Borok cukortartalmának meghatározására szolgáló módszerek összehasonlító vizsgálata

Fünf verschiedene Methoden der Zuckerbestimmung (Bertrand, Schoorl, Rebelein, Reischauer und AOAC) wurden miteinander verglichen. Dabei wurde die Verwendbarkeit der einzelnen Methoden zu genauen Messungen festgestellt. Auf Grund der Ergebnisse wird bei Anwendung der Rebelein- und der AOACMethode eine Korrektion von 1 5 g/Lit. bei Zuckergehalten unter 10 g/1. empfohlen. Five different methods of sugar determination (Bertrand, Schoorl, Rebelein, Reischauer and АОАС) were subjected to a comparative investigation. The suitability of the individual methods for exact measurements was established. On the basis of the results the use of a correction value of 1.5 g/1. is suggested at sugar contents below 10 g/1., in case of the Rebelein and AOAC methods

A climate profile indicator based comparative analysis of climate change scenarios with regard to maize cultures

Using ecological data compiled from scientific literature on pest, pathogen and weed species characteristic in maize cultures in Hungary, we defined monthly climate profile indicators and applied them to complete a comparative analysis of the historical and modelled climate change scenario meteorological data of the city of Debrecen. Our results call attention to a drastic decline of the competitive ability of maize as compared to several C4 and especially C3 plants. According to the stricter scenarios, the frequency of potential pest and pathogen damage emergency situations will grow significantly by the end of the century

Climatic effects on the phenology of geophytes

Nowadays, the scientific and social significance of the research of climatic effects has become outstanding. In order to be able to predict the ecological effects of the global climate change, it is necessary to study monitoring databases of the past and explore connections. For the case study mentioned in the title, historical weather data series from the Hungarian Meteorological Service and Szaniszló Priszter’s monitoring data on the phenology of geophytes have been used. These data describe on which days the observed geophytes budded, were blooming and withered. In our research we have found that the classification of the observed years according to phenological events and the classification of those according to the frequency distribution of meteorological parameters show similar patterns, and the one variable group is suitable for explaining the pattern shown by the other one. Furthermore, our important result is that the dates of all three observed phenophases correlate significantly with the average of the daily temperature fluctuation in the given period. The second most often significant parameter is the number of frosty days, this also seem to be determinant for all phenophases. Usual approaches based on the temperature sum and the average temperature don’t seem to be really important in this respect. According to the results of the research, it has turned out that the phenology of geophytes can be well modelled with the linear combination of suitable meteorological parameter

A növénytermesztési folyamatok modellezése = Modelling of crop production processes

Az elvégzett feladatokat négy nagyobb csoportba lehet sorolni: 1. Intelligens információs rendszerek építése és folyamatos karbantartása 2. Növényi növekedési modellek összegyűjtése, adaptálása, fejlesztése 3. Kockázatelemzés 4. Döntéstámogató rendszerek építése 1. Az információs rendszerek témakör a következő létrehozott fontos adatbázisokat tartalmazza: meteorológiai adatbázis historikus mért adatokra, meteorológiai adatbázis szcenáriókra, tartamkísérletek információi, talajtani információs rendszer, növényi termésátlagok megyei idősorai, zöldségtermesztési agroökológiai információs rendszer. 2. A növényi növekedési modellekkel kapcsolatban foglalkoztunk az agroökológiai táplálékhálózati rendszer populációdinamika biomassza modelljének a fenológiai általánosításával, nemzetközileg elfogadott modellek összegyűjtésével és összehasonlító tesztelésével, a növénytermesztés és időjárás sztochasztikus kapcsolatának elemzésével, a búza és kukorica modellek adaptálásával és hasznosítási lehetőségük feltárásával. 3. A kockázat mértékének növekedését az általánosított sztochasztikus dominancia-módszer egy közelmúltban egyszerűsített változatával, a kockázati averziótól is függő sztochasztikus hatásossági kritérium segítségével igazoltuk. | The completed tasks can be classified into four groups: 1. Constructing and continious maintainence of intelligent information systems: a. Database of historical meteorological data b. Database of climate scenario meteorological data c. Information of field experiments d. Soil information system e. Regional time series of plant yield averages f. Agroecological information system of vegetables 2. Collecting, adapting and development of plant growth models a. Phenological generalisation of the biomass model of the agroecological food web population dynamics. b. Collecting and comparative testing of internationally accepted models. c. Analysis of the stochastic relationship of the crop production and weather. d. Adaptation and revealing of the utilization possibilities of the wheat and maize models. 3. Risk analysis a. We justified the increasing of risk with a recently simplified version of the stochastic dominance method, namely with the stochastic effectiveness criteria depending on risk aversion. 4. Development of decision support systems: a. Planning of environmentally sparing agriculture, sustainable management, study of agrometeorological and pest dynamical conditions

Comparative analysis of the relationship between phenological phenomena and meteorological indicators based on insect and plant monitoring

Climate change is one of the biggest environmental problems of the 21st century. The most sensitive indicators of the effects of the climatic changes are phenological processes of the biota. The effects of climate change which were observed the earliest are the remarkable changes in the phenology (i.e. the timing of the phenophases) of the plants and animals, which have been systematically monitored later. In our research we searched for the answer: which meteorological factors show the strongest statistical relationships with phenological phenomena based on some chosen plant and insect species (in case of which large phenological databases are available). Our study was based on two large databases: one of them is the Lepidoptera database of the Hungarian Plant Protection and Forestry Light Trap Network, the other one is the Geophytes Phenology Database of the Botanical Garden of Eötvös Loránd University. In the case of butterflies, statistically defined phenological dates were determined based on the daily collection data, while in the case of plants, observation data on blooming were available. The same meteorological indicators were applied for both groups in our study. On the basis of the data series, analyses of correlation were carried out and a new indicator, the so-called G index was introduced, summing up the number of correlations which were found to be significant on the different levels of significance. In our present study we compare the significant meteorological factors and analyse the differences based on the correlation data on plants and butterflies. Data on butterflies are much more varied regarding the effectiveness of the meteorological factors

Effect of cropping technologies on the yield of dry bean variety ’Diana’

An experiment on three dry bean varieties (Start, Hópehely, Diana), using different sowing-times, fertilizers and plant densities was performed on sandy soil in the University of Debrecen, Centre for Agricultural Sciences, Research Institute of Nyíregyháza in 2015. The aim of the experiment was to study which treatment gives the highest yield, and whether the different treatments result in significant differences in the yields. In this paper the dry bean yield at ‘Diana’ variety is analyzed. The treatments were done with three fertilizer doses and three plant densities at different sowing-times (April 24; May 8; May 18). As a result of the high temperature and the drought during the growth season, the yields we harvested were in low, which shows the ecological sensitivity of the plant we examined. We concluded that the poorest yield was harvested at the third sowing-time. There was no significant difference in the yields at the first and second sowing- time. Examining all the three fertilizer treatments we applied at the experiment, we achieved the highest yields in the control plots. It might be due to the weak efficiency of nitrogen fertilizer under the extremely dry conditions. The highest yield was harvested at the control treatments during the second sowing-time. Regarding the effect of the plant densities, the highest crop yield was achieved at the treatment using 400 000 germs ha-1, followed by 300 000 germs ha-1 and 200 000 germs ha-1