Ecological Modelling Coupling a Land Use Model and an Ecosystem

Ecological Modelling 220 (2009) 2503–2511

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Ecological Modelling

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Coupling a land use model and an ecosystem model for a crop-pasture zone

Xia Xu a,∗, Qiong Gao a, Ying-Hui Liu a, Jing-Ai Wang b, Yong Zhang a a State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China b School of Geography, Beijing Normal University, Beijing 100875, China article info abstract

Article history: This paper describes the development of a land use model coupling ecosystem processes. For a given Received 12 November 2008 land use pattern in a region, a built-in regional ecosystem model (TESim) simulates leaf physiology of Received in revised form 24 April 2009 plants, carbon and nitrogen dynamics, and hydrological processes including runoff generation and run-on Accepted 27 April 2009 re-absorption, as well as runoff-induced soil erosion and carbon and nitrogen loss from ecosystems. The Available online 13 August 2009 simulation results for a certain period from 1976 to 1999 were then used to support land use decisions and to assess the impacts of land use changes on environment. In the coupling model, the land use type for a Keywords: land unit was determined by optimization of a weighted suitability derived from expert knowledge about Coupling model Land use change the ecosystem state and site conditions. The model was applied to the temperate crop-pasture band in Ecosystem process and function northern China (CCPB) to analyze the interactions between land use and major ecosystem processes and Soil erosion functions and to indicate the added value of the feedbacks by comparing simulations with and without The northern China crop-pasture band the coupling and feedbacks between land use module and ecosystem processes. The results indicated (CCPB) that the current land use in CCPB is neither economical nor ecologically judicious. The scenario with feedbacks increased NPP by 46.78gCm−2 a−1, or 32.23% of the scenario without feedbacks, also decreased soil erosion by 0.65 kg m−2 a−1, or 23.13%. Without altering the regional land use structure (proportions of each land use type). The system developed in this study potentially beneﬁts both land managers and researchers. © 2009 Published by Elsevier B.V.

1. Introduction enced; and estimating the speed of land use and land cover change. By using certain suppositions, the LUCC model can also be used to Optimal land use planning is a leading issue for terrestrial explore future land use patterns. ecosystem management in the world (Brody, 2003; Bousquet and Le A number of land use models have been developed in the Page, 2004). The multiple objectives of land use decisions can lead last decade based on methods of systems analysis (De Wit et al., to substantially different consequences in terms of ecosystem evo- 1988; Rabbinge and van Latesteijn, 1992; Kruseman et al., 1995; lution (Haberl et al., 2001; Krausmann, 2001; Verburg et al., 2004; Kuyvenhoven et al., 1995; Penning de Vries et al., 1995). Some mod- Krausmann et al., 2004). Policy makers and stakeholders have both els address urbanization and agricultural intensification (Brown et a current and a future right to decide on the use of land (FAO, 1995). al., 2000; Engelen et al., 1995; Hilferink and Rietveld, 1999; Lambin They thus require enhanced understanding of the opportunities for et al., 2000), by simulating land use changes either as a function and limitations to regional development. Spatially explicit dynamic of land use in the neighborhood and user’s expertise, or based models, that integrate land use changes and ecosystem processes on the empirical relationship between land use and driving fac- and functions, are important tools for the design, analysis and quan- tors (Pijanowski et al., 2000; Pontius et al., 2000). The Conversion titative understanding of future spatial patterns and impacts of land of Land Use and its Effects (CLUE) model is capable of simulating use change (Veldkamp and Lambin, 2001; Rutherford et al., 2008). multiple land use types simultaneously using competition between Lambin (1994) pointed out that the LUCC (Land Use and Land Cover land use types, without incorporation of the environmental effects Change) model can help scientists to make suppositions and to on land use (Veldkamp and Fresco, 1996; Verburg et al., 1999, 2002, answer major questions in land use research, including: elucidat- 2006). ing the socio-economic and biophysical variables explaining land The choice of model may have important implications in terms use and land cover change; determining the concrete area influ- of specific aspects of the resultant land use changes. Castella and Verburg (2007) applied two different LUCC modeling frameworks to the same research site. The integration of land use change ∗ Corresponding author. with ecosystem processes to improve future land use manage- E-mail address: [email protected] (X. Xu). ment remains an important subject that deserves more advanced

