Modeling the impact of global change on regional agricultural land use through an activity-based non-linear programming approach

Agricultural Systems 100 (2009) 31–42

Contents lists available at ScienceDirect

Agricultural Systems journal homepage: www.elsevier.com/locate/agsy

Modeling the impact of global change on regional agricultural land use through an activity-based non-linear programming approach Martin Henseler a, Alexander Wirsig b, Sylvia Herrmann c, Tatjana Krimly d,*, Stephan Dabbert d a

Martin Henseler, European Commission, Joint Research Centre (JRC), Institute for Prospective Technological Studies (IPTS), Edificio Expo, Avda. Inca Garcilaso s/n, 41092 Seville, Spain Terra Fusca Engineering and Consulting, Wollgrasweg 27, 70593 Stuttgart, Germany c Institute for Environmental Planning, Leibniz Universität Hannover, 30419 Hanover, Germany d Institute for Farm Management, Universität Hohenheim, Section Production Theory and Resource Economics, 70593 Stuttgart, Germany b

a r t i c l e

i n f o

Article history: Received 29 February 2008 Received in revised form 19 November 2008 Accepted 11 December 2008 Available online 20 January 2009 Keywords: Global change Regional optimization model Global change scenarios Agricultural production

a b s t r a c t Assessing the impact of climate change on agriculture is a new challenge for quantitative model-based policy analysis. The impact of climate change will vary strongly across regions depending on pre-existing climatic, agronomic, and political conditions. Most of the present modeling approaches, which aim to analyze the impact of global change on agriculture, deliver aggregated results both with regard to content and spatial resolution. To deliver results with a higher spatial resolution and to produce a more detailed picture of agricultural production, the county-based agro-economic model known as ACRE-Danube was developed. The German and Austrian part of the Upper Danube basin, a study area with great diversity in agricultural landscapes and climatic conditions, was chosen for study. For the analysis, two scenarios of climatic and socio-economic change were derived. The first and more economically and globally oriented scenario, termed ‘‘Full Liberalization,” included significant temperature increases. The second and more environmentally and regionally oriented ‘‘Full Protection” scenario included a moderate temperature increase. Both scenarios produce different results regarding agricultural income and land use. While the developments in the Full Protection scenario are small, the Full Liberalization scenario yields extreme regional changes in agricultural income, an increase in cereal production and extensive grassland farming. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction Global change refers to those changes that reflect the influence of humans on the earth, including changes in global climate, demographics, culture, socio-economics and technological developments (Crutzen, 2004). Agriculture is a form of land use with widespread application in Europe that may be strongly influenced by climatic changes, as well as by population growth, income growth, crop demand and animal demand (Rounsevell et al., 2006; Busch, 2006; Geist and McConnell, 2006). Modeling approaches that investigate the consequences of possible future scenarios may allow us to prepare for certain changes prior to their actual occurrence. Land use is determined by multiple processes at both global and regional scales. Thus, a single model representing only one scale may not be sufficient for simulating all the driving factors, which can operate at different levels (Verburg et al.,

* Corresponding author. Tel.: +49 711 459 22559; fax: +49 711 459 22555. E-mail address: [email protected] (T. Krimly). 0308-521X/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2008.12.002

2006). The influence of climate change on agriculture thus represents a new challenge to quantitative model-based policy analysis. With regard to agriculture, ‘‘location and context-specific modeling” (Buysse et al., 2007) is now more important than ever, as the ability to adapt to climate change will be strongly linked to location and region- and farm-specific behavior. The aim of this study is to give a more spatially detailed analysis on the impact of global change scenarios on agricultural land use in order to better inform future policy decisions. For this reason, we developed the county-based ACRE (Agro-eConomic pRoduction model at rEgional-level) model. The investigation area of ACREDanube, which is presented in this study, represents the German and Austrian part of the Upper Danube basin, the research area of the GLOWA-Danube project (Global Change in the Hydrological Cycle). This area supports a wide variety of agricultural landscapes and soil qualities, as well as great altitude and climate gradients, especially in the Alps and their forelands (Winter, 2005; Probeck et al., 2006). The following section describes the methodological approach used to model ACRE-Danube and briefly introduces the study area. Then, the assumptions behind the scenarios are discussed. Finally, the results for the counties are analyzed and discussed.

32

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

2. Economic modeling of land use/land cover changes Mathematical programming models are widely used to model land use/land cover changes since they are able to capture the core decision-making processes that drive agricultural land use/land cover change (Lambin et al., 2000). These models also have the unique ability to link economic elements with ecological and biophysical elements (Buysse et al., 2007). Scaling problems are a major concern in land use/land cover change modeling (Verburg et al., 2006). The data for the natural sciences must be detailed in terms of spatial resolution; economic data, on the other hand, deal with larger spatial units, both because spatially differentiated economic data are either difficult to obtain or do not exist (Umstätter, 1999) and because aggregation to a higher level reduces the complexity of the problem. Bottom-up approaches, which consider proximate decisions taking place on the local or regional level, are important to adequately reflect the relevant actors and factors of agricultural land use (Busch, 2006). Since conducting detailed farm surveys on a regional scale would be extraordinarily costly, data acquisition was limited to secondary statistical information. These data are available at certain administrative levels, depending on the kind of data (e.g., state, county or municipality level) (Umstätter, 1999). The Farm Accountancy Data Network (FADN) provides regular farm survey data for a large number of farms in Europe. However, being based on data from a sample of the agricultural holdings, the survey data might not represent complete agricultural production at a regional level. Positive Mathematical Programming (PMP) models may help in overcoming the problem of missing data (Umstätter, 1999). PMP is a qualified calibration method, and adequate measures are available to validate these models (Verburg et al., 2006). Feedback related to the use of models as policy support tools for stakeholders, in particular decision-makers, is another important issue in land use/land cover change modeling (Verburg et al., 2006). PMP models are able to take into account real observed behavior and project producer reactions to external changes. This quality makes PMP models particularly interesting for agricultural policy analysis (Buysse et al., 2007). 2.1. Existing modeling approaches There are few studies that explicitly consider the impact of global change on land use at a regional scale. A detailed review of quantitative agricultural land use/land cover change studies for Western Europe was conducted by Busch (2006). The various studies differed in their focuses, approaches to modeling land use changes, base years, time horizons and the numbers of scenarios, as well as their investigation areas and spatial scales. The ATEAM (Advanced Terrestrial Ecosystem Analysis and Modeling) study and the EURuralis (Europe’s Rural Areas) study used a top-down modeling approach to provide information on land use/land cover changes on different scales by applying downscaling procedures to continental level data (Busch, 2006). In the EURuralis study, climate change feedback is accounted for by reiterating global results from the biophysical model (IMAGE) (Meijl et al., 2006) at the national level, using the general economic equilibrium model (GTAP). The allocation of land at the local level is represented using a spatially explicit model that considers variations in biophysical, socioeconomic and policy characteristics (Verburg et al., 2006). In contrast, the projects ACCELERATES (Assessing Climate Change Effects on Land Use and Ecosystems; from Regional Analysis to the European Scale), GLOWA-Elbe (Global Change in the Hydrological Cycle) and SEAMLESS (System for Environmental and Agricultural Modeling; Linking European Science and Society) use bottom-up approaches to address decision-making at a regional level. The ACCELERATES project uses a farm model based on

