Development of grid square air temperature and precipitation data compiled from observed, forecasted, and climatic normal data

Ohno, Hiroyuki; Sasaki, Kaori; Ohara, Genji; Nakazono, Kou

doi:10.2480/cib.j-16-028

Cited by 146 publications

(80 citation statements)

References 5 publications

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“…The results of the estimations are shown by decades (2010s: 2011-2020; 2020 s: 2021-2030; 2030 s: 2031-2040; 2040 s: 2041-2050). For climate data from 2011 to 2015, we used the Mesh Agro-Meteorology Data (Mesh AM [26]), which was developed from observations at meteorological sites across Japan, and from 2016 to 2050, we used NIAESv2.7r [27], which is bias-corrected and 1 km-downscaled climate data from General Circulation Models (GCMs). To account for uncertainty in future climate projections, we used 10 future climate scenarios from 2 RCPs (RCP2.6 and RCP8.5) and 5 GCMs (CSIRO-Mk3-6-0, MIROC5, MRI-CGCM3, HadGEM2-ES, GFDL-CM3).…”

Section: Assessment Frameworkmentioning

confidence: 99%

“…The optimal values for the parameter T crit and k T were estimated from the observed data on the occurrence of CRG and the flowering date at four sites (six treatments) in Japan, and data on daily air temperature at the sites from Mesh AM [26]. The simplex method for optimization was used, as in Masutomi et al (2015) [13].…”

Section: Model Calibration and Validationmentioning

confidence: 99%

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Rice grain quality degradation and economic loss due to global warming in Japan

Masutomi

Takimoto

Shimamura

et al. 2019

Environ. Res. Commun.

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Global warming is predicted to have significant impacts not only on crop productivity but also on crop quality, leading to economic loss. In the present study, focusing on grain quality of rice in Japan, we assessed the impact of global warming on the occurrence of low quality rice grain called 'chalky rice grain', and the incurred economic loss. Using 10 future climate scenarios and a simulation model to estimate the occurrence of chalky rice grain, we showed that global warming will double the occurrence of chalky rice grain by the 2040 s under RCP8.5. This corresponds to one third of paddy rice fields in Japan showing a decrease in grain grade. This would produce an annual economic loss of 401.4 million US$/yr under RCP8.5 in the 2040 s. These results suggest that the development and implementation of national and local adaptation strategies are urgently needed for rice production in Japan.

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Section: Assessment Frameworkmentioning

confidence: 99%

Section: Model Calibration and Validationmentioning

confidence: 99%

Rice grain quality degradation and economic loss due to global warming in Japan

Masutomi

Takimoto

Shimamura

et al. 2019

Environ. Res. Commun.

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“…NARO developed a daily Agro-meteorological Grid Square Data system (AmGSD) [1]. AmGSD seamlessly combines three different data produced by the Japan Meteorological Agency (JMA), including data acquires from the Automated Meteorological Data Acquisition System (AMeDAS), nine-day numerical weather prediction and daily climatic normal value [6]. AmGSD provides meteorological dataset at approximately 1-km resolution from 1980 to the present, and new weather data are updated every day.…”

Section: Data Sourcementioning

confidence: 99%

Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications

Chinnachodteeranun

Honda

2016

Future Internet

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We developed a mechanism for seamlessly providing weather data and long-term historical climate data from a gridded data source through an international standard web API, which was the Sensor Observation Service (SOS) defined by the Open Geospatial Consortium (OGC). The National Agriculture and Food Research Organization (NARO) Japan has been providing gridded climate data consisting of nine daily meteorological variables, which are average, minimum, maximum of air temperature, relative humidity, sunshine duration, solar radiant exposure, downward longwave radiation, precipitation and wind speed for 35 years covering Japan. The gridded data structure is quite useful for spatial analysis, such as developing crop suitability maps and monitoring regional crop development. Individual farmers, however, make decisions using historical climate information and forecasts for an incoming cropping season of their farms. In this regard, climate data at a point-based structure are convenient for application development to support farmers' decisions. Through the proposed mechanism in this paper, the agricultural applications and analysis can request point-based climate data from a gridded data source through the standard API with no need to deal with the complicated hierarchical data structure of the gridded climate data source. Clients can easily obtain data and metadata by only accessing the service endpoint. The mechanism also provides several web bindings and data encodings for the clients' convenience. Caching, including the pre-caching mechanism, was developed and evaluated to secure an effective response time. The mechanism enhances the accessibility and usability of the gridded weather data source, as well as SOS API for agricultural applications.

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“…Morita 2008 reveals that white immature grain was observed when the daily mean temperature, averaged over the 20 days after the heading date, exceeded 27˚C. Nagahata et al 2006 report that the HD with a threshold temperature of 26˚C explained the generation of white immature grain in the growth chamber experiment with a japonica rice cultivar Koshihikari by manipulating the exposure period under high-temperatures 33˚C during day time and 26˚C during night time . Ishigooka et al 2017 utilize this HD index for the simulation of a crop model by fixing the exposure period to 20 days.…”

Section: Introductionmentioning

confidence: 99%

Estimating first-grade rice production due to high temperature after heading date utilizing the statistical data

Fukui

Nishihara

Tamaki

et al. 2019

J. Agric. Meteorol.

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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.

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Development of grid square air temperature and precipitation data compiled from observed, forecasted, and climatic normal data

Cited by 146 publications

References 5 publications

Rice grain quality degradation and economic loss due to global warming in Japan

Rice grain quality degradation and economic loss due to global warming in Japan

Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications

Estimating first-grade rice production due to high temperature after heading date utilizing the statistical data

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