M
Motoki Nishimori
Researcher at National Agriculture and Food Research Organization
Publications - 57
Citations - 1476
Motoki Nishimori is an academic researcher from National Agriculture and Food Research Organization. The author has contributed to research in topics: Climate change & Climate model. The author has an hindex of 17, co-authored 56 publications receiving 1112 citations.
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Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach
TL;DR: A Bayesian approach, the Markov Chain Monte Carlo (MCMC) technique, was applied to a newly developed large-scale crop model for paddy rice to optimize a new set of regional-specific parameters and quantify the uncertainty of yield estimation associated with model parameters as mentioned in this paper.
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Responses of crop yield growth to global temperature and socioeconomic changes.
Toshichika Iizumi,Jun Furuya,Zhihong Shen,Wonsik Kim,Masashi Okada,Shinichiro Fujimori,Tomoko Hasegawa,Motoki Nishimori +7 more
TL;DR: The results revealed that global mean yields of maize and soybean will stagnate with warming even when agronomic adjustments are considered, and this trend is consistent across socioeconomic assumptions.
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Global Patterns of Crop Production Losses Associated with Droughts from 1983 to 2009
TL;DR: In this paper, the global geographic pattern of drought-driven red-red color changes is analyzed. And the authors highlight the importance of climate extreme that reduces crop production and food security.
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How to analyze long-term insect population dynamics under climate change: 50-year data of three insect pests in paddy fields
TL;DR: The state-space model selected by Akaike’s information criterion indicates that the observed number of light-trap catches of C. suppressalis and N. cincticeps in summer increases with increasing temperatures in the previous winter, and the influence of temperature is not carried over to the next year.
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Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes
TL;DR: In this paper, the authors compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021-2060) and distant future (2061-2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias-correction methods, and three reference forcing data sets.