2001 Fiscal Year Final Research Report Summary
Management Optimization System for Precision Farming Japan model
| Project/Area Number |
12460112
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
農業機械学
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
SASAO Akira Tokyo University of Agriculture and Technology, Faculty of Agriculture, Professor, 農学部, 教授 (70032993)
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| Co-Investigator(Kenkyū-buntansha) |
SAKAI Kenshi Tokyo University of Agriculture and Technology, Faculty of Agriculture, Associate Professor, 農学部, 助教授 (40192083)
SHIBUSAWA Sakae Tokyo University of Agriculture and Technology, Faculty of Agriculture, Professor, 農学部, 教授 (50149465)
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| Project Period (FY) |
2000 – 2001
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| Keywords | precision farming / soil map / soil sensor / plant growth model / Lotka-Volterra model / nonlinear pattern / NIR spectroscopy / weed management |
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| Research Abstract |
The project has aimed at developing a decision support system for farm work management based on the spatiotemporal variability of soil fertility and growing crops in the field, considering how to introduce precision farming in Japan. Results were as follows.
1. Investigation was conducted during 1999 to 2000 on the competition of growth between white clover and weeds at 420 plots with different densities of white clover in two fields of different former crops, com and white clover. Results showed that cultivating the white clover inhibited the emergence and growth of weeds over all plots and that survivals were white clover for white clover plots and white clover for corn plots. A proposed Lotka Volterra model simulated well the dynamic behavior of growth competition between white clover and weeds ; and four behavior patterns could be classified.
2. The real-time soil spectrophotometer developed was used for collecting the soil reflectance under the ground in a 0.5 ha paddy and a 0.3 ha upland fields. With about 900 and 500 data sets obtained form the paddy field and the upland field, respectively, at 1 m data scanning intervals, precise soil parameter maps were generated for moisture, soil organic matter, nitrate nitrogen, pH and EC.
3. Analysis on the temporal variations in the soil parameter maps derived management maps for a decision support system on farm works, where three management units : stable high, stable low and unstable parts of soil parameters were clearly depicted.
4. Crop growth maps of wheat and silage corn plants were produced by kriging technique using 432 treatments at 72 locations in a 0,4 ha field. About ten growth parameters were obtained including crop height and SPAD value. Airborne multi-spectral images of growing crops gave NDVI maps with high correlation with SPAD maps.
5. The technical report was published.
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