Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/36785
標題: 台灣南部毛豆合格莢產量之氣象估計模式的驗證與實地測試
Validation and On-Farm Testing of Several Weather Models for Predicting the Marketable Pod Yield of Vegetable Soybean in Southern Taiwan
作者: 許煌城
Hsu, Huang-Cheng
關鍵字: Vegetable Soybean;毛豆;Validation;On-Farm Testing;驗證;實地測試
出版社: 農藝學系所
引用: 林隆新、林俊隆。2004。台灣南部毛豆合格莢產量之氣象估計模式的研究I. 建模。中華農學會報5(4):344-360。 陳庚鳳。1993。毛豆莢果特性之研究。國立中興大學農藝學研究所博士論文。臺灣。臺中。pp.194。 Anderson, L. R. and B. L. Vasials. 1985. Effect of planting date on two soybean cultivars: seasonal dry matter accumulation and seed yield. Crop Sci. 25:999-1004. Baier, W. 1979. Note on the terminology of crop-weather models. Agric. Meteorol. 20:137-145. Boote, K. J., J. W. Jones, and N. B. Pickering. 1996. Potential uses and limitations of crop models. Agron. J. 88:704-716. Carlson, J. B. 1973. Morphology. In: B. E. Caldwell ( ed. ) Soybean: Improvement, Production , and Use. pp:17-95. Am. Soc. Agron., Inc., Madison, WI. USA. Draper, N. R. and H. Smith. 1998. Applied Regression Analysis. 3rd ed. New York: Wiley. pp.706. Egli, D. B. and I. F. Wardlaw. 1980. Temperature response of seed growth characteristics of Soybean. Agron. J. 72:560-564. Elston, D. A. and M. F. Proe. 1995. Smoothing regression coefficients in an overspecified regression model with interrelated explanatory variables. Appl. Statist. 44 (3):395-406. Fehr, W. R. and C. E. Caviness. 1977. Stages of Soybean Development. Iowa State Univ. Coop. Ext. Serv. Spec. Rep. 80:1-12. Hadi, A. S. and R. F. Ling, 1998. Some cautionary notes on the use of principal components regression. Amer. Stat. 52:15-19. Hartwig, E. E. 1973. Varietal Development. In: B. E. Caldwell ( ed. ) Soybean: Improvement, Production, and Use. pp:187-210. Am. Soc. Agron. Madison Wis. Helland, I. S. 1988. On the structure of partial least squares regression. Commin. Statist. Simula. Comput. 17:581-607. Helland, I. S. 1990. Partial least squares regression and statistical models. Scand. J. Statist. 17:97-114. Hodges, T. and V. French. 1985. Soybean: Soybean growth stages modeled from temperature, daylength, and water availability. Agron. J. 77:500-505. Hoerl, A. E. and R. W. Kennard. 1970. Ridge regression: biased estimation for non-orthogonal problem. Technometrics 12:55-67. Hoskuldsson, A. 1988. PLS regression methods. Chemomet. 2:211-228. Katz, R. W. 1979. Sensitivity analysis of statistical crop-weather models. Agri. Meteor. 20:291-300. Kobayashi, K., and M. U. Salam. 2000. Comparing simulated and measured values using mean squared deviation and its components. Agron. J. 92:345-352. Major, D. J., D. R. Johnson, J. W. Tanner, and I. C. Anderson. 1975. Effects of daylength and temperature on soybean development. Crop Sci. 15:174-179. Martens, H., and T. Naes. 1989. Multivariate Calibration. 4thed. Wiley. Chichester. Meckel, L., D. B. Egli, R. E. Phillips, D. Radcliffe, and J. E. Leggett. 1984. Effect of moisture stress on seed growth in soybean. Agron. J. 76:647-650. Neter, J., M. H. Kutner, C. J. Nachtsheim, and W. Wasserman 1996. Applied Linear Statistical Models. Chicago: Irwin.pp.1408. Oreskes, N., K. Shrader-Frechette, and K. Belitz. 