臺灣博碩士論文加值系統

病害嚴重度指標(disease severity index, DSI)常被使用在評估田間病害嚴重度，它可用來表示一個區域的病害強度，農業專家常使用DSI來說明某一品種在抗病性的表現程度、且說明病害嚴重度與產量的關係、並決定某種殺菌劑之有效性。本研究之目的為探究DSI在比較處理(例如:品種、藥劑等)的應用，首先以模擬方式獲得田間抽樣之估計值，並將估計值以DSI的方式與不同類別區間之組中點加權平均來進行比較，結果顯示DSI方式之表現略優於組中點；在選擇評估方法上，Nearest percent estimate(NPE)與10%等距的類別量表的表現最佳，而不等距的類別量表以修正的10%類別量表(低嚴重度時加以細分)表現最好。此外，在比較兩處理是否有差異的檢定時，使用三種分析方法:統合分析(meta analysis)、無母數的Mann-Whitney U test與t檢定，當探討這些分析方法之優劣時，結果發現以統合分析的固定效應分析結果最佳；當變化不同之區集數與樣本數時，亦會影響分析方法的結果，當樣本數較少時，使用統合分析效果較差，而區集數較少時，則不適用Mann-Whitney U test進行檢定，此研究結果將可提供在DSI使用時適當分析方法之建議。

The disease severity index (DSI) is a single index number for summarizing disease severity data so that it can represent the gross intensity of the disease in a plot or region. The DSI be used to indicate the disease resistance of a cultivar, the effectiveness of fungicides and the relationship between the disease severity and the yield by agricultural researchers. The purpose of this study is to explore the application of DSI in a comparison of the treatment (e.g., varieties, fungicides, etc.). First, to compare treatments, the estimated values of field sampling are obtained by simulation methods. Subsequently, transformed into the category scale to calculate DSI. The calculated DSI were compared with the weighted average of the midpoint from the corresponding interval. From the result of the study, the use of DSI is superior to the midpoint. In the assessment methods, nearest percent estimate (NPE) and 10% category scale have the better results. Moreover, the performance of amended 10% category scale with additional grades at low severity is good in the category scale with unequal intervals. Furthermore, as for comparing the differences between the two treatments, three analytical methods (meta analysis, Mann-Whitney U test, and t-test) were used. In these analytical methods, the fixed effect model of the meta analysis is the best.Also,the results of the analysis method were affected by the plots or samples.Meta analysis is inferior for smaller sample sizes. Besides, Mann-Whitney U test is not suitable for testing when the numbers of plots was small.The results of this study could be helpful for appropriate analytical methods when using DSI.

中文摘要…………………………………………………….……………i
Abstract………………………………………………..….………………ii
一、 緒論………………………………………………………………..1
(一) 研究動機…………………………………...…………………1
(二) 文獻回顧………………………………………………...……5
