Associating wheat crop and undersown forage legumes in organic agriculture: Incidence of forage legumes species

One of the key issues of organic arable systems is to increase use of N2 fixation from legume plants while enhancing autonomy by the limitation of off-farm inputs. Wheat yield in organic agriculture is generally low and variable. Grain yield and protein content are strongly affected by N deficiency and weed competition (Casagrande et al., 2009). Previous research had clearly demonstrated the benefits of forage legumes to improve N balance and preserve weed infestation (den Hollander et al., 2007). Several authors highlighted the interest of crop mixtures combining cereal and legumes to provide higher overall productivity, enhance ecological services and improve economical profitability (Malezieux et al., 2008). Nevertheless, previous research also highlights how important it is to manage whether above- and belowground interactions between species to optimise benefits and limit competition. We propose here to analyse how the insertion of legumes species influences the performance of organic wheat (yield, grain protein content) but also the weeds population during and after crop cycle

Incidence of soil N fertility on the performance of organic forage legume-wheat mixtures.

One of the key issues of organic arable systems is to bring enough nitrogen in the crop rotation to ensure satisfying crop nutrition. Wheat yield in organic agriculture are generally low and variable. Grain yield and grain protein content are strongly affected by N deficiency and weed competition (Casagrande et al., 2009). Nevertheless, the autonomy of the organic cropping systems has to be improved while off-farm inputs have to be limited. The use of N2 fixation from legume plants should then be improved. Previous research had clearly demonstrated the benefits of forage legumes in association to improve N balance and control weed seed bank. However, it is also well known that legume N2 fixation could be limited depending on the soil N fertility. The functioning of such mixtures could then be disturbed by variations of the nitrogen fertility of the environment. The impact of soil N fertility has to be studied in order to manage whether above- and belowground interactions between species and to optimise benefits of the association

Optimal Probabilistic Forecasts for Counts

Optimal probabilistic forecasts of integer-valued random variables are derived. The optimality is achieved by estimating the forecast distribution nonparametrically over a given broad model class and proving asymptotic efficiency in that setting. The ideas are demonstrated within the context of the integer autoregressive class of models, which is a suitable class for any count data that can be interpreted as a queue, stock, birth and death process or branching process. The theoretical proofs of asymptotic optimality are supplemented by simulation results which demonstrate the overall superiority of the nonparametric method relative to a misspecified parametric maximum likelihood estimator, in large but .nite samples. The method is applied to counts of wage claim benefits, stock market iceberg orders and civilian deaths in Iraq, with bootstrap methods used to quantify sampling variation in the estimated forecast distributions.Nonparametric Inference; Asymptotic Efficiency; Count Time Series; INAR Model Class; Bootstrap Distributions; Iceberg Stock Market Orders.

Global ocean heat transport dominated by heat export from the tropical Pacific

Heat redistribution is one of the main mechanisms by which oceans regulate Earth’s climate. Analyses of ocean heat transport tend to emphasize global-scale seawater pathways and concepts such as the great ocean conveyor belt. However it is the divergence or convergence of heat transport within an oceanic region, rather than the origin or destination of seawater transiting through that region, which is most immediately relevant to Earth’s heat budget. Here we use a recent gridded estimate of ocean heat transport to reveal the net effect on Earth’s heat budget, the ‘effective’ ocean heat transport, by removing internal ocean heat loops that have obscured the interpretation of measurements. The result demonstrates the overwhelming predominance of the tropical Pacific which exports four times as much heat as is imported in the Atlantic and Arctic. It also highlights the unique ability of the Atlantic and Indian Oceans to transport heat across the Equator – Northward and Southward, respectively. However effective inter-ocean heat transports are smaller than expected, suggesting that global-scale seawater pathways only play a minor role in Earth’s heat budget

Asymptotic Properties of Approximate Bayesian Computation

Approximate Bayesian computation allows for statistical analysis in models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, its limiting shape, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance used within the method, mild regularity conditions on the summary statistics, and a condition linked to identification of the true parameters. Implications for practitioners are discussed.Comment: This 31 pages paper is a revised version of the paper, including supplementary materia

