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MSc Thesis - Research report written under supervision of Eric Visser Wheat productivity in semi-arid regions may be improved through deep fertiliser placement by the development of a deeper root system, but more knowledge of both the benefits and possible drawbacks of this method is required to optimize crop system management. Therefore, the aim of this study was to determine the effects of deep fertiliser on wheat aboveground biomass under drought and irrigated conditions and how changed root system architecture links to this. Spring wheat either received deep (27-37 cm), shallow (5-15 cm), or homogeneously distributed (5-47 cm) combined NPK fertiliser and was either subjected to full irrigation or topsoil drought in a semi-randomly, full-factorial design with six replicates. In drought, deep fertiliser resulted in a 30% greater aboveground biomass (P = 0.007) and a deeper root system compared to shallow fertiliser placement (P < 0.0001). Moreover, in the absence of drought no drawback due to the increased seed-fertiliser distance was observed, reflected by similar growth rates and aboveground biomass in the different fertiliser treatments. Root diameter increased within zones of high nutrient concentrations, which was most prominent in dry soils. Greater aboveground biomass with deep fertilizer placement could be explained by an increase in photosynthesis rate and stomatal conductance. Belowground, roots responded strongly to fertiliser position and drouht by an increase in root diameter at greater soil depth (P = 0.03), an increase in root density (P = 0.03) and a decrease in specific root length (P = 0.04). Here we showed that deeper fertiliser placement can alleviate the negative effects of drought by the development of a deeper root system and did not inflict a yield penalty in the absence of drought. Deep fertiliser placement therefore can be a promising strategy to improve drought-tolerance, especially in crop systems where seasonal water input is minimal.

本研究では，感情を基本因子ベクトルArousal – Valence – Dominance の合成ベクトルとして表現するという新しい発想のもと，申請者らが提案している音声中の感情知覚モデルを感情音声認識に適用し，感情が複数含まれる音声からそれぞれの感情の程度までを推定する手法を提案した。評価の結果，感情空間へのマッピングについて提案法が最もヒトの特性に近く，認識精度もGMM を用いた手法と比較して本手法が認識率で大きく優れていることが確認できた。 : This study proposed a method of emotion recognition in speech, which can estimate not only the emotion itself but the degree of each emotion from speech that plural emotions are included in. This method represents each emotion as a resultant vector of the basic factor vectors, Arousal ? Valence ? Dominance. As the results of applying this method with our already proposed emotion perception model to emotion recognition in speech, the mapping of speech to the emotional space is the most correspondent to human responses. In addition, the recognition accuracy is also greatly excellent at the recognition rate compared with that by GMM. 科研費の分科・細目：知覚情報処理・知能ロボティクス 研究者番号：20242571 研究期間：2010～2012 課題番号：22650032 研究種目：挑戦的萌芽研究 研究分野：音声情報処理

採集地: 広島県〜鳥取県 道後山 (備後〜伯耆 道後山) 国立科学博物館整理番号: TNS-VS-973776 科名: マツムシソウ科 = Dipsacaceae 整理番号: JH023776 採集日: 1976/9/2 原著和名: マツムシサウ 採集者: 萩庭丈壽