Leaf Blast Rule Model by use of AMeDAS-based Mesh Data
and Micrometeorological Data
Masahiro Kikusawa 1), Tsugio Takagi 2), Tsuneo Hayashi 3) & MasamiYamada 4)
1) Fukui Prefectural University, Fukui 910-1195 Japan
2) Japan Weather Association, Hokuriku branch, Toyama 930-0892 Japan
3) Agricultural Technology and Management Division, Fukui prefecture , Fukui
4) Fukui Agricultural Experiment Station, Fukui 918-8215 Japan
ABSTRACT
Disease forecasting is essential in blast disease
management. BLASTAM is a computer code based
on a rule model. It supports a grower to make a
decision on rice leaf blast and has been introduced
into the extension service in some prefectures in
Japan. BLASTAM uses weather data such as rainfall,
air temperature, wind velocity, and sunshine from
AMeDAS provided by Japan Meteorological Agency.
Fukui prefecture has 12 AMeDAS stations that
covers 17km-mesh and has newly 1 km-mesh weather
data interpolated from AMeDAS data through a
variational method. In addition we have observed
the micrometeorological phenomena in five paddy
fields by weather robots (Campbell kits) for three
years. The main focus of this paper is on the use of
mesh data and robot data to BLASTAM. It is shown
that AMeDAS-based mesh data does not have
sufficient accuracy to light rainfall and sunshine rate
for BLASTAM use and we shall examine the way to
improve the mesh data to be utilized successfully for
blast forecasting.
Key words: rice leaf blast, rule model, BLASTAM,
AMeDAS, mesh weather data, micrometeorological
data, weather robot
INTRODUCTION
Rice blast has brought severe damage in Japan. How
easily a rice leaf infects blast depends on how long
the leaf is wet and how much the temperature is
during wet period. In Japan BLASTAM1 based on a
Figure 1 AMeDAS and robots installed
in Fukui prefecture.

E-mail : mas@kiku.net
910-8580 Japan
rule model with AMeDAS data proposed by
Koshimizu (1988) have been utilized as a powerful
tool to forecast the dates suitable for infection.
BLASTAM would be effectively applied with mesh
weather data in the sense that we can recognize how
large the blast infected area is and how rapidly it is
growing. Mesh weather can also forecast future
weather in a few days, which would upgrade the blast
forecasting. As AMeDAS data have served against
disasters6, it may not measure light rain and sunshine,
and so are mesh data. We herein apply
AMeDAS-based mesh data and micrometeorological
data as input data in BLASTAM, and examine how
the mesh data are successfully utilized in forecasting
leaf blast.
METHODS
We have 12 AMeDAS stations6 and five weather
robots7 in Fukui prefecture (4189 km2). Those are
located as shown in Figure 1. In addition we have 1
km-mesh weather data8 based on AMeDAS. Each
robot measures wind velocity and direction, air, water
and soil temperature, vapor pressure, humidity,
rainfall and solar radiation but it does not measure
sunshine except one robot. On the other hand
AMeDAS generally serves not solar radiation but
sunshine rate. BLASTAM calculates wet period2, 3 by
means of rainfall, wind velocity and sunshine. A leaf
starts to be wet after rainfall under small sunshine
and wind. When sunshine rate and wind velocity are
bigger than a certain threshold, the leaf is no more to
be wet. Kobayashi (1984) proposed solar radiation
0.856 MJ/m2 and sunshine duration 120minutes for a
half day as a threshold to wet off. Those equal to 0.14
MJ/m2 and 20minutes (0.33 sunshine rate) every hour.
Sunshine rate 0.2 has been used in BLASTAM3 as a
threshold that was proposed by Koshimizu (1988).
First, we determine a solar radiation threshold
through BLASTAM simulation with robot data
assuming some threshold values. Secondly, we
examine how different the forecasting is whether we
use sunshine or solar radiation. Finally, we examine
hourly rainfall data during wet period and show
clearly why mesh data result in different forecasting
from that robot data does. Hereafter, “mesh” and
mesh data and micrometeorological data are used as
input data in BLASTAM.
Table 1 Suitable and semisuitable (level4) dates simulated by use of robot data (1999)
for some solar radiation thresholds r 1s.
(a) r 1>1.5MJ/m 2, (b) r 1=0.1MJ/m 2, (c) r 1=0.05MJ/m 2, and (d) r 1=0.01MJ/m 2.
