Search

La podredumbre carbonosa de la soja [Glycine max (L.) Merr.], ocasionada por el hongo polífago Macrophomina phaseolina (Tassi) Goid., es una enfermedad que ocasiona pérdidas económicamente importantes en el mundo. En el noroeste de Argentina esta patología es limitante para la producción de soja cuando se presentan condiciones de altas temperaturas y estrés hídrico. Debido a la importancia económica y a la falta de genotipos comerciales de soja resistentes a esta enfermedad, se planteó como objetivo evaluar el comportamiento de las líneas avanzadas de soja del programa de mejoramiento genético de la Estación Experimental Agroindustial Obispo Colombres (EEAOC) frente a la podredumbre carbonosa, en ensayos a campo inoculados con el hongo. Se evaluaron los parámetros de severidad e índice de unidades formadoras de colonias de M. phaseolina (IUFC) en estadio fenológico R7 del cultivo. Se evaluaron 12 líneas de soja en la campaña 2016/2017 y 24 líneas en el ciclo agrícola 2017/2018. Las líneas que presentaron los mejores comportamientos frente a M. phaseolina en los ciclos agrícolas evaluados fueron: D38M3-13/182, D51N59-14/247 y Munasqa RR, las cuales se comportaron como moderadamente resistentes cuando se evaluó el parámetro severidad, y como resistentes según el parámetro IUFC. Estas líneas podrían ser utilizadas en los programas de mejoramiento genético como progenitores para incorporar la resistencia frente a esta enfermedad, y mejorar así el manejo de la podredumbre carbonosa de la soja en el noroeste de Argentina de forma eficaz y amigable con el ambiente.

The current work addresses one of the key building blocks towards an improved understanding of flood processes and associated changes in flood characteristics and regimes in Europe: the development of a comprehensive, extensive European flood database. The presented work results from ongoing cross-border research collaborations initiated with data collection and joint interpretation in mind. A detailed account of the current state, characteristics and spatial and temporal coverage of the European Flood Database, is presented. At this stage, the hydrological data collection is still growing and consists at this time of annual maximum and daily mean discharge series, from over 7000 hydrometric stations of various data series lengths. Moreover, the database currently comprises data from over 50 different data sources. The time series have been obtained from different national and regional data sources in a collaborative effort of a joint European flood research agreement based on the exchange of data, models and expertise, and from existing international data collections and open source websites. These ongoing efforts are contributing to advancing the understanding of regional flood processes beyond individual country boundaries and to a more coherent flood research in Europe.

Klebsiella spp. have been isolated from many different environmental habitats but have mainly been associated with nosocomial acquired diseases in humans. Although there are many recently published sequenced genomes of members of this genus, there are very few studies on whole genome comparisons between clinical and non-clinical isolates, and it is therefore still an open question if a strain found in nature is capable of infecting humans/animals. Klebsiella michiganensis Kd70 was isolated from the intestine of larvae of Diatraea saccharalis but genome analysis revealed multiple genes associated with colonization and growth promotion in plants suggesting an endophytic lifestyle. Kd70 cells labeled with gfp confirmed capability of root colonization and soil application of Kd70 promoted growth in greenhouse grown sugarcane. Further genomic analysis showed that the Kd70 genome harbored fewer mammalian virulence factors and no pathogen island-like regions when compared to clinical isolates of this species, suggesting attenuated animal/human pathogenicity. This postulation was corroborated by in vivo experiments in which it was demonstrated that Kd70 was unable to infect the mouse urinary tract. This is to the best of our knowledge the first experimental example of a member of a pathogenic Klebsiella spp. unable to infect a mammalian organism. A proteomic comparison deduced from the genomic sequence between Kd70 and several other K. michiganensis strains showed a high similarity with isolates from many different environments including clinical strains, and demonstrated the existence of conserved genetic lineages within this species harboring members from different ecological niches and geographical locations. Furthermore, most genetic differences were found to be associated with genomic islands of clinical isolates, suggesting that evolutionary adaptation of animal pathogenicity to a large extent has depended on horizontal gene transfer. In conclusion our results demonstrate the importance of conducting thorough in vivo pathogenicity studies before presupposing animal/human virulence of non-clinical bacterial isolates.

The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

The variability of the second order moments of flood peaks with respect to geomorphoclimatic basin characteristics was investigated. In particular, the behaviour of the coefficient of variation (Cv) of the series of annual maximum floods was analysed with respect to its dependence on physically consistent quantities. The results achieved were in fairly good agreement with real world observed characteristics and interesting insights on the relationship between Cv and basin size were found. It appears that Cv is controlled mainly by the climate and by some water loss features. Many observations reported in the literature show a decrease of Cv with basin area A, usually ascribed to the limited spatial extent of extreme events, which leads to a decrease with area of the Cv of areal rainfall intensity. An increase of Cv with the area is also sometimes observed for small basins. Such different behaviours were accounted for by the concurrent effect on two parameters that affect the Cv (A) relationship, representative of the way rainfall losses and effective rainfall intensity scale with the basin area. Keywords: floods, climate, coefficient of variation, scaling.

This paper focuses on the seasonality of hydroclimatic extremes and on the problem of accounting for their non-homogeneous character in determining the design value. To this aim we devise a simple stochastic world in which extremes are produced by a non-homogeneous extreme value generation process. The design values are estimated in closed analytical form both in a peak over threshold framework and by using the standard annual maxima approach. In this completely controlled world of generated hydrological extremes, a statistical measure of the error associated to the adoption of a homogeneous model is introduced. The sensitivity of this measure, assumed in terms of return period ratio, to the typology and strength of seasonality is investigated. We find that seasonality induces a downward bias in design value estimators. The magnitude of the bias may be large when the peak over threshold approach is adopted, while the return period distortion is limited when the annual maxima are considered. An application to a prealpine to alpine transition region located in North-Western Italy is presented to better clarify the effects of disregarding seasonality in a real case.

Validation of probabilistic models based on goodness-of-fit tests is an essential step for the frequency analysis of extreme events. The outcome of standard testing techniques, however, is mainly determined by the the behavior of the hypothetical model, FX(x), in the central part of the distribution, while the behavior in the tails of the distribution, which is indeed very relevant in hydrological applications, is relatively unimportant for the results of the tests. The maximum-value test, originally proposed as a technique for outlier detection, is a suitable, but seldom applied, technique that addresses this problem. The test is specifically targeted to verify if the maximum (or minimum) values in the sample are consistent with the hypothesis that the distribution FX(x) is the real parent distribution. The application of this test is hindered by the fact that the critical values for the test should be numerically obtained when the parameters of FX(x) are estimated on the same sample used for verification, which is the standard situation in hydrological applications. We propose here a simple, analytically explicit, technique to suitably account for this effect, based on the application of censored L-moments estimators of the parameters. We demonstrate, with an application that uses artificially generated samples, the superiority of this modified maximum-value test with respect to the standard version of the test. We also show that the test has comparable or larger power with respect to other goodness-of-fit tests (e.g., chi-squared test, Anderson-Darling test, Fung and Paul test), in particular when dealing with small samples (sample size lower than 20–25) and when the parent distribution is similar to the distribution being tested.