IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Research article
erdApplication of Probabilistic Risk Assessment to the Chalky
Rice Grain Issue in JapanHIROKI ODA*
Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-city, Chiba, Japan
Email: 2215920513@edu.k.u-tokyo.ac.jp

MOMOKA MASANO
Shizuoka Prefectural Office, Shizuoka-city, Shizuoka, Japan

TAKASHI NOBUOKATokyo Electric Power Service Corporation, Koutou-ku, Tokyo, JapanKOSHI YOSHIDAThe University of Tokyo, Kashiwa-city, Chiba, Japan
Received 19 November 2023

Accepted 26 November 2023(*Corresponding Author)

Abstract In recent years, extreme weather events have become more frequent, and there areconcerns about the increase of chalky rice grains. Therefore, it is important to understand thefrequency and severity of high-temperature injuries to rice grains occurring nationwide inJapan. Thus, the objective of this research was to analyze the hazard of high temperature
through a probabilistic method using big weather data and to assess the risk of chalky ricegrain. In this research, we analyzed air temperature data from approximately 800 stations of
the AMeDAS (Automated Meteorological Data Acquisition System) in Japan and checked
the chronological changes. To assess the risk of chalky rice grain, the Probabilistic Risk
Assessment (PRA) method was applied and the risk for each of the 20 years from 1980 to1999 and from 2000 to 2019 was compared. Chalky rice grains, which reduce rice quality,
increase when the average temperature exceeds 26℃ during the first 20 days after the heading
date of rice. A comparison of the areas with air temperatures exceeding 26℃ at least once
per three years (33.3%) between the past and the recent 20 years shows that the risk of hightemperatures has increased in the inland areas of the southern Tohoku region and the southernpart of Ibaraki Prefecture. Subsequently, a multiple regression model was applied to identify
the factors affecting high temperature risk. The 20-day averaged daily mean temperature,
which has a probability of 33.3%, was set as the dependent variable, and longitude, altitude,
and urban area ratio were set as explanatory variables. As a result, regions located further
north experience greater temperature increases due to climate change, and regions with higher
urban rates also experience greater temperature increases due to anthropogenic effects.
Keywords climate change, climate hazard, agricultural damage, paddy rice quality

INTRODUCTION
In recent years, there have been many reports of high-temperature injury in rice (Terashima et al.,2001). The main symptoms are a decrease in grain filling rate and a decrease in grain weight per
grain (MAFF, 2006), which can reduce the inspection grade and yield, respectively, thereby
decreasing farmer income (Morita, 2008). The inspection grade is determined by the minimum grain
filling rate and the maximum proportion of rice classified as a chalky rice grain or other non-grain
filling categories (National Food Safety and Quality Association, 2002).
The reason CRGs appear "chalky" is due to empty spaces between starch granules in theendosperm, which cause light to scatter randomly (Tashiro and Ebata, 1975). In fact, in "chalky"areas caused by high temperatures, there have been observations of empty spaces betweenⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2amyloplasts, normally polyhedral amyloplasts that are round, and small amyloplasts that only containtwo or a few single-grain starches (Tashiro and Wardlaw, 1991; Zakaria et al., 2002).
Masutomi (2019) revealed that CRG begins to occur when the average temperature exceeds
26°C during the 20 days after rice heading. The study also presented the predicted spatial distributionof this damage under future climate conditions. However, in analyzing the risk of agricultural damage,it is important to consider not only the severity of the damage but also the probability (frequency) of
its occurrence.
OBJECTIVE
This study aims to use a probabilistic method and weather big data to analyze the risk of hightemperatures and assess the risk of CRGs in Japan.
METHODOLOGY
Probabilistic Risk Assessment
Probabilistic Risk Assessment (PRA) is a method of quantitatively evaluating risk using probability
theory and is commonly used in the safety assessment of structures. For example, the International
Atomic Energy Agency (IAEA) uses probabilistic risk assessment to evaluate the safety of nuclear
facilities. PRA involves integrating probability fields that represent the predicted seismic forces atthe construction site of the facility, represented as a hazard curve, with the load represented as a
fragility curve when specific seismic forces are applied, and evaluating the risk in terms of annual
damage probability (IAEA, 2010). In addition to structures, some studies quantitatively evaluate therisk that herbicides for rice fields pose to the surrounding ecosystem (Nagai et al., 2008). To assess
the risk of CRGs, we applied the PRA method and compared the risk for two 20-year periods: 19801999 and 2000-2019. CRGs, which can negatively impact the quality of rice, are more likely to occur
when the average temperature exceeds 26℃ during the first 20 days after the heading date of rice.
We utilized daily mean temperature data from Japan's Automated Meteorological Data
Acquisition System (AMeDAS), which consists of over 1600 observatories. However, due to theinclusion of recently established stations and the presence of missing values, we did not computedata for any station that had less than 90% data availability during the analysis period. After applying
this criterion, the number of stations eligible for our calculations was approximately 830. The dataof 20 days mean air temperature were extracted in descending order and the top five values per year
were selected for further analysis. Using Eq. (1), the annual exceedance probability was calculated
based on these values.
𝑃(𝑥) = 1 − 𝑒