Fukui dat e ( a) ( b) ( c) ( d)
June 16 ● ● ● ●
18 ●
19 ● 〇 〇 ●
20 ● ● ●
28 ● ● ● 〇
〇
July
4
13 ●
14 ● ●
15 ● ● ●
18 ●
19 ● ● ● ●
“robot”
will be referred to when
Ono dat e ( a) ( b) ( c) ( d)
●
June 7
16 ● ● ● ●
18 ●
19 ●
20 ● ● 〇 〇
25 ●
28 ● 〇 〇 〇
〇
July 1
〇
〇
〇
6
13 〇 〇
14 ● ● ● ●
15 ● ● ● ●
18 ●
19 ● ● ● 〇
Mikata dat e ( a) ( b) ( c) ( d)
June 17 ● ● ● ●
19 ● ● ●
20 ● ● ● 〇
28 ● ● ● 〇
●
●
●
〇
July
1
●
2
13 ● ● ● ●
14 ● ● ● 〇
18 ● ● ● ●
19 ● ● ● ●
AMeDAS-based
RESULTS and DISCUSSIONS
Solar radiation threshold
We need specify two threshold r1 and r2 of solar
radiation, which correspond to the sunshine rate
thresholds 0.1 and 0.22,3. We simulate dates suitable
for blast infection for cases of five sites in 1998 &
1999 and for several r1’s assuming r2= 2 r1. Table 1
marks the date suitable for blast infection4 as ● and
semi-suitable (level 4) date5 as ○ for the results of
three sites in 1999 and four r1’s.
It is pointed out that the smaller r1 is, the less
number of suitable days is forecasted in all sites.
Because the more solar radiation that interrupt
wetting appears, the lower threshold r1 is. The r1 has
a significant effect on a date whether it is suitable or
semi-suitable for infecting blast. It sometimes delays
the suitable date by one day.
We have a robot that has both pyranometer and
sunshine recorder. Comparing both sunshine and
solar radiation every minute, we surmise the r1 ranges
from 0.1 to 0.12 MJ/m2. According to Kobayashi
(1984) who proposed 0.14 as r2 that simply leads to
r1=0.07 MJ/m2 as a half of r2. There is not a
remark-able difference in Table 1 between the two
cases of r1=0.1 MJ/m2 and r1=0.05 MJ/m2. With
additional discussion10 we shall propose and use
r1=0.1 MJ/m2 and r2=0.2 MJ/m2 in the following
analysis.
Sunshine rate and solar radiation
Table 2 shows suitable and semi-suitable (level 4)
dates for three sites in 1999 and for four cases where
(a) uses AMeDAS-based mesh data and (d) uses
robot data. The cases (b) and (c) use mesh data
(temperature, wind velocity and rain) and robot data
(solar radiation) for two r1’s.
In Table 2, case (a) results in almost the same one
as (b) but both are different from (c) and (d). The
sunshine rate is generally 0.09 under cloudy and rainy
weather when the blast infection is likely to occur.
And even under such weak sunshine the robot
measures some solar radiation. As it is usually
smaller than 1.5 MJ/m2, any solar radiation to
Table 2 Forecast by mesh and robot (1999).
(a) mesh (sunshine) (b) mesh (radiation, r 1>1.5MJ )
(c) mesh (radiation, r 1=0.1MJ ) (d) robot (r 1=0.1MJ )
Fukui dat e
June
16
19
20
28
July
14
15
19
( a)
Ono
June
( b)
( c)
( d)
●
〇
●
●
●
●
●
●
●
●
●
●
●
●
●
●
dat e
16
18
19
20
27
28
1
6
13
14
15
18
19
20
( a)
( b)
( c)
( d)
●
●
●
●
〇
●
●
●
●
●
●
●
〇
〇
Mikata dat e
June
17
19
20
25
28
July
1
13
14
18
19
July
〇
〇
●
〇
〇
●
●
●
●
●
●
●
●
●
●
( a)
( b)
●
●
( c)
( d)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
interrupt wetting can not be taken into account in
case (b) just like in case (a) that is no sunshine.
sunshine rate as a wetting off rule3 so long as we use
AMeDAS or AMeDAS-based mesh data which
Figure 2 Hourly rainfall properties between mesh and robot (1999)
（ b） Fukui
（ a） Fukui
time mesh robot date time mesh robot
date
7/18
7/19
1900
2000
2100
2200
2300
2400
100
200
300
400
500
600
700
Suitable condition
Wet period(hour)
Average rainfall(mm)
Average wind speed(m/s)
Average temp.(degree C)
Average temp. pre 5days
(c) Ono
date time
7/13 1800
1900
2000
2100
2200
2300
2400
7/14 100
200
300
400
500
600
700
Suitable condition
Wet period(hour)
Average rainfall(mm)
Average wind speed(m/s)
Average temp.(degree C)
Average temp. pre 5days
It dose not make any sense to
suggest that wetting off rule should be
accounted for by solar radiation instead
of sunshine rate which has been used.