0304-3800/$ – see front matter © 2009 Published by Elsevier B.V. doi:10.1016/j.ecolmodel.2009.04.043 2504 X. Xu et al. / Ecological Modelling 220 (2009) 2503–2511 research. Spatially explicit landscape models have been developed understanding of the relationship between land use change and to address the impacts of spatial variation on ecosystem processes ecosystem processes. (Higgins et al., 2000; Curnutt et al., 2000; Gao et al., 1996; Gao and The region has a history of land use research and modeling: An Reynolds, 2003). Spatially explicit landscape models link a process- et al. (2002) reconstructed the land use structure of the CCPB; Liu based ecosystem model to a geographic information system (GIS). and Tong (2003) investigated the land use changes in the past 50 An example is the project NELUP (Watson and Wadsworth, 1996), years and estimated the effect of the changes on the quality of the in the United Kingdom, in which ecological models have been built eco-environmental system; Zhan et al. (2004) estimated the envi- within a GIS environment as a part of a computerized decision ronmental effects of land use change in the Inner Mongolia part of support system (DSS). NELUP integrates models based on eco- the CCPB; Shu et al. (2006) simulated the optimal land use patterns nomics, ecology and hydrology in one modeling framework with with the model LUOS basing on GLP (Grey Linear Programming) and a common database and user interface. Weber et al. (2001) linked CA (Cellular Automata) methods; and finally Gao et al. (2007) sim- three simulation models from agricultural economic, ecological and ulated the impacts of soil erosion and climate shift on the regional hydrological sciences, via GIS and examined land use changes in a carbon balance with a spatially coupled regional ecosystem model, peripheral German region and their effect on ecology and hydrol- TESim. ogy. More recently, similar approaches have been taken in the As the core of International research projects “Global Change following studies: Gao et al. (2003) introduced land use as a limiting and Terrestrial Ecosystems (GCTE)” and “land use/land cover change factor for dynamic vegetation development during their simulation (LUCC)” had relatively independent in-depth study from natural of primary production; Fu et al. (2003) analyzed the dynamic effect ecosystems and the change of terrestrial surface process under of five land use structures and seven land use trends on soil mois- human activities, as the deepening of research about these two ture; Ozaki et al. (2008) simulated effects of alteration of river basin aspects, researchers were increasingly aware of that the results land use on river water temperature; Wang et al. (2003) analyzed were unilateral if we independently studied the two aspects close the response of soil structure and nutrient dynamics to agricultural tied with, thus, the coupling research of land use change and eco- land use; Gao et al. (2004a,b) depicted the effects of the spatio- logical processes, and building a coupling model between the two temporal character of the northern climate and LUCC on regional aspects is a major trend of development. This paper presents a mod- net primary production; and Costanza et al. (2002) constructed the eling framework, TES–LUC, which couples the TESim model and PLM (Patuxent Landscape Model) model considering the reciprocity land use model (LUCC). The land use pattern depends on environ- of land use and ecological processes. mental factors including soil and Climate conditions and man-made The northern China crop-pasture band (CCPB) lies geographi- socio-economic factors, in the TES–LUC model, we considered only cally between 34.7◦N and 48.6◦N and 100.8◦E and 124.8◦E, with the former (environmental factors), thence it was a theoretical a total area of 725 527.9 km2 (Fig. 1)(Wang et al., 1999). It has a exploration. The TES–LUC model is applied to the CCPB to simu- population of approximately 60 million. Ecosystems in the region late potential changes in land use pattern, net primary production are under a typical continental monsoon climate. Annual mean and soil erosion under different decision scenarios. Results are dis- temperature is 14 ◦C in the South to 1 ◦C in the North. Annual pre- cussed in the context of land use planning and regional ecosystem cipitation decreases from approximately 580 mm in the Southeast management. to less than 200 mm in the Northwest. Vegetation patterns largely correlate with precipitation and temperature: with a small pro- 2. Model description portion of coniferous forests in the cold North and mountains: deciduous broadleaf forests in the Southeast and North: large pro- 2.1. Land use module portions of shrubs and grasses on the northwest side of the dry plateaus; and croplands distributed mostly in the middle and the Our land use model is based on the principles of the CLUE model Southeast regions. The region has been undergoing severe soil ero- (Veldkamp and Fresco, 1996; Verburg et al., 1999, 2002, 2006) sion, degradation and desertification because of inappropriate land for crop-pasture areas. Crop-pasture areas are often dominated by use and overgrazing by livestock. The rapid economic development three types of land use, crop fields, grasslands (pasture), and forests, of China over the past two decades exerts a great pressure on the whilst other types (buildings, water bodies, etc.) occupy only a small regions environments and ecosystem structure and functions. Opti- proportion. Land use in a crop-pasture region was thus grouped mal land use planning to achieve the solution of best compromise into to four types, i.e., crop fields (CROP), grasslands (GRAS), forests for both economic development and conservation of ecosystem (FORE), and others (OTHE). After initial gridding of the crop-pasture services represents a great challenge for research. It is a top pri- region into a number of discrete cells, CROP type land use was ority for both governmental policy makers and local practitioners. allocated to all grid cells meeting the following criteria: Judicious planning of land use has to be guided by comprehensive min fi (xi) ≥ 0.2 (1)

