跳到主要內容

臺灣博碩士論文加值系統

(34.204.180.223) 您好!臺灣時間:2021/08/05 23:52
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:黃啟銘
研究生(外文):Chi-Ming Huang
論文名稱:CERES-Rice作物模擬軟體之作物生長參數的敏感度分析與產量區間預測
論文名稱(外文):Sensitivity Analysis of Genetic Coefficients and Interval Prediction of Yield in CERES-Rice
指導教授:劉力瑜
指導教授(外文):Li-Yu Daisy Liu
口試委員:廖振鐸蔡育彰
口試日期:2015-06-04
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:農藝學研究所
學門:農業科學學門
學類:一般農業學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:80
中文關鍵詞:作物模擬軟體CERES-Rice敏感度分析產量區間預測
外文關鍵詞:crop modelCERES-Ricesensitivity analysisinterval prediction of yield
相關次數:
  • 被引用被引用:1
  • 點閱點閱:182
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
全球氣候變遷造成極端氣候發生頻度增加也影響全球糧食供應,世界第二大糧食作物、臺灣主要的糧食作物-水稻,也常作為氣候變遷研究之目標作物。最初用於預測作物生長發育的作物模擬軟體也因此越來越受到重視,在臺灣作物模擬軟體的研究及應用並不普遍,本研究欲利用作物模擬軟體預測水稻之產量,水稻研究中DSSAT CERES-Rice為最廣泛應用的作物模擬軟體之一。不過作物模擬軟體若輸入相同的設定及生長參數將會得到相同的模擬結果,如此無法反映出自然界中的個體變異。因此本研究目標利用區間估計代替作物模擬軟體的點估計以產生產量預測區間,首先利用Log Multivariate Normal與Uniform分布產生模擬資料,以3k 複因子混雜設計、複回歸分析與逐步變數篩選等方法篩選P1、P2R、P5、P2O、G1、G2、G3及G4等8個生長參數皆為重要的生長參數後,再以Log Multivariate Normal分布產生臺灣水稻品種-臺農67號與臺中秈10號之品種模擬資料得其模擬產量之10% 與90% 百分位數作為兩品種之產量區間分別為 (5360.4, 7049.6) Kg・ha-1及 (4995.4, 6881.6) Kg・ha-1,對照行政院農業委員會農業試驗所之TRIS台灣稻作資訊系統各育成品種之產量試驗資料,產量預測區間高估臺農67號之產量,低估臺中秈10號之產量。經本研究驗證CERES-Rice再經改善可做為臺灣水稻產量預測的工具之一,為改善作物模擬軟體於臺灣稉稻品種之模擬結果,有待後續參數化、模擬前的校正等以提升可信度與準確性,經驗證之模擬軟體也能推廣應用於臺灣農業,如政策評估、栽培管理、提供育種方向等多元的用途,促進臺灣農業更有效率的發展。

Climate change caused that increasing extreme climate occurred and resulted in food and water shortages all over the world. Rice, with the second-highest worldwide production and as one of the most important crops in Taiwan, was usually concerned as the target crop in crop models. In addition, the indica was the main cultivating and commercial variety instead of the japonica which was widely cultivated in Taiwan. Crop models were initially used to predict crop growth and development, but researches and applications in Taiwan were still scarce. DSSAT CERES-Rice was one of the widely used crop model in rice research. But there were some difficulties to create individual variations in the simulation because the same inputs would return the same outputs by crop model simulations. In this study, we aimed to use interval estimation to substitute point estimation to predict the yield intervals of two rice varieties in Taiwan, TNG67 and TCS10. First, we created simulation data from Log Multivariate Normal distribution and Uniform distribution then selected P1, P2R, P5, P2O, G1, G2, G3 and G4 as the effective genetic coefficients by 3k factorial confounding design, regression analysis and stepwise regression analysis. Second, we simulated simulation data of TNG67 and TCS10 from Log Multivariate Normal distribution then estimated 10% to 90% percentiles as the yield intervals, (5360.4, 7049.6) and (4995.4, 6881.6) respectively. Comparing to the yields of trials, from Rice Registered Varieties Database, Taiwan Agricultural Research Institute Council of Agriculture, Executive Yuan, the yields of TNG67 were overestimated and the yields of TCS10 were underestimated. CERES-Rice is able to be implemented to predict the yields of Taiwanese rice varieties To improve the simulation outputs, parameterizing varieties in Taiwan and calibration before simulation would be essential and efficient to promote the applications of crop models to agriculture in Taiwan, such as policy assessment, management decision, plant breeding and so on.