linear programming techniques to evaluate optimal land use allocation at the NUTS2 (Nomenclature of Territorial Units for Statistics) level (Audsley et al., 2006). The Nomenclature of Territorial Units for Statistics (NUTS) is a geocoding standard developed by the European Union for referencing the administrative divisions of European countries for statistical purposes (EC, 2004). For example, in Germany, NUTS1 represents the state level (Bavaria), NUTS2 represents the level of Regierungsbezirke (group of counties, e.g., Lower Bavaria), and NUTS3 represents the county level. The GLOWA-Elbe and SEAMLESS studies use regional agricultural sector models based on Positive Mathematical Programming (PMP) to simulate land use changes at the NUTS3 level (Gömann et al., 2005; Flichman et al., 2006). However, only GLOWA-Elbe takes into account the feedback between the macro and the regional scales; the study links the regional agricultural sector model (RAUMIS) and the general economic equilibrium model (WATSIM). Most of the studies reviewed use a downscale approach to simulate decision-making at the micro-level by combining disaggregated regional level information from economic models (e.g., within EURuralis, local processes are simulated at 1 km2 resolution). The validity of the results derived from this downscaling procedure, however, is unclear since consistent land cover data (e.g., CORINE) are rarely available for large areas (Verburg et al., 2006). In contrast, SEAMLESS integrates decision-making at the local and regional levels and the feedback yielded by these decisions combines a mathematical programming module (FSSIM-MP) with an agricultural management module (FSSIM-AM) (Flichman et al., 2006). Socio-economic parameters such as changes in policies, producer prices and technological advances are addressed at a high level of detail and with a particularly long projection period by the ACCELERATES study. In GLOWA-Elbe and EURuralis, these parameters are addressed over a medium-term time horizon. SEAMLESS uses the most complex approach to quantify socio-economic parameters, but it covers only the short-term. Climate-induced yield changes for medium-term projections are considered in detail in GLOWA-Elbe with respect to the spatial scale and the number of crops. ACCELERATES and ATEAM offer long-term projections with a good level of detail. In SEAMLESS, climate change scenarios are intended to be modeled at a later stage. Except for the model RAUMIS, which calculates parameters at a county (NUTS3) level and is used to model the impact of global change on agriculture in the German part of the Elbe basin in northern Germany, all the models described use top-down modeling approaches to investigate the impact of Global Change on land use/land cover changes at the NUTS1 or NUTS2 level. These surveys apply downscaling to obtain additional spatial information for their results. In general, only a limited number of production activities are taken into account, and different intensity levels of agricultural production are not considered. In all these surveys, only very aggregated quantitative results on agricultural land use change in Europe are available, which poses an obstacle to the discussion of future challenges to rural areas (Busch, 2006). Existing heterogeneities within the considered administrative units are not represented. 2.2. The ACRE model The ‘‘Agro-eConomic pRoduction model at rEgional level” (ACRE) for the Upper Danube basin uses a bottom-up approach to simulate regional agricultural land use. This model offers a high level of detail, both in terms of spatial resolution and the number of agricultural production activities included. The main objective of ACRE is to produce a regional analysis of the impact of global change and agricultural policy measures (e.g., quotas, subsidies) on agricultural land use. In ACRE, 24 food and non-food crops with different production intensities per crop as well as 15

33

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

production processes for livestock are considered at the county (NUTS3) level. 2.2.1. Process analytical approach ACRE is a comparative static optimization model that maximizes total gross margin at the regional level by calculating the optimal combination of different production activities for each county. Production factors within each county are aggregated to create a ‘single farm’ (regional farm approach). The shortest simulation period is one year. The model analyzes the most important processes and interactions in agricultural production. On arable land, cash crops or fodder crops for livestock production may be produced. The animals produce manure, which is used as fertilizer in crop production. Mineral fertilizer and feed concentrates are purchased. The applied amount of fertilizer per crop is calculated using a linear function that depends on the simulated crop yields (Winter, 2005). The prices for crops and animal products, as well as premiums, influence the total gross margin. Trade activities between the counties are not defined. As the Upper Danube basin has a good climatic water balance and is not expected to experience severe water problems within the next decade, irrigation is not integrated. 2.2.2. Calibration method and model The methodological approach used in ACRE is exemplary in explaining crop production activities. For detailed information concerning animal production activities, feeding activities or set-aside and corresponding restrictions of these activities, see Röhm (2001) and Winter (2005). This section draws on Section 3 ‘‘Theoretical framework” of Henseler et al. (2005). ACRE is based on the calibration method of PMP. A PMP model optimizes agricultural production by maximizing the objective value of a non-linear total gross margin function (Howitt, 1995). In comparison to Linear Programming (LP) models, PMP models have the following advantages: they are calibrated by the reference situation and avoid overspecialization; they react continuously to parameter variations and allow a flexible result calculation; and they tend to require fewer data. These features make PMP models particularly suitable for modeling regional agricultural production. A few years ago the PMP method was elaborated to include an additional sub-dimension: the variant activity (Röhm and Dabbert, 2003). This variant activity extension differentiates between, within crop production, two levels of activities with different degrees of substitution characteristics: total crop activities and variant activities. Hence, the extension allows the modeling of different production variant activities of a certain crop, e.g., intensive and extensive production variants. This approach allows the PMP model greater reactivity, since it can react by changing either the extension of variant activities or the total crop activities. This makes it particularly suitable for simulating such scenarios as agri-environmental measures. Generally, a PMP model is built in two steps: an LP model representing the observed statistical situation calculates dual values, which are then used to calibrate the non-linear functions of the PMP model. The system of non-linear functions has its optimum at the point where the marginal gross margins are equal. Graphically, this is where the non-linear functions intersect. Thus, the optimum value, or the maximum objective value, is determined by the non-linear function parameters (e.g., the slopes of the non-linear functions). In other words, the LP model produces shadow prices that are used to calculate non-linear function parameters. Dual values ensure the replication of production patterns as simulated by the LP model. Shadow prices represent the true values of prices for the scarce resource in an observed situation. Such a model, calibrated by estimating the non-linear function parameters with shadow prices,

essentially depicts an empirically observed value using a non-linear objective function. Fixed restrictions for the activities are not needed to determine an optimum: these restrictions often require exact data that are not available at the regional level. Technological and other limiting constraints, on the other hand, do appear in the PMP model. According to Röhm and Dabbert (2003), a variant activity version of PMP is described by the series of equations from Eqs. (1)– (12). Eq. (1) is the total gross marginal function (TGM), which is the objective function of the LP model. TGM is maximized by the LP model subject to Eqs. (2)–(6). X i;v is the optimized extension of ^ i;v is the extension of the variant the variant activities i, v, and X activity i, v observed in the calibration situation. The index i represents the total crop activity and index v represents the variant activity.

max f ðXÞ where f ðXÞ ¼ TGM ¼

XX ðX i;v  ½yi;v  pi;v þ SUBi;v  ci;v Þ i

ð1Þ

v

With i : total crop activity i ðe:g:; wheat; rye or grasslandÞ

v : crop production variant v ðe:g:; intensive or extensive productionÞ i; v : variant activity of crop i and production variant v ðe:g:; intensive wheat; extensive grasslandÞ X i;v : simulated acreage of variant activity i; v ½ha ^ i;v : observed acreage of variant activity ðstatisticÞ i; v ½ha X 1

yi;v : crop yields of variant activity i; v ½dt ha  pi;v : price for variant activity i; v ½EUR dt  1

1

SUBi;v : subsidies for variant activity i; v ½EUR ha  1

ci;v : variable costs for production of variant activity i; v ½EUR ha 

subject to XX XX ^ i;v Þ ðX i;v Þ  ðX i

v

i

ð2Þ

v

Eq. (2) represents the land resource constraint and produces the dual value kland which is used to calculate the shadow price of the marginal crop (activity).

X X ^ i;v Þ  ð1 þ e1 Þ ðX i;v Þ  ðX v

ð3Þ

v

The constraint on the amount of total crop activity is represented by Eq. (3). The sums of the variant activities (Xi,v) represent the corresponding total crop activities (Xi). The total crop activity restriction produces the dual value ki .

