Farm types as an interface between an agroeconomical model and CLUE-Naban land change model: Application for scenario modelling

Ecological Indicators 36 (2014) 766–778

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Farm types as an interface between an agroeconomical model and CLUE-Naban land change model: Application for scenario modelling Tarig Mohammed Gibreel a,∗ , Sylvia Herrmann b , Karin Berkhoff b , Ernst-August Nuppenau c , Alexandra Rinn c a

Justus-Liebig-Universität, Institut für Betriebslehre der Agrar-und Ernährungswissenschaften, Senckenbergstr. 3, 35390 Gießen, Germany Leibniz Universität Hannover, Institute for Environmental Planning, Herrenhäuser Straße 2, 30419 Hannover, Germany c Institute of Agricultural Policy and Market Research, Justus-Liebig-Universität, Senckenbergstr. 3, 35390 Giessen, Germany b

a r t i c l e

i n f o

Article history: Received 10 March 2013 Received in revised form 7 September 2013 Accepted 8 September 2013 Keywords: Land use change scenario CLUE-Naban land-change model GAMS VFHM model Farm type cluster analysis Rubber Planning support BAU-scenario

a b s t r a c t Land use planning has to consider different development goals, for instance, economic profit, biodiversity conservation, and the protection of traditional land use techniques. To evaluate different land use change scenarios for sustainable development, land use managers in the study area in southwest of China were provided with an integrated modelling approach. We applied the CLUE-Naban land-change model and a GAMS-based village farm household model (VFHM) to model a business as usual scenario (BAU) at the regional scale. The scenario was driven by the demand for different land cover types, as given by the VFHM model. In our approach, this aggregated demand was disaggregated to grid cells of 0.09 ha size with the help of the CLUE-Naban land-change model and the farm types were defined as interface between the two models. Two farm types with characteristic land management regimes and public land type were identified using cluster analysis. The results of the BAU-scenario show that the area of rubber plantation in the lowlands more likely to increase until the year 2025. Hemp was introduced as a cash crop in the highlands of the study area. Areas with the most land use changes were where land was converted from extensively-used cropland to intensively-used rubber plantations. In this paper, an organizational heuristic with two conceptual models for linking land change with driving forces and actors is presented. Therefore, the CLUE-Naban approach can contribute to improve land use planning, because this approach creates spatially explicit land use change scenarios at the regional scale and also considers the socioeconomic driving factors that influence land management issues as considered in the VFHM model. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction One of the most important goals of global change research is to understand the causes and consequences of land change (Lambin and Geist, 2006; Lambin et al., 2003; Rindfuss et al., 2004). Accordingly, land change has become a central research theme in the last decade (Turner and Robbins, 2008; Turner et al., 2007). Therefore, the aim of land change science is to understand the biophysical and human causes of the land use and land cover patterns and the dynamics affecting the structure and function of the earth system (Rindfuss et al., 2004).

∗ Corresponding author. Tel.: +49 0641 99 37316; fax: +49 0641 99 37319. E-mail addresses: [email protected], [email protected] (T.M. Gibreel), [email protected] (S. Herrmann), [email protected] (K. Berkhoff), [email protected] (E.-A. Nuppenau), [email protected] (A. Rinn). 1470-160X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2013.09.009

Land change models analyse the causes and consequences of land use changes to better understand the functioning of the land use system and to support land use planning and policy (Verburg et al., 2004). Simultaneously, land use models are used to explore the effects of possible future changes in driving factors on land use. In this sense, models are used to provide ex-ante assessments of policies or to serve as an early warning system for environmental impacts (Verburg et al., 2006a; Rounsevell et al., 2006). Many models are descriptive models, which simulate the functioning of the land use system and the spatially explicit layout of near future land use patterns (Verburg et al., 2004). In contrast, prescriptive models, aim at the calculation of optimized land use configurations that best match a set of goals and objectives. The models support land use planning and decisions and the model output is used in further studies, for instance in ecology. Consequently, comprehensive land-change modelling (for planning purposes) has to consider the three dimensions of sustainable development: ecology, economy, and society; and the modelling should be spatially explicit. Modelling alternative scenarios based