Fsite,crop + Fexpt,crop − Floss,crop >Ccrop (2)

where fi(xi) ∈ [0,1], for i =1,2,...,n, is a series of fuzzy membership functions (Chen et al., 2006) derived from the knowledge of agricultural and ecological experts, xi is a series of climate and soil fertility characteristics Fsite,crop is a site suitability factor statistically derived from the relationship between current land use and site n Fexpt,crop = w f x variables such as climate and topography, i=1 i i ( i) is the weighted average of the expert fuzzy membership functions, wi is the weight determined by the relative correlation between land use and xi, Floss,crop is a factor derived from past land use change data to quantify the likelihood of crop fields transferring to other land use types, thus Floss,crop characterizes the reciprocal of land use stability. Ccrop is the criteria of allocation for crop fields, a constant Fig. 1. Location of the China crop-pasture belt (CCPB). adjusted to accommodate land use demand from agricultural eco- X. Xu et al. / Ecological Modelling 220 (2009) 2503–2511 2505 nomics. Eqs. (1) and (2) require that the minimum membership probability that land use type i transfers to land use type j in the function value (which specifies the suitability) should be greater next period. ij is equal to the approximate number of sites trans- than 0.2, and that the overall suitability, i.e., suitability derived from ferred to land use type i in the later map divided by the total number site analysis plus that from experts minus the probability of loss by of sites of land use j in the earlier map. Floss,j is then calculated as transferring to other land use type, should be greater than criteria q specified by policy makers to meet the local agricultural economic demands. Floss,j = ij (6) After allocation of cells to the CROP land use type the remain- i=1,i =/ j ing grid cells were allocated to the other land use types using the following equation where q is the total number of land use types in the region. LU(r) = LTj, if max Fsite,k − Floss,k+Dk = Fsite,j − Floss,j + Dj (3) k=2,3,4 2.2. Ecosystem process simulator where LU(r) is the land use variable, r is the spatial location variable Ecosystem processes in our model were simulated using an (rth grid cell), LTj is the value of LU(r), specifying the type of land embedded model TESim which calculates ecosystem production, use. In the present study, LTj = CROP, or GRAS, or FORE or OTHE for carbon balance, soil erosion and nutrient cycling for terrestrial j = 1, 2, 3 and 4. Fsite,j and Floss,j have the same meanings as Fsite,crop ecosystems at different scales. The detailed model description of and Floss,crop, but the subscript j here indicates they are factors for TESim formulation, parameterization, validation and application to the jth land use type. Dj is a series of constants determined by socio- CCPB without land use change are detailed in previous publications economic demands for a specified land use structure. Hence land (Gao and Zhang, 1997; Gao and Yu, 1998; Gao and Reynolds, 2003; units (grid cells) will be allocated to types that have the maximum Gao et al., 2007). Mean annual NPP, annual soil erosion, SOM con- suitability and stability, adjusted by the demand. tent, and soil nitrogen are the four output variables used in the Site suitability Fsite,j was calculated using a standard logit func- present study. In particular annual NPP and soil erosion were used tion of multi-variables to show the effects of the land use model. a + m b X exp( j =1 j ) Fsite,j = (4) + a + m b X 2.3. Coupling the land use module with the ecosystem process 1 exp( j = j ) 1 simulator where X␰ for ␰ =1,2,3,..., m is a series of site variables. In our case, these variables were population density, P (number of people/km2), The coupling processes between the land use module and site slope, ˛ (◦), site elevation, H (m), mean annual temperature, ecosystem simulation within TES–LUC was realized as an iterative MAT (◦C), cumulative daily mean temperature during the growing procedure through the exchange of data. (Figs. 2 and 3). TESim was season, TAG (◦C), mean annual precipitation, MAP (mm), precipita- run for an initial land use pattern, the output values for SOM and soil nitrogen content from TESim were then used as ecological back- tion during the growth season, MAPg (mm), and soil organic matter (%). All of these special variables were quantified from DEM (Digital ground data of the land use module. After getting the ecological Elevation Model) or long-term climate data and population data, background data, the land use module calculated the suitability of with the exception of soil organic matter, which was estimated every land use type on every land unit, by adjusting Ccrop and Dj the desired amount of crop field, forest land and grassland can be from a simulation model. Coefficients aj and bj were estimated by regressing a land use variable on all of these site variables within allocated. This completes the first round of simulation. The newly the area of application using Eq. (4). The land use variable equals 1 adjusted land use may not meet the criteria set in the first round of if a site is currently occupied by land use type j, or zero otherwise. simulation, in which case TESim is run for a second round, and land Only those coefficients that were significant were used in the land use is allocated again. This process is repeated until all conditions use module. Eq. (4) is a logistically increasing/decreasing function are met, which signals the convergence of the procedure and the iteration stops. of all X, depending on the sign of bj. The membership function was constructed according to FAO’s classification standards and agricultural ecological experts’ knowl- 3. Application to CCPB edge. These functions take the following form ⎧ −1 3.1. Data preparation and parameterization of the land use ⎨⎪ x − x 2 + i i0 ,x

Fig. 2. The sketch map of data ﬂow.

3.2. Scenarios and simulation until all the land use criteria were met. Scenarios 3 required iteration between the land use module and TESim until all the land use A total of three land use scenarios were used in this study. S1 criteria were met. The iteration then stopped and the output of the was the current land use speciﬁed by the current land use map. mode was statistically analyzed. S2 retained the same ratios of area occupancy among all land use types but adjusted the pattern of distributions without coupling the ecosystem process module. S3 was a suitability scenario to allocate 4. Results all land use types according to the principle of maximum suitability. For the land use scenario S3 with feedbacks of TESim, TESim was 4.1. Land use patterns under three scenarios initialized with a set of typical values of it state variables (Gao et al., 2007) and run for 24 years until its soil pools were approximately For Scenario 2 (S2), with the proportion area of occupancy con- stabilized. Scenarios 2 only required running the land use module served, the different land use types were allocated to concentration X. Xu et al. / Ecological Modelling 220 (2009) 2503–2511 2507

Fig. 3. The data exchange between the two modules. area, such as the croplands were allocated to the south, the grass- plete adjustment may not be practical because of socio-economic land the most area of the west and the north. For Scenario 3 (S3), demands, but it indicates that the present land use patterns are not with the proportion area of occupancy conserved, the adjusted pat- favorable for regional ecosystem development. Currently areas of tern of land use showed interesting changes from the present land poor climate and soil conditions in the upper (northwest) region use pattern (S1) (Fig. 4). The croplands in S3 were allocated to places loaded with too great a production pressure. The balance between with better climate, soil and irrigation conditions, around the basins forests and grassland should mainly be determined by the balance of the Yellow River, Weihe River, Fenhe River, Luanhe River, Liaohe between environmental protection and animal husbandry. River, and Songhua River. Forests were mostly shifted to the cen- tral portion and mountainous area of CCPB, and grassland occupied 4.2. Impacts of the land use changes on regional ecosystem the west portion and northwest portion with less precipitation and production and soil erosion under three Scenarios poorer soil conditions. Most croplands in the northwest of present land use map Based on the simulated distributions of average net primary pro- were removed and became either grasslands or forests. This com- ductivity (NPP) and soil erosion by the with and without TESim

Fig. 4. Spatial distribution of land use types for the four scenarios within the CCPB. The scenario codes are explained in Table 3. 2508 X. Xu et al. / Ecological Modelling 220 (2009) 2503–2511

