Analysis of the association between the incidence of two spatially aggregated foliar diseases of strawberry

ABSTRACT Association of the incidence of leaf blight (caused by Phomopsis obscurans) and leaf spot of strawberry (caused by Mycosphaerella fragariae) was assessed at multiple scales in perennial plantings at several commercial farms over 3 years (1996 to 1998). For each field, the presence or absence of each disease was recorded from n = 15 leaflets in each of N approximately 70 evenly spaced sampling units, and the proportion of leaflets with blight, spot, and total disease (blight or spot) was determined. Individual diseases and total disease incidence were all well described by the beta-binomial distribution but not by the binomial distribution, indicating overdispersion of disease. The Jaccard similarity index was used to measure disease co-occurrence at the leaflet, sampling-unit, and field scales. Standard errors of this index for the lower two scales were obtained using the jackknife (resampling) procedure, and data randomizations were used to determine the expected Jaccard index for an independent arrangement of the two diseases, conditioned on the incidence and spatial heterogeneity of the observed disease data. Results based on these statistics showed that only 4 of 52 data sets at the leaflet level and no data sets at the sampling-unit level had Jaccard index values significantly different from that expected under an independent rearrangement of the two diseases. Rank correlation and cross-correlation statistics were calculated to determine the degree of covariation in incidence between the two diseases. Additionally, covariation between diseases was tested using a new procedure in the Spatial Analysis by Distance IndicEs (SADIE) class of tests. Covariation was detected in 21% of the data sets using rank correlation methods and in 15% of the data sets using the SADIE-based approach. The discrepancy between these two methods may be due to the rank correlation procedure not taking into account the effects of spatial pattern of disease incidence. There was no relationship between mean disease incidence per field of spot and blight or between degree of heterogeneity of the two diseases (as measured by theta of the beta-binomial distribution), demonstrating lack of covariation at the field scale. Incidence of leaflets with either disease (total disease incidence) could be well predicted using a linear combination of the estimated probabilities of leaf blight and leaf spot incidence based on independence of the two diseases. Heterogeneity of total disease incidence, measured with the estimated theta parameter of the beta-binomial distribution, could also be well predicted using a linear combination of the weighted theta values for leaf blight and leaf spot, with weights proportional to incidence of the individual diseases.     

Sampling for plant disease incidence

ABSTRACT Knowledge of the distribution of diseased plant units (such as leaves, plants, or roots) or of the relationship between the variance and mean incidence is essential to efficiently sample for diseased plant units. Cluster sampling, consisting of N sampling units of n individuals each, is needed to determine whether the binomial or beta-binomial distribution describes the data or to estimate parameters of the binary power law for disease incidence. The precision of estimated disease incidence can then be evaluated under a wide range of settings including the hierarchical sampling of groups of individuals, the various levels of spatial heterogeneity of disease, and the situation when all individuals are disease free. Precision, quantified with the standard error or the width of the confidence interval for incidence, is directly related to N and inversely related to the degree of heterogeneity (characterized by the intracluster correlation, rho). Based on direct estimates of rho (determined from the theta parameter of the beta-binomial distribution or from the observed variance) or a model predicting rho as a function of incidence (derived from the binary power law), one can calculate, before a sampling bout, the value of N needed to achieve a desired level of precision. The value of N can also be determined during a sampling bout using sequential sampling methods, either to estimate incidence with desired precision or to test a hypothesis about true disease incidence. In the latter case, the sequential probability ratio test is shown here to be useful for classifying incidence relative to a hypothesized threshold when the data follows the beta-binomial distribution with either a fixed rho or a rho that depends on incidence.     