T.M. Gibreel et al. / Ecological Indicators 36 (2014) 766–778

on this integrated approach provides decision-support for planning (Koh and Ghazoul, 2010). van Delden et al. (2011) give an overview of recent integrated modelling approaches for policy support. The transformation of land use and land cover is driven by a range of different factors and mechanisms. The key determinants of Land use change at different spatial and temporal scales are climate, technology and economics (Koomen et al., 2007). Economic drivers, such as agriculture commercialization, increasing export earnings, modern technologies, etc., are powerful factors that shape landscapes towards market oriented agricultural production (Nuppenau, 2008). Consequently, farmers are major contributors to change as identified by Bohnet and Potter (2003) in saying that; changing the attitudes to land use is well thought-out to be crucial factor for land use decisions. Since the 1980s households have become the basic units of land use decision-making in China (Shi and Chen, 2004). In the last decades the local communities in the study area of the Naban River Watershed National Nature Reserve, located in the Xishuangbanna prefecture of Yunnan province in South-West of China, were heavily influenced by the rapid development of the rubber industry. These communities were experiencing quick transformation from subsistence agricultural production to intensive cash crop cultivation. This transformation has been made possible mainly by the land tenure reformation and the formal governmental agricultural extension through introduction of new crops and varieties such as rubber (Hevea brasiliensis) and hybrid paddy rice (Oryza sativa) (Tang et al., 2010). Rubber has become the main cash crop for many farm households and changed the landscape as well as traditional farming systems, rapidly. As a result, the secondary forest areas, which were part of the highland swidden system, have been converted into permanent agricultural land. Rapid land use change in the study area has been characterized by increasing monoculture rubber plantations, which greatly affects the flora and fauna diversity and further deteriorates fragile mountainous ecosystems. In conclusion, commercialization of agricultural systems leads to greater market orientation of farm production; progressive substitution out of non-traded inputs in favour of purchased inputs; and the gradual decline of integrated farming systems and their replacement by specialized enterprises for mono-cropping. While economically a very efficient system, allowing for specialization in equipment and crop production, monocropping is also controversial, as it can damage the soil ecology (including depletion or reduction in diversity of soil nutrients) and provide an unbuffered niche for parasitic species, increasing crop vulnerability to opportunistic insects, plants, and microorganisms. The result is a more fragile ecosystem with an increased dependency on pesticides and artificial fertilizers (Kumar et al., 2012). To support decision-making in such circumstances, a multiscale modelling approach embedded in an interdisciplinary research process were advocated. To this end, the LIving LAndscape China (LILAC) Project developed an analytical framework and methods for resource use analysis and planning in the Naban River Watershed National Nature Reserve (see Wehner, 2010, this issue). The example of rubber plantations in a nature reserve in south-west of China clearly shows the conflict between the goals of monetary profit, nature conservation, and preservation of indigenous customs at a regional scale (Mittermeier, 2004; Sturgeon, 2005; Qiu, 2009; Ziegler et al., 2009). Rubber plantations have been introduced in the region in the 1980s by governmental law (Tang et al., 2010). Rubber restrains the natural vegetation of tropical rainforest and by 2007 it covered 10% of the nature reserve (Berkhoff and Herrmann, 2009b). Other cash crops such as Hemp (Cannabis sativa L.) or Jatropha (Jatropha integerrima) put further pressure on the region and accelerated forest destruction (Audsley et al., 2008). Thus, planners in the region need comprehensive and spatially explicit information on possible

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1600 1400 1200 1000 Mu

800 600 400 200 0 2002

2003

2004

2005

2006

2007

2008

Year Corn

Rubber

Rice

Area units: 1 mu = 0.066 ha. Fig. 1. Average land use activities trend in the study area (2002–2008).

development trajectories of the nature reserve. This information can then be used for sustainable Land use planning in the study area. Land use/cover change (LUCC) is not only driven by physical factors, but also by agro-economic factors. The need for approaches to integrate agro-economic and geo-/bio-physical drivers is widely recognized due to the complexity of human-nature systems to be managed (Pahl-Wostl, 2007) and the scale dependency of land use change drivers (Mottet et al., 2006). Numerous studies indicate that LUCC patterns depend significantly on social and agro-economic factors (Berkes and Seixas, 2005; Mottet et al., 2006; Gellrich et al., 2007; Gibon et al., 2010). Additionally, in other application areas the relevance of integrating economic information is considered, for instance, in catchment modelling (Kragt et al., 2011). Thus, to understand land use change, it is not only necessary the analysis of the spatial patterns of existing practices and their productivity, but also the analysis of the economic and social factors that affect the attitudes, perceptions and decision-making processes of the farmers themselves. Hence, in this research we present an organizational heuristic with two conceptual models for linking land change with driving forces and actors, in which a multiscale economic approach based on linear programming village farm household modelling (VFHM) with a grid-based land change modelling approach is combined. 2. Rubber expansion incentives in Xishuangbanna Rubber was first introduced to Xishuangbanna by the Chinese Central Committee in the early 1950s as a strategic industrial product to be produced on large-scale state collective farms. The recent expansion of rubber plantations is due to the growing rubber market. This is caused mainly by the enormous boost of vehicles industry in China, since rubber is a very essential material for manufacturing car tires. As an evidence, time series data (2002–2008) from three villages in the study area show an increase of the rubber cultivated area by 25% during that period (Fig. 1). At the same time the rice and corn (Zea mays) cultivated areas decreased by 32% and 33%, respectively. The increase in rubber plantation area is mainly caused by institutional change of the introduction of the household responsibility system in 1979, which replaces the production team system as the unit of production and income distribution. The household responsibility system granted farmers greater autonomy and long-term land security (Liu et al., 2006). Many studies of land use history have shown that the conversion of forest to cash crops or other commercially important simplified forest types is widespread in developing countries (Rao and Pant, 2001). This loss of forest can also be explained by the

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efficient economic use of land usually dictates a higher preference for agricultural production than for forest activities (Kammerbauer and Ardon, 1999). This is also the case in Xishuangbanna, where collective traditional forest uses (e.g., hunting, non-timber forest products and some grazing) produce much lower income than cash crops. In contrast to other parts of Asia, where rubber may be grown as part of mixed agro-ecosystems (Liu et al., 2006), rubber in Xishuangbanna is mainly grown in monoculture plantations. Monocultures are environmentally and economically risky. Producing only rubber makes farmers vulnerable to fluctuations in world markets, diseases and pests (Fox, 2009). Therefore, it is important to carefully address all the potential effects of rubber monoculture plantations, both positive and negative, (e.g., rural incomes changes, loss of agro-diversity, water catchments implications) and to consider a coordinated strategy to balance the agricultural commercialization Land use and environmental values (Liu et al., 2006).

