12 research outputs found
Uncertainty Principle for Control of Ensembles of Oscillators Driven by Common Noise
We discuss control techniques for noisy self-sustained oscillators with a
focus on reliability, stability of the response to noisy driving, and
oscillation coherence understood in the sense of constancy of oscillation
frequency. For any kind of linear feedback control--single and multiple delay
feedback, linear frequency filter, etc.--the phase diffusion constant,
quantifying coherence, and the Lyapunov exponent, quantifying reliability, can
be efficiently controlled but their ratio remains constant. Thus, an
"uncertainty principle" can be formulated: the loss of reliability occurs when
coherence is enhanced and, vice versa, coherence is weakened when reliability
is enhanced. Treatment of this principle for ensembles of oscillators
synchronized by common noise or global coupling reveals a substantial
difference between the cases of slightly non-identical oscillators and
identical ones with intrinsic noise.Comment: 10 pages, 5 figure
The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols
Climate change is expected to alter a multitude of factors important to agricultural
systems, including pests, diseases, weeds, extreme climate events, water resources,
soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration
([CO2]), temperature, andwater (CTW) will be the primary drivers of change
in crop growth and agricultural systems. Therefore, establishing the CTW-change
sensitivity of crop yields is an urgent research need and warrants diverse methods
of investigation. Crop models provide a biophysical, process-based tool to investigate crop
responses across varying environmental conditions and farm management techniques,
and have been applied in climate impact assessment by using a variety of
methods (White et al., 2011, and references therein). However, there is a significant
amount of divergence between various crop models’ responses to CTW changes
(R¨otter et al., 2011). While the application of a site-based crop model is relatively
simple, the coordination of such agricultural impact assessments on larger scales
requires consistent and timely contributions from a large number of crop modelers,
each time a new global climate model (GCM) scenario or downscaling technique
is created. A coordinated, global effort to rapidly examine CTW sensitivity across
multiple crops, crop models, and sites is needed to aid model development and
enhance the assessment of climate impacts (Deser et al., 2012)..
Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe
The main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed