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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/64897

    Title: 氣候變遷下稻作生長期之變化與產量模擬;Yield simulating and principle components analysis on the impacts of growing periods of rice under climate change
    Authors: 魏郁婷;Wei,Yu-ting
    Contributors: 土木工程學系
    Keywords: 氣候變遷;水稻;DSSAT作物模式;灌溉需水量;主成分分析;Climate change;Rice;Irrigation water requirement;principle components analysis
    Date: 2014-07-30
    Issue Date: 2014-10-15 14:33:02 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 受氣候環境變遷之影響,糧食問題為全球之重要議題,如何建立適當之水田灌溉操作管理方法,以確保農業生產以及水資源之有效利用,為水資源規劃之重要課題。本研究以桃園為研究區域,使用DSSAT(The Decision Support System for Agrotechnology Transfer) 作物生長模式,對不同種植日期(提前一周、正常、延後一周及延後兩周)之水稻進行模擬。探討氣候變遷下3個不同時期:近未來(2020-2039)、未來(2050-2069)及遠未來(2080-2099),稻作生長天數、產量、作物需水量及灌溉需水量之變化。作物生長與氣象環境之間的交感相當複雜,在不同時間與空間皆有不同反應。作物模式中需要各種的氣象資料及現地資料,其中氣象資料以溫度、降雨量及輻射量最廣為被探討。本研究將影響產量之因子運用多變量統計分析中之主成份分析法探討其變數對作物產量之影響。所採用之影響產量的八項因子分別為:開花前之累積日輻射量、開花前之累積生育度數、開花前之累積作物蒸散量、開花前之生長天數、開花後之累積日輻射量、開花後之累積生育度數、開花後之作物蒸散量及開花後之生長天數。通過現在(1985-2011) 、近未來(2020-2039)、未來(2050-2069年)及遠未來(2080-2099)資料之主成份分析。分析結果可以得知,在目前影響產量的主要成因為稻作開花前之輻射,而在氣候變遷情境中發現近未來、未來及遠未來影響產量的主要因素為稻作開花前之生長天數。;Food production is an important issue among the effects of climate and environmental change. Furthermore, how to perform an appropriate management in paddy irrigation is a significant topic of water resource planning. This research takes Taoyuan county as research region , uses DSSAT (The Decision Support System for Agrotechnology Transfer) model to simulate the growing of rice which were planted on different time, i.e., ahead of one week, regular, one week delayed and two weeks delayed , and Investigate the changing of days and output of rice farming on 3 different period under climate change, namely, near future (2020-2039), future (2050-2069), far future (2080-2099). The mutual influence between crop growing and meteorology is complicated. It is expected that different time and environment will lead to dissimilar reaction. Crop simulating model needs all kinds of meteorological data and Situ data. Among all the meteorological data, temperature, rainfall and radiation are probed in more detailed.

    This research uses principal components analysis of multivariate statistical analysis to investigate how these factors will influent output. The eight factors are listed below: 1.Solar radiation accumulation before bloom; 2.Growing degree before bloom; 3. Crop Evapotranspiration accumulation after bloom; 4. Growing days after bloom; 5. Solar radiation accumulation before bloom; 6. Growing degree after bloom; 7. Crop Evapotranspiration accumulation before bloom; 8. Growing days after bloom. The analysis was performed with meteorological data of nowadays (1985-2011), near future (2020-2039), future(2050-2069) and far future (2080-2099). The results show that the main factor to affect output is solar radiation accumulation before bloom in present, while the growing days before bloom is the main factor to affect output in the near future, future and far future under climate change.
    Appears in Collections:[土木工程研究所] 博碩士論文

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