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

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

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

Authors: Martin Henseler (Spain), Alexander Wirsig (Germany), Sylvia Herrmann (Germany), Tatjana Krimly (Germany), Stephan Dabbert (Germany).

Publication: Agricultural System.

Year: 2009.

Keywords: Global change, Regional optimization model, Global change scenarios, Agricultural production, Nonlinear programming.

Published in: Education, Technology
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  • Agronomic: bilimseltarımspatial resolution: The measure of how closely lines can be resolved in an imageCounty: ilçeAcre: dönüm an area of land containing 43,560 square feet. (4047 m2)
  • Basin: havza
  • NUTS: a geocodestandard for referencing the subdivisions of countries for statistical purposes
  • Cash crop(ekin, ürün,hasat); that is grown for sale rather than for personel food or for feeding to livestockFodder crop; yembitkisiArable land; land that is fit for farming
  • In constrained optimization in economics, the shadow price is the instantaneous change per unit of the constraint in the objective value of the optimal solution of an optimization problem obtained by relaxing the constraint. In other words, it is the marginal utility of relaxing the constraint, or, equivalently, the marginal cost of strengthening the constraint.In a business application, a shadow price is the maximum price that management is willing to pay for an extra unit of a given limited resource.[1] For example, if a production line is already operating at its maximum 40-hour limit, the shadow price would be the maximum price the manager would be willing to pay for operating it for an additional hour, based on the benefits he would get from this change.More formally, the shadow price is the value of the Lagrange multiplier at the optimal solution, which means that it is the infinitesimal change in the objective function arising from an infinitesimal change in the constraint. This follows from the fact that at the optimal solution the gradient of the objective function is a linear combination of the constraint function gradients with the weights equal to the Lagrange multipliers. Each constraint in an optimization problem has a shadow price or dual variable.The value of the shadow price can provide decision-makers with powerful insights into problems. For instance if you have a constraint that limits the amount of labor available to 40 hours per week, the shadow price will tell you how much you would be willing to pay for an additional hour of labor. If your shadow price is $10 for the labor constraint, for instance, you should pay no more than $10 an hour for additional labor. Labor costs of less than $10/hour will increase the objective value; labor costs of more than $10/hour will decrease the objective value. Labor costs of exactly $10 will cause the objective function value to remain the same.A covering LP is a linear program of the form:Minimize: bTy, Subject to: ATy ≥ c, y ≥ 0, such that the matrix A and the vectors b and c are non-negative.The dual of a covering LP is a packing LP, a linear program of the form:Maximize: cTx, Subject to: Ax ≤ b, x ≥ 0, such that the matrix A and the vectors b and c are non-negative.Kesinolarakçözümübulmuyoruzsadece alt sınırınıbuluyoruz.
  • Acreage : arazialanı, yüzölçümüa unit of land area equal to 4,840 square yards (0.405 hectare). Subsides: a sum of money granted from public funds to help an industry or business keep the price of a commodity or service low. Intensive-crop variant: Designating, or pertaining to, any system of farming or horticulture, usually practiced on small pieces of land, in which the soil is thoroughly worked and fertilized so as to get as much return as possible; opposed to extensive
  • South = güneyNorth = kuzeyThe most favorable arable regions are located south of the DanubeRiver. Very specialized arable farming may be found in the eastBavarian low mountain range, which is located in the southeasternpart of the basinTo the north of the DanubeRiver, the agronomic conditions for arable production are lessfavorable and the share of grassland accounts for 80–100% of theland.
  • Scenarios are images of the future, or alternative futures. They are neither predictions nor forecasts. Rather, each scenario is one alternative image of how the future might unfold. A set of scenarios assists in the understanding of possible future developments of complex systems.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.Qualitative: relating to or measured by quality. Often contrasted with quantitative. nicelendirilmesigüçverilerinaraştırılmasıişlemiquantitative : relating to or measured by quantity.
  • 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.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 simulation at the NUTS2-level from the crop growth model ROIMPEL (Mayr et al., 1996) based on HadCM3 climate projections (Mitchellet 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 foreight 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 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.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 thechallenge 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 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 nearlyfully 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 incomein 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 grasslanddominated 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 rootcrops. 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 isconverted 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 result in a higher increase of grassland yields with greater effects ofde-intensification than in extensive grassland areas.
  • Intensification-kuvvetlendirmeCounteractive=karşıkoyanBoosting = kuvvetlendirmeMarginal=değeriazolan
  • Diversification:çeşitlilikRural=large kırsalalanThis expands the possibility for evaluation within the agro-economic model in terms of environmental and cultural landscape functions
  • Irrigation:sulamaAn energy crop is a plant grown as a low cost and low maintenance harvest used to make biofuels, or directly exploited for its energy content.fuel that is derived from organic material (also biogas) Abandoned-terkedilmiştopraklarHamper –engelolmak
  • Normative-kuralcı
  • Transcript