We notice that there is a remarkable
difference between case (c) of mesh
and (d) of robot in Table 2. Both cases
use same solar radiation, and
0.0
1.1
2.4
0.0
2.0
0.5
1.0
0.6
1.9
0.0
0.0
0.0
0.0
0.0
0.8
1.6
0.2
2.2
0.8
0.3
1.0
1.3
0.0
0.0
0.0
0.0
●
●
13
0. 73
1. 68
22. 1
23. 1
13
0. 63
0. 38
21. 8
23. 1
6/27 1900
2000
2100
2200
2300
2400
6/28 100
200
300
400
500
600
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.4
0.3
0.2
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0
0. 00
1. 32
19. 7
22. 0
12
0. 12
0. 15
19. 4
22. 0
●
(d) Ono
mesh robot date time mesh robot
0.7
0.0
0.0 6/30 2200 1.0
0.5
2300 0.7
0.0
0.1
2.4
2400 3.8
0.7
1.1
3.0
7/1 100 2.7
0.0
0.2
2.1
200 2.9
0.0
0.1
1.5
300 2.0
0.0
0.3
1.1
400 1.0
0.0
0.2
1.9
500 2.0
0.0
0.1
0.0
600 0.0
0.0
0.1
0.1
0.0
700
0.0
0.1
0.1
800 0.0
0.7
0.5
0.2
900 0.0
0.0
0.3
1000 0.0
0.0
0.0
0.0
0.0
1.1
●
●
○
13
0. 11
0. 27
21. 2
22. 5
14
0. 30
0. 61
21. 2
22. 2
10
1. 61
0. 97
18. 2
21. 6
12
1. 08
0. 65
18. 4
21. 6
employ the
measures the rainfall greater than 0.56. Thus we shall
tempera-ture does not deal with the difference so long as suitable and
semi-suitable condition higher than level 4 are concerned. Thus wind
and rainfall must cause the difference between (c) and (d) in Table 2.
We shall check it up in the following section.
Rainfall characteristics on blast suitable dates
Figure 2 illustrates hourly rainfall and some average values during wet
period that are shaded in the figure. Case (a) is a typical case when the
rain greater than 0.5mm in the evening period long enough to bring a suitable condition since AMeDAS can
brings a suitable condition both in not miss the rainfall greater than 0.5mm. Final case of (d) is rather rare
mesh and robot forecasting. Case (b) is in the sense that robot misses a suitable condition while mesh does not.
the case when a light rain under weak This is the result some conditions are jumbled up. The wet period
wind and sunshine brings a blast begins from 23:00 in robot case, which is one hour late due to a heavy
suitable condi-tion in robot forecasting rain of 5.3mm while 3.1mm rain is recorded in mesh. Thus the wet
but not in mesh one. This is because no period is calculated as 10 hours in mesh and 9 hours in robot. But why
rain is recorded in mesh data as seen in is 9 hours in the robot? The solar radiation greater than 0.1MJ/m2
the figure. Case (c) is almost the same breaks off the wet period although a light rain
case as (b) but a 0.7mm rain makes wet
continues to fall after 7:00. You can find out a
pp.123-138.
semi-suitable mark ○ on July 1st of Ono site in Table
Ishiguro, K. 1998. Recent advances in forecasting of
1(a) in which the threshold is greater than 1.5MJ/m2.
rice blast disease. Journal of the Japanese
In Figure 2, Average temperature9 in mesh agrees
Agricultural Systems Society. 14(2) pp.111-118.
remarkably with that in robot. This means mesh data
Kobayashi, J. 1984. Studies on epidemic of rice leaf
can be well utilized as input data of BLASTAM once
blast, Pyricularia oryzae Cav., in its early stage.
wet period is calculated correctly. Average wind
Bulletin of the Akita Agricultural Experiment
velocity may not be well interpolated in mesh data 9.
Station. No.26 pp.1-84.
We can find the wind error happens at the level
Koshimizu,Y. 1988. A forecasting method for
smaller than 1m/s and it does not give a significant
occur-rence of rice leaf blast with AMeDAS data.
misforecasting because wind velocity threshold
Bulletin of the Tohoku National Agricultural
defined in wetting off rule3 is greater than the
Experiment Station. No.78 pp.67-121.
accuracy of its measurement.
Ohata,K. 1989. Rice diseases in Japan. Zenkoku
Noson Kyoiku Kyokai Publishing Co. Ltd. p.565.
CONCLUSIONS
Takagi,T. and N.Takenaka. 1999. The 1 km-mesh
(1) We throw out a suggestion that a threshold
information services in agricultural weather.