3. Linking the VFHM models to land-change models The general problem with linking the VFHM and land-change models is that they usually refer to different spatial units. The VFHM modelling is based on farmers’ resource input use decisions regarding market and policies. The observations of this behaviour are usually aggregated in space and time (Britz et al., 2011). Due to this aggregation, specific local environmental impacts cannot be properly addressed by the results of VFHM models. In contrast, land-change models must be spatially explicit to be able to simulate land use change (Turner et al., 2007). Therefore, land-change models tend to use information derived from remote sensing images as a primary source of information (Britz et al., 2011), whereas economic models use management or statistical data. The latter usually focus on the economy of individual parcels managed by an “operator”. The operator can be a single person, household, or group of people in the case of common property ownership (Nelson and Geoghegan, 2002). Economic modelling approaches are generally based on the assumption of rational choice; i.e. the objective of the operator is profit maximization (Gellrich et al., 2007). To understand the added value of linking economic models and land-change models, it is important to distinguish between land-cover and land use. Land-cover refers to the layer of features covering the land surface, which includes natural vegetation, crops, human-made structures and open ground. Thus, land-cover can be directly observed in the field or using remote sensing images. In contrast, the observation of land use can be difficult, because it includes land-management practices (Verburg, 2009). In general, land-change models are designed to model changes in land-cover. On the other hand, economic models are designed to model changes in land management. Linking economic models to land-change models will enable us to integrate management issues into a land-change model. With this approach it is possible to model land use instead of only model land-cover (van de Steeg et al., 2010). Most attempts at coupling economic and land-change models were done at the country level, or at regional scale, only for Europe (Britz et al., 2011), as example, the LUMOCAP model for assessing the impact of the Common Agricultural Policy (CAP) (van Delden et al., 2010). Only few studies coupled economic and landchange models at regional scale. Gaube et al. (2009) developed the integrated socio-ecological model SERD (Simulation of Ecological Compatibility of Regional Development) for modelling the interaction between VFHM and natural components of the integrated land system. Lobianco and Esposti (2010a,b) present the RegMAS model, an open-source, spatially explicit agent based modelling framework that allows assessing the economic and environmental outcomes at different scales. Roeder et al. (2010) couple a VFHM

model and an agent-based land change model at a regional scale. Their objective was more to propose management measures than to conduct a spatially explicit impact assessment of land use changes. One approach to linking land change models with economic models is using farm types as an interface between both models (Carmona et al., 2010). Farm types can be characterized by farm conditions such as the length of cultivation time, the type of crops cultivated, farming techniques, or the land tenure issues and ownership (Pare et al., 2008). They are mostly studied at the household level as the unit of production (van de Steeg et al., 2010). The same farm types can be defined in the VFHM models as well as in the land-change models. In this case, farm types can be used as interface between both models. Defining farm types as an interface enables us to relate the economic behaviour on regional scale (given by the results of the VFHM model) to space. Land use planning can profit from this approach because it allows us to relate the aggregated economic development scenarios to distinct spatial units. We used the farm type interface for modelling a business as usual scenario (BAU) for the study area, based on a GAMS (General Algebraic Modelling System) VFHM model and the CLUE-Naban land change model. 4. Study area The study was conducted in the Naban River Watershed National Nature Reserve (hereafter called Naban Nature Reserve) with an area of 271 km2 , located in the Xishuangbanna Prefecture of Yunnan Province in South-West China (22◦ N, 100◦ E) (Fig. 2). The Naban Nature Reserve is part of the Indo-Burma hotspot of biodiversity (Myers et al., 2000; Mittermeier, 2004). It represents the catchment of the Naban River, which is a tributary to the Mekong River. The Mekong River outlines the eastern boundary of the catchments. The subtropical climate is heavily influenced by the south-west monsoon with intense rainfalls from May to October; the average precipitation is around 1400 mm per year, depending on the elevation (Guardiola-Claramonte et al., 2008). Due to the climatic conditions in this most southern prefecture of China state farms and village farmers grow rubber (H. brasiliensis) as a cash crop in large areas (Xu et al., 1990, 2005a, 2006; Fu et al., 2005; Liu et al., 2006). The total area of rubber plantations in Xishuangbanna increased from 6130 ha in 1963 to 136,782 ha in 1998 (Wu et al., 2001), and continues to increase. The Naban-Nature-Reserve is located in Jing Hong County, close to the borders of Laos and Myanmar (Fig. 2). The area covers about 267 km2 of land, containing 32 villages with approximately 5500 inhabitants. The reserve was founded in 1991 and has just recently been upgraded to a National Nature Reserve. It is multi-ethnic, mainly populated by six different minority groups. The main source of income is agricultural production (Leshem et al., 2010). 5. Materials and methods The process of coupling the VFHM and the CLUE-Naban model (for further information see Wehner, 2010, this issue) was used for modelling the BAU-scenario for the years 2006–2025. We defined the status quo as the land-cover of the year 2006 because the most recent land cover map for the study area was available for this year. It served as input map and as starting year for scenario modelling. 5.1. The BAU-scenario definition In the BAU-scenario, the status quo of land management in the study area was extrapolated into the future (2007–2025). The basic assumption was that farmers would follow the same routines and trends. Thus, we assumed that rubber would remain a cash crop in

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Fig. 2. Location of Xishuangbanna Prefecture.

the study area, complemented by hemp, whose area of cultivation would continue to increase. The assumptions of the BAU-scenario were developed with the help of local experts who worked in the study area. Specifically, we included the following basic assumptions into the BAU-scenario (a) the area of tea is relatively constant. Due to the long lifespan of tea trees, the high tea price, and the cultural value of tea, (particularly old) tea trees are seldom cut, (b) rubber trees deliver yield starting at age 7. The maximum yield is obtained at age 17 or 18 (Clément-Demange et al., 1995) and then declines. Thus, from an economic point of view we assumed that rubber trees are cut after a maximum of 30 years, (c) hemp is already planted as cash crop in the Naban Nature Reserve (Wehner, 2010, this issue). We assumed that this development will continue in the future, based on the fact that a major hemp factory has been established close to the study area in 2009. Additionally, the government supports hemp production in China, by providing free hemp seeds and technical training. Hemp replaces rubber as a cash crop in the highland area of the Naban Nature Reserve because rubber cannot be grown above 1400 m. (d) Rubber is currently being bred for higher frost tolerance. Li et al. (2007) observed a trend of increasing rubber production at higher elevations (above 1000 m) between 1988 and 2003. We assumed that this trend will continue and, therefore, we increased the rubber growing limit to 1400 m in the BAU-scenario. This elevation was chosen according to recent developments in rubber breeding (Priyadarshan et al., 2003; Liu et al., 2006; Audsley et al., 2008), but also considering a potential temperature increases due to climate change (Cheng and Xie, 2008). And (e) the total area of irrigated land remains fixed by law, and only 10% area change in respect to the area present in 2006 was allowed.

For the BAU-scenario it was necessary to define the demand for the years 2007–2025 to drive the land-change model. To define this demand, we linked the CLUE-Naban model to the VFHM model using farming types as an interface between the two models. 5.2. The linear programming VFHM model The land demands for eight villages in the Naban Nature Reserve were modelled by using linear programming procedure in GAMS for the period from 2007 to 2025 for the following crop types: paddy-rice, corn, rubber, vegetable, hemp, forest, tea, and fallow. At the village level, three main agricultural development goals for scenario analysis were identified in stakeholder workshops, these were: maximizing farmers’ income, production of staple food (rice and corn), and the production of cash crops (rubber, tea and hemp), while maintaining a minimum level of consumption requirement from their own crop production. All models have the same general structure; however, some coefficients are different, which reflects different farm type conditions. The models try to mimic household production behavioural decisions. They maximize income simultaneously with subsistence needs, given the household specific endowment of resources, minimum consumption requirements, limits on off-farm employment, generic crop technology and prices. Fig. 3 shows a schematic illustration of the VFHM model methodology. The model aims to approach the real situation to such an extent that it does support a better understanding of the processes, which brings farmers to “design” a farming system, changing it, as well as provide possibilities to evaluate policy measures geared towards less landscape destruction. The model is capable of describing human behaviour in

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Objective (Net Revenue)

Crops • Requirements • Yield

Constraints • Shadow price Resources Endowment • Labour • Land • Capital

Household • Consumption Requirements

GAMS Optimal inputs Allocation • Land • Labour • Capital Simulation ‘What-if analyses

Fig. 3. The village-household model structure.

a local context and is competent of describing relevance in a global context. The developed mathematical programming model is dynamic. It enables us to determine an optimal allocation of land, labour and capital, given a set of goals and constraints. Nevertheless, a drawback of the mathematical programming model is that it produces vast results. Particularly using a ‘what-if’ analysis, we can show what happens if some parameters of the model are not known with great accuracy in future and scenarios are to be built (Shi and Chen, 2004). As shown in Fig. 3, the fundamental nature of linear programming models is the constrained optimization of an objective function (income “net revenue” maximization, effort minimization, etc.). The objective function specifies the preferences of a decision maker. The constraints deal with matters such as the production capacity and the availability of labour. In a linear programming model, it is possible to make a distinction between decision variables of which the values have to be determined by solving the model and exogenous parameters, which are used as inputs to the model and can be calculated or estimated from primary or secondary data sets. In this research project the linear programming model was applied to describe the relationship between policy change and land use decision making. Several features of this linear programming model were purposely introduced in this research in south-western China, are as follows: first, the model is designed to maximize net income, simultaneously incorporating farm and off-farm activities, subject to constraints on land, labour and capital resources. Second, the model is specified at the village farm household level using the data collected from eight villages in the Naban Nature Reserve to address village specific topics for regional analysis. Moreover, the model is specified at the village level, because in China many natural resources such as grazing land and fuel-wood are managed at the village level. Third, this model is a bio-economic model, as soil degradation processes occurs at a collective level rather than at an individual household level, accordingly the model can assess degradation. Fourth, moreover as mutual labour exchange often occurs between individual households, the labour constraint cannot be bounded strictly at the household level. Fifth, the model includes sale processes of tradable goods and selfconsumption processes of non-tradable goods. Sixth, as off-farm job opportunities for rural residents are limited under an imperfect labour market, in the model we placed a constraint of off-farm job opportunities. Seventh, as there is rarely mobility of land and management rights between households, land markets are missing, however a module related to land leasing was introduced in the model. Eighth, land use activities are constituted by annual cropping activities (rice, corn, hemp and vegetable), perennial tree

production (rubber and tea), forest and fallow. Finally, food expenditures obtained from self-consumption of farm products and food purchased from the markets. Food expenditure decisions are based upon linear consumption choices that combine quantity with prices of food products under the constraints of basic requirement of food preference of the households. 5.2.1. Land use optimization Land use optimization process consists of complex linkages between resources, constraints and objectives. This complexity calls for a structured approach. Thus, we developed a linear programming VFHM model, which solved by using the General Algebraic Modelling System (GAMS) software programme version 22.7. GAMS is a high-level modelling system for mathematical programming problems (for more information see: www.gams.com/). In GAMS, the structure of the VFHM is composed of sets, parameters, variables, and equations to obtain optimal solutions for the variables. In this case, an optimal land use allocation is calculated by the VFHM. Sets are units on which variables and parameters depend, which can be land units, crops, seasons, etc. The input data is converted into parameters and used to generate the output, which is stored in variables. Using equations, the parameters are converted into variables; furthermore, they defined the constraints of the model. GAMS was used to identify the linkage between the land units and the different crops that can be grown. A schematic overview of the VFHM for land use demand is illustrated in Fig. 3. The crops, resource, endowments (land, labour and capital) and household units refer to the ‘input’ and ‘constraint’ module of the VFHM. The right-hand side of the figure shows the different output of the VFHM system. 5.2.2. The objective function in the VFHM model From farm household theories we know that two types of farm household objectives are commonly distinguished in relation to land use. The first type is subsistence households where the prime goal is to have sufficient food (from home produce or bought on the market) to feed the family (Kruseman, 2001; Sadoulet and de Janvry, 1995). The second type is market-oriented households where family consumption is not a reason for concern; their goal is to earn an income. We have chosen to consider both household goals because many households in the Naban Nature Reserve villages are in fact subsistence households. While subsistence farmers may sell the surplus of cash crops on the market, they behave differently than market-oriented households by keeping part of their own farm production for home consumption. The model can easily be adapted for study areas where households are predominantly market or subsistence oriented. We assumed that food consumption indeed has the highest priority for the farm households in the Naban Nature Reserve villages, and that sufficient food for the family is a requirement that needs to be fulfilled, even if this conflicts with the asset accumulation objective. In mathematical terms, the household goal of sufficient food consumption is formulated as a “hard constraint” rather than as an objective function, meaning that no deviation is allowed. The constraint is then formulated as such: food consumption needs are to be at least at the amount required for a healthy and productive life. The objective function (in time) further states that the accumulation of household assets should be maximized. Assets can consist of money (savings), consumer goods (both durable and non-durable), or investment goods (e.g. land, livestock, vehicle, etc). Over the short-term, households need to generate income in order to obtain these assets. 5.2.3. The VFHM model variables and parameters The decision variables in the household model represent the choices of the decision maker. The farmers need to make decisions

T.M. Gibreel et al. / Ecological Indicators 36 (2014) 766–778 Table 1 The VFHM model variables and parameters. Variable/parameter types a. Land use activities 1. On-farm activities 1.1. Annual food crops production 1.2. Annual cash crops production 1.3. Perennial cash crops production 1.4. Land conversion b. Non-income generation activities 1. Consumption 2. Market purchase c. Decision variables 1. Land area 2. Working days d. Parameters and state variables 1. Expenditures 2. Production and harvesting costs 3. Capital 4. Output 5. Non-farm income 6. Marketing

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Table 2 The VFHM model constraints. Description

Production of rice, corn and vegetable. Production of rice, corn and vegetable. Production of hemp, rubber and tea. Conversion of crop or pasture land into fallow and forest.

Constraint types

Description

1. Production function

Defines the relationship between state and decision variables. States the household food needs from own farm production.

2. Consumption function 3. Financial constraints 3.1. Net revenue

3.2. Total farm revenue 3.3. Non-farm income

Home consumption of own produced food crops and tea. Purchases of food and non-food items. Amount of land allocated for different land activities. Man-days of hired and family labour used for land use activities.

3.4. Production costs 3.5. Available capital

4. Labour constraint

5. Land constraint Household food and non-food expenses. Costs of inputs used in production activities. Assigned capital to land use activities. Land use activities output per unit of land. Labour sale income. Marketed surplus of land use activities.

on activities that they can choose from a given set. In general, we can say that the decision describes the choice of the allocation of inputs among a number of alternative activities. The values of the decision variables are unknown on beforehand, and need to be calculated by the model. The decision variables define the allocation of inputs land, labour and capital for the farm activities. In the following detailed description of variables and parameters in the Naban Nature Reserve VFHM model were given as activities, decision variables and inputs. Table 1 summarizes the activities undertaken by the Naban Nature Reserve farm households, which derived from the household survey carried out in 2007 and 2008 mainly for the application of the VFHM model. These activities are on-farm activities, off-farm activities and non-income generation activities. 5.2.4. The VFHM model constraints The constraints describe the relations between state variables and decision variables. In the short run (decisions for one year) we can distinguish the following categories of constraints, production function, consumption function, financial constraints, labour constraint and land constraint (for details see Table 2). Provided that each land use activity has a certain yield per hectare, the production function estimated based on the yield parameter, which set up here to be deterministic. Although, yield tends to be highly variable and uncertain in real situation, we assumed that the village farm household takes into account the expected yield, such that the parameter can be treated as deterministic instead of stochastic. Note that land conversion is also defined as a land use activity. Land that is being converted (for example from annual to perennial crops “rubber”, or from arable land to pasture) usually has lower yields initially, whereas labour requirements are often higher. A typical case of land conversion is land degradation. Yields are high in the first year at the cost of future output. The relation between the current and future production possibilities is