    • 1. 04.07.2012END 603E- Operations Res. & Mathem. Mod.Article Presentation 507102103 Özcan ÇAVUŞOĞLU 504112306 Nevin DÖNMEZ 1
    • 2. Article Information• Name: Modeling the impact of global change on regional agricultural land use through an activity-based non-linear programming approach. 04.07.2012• Authors: Martin Henseler (Spain), Alexander Wirsig (Germany), Sylvia Herrmann (Germany), Tatjana Krimly (Germany), Stephan Dabbert (Germany).• Publication: Agricultural System.• Year: 2009.• Keywords: Global change, Regional optimization model, Global change scenarios, Agricultural production, Nonlinear programming. 2
    • 3. Agenda• Abstract• Introduction 04.07.2012• Literature Review• Methodology• Implementation• Results and Findings• Future Research, Discussion and Implementations 3• Conclusion
    • 4. Abstract• The impact of climate change will vary strongly across regions depending on • pre-existing climatic, • agronomic, 04.07.2012 • 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. 4
    • 5. Abstract• 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. 04.07.2012• 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. 5
    • 6. Introduction• 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 04.07.2012 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, in this study developed the county-based ACRE (Agro-eConomic pRoduction model at rEgional-level) model.• The investigation area of ACRE-Danube, 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). 6
    • 7. LiteratureApproaches• Existing Modeling Review 04.07.2012 7
    • 8. LiteratureApproaches• Existing Modeling Review 04.07.2012 8
    • 9. MethodologyThe ACRE (Agro-eConomic pRoduction model at rEgional ) Model• For the Upper Danube basin uses a bottom-up approach to simulate regional agricultural land use. 04.07.2012• 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 production processes for livestock 9 are considered at the county (NUTS3) level.
    • 10. MethodologyThe ACRE (Agro-eConomic pRoduction model at rEgional ) Model1. Process Analytical Approach 04.07.2012• 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. The prices for crops and animal products influence the total gross margin.• 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.• 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 10 is not integrated.
    • 11. MethodologyThe ACRE (Agro-eConomic pRoduction model at rEgional ) Model2. Calibration Method and Model 04.07.2012• ACRE is based on the calibration method of Positive Mathematical Programming (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; • they tend to require fewer data.• These features make PMP models particularly suitable for modeling regional agricultural production. 11
    • 12. MethodologyThe ACRE (Agro-eConomic pRoduction model at rEgional ) Model 04.07.20122. Calibration Method and Model• 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 12 replication of production patterns as simulated by the LP model.
    • 13. Methodology2. Calibration Method and Modela.Creating LP ModelObjective Function(Total Gross Marginal (TGM)) 04.07.2012 13
    • 14. Methodology2.Calibration Method and Model1.Creating LP ModelSubject to 04.07.2012 14
    • 15. Methodology2. Calibration Method and Modela.Creating LP ModelSubject to 04.07.2012 15
    • 16. Methodology2.Calibration Method and Modelb.Transforming from LP to NLP 04.07.2012 16
    • 17. Methodology2. Calibration Method and Modelc.Creating NLP ModelObjective Function: 04.07.2012Parameters 17
    • 18. Methodology 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. 04.07.2012 • 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). • The Upper Danube basin, which is the research area of the GLOWA-Danube project, • covers an area of 77,000 km2• Approximately 55% of the study area is used for agriculture. • extend across five countries (Fig. 1). • The largest portion of land lies in the south 18• Overall, the study area includes 74 counties (NUTS3-level), of Germany, with an area of 56,000 km2, with 58 belonging to Germany and 16 to Austria. The followed by that of Austria, with ~20,000 counties belong 12 administrative units based on the km2 NUTS2-level classification.
    • 19. MethodologyDevelopment of Global Change Scenarios 04.07.2012• 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). 19