^ i;v  ð1 þ e2 Þ X i;v  X

ð4Þ

Analogously, the restriction on the amount of the variant activities (Xi,v) is represented by Eq. (4), by which the dual value of the variant activities ki;v is produced. The perturbation coefficients e1 , e2 in Eqs. (3) and (4) are small positive numbers. These coefficients enlarge the restrictions of the ^ i;v Þ by a small value, ^ i and X observed amounts of the activities ðX which allows the LP model to produce dual values for each activity. Nevertheless, the number of constraints exceeds the number of variables by one, which is why one total crop activity constraint produces the dual value of zero. This problem requires a special method of calibration for the least profitable total crop activity, the marginal crop (or marginal

34

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

activity). This approach requires inter alia the shadow price for land kland (for details cf. Röhm and Dabbert, 2003; Röhm, 2001; Umstätter, 1999). To ensure that in the optimization, the substitution between variant activities takes place between variant activities rather than between total crop activities, Eq. (3) for the total crop activities must be more binding than is Eq. (4) for the variant activities: The value of e2 must be larger than that of e1 , resulting in higher dual values for the variant activities. The different shadow prices for total crop activity and variant activity result in differing sizes of non-linear function parameters. This allows the PMP model to optimize by changing the variant activities rather than the total crop activities.

e1 < e2

ð5Þ

X i;v  0

ð6Þ

Eq. (7) describes the classical version of the objective PMP function using only total crop activities. Thus, only the total crop activity (Xi and ki ) appears and the TGM is summed up by index i. Eq. (8) is the objective PMP function including variant activity extension. It shows the hierarchical relationship between variant activities and total crop activities. The curved brackets include the sums yielded by index v and the TGM sum yielded by index i. For a better overview, we replaced yield, price, subsidies, and cost terms in Eqs. (7) and (8) with the equation: GMi;v ¼ yi;v  pi;v þ SUBi;v  ci;v

TGM ¼

X i

    Xi X i X i  GMi þ ki  1  ^i X

ð7Þ

( " !# X X X i;v GMi;v  X i;v þ ki;v  X i;v  1  ^ i;v X v i 0 19 P X i;v > = X B C v X i;v  @1  P þki  A ^ i;v > X ; v

TGM ¼

ð8Þ

v

Eq. (9) represents the non-linear objective function for crop production as formulated in ACRE. This objective function implies a quadratic cost function. Eqs. (10)–(12) represent the parameters for the non-linear functions according to Röhm (2001). In the complete model, further constraints are illustrated in order to consider the requirements of production processes (e.g., animal feeding, herd management, and crop rotation) or market and policy demands (e.g., production quotas or obligatory set-aside). For details, see Röhm (2001) and Winter (2005).

TGM ¼

X X v

i

 X i;v  yi;v  pi;v þ SUBi;v

ci;v  di;v þ /i;v  X i;v þ ui;v 

di;v ¼ 1 

/i;v ¼

ki þ ki;v ci;v

ki;v ^ i;v ci;v  X

X

!!# X i;v

ð9Þ

v

ði:e: the coefficient axis interceptÞ

ð10Þ

ði:e:; the slope coefficient of variant activity levelÞ ð11Þ

ui;v ¼

ki;v P^ X i;v

ci;v 

v

ði:e:; the slope coefficient of total crop activity levelÞ ð12Þ To reach a realistic and empirically valid fit for the functions, empirical estimators may be calculated to compare theoretical outcomes to observable data (e.g., elasticity). Instead of using empiri-

cal parameters, however, the ACRE model was validated according to the quality of its forecasts. Winter (2005) derived test values which were based on the absolute percentage deviation of statistical and simulated production data. These values are comparable with the mean absolute deviation proposed by Hazell and Norton (1986). With a calculated weighted absolute deviation (WAD) of 7.68% the validation of ACRE-Danube was very accurate in ex-post forecasting the period from 1995 to 1999.

2.3. Model data and study area ACRE-Danube was calibrated using statistical production data at the county level for 1995, which was the base year for the GLOWA-Danube project. Agricultural data (e.g., yields, crop acreages, and livestock) at the county level are available and sufficiently precise. The production processes in ACRE are formulated according to the publications of the German Association for Technology and Structures in Agriculture (KTBL, 1995, 1997, 1999). These data collections represent an accurate standardized database for agricultural production in Germany. Soil differences are integrated into the ACRE model using the Agricultural Comparability Index (LVZ). This index provides information about the suitability of both grassland and arable land (Manthey, 1996) and determines, within the model, the Compensatory Allowances for Mountain or Less Favored Areas within the second pillar of the CAP and the production intensity (Winter, 2005). The Upper Danube basin, which is the research area of the GLOWA-Danube project, covers an area of 77,000 km2 and extends across five countries (Fig. 1). The largest portion of land lies in the south of Germany, with an area of 56,000 km2, followed by that of Austria, with 20,000 km2. The basin supports a wide variety of landscapes and ecosystems, and this variation is primarily attributed to the differences in altitude (Mauser, 2000). While Passau is 286 m above sea level, the highest elevation in the Alps lies 3600 m above sea level. Altitude also affects the climatic conditions in the area; precipitation, for example, ranges from 650– 2000 mm/m2. Approximately 55% of the study area is used for agriculture (Mauser and Ludwig, 2002). Due to the varying climatic conditions, the agricultural land use in the basin also differs. The Alps and their forelands, which are located in the south of Germany and also in the southerly region bordering Austria, are dominated by grassland and dairy production. Arable production is secondary and reserved for the production of cereals and forage (Winter, 2005). Northwards, the share of arable land increases up to 60%, and dairy production is replaced by fattening bulls in combination with silage maize production. In the Tertiary Hill Country, which is spaciously bordered by the Danube River to the north, the River Inn to the east and south-east, the lowlands around Munich to the south and the underflow of the River Lech to the west, the proportion of arable land accounts for up to 80–90%, and pig production is widespread. The most favorable arable regions are located south of the Danube River. Very specialized arable farming may be found in the east Bavarian low mountain range, which is located in the southeastern part of the basin (Krimly et al., 2008). To the north of the Danube River, the agronomic conditions for arable production are less favorable and the share of grassland accounts for 80–100% of the land. However, grassland use and cattle production in the east Bavarian low mountain range is less specialized than in the Alps (Winter, 2005). Northeast of the Danube, in the range of the Swabian Alb, which is bordered by the Danube River to the south-east, the borders of the study area to the north and the steep Albtrauf escarpment to the northwestern edge, unfavorable agronomic conditions exist due to mattock formations. Here, dairy cow farming is again common. The proportion of arable land ranges from 40%

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

35

Fig. 1. The Upper Danube basin. Source: adapted from Diercke (1992).

to70%, and it is used mainly for cereal production. Pig production remains widespread here as well. Overall, the study area includes 74 counties (NUTS3-level), with 58 belonging to Germany and 16 to Austria. The counties belong to 12 administrative units based on the NUTS2-level classification. 3. Development of global change scenarios The storylines A1 and B2 of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) constitute the scenario framework of this study (Nakic´enovic´ et al., 2000). The SRES storylines presented in Table 1 were created using detailed assumptions for the agricultural sector, derived from the ACCELERATES study (Abildtrup et al., 2006) and implemented for the Upper Danube study area (Henseler et al., 2008).