Calculated as the sum of all farmers’ income minus summation of the costs of production and living expenses. Equals to the summation of land use output sales at farm gate price. Defined as the sum of revenues from all off-farm wage labour activities. Consist of the costs of hired labour and the costs of the input used at market price. Calculated as the part of the revenues used for expenses on consumer goods, while the remainder added to the capital stock. States that the total amount of family labour used for productive activities cannot exceed the total amount of family labour available. States that the total available land equals the household land under collective forest at the village level in a year plus allocated area to cropping (annual and perennial) and non-cropping activities (fallow).

expressed in the land constraints. In order to avoid non-linearity in the production functions, we have chosen for a formulation where land, labour and capital are used in fixed proportions. Although, these proportions are allowed to differ according to the input requirements for different households, products, and time allowing for diversity in households, products, and technological progress characteristics. Concerning labour constraint, only family labour has an upper bound, hired labour is supposed to be available in unlimited supplies. Note that, family labour is not subdivided into male, female and other (child labour). This is done to avoid unnecessary complexity, and is justified if all family members can be involved in all productive tasks. 5.3. CLUE-Naban model adjustments according to BAU scenario assumptions The CLUE-s (The Conversion of Land Use and its Effects) model (Castella and Verburg, 2007; Overmars et al., 2007a; Verburg et al., 2002) is a widely used model to assess Land use change at regional scale. The application of the CLUE-s model to the Naban River Watershed National Nature Reserve is called CLUE-Naban (for further explanation see Wehner, 2010, this issue). CLUE-Naban was used to simulate land use changes based on empirically quantified relations, which established through logistic regression between land use and its driving factors. For the scenario calculations, the CLUE-Naban model had to be adjusted compared to the settings in the status quo approach (see Wehner, 2010, this issue). According to the BAU-scenario, hemp was introduced as a new cash crop. Only a small amount of hemp was available in the study area in the year 2006, but has not been classified in the land cover map due to its small coverage. Consequently, the influence of the driving factors on the allocation of hemp was unknown, because no logistic regression for this land-cover type had been conducted. For the introduction of a new land-cover type in the CLUE-Naban model, it was necessary to evaluate the regression factors for the new land-cover type to be able to subsequently produce a probability map. In the following, it is explained how we introduced hemp as a new land-cover type into the CLUE-Naban model. Although no

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information on the spatial distribution of hemp could be derived from remote sensing, statistical information on hemp was available, since the conducted survey showed that hemp was produced in six villages in the Naban Nature Reserve, with a total area of 144 ha. Our hypothesis regarding the probability of hemp allocation is that if a village already had a considerable amount of hemp in the year 2008, we assumed that the probability of hemp allocation in that village in the following years will be high. To implement this assumption, we standardized the area of hemp per village to be within a range of 0–0.5 (following the probability range of the CLUE-Naban model that covers a range of 0–1). Villages that had no hemp production in 2006 were assigned a probability value of 0.02 for hemp. As a result, we derived hemp probability values for all villages of the study area, ranging from 0.02 to 0.5. We further assumed that the probability of hemp allocation increased in the vicinity of a village. Thus, for every grid cell the closest distance to the next village was calculated. Following the same procedure, we standardized the distance values also to a range of 0–0.5. The standardized area probability values and the standardized distance probability values were then multiplied to calculate the total probability for the allocation of hemp. To obtain the regression factors that were necessary to introduce hemp as a new land-cover type into the CLUE-Naban model, linear regression between the total probability for hemp and the CLUE-Naban driving factors was estimated. A further adjustment was made to parameterize the improvements in rubber breeding and also to consider a potential temperature increase due to climate change. The rubber growing limit was increased in the BAU-scenario from 1200 m to 1400 m. Thus, the spatial restriction map allowed the establishment of new rubber plantations up to 1,400 metre. Finally, the area of tea was set to remain constant in the BAU-Scenario. This was reflected by the tea demand in the VFHM model. 5.4. Farm type cluster analysis The VFHM model estimated the demand for the land-cover types separately for eight village households in the study area. In contrast, the CLUE-Naban model needed the demand for the land-cover types for the whole area of the study region. To overcome this spatial misfit, we conducted a cluster analysis with the aim to translate the demand from a single village to the whole study area. In the cluster analysis, we aggregated villages according to their farm type based on the altitude. With the help of the farm types, we then could extrapolate the village demand of the VFHM model to the whole cluster area in CLUE-Naban. Two data sets were used for the cluster analysis; these are primary socio-economic data collected at the household level through direct interview by using structured questionnaires for eight villages (from the years 2008 and 2009), and agro-economic time series data collected for all 31 villages in the study area from Yunnan Provincial Statistics (2007). Additionally, elevation data obtained from NASA METI Japan (2009); and the percentage of rubber area per village, which derived from the land-cover map of the year 2006, were included in the cluster analysis. Firstly, the correlation between the statistical and empirical data sets was assessed for the eight villages for which both data sets were available. The purpose of this approach was to find out, which variables of the statistical data set could be considered equivalent to the variables of the empirical data. Table 3 shows the significant correlations between household data and village statistics geographical data, respectively. Secondly, based on the statistical and geographical available data from all 31 villages in the study area, hierarchical cluster analysis was conducted. The Ward method was applied allowing for two to six clusters, using standardization of data from 0 to 1. Fig. 4

shows the result for the identification of two clusters. We decided to use the two-cluster results because only in this case all clusters contained the villages that were modelled by the VFHM model. The following clusters were identified cluster-1 defines Rubber farming, this farm type is found in collective land and on lowland (mainly below 800 m). It is dominated by rubber plantations, which ensure high income for the farmers. Only few cattle are kept in this cluster. Three household villages were modelled by the VFHM model for this cluster. This farm type corresponds to Region 0 in the calculations of CLUE-Naban. Cluster-2 describes extensive highland farming; this type of farm is found in the collective land and at higher elevations above 800 m. Only small areas of rubber are observed in this region due to unsuitable climatic conditions, which results in a relatively low income for the village farmers. More cattle are kept in this cluster. Five villages were modelled by the VFHM model for this cluster. This farm type corresponds to Region 1 in the calculations of CLUE-Naban. And finally, cluster-3 characterizes the public-land, which corresponds to Region 2 in the calculations of CLUE-Naban. The translation of the demand estimated by the VFHM model to the input demand for the CLUE-Naban model was done in two steps: first, the VFHM model crop types were assigned to the landcover types of the CLUE-Naban model, as paddy rice and vegetables were summarized in the irrigated land-cover type. Agricultural production areas without irrigation as well as fallow were assigned to the rain-fed land-cover type. Second, the average demand values were calculated using the three household villages’ demands for the rubber farming cluster-1 and five household village demands for the extensive highland farming cluster-2. They were then integrated into the CLUE-Naban model using a five-year floating mean value. This was necessary to compensate for abrupt changes of demand produced by the linear programming approach of the VFHM model. With the procedure of the farm type clustering, we were able to integrate the VFHM model results into the land-change model. Regarding the demand definition, one further step was necessary to cover the whole area of the Naban-Nature-Reserve. The VFHM model only included the collective land area and excluded the public-land, because there is no agricultural production practiced on that land type. To define the demand for the public-land (referred to as “Region 2” in CLUE-Naban), the trend extrapolation were decided to be used. The past land area development in the public-land from the year 1988 to the year 2006 was extrapolated until the year 2025. This region was covered by 86% state forest in the year 2006, and land use activities were very limited. Thus, assuming trend continuation land cover in this region remained rather constant also in the scenario.

6. Results 6.1. Changes in land use shares modelled by the VFHM model The BAU-scenario is a conceptual baseline, which projects what could happen in the Naban-Nature-Reserve area if there were no changes in structural parameters and dynamics. It assumes that (a) the current land use and other policies that guide or shape development remains the same; (b) the current demographic trends will continue as expected and (c) future trends in land use follow past patterns. According to our model results, we provided information on how much percentage of rain-fed, irrigated land crops, shifting land/forest, rubber, tea and hemp will be needed in the Naban Nature Reserve villages for every single year in the analysis period from 2007 to 2025. In the lowland altitude villages, the results of the BAU-scenarios show that forest land share is subject to decline (see Fig. 5a), as

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Table 3 Correlation between household data, village statistics, and geographical data. Household data (8 villages)

Village statistics/geographical data (31 villages)

Correlation coefficient

Significance

Household size Farm size Rubber area Tea area Hemp area Diversity index Number of buffalos Number of pigs Farm income

Population Number of households % rubber area Median elevation Median elevation % rubber area Produced cattle Produced pigs Income

0.624 0.553 0.831 0.701 0.777 0.796 0.802 0.612 0.577

**

* **

The correlation is significant at a level of 0.05 (2-sided). The correlation is significant at a level of 0.01 (2-sided).

Fig. 4. Result of the cluster analysis (rubber farming Cluster-1 and extensive, highland farming cluster-2).

* ** ** ** ** ** * *

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Fig. 5. Demand input data for the CLUE-Naban model, derived from the VFHM model, demand for cluster-1 (lowland (a)) and for cluster-2 (highland (b)).

predicted to decline by an annual average of 0.03%. On the other hand, rubber area is increasing in the same altitude. Whereas, in the highland villages of Naban Nature Reserve rain-fed crops (i.e. corn, hemp as a new crop and tea) are dominant. The results in Fig. 5b show that, in general, changes in the land share of forest is potentially minor, when considering the time period from 2007 to 2019, as an annual average reduction of 0.02% in forest land share is expected. On the other hand the land share of tea is unlikely to change due to the fact that tea is a long-term investment and this tree is strongly linked to the villager’s culture. Moreover, the model shows that the irrigated land (rice 1% and vegetable 0%) share is expected to remain very low levels compared with other land use activities.

the base year (2006) to 74% in the last year (2025). The increment of the rubber plantation is mainly derived from the collective forest. While, the irrigated land, rain-fed land, and settlements remained rather constant. In contrast to the rubber farming cluster-1, the results in the extensive highland farming cluster-2 demonstrate higher variability, as hemp production increased considerably. In the year 2008 only 144 ha of hemp were available, which is expected to increase to 484 ha in the year 2025. While, collective forest and rain-fed land likely to decrease by 9% and 5%, respectively. The tea plantation area remained rather constant, whereas the area of rubber and irrigated land increased slightly. 7.1. CLUE-Naban land change map for the BAU-scenario (2025)

7. Changes in farm types The BAU-scenario shows the dominance of rubber plantations in the rubber farming cluster-1 as rubber share increases from 66% in

The CLUE-Naban model was used to allocate the VFHM model results spatially for the research area. The resulting map (Fig. 7) was compared with the initial land use map for the year 2006 (Fig. 6). The most striking pattern in the modelled BAU land-cover map for

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Fig. 6. Land-cover map for the initial year of the simulation (year 2006).

the year 2025 was the introduction of hemp as a new cash crop in the mountainous parts of the study area. In the initial year of the simulation (2006) hemp area was so small, to extent that they cannot be observed in the corresponding land-cover map (Fig. 6). As driven by industry demand on hemp and supported by the government, hemp simulated to cover 1.8% of the study area (485 ha) in the year 2025. Demand for hemp was only determined for the area of the extensive highland farming cluster-2. Major hemp areas were allocated in the south-west of the study area, mainly replaced rain-fed land, as well as collective forest. The share of rubber in the lowland area (rubber farming cluster-1) was 66% in the year

2006; it is more likely to increase to 76% in the year 2025 in the BAU-scenario. On the other hand, rubber replaced collective forest, mainly at the western and northern fringes of lowland area that border the extensive highland farming cluster-2 area. These areas are found at an elevation of around 900 m where still suitable for rubber plantations. That is to say, in these areas rubber expanded up to the hills, because the valley already was covered by rubber trees. The most dynamic region in the terms of land-cover change is located to the south-east of the reserve (Fig. 7); thus most of the land-cover changes in the period between the years 2006 and 2025 occurred in this area.

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Fig. 7. Modelled land cover map (year 2025) for the BAU-scenario. Dashed polygon marks the, most dynamic part of the study area with regard to land cover changes (region 0 = “rubber, farming” type; region 2 = public land; region 3 = “extensive highland farming” type).

8. Discussion and conclusions The goal of this study was to model a land change scenario based on integration of the VFHM model and a land change model using farm types as an interface to be able to include an aggregated inputs demand estimations of the VFHM model into the spatial dimension. With the modelling exercise of the BAU-scenario, it is possible to place the demand in space using the interface of farm types. Thus, it was possible to address as well land management

issues (as described in the farm types) rather than just modelling land-cover changes. The procedure of farm types clustering implies that the modelling results are mainly dedicated to the study area. An extrapolation of the findings to other study areas, therefore should consider if the other study areas share similar characteristics from the point of view chosen to classify the farms. An essential precondition to be able to do so is the availability of village boundary data as these boundaries provided the spatial unit necessary to couple the two models. The co-ordination of the land-change

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model and the VFHM model in this study could have been improved with regard to the definition of the land-cover types. Our modelling results were addressed for land use planning at the regional scale. The consideration of eight land-cover types was suitable for regional evaluation. However, for local evaluation and for application of the results for specific nature protection, a further distinction of land-cover types would be necessary. In particular, the forest class should be split up into, for instance, virgin forest, secondary forest, and additional classes. The availability of input data for modelling Land use change is often a problem. This is true in particular with respect to data accessibility from individual landowners at the farm level. In this study, to overcome this problem the empirical socio-economic data at the farm household level were linked to the village boundaries that were mapped in a participatory approach (for more information see Wehner, 2010, this issue). The selection of driving factors in this study was based upon interviews with farmers, expert knowledge, and field observations. Only limited data were available in the study area, thus no further selection of input data was done. The correlation of driving factors was tested before doing the logistic regression. The stable location factors can be turned into dynamic ones, if reliable information on their development for the modelled period is available. For example, in our BAU-Scenario we used a stable location factor of labour availability, which could be turned into a dynamic location factor if information on population development and the development of the demographic structure is available. Our model results support findings from van Doorn and Bakker (2007) that the land ownership or land use rights have a strong influence on land management and land use change. In our case, land use rights differentiated between public land and collective land. We introduced public land as a location factor into the CLUE-Naban model and it proved to be one of the most important driving factors of Land use change in the regression analysis. Thus, the importance of Land use rights should generally be assessed in land change analysis, even if related data are difficult to acquire. The results of the modelling exercise presented in this paper were dedicated to land use planning. For this purpose, alternative land change scenarios were modelled. These scenarios served as a discussion basis for land use planning. A Go-Green-scenario (see Cotter et. al., in this issue) complemented the BAU-scenario. The Go-Green-scenario storyline described a more balanced land management with regard to biodiversity conservation, alternative income sources for rubber cultivation, and cultural landscape practice. The two scenarios gave a broad insight into the development strategies that could be followed in the nature reserve. Thus, the BAU-scenario that was presented in this paper was only part of an integrated scenario modelling exercise, which provided spatially explicit information on land use change, which can be used to derive potential impacts of these changes regarding biodiversity, hydrology and social structure of the area under investigation. Considering the framework of “societal problem solving” (Sterk et al., 2011) our approach was explicitly designed to involve farm managers and land use planners at regional level.

Acknowledgements This work was conducted within the LILAC (“Living Landscapes China”) project, supported by the German Federal Ministry of Education and Research under promotional reference 0330797A. The invaluable comments made by the two anonymous reviewers have contributed to the improvement of this paper. The remaining errors and omissions are our responsibility.

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