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Podosphaera leucotricha. Venturia inaequalis. Botrytis cinerea ... Podosphaera tridactyla. Polystigma rubrum ... Sphaerotheca pannosa. Venturia carpophila.
VII. Alps-Adria Scientific Workshop

Stara Lesna, Slovakia, 2008

PRACTICAL APPLICATION OF AN INTEGRATED AGROMETEOROLOGICAL FORECASTING SYSTEM IN SOUTH-EAST HUNGARY Ferenc LANTOS1 – Zoltán GÖRÖG2 – István KRISTÓ1 – Tamás MONOSTORI1 1

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University of Szeged, Faculty of Agriculture, Hódmezıvásárhely, Hungary e-mail: [email protected] Mórakert TÉSZ, Mórahalom, Hungary

Abstract: An integrated agrometeorological forecasting system was built out in SE Hungary. The system is based upon 12 meteorological stations deployed at thoroughly selected sites in 3 counties. Collected and calculated meteorological data serve as basis for disease forecasting algorithms worked out for the typical fruits, vegetables and arable crops of the region. Experiences after the first years’ usage suggest that the disease forecasting system gives reliable data to make efficient management of plant pathogens possible. Keywords: agrometeorological forecasting, integrated pest management

Introduction Climate change and diversity of meteorological phenomena have a predictable impact on both the ecological and the economical environment of agricultural production (Dobó et al., 2006; Mikulec and Stehlová, 2006; Tanács et al., 2006; Vágó et al., 2006). Agrometeorological modeling, processing and analysis are essential parts of yield forecasting systems such as the MARS project (http://mars.jrc.it/marsstat/Crop_Yield_Forecasting/METAMP/INDEX.HTM). Integrated pest management (IPM) requires forecasting to make an effective plant protection technology based on prevention and on the use of contact pesticides of short persistence possible. Deployment of agrometeorological stations directly on the field of the crops to be protected can be the basis of delivering proper data for plant protection algorithms in each culture. IPM models based on weather data were established for several diseases e.g. potato late blight (Hansen et al., 1995), apple fire blight (Billing, 1990), apple scab (Mills, 1944). Models of this type can be integrated in complex computer programs such as GALATI-VITIS (Szıke et al., 1998). Here we report about the experiences after the first years’ application of an agrometeorological system deployed at 12 farms of various location in SE Hungary. We evaluated the correspondence between the diseases forecasted by the system and the actually detected infections. On this basis the protection algorithms can be corrected if necessary. Materials and methods The agrometeorological system was developed and delivered by the Boreas Ltd., Hungary: – Meteorological station (temperature and moisture sensor, wind speed and direction sensor, soil temperature sensor, soil moisture sensor, precipitation quantity sensor, precipitation or leaf moisture sensor, sunshine detector), to perform the necessary measurements – GPRS communication module to transfer the measured data

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Vol. 36, 2008, Suppl.

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Cereal Research Communications

MailBCUServer a central data collecting/processing program to store and process data and to forward them towards growers MultiStation4Web program to calculate the infection index of the given pathogen InterMet3, a data presentation program for basic agricultures to visualize data on the user’s side

The 12 agrometeorological stations were deployed in the following sites, in fields where the most characteristic vegetables or fruits of the given region were cultivated: Tataháza, Jánoshalma, Öttömös, Ruzsa, Üllés, Zsombó, Röszke, Balástya, Csengele, Tömörkény, Makó, Medgyesegyháza Meteorological data (basic and calculated) collected by the system: minimum and maximum air temperature (ºC), relative moisture (%), precipitation quantity (mm), leaf moisture (h), duration of solar radiation (h), soil temperature (ºC), soil moisture (%), positive heat unit (ºC day), active heat unit (ºC day), effective heat unit (ºC day), air drought (h), potential evaporation (mm) The fruits, vegetables and arable crops involved in the forecasting program: grape, apple, cherry, sour-cherry, apricot, peach, plum, potato, wheat, maize, sunflower, canola, tomato, pepper Collection of data happens via GSM technology. Each station is equipped with a GSM adaptor and forwards the measured parameters to the central server (MailBCUServer) of the Mórakert Ltd. in the form of a data package. Data are processed by a software (MultiStation4Web) installed on this server. Infection indexes are calculated by algorithms used by the software. The indexes give the basis of the protection method recommended by the consultant. Recommendations are forwarded to growers in the form of SMS. Furthermore, they can collect all the measured and calculated data through the InterMet3 data presentation program. Results and discussion Evaluation of meteorological data (primarily: leaf moisture, moisture, temperature) suggested that climatic conditions had been suitable for several parasites in the region over the year 2007 (Tab. 1). However, the probability levels given in Table 1 were determined on the basis of literary data of the diseases (e.g. Glits and Folk, 2000) and did not always reflect the current situation. Based on the disease forecasting algorithms of the system, the regularly occurring parasites in the region were e.g. Ph. infestans in potato (from 17.04. to 20.06., 4 times forecasted), V. inaequalis and P. leucotrycha in apple (2-3 times forecasted), T. deformans in peach (2 times forecasted). Home and international experiences confirm that before using an IPM model that was not field tested or validated for a specific location, the model should be tested for one or more seasons under local conditions to verify that it will work in the desired location (http://www.ipm.ucdavis.edu/DISEASE/DATABASE/diseasemodeldatabase.html#FUN GICIDES).

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VII. Alps-Adria Scientific Workshop

Stara Lesna, Slovakia, 2008

Table1. Probability of appearance of the most important diseases according to the meteorological data Culture apple

Disease Prob. Erwinia amylovora * Podosphaera leucotricha *** Venturia inaequalis *** Botrytis cinerea ** Monilia fructigena *** cherry, Taphrina cerasi * sourMycospherella cerasi * cherry Venturia cerasi *** Blumeriella jaapii *** Monilia laxa *** Coniothyrium prunicolum *** Gloeosporium fruchtigenum ** apricot Sphaerotheca pannosa *** Venturia carpophila *** Monilia laxa *** Tranzschelia pruni spinosae * Stigmina carpophila ** Apiognomonia erytrostoma *** peach Taphrina deformans *** Sphaerotheca pannosa *** Venturia carpophila *** Prob.: probability of appearance ***, **, *: 80-100%, 60-80%,