Rhynchosporium leaf scald disease incidence: seed source and spatial pattern

A programme of field trials for the study of the winter barley–Rhynchosporium commune pathosystem is reported. The associated seedborne disease rhynchosporium leaf scald is regarded as having an important impact on barley yields. The analysis in this study relates to the impact of the seed source (commercial or farm-saved seed) on disease incidence and to the spatial pattern of rhynchosporium leaf scald disease incidence. Disease incidence data were calculated from field data recorded as disease severity. Mean disease incidence was higher in the crops grown from farm-saved seed than in those grown from commercial seed, although great agronomic significance cannot be attached to this result. The spatial pattern of rhynchosporium leaf scald disease incidence was characterized in terms of the binary power law (BPL) and was indicative of an aggregated pattern. Programme-wide BPL results were described using a novel phytopathological application of a random coefficients model. These results have application in field sampling for rhynchosporium leaf scald disease.     

A generalized linear modeling approach for characterizing disease incidence in a spatial hierarchy

ABSTRACT Several statistical models are introduced to quantify the effect of heterogeneity on disease incidence relationships in a three-scale spatial hierarchy: the sampling unit level (highest), the leaf scale (intermediate), and the leaflet scale (lowest). The models are an extension of the theory previously developed for a two-scale hierarchy and were tested using data collected from strawberry leaf blight epidemics. Disease incidence at the sampling-unit scale (proportion of sampling units with one or more diseased leaflets) increased as a saturation-type curve with increasing leaflet or leaf disease incidence (proportion of leaflets or leaves diseased) as predicted by the good fit of the beta-binomial distribution to the leaflet and leaf data. The relationship could be accurately described, without curve fitting, by several simple nonlinear models, in which the aggregation of disease was represented by a modified binomial function incorporating an effective sample size that was either constant or dependent on mean incidence. The relationship between incidence at the leaflet and leaf scales could be modeled based on the combined sampling-unit models for leaflets and leaves. By taking the complementary log-log (CLL) transformation of incidence, the equations could be expressed as generalized linear models, and curve fitting used to estimate the parameters. Generally, curve fitting gave slight to no improvement in the accuracy of the predictions of incidence. These models have broad applicability in sampling for disease incidence, and results can be used to interpret how diseased individuals at the lowest level in a hierarchy are arranged within sampling units.     

Neural network classification of tan spot and stagonospora blotch infection periods in a wheat field environment

ABSTRACT Tan spot and Stagonospora blotch of hard red spring wheat served as a model system for evaluating disease forecasts by artificial neural networks. Pathogen infection periods on susceptible wheat plants were measured in the field from 1993 to 1998, and incidence data were merged with 24-h summaries of accumulated growing degree days, temperature, relative humidity, precipitation, and leaf wetness duration. The resulting data set of 202 discrete periods was randomly assigned to 10 modeldevelopment or -validation (n = 50) data sets. Backpropagation neural networks, general regression neural networks, logistic regression, and parametric and nonparametric methods of discriminant analysis were chosen for comparison. Mean validation classification of tan spot incidence was between 71% for logistic regression and 76% for backpropagation models. No significant difference was found between methods of modeling tan spot infection periods. Mean validation prediction accuracy of Stagonospora blotch incidence was 86 and 81% for backpropagation and logistic regression, respectively. Prediction accuracies of other modeling methods were 相似文献   

Spatial pattern analysis of hop powdery mildew in the pacific northwest: implications for sampling

ABSTRACT The spatial pattern of hop powdery mildew was characterized using 3 years of disease incidence data collected in commercial hop yards in the Pacific Northwest. Yards were selected randomly from yards with a history of powdery mildew, and two to five rows were selected for sampling within each yard. The proportion of symptomatic leaves out of 10 was determined from each of N sampling units in a row. The binomial and the beta-binomial frequency distributions were fit to the N sampling units observed in each row and to SigmaN sampling units observed in each yard. Distributional analyses indicated that disease incidence was better characterized by the beta-binomial than the binomial distribution in 25 and 47% of the data sets at the row and yard scales, respectively, according to a log-likelihood ratio test. Median values of the beta-binomial parameter theta, a measure of small-scale aggregation, were near 0 at both sampling scales, indicating that disease incidence was close to being randomly distributed. The variability in disease incidence among rows sampled in the same yard generally increased with mean incidence at the yard scale. Spatial autocorrelation analysis, used to measure large-scale patterns of aggregation, indicated that disease incidence was not correlated between sampling units over several lag distances. Results of a covariance analysis showed that heterogeneity of disease incidence was not dependent upon cultivar, region, or time of year when sampling was conducted. A hierarchical analysis showed that disease incidence at the sampling unit scale (proportion of sampling units with one or more diseased leaves) increased as a saturation-type curve with respect to incidence at the leaf level and could be described by a binomial function modified to account for the effects of heterogeneity through an effective sample size. Use of these models permits sampling at the sampling unit scale while allowing inferences to be made at the leaf scale. Taken together, hop powdery mildew was nearly randomly distributed with no discernable foci, suggesting epidemics are initiated from a well-distributed or readily dispersible overwintering population. Implications for sampling are discussed.     

