YOKOSAWA, Masayuki



Faculty of Human Sciences, School of Human Sciences

Job title




・環境摂動に対する不確実性を考慮した生態系の resilience と vulnerability の評価

Concurrent Post 【 display / non-display

  • Faculty of Human Sciences   School of Human Sciences (Online Degree Program)

  • Faculty of Human Sciences   Graduate School of Human Sciences

Education 【 display / non-display


    Tokyo Institute of Technology   Science of Engineering  


    Tokyo Institute of Technology   School of Science   Dept. of Physics  

Degree 【 display / non-display

  • Tokyo Institute of Technology   Master of Science

  • The University of Tokyo   PhD

Research Experience 【 display / non-display

  • 2013

    Shizuoka Univeristy   Department of Engineering   Professor

  • 1987

    National Institute of Agro-Environmental Sciences   Researcher


    Waseda University Faculty of Human Sciences   Professor

Professional Memberships 【 display / non-display


    American Geophysical Union


    The Physical Society of Japan


    The Society of Agricultural Meteorology of Japan


    The Japanese Society for Artificial Intelligence


    Ecological Society of Japan


Research Areas 【 display / non-display

  • Agricultural environmental engineering and agricultural information engineering

  • Agricultural environmental engineering and agricultural information engineering

  • Ecology and environment

  • Environmental load and risk assessment

  • Environmental impact assessment

Research Interests 【 display / non-display

  • spatial pattern

  • size structure

  • biogeochemistry

  • modeling

  • climate change

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Papers 【 display / non-display

  • A Bayesian inversion framework to evaluate parameter and predictive inference of a simple soil respiration model in a cool-temperate forest in western Japan

    Motomu Toda, Kazuki Doi, Masae I. Ishihara, Wakana A. Azuma, Masayuki Yokozawa

    ECOLOGICAL MODELLING   418   108918  2020.02  [Refereed]

     View Summary

    Careful modelling of soil carbon sequestration is essential to evaluate future terrestrial feedback to the earth climate system through atmosphere-surface carbon exchange. Few studies have evaluated, in bio- and geo-applications, parameter and predictive uncertainty of soil respiration models by considering the difference between observations and model predictions; i.e. residual error, which is assumed neither to be independent nor to be described by a normal (i.e. Gaussian) probability distribution with a mean of zero and constant variance. In this paper, we use 2-year observations of soil carbon flux from 2017 to 2018 (hereafter referred to as 'long-term simulation') obtained with two open-top chambers to estimate parameter and predictive uncertainty of a simple soil respiration model based on Bayesian statistics in a cool-temperate forest in western Japan. We also use a Gaussian innovative residual error model in which a generalised likelihood uncertainty estimation that accounts for correlated, heteroscedastic, non-normally distributed (i.e. non-Gaussian) residual error flexibly handles statistics varying in skewness and kurtosis. Results show that the effects of correlation and heteroscedasticity were eliminated adequately. Additionally, the posterior distribution of the residuals had a pattern intermediate to those of Gaussian and Laplacian (or double-exponential) distributions. Consequently, the predicted soil respiration rate, and range of uncertainty therein, well-matched the observational data. Furthermore, we compare results of parameter and predictive inference of the soil respiration model from the long-term simulation with those constrained of short-term simulations (i.e. 4-month subsets of the 2-year dataset) to determine the extent to which the approach used affects the estimation of parameter and predictive uncertainty. No significant difference in parameter estimates was found between the long-term simulation versus any of the short-term simulations, whereas short-term simulation analysis of the uncertainty at 50 %-i.e. between the lower (25 %) and upper (75 %) quartiles of the probability range-indicated distinctive variations in model parameters in summer when more vigorous activity of trees and organisms promotes carbon cycling between the atmosphere and ecosystem. Overall we demonstrate that the Bayesian inversion approach is useful as a means by which to evaluate effectively parameter and predictive uncertainty of a soil respiration model with precise representation of residual errors.


  • Dynamic measurements of earthworm respiration

    Yonemura Seiichiro, Kaneda Satoshi, Kodama Naomi, Sakurai Gen, Yokozawa Masayuki

    Journal of Agricultural Meteorology   75   103 - 110  2019.03  [Refereed]

  • Effectively tuning plant growth models with different spatial complexity: A statistical perspective

    Yoshiaki Nakagawa, Masayuki Yokozawa, Akihiko Ito, Toshihiko Hara

    ECOLOGICAL MODELLING   361   95 - 112  2017.10  [Refereed]

