Improving node number simulation in soybean

The objective of this study was to improve the simulation of node number in soybean cultivars with determinate stem habits. A nonlinear model considering two approaches to input daily air temperature data (daily mean temperature and daily minimum/maximum air temperatures) was used. The node number on the main stem data of ten soybean cultivars was collected in a three-year field experiment (from 2004/2005 to 2006/2007) at Santa Maria, RS, Brazil. Node number was simulated using the Soydev model, which has a nonlinear temperature response function [f(T)]. The f(T) was calculated using two methods: using daily mean air temperature calculated as the arithmetic average among daily minimum and maximum air temperatures (Soydev tmean); and calculating an f(T) using minimum air temperature and other using maximum air temperature and then averaging the two f(T)s (Soydev tmm). Root mean square error (RMSE) and deviations (simulated minus observed) were used as statistics to evaluate the performance of the two versions of Soydev. Simulations of node number in soybean were better with the Soydev tmm version, with a 0.5 to 1.4 node RMSE. Node number can be simulated for several soybean cultivars using only one set of model coefficients, with a 0.8 to 2.4 node RMSE.

Colony-colony interactions between highly invasive ants

Among invasive species, ants are a particularly prominent group with enormous impacts on native biodiversity and ecosystem functioning. Globalization and on-going climate change are likely to increase the rate of ant invasions in the future, leading to simultaneous introductions of several highly invasive species within the same area, Here, we investigate pairwise interactions among four highly invasive species, Linepithema humile,Lashis neglectus, Pheidole megacephala and Wasmannia auropunctata, at the whole colony level, using a laboratory set-up. :Each colony consisted of 300 workers and one queen. The number of surviving workers in the competing colonies was recorded daily over 7 weeks. We modelled the survival of each colony during pairwise colony interactions, using a nonlinear model characterizing the survival dynamics of each colony individually. The least dominant species was P. megacephala, which always went extinct. Interactions among the three other species showed more complex dynamics, rendering the outcome of the interactions less predictable. Overall, W auropunctata and L neglectus were the most dominant species. This study shows the importance of scaling up to the colony level in order to gain realism in predicting the outcome of multiple invasions.

Bimodal pore distribution on soils under conservation management system for coffee crop

This study aims at detailing bimodal pore distribution by means of water retention curve in an oxidic-gibbsitic Latosol and in a kaolinitic cambisol Latossol under conservation management system of coffee crop. Samples were collected at depths of 20; 40; 80; 120 and 160 cm on coffee trees rows and between rows under oxidic-gibbsitic Latosol (LVd) and kaolinitic cambisol Latossol (LVAd). Water retention curve was determined at matrix potentials (Ψm) -1; -2; -4; -6; -10 kPa obtained from the suction unit; the Ψm of -33; -100; -500; -1,500 kPa were obtained by the Richards extractor, and WP4-T psychrometer was used to determine Ψm -1,500 to -300,000 kPa. The water retention data were adjusted to the double van Genuchten model by nonlinear model procedures of the R 2.12.1 software. Was estimated the model parameter and inflection point slope. The system promoted changes in soil structure and water retention for the conditions evaluated, and both showed bimodal pores distribution, which were stronger in LVd. There was a strong influence of mineralogy gibbsitic in the water retention more negative than Ψm -1500 kPa, reflected in the values of the residual water content.

How Much Inflation is Necessary to Grease the Wheels?

This paper studies Tobin's proposition that inflation "greases" the wheels of the labor market. The analysis is carried out using a simple dynamic stochastic general equilibrium model with asymmetric wage adjustment costs. Optimal inflation is determined by a benevolent government that maximizes the households' welfare. The Simulated Method of Moments is used to estimate the nonlinear model based on its second-order approximation. Econometric results indicate that nominal wages are downwardly rigid and that the optimal level of grease inflation for the U.S. economy is about 1.2 percent per year, with a 95% confidence interval ranging from 0.2 to 1.6 percent.