二、 材料與方法………………………………………………………10
(一) 模擬假設檢定…………………………………………….…10
(二) 病害介紹與原始數據……………………………….………12
1. 山核桃瘡痂病………………………………………...……12
2. 病害數據的真實值與估計值……………...………………13
(三) 模擬數據表示型式……………………………….…………14
1. 評估方式………………………………………………...…14
2. 病害嚴重程度指標……………………………...…………15
(四) 分析方法…………………………………………….………16
1. 固定效應模型………………………………………...……18
2. 隨機效應模型……………………………………...………20
三、 研究結果…………………………………………………………23
(一) 真實值與模擬資料之特性…………….……………………23
(二) 分析方法應用於DSI上之比較……………….……………23
1. 資料表示型式之比較………………………...……………24
2. 不同評估方式之比較…………………………...…………25
3. 分析方法之比較……………………………………...……25
(三) 樣本數與區集數之影響………………………...…..………26
1. 樣本數與區集數變動對分析方式的影響……..………….26
2. 給定樣本與區集數，探討分析方式與病害的關係…..….27
四、 討論………………………………………………………………28
(一) 綜合論述………………………………….…………………28
(二) 量表的資料型式之影響……………….……………………29
(三) 評估方式的影響…………………………….………………29
(四) 統合分析……………………………………….……………30
(五) 樣本數與區集數的影響………………………….…………31
(六) 樣本與區集的決定……………………….…………………32
(七) 最佳方式的選擇…………………………………………….33
(八) 試驗設計與統合分析之比較和實際田間設計…………………………………………………………….34
五、 參考文獻…………………………………………………………35
圖表目次………………………………………………………..………39
圖一、(a) 80筆病害真實值與其由15位評估者評估所得的病害估計
值之散佈圖。(b)估計值的平均與真實值的關係，以線性關係
(μ_rater=θ×Y_actual+c)進行配適，參數值(標準差)為θ
=1.06014 (0.03228)，c=2.5134 (0.97254)，配適所得
R^2 (coefficient of determination)=0.99。(c) 估計值的平均
之標準差與真實值的關係，以多項式σ_rater=a×〖Y_actual〗^2+
b×Y_actual+c進行配適，參數值(標準差)分別為a=
-0.001665 (0.00163)，b=0.2684 (0.12122)，c=
2.160534(1.287827)，配適所得R^2=0.8378574。……..…40
圖二、對於兩種計算方式DSI(%)與組中點的加權平均值之檢定力與
真實值之關係，將拒絕假設的機率(當假設不為真時)與真實值
變動(5~70%)作圖，分別以統合分析的固定效應與隨機效應模
式、t檢定以及Mann-Whitney U test分析三種不等距的量表H-
R scale、H-B scale和Amended 10% categorical scale的結果呈
現。假設真實的母體平均差(μ_∆)為5%，母體標準差(φ)為5%，
顯著水準(α)為0.05。……….…………………..………………42
圖三、對於不同評估方法之檢定力與真實值的關係，將拒絕假設的
機率(當假設不為真時)與真實值變動(5~70%)作圖，分析方法
為統合分析的固定效應模型，假設真實的母體平均差(μ_∆)為
5%，母體標準差(φ)為5%，顯著水準(α)為0.05。(a)NPE、不
等距的類別量表以DSI(%)做為資料型式以及10%和20%
increments之結果。(a)NPE、不等距的類別量表以組中點的加
權平均值做為資料型式以及10%和20% increments之結
果。…………………………..…………………..………………44
圖四、不同統計分析方法分析結果之檢定力與真實值之關係，將拒
絕假設的機率(當假設不為真時)與真實值變動(5~70%)作圖，
假設真實的母體平均差(μ_∆)為5%，母體標準差(φ)為5%，顯
著水準(α)為0.05。分析方法一: 統合分析的固定效應模型，
分析方法二: 統合分析的隨機效應模型，分析方法三: Mann-
Whitney U test，分析方法四: t檢定。結果以不同評估方法分
別呈現，(a) NPE，(b) H-R scale的DSI(%)，(c) H-B scale的
DSI(%)，(d) Amended 10% categorical scale的DSI(%)，(e)
10% increments，(f) 20% increments。………….…..………46
圖五、不同統計分析方法分析結果之型一誤差與真實值之關係，將
拒絕假設的機率(當假設為真時)與真實值變動(5~70%)作圖，
假設真實的母體平均差(μ_∆)為0%，母體標準差(φ)為5%，顯
著水準(α)為0.05。分析方法一: 統合分析的固定效應模型，
