Sweet and sour cherry need 550–600 mm yearly precipitation. The critical period is 1–1.5 month after flowering, it is normallybetween 15.April – 15. June in Hungary. The rain induced fruit cracking is also a critical and costly problem for cherry growers. Fruits grownunder arid conditions are less resistant against rainfall during harvest and up to 50–60% crack damage may occur. A computer program wasdeveloped to calculate the precipitation related production risks of sweet cherry. Focus of the research was Zala county. Spatial distributionof precipitation was compared in two directions (East and North of Zala county) based on the data of meteorological stations. The first resultsindicate that the developed method estimates the risks quite well, compared to the farm experiment results. The developed computer programcan be parameterised according to the user’s requirements, this allows to take into account the real variety structure of a given orchard

J., Nyéki

K., Szentteleki

M., Gaál

M., Soltész

Z., Mézes

Z., Szabó

University of Debrecen Electronic Archive

Introduction
Beside minimum annual water demand the yearly
temporal and spatial distribution of rainfall is essential for
fruit development. There are two important factors of sweet
cherry production:
– Rainfall between blooming and harvest should be
well balanced, a weekly 10–15 mm of rain ensures
optimal fruit weight increase. Constant rain
distribution in the above mentioned period on the
other hand gives a good condition for the fruits and
reduces the effect of rainfall in the harvest period
(crack damage of fruits and development of fungi).
Damaged fruits can be hardly sold on the market
which means financial loss for the producer (Nyéki et
al., 1997; Papp, 2004).
– The second factor is in connection with the 10–15
days period before harvest. If there is significant
rainfall during, the fruit crack damage appears.
Reason is osmotic concentration difference of the
liquid of the fruit inside and the rain water on the
surface of the crop. If more than 50% of the crop has
crack damage than the product can be sold only for
juice production industry for a much lower than fresh
market price (Sekse, 1998).
Materials and methods
With appropriate meteorological data for fifty to hundred
years we can analyze if these requirements for quality fruit
production occurred at the past. The volume and distribution
of rainfall was partly fulfilled the above mentioned factors in
the past but never fully satisfied the criteria of cherry fruit.
Every year a risk factor can be calculated based on the
historical meteorological data. Permanent and well balanced
precipitation can be easily monitored by computer software
in any time period during the last 100 years (Erdélyi et al.,
2006). Criterion could be the number of weeks when 10–15
mm rain occurred or not. In this research we calculated the
number of weeks during the investigated season when there
was the appropriate precipitation amount. The investigated
period was with variable length – as the ripening times are
different – between 15. April and the beginning of harvest.
We put the harvest time between 25. May and 10. July
including early, mid and late harvest sweet cherry varieties.
Average water supply indicators for different growing
periods have been calculated for every year. Result is the K1
risk factor, which shows the being of permanent and well
balanced precipitation between April and June (Ladányi,
2008; Erdélyi, 2006).
Harvest period was marked between 20. May and 10.
July, namely the 145th and 191st day of the year. Four risk
factors were calculated for every harvest day according to the
following algorithm:
– K2 risk factor: 10-6. days before harvest at least 15 mm
rain occur 1x=0.15; 2x=0.4; 3x=0.7
– K3 risk factor: 5-1. days before harvest at least 10 mm
rain occur 1x=0.2; 2x=0.5; 3x=0.8
– K4 risk factor: during harvest at least 5 mm rain occur
1x=0.8; 2x=1
The overall risk factor is like follows:
– K2+K3+K4=K5; if K5>1, than K5=1.
The developed computer program can be parameterised
according to the user’s requirements. The necessary
calculations for any year or time period such as decade,
Correlation of precipitation distribution and quality sweet
cherry production
Szenteleki, K.1, Gaál, M.1, Mézes, Z.2, Soltész, M.2, Nyéki, J.2 & Szabó, Z.2
1Corvinus University of Budapest, Hungary
2University of Debrecen, Hungary
Summary: Sweet and sour cherry need 550–600 mm yearly precipitation. The critical period is 1–1.5 month after flowering, it is normally
between 15. April – 15. June in Hungary. The rain induced fruit cracking is also a critical and costly problem for cherry growers. Fruits grown
under arid conditions are less resistant against rainfall during harvest and up to 50–60% crack damage may occur. A computer program was
developed to calculate the precipitation related production risks of sweet cherry. Focus of the research was Zala county. Spatial distribution
of precipitation was compared in two directions (East and North of Zala county) based on the data of meteorological stations. The first results
indicate that the developed method estimates the risks quite well, compared to the farm experiment results. The developed computer program
can be parameterised according to the user’s requirements, this allows to take into account the real variety structure of a given orchard.
Key words: sweet cherry, fruit cracking, fruit quality
International Journal of Horticultural Science 2010, 16 (4): 39–43.
Agroinform Publishing House, Budapest, Printed in Hungary
ISSN 1585-0404
40
century and etc. can be calculated fast and exactly with this
software. The software is able to make a separation by
growing period of the different varieties such as early, mid
and late harvest sweet cherries. The risk factors can be
estimated in a weekly basis for the whole harvest period for
decades in the past.
Meteorological data were provided by OMSZ (Hungarian
Meteorological Service) partly from public communications,
internet sites and from official data by contracting with OMSZ
Éghajlati Osztály (Hungarian Meteorological Service – Climate
Department). Data for the precipitation map – baseline period,
1961–90 – were obtained from the CRU CL 2.0 database (New
et al., 2002), which has 10’grid spatial resolution.
Szenteleki, K., Gaál, M., Mézes, Z., Soltész, M., Nyéki, J. & Szabó, Z.
0,00
0,10
0,20
0,30
0,40
0,50
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0,80
2001  2002  2003  2004  2005 2006  2007  2008 2009
 