0.1MJ/m2 of solar radiation may be used in
Bulletin
of
the
Japanese
agricultural
BLASTAM corresponding to a threshold 0.1 of
Meteorolo-gical Society (Hokuriku branch)
sunshine rate.
No.24. pp.3-5.
(2) Sunshine rate is measured as 0.0 by AMeDAS
during wet period when it is usually cloudy and
NOTES
rainy, and so is mesh data. Thus BLASTAM
analysis that uses mesh data forecasts blast
1 Leaf blast infection and BLASTAM
suitable days more than they are. So we shall
Rice blast fungus (Pyricularia oryzae) is germinated
propose to employ solar radiation instead of
in a water drop on a leaf surface and penetrates into
sunshine rate as wetting off rule.
leaf surface cells. Then a lesion is appeared and a
(3) BLASTAM analysis that uses mesh data forecasts
spore is formed on the leaf. The disease is infected to
blast suitable days less than they are because
other rice plant by scattered spore and its penetration
AMeDAS misses light rainfall less than 0.5mm
to leaf cells (Ishiguro 1998). An incubation period
and so is AMeDAS-based mesh data.
after infection is longer with low temperature, and
(4) Wind velocity in mesh is not well interpolated in
shorter with high temperature. Normally, the
small range but it does not matter because the
incubation period is nearly 5 days (Ohata 1989).
wind threshold in wetting off rule is big enough
From these facts, it is necessary to know a wet
to forgive the error.
condition and temperature of a rice plant leaf in order
(5) Temperature is quite well interpolated in mesh
to forecast a development of the blast disease. It is
which means mesh data is used effectively in
not easy to measure directly the moisture condition of
BLASTAM once we correctly estimate wet
a leaf surface. Kobayashi (1984) suggested a new
period.
method to judge an infection suitable level by an
(6) Mesh data will be successfully utilized in
estimation of wetting period of leaf surface using
BLASTAM with solar radiation and with tuning
precipitation, wind velocity, sunshine rate and
up its precipitation accuracy in small range by
temperature condition. On the basis of Kobayashi's
such data that are measured by radar device.
concept, Koshimizu (1988) developed a forecasting
system named BLASTAM that uses AMeDAS and
REFERENCES
Hayashi & Koshimizu(1988) coded it for computer
Hayashi, T. and Y. Koshimizu. 1988. Development of
use. BLASTAM was adapted in Tohoku district at
computer code BLASTAM to forecast leaf blast
first, but now it is also adapted in other areas. For
occurrence. Bulletin of the Tohoku National
example, JPP network of the Plant Protection Society
Agricultural
Experiment
Station.
No.78
provides real-time result of BLASTAM at the
national AMeDAS observation points through the
Internet.
2 Wetting rule
Basically wetting calculation begins from 16:00
every day. When it rains between 16:00 and 6:00 next
morning, wetting begins and keeps on from one hour
before a rainfall until wetting off occurs3. If the
wetting continues over 7:00 next morning, we stop
calculating wet period once the suitable condition4 is
satisfied and start the calculation for the next day.
Wetting does not begin due to a rain with the wind
greater than 4 m/s. When it rains between 6:00 and
15:00 under sunshine rate less than 0.1 and wind less
than 3 m/s, three hours around the rainfall are
involved in the wetting period. And also one hour
between the two wetting periods is supposed to be
wet under the condition of small sunshine (<0.1) and
wind (<3m/s).
3 Wetting off rule
The wetting from 15:00 to 7:00 breaks off when
sunshine rate adds up to 0.2 since a wetting began.
The sunshine rate of 0.1 with rain is supposed to be
zero. The wetting from 16:00 to 4:00 breaks off when
the wind greater than 4 m/s blows. It also breaks off
at two hours after the wind greater than 3 m/s in
3-hour average blows. The wetting from 4:00 to 7:00
breaks off by the wind greater than 3m/s that is
regarded as 2 m/s if it blows with some rain. If there
is a strong rain greater than 4 mm/h or the 3mm/h-rain
that continues for two hours, nine hours ahead and
behind the onset of the rainfall during the wetting
period is invalidated because no infections are
expected.
4 Suitable condition to infect blast
When wetting period is longer than 10 hours under
the following three conditions being satisfied, we
consider the day is suitable for blast infection.
Condition 1: Average temperature during wetting
period is 15-25deg.