On the observability of turbulent transport rates by Argo: supporting evidence from an inversion experiment

Although estimation of turbulent transport parameters using inverse methods is not new, there is little evaluation of the method in the literature. Here, it is shown that extended observation of the broad scale hydrography by Argo provides a path to improved estimates of regional turbulent transport rates. Results from a 20 year ocean state estimate produced with the ECCO v4 non-linear inverse modeling framework provide supporting evidence. Turbulent transport parameter maps are estimated under the constraints of fitting the extensive collection of Argo profiles collected through 2011. The adjusted parameters dramatically reduce misfits to in situ profiles as compared with earlier ECCO solutions. They also yield a clear reduction in the model drift away from observations over multi-century long simulations, both for assimilated variables (temperature and salinity) and independent variables (bio-geochemical tracers). Despite the minimal constraints imposed specifically on the estimated parameters, their geography is physically plausible and exhibits close connections with the upper ocean ocean stratification as observed by Argo. The estimated parameter adjustments furthermore have first order impacts on upper-ocean stratification and mixed layer depths over 20 years. These results identify the constraint of fitting Argo profiles as an effective observational basis for regional turbulent transport rates. Uncertainties and further improvements of the method are discussed

Board involvement in strategy : advancing the governance of sport organizations

This study investigated how boards of national sport organizations might enhance their strategic capability. Utilizing an action research method and focusing on the case of New Zealand Football (soccer), findings established that greater board involvement in strategy advanced the board\u27s ability to perform its strategic function. Further findings determined the importance of shared leadership between the board and the CEO, the complex interplay in balancing this relationship and the need to integrate strategy into board processes

Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models

A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics for the observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a 'match' between observed and simulated summaries are retained, and used to estimate the inaccessible posterior. With no reduction to a low-dimensional set of sufficient statistics being possible in the state space setting, we define the summaries as the maximum of an auxiliary likelihood function, and thereby exploit the asymptotic sufficiency of this estimator for the auxiliary parameter vector. We derive conditions under which this approach - including a computationally efficient version based on the auxiliary score - achieves Bayesian consistency. To reduce the well-documented inaccuracy of ABC in multi-parameter settings, we propose the separate treatment of each parameter dimension using an integrated likelihood technique. Three stochastic volatility models for which exact Bayesian inference is either computationally challenging, or infeasible, are used for illustration. We demonstrate that our approach compares favorably against an extensive set of approximate and exact comparators. An empirical illustration completes the paper.Comment: This paper is forthcoming at the Journal of Computational and Graphical Statistics. It also supersedes the earlier arXiv paper "Approximate Bayesian Computation in State Space Models" (arXiv:1409.8363

Scaling analysis of deformation field within granular materials: application to strain localization

Discrete element method (DEM) simulations using periodic boundary conditions and molecular dynamics are conducted on a frictional granular media. Two dimensional strain controlled biaxial tests are carried out on an assembly of circular particles interacting via elastic contacts and Coulomb friction. The spatial correlations that take place within the deformation field along the loading path are tracked by a scaling analysis of the continuous strain rate field. This method allows us to discuss the degree of strain localization occurring throughout the test. The analysis of the correlation length in the early stages of macroscopic deformation leads to the identification of two distinct behaviors. First, a divergence of the correlation length on the first deformation invariant, i.e. the divergence, is reported at the onset of macroscopic dilation. This suggests an interpretation of the contraction peak as a critical point. Secondly, an increase of the correlation length on the second deformation invariant, i.e. the shear, is also observed before the peak load. However, saturation remains on the scaling law. We argue that this second behavior is associated to macroscopic shear banding: our analysis accurately gives its outbreak on the stress versus strain curve. Finally, a dependence of the correlation length as a function of the deformation window considered is reported. This shows that scaling properties within the deformation field emerge from long range interactions within an assembly of rigid frictional particles