口試委員會審定書…………………………………………………… i
謝誌………………………………………………………….………… ii
摘要…………………………………………………………………… iii
Abstract……………………………………………………………...… iv
目錄…………………………………………………………………… vi
壹、 前言…………………………………………………………… 1
1.1全球人口與水稻生產概況…………………………………… 1
1.2水稻種類與臺灣稻米發展簡史……………………………… 3
1.3稻作生產與作物模擬軟體…………………………………… 4
1.4臺灣作物模擬軟體應用的現況……………………………… 6
貳、 材料與方法…………………………………………………… 9
2.1作物模擬軟體………………………………………………… 9
2.1.1 DSSAT ………………………………………………… 9
2.1.2 CERES-Rice …………………………………………… 9
2.2作物生長參數………………………………………………… 10
2.2.1 CERES-Rice預設資料………………………………… 11
2.2.2模擬資料………………………………………………… 11
2.2.2.1 Log Multivariate Normal ………………………… 12
2.2.2.2 Uniform ………………………………………… 12
2.3篩選作物生長參數…………………………………………… 13
2.3.1 3k 複因子混雜設計…………………………………… 14
2.3.2複回歸分析與逐步變數篩選………………………… 14
2.4產量區間預測………………………………………………… 16
2.4.1群集分析……………………………………………… 16
2.4.2產量區間預測………………………………………… 18
參、 結果…………………………………………………………… 19
3.1篩選作物生長參數…………………………………………… 19
3.2比較預設資料與模擬資料…………………………………… 20
3.3預測產量區間………………………………………………… 21
肆、 討論…………………………………………………………… 25
4.1模擬方法的差異……………………………………………… 25
4.2產量區間預測………………………………………………… 28
伍、 總結…………………………………………………………… 31
陸、 表……………………………………………………………… 33
Table 1: The definition of growth periods in CERES-Rice ……… 33
Table 2: The definition of genetic coefficients in CERES-Rice … 34
Table 3: The default varieties in CERES-Rice …………………… 35
Table 4: The genetic coefficients of TNG67 and TCS10 ………… 35
Table 5: The outputs of sensitivity analysis ……………………… 36
Table 6: The means, coefficients of variation and p-values of
Kolmogorov-Smirnov test between default data and
simulation data ………………………………………… 38
Table 7: The varieties in the same group with TNG67 and
TCS10 by clustering …………………………………… 39
Table 8: The varieties in the different group with TNG67 and
TCS10 by clustering …………………………………… 40
Table 9: The means and coefficients of variation from default
data and the two clustered group ……………………… 41
Table 10: The descriptive statistics of the yields from TNG67
and TCS10 ……………………………………………… 41
Table 11: The trial yields of TNG67 and TCS10 ………………… 42
Table 12: The means and the coefficients of variation of the
genetic coefficients from TNG67 by yields ……………… 43
Table 13: The means and the coefficients of variation of the
genetic coefficients from TCS10 by yields ……………… 43
柒、 圖……………………………………………………………… 44
Figure 1: The growth periods in ORYZA 2000 …………………… 44
Figure 2: The boxplots of genetic coefficients from default data
and simulation data ……………………………………… 45
Figure 3: The yield distributions of default data and simulation
data ……………………………………………………… 46
Figure 4: The dendrogram by cluster analysis …………………… 47
Figure 5: The histogram of TNG67 simulated yield ……………… 48
Figure 6: The histogram of TCS10 simulated yield ……………… 49
捌、 參考文獻……………………………………………………… 50
附錄…………………………………………………………………… ix


Akaike, H. (1974). A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723.
Bannayan, M., Crout, N.M.J., and Hoogenboom, G. (2003). Application of the CERES-Wheat Model for Within-Season Prediction of Winter Wheat Yield in the United Kingdom. Agron. J. 95, 114–125.
Bannayan, M., Kobayashi, K., Marashi, H., and Hoogenboom, G. (2007). Gene-based modelling for rice: An opportunity to enhance the simulation of rice growth and development? J. Theor. Biol. 249, 593–605.