0,00
0,10
0,20
0,30
0,40
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0,60
0,70
0,80
2001 2002 2003 2004 2005 2006 2007 2008 2009
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
2001 2002 2003 2004 2005 2006 2007 2008 2009
0,00
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0,80
2001 2002 2003 2004 2005 2006 2007 2008 2009
0,00
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0,80
2001 2002 2003 2004 2005 2006 2007 2008 2009
0,00
0,10
0,20
0,30
0,40
0,50
0,60
2001 2002 2003 2004 2005 2006 2007 2008 2009
Figure 1. Water supply of sweet cherry during the growing period (K1)
Zala county / Nagykanizsa Zala county / Zalaegerszeg
Gyôr county / Gyôr Veszprém county / Pápa
Somogy county / Siófok Vas county / Szombathely
41
Results
Rainfall with positive effect on sweet cherry quality
Research has been made in fourWest-Hungarian counties
(Gyôr-Moson-Sopron, Vas, Zala and Somogy) including five
weather stations. In the first step the cumulated weekly
precipitation was calculated from 15. April to every harvest
day. We made a weekly analysis according to the minimal
criterion of 10–15 mm rainfall and the average of these
frequency values were calculated for the whole period.
Satisfaction rate of this criterion is shown in Figure 1.
According to the graphs natural water supply of cherry is
uncertain during the period between flowering and harvest but
obviously the best in Zala county. The research has been made
for long time scale – 50 years for all the country but 100 years
for the centre region and Great Hungarian Plain. Situation is
more critical in Gyôr-Moson-Sopron county and Vas county
but the worst is in Somogy county (Siófok) and this is perhaps
the reason why the modern orchards can be made only with
irrigation system in this area (Bartholy J. et al. 2009).
Our results are not surprising if we make the precipitation
map of Hungary during April – June (Figure 2.).
Calculation and representation of the well balanced
precipitation index gives a good opportunity to compare
different production areas. The centre of the comparison was
Nagykanizsa, from it two groups have been created according
to the geographical position (1. north of Nagykanizsa; 2. east
of Nagykanizsa). We have a time scale for almost 60 years in
the past and according to the calculations Nagykanizsa has
the best precipitation index for sweet cherry. In the north
direction can be seen that the precipitation index has lower
value by the distance from Nagykanizsa (Figure 3).
The reason of the second direction (east-west) is to show
the differences across the country. Base is Nagykanizsa like
before and the border point is Újfehértó (Debrecen area). The
well balanced precipitation index is going down as the
distance is higher from Nagykanizsa, in the middle of the
country (Kecskemét) has the smallest value, but until the
eastern border the index value is raising again. A remarkable
result is that the precipitation index of Gyôr area is almost the
same like the value of mid Hungarian cities (Kecskemét
area). The precipitation map (Figure 2) shows the same result
but it should be emphasized that the precipitation index is a
better standard than the precipitation sum of a given period
(Soltész et al., 1999; Thurzó et al., 2007).
Rainfall with negative effect on sweet cherry quality
During ripening and harvest period rainfall can be a hazard
for the fruit quality. If the water supply of this period is too
high cracking damage occur, which disables the selling on
fresh market as a first class fruit. In
some extreme years the cracking
damage can be up to 80–100% making
the harvest less productive
(Christiensen, 1996; Csiszár, 2004).
Some experiments showed a good
result of spraying with pesticides
