| Project/Area Number |
18K14452
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 39020:Crop production science-related
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| Research Institution | Gifu University |
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Principal Investigator |
Tanaka Takashi 岐阜大学, 応用生物科学部, 助教 (20805436)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 大規模区画圃場 / 小麦 / 大豆 / リモートセンシング / 2年3作輪作体系 / 空間変動解析 / 作業体系 / 水稲 / 大区画圃場 |
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| Outline of Final Research Achievements |
The final goal of this study was to provide a solution for improving crop productivity in large-scale paddy. For that purpose, this study evaluated the effect of previous field management, cropping sequence, and edaphic factors on the crop yield and quality. The study mainly established: 1) the prediction model for winter wheat yield and grain protein using UAV-based remote sensing and machine learning; 2) the efficient soil sampling approach for digital soil mapping; and 3) the spatial linear mixed model that can account for the spatial variations in yield residuals due to the soil properties and previous crop management.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果として、大規模区画水田において、収量の空間変動は無視できない上に、調査で観測できなかった土壌要因の影響が大きかった。そのため、大規模区画水田における収量・品質の変動要因解析には、本研究の成果で実装した空間線形混合効果モデルを要因解析に用いることが望ましい。本手法は、現場レベルで普及がすすむリモートセンシングや収量コンバインなどの空間データを有効利用できるため、農家圃場調査の基盤技術となりうる。
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