caa a22 4500 445864478 CHVBK 20180317145453.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s11119-010-9179-0 doi (NATIONALLICENCE)springer-10.1007/s11119-010-9179-0 Integrating remote sensing and GIS for prediction of rice protein contents [Elektronische Daten] [Chanseok Ryu, Masahiko Suguri, Michihisa Iida, Mikio Umeda, Chungkeun Lee] 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 2years 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. Springer Science+Business Media, LLC, 2010 Remote sensing nationallicence Geographic information system nationallicence Protein content nationallicence Key management factors nationallicence Ryu Chanseok Environmental Science and Technology, Graduate School of Agriculture Kyoto University, Kitashirakawa Oiwake-cho, 606-8502, Sakyo-ku, Kyoto, Japan aut Suguri Masahiko Environmental Science and Technology, Graduate School of Agriculture Kyoto University, Kitashirakawa Oiwake-cho, 606-8502, Sakyo-ku, Kyoto, Japan aut Iida Michihisa Environmental Science and Technology, Graduate School of Agriculture Kyoto University, Kitashirakawa Oiwake-cho, 606-8502, Sakyo-ku, Kyoto, Japan aut Umeda Mikio Kyoto University, Yoshida Hon-machi, 606-8501, Sakyo-ku, Kyoto, Japan aut Lee Chungkeun National Academy of Agricultural Science, RDA, 441-707, Suwon, Korea aut Precision Agriculture Springer US; http://www.springer-ny.com 12/3(2011-06-01), 378-394 1385-2256 12:3<378 2011 12 11119 https://doi.org/10.1007/s11119-010-9179-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11119-010-9179-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ryu Chanseok Environmental Science and Technology, Graduate School of Agriculture Kyoto University, Kitashirakawa Oiwake-cho, 606-8502, Sakyo-ku, Kyoto, Japan aut NATIONALLICENCE 700 1- Suguri Masahiko Environmental Science and Technology, Graduate School of Agriculture Kyoto University, Kitashirakawa Oiwake-cho, 606-8502, Sakyo-ku, Kyoto, Japan aut NATIONALLICENCE 700 1- Iida Michihisa Environmental Science and Technology, Graduate School of Agriculture Kyoto University, Kitashirakawa Oiwake-cho, 606-8502, Sakyo-ku, Kyoto, Japan aut NATIONALLICENCE 700 1- Umeda Mikio Kyoto University, Yoshida Hon-machi, 606-8501, Sakyo-ku, Kyoto, Japan aut NATIONALLICENCE 700 1- Lee Chungkeun National Academy of Agricultural Science, RDA, 441-707, Suwon, Korea aut NATIONALLICENCE 773 0- Precision Agriculture Springer US; http://www.springer-ny.com 12/3(2011-06-01), 378-394 1385-2256 12:3<378 2011 12 11119 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer