P ATRONES DE DISEÑO

Several mitigation options exist in the agriculture sector: reducing emissions (efficient management of carbon and nitrogen flows significant to decrease CO2, N2O and CH4 released by farm practices); enhancing removals (reserving carbon in ecosystems, particularly soil carbon sequestration and vegetative carbon storage) and avoiding or displacing emissions (converting crops and residues into biofuel) (IPCC, 2007b). Carbon removal and sinks have become the central focus of discussion in the literature on agricultural mitigation, because these practices and technologies potentially provide FS co-benefits.

Soil carbon sequestration transfers atmospheric CO2, into long-lived pools and prevents its immediate re-emission (Lal, 2004). At the global scale, about 1,500 Gt of carbon is sequestered in the soil pool, 2 times higher than that of the atmospheric pool and 3 times higher than that of the biotic pool. Carbon is sequestered in planetary soil at an estimated rate, though large variations from 0.4 to 1.2 Gt C yr^-1 and SOC potentially enhance FS globally. For example, an increase of 1 ton of soil carbon pool of degraded farmland soil could increase crop yield by 20 to 40 kg ha^-1 for wheat, 10 to 20 kg ha^-1 for maize, and 0.5 to 1.0 kg ha^-1 for cowpea (Lal, 2004).

Besides SOC, vegetation or plant biomass is considered a significant pool for carbon storage (IPCC, 2007b). Long rotation agricultural systems such as agroforestry, home-gardens and perennial plantations can sequester substantial amounts of carbon in plant biomass and in long-lasting wood products. Such areas on earth could store 1.1 to 1.2 billion tons of carbon for a

period of 50 years. Agroforestry systems could sequester from 12 to 228 tons C ha^-1 (Albrecht and Kandji, 2003). Many mitigation opportunities could be created from current technologies and hence, can be implemented immediately. However, technological development and innovation will play a key role in driving mitigation measures in the future.

There have been several studies investigating the economics of agricultural GHG mitigation, including costs and benefits of carbon farming to practitioners, the efficiency of mitigation strategies and the effectiveness of policy incentives. Recent review of carbon farming economics carried out by Tang et al. (2016) indicate that many carbon farming studies popularly combine biophysical and economic models to evaluate mitigation options in terms of feasibility and practical soundness. The output of biophysical models (e.g. on-farm emission or storage) are then applied in economic models to estimate farm revenues and costs.

Biophysical models

In most cases, biophysical models are constructed from information on soil types, climate, current or past land use records, plant types, and livestock structure. These models estimate, among other things, crop and livestock yields, vegetation growth, GHG emission levels, and soil carbon levels. The popular applied models include: (i) CENTURY, a generalized-biogeochemical ecosystem model simulating nutrient dynamics; (ii) APSIM (Agricultural Production Systems Simulator), a process-based model on a paddock scale; (iii) NCAT (National Carbon Accounting Toolbox), an Australian predictive model for carbon flows in forest and agricultural systems; (iv) EPIC (Environmental Policy Integrated Climate), a model that operates on a daily time step and simulates crop production, soil carbon and nitrogen; and (v) CALM (Carbon Accounting for Land Managers), an online calculator that can be used to estimate GHG emissions on a farm scale. These models commonly estimate soil carbon changes and enable simulating multiple carbon farming practices (crop rotation, fertilization, and tillage). Nonetheless, these models are limited in wide application since they are complex, process- and specific parameter-based. New application requires a large number of variable inputs to reset parameters (Tang et al., 2016).

A part from these models, carbon footprint analysis, a life cycle assessment (LCA)–based approach, has been developed and widely applied in various sectors worldwide to measure sustainability or GHG-intensiveness of any good or service (Franchetti and Apul, 2013). In ISO/TS 14067 standard, carbon footprint of products (CFP) is defined as “the quantity of GHGs expressed in terms of CO2-e, emitted into the atmosphere by individual, organization, process,

product, or event from within a specified boundary”. In principal, the standard can be implemented as a full or partial LCA. CFP is the “sum of GHGs and removals in a product system, expressed as CO2 equivalents and based on a life cycle assessment using the single impact category of climate change” and partial CFP is the “sum of greenhouse gas emissions and removals of one or more selected process(es) of a product system, expressed as CO2 equivalents and based on the relevant stages or processes within the life cycle” (ISO, 2013).

This CFP is originally and intensively applied to assess material and energy in industrial production but recently it has been used widely in agriculture and aquaculture. Several studies have been conducted to investigate all environmental impacts (full LCA) or only global warming potential (a CFP). In aquaculture, examples include: LCA intensive and semi-intensive shrimp farming systems in Hainan Province, China (Cao et al., 2011); LCA eco-labeling in farmed shrimp product (Mungkung et al., 2006); LCA food production in integrated agriculture–

aquaculture systems in the Mekong Delta, Viet Nam (Phong et al., 2011); CFP of farmed catfish Viet Nam (Bosma et al., 2011; Henriksson et al., 2015); and LCA organic and conventional mangrove-shrimp farms in Viet Nam (Jonell and Henriksson, 2015).

There are many LCA and CFP studies on agricultural practices published in the literature. Among these, few have focused intensively on carbon footprint, the interest of this research, including CFP wheat production in Australia (Biswas et al., 2008), CFP in banana supply chain in Costa Rica (Svanes and Aronsson, 2013), CFP rice production in California, U.S.A (Brodt et al., 2014), CFP in maize production in China (Wang et al., 2015), and CFP organic and conventional Darjeeling tea in India (Cichorowski et al., 2015). Many of these studies have set their boundaries at the farm-gate and few attempted to go beyond the farm (e.g. Darjeeling tea). For perennial cropping systems, there have been 103 peer-reviewed LCA/CFP studies on 14 products, most also focusing on the farm level (Bessou et al., 2013). LCA/CFP studies on tea production in Southeast Asia and in Viet Nam are extremely scarce. This research contributes to this literature by conducting a partial or “cradle-to-gate” CFP for fresh tea production to evaluate the mitigation potential in this system.

Economic models

The processes of estimation of costs, revenues or trade-offs associated with carbon sequestration or emissions typically involve either econometrics-based simulations or mathematical programming techniques. In econometric models, production functions can be combined with a

discrete land use decision simulation. Simulated site-specific data and farm production from biophysical models are used to estimate production functions (net returns, cost and price) for simulating in economic models (Tang et al., 2016). Several studies have used economic simulation models to estimate the economic possibility of carbon sequestration practices and evaluate associations between farm profitability, spatial heterogeneity, and policy incentives (Antle et al., 2003; 2007; Capalbo et al., 2004).

Mathematical programming models have also been applied in analyzing economic optimization of certain mitigation options constrained by farm resources (Tang et al., 2016). Solving the problem of optimal resource allocation provides farmers with sound solutions for integrating climate-smart practices into farm activities. A linear programming model has been used to maximize overall farm GMs and simulate the marginal mitigation costs of GHG in the EU (De Cara et al., 2005; De Cara and Jayet, 2011) and in the UK (MacLeod et al., 2010). Both of them conclude that agriculture could generally lower mitigation costs. Some researchers have tried this technique to maximize overall farm profit rather than GMs in analyzing carbon sequestration in crop production systems (Kragt et al., 2012). González-Estrada et al. (2008) also followed this approach but they integrated FS requirement into the model to ensure food supply for farmers.

However, none of the above models take into account potential changes in crop output as a result of changes in SOC levels and vice versa (Tang et al., 2016). Evidence base for carbon farming economics in Asia and Viet Nam is limited in the literature.

Costs of agricultural mitigation options

Agriculture offers a variety of cost-effective, high economic potential GHG mitigation options.

According to IPCC (2007b), agricultural actions are found to be cost competitive compared with non-agricultural technologies (e.g., energy, transportation, forestry) in achieving long-run climate targets. Long-term estimations, exception soil carbon management options, show that non-CO2

crop and livestock abatement options could cost-effectively contribute 270–1520 MtCO2-e yr^-1 globally in 2030 with carbon prices up to 20 US$/tCO2-e, or 640–1870 MtCO2-e yr^-1 with carbon prices up to 50 US$/tCO2-e. If all gases are considered, mitigation economic potential from agriculture is estimated to be 1500-1600, 2500-2700, and 4000-4300 MtCO2-e yr^-1 at carbon prices of up to 20, 50 and 100 US$/tCO2-e, respectively. In which, two-thirds of the potential is from developing countries.

Achieving those potentials, however, requires us to understand and estimate costs associated with the options. McCarthy et al. (2011) categorized costs related to the adoption of SLM technologies into five groups: investment costs (expenditure for on-farm structure); maintenance costs (recurrent expenses and periodic costs); opportunity costs (benefit forgone by allocating own resources to SLM practices instead of to other alternatives); transaction costs (bargaining, negotiation, monitoring and enforcement), and risk costs (uncertainty). Out of these, opportunity costs are the most important in enabling the transitions from conventional to CSA practices.

Investment costs, including up-front and maintenance costs, must be satisfied by increased yields in the future and higher benefits than costs discounted over a certain timeline to sustain SLM in the long term (Branca et al., 2009; Lipper et al., 2011; McCarthy et al., 2011). Literature summaries indicate that land-based agricultural mitigation options cost from 0 to 2,060 US$ ha^-1 (establishment costs) and from 12 to 814 US$ ha^-1 yr^-1 (maintenance costs) (Lipper et al., 2011).

The cost estimates of some carbon farming practices vary between $3 and $130/ tCO2-e in 2012 US dollars (Tang et al., 2016). In short, costs associated with agricultural GHG mitigation options show wide variation, depending on the mitigation strategies, spatial locations and scenario considered.