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題名 | 應用主成份分析評估氣候變遷對作物產量因子之影響=Assessment of Climate Change Impact on Crop Yields in Northern Taiwan Using Principal Component Analysis |
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作者姓名(中文) | 吳瑞賢; 李明旭; 方紀棠; | 書刊名 | 農業工程學報 |
卷期 | 60:3 2014.09[民103.09] |
頁次 | 頁68-81 |
分類號 | 430.1637 |
關鍵詞 | 氣候變遷; 水稻; DSSAT作物模式; 主成份分析; Climate change; Rice; DSSAT; PCA; |
語文 | 中文(Chinese) |
中文摘要 | 氣候變遷為長期氣候特徵發生變化,而糧食生產受氣候影響,當氣候發生改變時,溫度、降雨型態等各種氣候因子皆會發生改變,而進一步對糧食生產造成影響,如作物品質、產量等。台灣最主要之糧食作物為水稻,其產量為農業生產及糧食安全之重要指標。本研究使用台灣氣候變遷推估與資訊平台建置(TCCIP)所提供之SRES (Special Report on Emissions Scenarios, SRES)情境模擬之未來氣候分析預測情境,採用A1B之情境,並使用氣象合成模式(WGEN)繁衍日解析之降雨量與日均溫,進而推估日最高溫、日最低溫及日輻射量。再利用The Decision Support System for Agrotechnology Transfer (DSSAT)作物模式評估未來氣候變遷下稻作生長天數及產量之變化。 DSSAT模式所需之基本氣象資料主要有下列四項:日輻射量、日最高溫、日最低溫及日降雨量。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,選取影響產量之因子有開花前之累積日輻射量、開花前之累積生育度數、開花前之累積作物生長需水量、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、開花後之作物生長需水量及開花後之生長天數,進行基期資料(1985~1990)與近未來(2020~2039)資料之主成份分析,分析結果可以得知在基期條件下影響產量的主要因子為稻作開花後之溫度。在近未來條件下系集模式及CM3模式中影響產量的主要成因為稻作開花前之溫度,而MK3_0模式中影響產量的主要成因為稻作開花前之日輻射量。 |
英文摘要 | Climate change affects all agriculture activities. When long-term climate pattern has changed, the weather factors, such as temperature and rainfall, might affect the quality and quantity of crop growth. Paddy rice is the most important crop productions in Taiwan and accounts for more than 70% of total water resources usage. The quantity of rice productions is a very important index for food security and agriculture management. This study utilizes the Decision Support System for Agrotechnology Transfer (DSSAT) model to analyze the variations of growth days and quantity of paddy rice under climate change. The Weather Generator Model (WGEN) was used to generate daily rainfall and daily mean temperature. Maximum and minimum daily temperature and solar radiation were then estimated by regression functions of daily mean temperature and daily rainfall from historical data. Rice productions were estimated by the DSSAT model. The Taiwan Climate Change Projection and Information Platform Project (TCCIP) provides future climate projections and the A1B scenarios of Special Report on Emissions Scenarios were selected in this study. To understand the dominant factors affecting crop yields under climate change, the Principal Component Analysis(PCA) was applied to analyze DSSAT results for both periods of baseline data (1985~1990) and near future data (2020~2039). Accumulated solar radiation, accumulated growing degree, crop water requirement and growing days were retrieved for performing PCA. Climate variations projected by ensemble models and CM3 model showed accumulated growing degree before blossom is the most important factor, while in MK3_0 mode is the accumulated solar radiation before blossom. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。