The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisitionsessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of eithermonomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to beavailable for research purposes through the BioSecure Association during 2008.

Effects of nitrogen supply on wheat and on soil nitrate

Field experiments were conducted at two locations during two growing seasons in the Ebro Valley (Spain), to evaluate the effects of N fertilization on yield and quality of Mediterranean-type wheat in irrigated conditions. Seven N treatments and a control were investigated. The average grain yields ranged from 2117 to 5551 kg ha-1 depending on the year and location. Grain protein ranged from 14.25 to 16.9%, and other quality parameters such as the dough strength (W) also varied with year and location, confirming the suitability of Mediterranean-type wheat and the climate for the production of good bread-making quality wheat. However, grain yields are normally low and both yields and quality can be greatly affected by the variability of this type of climate, even under irrigation. Under these conditions, grain yield increases were mainly due to an increase in the number of grains per m2 without a reduction in the N content per spike, suggesting that N in the grain was not source-limited, possibly due to the lower grain yields and relatively high soil nitrate concentrations. In soils with lower initial soil NO-3N contents, better grain yields could be achieved by applying a N fertilizer rate of about 100 kg N ha-1, whereas in soils with high initial NO-3N contents, no N or a maximum rate of 50 kg N ha-1 is needed to obtain a good grain quality, showing the possibility of producing high-quality wheat with a low amount of N fertilizer and thus increasing the sustainability of the cropping system.

Use of a pre-sidedress soil nitrate test (PSNT) to determine nitrogen fertilizer requirements for irrigated corn

In the present study, a 2-year N rate response experiment was conducted in different fields to monitor NO3-N soil profiles, N accumulation by the crop and final crop performance, in order to assess if soil NO3-N at pre-sidedressing (Pre-Sidedress Soil Nitrate Test, PSNT) is a reliable indicator for soil N availability for corn in the irrigated area served by canal d’Urgell (Lleida, Spain), and if the test can be used to separate responsive fields from non-responsive fields to sidedress N fertilizer applications. Preliminary soil N availability (N sidedress fertilizer rate + PSNT) critical levels to identify fields that need supplementary N fertilizer applications were established at ca. 300 and 210 kg NO3-N·ha–1, for PSNTrooting–zone and PSNT0–30 cm, respectively (for a yield goal of 14 t grain·ha–1).

A priori parameterisation of the CERES soil-crop models and tests against several European data sets

Mechanistic soil-crop models have become indispensable tools to investigate the effect of management practices on the productivity or environmental impacts of arable crops. Ideally these models may claim to be universally applicable because they simulate the major processes governing the fate of inputs such as fertiliser nitrogen or pesticides. However, because they deal with complex systems and uncertain phenomena, site-specific calibration is usually a prerequisite to ensure their predictions are realistic. This statement implies that some experimental knowledge on the system to be simulated should be available prior to any modelling attempt, and raises a tremendous limitation to practical applications of models. Because the demand for more general simulation results is high, modellers have nevertheless taken the bold step of extrapolating a model tested within a limited sample of real conditions to a much larger domain. While methodological questions are often disregarded in this extrapolation process, they are specifically addressed in this paper, and in particular the issue of models a priori parameterisation. We thus implemented and tested a standard procedure to parameterize the soil components of a modified version of the CERES models. The procedure converts routinely-available soil properties into functional characteristics by means of pedo-transfer functions. The resulting predictions of soil water and nitrogen dynamics, as well as crop biomass, nitrogen content and leaf area index were compared to observations from trials conducted in five locations across Europe (southern Italy, northern Spain, northern France and northern Germany). In three cases, the model’s performance was judged acceptable when compared to experimental errors on the measurements, based on a test of the model’s root mean squared error (RMSE). Significant deviations between observations and model outputs were however noted in all sites, and could be ascribed to various model routines. In decreasing importance, these were: water balance, the turnover of soil organic matter, and crop N uptake. A better match to field observations could therefore be achieved by visually adjusting related parameters, such as field-capacity water content or the size of soil microbial biomass. As a result, model predictions fell within the measurement errors in all sites for most variables, and the model’s RMSE was within the range of published values for similar tests. We conclude that the proposed a priori method yields acceptable simulations with only a 50% probability, a figure which may be greatly increased through a posteriori calibration. Modellers should thus exercise caution when extrapolating their models to a large sample of pedo-climatic conditions for which they have only limited information.