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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81864
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dc.contributor.advisor劉力瑜(Li-yu Daisy Liu)
dc.contributor.authorKai-Che Shihen
dc.contributor.author施愷哲zh_TW
dc.date.accessioned2022-11-25T03:05:20Z-
dc.date.available2023-06-01
dc.date.copyright2021-08-18
dc.date.issued2021
dc.date.submitted2021-06-24
dc.identifier.citationArrouays, D., Richer-de-Forges, A., Chen, S., Saby, N., Martin, M., Libohova, Z., . . . Padarian, J. (2017). GlobalSoilMap history and main achievements. Paper presented at the GlobalSoilMap-Digital Soil Mapping from Country to Globe: Proceedings of the Global Soil Map 2017 Conference, July 4-6, 2017, Moscow, Russia. Attia, A., El-Hendawy, S., Al-Suhaibani, N., Tahir, M. U., Mubushar, M., dos Santos Vianna, M., . . . Datta, A. Sensitivity of the DSSAT model in simulating maize yield and soil carbon dynamics in arid Mediterranean climate: Effect of soil, genotype and crop management. Field Crops Research, 260(2021), 107981. B.P.Malone. (2009). ithir: Functions and Algorithms Specific to Pedometrics. R package version 1.0. Bishop, T. F. A., McBratney, A. B., Laslett, G. M. (1999). Modelling soil attribute depth functions with qual-area quadratic smoothing splines. GEODERMA, 91, 27-45. Boonwichai, S., Shrestha, S., Babel, M. S., Weesakul, S., Datta, A. (2018). Climate change impacts on irrigation water requirement, crop water productivity and rice yield in the Songkhram River Basin, Thailand. Journal of Cleaner Production, 198, 1157-1164. doi:10.1016/j.jclepro.2018.07.146 Brady, N. C., Weil, R. R. (2014). Elements of the Nature and Properties of Soils. Pearson Education, Inc. Campbell, N., Mulcahy, M., Mcarthur, W. (1970). Numerical classification of soil profiles on the basis of field morphological properties. AUSTRALIAN JOURNAL OF SOIL RESEARCH(8), 43–58. Chen, Z.-S., Hseu, Z.-Y., Tsai, C.-C. (2015). The Soils of Taiwan (Vol. 137): World Soils Book Series. Ghorbani, M. A., Shamshirband, S., Haghi, D. Z., Azani, A., Bonakdari, H., Ebtehaj, I. (2017). Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil and Tillage Research, 172, 32-38. Gijsman, A. J., Thornton, P. K., Hoogenboom, G. (2007). Using the WISE database to parameterize soil inputs for crop simulation models. Computers and Electronics in Agriculture, 56(2), 85-100. doi:10.1016/j.compag.2007.01.001 Han, E., Ines, A. V. M., Koo, J. (2019). Development of a 10-km resolution global soil profile dataset for crop modeling applications. Environ Model Softw, 119, 70-83. doi:10.1016/j.envsoft.2019.05.012 He, W., Yang, J., Zhou, W., Drury, C., Yang, X., Reynolds, W., . . . Li, Z. (2016). Sensitivity analysis of crop yields, soil water contents and nitrogen leaching to precipitation, management practices and soil hydraulic properties in semi-arid and humid regions of Canada using the DSSAT model. Nutrient Cycling in Agroecosystems, 106(2), 201-215. Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B., Ribeiro, E., . . . Gonzalez, M. R. (2014). SoilGrids1km--global soil information based on automated mapping. PLoS One, 9(8), e105992. doi:10.1371/journal.pone.0105992 Hoogenboom, G., Porter, C., Boote, K., Shelia, V., Wilkens, P., Singh, U., . . . Moreno, L. (2019). The DSSAT crop modeling ecosystem. Advances in crop modelling for a sustainable agriculture, 173-216. Jagtap, S. S., Lall, U., Jones, J. W., Gijsman, A. J., Ritchie, J. T. (2004). Dynamic Nearest-Neighbor Method for Estimating Soil Water Parameters. Transactions of the ASAE, 47(5), 1437-1444. doi:10.13031/2013.17623 Jauregui, M. A., Quirino, P. (1985). Spline response functions for direct and carry over effects involving a single nutrient. Soil Science Society of America Journal, 49(49), 140–145. JENNY, H. (1941). Factors of Soil Formation: a System of Quantitative Pedology. New York and London: McGraw-Hill. Jones, P. G., Thornton, P. K. (2003). The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global environmental change, 13(1), 51-59. Koo, J., Dimes, J. (2013). HC27 generic soil profile database. Harvard Dataverse Ver. 4. Malone, B. P., McBratney, A. B., Minasny, B., Laslett, G. M. (2009). Mapping continuous depth functions of soil carbon storage and available water capacity. GEODERMA, 154(1-2), 138-152. doi:10.1016/j.geoderma.2009.10.007 Moore, A. W., Russell, J. S., Ward, W. T. (1972). Numerical analysis of soils: a comparison of three soil profile models with field classifications. Journal of Soil Science, 23(23), 193–209. Nehbandani, A., Soltani, A., Taghdisi Naghab, R., Dadrasi, A., Alimagham, S. M. (2020). Assessing HC27 Soil Database for Modeling Plant Production. International Journal of Plant Production, 14(4), 679-687. doi:10.1007/s42106-020-00114-4 Odgers, N. P., Libohova, Z., Thompson, J. A. (2012). Equal-area spline functions applied to a legacy soil database to create weighted-means maps of soil organic carbon at a continental scale. GEODERMA, 189-190, 153-163. doi:10.1016/j.geoderma.2012.05.026 QGIS.org. (2021). QGIS Geographic Information System. QGIS Association. http://www.qgis.org. Romero, C. C., Hoogenboom, G., Baigorria, G. A., Koo, J., Gijsman, A. J., Wood, S. (2012). Reanalysis of a global soil database for crop and environmental modeling. Environmental Modelling Software, 35, 163-170. Rossel, R. V., Chen, C., Grundy, M., Searle, R., Clifford, D., Campbell, P. (2015). The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research, 53(8), 845-864. Saxton, K., Rawls, W. J., Romberger, J. S., Papendick, R. (1986). Estimating generalized soil‐water characteristics from texture. Soil Science Society of America Journal, 50(4), 1031-1036. Singh, K., McClean, C. J., Buker, P., Hartley, S. E., Hill, J. K. (2017). Mapping regional risks from climate change for rainfed rice cultivation in India. Agricultural Systems, 156, 76-84. doi:10.1016/j.agsy.2017.05.009 Soil Science Glossary Terms Committee (2008). Glossary of soil science terms: 2008. American Society of Agronomy, USA Tyagi, S., Singh, N., Sonkar, G., Mall, R. (2019). Sensitivity of evapotranspiration to climate change using DSSAT model in sub humid climate region of Eastern Uttar Pradesh. Modeling Earth Systems and Environment, 5(1), 1-11. 吳瑞賢、李明旭、方紀棠 (2014)。應用主成份分析評估氣候變遷對作物產量因子之影響。農業工程學報。60(3): 68-8。 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(4): 16-28。 秦松林 (2013)。DSSAT 作物模式與統計時間序列應用於預測臺灣氣候變化對水稻產量影響之比較。臺灣大學農藝學研究所學位論文。 陳俊仁、姚銘輝、陳宣蘋、廖芳瑾 (2014)。糧食生產評估系統之建置:以水稻生產為例。台灣農業研究。63(1): 84-90。