𝑛
𝑇

(− )

(1)

where, x: 20 days mean air temperature (℃), T: observation period, n: The number of occurrencesof mean temperatures above x℃.
Multiple Regression Analysis
To identify the factors that have the most significant impact on the increase in temperature resulting
from global warming and anthropogenic effects, multiple regression analysis was conducted. The
dependent variable was the 20-day averaged daily mean temperature with an annual exceedance
probability of 33.3% (i.e., once in the 3-year probability of occurrence), while latitude and elevation
were chosen as explanatory variables due to their well-established negative correlation withtemperature. The reason for adopting a threshold of 33.3% can be exemplified by Sompo Japan
Thailand. This company has set the frequency of early drought coverage in its weather index
insurance for rice drought damage in Thailand to about once every three years. Additionally, we
included urban land use coverage as an explanatory variable due to its known role in raisingⒸ ISERD

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temperatures. For latitude and elevation, we used the coordinates and elevation data of AMeDAS
stations published by the Japan Meteorological Agency. For urban land use coverage, we utilized
land use data in 2010 from JAXA, calculating urban land use coverage within a buffer of 10 km indiameter centered on the AMeDAS station being examined following Fujibe (2011).
RESULTS AND DISCUSSION
To analyze the risk of CRGs, we calculated the annual probability of 20-day averaged daily mean
temperature exceeding 26°C for the recent 20 years (2000-2019) in Fig 1. The probability ofexceeding 26°C is low in Hokkaido or the northern part of Japan, where temperatures are relatively
low throughout the year, and in high-elevation areas (mountainous regions), indicating a low risk ofhigh-temperature injury. In the areas south and western parts of Japan, the annual probability ofexceeding 26°C frequently occurs, except in high-elevation areas.
Eligible stations: 830

Fig. 1 Probability of 20-day averaged daily mean temperature
exceedance by 26°C for the last 20 years (2000-2019)
To examine the changes in the probability of annual exceedance of 26°C for the 20-day averaged
daily mean temperature over the past 20 years and the recent 20 years, we calculated the differenceof temperature with a probability of 33.3% between the two periods. Yoshida et al. (2019) indicatedthat the most frequent climate hazard in Northeast Thailand occurs once every four years. The
concern of this research lies in high-frequency climate hazards that are not covered by insurance,
hence setting a threshold of 33.3%, which means a frequency of once every three years. To identify
locations with significant differences, we used color-coded observation points based on thesignificance of the difference. As shown in Fig. 2, locations with insufficient data are represented ingray, those with negative change are depicted in blue, locations with no change are represented ingreen, locations with a change greater than 0°C and less than 0.3 are depicted in yellow, and locations
with a change greater than 0.3°C are depicted in red. Notably, Fig. 2 has more 'No Data' points thanFig. 1. This is because each station needed to have over 90% data availability in both time periods.
This stricter rule reduced the number of sites with sufficient data, resulting in an increased numberof locations categorized as 'No Data'. With a few exceptions, most of the sites depicted in red arelocated slightly inland from the coast. This is likely due to the high heat capacity of the oceans incoastal areas, which makes it difficult for temperature changes to occur. There are many locations inthe southern part of the Ibaraki prefecture where the risk of high-temperature injury has increased,Ⓒ ISERD

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consistent with the findings of Masutomi et al. (2019), who observed a decreasing trend in the ratioof first-class rice in future climates.
Eligible stations: 799