In correspondence to the ACCELERATES study, the fossil intensive scenario group A1FI was selected for the A1 storyline. The scenarios were labeled according to their level of market liberalization and their protection of agricultural production through public expenditures. ‘‘Full Liberalization” is characterized by high technological advances and low public expenditures; ‘‘Full Protection” is characterized by low technological advances and high public expenditures. Table 2 presents the scenario assumptions for climate change (i.e., percentage crop yield changes) and socio-economic change (i.e., crop yield changes due to technological progress, subsidies, and prices). Data for these scenarios were available in different spatial resolutions. While climate change data for crop yield are available at the NUTS2-level, the socio-economic data were available only for the complete model region.

Table 1 Description of Special Report on Emission Scenarios (SRES). SRES storyline

Orientation of future development

Description

A1

Economic-global

Economic development  very rapid economic growth  global population peaks in mid-century and declines thereafter  rapid introduction of new and more efficient technologies  emphasis on substantial reduction in regional differences in per capita income High temperature increase

B2

Environmental-regional

Economic development  intermediate levels of economic development  continuously increasing global population, at a rate lower than A1  less rapid and more diverse technological change than in B1 and A1  emphasis on local solutions to economic, social, and environmental sustainability Moderate temperature increase

Source: Authors’ compilation.

36

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

Table 2 Changes of crop yields due to climatic changes and technological progress for the scenarios Full liberalization and Full protection in 2020. Changes of crop yield of selected crops for the scenario in storylines A1 and B2 in the year 2020 Percentage crop yield due to climate change in 2020 (100% = regional yields at NUTS3 scale) NUTS2 regions

Spatial resolution: NUTS2 Swabia Central Franconia Upper Palatinate Lower Bavaria Upper Bavaria Tübingen Freiburg Stuttgart Vorarlberg Tyrol Salzburg Upper Austria

Full liberalization

Full protection

Cerealsa

Fodder cropsb

Grasslandc

Cerealsa

Fodder cropsb

Grasslandc

106 112 109 109 109 107 106 108 109 97 107 108

142 141 118 124 89 151 124 123 150 no data 136 130

107 110 113 113 113 102 107 105 108 108 103 109

108 112 112 109 111 107 106 108 110 98 108 107

143 145 125 127 91 157 128 127 153 no data 129 131

107 112 114 112 114 104 107 105 108 109 105 108

Percentage crop yield due to technological progress in 2020 (100% = regional yields at NUTS3 scale)

Spatial resolution: MODEL REGION Technological progressd

Full liberalization

Full protection

167

104

a

Changes in cereal yields are represented here by the change in yield of the most relevant cereal crops in each region, such as winter wheat. b Changes in fodder crop yields are represented here by the change in yield of the most relevant fodder crops in each region, such as silage maize. c Yield changes of grassland are representative for intensive and extensive grasslands. Note: nitrogen availability is assumed to be non-limiting in the calculation of crop yields. Source: authors’ calculations based on Simota (2007). d Technological progress on yield for crops and grassland. Source: based on Abildtrup et al. (2006).

To simulate the short-term impacts of climate change (temperature, precipitation, and CO2 fertilization) on agriculture for the next decade, we used crop-specific and spatially explicit yield simTable 3 Changes of subsidies and prices due to socio-economic changes for the scenarios Full Liberalization and Full Protection in 2020. Percentage subsidies for UAA and environmental programs due to socio-economic changes in 2020 (100% = regional subsidies in the reference year 1995)

Spatial resolution: MODEL REGION Subsidies for UAAa Environmental programsb

Full liberalization

Full protection

0 81

107 121

Percentage subsidies for crop and animal specific subsidies and prices due to socioeconomic changes in 2020 (100% = regional prices and subsidies at NUTS3 scale) Land use/commodities

Spatial resolution: MODEL REGION Cereals Grain maize Legumes Oilseeds Root crops Silage maize Clover Special crops Grassland Set-aside Milk and eggs Meat (poultry, beef, and pork)

Full liberalization

Full protection

Subsidy

Price

Subsidy

Price

0 0 0 0 0 0 0 0 0 0 0 0

83 91 81 82 88 0 0 82 0 0 88 96

101 89 85 115 107 89 107 107 107 112 0 0

112 106 108 106 124 0 0 106 0 0 119 110

a In German districts utilized agricultural area (UAA) is related to clover and grassland. In Austrian districts changes of UAA are related to payments according to the historic model, i.e., to all crops and grassland. b Implemented for the respective agri-environmental program of the regions: MEKA = Marktentlastungs- und Kulturlandschaftsausgleich: the regional environmental program in the federal state Baden-Wuerttemberg in Germany; KULAP = Kulturlandschaftsprogramm: the regional environmental program in the federal state Bavaria in Germany; ÖPUL = Österreichisches Programm für eine umweltgerechte und den natürlichen Lebensraum schützende Landwirtschaft: the regional environmental program in Austria.

ulation at the NUTS2-level from the crop growth model ROIMPEL (Mayr et al., 1996) based on HadCM3 climate projections (Mitchell et al., 2004). ROIMPEL is an agro-climatic simulation model for crop yields that uses soil and terrain information, such as soil texture and organic matter, as well as weather and climatic variables, such as monthly values of average daily temperature and monthly cumulative precipitation (Audsley et al., 2006). The results for eight different crops plus grassland and 12 NUTS2 regions were allocated for each crop and modeled by county, respectively; these were then used as input parameters for ACRE (Table 2). More details are described in Henseler et al. (2008). Table 2 presents the selected percentage yield changes for cereals and grassland as available for the NUTS2 regions of the study area. The percentage change in yields due to climate change ranged from 10% to +20% of reference yield for cereals and grassland. For fodder crops, these values ranged from 10% to +60%, with comparable percentages under the Full Liberalization scenario and Full Protection scenario. To generate the yield values, the percentage of technological progress was added to the crop yield percentage due to climate change. This created values of +67% in the Full Liberalization and +4% in the Full Protection scenario. For example, the total value for the NUTS2 region ‘Swabia’ results in +73% for cereals in the Full Protection scenario. To overcome the challenge of building methodologically consistent scenarios, we used these crop yield changes, but it is important to keep in mind that these changes are quite extreme under the Full Liberalization scenario due to the assumed technological progress. The CAP (Common Agricultural Policy) reform 2003 begun in 2005 and projected to end in 2013 is used as a reference scenario. This policy is assumed to remain constant until 2020, without changes in crop yields. Changes in subsidies were calculated based on the single farm payments (SFP) under the CAP scenario. Changes in environmental payments were calculated based on the agri-environmental programs present in the respective regions. Price changes were calculated based on the calibration prices in ACRE (Henseler et al., 2008). Table 3, for example, shows that in the Full Liberalization

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

37

scenario, the subsidies were cancelled, while in the Full Protection scenario they were increased. Market prices tended to decrease in the Full Liberalization scenario and to increase in the Full Protection scenario.

result in a higher increase of grassland yields with greater effects of de-intensification than in extensive grassland areas.