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81864-
dc.description.abstract臺灣擁有豐富且多元的土壤生成因子,因而孕育出多樣化的土壤。土壤影響作物根系水分及養分的吸收,在農業生產上扮演舉足輕重的角色。過去已進行諸多土壤調查工作,以臺灣農地生產面向會考量的因子為目標,在臺灣各地進行詳盡的調查。其中1979年調查完成的詳測土壤圖包含屬性資料以及座標資料,其多應用於特定位置的土壤性質評估,較少以大規模方式使用。另外,在了解土壤因子對於作物生產影響的研究上,我們通常使用作物模式進行評估。藉由調整作物栽培參數,包含作物品種、氣候條件、土壤以及栽培管理策略等,可以模擬作物真實生長情形。本研究使用DSSAT模式進行研究。在過去的DSSAT模擬研究中,土壤參數通常予以忽略,並不會依據真實的土壤性質選擇土壤參數,主因是臺灣並沒有類似資源提供模式模擬者使用。前人的研究,鮮少將作物模式及土壤調查資源結合利用的案例。本研究將這兩項有力的工具結合,將詳測土壤圖內的土壤質地資料,進行含水量參數與土壤質地間的轉換,再透過等面積二次平滑樣條函數對參數進行估計,使之成為符合GlobalSoilMap六層標準土壤剖面的數位土壤資訊,將詳測土壤圖資轉換為網格資料,產製涵蓋全臺農業用地之土壤檔,命名為DH.SOL。與文獻資料相比,使用本土壤檔進行的全臺灣以及桃園地區模擬產量之敏感度分析,顯示使用本研究土壤檔的模擬結果對於產量較敏感,意即更能區別土壤差異,提高土壤因素影響模擬的精準度。本研究產製之土壤檔,為臺灣第一個符合在地土壤條件的DSSAT土壤檔,未來不論是大規模的作物生產模擬,或是評估氣候變遷的因應策略,皆可應用本研究之成果,獲得更好的模擬成效。zh_TW
dc.description.provenanceMade available in DSpace on 2022-11-25T03:05:20Z (GMT). No. of bitstreams: 1
U0001-2306202112224100.pdf: 1159992 bytes, checksum: 4bcc1628e788f4620448e2365c3d3a42 (MD5)
Previous issue date: 2021
en
dc.description.tableofcontents1 前言 1 1.1 土壤在農業栽培的重要性 1 1.2 DSSAT作物模式 1 1.2.1 DSSAT 1 1.2.2 DSSAT 模式在土壤參數的設定 2 1.2.3 敏感度分析 (Sensitivity Analysis) 3 1.3 臺灣土壤圖資 3 1.4 土壤資料庫系統 4 1.4.1 GlobalSoilMap 計畫 4 1.4.2 ISRIC網格土壤圖資 (SoilGrids) 5 1.4.3 DSSAT 土壤檔 (.SOL) 5 1.5 樣條函數 (Spline) 6 1.5.1 建構土壤屬性剖面函數 (Soil Attribute Depth Functions) 6 1.5.2 樣條函數 (Spline) 擬合土壤屬性剖面函數 7 1.6 論文架構 8 2 材料與方法 9 2.1 土壤資料 9 2.1.1 詳測土壤圖 (DSM) 9 2.1.2 網格文獻土壤檔Literature gridded soil dataset (LSD) 10 2.1.3 DSSAT土壤性質與含水量參數對照 11 2.2 等面積二次平滑樣條函數 13 2.3 三維土壤資訊建構流程 14 2.3.1 詳測土壤圖 (DSM) 處理 14 2.3.2 文獻土壤檔 (LSD) 處理 15 2.3.3 DSM多邊形與LSD網格的座標對應 15 2.4 DSSAT敏感度分析 17 2.5 DSM產量網格化 17 2.6 評估土壤因素對於水稻產量影響:以全臺及桃園地區為例 18 3 結果 19 3.1 LSD在DSM的投影 19 3.2 DH.SOL土壤檔產製 19 3.3 繪製LSD的全臺模擬結果 20 3.4 繪製DH.SOL的全臺模擬結果 20 3.5 比較兩種土壤檔對於水稻產量影響 21 3.5.1 全臺的產量模擬結果 21 3.5.2 桃園地區產量模擬結果 23 4 討論 24 4.1 本研究產製的DH.SOL與LSD之差異 24 4.2 敏感度分析 24 4.3 DH.SOL未來可利用性 25 4.4 網格對應技巧改善 26 4.5 土壤含水量參數估計方法的改善 27 4.6 土壤資料可以精進的方向 28 5 結論 29 參考文獻 30 附錄 A
dc.language.isozh-TW
dc.subject等面積二次平滑樣條函數zh_TW
dc.subject詳測土壤圖zh_TW
dc.subject產量評估zh_TW
dc.subject網格化zh_TW
dc.subjectDSSAT模式zh_TW
dc.subject水稻zh_TW
dc.subjectDSSATen
dc.subjectYield Evaluationen
dc.subjectRiceen
dc.subjectEqual Area Quadratic Smoothing Splineen
dc.subjectGriddingen
dc.subjectTaiwan Detailed Soil Mapen
dc.titleDSSAT模式結合網格化土壤資訊預測水稻產量之可行性評估zh_TW
dc.titleAssessment of Feasibility to Combine DSSAT Model and Gridded Soil Information for Rice Yield Predictionen
dc.date.schoolyear109-2
dc.description.degree碩士
dc.contributor.oralexamcommittee莊愷瑋(Hsin-Tsai Liu),陳虹諺(Chih-Yang Tseng)
dc.subject.keyword詳測土壤圖,網格化,DSSAT模式,等面積二次平滑樣條函數,水稻,產量評估,zh_TW
dc.subject.keywordTaiwan Detailed Soil Map,Gridding,DSSAT,Equal Area Quadratic Smoothing Spline,Rice,Yield Evaluation,en
dc.relation.page75
dc.identifier.doi10.6342/NTU202101104
dc.rights.note同意授權(全球公開)
dc.date.accepted2021-06-24
dc.contributor.author-college生物資源暨農學院zh_TW
dc.contributor.author-dept農藝學研究所zh_TW
dc.date.embargo-lift2023-06-01-
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