Bozdogan, H. (2000). Akaike’s Information Criterion and Recent Developments in Information Complexity. J. Math. Psychol. 44, 62–91.
Everitt, B.S., Landau, S., Leese, M., and Stahl, D. (2011). Cluster Analysis (Wiley).
Field, C.B., Barros, V.R., Mach, K.J., Mastrandrea, M.D., van Aalst, M.K., Adger, W.N., Arent, D.J., Barnett, J., Betts, R.A., Bilir, T.E., et al. (2014). Technical Summary. Clim. Change 2014 Impacts Adapt. Vulnerability 35–94.
He, J., Jones, J.W., Graham, W.D., and Dukes, M.D. (2010). Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agric. Syst. 103, 256–264.
Hijioka, Y., Lin, E., Pereira, J.J., Corlett, R.T., Cui, X., Insarov, G., Lasco, R., Lindgren, E., and Surjan, A. (2014). Asia. Clim. Change 2014 Impacts Adapt. Vulnerability 1327–1370.
Holzworth, D.P., Snow, V., Janssen, S., Athanasiadis, I.N., Donatelli, M., Hoogenboom, G., White, J.W., and Thorburn, P. (2015). Agricultural production systems modelling and software: Current status and future prospects. Environ. Model. Softw. In press.
Hoogenboom, G., Jones, J.W., Porter, C.H., Wilkens, P.W., Boote, K.J., Hunt, L.A., Singh, U., Lizaso, J.L., White, J.W., Uryasev, O., et al. (2012). Decision Support System for Agrotechonology Transfer (DSSAT) (Honolulu, Hawaii: University of Hawaii).
Isah, A., Abdullahi, U., and Waziri, V.O. (2013). A Hierarchical Cluster Analysis and Simulation of State Capitals in Nigeria for Tourism Exploration. Int. J. Latest Res. Sci. Technol. 437–441.
Jeuffroy, M.-H., Casadebaig, P., Debaeke, P., Loyce, C., and Meynard, J.-M. (2013). Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review. Agron. Sustain. Dev. 34, 121–137.
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., and Ritchie, J.T. (2003). The DSSAT cropping system model. Eur. J. Agron. 18, 235–265.
Kutner, M., Nachtsheim, C., Neter, J., and Li, W. (2004). Applied Linear Statistical Models (Boston: McGraw-Hill/Irwin).
Laborte, A.G., de Bie, K. (C. A.J.M.), Smaling, E.M.A., Moya, P.F., Boling, A.A., and Van Ittersum, M.K. (2012). Rice yields and yield gaps in Southeast Asia: Past trends and future outlook. Eur. J. Agron. 36, 9–20.
Robert, N. (2002). Comparison of stability statistics for yield and quality traits in bread wheat. Euphytica 128, 333–341.
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (Hoboken, NJ: Wiley).
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S. (2007). Global Sensitivity Analysis: The Primer (John Wiley & Sons, Ltd).
Suzuki, R., and Shimodaira, H. (2006). Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542.
Tsuji, G.Y., Hoogenboom, G., and Thornton, P.K. (2002). Understanding Options for Agricultural Production (Springer-Science+Business Media, B.V.).
White, J.W., Hoogenboom, G., Kimball, B.A., and Wall, G.W. (2011). Methodologies for simulating impacts of climate change on crop production. Field Crops Res. 124, 357–368.
Wikarmpapraharn, C., and Kositsakulchai, E. (2010). Evaluation of ORYZA2000 and CERES-Rice Models under Potential Growth Condition in the Central Plain of Thailand. Thai J. Agric. Sci. 43, 17–29.
Zajac, Z.B. (2010). Global Sensitivity and Uncertainty Analysis of Spatially Distributed Watershed Models. Ph.D. Dissertation. University of Florida.
呂秀英 (1997). 作物模式化的意義及展望. 臺灣農業雙月刊 33, 95–114.
呂秀英 (2000a). 作物生長模式之發展與應用. 近代作物科學發展研討會論文集 2, 1–17.
呂秀英 (2000b). 作物模式在改良基因型適應性上的應用. 科學農業 48, 199–205.
呂坤泉, 許志聖, 楊嘉凌 (2005). 水稻的栽培生態分類與稻米市場分類. 台中區農業專訊 50, 24–27.
周玫君 (2004). 氣候變遷及乾旱灌溉用水移用對水稻潛能產量影響. 碩士論文. 國立臺灣大學.