containing Ca (Simon, 2006; Simon et
al., 2007). On the other hand the
schedule of the harvest has an effect on
fruit quality, because right after rain
harvesting is not recommended as the
crack damage occurs in the
compartments with fungi on the top of
the fruit (Demirsoy et al., 1998; Simon
et al., 2008). If harvest is done by
machine than we recommend a 1–2
days brake after a significant rainfall
otherwise fruits falling down on the
receiving hood could have 30–40%
more cracking than a few days later
(Soltész et al., 2006; Simon, 2003).
According to the introduction of
the article the precipitation risk during
harvest period was calculated on a
daily basis but is more transparent to
publish on weekly terms for the period
last week of May – 10. July. Cracking
damage has a more direct and exact
effect on fruit quality than the
precipitation evenness during spring.
Correlation of precipitation distribution and quality sweet cherry production
Figure 2. Precipitation during April – June, 1961–1990 (source: CRU database)
0,00 0,10 0,20 0,30 0,40 0,50
Nagykanizsa
Zalaegerszeg
Szombathely
Pápa
Gyôr
0,00 0,10 0,20 0,30 0,40 0,50
Nagykanizsa
Keszthely
Siofok
Kecskemét
Debrecen
Figure 3. The well balanced precipitation indexes across the country, 1951–2009
North of Nagykanizsa East of Nagykanizsa
42
Amount of precipitation in the harvest period directly affects
the percentage of rain induced fruit cracking.
The developed risk factor of fruit crack damage has an
average value between 10–40% except some years with extreme
weather. Such a year was 2009 when our risk estimation in
Nagykanizsa gave a result of 48% of crack damage. A farm in
Nagykutas (Zala county) made a detailed statistic of the rain
induced fruit cracking damages, given in Table 1.
Average fruit cracking damage of the 13 varieties was
42%. There are two weather stations in the surroundings of the
Nagykutas farm: Nagykanizsa and Zalaegerszeg. The fruit
cracking risk was calculated for both weather stations. The
obtained results for the year 2009 are presented inFigure 5.Based
on the data from Zalaegerszeg we got 36% for risk of fruit
damage. The farm is near to these weather
stations so our estimation gave proper
result compared to the field experiment.
Our computer software is able to handle
varieties with different ripening time. In
the abovementioned estimation we did not
use this parameter in the software but if
there is a concrete farm with certain
variety structure the model can be more
exact. For example the graph below shows
the detailed calculation of the expected
fruit crack damage every week between
25. May – 10. July.
Figure 5. shows that the risk level of
cracking damage in Zalaegerszeg was
lower in each and everyweek of the harvest
period than in Nagykanizsa. Long-term
calculations based on 60 years historical
data gave the same result, there is
approximately 5% difference in the risk
level of the two stations. Of course there is
a probability of change because every year
is different but the aim of long term
historical data analysis is to get a realistic
risk assessment of the fruit production in an
area. It is recommended to make these
calculations for example before planting a
new orchard to choose between early, mid
and late varieties also based on the risk level
of the future production.
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Correlation of precipitation distribution and quality sweet cherry production


Correlation of precipitation distribution and quality sweet cherry production

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Correlation of precipitation distribution and quality sweet cherry production

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