Comparative spatial analysis of the sooty blotch/flyspeck disease complex,bull’s eye and bitter rots of apples

The incidence of sooty blotch/flyspeck (SBFS) and bitter and bull’s eye rots were assessed in a Fuji apple orchard during two seasons. Using a regular sampling design, 252 trees were selected and 20 fruits per tree were sampled at harvest and scored for disease incidence. For bitter and bull’s eye rots, additional assessments were made on all symptomless fruit after a 30‐day period of storage. Randomness in the spatial pattern was assessed using beta‐binomial analysis of incidence data for three sampling scales (one, three or six adjacent trees as sampling units) and using Spatial Analysis by Distance Indices (sadie ) for disease counts for the 3‐tree sampling scale. sadie was also used for testing spatial associations between a pair of diseases, between years for the same disease or between rotted and latently infected fruit. Using a toroidal‐shifts procedure, 360 maps of disease counts were created based on the observed data, which were further analysed using sadie . Most datasets showed an aggregated spatial pattern, which was more consistent for the two fruit rots than SBFS, which showed distinct patterns depending on the year or method of analysis. The two fruit rots were spatially associated in most situations but SBFS and bull’s eye rot were dissociated in one season. Results from virtual orchards showed that the patterns observed in the original maps may accurately represent those in similar apple‐growing areas. Hypotheses regarding aspects of ecology and epidemiology of pathogens studied and potential efficacy of control measures in the region are discussed.     

Adaptation of the New Zealand Psa risk model for forecasting kiwifruit bacterial canker in Korea

Bacterial canker of kiwifruit, caused by Pseudomonas syringae pv. actinidiae (Psa), is a severe threat to kiwifruit production in Korea. An existing infection risk model from New Zealand was adopted to respond to this epidemic. Disease incidence (proportion of diseased leaves on each vine) and weather (hourly temperature, rainfall and relative humidity) data required to develop the model were collected and analysed in the study. Disease incidence data were used to modify and validate the existing model. Because the Psa risk model was originally developed in a region where the characteristic climatic conditions are completely different from those in Korea, the temperature and rainfall functions of the existing model were modified. Analyses using statistical correlation and prediction–realization tables revealed that the modified model is valid with high agreement (a correlation coefficient of 0.85 and an accuracy of 85.7%, respectively) between the observed disease incidence and simulated disease risk from the model. The model was also found to be more highly sensitive to the presence or absence of rainfall than any other weather variable inputs. Uncertainty in simulated disease risk was measured based on the level of uncertainty in temperature input from weather forecasts. Overall, these results indicate that the modified Psa risk model can be used to provide practical and applicable information for timely disease control to the kiwifruit growers in Korea.     

