1,311 research outputs found
Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design
Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway
Integrating remote sensing datasets into ecological modelling: a Bayesian approach
Process-based models have been used to simulate 3-dimensional complexities of
forest ecosystems and their temporal changes, but their extensive data
requirement and complex parameterisation have often limited their use for
practical management applications. Increasingly, information retrieved using
remote sensing techniques can help in model parameterisation and data
collection by providing spatially and temporally resolved forest information. In
this paper, we illustrate the potential of Bayesian calibration for integrating such
data sources to simulate forest production. As an example, we use the 3-PG
model combined with hyperspectral, LiDAR, SAR and field-based data to
simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and
SAR data are used to estimate LAI dynamics, tree height and above ground
biomass, respectively, while the Bayesian calibration provides estimates of
uncertainties to model parameters and outputs. The Bayesian calibration
contrasts with goodness-of-fit approaches, which do not provide uncertainties
to parameters and model outputs. Parameters and the data used in the
calibration process are presented in the form of probability distributions,
reflecting our degree of certainty about them. After the calibration, the
distributions are updated. To approximate posterior distributions (of outputs
and parameters), a Markov Chain Monte Carlo sampling approach is used (25
000 steps). A sensitivity analysis is also conducted between parameters and
outputs. Overall, the results illustrate the potential of a Bayesian framework for
truly integrative work, both in the consideration of field-based and remotely
sensed datasets available and in estimating parameter and model output uncertainties
Simple model for the power-law blinking of single semiconductor nanocrystals
We assign the blinking of nanocrystals to electron tunneling towards a uniform spatial distribution of traps. This naturally explains the power-law distribution of off times, and the power-law correlation function we measured on uncapped CdS dots. Capped dots, on the other hand, present extended on times leading to a radically different correlation function. This is readily described in our model by involving two different, dark and bright, charged states. Coulomb blockade prevents further ionization of the charged dot, thus giving rise to long, power-law distributed off and on times
Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model
Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential
(GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity
to predict N2O emissions in relation to environmental conditions and crop management. Biophysical
models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to
explore these relationships, but are fraught with high uncertainties in their parameters due to their
variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O
submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the
nitrification and denitrification processes, which are modelled as the product of a potential rate with
three dimensionless factors related to soil water content, nitrogen content and temperature. These
equations involve a total set of 15 parameters, four of which are site-specific and should be measured on
site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior
information on the model parameters based on the literature review, and assigned them uniform
probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was
subsequently developed to update the parameter distributions against a database of seven different
field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm.
This site-specific calibration significantly reduced the spread in parameter distribution, and the
uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73%
across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently
applied simultaneously to all data sets, to obtain better global estimates for the parameters initially
deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the
uncalibrated model. These global parameter values may be used to obtain more realistic estimates of
N2O emissions from arable soils at regional or continental scales
Combustion in swirling flows, modelling of premixed turbulent combustion in swirling flows using large eddy simulations and tabulated chemistry
DNA is a co-factor for its own replication in Xenopus egg extracts
Soluble Xenopus egg extracts efficiently replicate added plasmids using a physiological mechanism, and thus represent a powerful system to understand vertebrate DNA replication. Surprisingly, DNA replication in this system is highly sensitive to plasmid concentration, being undetectable below ∼10 pM and highly efficient above ∼75 pM. DNA replication at the high plasmid concentration does not require plasmid–plasmid contacts, since replication is not inhibited when plasmids are immobilized in agarose prior to addition of egg extract. The absence of replication at low plasmid concentration is due to a defect in the assembly of pre-replication complexes (pre-RCs). pre-RC assembly requires contact-independent communication between plasmids. Our results show that in Xenopus egg extracts, aggregation of multiple replication forks is not required for efficient replication of plasmid DNA, and they suggest that DNA functions as a co-factor for its own duplication
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