Influence of climate change on short term management of field crops – A modelling approach

Aurbacher, Joachim; Parker, Phillip S.; Sánchez, Germán A. Calberto; Steinbach, Jennifer; Reinmuth, Evelyn; Ingwersen, Joachim; Dabbert, Stephan

doi:10.1016/j.agsy.2013.04.005

Cited by 43 publications

(41 citation statements)

References 64 publications

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“…Schaller and Weigel, 2007;Olesen et al, 2011;Aurbacher et al, 2013). While most studies predicted a more or less slight decrease in cereal yield, simulation results in the present paper showed an increasing whole crop yield for winter rye until 2100.…”

Section: Simulation Of Dm Yield Under Climate Changecontrasting

confidence: 53%

Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future climate change

Graß

Thies

Kersebaum

et al. 2015

European Journal of Agronomy

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Section: Simulation Of Dm Yield Under Climate Changecontrasting

confidence: 53%

Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future climate change

Graß

Thies

Kersebaum

et al. 2015

European Journal of Agronomy

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“…In terms of adaptation to climate change, "adaptive capacity can be thought of as an emergent property, one for which ABM is a suitable analytic tool" (Patt and Siebenhüner 2005:317). Agent-based models have been used to explore a range of topics, such as the relationships between climate change and local institutions (Wang et al 2013), adaptive decision making (Janssen and de Vries 1998, Ziervogel et al 2005, Aurbacher et al 2013, migration (Smith et al 2008, Kniveton et al 2011, Hassani-Mahmooei and Parris 2012, and land use (Yan et al 2013, Zhang et al 2013. However, there is not yet a clear strategy for incorporating climate data into ABMs as a dynamic input.…”

Section: State-and-transition Modelsmentioning

confidence: 99%

Integrating Research Tools to Support the Management of Social-Ecological Systems Under Climate Change

Miller¹,

Morisette²

2014

E&S

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ABSTRACT. Developing resource management strategies in the face of climate change is complicated by the considerable uncertainty associated with projections of climate and its impacts and by the complex interactions between social and ecological variables. The broad, interconnected nature of this challenge has resulted in calls for analytical frameworks that integrate research tools and can support natural resource management decision making in the face of uncertainty and complex interactions. We respond to this call by first reviewing three methods that have proven useful for climate change research, but whose application and development have been largely isolated: species distribution modeling, scenario planning, and simulation modeling. Species distribution models provide datadriven estimates of the future distributions of species of interest, but they face several limitations and their output alone is not sufficient to guide complex decisions for how best to manage resources given social and economic considerations along with dynamic and uncertain future conditions. Researchers and managers are increasingly exploring potential futures of social-ecological systems through scenario planning, but this process often lacks quantitative response modeling and validation procedures. Simulation models are well placed to provide added rigor to scenario planning because of their ability to reproduce complex system dynamics, but the scenarios and management options explored in simulations are often not developed by stakeholders, and there is not a clear consensus on how to include climate model outputs. We see these strengths and weaknesses as complementarities and offer an analytical framework for integrating these three tools. We then describe the ways in which this framework can help shift climate change research from useful to usable.

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“…Consequently, to apply PMF in the sense of a full crop growth model for agricultural application, a management module is required that considers typical management strategies in agriculture. Instead of full inclusion of this management tool in the PMF model itself, we promote following the idea of the framework strategy of PMF as well as CMF and apply an external farm management model (Aurbacher et al, 2013;Windhorst et al, 2012).…”

Section: Cross Comparison Of Sites and Parameter Setsmentioning

confidence: 99%

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

et al. 2014

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Abstract.Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures -for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil-plant interface.We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten-Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation.The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 10 6 model runs randomly drawn from a uniform distribution.The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model.

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Influence of climate change on short term management of field crops – A modelling approach

Cited by 43 publications

References 64 publications

Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future climate change

Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future climate change

Integrating Research Tools to Support the Management of Social-Ecological Systems Under Climate Change

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

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