4. Results

5.1. Implications of scenario assumptions for results

In each of the two Global Change scenarios, changes in agricultural income and land were analyzed in comparison to the baseline (CAP) scenario. The results are shown in Fig. 2. The Full Liberalization scenario predominantly leads to a decrease in agricultural income. The changes in income range from 5% to 40% compared to the CAP scenario and reveal the occurrence of regional variation. In counties located in the most favorable arable regions along the Danube River, the changes in income are very low. Income decreases significantly, as much as 40%, in counties with a high share of grassland, e.g., in the east Bavarian low mountain range in the northeast or in the alpine forelands in the south of Germany. The main reason for these severe losses is the cancelled single farm payments, which can lead to strong structural changes in land use in these regions and even result in farm abandonment. In the counties with a high share of arable land and favorable agronomic conditions, the cancelled subsidies and lower prices may be nearly fully compensated by strong yield increases for cash crops due to technological progress (+67%) assumed under this scenario. More moderate technological progress, however, would also cause income losses in some counties. In the Austrian Alps in the south of the study area where extensive grassland farming represents the major agricultural land use, relative losses in total gross margins are smaller than those occurring in the region bordering Germany. In contrast to Germany, single farm payments in Austria were modeled according to the historic model of the single farm payment scheme. These farms are in grassland regions and have a value that is significantly lower in terms of Euros per hectares than counterpart farms in Germany. Thus, the relative loss of income in Austria is smaller. In contrast, the Full Protection scenario produces increases of about 20% for all counties in the development of agricultural income due to the assumed increase in subsidies. The heterogeneity of the counties becomes evident in the model. This could not be represented by a model that is calculated with aggregated data at the NUTS2-level. Changes in agricultural land use are observed as the changes in cereal production and extensive grassland use (Fig. 2). In the Full Protection scenario, the areas of both land use types remain similar to the baseline scenario. In the Full Liberalization scenario, cereal production also remains more or less unchanged in the grassland dominated southern half of the study area, but increases in the northern half, except in the far west. In the counties north of the Danube, the increases in fodder crop yields result in a smaller area of fodder crop production, which is accompanied by an extension of cereal production. The counties to the south of the Danube River are more specialized on cash crop production. Here, root crop areas in particular are reduced and replaced by cereal production areas. Diminished prices for root crops decrease gross margins and production of root crops. The de-intensification of grassland appears in regions that are dominated by grassland and dairy farming in the south, in the east Bavarian low mountain range, the alpine foreland and the Austrian Alps. Here, the increase of grassland yields results in decreased grassland area for fodder production. Intensive grassland production is converted to non-intensive grassland use. This scenario represents a possible danger of grassland abandonment. Extreme increases of extensive grassland area are observed, e.g., in counties in the southwest of the study area. Here, high proportions of intensive grassland

The use of simulated yield data from the crop growth model ROIMPEL allows us to appropriately consider the influence of climate (temperature and precipitation) and elevated CO2 levels on crop yield in the Upper Danube study area. However, ROIMPEL simulation results must be considered carefully with respect to the model input, since the spatial level of the data does not consider variability within NUTS2 regions. ROIMPEL furthermore suggests overall productivity increases for all crops in 2020 for all IPCC SRES scenarios, particularly for oilseeds and maize. However, important factors and potential interactions, for example, pests, pathogens and weed infestations, are not at all taken into account. Nevertheless, the climate change yield data of ROIMPEL were the only ones available for the model region with a high spatial resolution on NUTS2-level, which is why we used them in this study. In addition, the assumptions on the rates of crop yield growth due to technical advances for the EU15 (up to 67% compared to the baseline) appear to be overestimated. Analyses of regional historic yield trends, using wheat as a proxy for the upper boundary of productivity, indicate that technology has had a smaller effect on productivity in the past. According to Sterzel (2004), linear fit lines of historic long-term wheat yield series (1950–2003) show a moderate positive slope of 0.08t ha1 y1 (R2 = 0.92) and 0.09t ha1 y1 (R2 = 0.91), for Baden-Württemberg and Bavaria, respectively. Assuming that future productivity increases are likely to occur from the same sources (Hafner, 2003; Ewert et al., 2005, 2006), potential yield increases at a regional level would be considerably lower than stated under the scenario assumptions. Also, the prices of agricultural commodities have tended to increase over the past three years, and future prices are forecasted to increase further. Thus, the price estimations in the Full Liberalization scenario are contrary to currently observed market developments. The large differences between the agricultural land use changes obtained from ACRE results and the changes documented in the corresponding SRES scenario description occur for two reasons: (a) the original SRES land cover scenarios provided agricultural land use changes for global cropland and grassland areas were obtained from Integrated Assessment Models (IAMs) (Nakic´enovic´ et al., 2000). However, since the Third Assessment Report numerous new models have emerged that can generate regional agricultural land use changes potentially very different from those generated by the IAMs (Busch, 2006), which makes the validity of those estimates more questionable. (b) The Fourth Assessment Report shows that the SRES land cover scenarios lack internal consistency; for instance, they exclude climate change impacts on future land use. It recommends therefore the use of more sophisticated downscaling approaches for regional level assessment (Carter et al., 2007). In this context the Fourth Assessment Report explicitly refers to some of the European studies at regional scale (e.g., Meijl et al., 2006; Audsley et al., 2006). The results from the ACRE calculations correspond with recent findings for Middle European regions published by several other authors. Given these results, terminating subsidies and boosting yields through technological development may lead to strong reductions in grassland farming (Klijn et al., 2005; Meijl et al., 2006). The result would be intensification in favorable regions and de-intensification in marginal regions (Gömann et al., 2005; Audsley et al., 2006). In contrast, a rise in public spending may ensure the maintenance of the current landscape, in particular the preservation of cultural

5. Discussion

38

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

Fig. 2. Development of agricultural income, cereals production, and extensive grassland in percentage points of TGM/UAA in comparison to CAP scenario for the NUTS3 counties under Full Liberalization and Full Protection scenarios.

landscapes and agricultural income levels, despite small increases in productivity (Klijn et al., 2005). If recent developments in agricultural output price levels (OECD-FAO, 2008) persist in the longterm, this would be a counteractive force.

5.2. The use of positive mathematical programming methods The use of Positive Mathematical Programming (PMP) allows projections based on past observations of the cost function and

39

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

thus reflects real farmer behavior (Buysse et al., 2007). However, the dependence on the availability of regionalized data creates some restrictions in areas where statistical data are missing or incomplete. Another disadvantage is the difficulty in calibrating production functions for activities where no observations are available (Buysse et al., 2007). This is a particular restriction for the simulation of long-term scenarios and the uptake of new activities due to climate change adaptation needs (e.g., water stress tolerant crops). This quality makes the PMP models more suitable to modeling medium-term future scenarios than to making long-term projections. Thus the most valuable use of PMP is to model modifications of the existing (calibrated) situation; in such situations, it can give a reliable projection of the consequences of change. The inclusion of new measures or activities is only possible if they are in line with the existing calibration (Buysse et al., 2007). 5.3. Implications of the use of a quadratic cost function on the results As shown in Section 2.2 the formulation in ACRE produces a quadratic gross marginal function, which is a common approach when using the PMP method in explanatory models (Paris, 1988; Howitt, 1995), as well as in applied regional models (e.g., Röhm, 2001; Wälzholz, 2003; van Welsum et al., 2007; Fragoso et al., 2008). The quadratic cost function is assumed due to simplicity, the lack of data and the lack of strong arguments for other types of functions (Heckelei, 2002). However, other functions are possible, and they would reflect farmer decisions in a different way. Several studies deal with approaches where the functions of the programming models are estimated by empirical information (e.g., supply elasticities) (e.g., Heckelei, 2002; Buysse et al., 2007; Helming and Berkum van, 2008). As those empirical data are not available for the Upper Danube catchment, ACRE uses the quadratic cost function. Its parameters are calculated by using the shadow prices of the linear model in the baseline situation, which represent the empirical data of the reference situation. However, the use of functional forms other than quadratic is theoretically possible. To examine the impact of using different cost functions, a less complex model was developed where the functional form of the cost function was varied. Based on the general assumption of the PMP approach that farmers have maximized their profits in the reference situation, we suppose that for every crop they produce at an acreage Xi they have to face decreasing marginal gross margins dGM/dXi. Therefore, the functional form of the objective function is formulated as follows:

TGM ¼

X



yi;r  pi;r þ SUBi;r X i;r

tion of the ACRE model region and includes the activities i: winter wheat, winter barley, spring barley, oats, rapeseed, set-aside, and land abandoning. Three different regional farms r were defined which differ in their intensity of crop production (high, medium, and low). Production data of these regional farms were derived from the statistical distribution of the acreage and yield of the most relevant crops over all counties in the model region (Table 4). To represent crop production in the regional farm with high production intensity, the acreages and yields of the intensive land use types – winter wheat, winter barley and rape seed – were derived from the upper quartile (75% quartile) and those of the extensive land use types – oats, spring barley and set-aside – from the lower quartile (25% quartile) and vice versa. The average production costs of the whole region were used as the production costs for all modeled regional farms. The results of the different cost models are shown in Table 5. In the Full Protection scenario the decrease of set-aside area slightly lowers with increasing power of the cost function in all regional farms. While in the Full Liberalization scenario the variation of the cost functions only leads to minor differences in the regional farms with a high or medium production intensity, the results clearly differ in the regional farm with low production intensity. Comparing thus the results for different specifications of the objective function under different scenarios and for different regions we find – as expected – that this choice of objective function influences the results. This influence is comparatively small in five of the six cases investigated. However, in the extensive region in the Full Liberalization scenario the differences are quite noticeable. From the example calculation no argument arises that makes one of the three functional forms investigated the superior one. As we did not have data that would support an empirical estimation of the coefficients of the objective function and as there are no strong arguments for other types of functions the quadratic form is the most simple. From the viewpoint of philosophy of science in such cases the simplest solution should be the best. Also

Table 5 Scenario results of the example model with different cost functions. Intensity of production Power of cost function

High

i;r



þ ci;r  ui;r  X i;r :  ci;r  di;r  X power i;r The power is varied (1.5, 2, and 3) and the calibration parameters di and ui determine the slope of the cost function and thus, the increase of the marginal costs. The model simulates crop produc-

Medium

Table 4 Land use in the regional farms of the example model. Productionintensity

High Medium Low

Chosen quartile over all counties of the model region Low Winter wheat, winter barley (%)

Spring barley, oats (%)

Rape seed (%)

Setaside (%)

75 50 25

25 50 75

75 50 25

50a 50 75

a The 25% quartile would not be plausible here, because the regional farms with a high intensity of production have a high share of cash crops and thus a large share of set-aside area.

Winter wheat Winter barley Spring barley Oats Rapeseed Set-aside Abandoned UAA Winter wheat Winter barley Spring barley Oats Rapeseed Set-aside Abandoned UAA Winter wheat Winter barley Spring barley Oats Rapeseed Set-aside Abandoned UAA

Scenario REFa

Deviation from REFa scenario in percentage points of UAAb

% of UAAb

Full Liberalization

Full Protection

1.5, 2.0, 3.0

1.5

2.0

3.0

1.5

2.0

3.0

46 30 1 2 11 10 0 39 22 10 9 9 11 0 6 2 36 26 4 26 0

8 4 0 0 2 10 0 7 3 2 1 2 11 0 2 1 12 9 0 26 3

7 4 0 0 1 10 0 6 3 2 1 1 11 0 1 0 8 6 0 26 10

6 4 0 0 0 10 0 5 3 1 1 0 11 0 1 0 5 4 0 26 16

5 3 0 0 1 10 0 4 2 1 1 1 10 0 1 0 4 2 0 8 0

4 2 0 0 1 7 0 3 2 1 1 1 7 0 1 0 3 2 0 6 0

2 2 0 0 1 5 0 2 1 1 0 0 5 0 0 0 2 1 0 4 0

Small deviations from the sum of 100% are caused by rounding errors. a REF: reference situation. b UAA: utilized agricultural area.

40

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

Ex-post analysis of an earlier version of ACRE showed a good forecasting behavior of ACRE with a quadratic form (see Section 2.2). With this line of argument we believe that the choice of the quadratic form can be justified, even if the justification is not as complete and as much based on empirical data as we would wish. Finding better ways to derive the type of the objective function for PMP models remains an important task for further research. 5.4. Additional benefits of ACRE to current modeling of agricultural land use changes ACRE reflects the decision-making process of the regional farmer in great detail both in terms of spatial scale and content. In doing this, the appropriate spatial level representing the socio-economic and biophysical conditions as well as the decision-making level of the main actors (Busch, 2006) is addressed in the simulation of climate change adaptation. ACRE provides detailed land use information and addresses various adaptation reactions by both considering multiple production activities and the close interrelations of variant activities pertinent to any one crop. Therefore, ACRE is suitable for the evaluation of agro-environmental policy measures under the second pillar of the CAP or the Cross Compliance regulation. The highly detailed picture of agricultural activities that ACRE provides may be used as the basis for diversification strategies in rural areas. Diversification is a main focus of the ‘changed rural paradigm’ (OECD, 2006), which takes the different indigenous potentials of rural regions into account. Currently, an extended effort is underway by various research institutions to operationalize these differences (e.g., IPTS, 2008; CAP-IRE, 2008; Klijn et al., 2005). For the agricultural sector, ACRE contributes to the understanding of differentiation by regional modeling. Furthermore, the ACRE model is flexible and allows additional indicators, which might address the demands of different groups of society, e.g., protection of cultural landscapes, ecological services and socio-economic benefits, to be integrated. This expands the possibility for evaluation within the agro-economic model in terms of environmental and cultural landscape functions (cf. Millennium Ecosystem Assessment, 2005). ACRE covers a large part of southern Germany. Therefore, it allows for coverage of a substantial area of highly diversified regions in Europe in terms of landscape and farm structure. This makes it particularly suitable for modeling different scenarios which examine the effects of funds for societal functions of agriculture (e.g., preservation of extensive grassland, sustainable resource management).

reforestation, hampers the results of land use changes, in particular for abandoned agricultural land. Incorporation of alternative land use options is likely to cause reductions in agricultural area. Land owners in these areas will be partially compensated by biofuel production and reforestation. The lack of irrigation in the current model is unlikely to affect the results significantly. Irrigation in the Upper Danube study area is only practiced to a minor extent and restricted almost entirely to special crops such as vegetables. The impact of climate change occurring within the next decade is unlikely to provoke a significant increase in irrigation in the investigation area. 5.6. Further development and potential of ACRE in regional land use modeling To improve the description of farmer reactions, the coupling of ACRE with a multi-agent system (MAS) may be useful. The first steps have been taken in the GLOWA-Danube project to develop a farming decision-making framework that works on a 1 km2 grid and interacts with other (ecological) models (Apfelbeck et al., 2007). The further development of this coupling will allow the transformation of ACRE results to the local level. There, results may be directly connected to spatially explicit GIS-based models. Additionally, this raises the possibility of using ACRE results in stakeholder processes (e.g., with regional institutions). The incorporation of ACRE into a model chain with market models would facilitate the inclusion of dynamic frame conditions to describe the economic impact of global change (cf. SEAMLESS: Flichman et al. (2006); EUruralis: Klijn et al. (2005)). To widen the scope of policy support to the second pillar of the CAP, further adaptations of ACRE are necessary. Currently, several options of the second axis (environment) have already been implemented (e.g., intercropping on arable fields, erosion control, grassland protection, compensation payments for less favored areas). However, the description of axis 3 (improvement of rural living conditions) is still a challenge. For this parameter, a connection to other social models via common parameters might be a feasible option. Furthermore, upcoming activities such as energy crops should be considered in the model. The direct coupling of ACRE with a regional ecological model via the parameters ‘‘land use” and ‘‘yield” has already been performed for the Neckar basin study (Henseler et al., 2006) and it may be easily extended to include other integral parameters. This would allow for an evaluation of the effects of environmental measures in the second pillar.

