A germination study of herbicide-resistant field poppies in Spain

Field poppy, Papaver rhoeas L., is a very common weed in winter cereals in North-Eastern Spain. Its control is becoming difficult due to expanding herbicide resistance. To control field poppies there are alternative strategies such as non-chemical control that take into account the weed emergence period. However, there is a lack of knowledge of P. rhoeas emergence patterns in semi-arid conditions. Thus, here we conducted pot experiments on the emergence of P. rhoeas. We aimed to describe the emergence period and to quantify the emergence of a susceptible and of a herbicide-resistant P. rhoeas population at two locations in Catalonia, Spain, from 1998 to 2001 and until 2004 at one of them. Therefore, pots containing seeds of both populations were established at the two locations and emergence was recorded monthly. We studied the origin of the population, the sowing location, the effect of cultivation and the sowing year. First, we found that the main emergence peaks in our experiments occurred in autumn, accounting for between 65.7 and 98.5% of the annual emergence from October to December, and only little emergence was recorded in spring. This emergence pattern is different from those found in the literature corresponding to Northern European countries, where in some cases main flushes occur only in autumn, in spring and winter or only in spring. The emergence was mainly affected by cultivation, but the effect of light stimulus was observed several months later. As a consequence, cultivation should be done in early autumn, promoting emergence during the whole autumn and winter so that emerged seedlings can be controlled before sowing a spring crop. Second, most experiments showed that the emergence was significantly higher in the first autumn than in the following seasons, e.g. 4.1% emergence in the first year and only 2.1, 2.3, 0.5 and 0.6% new emergence at one of the locations for the second, third, fourth and fifth years. Thus, after having a severe P. rhoeas infestation causing a big seed rain, emergence should be stimulated by autumn cultivation in the following season and seedlings controlled by trying to deplete the soil seed bank as much as possible. Despite the fact that emergence will be staggered throughout several years and that there was a significant relationship between rainfall and emergence, so that dry years will cause a smaller emergence rate of the weed, these findings define a cultural management strategy to reduce P. rhoeas infestations and to contribute to integrated weed management strategies combining it with other tools.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Forecasting tourism demand using consumer expectations

Abstract Purpose- There is a lack of studies on tourism demand forecasting that use non-linear models. The aim of this paper is to introduce consumer expectations in time-series models in order to analyse their usefulness to forecast tourism demand. Design/methodology/approach- The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months). Findings- Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed. Research limitations/implications- This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value- This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level. Keywords Tourism, Forecasting, Consumers, Spain, Demand management Paper type Research paper

On cumulant techniques in speech processing

This paper analyzes applications of cumulant analysis in speech processing. A special focus is made on different second-order statistics. A dominant role is played by an integral representation for cumulants by means of integrals involving cyclic products of kernels.

Improving Pitch Tracking Performance in Hard Noise Conditions by a Preprocessing Based on Mathematical Morphology

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing and has a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to one-dimensional context