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Aflatoxins (AFs) are harmful secondary metabolites produced by various moulds, among which Aspergillus flavus is the major AF-producer fungus. These mycotoxins have carcinogenic or acute toxigenic effects on both humans and food producing animals and, therefore, the health risks and also the potential economic damages mounted by them have led to legal restrictions, and several countries have set maximum allowable limits for AF contaminations in food and feed. While colonization of food and feed and AF production by A. flavus are highly supported by the climatic conditions in tropical and subtropical geographic regions, countries in the temperate climate zones are also increasingly exposed to AF-derived health risks due to climate change. In the present study, we have reviewed the available mathematical models as risk assessment tools to predict the possibility of A. flavus infection and levels of AF contaminations in maize in a changing climatic environment. After highlighting the benefits and possible future improvements of these models, we summarize the current agricultural practices used to prevent or, at least, mitigate the deleterious consequences of AF contaminations

Three Debrecen maize hybrids of different genotypes (Debreceni 285, Debreceni 377 and Debreceni 382) were examined on chernozem soil in a field experiment. During the two years of the experiment (2009–2010), we wanted to get to know how the examined hybrids reach to different sowing dates and what impact early, optimal and late sowing has on yield. In 2009, balanced soil and air temperature resulted in steady emergence. However, the low temperature in early April and the cooling down in mid-May 2010 caused a delayed emergence. The grain moisture content at harvesting and the high yield showed a strong crop year effect. In 2010, yield was much lower (1.664 t ha-1) and grain moisture was significantly higher (34%)than in 2009. In 2009, early sowing resulted in yield decrease (P

This paper describes a dynamic map representation method which provides a flexible, spectacular and cost-effective opportunity for the illustration and description of spatial data due to its parametrability, web-based publication and the free sowftare it uses in multi-user circumstances. The tasks of the database serves and the processing were performed by an ASUS WL-500 G Premium v2 router and a 80 GB hard disk. The database contains the measured data of the nitrogen fertilisation experiment established on the Látókép Experiment Site of the Centre for Agricultural Sciences of the University of Debrecen. The tests showed that the generation time of the processor which was run through the router did not significantly increase. Therefore, the configuration developed by us is suitable for users who do not wish to invest into a large and expensive server, but they still want to view their data quickly and easily, as well as to reach them from anywhere. The available data were not sorted into a database which was performed with Quantum GIS in a way to have an optimum database structure which is adjusted to the expected areas of use and the expected running speeds were also taken into consideration. The processor which processes the database items was written in PHP language. The main role of the processor is that it produces a KML file real time which is suitable for viewing with a given map viewer client (e.g. Google Earth). This application makes it possible to view information related to geographical objects, values stored in the database or those calculated by the processor on a map in 2D or 3D in a versatile way.

This study presents a PHP-based model capable of calculating maize leaf area index. The model calculates LAI from emergence to 75% silking. The basis of calculation is represented by the daily average temperature values. The usability of the model was tested using three years' temperature and LAI data series from the values obtained by the weather station set up at the Látókép Experiment Site of the University of Debrecen, Centre for Agricultural Sciences between 1994 and 1996. During the running of the model, it was observed that temperature affects the intensity of leaf development to a various extent.