5.5. Limitations of ACRE and implications for simulation results

6. Conclusions

Limitations of the current model include the exclusion of trade, the use of exogenous prices, and the lack of common alternative land use options. The exclusion of endogenous prices and market policies in ACRE may be justified for two reasons: first, modeling the impact of climate changes, direct transfers, and the second pillar policies demands models which take specific regional natural conditions as well as specific farm characteristics explicitly into account. To model the farm reaction on regional level sufficiently, complex micro–macro linked models are required, which would incorporate economic and biophysical sub-modules and ideally integrate international trade through the incorporation of a global economic model (Henning, 2008). Second, agro-environmental measures are difficult to integrate into classical micro-economic models since specific relations and important interactions are not yet fully understood (Henning, 2008). That being said, however, the use of exogenous prices in the current study instead of a market model may restrict the credibility of the scenarios. The lack of common alternative land use options, such as energy crops or

Detailed regional information on the consequences of global change is essential to regional decision-makers (e.g., agri-environmental programs, less favored areas, and creation of protected areas). ACRE takes economic decision-making into account by using a normative optimization approach. The model describes the actors of regional agricultural land use with a great amount of detail. Its particular strengths consist of a high regional resolution on which regional decision-makers can be addressed, an increase in reliability through the exact reproduction of the reference situation (crop and livestock production activities) and the prevention of jumpy model behavior (cf. Buysse et al., 2007).

Acknowledgement GLOWA-Danube (Global Change in the Hydrological Cycle) is funded by the German Federal Ministry of Education and Research (BMBF): www.glowa-danube.de.

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

References Abildtrup, J., Audsley, E., Fekete-Farkas, M., Giupponi, C., Gylling, M., Rosato, P., Rounsevell, M., 2006. Socio-economic scenario development for the assessment of climate change impacts on agricultural land use: a pairwise comparison approach. Environmental Science and Policy 9 (2), 101–115. Apfelbeck, J., Huigen, M., Krimly, T., 2007. The importance of spatial, temporal and social scales in integrated modelling; simulating the effects of climatic change on county- and farm-level decision making in the Danube catchment area. In: 81st Annual Conference, AES (Agricultural Economics society), Reading 02.04.07–04.04.07. Audsley, E., Pearn, K.R., Simota, C., Cojocaru, G., Koutsidou, E., Rounsevell, M.D.A., Trnka, M., Alexandrov, V., 2006. What can scenario modelling tell us about future European scale agricultural land use, and what not? Environmental Science and Policy 9 (2), 148–162. Busch, G., 2006. Future European agricultural landscapes – what can we learn from existing quantitative land use scenario studies? Agriculture, Ecosystems, and Environment 114, 121–140. Buysse, J., van Huylenbroeck, G., Lauwers, L., 2007. Normative, positive and econometric mathematical programming as tools for incorporation of multifunctionality in agricultural policy modelling. Agriculture, Ecosystems, and Environment 120 (1), 70–81. CAP-IRE, 2008. Assessing the multiple impacts of the common agricultural policies (CAP) on rural economies (CAP-IRE). www.cap-ire.eu. Carter, T.R., Jones, R.N., Lu, X., Bhadwal, S., Conde, C., Mearns, L.O., O’Neill, B.C., Rounsevell, M.D.A., Zurek, M.B., 2007. New assessment methods and the characterisation of future conditions. Climate change 2007: impacts, adaptation and vulnerability. In: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E. (Eds.), Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, pp. 133–171. Crutzen, P.J., 2004. Anti-Gaia. In: Steffen, W., Sanderson, A., Tyson, P.D., Jäger, J., Matson, P.A., Moore, B., III, Oldfield, F., Richardson, K., Schellnhuber, H.-J., Turner, B.L., II, Wasson, R.J. (Eds.), Global Change and the Earth System. Springer Verlag, Berlin. Diercke, C., 1992. Weltatlas, third ed. Westermann Schulbuchverlag, Braunschweig. EC, 2004. Regions: nomenclature of territorial units for statistics. European Commission, p. 39. Ewert, F., Rounsevell, M.D.A., Reginster, I., Metzger, M.J., Leemanns, R., 2005. Future scenarios of European agricultural land use I: estimating changes in crop productivity. Agriculture, Ecosystems, and Environment 107 (2005), 101–116. Ewert, F., Rounsevell, M., Reginster, I., Metzger, M., Leemans, R., 2006. Technology development and climate change as drivers of future land use. In: Brouwer, F., MacCarl, B.A. (Eds.), Agriculture Beyond 2015. Springer, Dordrecht, ISBN 14020-4063-6. Flichman, G., Donatelli, M., Louhichi, K., Romstad, E., Heckelei, T., Auclair, D., Garvey, E., van Ittersum, M., Janssen, S., Elbersen, B., 2006. Quantitative models of SEAMLESS-IF and procedures for up- and downscaling. SEAMLESS Report No.17. SEAMLESS Integrated Project, EU Sixth Framework Programme, Contract No. 010036-2, p. 112, www.SEAMLESS-IP.org. Fragoso, R., Carvalho, M.L., Henriques, P.D., 2008. Positive mathematical programming: a comparison of different specification rules. In: European Association of Agricultural Economists, 2008 International Congress, August 26–29, Ghent, Belgium. (accessed 31.10.08). Geist, H., McConnell, W., 2006. Causes and trajectories of land-use/cover change. In: Lambin, E.F., Geist, H. (Eds.), Land-Use and Land-Cover Change – Local Processes and Global Impacts. Springer, Heidelberg, pp. 41–69. Gömann, H., Kreins, P., Herrmann, S., Wechsung, F., 2005. Impacts of global changes on agricultural land-use in the German Elbe region: results of an operational modelling tool for planning, monitoring and agri-environmental policy counselling [CD-ROM]. In: 21st European Regional Conference – ERC 2005 – Integrated Land and Water Resources Management: Towards Sustainable Rural Development, May 15–19, 2005 – Frankfurt (Oder). Germany – Slubice, Poland, p. 11. Hafner, S.D., 2003. Trends in maize, rice, and wheat yields for 188 nations over the past 40 years: a prevalence of linear growth. Agriculture, Ecosystems, and Environment 97, 275–283. Hazell, P.B.R., Norton, R.D., 1986. Mathematical programming for economic analysis in agriculture. Macmillan Publishing Company, New York. Heckelei, T., 2002. Calibration and Estimation of Programming Models for Agricultural Supply Analysis. Habilitation Thesis, University of Bonn, Germany. Helming, J.F.M., Berkum van, S., 2008. Effects of abolition of the EU milk quota system for Dutch agriculture and environment. In: Paper prepared for the XIIth Congress of the European Association of Agricultural Economists, EAAE 2008 Congress ‘‘People, Food and Environments: Global Trends and European Strategies”, Ghent, Belgium, August 26–29, 2008. (Published on conference CD). Henning, C.H.C.A., 2008. The Health Check: a starting point of the end of the ‘Old CAP’? Agrarwirtschaft 57 (3/4), 149–154. Henseler, M., Wirsig, A., Krimly, T., 2005. Development, testing and application of ACRE: an agro-economic production model on regional level. In: Poster paper for 11th International Congress of the EAAE (European Association of Agricultural Economists): The Future of Rural Europe in the Global Agri-Food System, Copenhagen (Denmark), 24–27.08.05, published on Conference-CD: KVL (2005): Food and Resource Economic Institute: XIth Congress European