1994. Verification, validation, and confirmation of numerical models in the earth sciences. Science 263:641-646. Rawlings, J. O. 1988. Applied Regression Analysis: A Research Tool. Belmont, California: Wadsworth. pp.562. SAS Institute. 1999. The PLS Procedure. In: SAS/STATR User’s Guide, Version 8, pp. 2691-2735. Cary, NC: SAS Institute Inc. Seddigh, M. and G. D. Jolliff. 1984. Effect of night temperature on dry matter partitioning and seed growth of indeterminate field growth soybean. Crop Sci. 24:704-709. Sekulic, S., M. B. Seasholtz, Z. Wang, and B. R. Kowaiski. 1993. Nonlinear multivariate calibration methods in analytical chemistry. Ana. Chem. V.65 N.19:835A-845A. Smiciklas, K. D., R. E. Mullen, R. E. Carlson, and A. D. Knapp, 1992. Soybean seed quality response to drought stress and pod position. Agron. J. 84:166-170. Snee, R D. 1977. Validation of regression models: method and example. Technometrics 19:415-428. Shibles, R. M., I. C. Anderson, and A. H. Gibson. 1975. Soybean. In: L. T. Evans ( ed. ) Crop Phsiology. pp:151-190. Cambridge University Press. New York. Summerfield, R. J., and E. H. Roberts. 1984. Photo-thermal regulation of flowering in soybean. In: R. Shibles ( ed. ) World Soybean Research Conference. III: Proceedings. pp:848-857. Westview Press. Boulder and London. van Schaik, P. H., and A. H. Probst. 1958. Effect of some enviroment factors on flower production and reproductive efficiency. Agron. J. 50:192-197. Wallach, D., and B. Goffinet. 1987. Mean squared error of prediction in models for studying ecological and agronomic systems. Biometrics 43:561-573. Whisler, F. D.,B. Acock, D. N. Baker, R. E. Fye, H. F. Hodges, J. R. Lambert, H. E. Lemmon, J. M. McKinion and V. R. Reddy. 1986. Crop simulation models in agronomic systems. Adv. Agron. 40:141-208. Wold, S. 1994. PLS for Multivariate Linear Modeling. In "Methods and Principles in Medicinal Chemistry", ed. H. van de Waterbeemd. Weinheim, Germany: Verlag-Chemi.
摘要: 
為了估計台灣南部之毛豆合格莢產量,林 和 林( 2004 )藉由脊回歸法、主成份回歸法及淨最小平方回歸法開發了六個氣象估計模式。本研究之目的在評估這些模式的估計能力。
首先,我們在收集建模數據的同一地點( 高雄區農改場 ),以相同的材料與方法進行一系列播種期×收穫期的田間試驗,總共獲得32筆毛豆高雄五號之發育進度、合格莢產量、及其生長期間的氣象數據。以此32筆與建模數據完全獨立的驗證數據對前述六個模式進行驗證的結果顯示:除了以脊回歸法配適的兩個模式沒有準確的估計之外,循主成份回歸法與淨最小平方回歸法配適的四個模式都有相當好的表現。其中一個由主成份回歸法配適的模式表現最好:其估計近乎無偏,並能充分呈現毛豆合格莢產量對氣象變化的反應;其估計值與觀測值間的相關係數高達0.86,而估計的最大相對誤差則在22%以下。
其次,為了測試在驗證時表現較好的四個模式之實地應用性,我們分別在屏東縣美濃鎮與萬丹鄉各洽請兩個生產毛豆的農戶依其慣行的田間管理方式在當地之春作與秋作進行栽培試驗;同時,在高雄場亦設置一栽培試驗田以供對照。由此總共獲得10筆毛豆高雄五號之發育進度、合格莢產量、及其生長期間的氣象數據。以此10筆數據對四個模式之估計能力進行實地測試的結果極不理想:雖然估計的最大相對誤差仍在22%以下,但估計值與實測產量間的相關係數僅介於0.6177~0.6311( 皆未達0.05顯著水準 )。對四個農戶之毛豆合格莢產量而言,實測值一致地高於其模式估計值;而各個農戶之實測平均產量也一致地高於高雄場之實測平均產量。因此我們在模式中加上各個農戶之地點效應( 以各個農戶之平均產量減去高雄場之平均產量間的差額為其估值 ),以調校估計上的偏差。調校後的模式再以實地數據測試時,估計的相對誤差之最大值降低至11%以下;而估計值與實測產量間的相關係數則升高至0.92以上。這些結果顯示:由於建模數據的限制,不論是由淨最小平方回歸法配適的兩個模式或由主成份回歸法配適的兩個模式都只是地點專一性的模式,在應用上必須非常謹慎。
URI: http://hdl.handle.net/11455/36785
其他識別: U0005-1201200700324800
Appears in Collections:農藝學系

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