スイトウ サイバイ ニ オケル ホジョウ ジョウホウ ブンセキ オヨビ リモート センシング ギジュツ ノ オウヨウ

京都大学0048新制・課程博士博士(農学)甲第10891号農博第1397号新制||農||889(附属図書館)学位論文||H16||N3902(農学部図書室)UT51-2004-G738京都大学大学院農学研究科地域環境科学専攻(主査)教授 梅田 幹雄, 教授 笈田 昭, 教授 池田 善郎学位規則第4条第1項該当Doctor of Agricultural ScienceKyoto UniversityDA

Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing

Airborne hyperspectral remote sensing was adapted to establish a general-purpose model for quantifying nitrogen content of rice plants at the heading stage using three years of data. There was a difference in dry mass and nitrogen concentration due to the difference in the accumulated daily radiation (ADR) and effective cumulative temperature (ECT). Because of these environmental differences, there was also a significant difference in nitrogen content among the three years. In the multiple linear regression (MLR) analysis, the accuracy (coefficient of determination: R2, root mean square of error: RMSE and relative error: RE) of two-year models was better than that of single-year models as shown by R2 ≥ 0.693, RMSE ≤ 1.405 g m−2 and RE ≤ 9.136%. The accuracy of the three-year model was R2 = 0.893, RMSE = 1.092 g m−2 and RE = 8.550% with eight variables. When each model was verified using the other data, the range of RE for two-year models was similar or increased compared with that for single-year models. In the partial least square regression (PLSR) model for the validation, the accuracy of two-year models was also better than that of single-year models as R2 ≥ 0.699, RMSE ≤ 1.611 g m−2 and RE ≤ 13.36%. The accuracy of the three-year model was R2 = 0.837, RMSE = 1.401 g m−2 and RE = 11.23% with four latent variables. When each model was verified, the range of RE for two-year models was similar or decreased compared with that for single-year models. The similarities and differences of loading weights for each latent variable depending on hyperspectral reflectance might have affected the regression coefficients and the accuracy of each prediction model. The accuracy of the single-year MLR models was better than that of the single-year PLSR models. However, accuracy of the multi-year PLSR models was better than that of the multi-year MLR models. Therefore, PLSR model might be more suitable than MLR model to predict the nitrogen contents at the heading stage using the hyperspectral reflectance because PLSR models have more sensitive than MLR models for the inhomogeneous results. Although there were differences in the environmental variables (ADR and ECT), it is possible to establish a general-purpose prediction model for nitrogen content at the heading stage using airborne hyperspectral remote sensing

Model for predicting the nitrogen content of rice at panicle initiation stage using data from airborne hyperspectral remote sensing

Airborne hyperspectral remote sensing was used to provide data for a general-purpose model for predicting the nitrogen content of rice at panicle initiation stage using three years of data. There were significant differences between the vegetation data which were affected by the uptake of nitrogen from the soil depending on weather conditions. Therefore, the reflectance values obtained for one year may exhibit a different trend, due to the lack of vegetation. When the partial least squares regression (PLSR) models were estimated using all combinations of the three-year data, except for the model incorporating the data from 2005, correlation coefficients (r) were greater than 0.758, and the root mean squared error (RMSE) of prediction of the full-cross validation was less than 0.876 g m-2. The accuracy of the 2003-2004-2005 model was determined using five latent variables (PCs), with r = 0.938 and RMSEP = 0.774 g m-2. There were two different patterns for the regression coefficients associated with the NIR or red-edge regions. When the 2003-2004 model was validated using the data from 2005, the prediction error of the PLSR model was 1.050 g m-2. This became 2.378 g m-2 for the 2003-2005 model using the data from 2004 and 5.061 g m-2 for the 2004-2005 model with the data from 2003. There were similarities and differences for each latent variable between the 2003-2004 model and the 2003-2004-2005 model. The 2003-2004-2005 model might be more suitable for use as a general-purpose model, because it is possible to consider and validate all of the three years data

Integrating remote sensing and GIS for prediction of rice protein contents

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 0.250, 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

Cobalt polyoxometalate-derived CoWO4 oxygen-evolving catalysts for efficient electrochemical and photoelectrochemical water oxidation

Highly efficient water-oxidation catalysts (WOCs) were readily prepared through the simple heat treatment of cobalt-containing polyoxometalate [Co4(H2O)2(PW9O34)2]10&amp;#8722; (POM). The annealing of soluble POM molecules at high temperatures in air led to the formation of insoluble nanoparticles, of which the crystal structure and catalytic activity can be controlled by the annealing temperature. POMs were converted to amorphous and crystalline CoWO4 nanoparticles when annealed at 400 and 500&amp;#8239;??C, respectively. Interestingly, amorphous CoWO4 nanoparticles exhibited excellent catalytic activity near the neutral pH of pH 8.0, making them superior to both pristine POM and POM-derived crystalline CoWO4 nanoparticles. X-ray absorption and photoelectron spectroscopies combined with density functional theory (DFT) calculations revealed that their outstanding performance was resulted from the generation of large amounts of oxygen vacancies upon annealing, leading to the optimum distance between the nearest Co ions for the Langmuir-Hinshelwood (LH) mechanism. Based on these findings, we could readily immobilize CoWO4-based WOCs on the surfaces of various electrodes for efficient electrochemical and photoelectrochemical water oxidation through the annealing of POMs pre-adsorbed onto the desired electrode surface. This study may provide insights not only for the synthesis of efficient electrocatalysts derived from POMs but also for their immobilization onto the desired electrode surface for practical applications

Small RNA and transcriptome deep sequencing proffers insight into floral gene regulation in <it>Rosa </it>cultivars

Abstract Background Roses (Rosa sp.), which belong to the family Rosaceae, are the most economically important ornamental plants—making up 30% of the floriculture market. However, given high demand for roses, rose breeding programs are limited in molecular resources which can greatly enhance and speed breeding efforts. A better understanding of important genes that contribute to important floral development and desired phenotypes will lead to improved rose cultivars. For this study, we analyzed rose miRNAs and the rose flower transcriptome in order to generate a database to expound upon current knowledge regarding regulation of important floral characteristics. A rose genetic database will enable comprehensive analysis of gene expression and regulation via miRNA among different Rosa cultivars. Results We produced more than 0.5 million reads from expressed sequences, totalling more than 110 million bp. From these, we generated 35,657, 31,434, 34,725, and 39,722 flower unigenes from Rosa hybrid: ‘Vital’, ‘Maroussia’, and ‘Sympathy’ and Rosa rugosa Thunb. , respectively. The unigenes were assigned functional annotations, domains, metabolic pathways, Gene Ontology (GO) terms, Plant Ontology (PO) terms, and MIPS Functional Catalogue (FunCat) terms. Rose flower transcripts were compared with genes from whole genome sequences of Rosaceae members (apple, strawberry, and peach) and grape. We also produced approximately 40 million small RNA reads from flower tissue for Rosa, representing 267 unique miRNA tags. Among identified miRNAs, 25 of them were novel and 242 of them were conserved miRNAs. Statistical analyses of miRNA profiles revealed both shared and species-specific miRNAs, which presumably effect flower development and phenotypes. Conclusions In this study, we constructed a Rose miRNA and transcriptome database, and we analyzed the miRNAs and transcriptome generated from the flower tissues of four Rosa cultivars. The database provides a comprehensive genetic resource which can be used to better understand rose flower development and to identify candidate genes for important phenotypes.</p