Relationship between incidence and severity of cashew gummosis in semiarid north-eastern Brazil

The incidence–severity relationship for cashew gummosis, caused by Lasiodiplodia theobromae , was studied to determine the feasibility of using disease incidence to estimate indirectly disease severity in order to establish the potential damage caused by this disease in semiarid north-eastern Brazil. Epidemics were monitored in two cashew orchards, from 1995 to 1998 in an experimental field composed of 28 dwarf clones, and from 2000 to 2002 in a commercial orchard of a single clone. The two sites were located 10 km from each other. Logarithmic transformation achieved the best linear adjustment of incidence and severity data as determined by coefficients of determination for place, age and pooled data. A very high correlation between incidence and severity was found in both fields, with different disease pressures, different cashew genotypes, different ages and at several epidemic stages. Thus, the easily assessed gummosis incidence could be used to estimate gummosis severity levels.     

An effective sample size for predicting plant disease incidence in a spatial hierarchy

ABSTRACT For aggregated or heterogeneous disease incidence, one can predict the proportion of sampling units diseased at a higher scale (e.g., plants) based on the proportion of diseased individuals and heterogeneity of diseased individuals at a lower scale (e.g., leaves) using a function derived from the beta-binomial distribution. Here, a simple approximation for the beta-binomial-based function is derived. This approximation has a functional form based on the binomial distribution, but with the number of individuals per sampling unit (n) replaced by a parameter (v) that has similar interpretation as, but is not the same as, the effective sample size (n(deff) ) often used in survey sampling. The value of v is inversely related to the degree of heterogeneity of disease and generally is intermediate between n(deff) and n in magnitude. The choice of v was determined iteratively by finding a parameter value that allowed the zero term (probability that a sampling unit is disease free) of the binomial distribution to equal the zero term of the beta-binomial. The approximation function was successfully tested on observations of Eutypa dieback of grapes collected over several years and with simulated data. Unlike the beta-binomial-based function, the approximation can be rearranged to predict incidence at the lower scale from observed incidence data at the higher scale, making group sampling for heterogeneous data a more practical proposition.     

Relationships between incidence and severity of fusarium head blight on winter wheat in ohio

ABSTRACT To determine the relationship between incidence (I; proportion of diseased spikes) and severity (S; mean proportion of diseased spikelets per spike) for Fusarium head blight of wheat and to determine if severity could be predicted reliably from incidence data, disease assessments were made visually at multiple sample sites in artificially and naturally inoculated research and production fields between 1999 and 2002. Ten distinct data sets were collected. Mean disease intensity ranged from 0.023 to 0.975 for incidence and from 0.0003 to 0.808 for severity. A model based on complementary log-log transformation of incidence and severity performed well for all data sets, based on calculated coefficients of determination and random residual plots. The I-S relationship was consistent among years and locations, with similar slopes for all data sets. For 7 of the 10 data sets and for the pooled data from all locations and years, the estimated slope from the fit of the model ranged from 1.03 to 1.26. Time of disease assessment affected the relationship between incidence and severity; however, the estimated slopes from each assessment time were also close to 1. Based on the width of the 95% prediction interval, severity was estimated more precisely at lower incidence values than at higher values. The number of sampling units and the index of dispersion of disease incidence had only minor effects on the precision with which S was predicted from I. The estimation of mean S from I would substantially reduce the time required to assess Fusarium head blight in field surveys and treatment comparisons, and the observed relationship between I and S could be used to identify genotypes with some types of disease resistance.     

Logistic regression modeling of prevalence of soybean sclerotinia stem rot in the north-central region of the United States

ABSTRACT Regional prevalence of soybean Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum, was modeled using tillage practices, soil texture, and weather variables (monthly air temperature and monthly precipitation from April to August) as inputs. Logistic regression was used to estimate the probability of stem rot prevalence with historical disease data from four states of the north-central region of the United States. Potential differences in disease prevalence between states in the region were addressed using regional indicator variables. Two models were developed: model I used spring (April) weather conditions and model II used summer (July and August) weather conditions as input variables. Both models had high explanatory power (78.5 and 77.8% for models I and II, respectively). To investigate the explanatory power of the models, each of the four states was divided into small geographic areas, and disease prevalence in each area was estimated using both models. The R(2) value of the regression analysis between observed and estimated SSR prevalence was 0.65 and 0.71 for models I and II, respectively. The same input variables were tested for their significance to explain the within-field SSR incidence by using Poisson regression analysis. Although all input variables were significant, only a small amount of variation of SSR incidence was explained, because R(2) of the regression analysis between observed and estimated SSR incidence was 0.065. Incorporation of available site-specific information (i.e., fungicide seed treatment, weed cultivation, and manure and fertilizer applications in a field) improved slightly the explained amount of SSR incidence (R(2) = 0.076). Predicted values of field incidence generally were overestimated in both models compared with the observed incidence. Our results suggest that preseason prediction of regional prevalence would be feasible. However, prediction of field incidence would not, and a different site-specific approach should be followed.     