姚銘輝, 陳守泓 (2009). 氣候變遷下水稻生長及產量之衝擊評估. 作物、環境與生物資訊 6, 141–156.
姚銘輝, 盧虎生, 朱鈞, 蔡金川 (2000). DSSAT模式在預測水稻產量及氣候變遷衝擊評估之適用性探討. 中華農業研究 49, 16–28.
楊嘉凌, 鄭佳綺, 許志聖 (2012a). 世界稻米產銷概況. 台中區農業專訊 76, 4–8.
楊嘉凌, 鄭佳綺, 許志聖 (2012b). 亞洲國家稻米生產概況. 台中區農業專訊 76, 9–14.
沈明來 (2010). 試驗設計學 (九州圖書文物有限公司).
羅秋雄, 張金城 (2003). 作物施肥手冊. 桃園區農業改良場土壤保育研究 1–154.
蔡政廷 (1989). 機制性作物模式適用性之評估. 碩士論文. 國立中興大學.
謝兆樞 (2014). 臺灣稻米古典名種談.
連宛渝 (2000). 氣候變遷對台灣水稻灌溉需水量及潛能產量之影響. 碩士論文. 國立臺灣大學.
鄧耀宗 (2003). 台灣稻作之回顧與展望. 行政院農業委員會高雄區農業改良場研究彙報 14, 1–23.
陳亭羽 (2012). 氣候變遷對桃園地區水稻產量及灌溉需水量之影響. 碩士論文. 國立中央大學.
陳啟烈 (1997). EPIC作物模式對臺灣作物輪作方式下作物產量預估之應用研究. 博士論文. 國立臺灣大學.
謝兆樞 (2014). 臺灣稻米古典名種談 https://www.facebook.com/jawar.lion/notes.
FAOSTAT. Production of BROWSE DATA. Food and Agriculture Organization of the United Nations. http://faostat3.fao.org/browse/Q/QC/E.
TRIS台灣稻作資訊系統. 行政院農業委員會農業試驗所 http://tris.tari.gov.tw:8080/default.jsp.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
1. 山井湧〈明末清初的經世致用之學〉,《史學評論》第十二期,(台北,史學評論社,1986年)
2. 王光宜〈明代女教書的體例與內容簡介〉,(《明代研究通訊》第二期,1999年7月)
3. 王雅各〈男性研究:一個新的研究領域〉,(《婦女與兩性研究通訊》41期,1996年12月)
4. 李承貴〈「貞節」觀念的歷史演變及現代啟迪〉,(《孔孟學報》第75期)
5. 李國彤〈明清之際的婦女解放思想綜述〉,《近代中國婦女史研究》第三期,(台北,中央研究院近代史研究所,1995年8 月)
6. 林麗月〈從「杜騙新書」看晚明婦女生活的側面〉,(《近代中國婦女史研究》第3期,1995年8月)
7. 胡曉真〈「皇清盛世」與名媛閫道─評介Susan Mann:Precious Records: Women in China’s Long Eighteenth Century〉,(《近代中國婦女史研究》第6期,1998年8月)
8. 胡曉真〈最近西方漢學界婦女文學史研究之評介〉,(《近代中國婦女史研究》第2期,1994年)
9. 高彥頤〈「空間」與「家」-論明末清初婦女的生活空間〉,(《近代中國婦女史研究》第3期,台北,中央研究院近代史研究所,1995年8月)。
10. 康正果〈重新認識明清才女〉,(《中外文學》,第22卷第6期,1993 年11月)
11. 康正果〈邊緣文人的才女情結及其所傳達的情意─《西青散記初探》〉,(《九州學刊》,第6卷第2期,1994年7月)
12. 陳翠英《閱讀才子佳人小說:性別觀點》,(《清華學報》,第三十卷第三期,2000年9月)
13. 華瑋〈世間只有情難訴-是論湯顯祖的情觀與他劇作的關係〉,(《大陸雜誌》第86卷第6期,1993年6月)
14. 溝口雄三〈論明末清初時期在思想史上演變的意義〉,(《史學評論》第十二期,台北,史學評論社,1986年)
15. 蔣竹山〈湯斌禁毀五通神──清初政治菁英打擊通俗文化的個案〉,(《新史學》,1995年6月6卷2期)
 
無相關點閱論文