Fig. 5. Spatial distribution of NPP for the four land use scenarios within the CCPB. The scenario codes are explained in Table 3. model for the three scenarios (Figs. 5 and 6), we were able to sum- decrease of soil erosion took place at the most area of the study marize and compare the respective regional averages (Table 3), area. for the time period from 1976 to 1999. Comparing the distri- The regional averages of production and soil erosion (Table 3) butions of NPP and soil erosion with the land use patterns, we showed that the present land use (S1) had the lowest regional NPP. found that the patterns of NPP and soil erosion largely follow The scenario without the feedbacks of ecosystem (S2) increased that of land use. Forest land has the greatest NPP but moder- NPP by 3.82gCm−2 a−1, or 2.7% of the regional average NPP for ate soil erosion. On the other hand, croplands without irrigation the present scenario. The suitability scenario with the feedbacks have the smallest NPP but greatest soil erosion in most areas. of ecosystem (S3) increased NPP by 50.6gCm−2 a−1, or 35.8% of The simulation also showed that grasslands in general had mod- the regional average NPP for the present scenario. The scenario erate NPP, but the least soil erosion. By the spatial pattern map without the feedbacks of ecosystem (S2) had highest soil erosion, of the NPP and soil erosion added value between with and with- increased soil erosion by 0.37 kg m−2 a−1, or 11.97% of the regional out feedbacks, the area of increasing NPP was the north, the average soil erosion for the present scenario. The suitability sce-

Fig. 6. Spatial distribution of soil erosion for the four land use scenarios within the CCPB. The scenario codes are explained in Table 3. X. Xu et al. / Ecological Modelling 220 (2009) 2503–2511 2509

Table 1 Research, Chinese Academy of Sciences for the region. West and Parameters for site suitability functions in Eq. (4) and loss likelihood in Eqs. (2), Wali (2002) developed a model with a similar approach to TES–LUC (3) and (6) for CCPB. bj for c = 1,2,...,9 are the coefﬁcients for population density 2 ◦ ◦ to address regional carbon budgets and soil erosion as affected by (person/km ), site slope ( ), site elevation (m), MAP (mm), MAPg (mm), MAT ( C), ◦ ◦ MATg ( C), mean annual cumulative temperature above 10 C during growing sea- land use change in the old mine lands. They found that the annual son (◦C), SOM (%) respectively. Land use types codes: CROP, GRAS, FORE, OTHE for soil erosion after rehabilitation averaged at 780 Mg km−2 a−1. Baja croplands, grasslands, forests, others. The listed values were statistically signiﬁcant et al. (2007) used the erosion tolerance of 1120 Mg km−2 a−1 in the regression analysis. Empty cells in the table were replaced by zeros in the for agricultural areas. Erosion rates calculated by this model subsequent simulation calculation. were far beyond these limits, implying that there is still a long Land use CROP (j = 1) GRAS (j = 2) FORE (j = 3) OTHE (j =4) way to go to reverse the trend of environmental degradation in aj 1.563 2.605 −20.244 −1.036 China. 2 −1 − − − bj1(km person ) 0.007 0.003 0.006 0.006 The present simulation indicated that S2, with adjusted land use ◦−1 bj2 ( ) −0.017 1.017 −0.021 −1 distribution resulting from the coupling and feedbacks between the bj3 (m ) −0.010 0.010 −0.001 −1 land use module and ecosystem processes module without altering bj4 (mm ) −0.005 0.018 1.279 −1 bj5 (mm ) −0.020 the area of occupancy among land use types enhanced the ecosys- ◦ −1 bj6 ( C ) 0.420 −0.059 −0.521 −0.021 tem production and reduced soil erosion, implying the present land ◦ −1 bj7 ( C ) 0.211 0.718 use regime and without taking into account impacts between the ◦ −1 bj8 ( C ) −0.003 −0.005 −1 land use changes and regional ecosystem processes are not judi- bj9 (% ) −0.017 0.039 cious for either the economy or the environment and should be Floss,j 0.042 0.048 0.021 0.063 rearranged. The model presents a pattern of more locally concentrated Table 2 land use in comparison with the actual current pattern of land Parameters of the membership functions and their weights in Eqs. (1) and (5) for croplands in CCPB. use. The simulation indicates that this adjusted pattern with the feedbacks between the two models should yield both higher eco- w Parameters x10 Bi i nomic value (as indicated by the higher NPP) and better ecological MAT (◦C) 10.0 10.0 0.130 restoration and environmental protection (as indicated by higher ◦ MATg ( C) 16.0 3.0 0.130 regional NPP and lower regional soil erosion). This concentrated ◦ MAAT10 ( C) 3000.0 200.0 0.138 allocation of land use types by the TES–LUC model, without con- MAP (mm) 600.0 100.0 0.141 sidering the relationship between pattern and processes (Aguiar MAPg (mm) 500.0 100.0 0.136 SOM (%) 0.967 0.333 0.096 and Sala, 1999), is interesting. Conglomerated patterns in gen- NSOIL (%) 0.092 0.033 0.090 eral represent a greater resistance to degradation when combined ◦ SLOPE ( ) 3.0 6.0 0.138 environmental conditions are unfavorable to a landscape or region The eight variables are mean annual temperature (MAT), mean daily tempera- because the conglomeration provides greater connection and com- ture during growth season (MATg), mean annual accumulative temperature for munication between individuals within a large patch (Gardner and ◦ daily mean temperature above 10 C(MAAT10 ), mean annual precipitation (MAP), O’Neill, 1990). However, such a connective pattern also implies mean growth season precipitation (MAPg), SOM, total nitrogen concentration in soil (NSOIL), and slope of the site (SLOPE). a lower speed for ecosystem recovery because of the smaller boundary to area ratio (Cao et al., 2004). For example, a gener- ally connective pattern means less potential to use runoff water −2 −1 nario (S3) decreased soil erosion by 0.28 kg m a ,or9%ofthe (Aguiar and Sala, 1999). In addition, these concentrated patterns regional average soil erosion. By comparing simulations with and also imply a loss of landscape diversity and potentially biodiver- without coupling and feedbacks, if coupling the two models, it sity. −2 −1 would increased NPP by 46.78gCm a , or 32.23% of the with- Another issue arises from regional economic development. A out feedbacks of ecosystem processes. With the feedbacks, it also locally concentrated allocation of land use type necessitates more −2 −1 decreased soil erosion by 0.65 kg m a , or 23.13% of the without intensive regional collaboration and exchange of products from feedbacks of ecosystem processes. agriculture and animal husbandry, or potential relocation of the human population within the region. This may infer different envi- 5. Discussion ronmental impacts and these possibilities should be taken into consideration in future research. Our results imply that agriculture is the most important causal factor for land degradation, as indicated by the strong relationship between soil erosion and cropland degradation, a conclusion that 6. Concluding remarks has also been drawn in previous studies (Lal, 2004). Both simulated soil erosion rates and NPP for the present This paper presents a modeling framework for integrating land land use pattern are comparable to those obtained by Gao et use and ecological processes at a regional level. By comparing simu- al. (2007) for the area despite use of different land use maps. lations with and without coupling and feedbacks between the land The regional erosion values are also similar to those calculated use module and ecosystem processes module, It demonstrates the by the Institute of Geographical Science and Natural Resources potential of spatially detailed coupling modeling tools in the evaluation of and decision making for land use special allocation, outlining Table 3 options for different land use allocation in both quantity and spatial Regional average NPP and soil erosion for each land use scenario. structure. A spatially explicit process-land use coupling approach

