MANAGEMENT DECISIONS ON FARM-LEVEL AND THEIR LINK TO WEATHER REQUIREMENTS: A CASE STUDY FOR THE UPPER DANUBE RIVER BASIN

It is undeniable that the global warming has already affected the Earth’s biota, whereby the rise of air temperature is an important factor. Agricultural systems are also affected by climate change via the interrelated biophysical layers. Climate influences farmers` decisions in crop management. To simulate the interactions between climate/weather and the different crop management activities an agent based modelling approach is used, in which farmers` decision making is based on crop requirements from literature. To validate these decision algorithms on how farmers arrange their daily crop management decisions like planting, fertilizing, and harvesting due to changes of climate parameters, a statistical analysis of empirical data (1970-2003) on temperature and different crop growth stages, which represent different management activities, was carried out. Results show that every crop has to be considered separately and the requirements of the different crops on temperature have to be observed in different ways. There are crops which have a low germination temperature, for those the average daily temperature shows no relation with the planting day. In this case the temperature sum in a specific period is more precise. On the opposite side crops with a high germination temperature show significant correlation results with the average daily temperature of a period before the planting day.Crop management, heuristic decisions, regional climate change, Crop Production/Industries, Farm Management,

The importance of spatial, temporal and social scales in Integrated modeling; simulating the effects of climatic change on district- and farm-level decision making in the Danube catchment area

Many scientific publications discussing the effects of climate change on the agricultural system express these in terms of changing crop production at coarse spatial and temporal scales. But in agro-economy, where crop production is the result of the interaction between bio-physical and management components, the temporal drivers operate at much smaller resolutions. Climate change affects the agricultural system via the interrelated, bio-physical layers of air, water, soil and crops. Furthermore, it influences the farm-system manager in their choice of their crops. In our paper the main question is how to deal systematically with the different time extents and time resolutions when studying agricultural management impacts due to climatic change. Agent based modeling offers an elegant way to tackle such challenges, where agents represent simplified farm managers. The agricultural management model is dynamically connected to a regional agro-economic model, a ground water model, a crop growth model and a soil model. Hence, we endogonize climatic change and make its effects a (risk)-factor in the agents considerations along different temporal scales. This paper reports on the fundamental issues regarding use of different temporal modeling scales with several clear practical examples

The importance of spatial, temporal and social scales in Integrated modeling; simulating the effects of climatic change on district- and farm-level decision making in the Danube catchment area

Many scientific publications discussing the effects of climate change on the agricultural system express these in terms of changing crop production at coarse spatial and temporal scales. But in agro-economy, where crop production is the result of the interaction between bio-physical and management components, the temporal drivers operate at much smaller resolutions. Climate change affects the agricultural system via the interrelated, bio-physical layers of air, water, soil and crops. Furthermore, it influences the farm-system manager in their choice of their crops. In our paper the main question is how to deal systematically with the different time extents and time resolutions when studying agricultural management impacts due to climatic change. Agent based modeling offers an elegant way to tackle such challenges, where agents represent simplified farm managers. The agricultural management model is dynamically connected to a regional agro-economic model, a ground water model, a crop growth model and a soil model. Hence, we endogonize climatic change and make its effects a (risk)-factor in the agents considerations along different temporal scales. This paper reports on the fundamental issues regarding use of different temporal modeling scales with several clear practical examples.Environmental Economics and Policy, Farm Management,

Agent Based Modelling in Land Use and Land Cover Change Studies, Interim Report IR-03-044. Available at

Web: www.iiasa.ac.a

MANAGEMENT DECISIONS ON FARM-LEVEL AND THEIR LINK TO WEATHER REQUIREMENTS: A CASE STUDY FOR THE UPPER DANUBE RIVER BASIN

It is undeniable that the global warming has already affected the Earth’s biota, whereby the rise of air temperature is an important factor. Agricultural systems are also affected by climate change via the interrelated biophysical layers. Climate influences farmers` decisions in crop management. To simulate the interactions between climate/weather and the different crop management activities an agent based modelling approach is used, in which farmers` decision making is based on crop requirements from literature. To validate these decision algorithms on how farmers arrange their daily crop management decisions like planting, fertilizing, and harvesting due to changes of climate parameters, a statistical analysis of empirical data (1970-2003) on temperature and different crop growth stages, which represent different management activities, was carried out. Results show that every crop has to be considered separately and the requirements of the different crops on temperature have to be observed in different ways. There are crops which have a low germination temperature, for those the average daily temperature shows no relation with the planting day. In this case the temperature sum in a specific period is more precise. On the opposite side crops with a high germination temperature show significant correlation results with the average daily temperature of a period before the planting day

MODELING ENDOGENOUS RULE CHANGES IN AN INSTITUTIONAL CONTEXT: THE ADICO SEQUENCE

Agent-based modeling is being increasingly used to simulate socio-techno-ecosystems that involve social dynamics. Humans face constraints that they sometimes wish to challenge, and when they do so, they often trigger changes at the scale of the social group too. Including such adaptation dynamics explicitly in our models would allow simulation of the endogenous emergence of rule changes. This paper discusses such an approach in an institutional framework and develops a sequence that allows modeling of endogenous rule changes. Parts of this sequence are implemented in a NetLogo KISS model to provide some illustrative results.Endogenous rule change, norm, institution, agent-based modeling, simulation, emergence

§Institute for Agricultural Economics and Social Sciences in the Tropics and Subtropics,

The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence ’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning— Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation

Comparison of empirical methods for building agent-based models in land use science

International audienceThe use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM