The purpose of phenometry is to examine the measurable parameters of the plants in order to follow up the consequences of weather processes. We should fi nd the reasons, why the diameter of fruits grows larger in one season and smaller in the next. Variation may occur as a response to insuffi cient provision of water or nutrients, but also beca...use of pathological effects and of extremely high or low temperatures, moreover, of extraordinary heavy fruit load. There are phenometrical characteristics, which consider the fi nal consequences (density of fl owers, fruits set, drop of fruits), whereas other parameters could be followed up (size, length and width of fruits) as the dynamic components of growth. The quantitative parameters of growth are functionally related to each other, where the weather conditions, soil humidity and nutrients are on the input side, thus it is possible to model the growth of fruits as a function of the environment. Initially, the relations between the main weather variables and the phenometrical data have to be cleared. In the present study, the interactions between the mentioned phenomena are presented and numerically defi ned.
The aim of this paper was to investigate the fl owering characteristic of apples and their relationship to meteorological parameters. The trees observed are grown at Újfehértó, Eastern Hungary in the plantation of an assortment (gene bank) with 586 apple varieties. Each of the varieties were observed as for their dates of subsequent phenopha...ses, the beginning of bloom, main bloom and the end of bloom over a period between 1984 and 2001 during this period the meteorological data-base keeps the following variables: daily means of temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), daily precipitation sums (mm), daily sums of sunny hours, daily means of the differences between the day-time and night-time temperatures (°C), average differences between temperatures of successive daily means (°C). Between the 90th and 147th day of the year over the 18 years of observation. The early blooming varieties start blooming at 10–21April. The varieties of intermediate bloom start at the interval 20 April to 3 May, whereas the late blooming group start at 2–10 May. Among the meteorological variables of the former autumnal and hibernal periods, the hibernal maxima were the most active factor infl uencing the start of bloom in the subsequent spring.
The trees observed are grown at Ujfehert6, Eastern Hungary in a gene bank with 555 pear cultivars. Each of the cultivars was monitored for its dates of: the beginning of bloom, main bloom and the end of bloom and ripe phenophasis separately between I 984 and 2002. We analyzed the statistical features, frequency, distribution of these phenophasi...s and its' correlation the meteorological variables bet ween the interval. During this period the meteorological database recorded the following variables: daily mean temperature (°C), daily maximum temperature (0C), daily mini m um temperature (0C), daily precipitation (mm), daily hours of bright sunshine, daily means or the differences between the day-time and night-time temperatures (0C). For the analysis of data the cultivars have been grouped according to dates of maturity, blooming period as well as types of the seasons. Groups of maturity dates: summer ripe, autumnal ripening, winter ripe cultivars. Groups of blooming dates: early blooming, intermediate blooming, late blooming cultivars. At all the separated groups we analyzed the relationship between phenophasis and meteorological variables. During the 18 years of observation , the early blooming cultivars started blooming on 10-21 April, those of intermediate bloom date started flowering bet ween 20 April and 3 May, whereas the late blooming group started on 2- 10 May. Among the meteorological variables of the former autumn and winter periods, the winter maxima were the most active factor influencing the start dates of bloom in the subsequent spring. For the research of fruit growing-weather relationships we used simple, well known statistical methods, correlation and regression analysis. We used the SPSS 1 1.0 software for the linear regression fitting and for calculation of dispersions as well. The 1ables made by Excel programme.