    • 20. MethodologyDevelopment of Global Change Scenarios• 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 selected percentage yield changes for cereals and grassland as available for the NUTS2 regions of the study area. 04.07.2012• Table 2 presents the scenario assumptions for • climate change (i.e., percentage crop yield changes) • socio-economic change (i.e., crop yield changes due to technological progress, subsidies, and prices). 20
    • 21. MethodologyDevelopment of GlobalChange Scenarios• Table 3, shows that in the 04.07.2012 Full Liberalization 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. 21
    • 22. Methodology• Reference Scenario: 04.07.2012 • 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. 22
    • 23. Results and Findings• In each of the two Global Change scenarios, changes in agricultural income and land were analyzed in comparison to 04.07.2012 the baseline (CAP) scenario. 23
    • 24. Results and Findings• While the developments in the Full Protection scenario are small, the Full Liberalization scenario yields extreme regional changes in agricultural income. 04.07.2012 24
    • 25. Results and Findings• While the developments in the Full Protection scenario are small, the Full Liberalization scenario yields an increase in cereal production. 04.07.2012 25
    • 26. Results and Findings• While the developments in the Full Protection scenario are small, the Full Liberalization scenario yields extensive grassland farming. 04.07.2012 26
    • 27. Future Research, Discussion andImplementations1. Implications of scenario assumptions for results• The results from the ACRE calculations correspond with recent findings for 04.07.2012 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.• The result would be intensification in favorable regions and de-intensification in marginal regions.• In contrast, a rise in public spending may ensure the maintenance of the current landscape, in particular the preservation of cultural landscapes and agricultural income levels, despite small increases in productivity.• If recent developments in agricultural output price levels (OECD-FAO, 2008) persist in the long-term, this would be a counteractive force. 27
    • 28. Future Research, Discussion andImplementations2. The use of positive Mathematical Programming Methods• The use of Positive Mathematical Programming (PMP) allows 04.07.2012 projections based on past observations of the cost function and reflects real farmer behavior.• However, PMP models more suitable to modeling medium-term future scenarios than to making long-term projections, because of the difficulty in calibrating.• 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 . 28
    • 29. Future Research, Discussion andImplementations3. Implications of the use of a quadratic cost function on theresults 04.07.2012 • The formulation in ACRE produce a quadratic gross marginal function. • The quadratic cost function is assumed due to simplicity , the lack of data and the lack of strong arguments for other types of functions. • To examine the impact of using different cost functions, a less complex model could be developed for future work. 29
    • 30. Future Research, Discussion andImplementations4. Additional benefits of ACRE to current modeling of agricultural landuse changes 04.07.2012• The highly detailed picture of agricultural activities that ACRE provides may be used as the basis for diversification strategies in rural areas.• 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.• 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). 30
    • 31. Future Research, Discussion andImplementations5. Limitations of ACRE and implications for simulation results 04.07.2012• 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 lack of common alternative land use options, such as energy crops or reforestation, hampers the results of land use changes, in particular for abandoned agricultural land.• The lack of irrigation in the current model is unlikely to affect the results significantly 31
    • 32. Future Research, Discussion andImplementations6. Further development and potential of ACRE in regional landuse modeling 04.07.2012• 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.• Furthermore, upcoming activities such as energy crops should be considered in the model 32
    • 33. Conclusion• 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). 04.07.2012• 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 33 behavior.
    • 34. ? ? ? ? 04.07.201234

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