economics Economic models of carbon farming are used to estimate the revenue or costs and trade-offs associated with carbon sequestration or reducing emissions. The models can be econometric-based simulation or mathematical programming models. The econometric ones may combine econometric production functions with a discrete land-use decision simulation model. Site-specific data and farm production simulated by biophysical models are used to estimate production functions. These production functions provide the expected net returns of land-use decisions and parameters of supply, cost, and price distributions for the economic simulation model. Such a model may be used to compare the expected returns for different production activities on different land sites. The selected production activity can subsequently be used in the biophysical model to estimate changes in on-farm GHG emissions. A number of studies have used econometric simulation models to estimate the economic possibility of carbon farming. Antle et al. (2001) developed a stochastic, site-specific econometric-process model, and this model has been used in a series of papers based on a dryland grain production system in central US and a slow formation terrace farming system in north Peru. They evaluated the relationships between carbon farming profitability, spatial heterogeneity, and policy incentives (Antle et al., 2002b, 2003; Capalbo

et al., 2004; Mooney et al., 2004; Antle et al., 2007a, b). They found that the mitigation cost of soil carbon is determined by the type of policy incentives, the site-specific characteristics of the areas, and the amount of carbon sequestrated. Mathematical programming models have also been employed to analyse carbon farming economics. These models are generally designed around assumptions about farmers' behaviour. Mathematical programming techniques are used to solve for an objective function constrained by the available farm resources. The solutions of these optimisation models represent the optimal resource allocations made by farmers. De Cara et al. (2005) and De Cara and Jayet (2011) used a linearprogramming model to maximise overall farm gross margins and simulate the marginal mitigation costs of N2O and CH4 in 15 member states of the European Union. The model consists of a set of independent linear-programming models for multiple farm types to represent the heterogeneity in mitigation costs. The authors conclude that mitigation by the agricultural sector could contribute to lower overall mitigation costs. A similar approach was followed by MacLeod et al. (2010), who present a farm level linearprogramming model that maximises total gross margins and produces marginal GHG mitigation cost curves in the UK. They show that a potential abatement of 5.2 Mt CO2e per year can be accomplished at a cost of less than £100/tCO2e in the UK agricultural sector. Some mathematical programming models used in analysing carbon farming economics aim to maximise overall farm profit rather than gross margin. A typical example is MIDAS (Model of an Integrated Dryland Agricultural System), a steady-state linearprogramming model. MIDAS has been used to conduct economic analyses of carbon sequestration in crop production systems (e.g. Kragt et al., 2012). Analyses in Western Australia (Flugge and Abadi, 2006; Kragt et al., 2012; Thamo et al., 2013) concluded that the cost of carbon farming practices may be substantial. Another example is lez-Estrada et al. (2008), which also aims the model used in Gonza to maximise profits but adds a food security requirement to ensure food supply for farmers. They employed this multiple-criteria mathematical-programming model to address the potential impacts of carbon sequestration practices on farm income in Ghana. There are also cases where the economic model's objective is to minimise the net present value (NPV) of a stream of mitigation

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K. Tang et al. / Journal of Environmental Management 172 (2016) 49e57

costs. Yaron (2001), Parks and Hardie (1995), Stainback and Alavalapati (2002), Zelek and Shively (2003), Nijnik (2004), Hunt (2008), and Hoang et al. (2013) focused on the NPV of costs for afforestation in Cameroon, the USA, Philippines, Ukraine, Australia, and Vietnam, respectively. Khakbazan et al. (2009) developed a whole-farm budgeting model to calculate the NPV of the costs of GHG mitigation by nitrogenous fertiliser in Canada. Those studies generally found that the time frame of analysis and discount rate used will have an obvious impact on the assessed NPV of mitigation costs. The discounting rates used have varied from 1 to 25 percent, which makes it difficult to compare results across studies. None of the models we reviewed consider potential changes in crop yield in responses to changes in soil organic carbon level. The introduction of more flexible yield responses to soil organic carbon level may contribute to lower mitigation costs. Most economic models described above look at the supply of GHG mitigation by farmers. They estimate the mitigation costs for farm's carbon farming practices and then provide comparisons between the estimated costs and the potential financial incentives provided by the government or other buyers of GHG. These analyses assume that the market demand for GHG mitigation is unlimited (e.g. emissions permits can be traded without limits at a constant price (Key and Tallard, 2012)). Few studies have considered the market demand side in their economic models. An exception is Bakam et al. (2012), who designed a carbon trading scheme which assumes that the permit price is affected by both supply and demand. They argued that this trading scheme appears to be more effective than carbon tax and nitrogen tax in practices. Future economic models could also be designed to consider the economic impacts generated by varying market demand for GHG mitigation. 3.3. Economic incentives and policy measures Currently, few mandatory policy incentives for agricultural GHG mitigation activities have been implemented. New Zealand is the only country that has legislated mandatory inclusion of agriculture in a market-based instrument to reduce emissions from 2015 (Cooper et al., 2013).3 Some examples exist that allow farmers to sell GHG reduction credits in voluntary markets, e.g. the Regional Greenhouse Gas Initiative in the US (Regional Greenhouse Gas Initiative, 2015) and the Emission Reduction Fund in Australia (Department of the Environment, 2014a). However, none of the papers reviewed for this study has empirically evaluated the economic impacts of such voluntary policies on agriculture. Existing analyses have focused on hypothetical policy instruments such as per-tonne payments (carbon credits), per-hectare payments, and cost-based payments. Many studies consider the effects of output-oriented per-tonne payments (e.g. Callaway and McCarl, 1996; Peters et al., 2001; Sohngen and Sedjo, 2006; Schneider et al., 2007; Golub et al., 2009; Skidmore et al., 2014). Under per-tonne payments, farmers are assumed to receive payments from the government or a carbon market for the amount of GHG mitigation produced by altering land use and farm management practices (Schneider and McCarl, 2005; Lee et al., 2007; Antle et al., 2010; Delacote et al., 2014). Carbon credits are treated as tradable goods (Kurkalova, 2005; Pohjola and Valsta, 2007; Beach et al., 2008; Butler et al., 2009). A common practice in the literature is to assess a range of carbon prices (per tonne payments). For example, Antle et al. (2003) assumed a pertonne payment ranging from $10 to $100 in increments of $10, and De Cara and Jayet (2011) described an emission price varying

3 At the time of writing, the authors are not aware of any other countries that require the agricultural sector to pay for its GHG emissions.

from V0 to V10,000 per tCO2e. Rather than output-oriented carbon prices, per-hectare payments are input-based. Farmers can receive payments from the government based on the area of land enrolled in a GHG mitigation programme. For example, Antle et al. (2001) analysed the effects of a fixed annual per hectare payment, ranging from $5 to $50 in increments of $5. Similar assumptions were used by Alavalapati et al. (2002), Antle et al. (2002b, 2003), Capalbo et al. (2004), Lubowski et al. (2006), and Guthrie and Kumareswaran (2009). Payments usually come in the form of cash, but sometimes they can also be provided as a combination of cash and physical goods. Xu et al. (2007) mentioned the Chinese “Grain-For-Green” project,4 in which the central government provided 150 kg of food and 20 yuan5 in cash to subsidize every mu6 of land converted to forest for 5e8 years. Cost-based payments have also been assessed by several studies. In this scheme, farmers can be compensated based on the opportunity costs and conversion costs of their agricultural GHG mitigation activities. Parks and Hardie (1995) represented a contract payment covering half of the forest establishment costs and provide an annual rental fee for ten years. Zelek and Shively (2003) proposed a payment for the opportunity costs plus conversion costs. Generally these studies concluded that the cost-based payments, especially the schemes based on least cost per tonne, are cost effective. The majority of empirical studies cover a single payment scheme, although there have been some studies that compare alternative schemes. Antle et al. (2003) compared the efficiency of a fixed annual per-hectare payment and a per-tonne payment and found that the per-tonne contract is more efficient after accounting for measurement costs. Bakam et al. (2012) simulated a per-tonne incentive and a credit trading scheme, arguing that the costeffectiveness of incentives depends on the incentive rate and the amount of free credits allocated to farmers.

3.4. Economic estimates of carbon farming strategies7 3.4.1. Carbon sequestration strategies Carbon sequestration is the process of storing carbon in terrestrial vegetation or soils, thus removing it from the atmosphere (Pendell et al., 2007; Luo et al., 2011). Soil can be a source (CO2, CH4 and N2O) or sink (CO2 and CH4) of GHGs depending on the change in (permanent) land use and the changes in management within existing land uses (Lal, 1999). It has been estimated that 1500 Gt of carbon is stored in soils globally, approximately double the amount of carbon in the atmosphere and three times the quantity stored in terrestrial biomass (Bernoux et al., 2006). Given the considerable potential of soil carbon sequestration, researchers have attempted to evaluate the economic impacts of a number of strategies which can enhance carbon storage in agricultural soils. Switching from conventional to conservation tillage, including no-till and minimum-till, could decrease carbon oxidation and CO2 emission from soil as well as increase carbon sequestration (Lal and Kimble, 1997; Grace et al., 2010; Luo et al., 2010a, b). Conservation tillage is potentially economically feasible for carbon farming in various locations (Fig. 1). In the Central US, even without any policy

4 Though carbon sequestration was not part of the original project objectives, the large-scale application of the ‘‘Grain-For-Green’’ project will contribute to reducing atmospheric CO2 over the long run. 5 1 USD ¼ 8.27 yuan in 2004. 6 1 ha ¼ 15 mu. 7 All prices used in Section 3.4 have been converted to 2012 US Dollar using Purchasing Power Parities for private consumption and Consumer Price Index.

K. Tang et al. / Journal of Environmental Management 172 (2016) 49e57

$140

Hunt (2008)

Kragt et al. (2012)

Stavins (1999)

Thamo et al. (2013)

Flugge and Abadi (2006)

Yaron (2001)

Grace et al. (2010)

Cacho et al. (2003)

De Jong et al. (2000)

Antle et al. (2007)

Antle et al. (2001,2002b)

McCarl and Schneider (2001)

Wise and Cacho (2005)

Grace et al. (2010)

Nijnik (2004)

Fisher et al. (2011)

Hoang et al. (2013)

$20

Bellassen and Gitz (2008)

$40

Zelek and Shively (2003)

$80

Parks and Hardie (1995)

$100

Tschakert (2004)

afforesta on conserva on llage con nuous cropping rota onal cropping

$120

$60

53

$0 Fig. 1. Mitigation costs of different carbon sequestration strategies81(in 2012 US Dollar).

Table 2 Summary of carbon farming economics studies that have estimated carbon mitigation costsa. Strategy

Study

Region

Target farm system

Mitigation cost ($/t CO2e)b

Conservation tillage

McCarl and Schneider (2001) Antle et al. (2007a) Grace et al. (2010) Grace et al. (2012) Antle et al. (2001, 2002b)

USA

n.a.

12.97

Central USA South-eastern Region of Australia India's Indo-Gangetic Plain Montana USA

Dryland grain production system Dryland grain production system Rice-wheat agricultural systems Dryland grain production system

19.31 10.84 25 20.34 (mean)

Old Peanut Basin, Senegal Wheatbelt, Western Australia USA South central USA The Central Highlands of Chiapas, southern Mexico Mount Cameroon region, Cameroon Gippsland and Mount Gambier, Australia Mindanao Philippines Ukraine Argentinean Patagonia Sumatra, Indonesia Wheatbelt, Western Australia North Queensland Australia Congo Basin, Cameroon East and middle Tanzania Bac Kan province, Vietnam Wheatbelt, Western Australia

Semi-arid mixed agriculture Dryland grain production system n.a. n.a. Subtropical to temperate, subhumid agriculture Tropic rainforest agriculture Temperate treeecrop agroforest Tropical agriculture Wastelands and low-profit agricultural lands n.a. Tropical treeecrop agroforest Dryland grain production system Wet tropic agriculture Tropic rainforest agriculture Savanna agriculture Subtropical agriculture Dryland grain production system

