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END 603E- Operations Res. & Mathem. Mod.

Article Presentation

507102103 Özcan ÇAVUŞOĞLU504112306 Nevin DÖNMEZ 1

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Article Information• 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.

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Agenda

• Abstract• Introduction• Literature Review• Methodology• Implementation• Results and Findings• Future Research, Discussion and

Implementations• Conclusion

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Abstract• 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.

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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.

• 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.

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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 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).

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Literature Review

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• Existing Modeling Approaches

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Literature Review

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• Existing Modeling Approaches

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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.

• 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 are considered at the county (NUTS3) level.

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MethodologyThe ACRE (Agro-eConomic pRoduction model at rEgional ) Model

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. 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 is not integrated.

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MethodologyThe ACRE (Agro-eConomic pRoduction model at rEgional ) Model

2. Calibration Method and Model• 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.

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MethodologyThe ACRE (Agro-eConomic pRoduction model at rEgional ) Model

2. 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 replication of production patterns as simulated by the LP model.

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Methodology2. Calibration Method and Modela.Creating LP Model

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Objective Function(Total Gross Marginal (TGM))

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Subject to

Methodology2.Calibration Method and Model1.Creating LP Model

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Methodology

Subject to

2. Calibration Method and Modela.Creating LP Model

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Methodology2.Calibration Method and Modelb.Transforming from LP to NLP

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Methodology2. Calibration Method and Modelc.Creating NLP ModelObjective Function:

Parameters

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MethodologyModel 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).

• The Upper Danube basin, which is the research area of the GLOWA-Danube project,• covers an area of 77,000 km2• extend 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

• Approximately 55% of the study area is used for agriculture.

• Overall, the study area includes 74 counties (NUTS3-level), with 58 belonging to Germany and 16 to Austria. The counties belong 12 administrative units based on the NUTS2-level classification.

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Methodology

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).

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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.

• 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).

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Methodology

Development of Global Change Scenarios• Table 3, shows that in the

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

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Methodology• Reference Scenario:

• 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.

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Results and Findings• In each of the two Global Change scenarios, changes in

agricultural income and land were analyzed in comparison to the baseline (CAP) scenario.

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Results and Findings

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• While the developments in the Full Protection scenario are small, the Full Liberalization scenario yields extreme regional changes in agricultural income.

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Results and Findings

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• While the developments in the Full Protection scenario are small, the Full Liberalization scenario yields an increase in cereal production.

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Results and Findings

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• While the developments in the Full Protection scenario are small, the Full Liberalization scenario yields extensive grassland farming.

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Future Research, Discussion and Implementations

1. Implications of scenario assumptions for results

• 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.

• 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.

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Future Research, Discussion and Implementations2. 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 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 .

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Future Research, Discussion and Implementations3. Implications of the use of a quadratic cost function on the results

• 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.

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Future Research, Discussion and Implementations

4. Additional benefits of ACRE to current modeling of agricultural land use changes

• 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).

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Future Research, Discussion and Implementations5. Limitations of ACRE and implications for simulation results

• 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

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Future Research, Discussion and Implementations6. 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.

• Furthermore, upcoming activities such as energy crops should be considered in the model

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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).

• 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.

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