Fig. 2 Difference in temperature for two periods with a yearlyexceedance probability of 33.3%
The data were organized for multiple regression analysis with the dependent variable being thedifference of past and recent 20-day averaged daily mean temperature which occurs in 33.3%
probability of the year and the explanatory variables being latitude, elevation, and urban land use
ratio within a 10 km diameter buffer centered on the AMeDAS observatory. In the analysis, points
with more than 90% of the observation period for substantial explanatory variables were extracted
and examined. Applying this rule reduced the sample size to 799. The explanatory variables werestandardized using the following Eq. (2), and multiple regression analysis was conducted using thestandardized variables.
𝑥′ =

𝑥−𝑥

(2)

𝑠

where, 𝑥: mean value in sample, s: standard deviation in sample.
The difference in temperature between two periods with a yearly exceedance probability of
33.3% was used as the dependent variable, and latitude and urbanization rate were used as
explanatory variables in a multiple regression analysis. Table 1 shows the coefficients and p-values
for the results. The coverage of urban land use within a 10 km buffer is represented by the percentage
of JAXA land use maps classified as "urban," so it is referred to as the "urban rate" in the table. From
this multiple regression analysis, it was found that there were statistically significant results forlatitude and urban land use coverage at the 1% level. The partial regression coefficients for the
explanatory variables were 0.124 for latitude and 0.279 for urban land use coverage. This indicatesthat regions located further north experience greater temperature increases due to climate change andthat regions with higher levels of urbanization also experience greater temperature increases due toanthropogenic effects. Among these, it was also shown that urbanization has a greater contribution
to temperature increases. Also, the analysis suggested that the impact of urbanization on temperaturerise is more pronounced in northern regions compared to southern regions. In Fig. 2, most points nearcities in northeastern regions are shown in red, while there are fewer red points near cities in southern
regions. This means that cities in different places are affected differently by urbanization when it
comes to temperature changes.
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Table 1 Results of the multiple regression analysis
Intercept
Elevation
Latitude
Urban rate

coefficients
0.000-0.0290.124
0.279

P-value
1.000
0.401
0.000
0.000

CONCLUSION
In this study, hazard curves were probabilistically created from nationwide AMeDAS data mainly
using temperature as a meteorological factor, and based on the results, the risk of chalky rice grain
was examined using a probabilistic risk assessment method. The 26°C yearly exceedance probabilityof the 20-day averaged daily mean temperature was calculated for the past 20 years (1980-1999) andthe recent 20 years (2000-2019) at all AMeDAS observatories in Japan, and the change in the two
periods was calculated to analyze the change of high-temperature risk. When the acceptablefrequency of damage for farmers was set to 33.3%, it was shown that the risk of high-temperaturedamage in inland areas of the southern Tohoku region and the southern area of Ibaraki Prefecture has
increased in the recent 20 years. The characteristics of points where the risk of high-temperaturedamage has greatly increased were inland areas slightly away from the coast. On the other hand, therisk of high-temperature damage decreased in the northern part of the Tohoku region, especiallysurrounding areas near the ocean. To quantitatively measure regional differences in temperature
increases due to climate change, multiple regression analysis was conducted with the change intemperature with a yearly exceedance probability of 33.3% as the dependent variable and latitude,
elevation, and urbanization rate as dependent variables. It was found that there were statisticallysignificant results for latitude and urban land use coverage at the 1% level. The partial regression
coefficients for the explanatory variables were 0.124 for latitude and 0.279 for urban land use
coverage. This shows that climate change affects the northern part more and might become
significant in the agricultural land located near the urban city area. In this study, we just used 26°C
Celsius as a threshold of CRG and 33.3% probability as a farmer's acceptable frequency level,
however further research is needed about the rice varieties or farmer's perception.
To tackle the issue of Chalky Rice Grains (CRGs), various strategies have been suggested.
Morita et al. (2015) recommended applying enough nitrogen during or after the panicle initiation
stage. Similarly, Chiba et al. (2013) suggested using deep-flood irrigation to control the temperature
at the surface of rice paddies. In addition to these immediate solutions, Masutomi (2019) proposed
the development and use of rice varieties that can better withstand high temperatures, as a long-term
approach. Although these methods have been effective in reducing CRGs, they often involve
significant costs. Therefore, it is important for future research to include detailed cost-benefit
analyses to identify the most cost-effective adaptation strategies.
ACKNOWLEDGEMENTS
This research was supported by the MEXT/JSPS Grant-in-Aid for Scientific Research (KAKENHI)
no.19H03069 and no.20KK0346.
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