Spatial patterns of spear rot in oil palm plantations in Surinam

As the aetiology of spear rot of oil palm is unknown, indirect methods were applied to study its putative infectiousness by analysing data from commercial oil palm plantations in Surinam. Geostatistics and gradient analysis were used to examine the spatial variation of spear rot disease in 13 blocks at two plantations. In two blocks, which had low spear rot incidence initially, the variogram indicated that affected trees were not spatially related, suggesting that infection came from various distant sources. Later, the semivariances in one of these two blocks and in seven others, calculated for successive dates, showed a linear increase with distance. The variograms for four blocks showed a nonlinear increase in variance. Over the years, the variograms suggested that the variation in spear rot was anisotropic, with more spatial dependence in a westerly direction. Classical analysis of disease gradients over time confirmed that there was a preferential direction of disease spread. The data are compatible with the following hypotheses: (1) spear rot is an infectious disease; (2) the causal agent of spear rot is vector-borne, the vector being displaceable by wind; and (3) spear rot appears in two distinct phases, phase 1 being characterized by few randomly scattered trees, phase 2 by focal spread of disease starting from such scattered trees. The trigger of the change from phase 1 to phase 2 remains unknown.     

Use of bayesian methods to improve prediction of panicle and shoot blight severity of pistachio in california

ABSTRACT Panicle and shoot blight, caused by a Fusicoccum sp., is one of the major aboveground diseases of pistachio in California. The effects of temperature, number of continuous rainy days in April and May, irrigation system, and incidence of latent infection of the Fusicoccum sp. on severity of panicle and shoot blight of pistachio leaves and fruit have been quantified previously, using data collected from 1999 through 2001. A predictive model for leaves and another model for fruit with good explanatory power were generated. In 2003 and 2004, newly collected data were used to evaluate the two models with non-Bayesian and Bayesian methods. The 95% credible (i.e., confidence) intervals of initial (before modification with non-Bayesian and Bayesian methods) and updated parameter estimates were used to investigate their prognostic validity. In 2003, the non-Bayesian analysis resulted in all parameter estimates, with the exception of cumulative daily mean temperature from 1 June until harvest, having different 95% confidence intervals than the parameter estimates of the original models. In addition, the parameter estimates for drip irrigation for the leaf infection and the parameter estimates for drip irrigation and number of continuous rainy days in April and May for fruit infection were not statistically significant. With Bayesian methods, the reestimated model parameters had overlapping 95% credible intervals with the initial estimated parameters, except for the number of continuous rainy days in April and May. When the two sets of modified parameter estimates were used to predict disease severity, statistically significant (alpha = 0.05) differences between observed and predicted disease severities were found with non-Bayesian analysis for leaf infection in three locations and with Bayesian analysis for fruit infection in one orchard. The parameter estimates were modified again at the end of the 2004 season and were all statistically significant with both non-Bayesian and Bayesian methods. Both sets of parameter estimates gave predictions that were not significantly different from observed disease severity on leaves and fruit in all monitored orchards in 2004. In summary, Bayesian methods gave more consistent results when used to update parameter estimates with new information and yielded predictions not statistically different from observed disease severity in more cases than the non-Bayesian analysis.     