     View Summary

    Forest gap models (non-spatial, patch- and individual-based models) and size structure models (non spatial stand models) rely on two assumptions: the mean field assumption (A-I) and the assumption that plants in one patch do not compete with plants in other patches (A-II). These assumptions lead to differences in plant size dynamics between these models and spatially explicit models (or observations of real forests). Therefore, to more accurately replicate dynamics, these models require model tuning by (1) adjusting model parameter values or (2) introducing a correction term into models. However, these model tuning methods have not been systematically and statistically investigated in models using different patch sizes.
    We used a simple spatially explicit model that simulated growth and competition processes, and rewrote it as patch models. The patch sizes of the patch models were set between 4 and 1500 m(2). First, we estimated the parameter values (the intrinsic growth rate, metabolic loss, competition coefficient, and competitive asymmetry) of these models that best reproduce plant size growth under competition using field data from a Sakhalin fir stand, and compared the parameter values among the models. Second, we introduced correction terms into the patch models and estimated the optimal correction term for reproducing plant size growth under competition using the field data.
    The estimated parameter values of the patch models for all patch sizes differed greatly from those of the spatially explicit models. Therefore, parameter values should not be shared between spatially explicit models and patch models. In addition, the parameter value sets for the models with small patches differed from those with large patches. This is because parameter values for small patches mainly improve biases of A-II, while those for large patches mainly improve biases of A-I. Therefore, parameter values should not be shared between patch models with small patches and with large patches.
    The estimated correction term in the patch models with large patches excluded the competitive effects of small and medium-sized plants on their neighbors, even though these effects exist in real stands. This exclusion can be ascribed to the discrepancy between their competition in real plant populations and A-I. Therefore, the competitive effects of small and medium-sized plants should not be included in patch models with large patches. Finally, the reproducibility of the models tuned with correction terms was higher than those with adjusted parameters. (C) 2017 Elsevier B.V. All rights reserved.


  • A Model of Silicon Dynamics in Rice: An Analysis of the Investment Efficiency of Si Transporters

    Gen Sakurai, Naoki Yamaji, Namiki Mitani-Ueno, Masayuki Yokozawa, Keisuke Ono, Jian Feng Ma

    FRONTIERS IN PLANT SCIENCE   8  2017.07  [Refereed]

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    Silicon is the second most abundant element in soils and is beneficial for plant growth. Although, the localizations and polarities of rice Si transporters have been elucidated, the mechanisms that control the expression of Si transporter genes and the functional reasons for controlling expression are not well-understood. We developed a new model that simulates the dynamics of Si in the whole plant in rice by considering Si transport in the roots, distribution at the nodes, and signaling substances controlling transporter gene expression. To investigate the functional reason for the diurnal variation of the expression level, we compared investment efficiencies (the amount of Si accumulated in the upper leaf divided by the total expression level of Si transporter genes) at different model settings. The model reproduced the gradual decrease and diurnal variation of the expression level of the transporter genes observed by previous experimental studies. The results of simulation experiments showed that a considerable reduction in the expression of Si transporter genes during the night increases investment efficiency. Our study suggests that rice has a system that maximizes the investment efficiency of Si uptake.


  • A model of silicon dynamics in rice

    Gen Sakurai, Naoki Yamaji, Namiki Mitani-Ueno, Masayuki Yokozawa, Keisuke Ono, Jian Feng Ma

    Frontiers in Plant Science   8   E31-E34  2017  [Refereed]


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Books and Other Publications 【 display / non-display

  • Indo-Pacific Climate Variability and Predictability

    ( Part: Joint author)

    World Scientific Publishing  2016.01

  • 地球環境変動の生態学

    横沢正幸, 櫻井 玄( Part: Joint author)

    共立出版  2014.03

Misc 【 display / non-display

  • イネにおける作物体内ミネラル輸送ダイナミクスの解析手法の開発

    櫻井玄, 小野圭介馬建鋒, 山地直樹, 三谷奈見季, 横沢正幸

    農業環境変動研究センター研究成果情報    2018.03

  • 気候変動に対応した循環型食料生産等の確立のためのプロジェクト 第1編 農業分野における温暖化緩和技術の開発 第1章 農地及び草地におけるモニタリング・モデリングと全国評価 3 全国スケールでの温暖化緩和ポテンシャルの評価

    白戸康人, 井上吉雄, 岡本勝男, 石塚直樹, 杉浦裕義, 高橋司, 小原繁, 川守田真紀, 岩淵幸治, 額田光彦, 根本知明, 折本善之, 飯村強, 藤田裕, 郷内武, 手塚誉裕, 加藤治, 榊英雄, 山田一宇, 橋本享子, 峯田絵里, 岸本文紅, 高田裕介, 三島慎一郎, 横沢正幸, 米村正一郎, 須藤重人, 矢ケ崎泰海, 木村園子ドロテア