分析方法二: 統合分析的隨機效應模型，分析方法三: Mann-Whitney U test，分析方法四: t檢定。結果以不同評估方法分別呈現，(a) NPE，(b) H-R scale的DSI(%)，(c) H-B scale的DSI(%)，(d) Amended 10% categorical scale的DSI(%)，(e) 10% increments，(f) 20% increments。…………….....……..…48
圖六、對於統計分析方法之檢定力與樣本數變動的關係，將拒絕假
設的機率(當假設不為真時)與樣本數變動(n=5~50)作圖，資料
採用H-R scale的DSI(%)，分別在真實值(μ_A)為5%、25%、
50%下，考慮區集數(rep)為2、4、6、8時的結果，假設真實
的母體平均差(μ_∆)為5%，母體標準差(φ)為5%，顯著水準(α)
為0.05。分析方法一: 統合分析的固定效應模型，分析方法
二: 統合分析的隨機效應模型，分析方法三: Mann-Whitney U
test，分析方法四: t檢定。………………..………..…………50
圖七、在給定的樣本數(n)與區集數(rep)下，對於不同分析方法與真
實值的關係，將拒絕假設的機率(當假設不為真時)與真實值
變動(5~50%)作圖，資料採用H-R scale的DSI(%)，假設真實
的母體平均差(μ_∆)為5%，母體標準差(φ)為5%，顯著水準(α)
為0.05。分析方法一: 統合分析的固定效應模型，分析方法
二: 統合分析的隨機效應模型，分析方法三: Mann-Whitney U
test，分析方法四: t檢定。(a)區集數2樣本數30，(b)區集數
3樣本數30，(c)區集數4樣本數10，(d)區集數4樣本數
20，(e)區集數4樣本數30，(f)區集數4樣本數40。………52
圖八、在給定的樣本數(n)與區集數(rep)下，對於不同分析方法與真
實值的關係，將拒絕假設的機率(當假設不為真時)與真實值
變動(5~70%)作圖，資料採用H-R scale的DSI(%)，假設真實
的母體平均差(μ_∆)為5%，母體標準差(φ)為5%，顯著水準(α)
為0.05。分析方法一: 統合分析的固定效應模型，分析方法
二: 統合分析的隨機效應模型，分析方法三: Mann-Whitney U
test，分析方法四: t檢定。(a)區集數5樣本數30，(b)區集數
5樣本數40，(c)區集數6樣本數20，(d)區集數6樣本數
30，(e)區集數7樣本數30，(f)區集數8樣本數40。…....…54
圖九、給定處理A為30% (實線)和處理B為35% (虛線)，標準差
(φ)為5%，模擬NPE以及H-R scale資料的分布。每區集的
樣本數為25，兩處理分別重複模擬出5000個區集，計算組
中點或DSI(%)之分布，資料表示形式分別為: NPE的加權平
均值、組中點的加權平均值，以及等級DSI(%)。………….55
圖十、實際應用比較兩處理間的效應時，在4個區集以及25個樣本
下，提供評估方法、資料表示型式以及分析方法之建議。..57
圖十一、研究設計之流程圖。…………………………………………..58

李宛柔、林怡君、于耀華與賴玉玲。2009。後設分析之介紹。牙醫學雜誌。29-2: 63-68。
林資荃。2014，統合分析(Meta-analysis)簡介。RegMed。48: 6-17。
莊其穆。2011。臨床醫師如何閱讀統合分析(Meta-analysis)的論文。台灣醫界。54: 18-26。
Bero, L., and D. Rennie. 1995. The Cochrane Collaboration. Preparing, maintaining, and disseminating systematic reviews of the effects of health care. JAMA. 274: 1935-1938.
Bock, C. H., T. R. Gottwald, P. E. Parker, P. E. F. Ferrandino, S. Welham, F. van den Bosch, and S. Parnell. 2010. Some consequences of using the Horsfall-Barratt scale for hypothesis testing. Phytopathology 100: 1031–1041.
Bock, C. H., B. W. Wood, and T. R. Gottwald. 2013a. Pecan scab severity—Effects of assessment methods. Plant Dis. 97: 675–684.
Bock, C. H., B. W. Wood, F. van den Bosch, S. Parnell, and T. R. Gottwald. 2013b. The effect of Horsfall-Barratt category size on the accuracy and reliability of estimates of pecan scab severity. Plant Dis. 97: 797–806.
Bock, C. H., M. W. Hotchkiss, and B.W. Wood. 2016. Assessing disease severity: accuracy and reliability of rater estimates in relation to number of diagrams in a standard area diagram set. Plant Pathol. 65: 261-272.