Comparison of sampling procedures for two rice diseases: leaf blast and tungro

The precision and accuracy of eight random and systemic sampling methods, along with various sample sizes, were compared by means of a sampling simulation program with actual field data for two rice diseases, leaf blast and tungro. Three severity levels of leaf blast and two incidence levels of tungro were used. Precision depended primarily on disease intensity, followed by the sample size and the sampling method. Relative accuracy did not prove to discriminate sampling methods adequately, but simulated absolute accuracy is able to identify biases of systematic sampling paths. The results emphasize the necessity of pilot sampling at various stages of epidemics. The usefulness of simulated sample sizes and sampling methods based on real data is also demonstrated. With this approach a more practical combination of sample size and method may be found for different levels of disease intensity using precision and absolute accuracy as criteria.     

我国甘蔗主要杂交亲本黄叶病病原鉴定及田间发病率

甘蔗黄叶病是一种发生普遍、危害严重的病毒病害,选育抗病品种是控制该病发生与蔓延的有效措施.利用逆转录-聚合酶链反应(RT-PCR)、组织印迹杂交免疫测定(TBIA)和抗原直接包被酶联免疫吸附(DAC-ELISA)方法对我国43份甘蔗主要杂交亲本黄叶病病原(Sugarcane yellow leaf virus,SCYLV)进行鉴定,并调查SCYLV田间自然侵染条件下甘蔗黄叶病发病率.已明确36份亲本材料感染或未感染SCYLV,其中32份鉴定结果为阳性,品种(系)感病率达88.9％.甘蔗植株田间自然发病率可分为无发病、发病率低、中等和高4个等级,方差分析表明,各等级间的植株发病率差异达极显著水平(P＜0.01).从美国引进的多数CP和HoCP系列亲本发病率较高,而多数崖城和新台糖系列亲本材料的发病率较低,可作为甘蔗抗黄叶病杂交亲本.     

Rainfall and temperature distinguish between Karnal bunt positive and negative years in wheat fields in Texas

Karnal bunt of wheat, caused by the fungus Tilletia indica, is an internationally regulated disease. Since its first detection in central Texas in 1997, regions in which the disease was detected have been under strict federal quarantine regulations resulting in significant economic losses. A study was conducted to determine the effect of weather factors on incidence of the disease since its first detection in Texas. Weather variables (temperature and rainfall amount and frequency) were collected and used as predictors in discriminant analysis for classifying bunt-positive and -negative fields using incidence data for 1997 and 2000 to 2003 in San Saba County. Rainfall amount and frequency were obtained from radar (Doppler radar) measurements. The three weather variables correctly classified 100% of the cases into bunt-positive or -negative fields during the specific period overlapping the stage of wheat susceptibility (boot to soft dough) in the region. A linear discriminant-function model then was developed for use in classification of new weather variables into the bunt occurrence groups (+ or -). The model was evaluated using weather data for 2004 to 2006 for San Saba area (central Texas), and data for 2001 and 2002 for Olney area (north-central Texas). The model correctly predicted bunt occurrence in all cases except for the year 2004. The model was also evaluated for site-specific prediction of the disease using radar rainfall data and in most cases provided similar results as the regional level evaluation. The humid thermal index (HTI) model (widely used for assessing risk of Karnal bunt) agreed with our model in all cases in the regional level evaluation, including the year 2004 for the San Saba area, except for the Olney area where it incorrectly predicted weather conditions in 2001 as unfavorable. The current model has a potential to be used in a spray advisory program in regulated wheat fields.     