41

Association of Agricultural Economists – The Future of Rural Europe in the Global Agri-Food System. Henseler, M., Wirsig, A., Krimly, T., 2006. An agro-economic regional model for the Neckar river basin – first results of politic scenario calculations. In: International Conference on Regional and Urban Modeling, Brussels (Belgium), 01–02.06.06. (accessed 18.09.06). Henseler, M., Wirsig, A., Krimly, T., Dabbert, S., 2008. The influence of climate change, technological progress and political change on agricultural land use: calculated scenarios for the Upper Danube catchment area. Agrarwirtschaft 57 (3/4), 207–219. Howitt, R.E., 1995. Positive mathematical programming. American Journal of Agricultural Economics 77, 329–342. IPTS (European Commission, Directorate-General–Joint Research Centre, Institute for Prospective Technological Studies), 2008. Building a Typology of European Rural Areas for the Spatial Impact Assessment of Policies (TERA-SIAP). (accessed 05.11.08). Klijn, J.A., Vullings, L.A.E., van den Berg, M., van Meijl, H., van Lammeren, R., van Rheenen, T., Veldkamp, T., Verburg, P., Westhoek, H., Eickhout, B., 2005. The EURURALIS Study: Technical Document. Alterra-rapport, 1196 Alterra, Wageningen. Krimly, T., Apfelbeck, J., Huigen, M., Dabbert, S., 2008. Kapitel 2.9.2. Teilprojekt Agrarökonomie – Entscheidungsmodell zur Land- und Wassernutzung in der Landwirtschaft – Das Modell DeepFarming. In: GLOWA-Danube Projekt, Universität München (LMU) (Hrsg.): Global Change Atlas, Einzugsgebiet Obere Donau. GLOWA-Danube Projekt, Universität München. KTBL (Kuratorium für Technik und Bauwesen in der Landwirtschaft), 1995. Datensammlung Betriebsplanung 1995/96 Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL). Landwirtschaftsverlag GmbH, Münster. KTBL (Kuratorium für Technik und Bauwesen in der Landwirtschaft), 1997. Datensammlung Betriebsplanung 1997/98, Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL). Landwirtschaftsverlag GmbH, Münster. KTBL (Kuratorium für Technik und Bauwesen in der Landwirtschaft), 1999. Datensammlung Betriebsplanung 1999/00, Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL). Landwirtschaftsverlag GmbH, Münster. Lambin, E.F., Rounsevell, M.D.A., Geist, H.J., 2000. Are agricultural land-use models able to predict changes in land-use intensity? Agriculture, Ecosystems, and Environment 82 (1–3), 321–331. Manthey, R.P., 1996. Betriebswirtschaftliche Begriffe für die landwirtschaftliche Buchführung und Beratung, seventh ed., vol. 14. In: Schriftenreihe des Hauptverbandes der landwirtschaftlichen Buchstellen und Sachverständigen (HBLS). Verlag Pflug u. Feder, Sankt Augustin, p. 79. Mauser, W., 2000. GLOWA–DANUBE integrative techniques, scenarios and strategies regarding global changes of the water cycle, www.glowadanube.de/frameset.htm. Mauser, W., Ludwig, R., 2002. A research concept to develop integrative techniques, scenarios and strategies regarding Global Changes of the water cycle, vol. 10. In: Beniston, M. (Ed.), Climatic Change: Implications for the hydrological cycle and for water management – Advances in Global Change Research, 2002, pp. 171– 188. Mayr, T.R., Rounsevell, M.D.A., Loveland, P.J., Simota, C., 1996. Agro-climatic change and European suitability: regional modeling at monthly time steps. International Agrophysics 10 (3), 155–170. Meijl, H.van, van Rheenen, T., Tabeau, A., Eickhout, B., 2006. The impact of different policy environments on agricultural land use in Europe. Agriculture, Ecosystems, and Environment 114 (1), 21–38. Millennium Ecosystem Assessment, 2005. Ecosystems and Human Well-being Synthesis report. Island Press, Washington, DC. Mitchell, T.D., Carter, T.R., Jones, P.D., Hulme, M., New, M., 2004. A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901–2000) and 16 scenarios (2001–2100). Working paper. Tyndall Centre for Climate Change Research, University of East Anglia, Norwich. Nakic´enovic´, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., Gregory, K., Grubler, A., Jung, T.Y., Kram, T., Emiliola Rovere, E., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L., Riahi, K., Roehrl, A., Rogner, H.-H., Sankovski, A., Schlesinger, M.E., Shukla, P.R., Smith, S., Swart, R.J., van Rooyen, S., Victor, N., Dadi, Z., 2000. Special Report on Emissions Scenarios. Cambridge University Press, Cambridge, UK. OECD, 2006. Rural Policy Reviews: The New Rural Paradigm – Policies and Governance. OECD Publishing. OECD-FAO, 2008. OECD-FAO Agricultural Outlook 2008–2017 – Summary in German. (accessed 23.07.08). Paris, Q., 1988. PQP, PMP, Parametric Programming and Comparative Statics. Chapter 11 in Notes for AE 253. Department of Agricultural Economics, University of California, Davis, USA. Probeck, M., Colgan, A., Krimly, T., Zarate, M., Schneider, K., 2006. Landbedeckung und Landnutzung. In: GLOWA-Danube Projekt, Universität München (LMU)(Hrsg.): Global Change Atlas, Einzugsgebiet Obere Donau. GLOWADanube Projekt, Universität München. Röhm, O., 2001. Analyse der Produktions- und Einkommenseffekte von Agrarumwelt-programmen unter Verwendung einer weiterentwickelten Form der Positiven Quadratischen Programmierung, Berichte aus der Agrarwissenschaft. Dissertation, Universität Hohenheim, Shaker Verlag, Aachen.

42

M. Henseler et al. / Agricultural Systems 100 (2009) 31–42

Röhm, O., Dabbert, S., 2003. Integrating agri-environmental programs into regional production models – an extension of positive mathematical programming. American Journal of Agricultural Economics 85, 254–265. Rounsevell, M.D.A., Berry, P.M., Harrison, P.A., 2006. Future environmental change impacts on rural land use and biodiversity: a synthesis of the ACCELERATES project. Environmental Science and Policy 9 (2), 93–100. Simota, C., 2007. Crop yield data from calculations with the ROIMPEL crop growth model for the Upper Danube basin. National Research Institute for Soil Science, Bucharest, Romania. Unpublished. Sterzel, T., 2004. Correlation analysis of climate variables and wheat yield data on various aggregation levels in Germany and the EU 15 using GIS and statistical methods, with a focus on heat wave years. Diploma Thesis, Humboldt University, Berlin. Umstätter, J., 1999. Calibrating regional production models using positive mathematical programming – an agro-environmental policy analysis in Southwest Germany. Shaker, Aachen.

Van Welsum, P., Helming, J., Stuyt, L., Schonwenber, E., Groenendijk, P., 2007. Spatial planning for lowland stream basins using a bioeconomic model. Environmental Modelling and Software 23 (2008), 569–578. Verburg, P.H., Kok, K., Pontius Jr., R.G., Veldkamp, A., 2006. Modeling land-use and land-cover change. In: Lambin, E.F., Geist, H. (Eds.), Land-Use and Land-Cover Change – Local Processes and Global Impacts. Springer, Heidelberg, pp. 117– 131. Wälzholz, A., 2003. Effekte des Beitritts Polens zur EU auf das Angebotsverhalten polnischer Molkereien. PhD-Thesis, Fakultät für Agrarwissenschaften der Georg-August-Universität Göttingen. (accessed 21.10.08). Winter, T., 2005. Ein Nichtlineares Prozessanalytisches Agrarsektormodell für das Einzugsgebiet der Oberen Donau - Ein Beitrag zum Decision-Support-System Glowa-Danubia, PhD-Thesis, Universität Hohenheim, Stuttgart. (accessed 23.07.08).