| Project/Area Number |
16580201
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Irrigation, drainage and rural engineering/Rural planning
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| Research Institution | Kitasato University |
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Principal Investigator |
TSUTSUMI Satoshi Kitasato University, Veterinary Medicine, Professor, 獣医畜産学部, 教授 (40092275)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMA Eikichi Kitasato University Veterinary Medicine, Veterinary Medicine, Associate Professor, 獣医畜産学部, 助教授 (40196457)
TANAKA Katsuyuki Kitasato University Veterinary Medicine, Veterinary Medicine, Professor, 獣医畜産学部, 教授 (20146517)
HATTORI Toshihiro Kitasato University Veterinary Medicine, Veterinary Medicine, Lecturer, 獣医畜産学部, 講師 (10276165)
SHIMADA Hiroshi Akita Pref. University, Lecturer, 短期大学部, 講師 (00196487)
YOSHINO Kunihiko Tsukuba University, Associate Professor, 大学院・システム情報工学研究科, 助教授 (60182804)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2004: ¥2,100,000 (Direct Cost: ¥2,100,000)
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| Keywords | Catchments area / Water pollution / Livestock waste / Grassland / Remote Sensing / Hyper-spectral imaging sensor / ラジコンヘリ / 圃場管理 / 生物生産情報 / 画像分析 |
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| Research Abstract |
Recently, the river water quality has becomes a problem in the livestock farming areas and the actual state grasping is demanded.
In this paper, the effect of outflow water from catchment areas in livestock farming on water quality ware investigated in Aomori Prefecture. As a result, total nitrogen concentration was proportional to the grassland area ratio and increased and outflow rate of. total nitrogen from the grassland was 3.3 %. It has been shown that the load of SS, TP and DTP exceed 90 percent in snowmelt. In addition, it has been shown that SS, TP and DTP were discharged during the daytime and DTN was discharged during the nighttime.
A system was developed to estimate grass chemical composition values at the field level. Six important grass chemical compositions were estimated using a sensing system with a hyper-spectral imaging sensor mounted on a tractor. Calibration models were obtained by using multiple linear regression, and accuracy was evaluated with three statistical analysis methods. These methods were the standard error of cross validation (SECV), the coefficient of determination, and the Bias used by cross-validation. The calibration models were evaluated the evaluation index (El). Results indicated good potential for the sensing system to estimate grass chemical composition.
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