Spatial pattern analysis of strawberry leaf blight in perennial production systems

ABSTRACT Spatial pattern of the incidence of strawberry leaf blight, caused by Phomopsis obscurans, was quantified in commercial strawberry fields in Ohio using statistics for heterogeneity and spatial correlation. For each strawberry planting, two transects were randomly chosen and the proportion of leaflets (out of 15) and leaves (out of five) with leaf blight symptoms was determined from N = 49 to 106 (typically 75) evenly spaced sampling units, thus establishing a natural spatial hierarchy to compare patterns of disease. The beta-binomial distribution fitted the data better than the binomial in 92 and 26% of the 121 data sets over 2 years at the leaflet and leaf levels, respectively, based on a likelihood ratio test. Heterogeneity in individual data sets was measured with the index of dispersion (variance ratio), C(alpha) test, a standard normal-based test statistic, and estimated theta parameter of the beta-binomial. Using these indices, overdispersion was detected in approximately 94 and 36% of the data sets at the leaflet and leaf levels, respectively. Estimates of the slope from the binary power law were significantly (P < 0.01) greater than 1 and estimates of the intercept were significantly greater than 0 (P < 0.01) at both the leaflet and leaf levels for both years, indicating that degree of heterogeneity was a function of incidence. A covariance analysis indicated that cultivar, time, and commercial farm location of sampling had little influence on the degree of heterogeneity. The measures of heterogeneity indicated that there was a positive correlation of disease status of leaflets (or leaves) within sampling units. Measures of spatial association in disease incidence among sampling units were determined based on autocorrelation coefficients, runs analysis, and a new class of tests known as spatial analysis by distance indices (SADIE). In general, from 9 to 22% of the data sets had a significant nonrandom spatial arrangement of disease incidence among sampling units, depending on which test was used. When significant associations existed, the magnitude of the association was small but was about the same for leaflets and leaves. Comparing test results, SADIE analysis was found to be a viable alternative to spatial autocorrelation analysis and has the advantage of being an extension of heterogeneity analysis rather than a separate approach. Collectively, results showed that incidence of Phomopsis leaf blight was primarily characterized by small, loosely aggregated clusters of diseased leaflets, typically confined within the borders of the sampling units.     

Effect of Strawberry Density on the Spread of Anthracnose Caused by Colletotrichum acutatum

ABSTRACT Spread of strawberry anthracnose, resulting from the rain splash dispersal of Colletotrichum acutatum conidia, was determined in field plots by assessing fruit disease incidence at a range of distances from an introduced point source of infected fruit with sporulating lesions. Four within-row plant densities were established in replicated plots in each of 2 years. A generalized linear model with a logit link function and binomial distribution for incidence was used to quantify the effects of distance and side of the row relative to the inoculum source, plant density treatment, and their interactions on disease incidence. At all assessment times, there was a significant (P 相似文献   

Inoculation with <Emphasis Type="Italic">Sclerotium rolfsii</Emphasis>, cause of stem rot in Jerusalem artichoke,under field conditions

Stem rot caused by Sclerotium rolfsii is an important problem of Jerusalem artichoke, and breeding of Jerusalem artichoke for resistance to stem rot requires effective screening methods. The objective of this study was to compare methods for inoculating Jerusalem artichoke with S. rolfsii under field conditions. A 4 × 2 × 3 factorial in a randomized complete block with four replications was used in two environments characterized by different rates of fertilizer application (recommended rate and low rate) in the rainy season. The factors included four Jerusalem artichoke varieties (HEL280, HEL278, HEL256 and JA49), two levels of wounding (wounded and not wounded) and three methods of inoculation. The inoculation methods consisted of: 1) non-inoculated natural infection; 2) attaching one colonized sorghum seed at the crown of plants (single sorghum seed method); and 3) spreading 30 g m^?2 of colonized sorghum seeds (broadcast inoculation method). Jerusalem artichoke varieties and inoculation methods were significantly different for disease incidence, whereas the difference between wounded and non wounded treatments was not significant. Significant interactions were found between the variety and wounding method, the variety and inoculation method, wounding method and inoculation method, and inoculation method and environments. Natural infection resulted in the lowest disease incidence (32.2 %), whereas the single sorghum seed and the broadcast inoculation methods had a high disease incidence (79.0 % and 77.3 % respectively) and were not signnificantly different from each other. Broadcast inoculation did not allow differentiation of Jerusalem artichoke varieties for disease incidence, whereas single seed inoculation could better identify the differences among Jerusalem artichoke varieties.