Effet de la température sur les interactions trophiques et intraguildes au sein d’un système plante-herbivore-ennemis naturels : modélisation et approches expérimentales

Doctorat réalisé en cotutelle entre l'Université de Montréal et l'Université Paul Sabatier-Toulouse III

Prévention de la maladie de Lyme : facteurs sociaux et priorisation des interventions

La maladie de Lyme est la maladie vectorielle la plus fréquente dans les pays tempérés et est en émergence dans plusieurs régions du monde. Plusieurs stratégies de prévention existent et comprennent des interventions qui visent les individus, comme le port de vêtements protecteurs, et d’autres qui sont implantées au niveau collectif, dont des interventions de contrôle des tiques dans l’environnement. L’efficacité de ces stratégies peut être influencée par divers facteurs, dont des facteurs sociaux tels que les connaissances, les perceptions et les comportements de la population ciblée. Elles peuvent également avoir des impacts parallèles non désirés, par exemple sur l’environnement et l’économie, et ces derniers peuvent s’opposer aux bénéfices des interventions jusqu’à remettre en cause la pertinence de leur mise en œuvre. Aussi, ces facteurs sociaux et les impacts des interventions sont susceptibles de varier selon la population ciblée et en fonction du contexte épidémiologique et social. L’objectif de cette thèse était donc d’étudier les principaux facteurs sociaux et enjeux d’importance à considérer pour évaluer l’efficacité et prioriser des interventions de prévention pour la maladie de Lyme dans deux populations exposées à des contextes différents, notamment en ce qui concerne leur situation épidémiologique, soient au Québec, où l’incidence de la maladie de Lyme est faible mais en émergence, et en Suisse, où elle est élevée et endémique depuis plus de trois décennies. L’approche choisie et le devis général de l’étude sont basés sur deux modèles théoriques principaux, soient le modèle des croyances relatives à la santé et celui de l’aide à la décision multicritère. Dans un premier temps, les facteurs associés à la perception du risque pour la maladie de Lyme, c’est-à-dire l’évaluation cognitive d’une personne face au risque auquel elle fait face, ont été étudiés. Les résultats suggèrent que les facteurs significatifs sont différents dans les deux régions à l’étude. Ensuite, l’impact des connaissances, de l’exposition, et des perceptions sur l’adoption de comportements préventifs individuels et sur l’acceptabilité des interventions de contrôle des tiques (acaricides, modifications de l’habitat, contrôle des cervidés) a été comparé. Les résultats suggèrent que l’impact des facteurs varierait en fonction du type du comportement et des interventions, mais que la perception de l’efficacité est un facteur commun fortement associé à ces deux aspects, et pourrait être un facteur-clé à cibler lors de campagnes de communication. Les résultats montrent également que les enjeux relatifs aux interventions de contrôle des tiques tels que perçus par la population générale seraient communs dans les deux contextes de l’étude, et partagés par les intervenants impliqués dans la prévention de la maladie de Lyme. Finalement, un modèle d’analyse multicritère a été développé à l’aide d’une approche participative pour le contexte du Québec puis adapté pour le contexte suisse et a permis d’évaluer et de prioriser les interventions préventives selon les différentes perspectives des intervenants. Les rangements produits par les modèles au Québec et en Suisse ont priorisé les interventions qui ciblent principalement les populations humaines, devant les interventions de contrôle des tiques. L’application de l’aide à la décision multicritère dans le contexte de la prévention de la maladie de Lyme a permis de développer un modèle décisionnel polyvalent et adaptable à différents contextes, dont la situation épidémiologique. Ces travaux démontrent que cette approche peut intégrer de façon rigoureuse et transparente les multiples perspectives des intervenants et les enjeux de la prévention relatifs à la santé publique, à la santé animale et environnementale, aux impacts sociaux, ainsi qu’aux considérations économiques, opérationnelles et stratégiques. L’utilisation de ces modèles en santé publique favoriserait l’adoption d’une approche « Une seule santé » pour la prévention de la maladie de Lyme et des zoonoses en général. Mots-clés : maladie de Lyme, prévention, facteurs sociaux, perception du risque, comportements préventifs, acceptabilité, priorisation des interventions, contrôle des tiques, aide à la décision multicritère, analyse multicritère, Québec, Suisse, « Une seule santé »