129.71 51.90 41.54 50.60 20.67

Continuous cropping Crop rotation Afforestation

Tschakert (2004) Kragt et al. (2012) Parks and Hardie (1995) Stavins (1999) De Jong et al. (2000) Yaron (2001) Cacho et al. (2003) Zelek and Shively (2003) Nijnik (2004) Olschewski et al. (2005) Wise and Cacho (2005) Flugge and Abadi (2006) Hunt (2008) Bellassen and Gitz (2008) Fisher et al. (2011) Hoang et al. (2013) Thamo et al. (2013)

26.18 25.69 12.59 6.82 7.78 12.48 36.13 50.54 3.35 6.84 5 50

Notes: aSome studies only provide incentive prices for farmers rather than calling these direct mitigation costs. We assume that the lowest acceptable incentive price equals to the mitigation cost. bAll prices have been converted to 2012 US Dollar as follows: First all prices are converted into US Dollar in the year of the analysis by using annual Purchasing Power Parities for private consumption; then those dollar prices are discounted using the Consumer Price Index provided by US Bureau of Labour Statistics. c n.a. ¼ not available.

incentives, no-till systems can be profitable and sequestrate about 1.51e2.87t CO2e per hectare per year (Pendell et al., 2007). If carbon credits are provided at a price of $19.31/t CO2e, this region could sequestrate approximately 3.12 million tonnes CO2e per year (Antle et al., 2007a). In the southern region of Australia, no-till could achieve a net sequestration rate of 0.30e2.33t CO2e/ha/year while minimum-till has a net sequestration rate of 0.10e1.18t CO2e/ha/ year (Grace et al., 2010). The adaption of no-till with a carbon payment at $10.84/t CO2e in this region could result in about 0.32 million tonne CO2e being sequestrated annually (Grace et al., 2010). In India's Indo-Gangetic Plain, applying no-till in rice-wheat agricultural systems with a carbon payment at $25/t CO2e could achieve a sequestration rate of around 0.3t CO2e/ha/year (Grace et al.,

2012). Continuous cropping shortens the fallow period and thus decreases the rate of soil organic carbon decomposition and can lead to an increase in soil carbon stocks. Studies are optimistic about the economic potential of continuous cropping for agricultural GHG mitigation in both developed and developing countries. In the US state of Montana, the marginal costs for switching from a cropfallow rotation or permanent grass to continuous cropping system were estimated at around $20/t CO2e (Antle et al., 2001, 2002b). In Ghana, adapting continuous cropping for carbon farming could increase the net present value of farm profits by lez-Estrada et al., 2008). between 2 and 32 percent (Gonza Rotational cropping can decrease carbon oxidation and increase

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aggregate stability and the concentration of soil carbon (Blair and Crocker, 2000). The economic potential for rotational cropping differs across locations (Table 2). In Western Australia, the average cost of rotations comprising combinations of 10 crops has been estimated at more than $51.89/t CO2e (Kragt et al., 2012), while the cost of a millet-groundnuts rotation in semi-arid western Senegal has been estimated at more than $129.71/t CO2e (Tschakert, 2004). There are many studies that consider afforestation on agricultural land as a sequestration strategy (Fig. 1). Generally, in developing countries, conversion of agricultural land to forest could be achieved at relatively low costs, ranging from $20.67/t CO2e in Mexico (De Jong et al., 2000) to $5/t CO2e in Vietnam (Hoang et al., 2013). In developed countries, the costs are higher, ranging from $25.69 to $53/t CO2e (Parks and Hardie, 1995; Stavins, 1999; Cacho et al., 2003; Flugge and Abadi, 2006; Hunt, 2008; Thamo et al., 2013) (Table 2). Some studies argue that the economic feasibility of carbon farming through afforestation or reforestation is limited (Flugge and Abadi, 2006; Hunt, 2008). The cost of other carbon sequestration strategies such as increasing retention of crop residues and restoring wetlands on farm lands have also been studied (Kragt et al., 2012; Hansen, 2009). Overall, current findings indicate that the economic returns of those strategies are limited. In the studies reviewed for this paper, the estimated costs of agricultural sequestration strategies vary widely: between $3 and $130/t CO2e (Table 2). The estimated mitigation costs depend on the region of analysis, the farming system and the mitigation strategy analysed. Developed countries may achieve a relatively low-cost carbon sequestration by adopting conservation tillage and continuous cropping. In developing countries, continuous cropping and afforestation are potentially viable agricultural sequestration strategies.

3.4.2. GHG emission mitigation strategies The vast majority of carbon farming studies focus on carbon sequestration. However, as agriculture is responsible for a large proportion of global CH4 and N2O emissions (Povellato et al., 2007; Jackson et al., 2009), it is important to also explore the costeffectiveness of emission mitigation strategies for non-CO2 GHGs. Research has shown that, in cropping systems, GHG emission can be reduced by placing fertiliser in the soil at recommended rates and time (Khakbazan et al., 2009). There is debate about the economic potential of fertiliser management. For example, Pendell et al. (2007) argued that manure systems (beef cattle) could be effective in north-eastern Kansas if enough incentives are available (net returns ranging from $58.46 to $149.83/ha). Khakbazan et al. (2009), on the other hand, demonstrated that the economic potential of changing fertiliser rates to reduce carbon emissions is limited in western Canada. Livestock management is another potential GHG mitigation strategy. Converting from continuously grazing livestock on a single field to rotating livestock on multiple paddocks (called ‘rotational grazing’) may reduce GHG emissions. Bosch et al. (2008) showed that e for cow-calf and dairy farms in Virginia, US e rotational grazing could increase income by 6e19 percent per hectare while reducing GHG emissions, provided suitable financial support would be provided.

8 Some studies only provide acceptable incentive price for farmers without direct mitigation costs. Here we assume that the lowest acceptable incentive price equals to the mitigation cost.

4. Current issues in the economics of carbon farming While a great deal of research has investigated the cost and potential economic return of agricultural GHG mitigation strategies, there are still issues that require more attention.

4.1. Incorporating co-benefits of carbon farming Carbon farming is believed to lead to a number of potential cobenefits. These additional co-benefits include water quality improvement (Jackson et al., 2005; Pattanayak et al., 2005), soil health improvements (Plantinga and Wu, 2003; Feng et al., 2007), and biodiversity protection (Nelson et al., 2008), but also social , 2008), food sebenefits such as poverty relief (Ebeling and Yasue curity (Lal, 2010), public health benefit (Friel et al., 2009), infrastructure improvement (Garnaut, 2011), and indigenous community development (Department of the Environment, 2014b). Studies have shown that the co-benefits of carbon farming are an important motivator for farmers to undertake carbon management. For example, Miller et al. (2012, 2014) found that private forestland owners in the Lake States region of the US may be more interested in the co-benefits of afforestation than the income from selling carbon credits per se. The combination of GHG emission mitigation and its associated co-benefits will have an impact on the results of bio-economic analyses of carbon farming, and sometimes these impacts may be considerable (Lal, 2010, 2013). For instance, Feng and Kling (2005) stated that in the Upper Mississippi River basin 3.29 million tonnes of carbon sequestration yields multiple cobenefits in the form of reduced soil erosion, nitrogen runoff, and nitrogen leaching. However, economic studies focussing on carbon farming usually do not include co-benefits in their assessment. Except for EPIC, none of the biophysical models we reviewed estimate co-benefits of carbon farming. Most empirical studies focus on the possible amount of GHGs mitigated, thereby neglecting any potential nonGHG environmental and social co-benefits of carbon farming. A possible barrier is that it is difficult to adequately measure some of the co-benefits. For instance, social co-benefits, such as local community development, are hard to quantify due to other confounding factors such as economic and social developments and changes in behaviour over time (Bustamante et al., 2014). There may also be limited scientific knowledge and biophysical data available about the extent of co-benefits generated. The omission of co-benefits means that current literature potentially underestimates the economic value of carbon farming practices. The existence of additional socio-economic and environmental co-benefits means that agricultural abatement activities that produce co-benefits could potentially yield a higher carbon price. Such premiums would compensate farmers for the costs they incur to reduce GHG emissions, thus enhancing the economic attractiveness of carbon farming. However, it should be pointed out that co-benefits may not accrue to the individual farmer but rather may be public goods. That complicates the calculus, particularly if farmers aren't motivated to act in the name of public goods. To improve our understanding of the benefits of carbon farming, research is needed on the overall (co-)benefits of carbon farming activities. Interdisciplinary studies will increase our knowledge about the extent of co-benefits of carbon farming and what values such co-benefits provide. The development of integrated models, incorporation of environmental valuation, and application to various carbon farming scenarios are necessary to increase our empirical understanding of the co-benefits of carbon farming. Only with such increased knowledge can we improve the design of carbon farming policies.

