Integrating remote sensing and GIS for prediction of rice protein contents |
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Authors: | Chanseok Ryu Masahiko Suguri Michihisa Iida Mikio Umeda Chungkeun Lee |
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Institution: | (1) Environmental Science and Technology, Graduate School of Agriculture Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan;(2) Kyoto University, Yoshida Hon-machi, Sakyo-ku, Kyoto 606-8501, Japan;(3) National Academy of Agricultural Science, RDA, Suwon, 441-707, Korea |
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Abstract: | In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select
the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management
factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and
PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer.
There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average
of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI
model (r
2 > 0.424, RMSE < 0.256%) was better than for the Field GreenNDVI model (r
2 > 0.250, RMSE < 0.298%). A general-purpose model (r
2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare
the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending
on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of
the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based
on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC
at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the
amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management,
it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to
select the key management factor by the difference in the Field GreenNDVI. |
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