The influence of physical processes on extratropical singular vectors

An investigation is made of the impact of a full linearized physical (moist) parameterization package on extratropical singular vectors (SVs) using the ECMWF integrated forecasting system (IFS). Comparison is made for one particular period with a dry physical package including only vertical diffusion and surface drag. The crucial extra ingredient in the full package is found to be the large-scale latent heat release. Consistent with basic theory, its inclusion results in a shift to smaller horizontal scales and enhanced growth for the SVs. Whereas, for the dry SVs, T42 resolution is sufficient, the moist SVs require T63 to resolve their structure and growth. A 24-h optimization time appears to be appropriate for the moist SVs because of the larger growth of moist SVs compared with dry SVs. Like dry SVs, moist SVs tend to occur in regions of high baroclinicity, but their location is also influenced by the availability of moisture. The most rapidly growing SVs appear to enhance or reduce large-scale rain in regions ahead of major cold fronts. The enhancement occurs in and ahead of a cyclonic perturbation and the reduction in and ahead of an anticyclonic perturbation. Most of the moist SVs for this situation are slightly modified versions of the dry SVs. However, some occur in new locations and have particularly confined structures. The most rapidly growing SV is shown to exhibit quite linear behavior in the nonlinear model as it grows from 0.5 to 12 hPa in 1 day. For 5 times this amplitude the structure is similar but the growth is about half as the perturbation damps a potential vorticity (PV) trough or produces a cutoff, depending on its sign.

Robust identification for linear-in-the-parameters models

In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness, including three algorithms using combined A- or D-optimality or PRESS statistic (Predicted REsidual Sum of Squares) with regularised orthogonal least squares algorithm respectively. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalisation scheme in orthogonal least squares or regularised orthogonal least squares has been extended such that the new algorithms are computationally efficient. A numerical example is included to demonstrate effectiveness of the algorithms. Copyright (C) 2003 IFAC.

Early warning with calibrated and sharper probabilistic forecasts

Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In order to enhance the quality of the density forecasts, one can mix them with the unconditional density. This paper examines the value of combining conditional density forecasts with the unconditional density. The findings have positive implications for issuing early warnings in different disciplines including economics and meteorology, but UK inflation forecasts are considered as an example.

Gaussian quadrature 4D-VAR

A new incremental four-dimensional variational (4D-Var) data assimilation algorithm is introduced. The algorithm does not require the computationally expensive integrations with the nonlinear model in the outer loops. Nonlinearity is accounted for by modifying the linearization trajectory of the observation operator based on integrations with the tangent linear (TL) model. This allows us to update the linearization trajectory of the observation operator in the inner loops at negligible computational cost. As a result the distinction between inner and outer loops is no longer necessary. The key idea on which the proposed 4D-Var method is based is that by using Gaussian quadrature it is possible to get an exact correspondence between the nonlinear time evolution of perturbations and the time evolution in the TL model. It is shown that J-point Gaussian quadrature can be used to derive the exact adjoint-based observation impact equations and furthermore that it is straightforward to account for the effect of multiple outer loops in these equations if the proposed 4D-Var method is used. The method is illustrated using a three-level quasi-geostrophic model and the Lorenz (1996) model.