S1 S2 S3 helps to clarify and assess interrelationships between geographical conditions, ecological processes, land use, and ecosystem functions. NPP (g C m−2 a−1) 141.4 145.22 192.0 − − Given a particular land use rule generated from human demands Soil erosion (kg m 2 a 1) 3.09 3.46 2.81 and/or other political or economic regulations, TES–LUC is able S1—present land use, S2—Reallocation of land use without feedbacks of the land to determine possible land use patterns and to assess the impact use module and ecosystem processes with the present area proportionality module, S3—reallocation of land use with feedbacks between the two modules with the of these land use patterns. Thus the model can contribute to the present area proportionality using the suitability principle. objective evaluation of policy decisions based on different criteria, 2510 X. Xu et al. / Ecological Modelling 220 (2009) 2503–2511 i.e. conservation goals, environmental improvement and economic Hilferink, K., Rietveld, P., 1999. Land use scanner: an integrated GIS based model development. for long term projections of land use in urban and rural areas. J. Geogra. Syst. 1, 155–177. Krausmann, F., Haberl, H., Schulz, N.B., 2004. Land use change and socio-economic metabolism in Austria—part I: driving forces of land-use change: 1950–1995. Acknowledgment Land Use Policy 20, 1–20. Krausmann, F., 2001. Land use and industrial modernization: an empirical analysis of This research was jointly supported by National Science Foun- human inﬂuence on the functioning of ecosystems in Austria 1830–1995. Land Use Policy 18, 17–26. dation of China grants 30590384 and 40671071. Kruseman, G., Ruben, R., Hengsdijk, H., van Ittersum, M., 1995. Farm household modelling for estimating the effectiveness of price instruments in land use policy, Netherlands. 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