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4.2. Analysis of multiple mitigation strategies in carbon farming To date, most studies have assessed the mitigation potential of single mitigation strategies. Although there are some studies that have explored a combination of strategies and their combined effects (Bonesmo et al., 2012; Thamo et al., 2013), we still know little about the impacts of employing multiple mitigation strategies in the agricultural sector. An important reason for jointly assessing multiple mitigation strategies is to avoid double counting of emission reductions. Since some mitigation strategies may work on the same source of emission, analysing carbon farming with combinations of multiple mitigation strategies can potentially improve the accuracy of estimates (Del Prado et al., 2010). In addition, identifying the comprehensive range of benefits and costs of various combinations of strategies is an essential step toward designing effective mitigation policies (Plantinga and Wu, 2003). There are currently few bio-economic models that are capable of systematically exploring the complex interactions between multiple mitigation strategies, the inherent site-specific factors affecting GHG emissions, and their effects on farm economics. For example, biophysical models may consider crop and fertiliser emissions but don't integrate emissions from livestock. Designing bio-economic models that allow for a richer analysis of the integrated impacts of the combinations of multiple mitigation strategies is needed. It is advisable that such integrated bio-economic models are designed at a whole-farm level to allow an examination of the complex inter-enterprise interactions and optimal farm management decisions (Robertson et al., 2012). Moreover, whole-farm systems models can help to improve national inventories. Wholefarm models that integrate the joint effects of multiple management practices could improve the way in which the impacts of multiple mitigation strategies are incorporated into national inventories (Bonesmo et al., 2012). 5. Conclusions and research priorities This study reviewed 62 economic analyses published between 1995 and 2014 that investigate the economics of agricultural GHG mitigation. A general lesson from this review is that agricultural GHG mitigation is potentially attractive, depending on the farming system, location and mitigation practices analysed. Biophysical models are typically used to evaluate the changes caused by agricultural activities and farm management practices targeting GHG mitigation. The estimated results from biophysical models are then integrated into various economic models to simulate the economic impacts of adopting carbon farming practices. A key focus of previous studies has been the cost-effectiveness of agricultural GHG mitigation strategies. Cost estimates vary widely, depending on the mitigation strategies, spatial locations, and policy scenarios considered. We showed that there are few studies that considered the cobenefits of carbon farming in their analyses. The presence of cobenefits may increase the social value of carbon farming and motivate farmers to adopt mitigation practices. It has been shown that, even if policy incentives are available and mitigation practices are cost-effective, not all farmers will adopt mitigation activities (Miller et al., 2014). Therefore, additional information is needed to understand how farmers' interests and attitudes influence the adoption of carbon farming practices. There are studies ongoing that consider farmers' motivations to undertake GHG mitigation practices (Kragt et al., 2014). Most studies focus on a single GHG mitigation strategy, rather than evaluating the integrated impacts of multiple strategies. These shortcomings imply that the estimates obtained in the literature

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may need to be interpreted with caution. Studies that evaluate the impacts of adopting multiple GHG mitigation strategies at a wholefarm level will be useful to assess the joint effects of potential mitigation strategies and to clarify the potential impacts of alternative policy incentives. More research is needed to fully capture the whole range of trade-offs associated with GHG mitigation approaches. One of the biggest challenges facing agricultural economists is to assess the full extent of the trade-offs involved in carbon farming. We need to further improve our biophysical, social and economic knowledge about the multiple impacts of carbon farming strategies, and develop better and integrated models, to estimate the full costs and benefits of agricultural GHG mitigation to farmers and the rest of society. References Alavalapati, J.R., Stainback, G.A., Carter, D.R., 2002. Restoration of the longleaf pine ecosystem on private lands in the US South: an ecological economic analysis. 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