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harveypass铜虫 (小有名气)
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首先说一下啊，文章投了3个月才回复我，结果是“reject and resubmit",你们说这文章我按他们的意思改回去后，接受的可能性大不大？他们给的意见我基本都可以完成的。要补的数据我都有现成的。 Dear **, Your manuscript # PP-entitled "**" which you submitted to Plant Pathology has now completed its review process. I regret to inform you that it is unacceptable, in its present form, for publication in the journal. Comments and criticisms of the referee(s) can be found at the bottom of this letter. I hope you will be able to revise your manuscript guided by the comments on the referees' reports, and resubmit it. It is very important that you refer to our online guidelines for electronic artwork available at http://authorservices.wiley.com/bauthor/illustration.asp when preparing your figures. Thank you for considering Plant Pathology for the publication of your research. I look forward to receiving a revised manuscript. Yours sincerely, Professor Matt Dickinson Senior Editor, Plant Pathology Matthew.Dickinson@nottingham.ac.uk Please note that you have a file attached to your decision letter. Go to your Author Centre, click on the 'Manuscripts with Decision' queue and then on the right under 'Status' click on 'View Decision Letter'. The attachment is at the bottom of the decision letter. Referee: 1 Comments to the Author This work presents a two-years experiment on Rice, with the aim to “validate the direct effect of canopy structure parameters on rice sheath blight”. Nitrogen fertilisation and hill density are used to vary canopy structure in fields. Interestingly they both induce strong change in canopy structure and in disease development. For instance in 2009, there is no disease development at all for the 0 nitrogen treatment. However, the present work presents several major limits. The authors need to clarify the goal of their paper and therefore the way the results are analysed and presented. In the introduction, the authors present their goal as to “validate the direct effect of canopy structure parameters on rice sheath blight”. What does “validate” mean in this sentence? Several published works have shown that canopy structure can influence disease epidemics. This effect is known to vary between seasons and to depend on the pedo-climatic context of the experiments. What does “direct effect” mean in this sentence? The effects of canopy structure on disease development are difficult to assess directly because it is difficult to disentangle the effect of each canopy structure parameter as they act simultaneously, in interaction with each other and with the climate. It is necessary to present in the introduction the hypotheses issued from the literature on the link between the two practices (N and density), the canopy structure changes, and the epidemics. Literature dedicated to understand the effect of architecture on disease development is missing in this publication (eg. see references at the end). The authors need to explain the choice of the canopy structure parameters chosen to be measured in the experiments: “tiller number, LAI, biomass, contact frequency, light transmittance, SPAD measurement, number of dead leaves”. Others parameters such as plant height or phyllochron could also be of interest for their work. One of the limits is that here the parameters are static, and interaction between architecture and disease development is based on dynamic interactions (for instance, dynamics of tillering could be more informative than the final number). Moreover, these canopy parameters are treated identically in the statistical analyses (linking disease development and canopy variables), but they are different types of parameters. Some parameters are “integrated canopy” variables (such as LAI or biomass), others are more precise architecture and “explicative” (tiller numbers, contact frequency) and others are environmental measures strongly influenced by the canopy structure (light transmittance). Some of these parameters are certainly strongly correlated (number of tillers and LAI for instance). It would be more informative to analyse each parameter in the light of the hypotheses that underly this work. These remarks imply new analyse of the results and significant rewriting of the manuscript. Here are some more specific remarks in the different sections of the manuscript. Introduction Page 4 lines 77-82: This paragraph should be improved in order to better present the potential effects of architecture on epidemics and to identify the parameters that are measured in the present study (cf. above). This is where you should present your hypotheses. Page 4 lines 86-89: Please better explain the specificity of your work compared to Duy et al. 2004 and San-oh et al. 2004. Pages 4 lines 89-93: I find these sentences difficult to understand. Material and Method Page 8: Concerning pathogen inoculation, please clarify the number of leaves inoculated. Page 9, line 177: is it total LAI or green LAI? Page 9, line 196 -199: is the lesion length measured only on inoculated leaves? Or is it also measured on plants of the same treatments but non inoculated? The total lesion length could be divided by the total leaf surface in order to disentangle effects linked to the larger leaf surface area and to the physiological leaf status. Page 10, line 215: Test the correlation between the parameters Results A first paragraph describing the disease development for the two years for early and late seasons is needed (some sentences of the discussion could be used for that). Description of the effect of inoculation on disease development (in terms of sheath blight index) is also needed. It is not clear comparing figure 1d and figure 2d that inoculation resulted in higher epidemics (index is around 60% in the two cases). This could change the conclusion of the paper. Presentation of the main climatic differences between the two years would be helpful for understanding the results. The manuscript would benefit from highlighting the effect of the two practices and their interaction with epidemic development. These effects are strong and vary with the climate. I would advice to split the results concerning the lesion length and concerning the SB index (presenting thus together inoculated and non inoculated). Lesion length indicates lesion development in the leaf (linked to resistance, microclimate, physiological status etc…), but SB index reflects epidemic development in the canopy (with other canopy parameters involved). The figures 1, 2, 3, 4 and 5 do not show the specific effects of the nitrogen treatment (page 11, lines 231-232, page 12, lines 249-250, 259-260). References : Ando K, R. Grumet, K. Terpstra and J. D. Kelly. 