Cardoso, J. E., A. A. Santos, A. G. Rossetti, and J. C. Vidal. 2004. Relationship between incidence and severity of cashew gummosis in semiarid north-eastern Brazil. Plant Pathol. 53: 363–367.
Chaube, H. S., and U.S. Singh. 1991. Plant Disease Management: Principles and Practices. Boca Raton, FL, USA: CRC Press.
Chen, D. G., and K. E. Peace. 2013. Applied Meta-analysis with R. Chapman &Hall/CRC: Boca Raton.
Chester, K. S. 1950. Plant disease losses: their appraisal and interpretation. Plant Dis. Rep. Suppl. 193: 190-362.
Chiang, K. S., C. H. Bock, I. H. Lee, M. El Jarroudi, and P. Delfosse. 2016a. Plant disease severity assessment - how rater bias, assessment method and experimental design affect hypothesis testing and resource use efficiency. Phytopathology 106: 1451–1464.
Chiang, K. S., C. H. Bock, M. El Jarroudi, P. Delfosse, I. H. Lee, and H. I. Liu. 2016b. Effects of rater bias and assessment method on disease severity estimation with regard to hypothesis testing. Plant Pathol.65: 523–535.
Chiang, K. S., H. I. Liu, and C. H. Bock. 2017. A discussion on disease severity index values: warning on inherent errors and suggestions to maximize accuracy. Annals of Applied Biology. (Accepted) DOI: 10. 1111/aab. 12362
Chiang, K. S., S. C. Liu, C. H. Bock, and T. R. Gottwald. 2014. What interval characteristics make a good categorical disease assessment scale? Phytopathology 104: 575–585.
Cochrane, A. L. 1979. 1931-1971: a critical review, with particular reference to the medical profession. In: Medicines for The Year 2000. London: office of Health Economics. 1979: 1-11.
Corrêa, F. M., J. S. S. Bueno Filho, and M. G. F. Carmo. 2009. Comparison of three diagrammatic keys for the quantification of late blight in tomato leaves. Plant Pathol. 58: 1128–1133.
Gafni, A., C. E. Calderon, R. Harris, K. Buxdorf, A. Dafa-Berger, E. Zeilinger-Reichert, and M. Levy.2015. Biological control of the cucurbit powdery mildew pathogen Podosphaera xanthii by means of the epiphytic fungus Pseudozyma aphidis and parasitism as a mode of action. Frontiers in Plant Science 6: 132.
Galv´an, G. A., W.A. Wietsma, S. Putrasemedja, A.H. Permadi, and C.Kik. 1997. Screening for resistance to anthracnose (Colletotrichum gloeosporioides Penz.) in Allium cepa and its wild relatives. Euphytica 95: 173–178.
Ghose, L., F. A. Neela, T. C. Chakravorty, M. R. Ali, and M. S. Alam. 2010. Incidence of leaf blight disease of Mulberry plant and assessment of changes in amino acids and photosynthetic pigments of infected leaf. Plant Pathology J. 9: 140–143.
Glass, G. V. 1976. Primary, secondary and meta-analysis of research. Educ. Res. 5: 3-8.
Grau, C.R., V. L. Radke, and F. L. Gillespie. 1982. Resistance of soybean cultivars to Sclerotinia sclerotiorum. Plant Dis. 66: 506–508.
Higgin, J. P., S. G. Thompson, J. J. Deeks, and D. G. Altman. 2003. Measuring inconsistency in meta-analyses. British Medical Journal 327: 557–560.
Horsfall, J.G., and J. W. Heuberger. 1942. Measuring magnitude of a defoliation disease of tomatoes. Phytopathology 32: 226-232.
Huang, Y.H., R. C. Wang, C. H. Li, C. W. Zuo, Y. R. Wei, L. Zhang, and G. J. Yi. 2012. Control of Fusarium wilt in banana with Chinese leek. Eur. J. Plant Pathol.134: 87–95.
Hunter, R. E., and D. D. Roberts. 1978. A disease grading system for pecan scab. Pecan Q. 12: 3–6.
Hunter, R.E. 1983. Influence of scab on late season nut drop of pecans. Plant Dis. 67:806–807.