    農林水産省農林水産技術会議事務局研究成果   ( 557 ) 64‐69  2016.03


  • Characterizing the reliability of global crop prediction based on seasonal climate forecasts

    Toshichika Iizumi, Masayuki Yokozawa, Gen Sakurai, Hirofumi Sakuma, Jing-Jia Luo, Andrew J. Challinor, Toshio Yamagata

    World Scientific Series on Asia-Pacific Weather and Climate   7   281 - 304  2016  [Refereed]

     View Summary

    Reliable crop prediction based on seasonal climate forecasts can be achieved when a strong climate- crop relationship exists and there are reliable forecasts of the climatic constraints on crops. Here, we present global assessments of the climatic constraints on crops (maize, soybeans, rice, and wheat), the degree of the climate-crop relationship, and the reliability of seasonal forecasts of dominant climatic constraints based on statistical crop models and ensemble seasonal climate forecasts. We then classify the reliability of within-season crop prediction into four categories based on the degree of the climate-crop relationship and the reliability of the climate forecasting: (I) reliable
    (II) less reliable due to the low reliability of climate forecasting
    (III) not reliable due to the low reliability of climate forecasting and a weak climate-crop relationship
    and (IV) less reliable due to a weak climate-crop relationship. The results showed that a strong climate-crop relationship exists in the area that produces 24-38% of the global crop production. On a global scale, 51-59% of the maize and soybean production is sensitive to soil moisture level during the reproductive growth period, whereas 47-53% of the rice and wheat production is sensitive to temperature. Due to the greater reliability of temperature forecasts, crop prediction is reliable in those areas in which the crop yield is temperature-sensitive and temperature forecasts are reliable. The categorized reliability of crop prediction indicated that improvements of soil moisture forecasts in 30-50°N during July- October and in 30-40 ? S during February-April are needed for better maize and soybean prediction, whereas improved temperature forecasts in 20-60 ° N during March-August are keys to rice and wheat prediction. This study established a novel way of assessing the reliability of crop prediction, which will enable decision-making and allow researchers to prioritize the direction of new research to improve crop prediction in a given area for global food prediction.


  • 森林の炭素動態モデル構築に向けて―リター及び土壌有機物分解サブモデルのパラメータ推定―

    鈴木静男, 鈴木静男, 永井勝, 小嵐淳, 安藤麻里子, 横沢正幸, 横沢正幸, 原登志彦, 日浦勉, 渡邉博史, 波松香苗, 多胡靖宏, 中村裕二, 久松俊一

    富士学会発表要旨集   ( 15 ) 10‐11  2015.09


  • 土壌中CO2発生量鉛直分布の推定方法の開発

    櫻井玄, 米村正一郎, 横沢正幸, 岸本文紅, 村山昌平, 大塚俊之

    農業環境技術研究所研究成果情報   31   52 - 53  2015.03


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Awards 【 display / non-display

  • 日本農業気象学会論文賞


Research Projects 【 display / non-display

  • Modeling forest ecosystem responses to environmental change implementing spatial heterogeneity of trees

    Project Year :


    Authorship: Principal investigator

  • Elucidating impacts of variation in major crop production induced by abnormal weather events on World food supply and malnutrition population

    Project Year :


    Authorship: Principal investigator

Presentations 【 display / non-display

  • Detecting changes in traits of forest after extreme climate episode using model data fusion

    M. yokozawa

    The International Society for Ecological Modelling Global Conference 2016 

    Presentation date: 2016.05

  • Modeling the climate change adaptation of crop production using irrigation over water-limited region

    Masashi Okada, Toshichika Iizumi, Gen Sakurai, Toru Sakai, Masayuki Yokozawa

    2014 AGU Fall Meeting 

    Presentation date: 2014.12

  • The past impact of climate change on major crop yield

    Gen Sakurai, Toshichika Iizumi, Motoki Nishimori, Masashi Okada, Masayuki Yokozawa  [Invited]

    2014 AGU Fall Meeting 

    Presentation date: 2014.12

  • Evaluating the synchronicity in yield variations of staple crops at global scale

    Masayuki Yokozawa

    2014 AGU Fall Meeting 

    Presentation date: 2014.12

Specific Research 【 display / non-display

  • 異常気象による作物生産性変動のナウキャスティングに向けたモデル開発


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Syllabus 【 display / non-display

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Teaching Experience 【 display / non-display

  • 生物圏科学特別講義I


  • 基礎生態学


  • 個体群生態学特論


  • 自然環境論


  • 生態モデリング特論


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Committee Memberships 【 display / non-display

  • 2020.03

    Global Change Biology  Global Change Biology, Editorial Advisory Board member

  • 2017.09

    環境省  気候変動の影響観測・監視の推進に向けた検討チーム 座長

  • 2005

    日本農業気象学会  評議員