2021 Fiscal Year Final Research Report

Analyses of negative factors affecting the cut flower productivity of the rose plant community for optimizing the greenhouse environmental conditions by using system dynamics models

Research Project

PDF

Project/Area Number	18H02197
Research Category	Grant-in-Aid for Scientific Research (B)
Allocation Type	Single-year Grants
Section	一般
Review Section	Basic Section 39030:Horticultural science-related
Research Institution	Kyoto University
Principal Investigator	Motoaki DOI 京都大学, 農学研究科, 教授 (40164090)
Co-Investigator(Kenkyū-buntansha)	後藤丹十郎岡山大学, 環境生命科学学域, 教授 (40195938) 稲本勝彦国立研究開発法人農業・食品産業技術総合研究機構, 野菜花き研究部門, グループ長補佐 (50223235)
Project Period (FY)	2018-04-01 – 2022-03-31
Keywords	園芸技術 / バラ / 成育シミュレーション / 炭素収支 / システムダイナミクスモデル / 高精度化 / 二酸化炭素施用
Outline of Final Research Achievements	To establish a methodology for optimizing the greenhouse environment control, a system dynamics plant model predicting the flowering date, yield and quality of arching roses was developed and negative factors affecting the cut flower productivity were elucidated by analyzing the errors between the measured value and the predicted value. As the error factors of carbon gain by bending shoots, overestimation of dark respiration during the night hours and photosynthetic rates during the morning hours of fair days and underestimation of photosynthetic rates during summer resulting from the seasonal changes of LAI were elucidated. It was necessary to set both high and low threshold temperatures when the days to flowering were predicted by using real temperatures. The carbon dioxide concentration was a factor affecting the photosynthetic rate but high carbon dioxide acclimatization occurred depending on the sink activities of harvest shoots.
Free Research Field	園芸科学
Academic Significance and Societal Importance of the Research Achievements	園芸生産において，施設環境管理などの栽培管理には科学的な最適化の理論が欠落している．本研究ではバラの切り花生産を例として，システムダイナミクスモデルによって開花日・収量・品質を予測する作物モデルを構築し，実測値と予測値との間の誤差を解析することで，モデルの高精度化を図りうることを示した．具体的には，当初のモデルでは欠落していた光合成誘導反応，高二酸化炭素順化，LAIの季節変動，収穫枝のシンク活性の変化などの要因が誤差の要因として抽出され，その一部をモデルに取り込むことで，より精度の高い作物モデルが構築でき，生産者の意思決定支援ツールとして利用できることを示した．