2005. Manipulation of plant architecture to enhance crop disease control. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 2007 2, No. 026 Bahat, A., I Gelernter, M.B. Brown and Z. Eyal 1980 Factors affecting the vertical progression of Septoria Leaf Blotch in Short-Statured Wheats. Phytopathology 70 (3): 179-184 Lovell, D.J., Parker, S.R., Hunter, T., Royle, D.J. and Coker, R.R., 1997. Influence of crop growth and structure on the risk of epidemics by Mycosphaerella graminicola (Septoria tritici) in winter wheat. . Plant Pathology, 46(1): 126-138. Robert C., Fournier C.,Andrieu B., Ney B. 2008. Coupling a 3D virtual wheat (Triticum aestivum) plant model with a Septoria tritici epidemic model (Septo3D): a new approach to investigate plant-pathogen interactions linked to canopy architecture. Functional Plant Biology, 35 (10) p 997-1013. Walters D.R. & I.J. Bingham (2007). Influence of nutrition on disease development caused by fungal pathogens: implications for plant disease control, Annals of Applied Biology, 1-18 Gan Y, Gossen BD, Li L, Ford G, Banniza S. 2007. Cultivar type, plant population, and ascochyta blight in chickpea. Agronomy Journal, 99: 1463-1470. Skirvin DJ. 2004. Virtual plant models of predatory mite movement in complex plant canopies. Ecological Modelling, 171: 301-313. Jurke CJ, Fernando WGD. 2008. Effects of seeding rate and plant density on sclerotinia stem rot incidence in canola. Archives of Phytopathology and Plant Protection, 41: 142-155. Referee: 2 Comments to the Author Please see comments in the attached file. Editor: Paveley, Neil Comments to the Author: Firstly I hope you will accept my apologies for the delay in consideration of your manuscript. This was due to difficulty finding reviewers and my own workload. I know as an author how frustrating delays of this kind can be. At the suggestion of the Senior Editor, I acted as one of the reviewers. I am not able to recommend acceptance of the manuscript in its current form, for the reasons which are set out in some detail in the two reviews. However the work described could make a useful contribution to the literature, so I hope you will consider substantially re-working the manuscript and re-submitting it to Plant Pathology for review if you are able to address the points raised. The stated aim of the work reported here is to determine the direct effects of canopy structure parameters [in fact, variables] on sheath blight development in rice. This is a potentially useful area of work - as the authors state, variation in canopy structure can confound resistance phenotyping. A substantial field data set is presented which could add usefully to the literature. However, the paper requires substantial re-working if it is to achieve the stated aim. This requires reasonable proof of a direct (and therefore, by implication, causal) relationship between one or more canopy structure variables and disease severity. Data for potentially relevant variables are given, but the text and presentation of data are not sufficiently well structured to argue convincingly for causal relationships. The text should give a clear hypothesis: a mechanistic description of how variation in the independent canopy variables measured would be expected to relate to the dependent variables. And a more rigorous analysis should test the hypothesis. Currently, relationships between dependent and independent variable are tested by correlation. Where a causal relationship is being advocated, regression analysis is usually employed and the form of the relationship is usually shown as a scatter plot, with a fitted function. Furthermore, in the data presented the independent variables appear to be highly correlated, but this is not dealt with adequately in the analysis. Some form of stepwise multiple regression may help to discriminate between the key variable/s which are mechanistically related to the dependent variables and those which are correlated with the independent variable only because they are themselves correlated with the key variable/s. I suspect that multiple regression would show that most of the variables do not retain explanatory value, once one or two of the variables are included in the model. Once the analysis and interpretation are more certain, the authors could then return to re-consider the stated practical implications of their findings in the discussion. Two areas need more careful consideration. Firstly, the extent to which specific canopy variables might be used to reduce the confounding of host partial resistance phenotyping (as, for example, height and heading date are used in the analysis of resistance against splash-borne diseases in wheat) in order to identify resistance QTL. Secondly, whether ‘toothpick’ inoculation is really a good method for assessing host resistance, as claimed. 100% infection was achieved, suggesting that the method is unlikely to detect resistance which reduces infection efficiency. Specific points: 1. Consideration of the physiological variables is rather haphazard and does not clearly describe their hierarchical, mechanistic and statistical inter-relationships. 2. The variable ‘tiller number’ is more correctly ‘shoot number’ as the count does not differentiate between tillers and main shoots. 3. The lodging and yield data could usefully be included to show, for example, the extent to which a larger canopy might be more diseased, but perhaps better able to maintain light interception and hence yield? 4. The ShBI scoring system is a symptom location scale. So it is not perhaps surprising that inoculation placed in a particular location did not give rise to any variation in ShBI? 5. Consideration needs to be given to whether the independent variables could not themselves have been affected by the dependent variables; particularly when they are measured after the natural epidemic has started or artificial inoculation has been applied. 6. A fixed number of hills were inoculated, so the area inoculated would vary between treatments. The possibility that this could have introduced bias should be discussed. [ Last edited by harveypass on 2012-4-3 at 13:41 ] ... [ Last edited by harveypass on 2013-4-26 at 21:04 ] |
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