Abstract
Several eco-physiological process-based crop models have been used in combination with climate models to predict agricultural yield to assess the impact of climate change. However, the quality degradation of rice caused by the influence of climate change is a prevailing problem. Although there is extensive elucidation of the mechanism of the occurrence of white immature grain because of high temperatures resulting in quality degradation, there are fewer studies that incorporate this into their prediction models. In this study, a statistical model to estimate the first-grade rice ratio was developed for three major rice cultivars in Japan. Parameters for a heat-dose index were estimated by employing the particle swarm optimization method and parameters for the statistical model were estimated with the maximum likelihood method. Parameters of the statistical model varied depending on the cultivar variety. It was observed that the statistical model showed varied prediction accuracy for the first-grade rice ratio based on the temperature that was incorporated into model, that is, daily mean, maximum, or minimum temperatures. Our result can generate more accurate predictions of the impact of climate change on rice production, incorporating the farmers’ choice of adaptation to climate change, including the shift in transplanting day.
| Original language | English |
|---|---|
| Pages (from-to) | 217-224 |
| Number of pages | 8 |
| Journal | journal of agricultural meteorology |
| Volume | 75 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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Keywords
- Climate change
- Cultivar
- Oryza sativa
- Statistical data
ASJC Scopus subject areas
- Agronomy and Crop Science
- Atmospheric Science
Cite this
Estimating first-grade rice production due to high temperature after heading date utilizing the statistical data. / Fukui, Shin; Nishihara, Yukinaga; Tamaki, Emi; Takahashi, Daisuke; Ishigooka, Yasushi; Yoshida, Ryuhei.
In: journal of agricultural meteorology, Vol. 75, No. 4, 01.01.2019, p. 217-224.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Estimating first-grade rice production due to high temperature after heading date utilizing the statistical data
AU - Fukui, Shin
AU - Nishihara, Yukinaga
AU - Tamaki, Emi
AU - Takahashi, Daisuke
AU - Ishigooka, Yasushi
AU - Yoshida, Ryuhei
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Several eco-physiological process-based crop models have been used in combination with climate models to predict agricultural yield to assess the impact of climate change. However, the quality degradation of rice caused by the influence of climate change is a prevailing problem. Although there is extensive elucidation of the mechanism of the occurrence of white immature grain because of high temperatures resulting in quality degradation, there are fewer studies that incorporate this into their prediction models. In this study, a statistical model to estimate the first-grade rice ratio was developed for three major rice cultivars in Japan. Parameters for a heat-dose index were estimated by employing the particle swarm optimization method and parameters for the statistical model were estimated with the maximum likelihood method. Parameters of the statistical model varied depending on the cultivar variety. It was observed that the statistical model showed varied prediction accuracy for the first-grade rice ratio based on the temperature that was incorporated into model, that is, daily mean, maximum, or minimum temperatures. Our result can generate more accurate predictions of the impact of climate change on rice production, incorporating the farmers’ choice of adaptation to climate change, including the shift in transplanting day.
AB - Several eco-physiological process-based crop models have been used in combination with climate models to predict agricultural yield to assess the impact of climate change. However, the quality degradation of rice caused by the influence of climate change is a prevailing problem. Although there is extensive elucidation of the mechanism of the occurrence of white immature grain because of high temperatures resulting in quality degradation, there are fewer studies that incorporate this into their prediction models. In this study, a statistical model to estimate the first-grade rice ratio was developed for three major rice cultivars in Japan. Parameters for a heat-dose index were estimated by employing the particle swarm optimization method and parameters for the statistical model were estimated with the maximum likelihood method. Parameters of the statistical model varied depending on the cultivar variety. It was observed that the statistical model showed varied prediction accuracy for the first-grade rice ratio based on the temperature that was incorporated into model, that is, daily mean, maximum, or minimum temperatures. Our result can generate more accurate predictions of the impact of climate change on rice production, incorporating the farmers’ choice of adaptation to climate change, including the shift in transplanting day.
KW - Climate change
KW - Cultivar
KW - Oryza sativa
KW - Statistical data
UR - http://www.scopus.com/inward/record.url?scp=85073568299&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073568299&partnerID=8YFLogxK
U2 - 10.2480/agrmet.D-18-00051
DO - 10.2480/agrmet.D-18-00051
M3 - Article
AN - SCOPUS:85073568299
VL - 75
SP - 217
EP - 224
JO - J. AGRICULTURAL METEOROLOGY
JF - J. AGRICULTURAL METEOROLOGY
SN - 0021-8588
IS - 4
ER -