Condition 2 : Wetting period is long enough to keep
the blast fungus penetrating into the leaf at a fixed
rate. The period ranges from 10 to 17 hours
depending on the mean temperature during wetting
period as follows:17hrs(15deg.), 15hrs(16deg.),
4hrs(17deg.), 13hrs(18deg.), 2hrs(19deg.), 11hrs
(20-21deg.) and 10hrs (22-25deg.)
Condition 3: Average temperature during five days
before wetting is 20-25deg.
5 Semi-suitable condition to infect blast
When wet period is longer than 10 hours but all the
three conditions mentioned above are not satisfied,
we call the day as semi-suitable day and classifies it
by four levels as follows: under out of conditions 1,2
and 3, level 1 and level 2 are referred to the case that
the temperature is lower and higher than the range on
condition 3, respectively. Level 3 is out of conditions
1 and 2 and level 4 is just out of condition 2.
6 AMeDAS data
JMA (Japan Meteorological Agency ） operates a
mesoscale observation network called AMeDAS
(Automated Meteorological Data Acquisition
System). This system consists of about 1,300
observing stations at mean spatial intervals of 17 km;
All
stations
monitor
hourly
precipitation
automa-tically, and more than 800 stations among
these also monitor air temperature, wind direction &
velocity, and sunshine duration. This system was
installed in 1974 and has been in operation of
monitoring local heavy rains and storms. Main
purpose of the system is disaster prevention, so the
instruments are designed to measure accurately heavy
rain or strong wind. Rain gauge is tipping bucket type
and the minimum observing unit is 0.5mm/h. So it is
difficult to measure correctly a light rain.
Anemometer is aero-vane type and rotating
pyrheliometer is used for sunshine duration.
7 Robot data
Campbell kid is installed. It has Young cup type
rotation anemometer, temperature probe (accuracy of
0.2 deg.), tipping bucket rain gauge (accuracy of 0.1
mm），pyranometer (it responses to 400-1100 nm
wave with the accuracy of 200 W/m2 and hourly
global radiation greater than 0.0001 MJ/m2. One kit
in Fukui has Yokokawa’s sunshine recorder.
8 Mesh data (Takagi, 1999)
The 1 km-mesh weather data (temperature,
precipita-tion, wind velocity/direction and sunshine
duration) are served from current state up to 51 hours
later and those are updated twice a day. Data are
interpolated by JMA GPV (grid point of value; its
horizontal resolution is 20 km) and is calibrated
hourly by AMeDAS data.
(1) The 1 km-mesh horizontal grid point data
Variational method is adopted for spatial
interpola-tion to make 1 km-mesh data from 20 km
grid data (GPV). As for air temperature, 20 km mesh
data is at first converted to the value at sea-level by
lapse rate (0.5deg./100m). And then 1 km-mesh data
is inter-polated by using variational method and the
data is restored to the original height above sea level.
As for precipitation and sunshine, every 20km-mesh
data are interpolated by using variational method.
Wind data is decomposed into vector components,
and is inter-polated and restored to wind direction &
velocity.
(2) Calibration by AMeDAS
As for air temperature, a difference between
AMeDAS data (observed) and mesh data (calculated
and pre-corrected) is determined as a correcting
parameter. The parameter weighted in proportion to
distance is added to mesh data. For precipitation,
sunshine duration, wind direction & wind velocity,
wind data is at first decomposed into vector
components. The correcting parameter is derived as
the ratio of mesh data to AMeDAS data. It is
weighted in the same way as temperature and then it
multiplies mesh data.
9 Comments on mesh data (Takagi, 1999)
Air temperature on the mesh is accurately
calculated by the interpolation, which consider its
variation due to the elevation and local characteristics
of the air mass. It is difficult to estimate rainfall
accurately because we need more rain gauges and the
rain gauge of AMeDAS is not suitable to measure
light rain less than 0.5mm. We have problem in
sunshine duration data. The sunshine duration is
counted due to the direct radiation, leaving scattered
radiation out of account. Thus it is mostly zero when
sky is overcast and direct radiation is not perceived.
Wind data in the present system are not accurate
either because topographical effect is not taken into
account and less number of observation points for the
wind varies much locally site by site when it rains.
10 Solar radiation threshold r1
Sunshine duration is counted as long as direct solar
radiation is greater than 120W/s. So the direct solar
radiation in an hour is at least 0.432 MJ/m2
(=120*3600*10-6) when sunshine duration is 60
minutes or sunshine rate is 1.0. From the argument
the solar radiation threshold r1 that corresponds to
sunshine rate threshold 0.1 equals to 0.0432 MJ/m2.
The pyranometer of robot measures global radiation.
The direct radiation is a part of global radiation and
its ratio may be small during cloudy evening and
morning. From these grounds the global radiation
threshold r1 is likely to be 0.1 MJ /m2.