Development of a rice yield prediction system based on stochastic photosynthesis model and rice growth model(Fostering Joint International Research)

2019 Fiscal Year Final Research Report

• Project/Area Number: 16KK0169
• Research Category: Fund for the Promotion of Joint International Research (Fostering Joint International Research)
• Allocation Type: Multi-year Fund
• Research Field: Agricultural environmental engineering/Agricultural information engineering
• Research Institution: Tokyo University of Agriculture and Technology
• Principal Investigator: TATSUMI Kenichi 東京農工大学, (連合)農学研究科(研究院), 准教授 (40505781)
• Project Period (FY): 2017 – 2019
• Keywords: イネ生長応答モデル / 野外圃場 / 米国 / 光合成能力 / 日射環境

Outline of Final Research Achievements

Development of a stochastic photosynthesis model based on photosynthetic characteristics and received light amount by canopy as main variables was performed. And, model parameter sequential estimation based on Bayesian estimation for compensating shortage of measured data was applied to develop a generalized rice growth model. The developed models were used to reproduce the rice biomass and yield in the field. It was confirmed that it was generally possible to express the time-series variation of biomass and yield. In addition, we studied the construction of a technology platform system that enables the evaluation of cultivation risks and its application methods.

Free Research Field

農業情報気象学，農業環境工学

Academic Significance and Societal Importance of the Research Achievements

実測データの偏りや不足を補うことができ，多様な環境場において気候の変動や品種の多様性に対する栽培リスクの定量的評価を可能とする技術基盤システムの構築には学術的意義があると考えている．また，多様なシナリオ下におけるイネ成長シミュレーションを可能にし，現場の生産者レベルでの栽培管理の省力化を実現させるための技術として社会的意義があると考えている．