Jones, D. R. 1995. Meta-analysis: weighing the evidence. Star Med. 14: 137-149.
Kobriger, K. M., and D. J. Hagedorn. 1983. Determination of bean root rot potential in vegetable production fields of Wisconsin’s central sands. Plant Dis. 67: 177–178.
Kolkman, J. M., and J. D. Kelly. 2002. Agronomic traits affecting resistance to white mold in common bean. Crop Sci. 42: 693–699.
Koitabashi, M. 2005. New biocontrol method for parsley powdery mildew by the antifungal volatiles-producing fungus Kyu-W63. J. Gen. Plant Pathol. 71: 280–284.
Kora, C., M. R. McDonald, and G. J. Boland. 2005. Epidemiology of Sclerotinia rot of carrot caused by Sclerotinia sclerotiorum. Can. J. Plant Pathol. 27: 245–258.
Lamari, L. 2002. ASSESS: Image Analysis Software for Plant Disease Quantification. The American Phytopathological Society, St. Paul, MN.
Lazarovits, G., J. Hill, G. Patterson, K. L. Conn, and N. S. Crump. 2007. Edaphic soil levels of mineral nutrients, pH, organic matter, and cationic exchange capacity in the geocaulosphere associated with potato common scab. Phytopathology 97: 1071–1082.
Madden, L.V., G. Hughes, and F. van den Bosch. 2007. The Study of Plant Disease Epidemics. American Phytopathological Society, St. Paul, MN.
Nitzan, N., T. F. Cummings, D. A. Johnson, J. S. Miller, D. L. Batchelor, C. Olsen, R. A. Quick, and C. R. Brown. 2008. Resistance to root galling caused by the powdery scab pathogen Spongospora subterranea in potato. Plant Dis. 92: 1643–1649.
Nsabiyera, V., M. Ochwo-Ssemakula, and P. Sseruwagi. 2012. Hot pepper reaction to field diseases. African Crop Sci. J. 20: 77–97.
Nutter, F. W., and P. M. Schultz. 1995. Improving the accuracy and perception of disease assessments: Selection of methods and use of computer-aided training programs. Can. J. Plant Pathol. 17: 174-184.
Pearson, K. 1904. Report on certain enteric fever inoculation statistics. Br. Med. J. 3: 1243-1246.
Raupach, G.S., L. Liu, J. F. Murphy, S. Tuzun, and J. W. Kloepper. 1996. Induced systemic resistance in cucumber and tomato against cucumber mosaic cucumovirus using plant growth-promoting rhizobacteria (PGPR). Plant Dis. 80: 891–894.
Ruppel, E.G., and R. J. Hecker. 1982. Increased severity of Rhizoctonia root rot in sugar beet treated with systemic insecticides. Crop Protection 1: 75–81.
Shah, D.A., and L. V. Madden. 2004. Nonparametric analysis of ordinal data in designed factorial experiments. Phytopathology 94: 33–43.
Sherwood, R. T., C. C. Berg, M. R. Hoover, and K. E. Zeiders. 1983. Illusions in visual assessment of Stagonospora leaf spot of orchard grass. Phytopathology 73: 173-177.
Stovold, G. E., and H. J. P. Smith. 1991. The prevalence and severity of diseases in the coastal soybean crop of New South Wales. Aust. J. Exp. Agr. 31: 545–550.
Vieira, R. F., T. J. Paula Júnior, H. Teixeira, and J. E. D. S. Carneiro. 2010. White mold management in common bean by increasing within-row distance between plants. Plant Dis. 94: 361–367.
Vieira, R. F., T. J. Paula Júnior, J. E. S. Carneiro, H. Teixeira, and T. F. N. Queiroz. 2012. Management of white mold in type III common bean with plant spacing and fungicide. Trop. Plant Pathol. 37: 95–101.
Yadav, N. V. S., S. M. de Vos, C. H. Bock, and B. W. Wood. 2013. Development and validation of standard area diagrams toaid assessment of pecan scab symptoms on fruit. Plant Pathol. 62: 325–335.