Optimal linearization trajectories for tangent linear models

We examine differential equations where nonlinearity is a result of the advection part of the total derivative or the use of quadratic algebraic constraints between state variables (such as the ideal gas law). We show that these types of nonlinearity can be accounted for in the tangent linear model by a suitable choice of the linearization trajectory. Using this optimal linearization trajectory, we show that the tangent linear model can be used to reproduce the exact nonlinear error growth of perturbations for more than 200 days in a quasi-geostrophic model and more than (the equivalent of) 150 days in the Lorenz 96 model. We introduce an iterative method, purely based on tangent linear integrations, that converges to this optimal linearization trajectory. The main conclusion from this article is that this iterative method can be used to account for nonlinearity in estimation problems without using the nonlinear model. We demonstrate this by performing forecast sensitivity experiments in the Lorenz 96 model and show that we are able to estimate analysis increments that improve the two-day forecast using only four backward integrations with the tangent linear model. Copyright © 2011 Royal Meteorological Society

Four-dimensional data assimilation and balanced dynamics

The problem of spurious excitation of gravity waves in the context of four-dimensional data assimilation is investigated using a simple model of balanced dynamics. The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode, and can be initialized such that the model evolves on a so-called slow manifold, where the fast motion is suppressed. Identical twin assimilation experiments are performed, comparing the extended and ensemble Kalman filters (EKF and EnKF, respectively). The EKF uses a tangent linear model (TLM) to estimate the evolution of forecast error statistics in time, whereas the EnKF uses the statistics of an ensemble of nonlinear model integrations. Specifically, the case is examined where the true state is balanced, but observation errors project onto all degrees of freedom, including the fast modes. It is shown that the EKF and EnKF will assimilate observations in a balanced way only if certain assumptions hold, and that, outside of ideal cases (i.e., with very frequent observations), dynamical balance can easily be lost in the assimilation. For the EKF, the repeated adjustment of the covariances by the assimilation of observations can easily unbalance the TLM, and destroy the assumptions on which balanced assimilation rests. It is shown that an important factor is the choice of initial forecast error covariance matrix. A balance-constrained EKF is described and compared to the standard EKF, and shown to offer significant improvement for observation frequencies where balance in the standard EKF is lost. The EnKF is advantageous in that balance in the error covariances relies only on a balanced forecast ensemble, and that the analysis step is an ensemble-mean operation. Numerical experiments show that the EnKF may be preferable to the EKF in terms of balance, though its validity is limited by ensemble size. It is also found that overobserving can lead to a more unbalanced forecast ensemble and thus to an unbalanced analysis.

Body-centric modelling, identification, and acceleration tracking control of a quadrotor UAV

This paper presents the mathematical development of a body-centric nonlinear dynamic model of a quadrotor UAV that is suitable for the development of biologically inspired navigation strategies. Analytical approximations are used to find an initial guess of the parameters of the nonlinear model, then parameter estimation methods are used to refine the model parameters using the data obtained from onboard sensors during flight. Due to the unstable nature of the quadrotor model, the identification process is performed with the system in closed-loop control of attitude angles. The obtained model parameters are validated using real unseen experimental data. Based on the identified model, a Linear-Quadratic (LQ) optimal tracker is designed to stabilize the quadrotor and facilitate its translational control by tracking body accelerations. The LQ tracker is tested on an experimental quadrotor UAV and the obtained results are a further means to validate the quality of the estimated model. The unique formulation of the control problem in the body frame makes the controller better suited for bio-inspired navigation and guidance strategies than conventional attitude or position based control systems that can be found in the existing literature.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.

Orthogonal packing of identical rectangles within isotropic convex regions

A mixed integer continuous nonlinear model and a solution method for the problem of orthogonally packing identical rectangles within an arbitrary convex region are introduced in the present work. The convex region is assumed to be made of an isotropic material in such a way that arbitrary rotations of the items, preserving the orthogonality constraint, are allowed. The solution method is based on a combination of branch and bound and active-set strategies for bound-constrained minimization of smooth functions. Numerical results show the reliability of the presented approach. (C) 2010 Elsevier Ltd. All rights reserved.