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.     

Comments regarding the binary power law for heterogeneity of disease incidence

The binary power law (BPL) has been successfully used to characterize heterogeneity (overdispersion or small-scale aggregation) of disease incidence for many plant pathosystems. With the BPL, the log of the observed variance is a linear function of the log of the theoretical variance for a binomial distribution over the range of incidence values, and the estimated scale (?) and slope (b) parameters provide information on the characteristics of aggregation. When b = 1, the interpretation is that the degree of aggregation remains constant over the range of incidence values observed; otherwise, aggregation is variable. In two articles published in this journal in 2009, Gosme and Lucas used their stochastic simulation model, Cascade, to show a multiphasic (split-line) relationship of the variances, with straight-line (linear) relationships on a log-log scale within each phase. In particular, they showed a strong break point in the lines at very low incidence, with b considerably >1 in the first line segment (corresponding to a range of incidence values usually not observed in the field), and b being ?1 in the next segment (corresponding to the range of incidence values usually observed). We evaluated their findings by utilizing a general spatially explicit stochastic simulator developed by Xu and Ridout in 1998, with a wide range of median dispersal distances for the contact distribution and number of plants in the sampling units (quadrats), and through an assessment of published BPL results. The simulation results showed that the split-line phenomenon can occur, with a break point at incidence values of ?0.01; however, the split is most obvious for short median dispersal distances and large quadrat sizes. However, values of b in the second phase were almost always >1, and only approached 1 with extremely short median dispersal distances and small quadrat sizes. An appraisal of published results showed no evidence of multiple phases (although the minimum incidence may generally be too high to observe the break), and estimates of b were almost always >1. Thus, it appears that the results from the Cascade simulation model represent a special epidemiological case, corresponding primarily to a roughly nearest-neighbor population-dynamic process. Implications of a multiphasic BPL property may be important and are discussed.     

Distribution pattern and sequential sampling plan for spotted pod borer,Maruca vitrata (Fabricius) (Lepidoptera: Crambidae) on pigeon pea,Cajanus cajan L

Spatial distribution of legume pod borer, Maruca vitrata (Fabricius) (Lepidoptera: Crambidae) on Pusa 992 pigeon pea cultivar during kharif (rainy season) 2011 and 2012 was analyzed through Taylor's power law (TPL) and Iwao's mean crowding (IMC) regression. M. vitrata exhibited an aggregated distribution pattern on the crop. The pooled data for the two years fitted well to the TPL (a = 1.05, b = 1.19, R² = 0.967) and the IMC (α = ?0.17, β = 1.124, R² = 0.99). The optimal sample sizes with the TPL parameters increased with an increased precision level. Based on the TPL parameters, the decision lines of sequential sampling for M. vitrata were determined to be d = 3n ± 2.5√n. The sequential sampling plan would economize decision-making for an effective management of M. vitrata.     

Incidence-density relationship of pear scab (Venturia nashicola) on fruits and leaves

Lesion-count data on fruits/leaves from two regions of China and on leaves from controlled-environment studies were used to investigate incidence-density [incidence of leaves/fruits with lesion(s) and average number of lesions per leaf/fruit] and incidence-incidence [incidences of leaves and shoots with lesion(s)] relationships. Few of the datasets for the number of lesions per fruit/leaf could be fitted satisfactorily by a Poisson distribution. Three two-parameter distributions (negative binominal, Neyman type A and Polya-Aeppli) provided significantly better fit than the Poisson distribution, indicating a degree of aggregation in the number of lesions on a single leaf/fruit. However, many datasets could still not satisfactorily be fitted by these distributions. The dynamics of aggregation of lesions on leaves/fruits was well described by Taylor's power-law model. Regression models provided accurate predictions of the average number of lesions per leaf/fruit from the incidence of leaves or fruits with lesion(s). Nevertheless, the incidence-density relationship varied considerably between regions and between leaf and fruit scab. Field data also indicated that the number of scabbed leaves per shoot showed some degree of aggregation. The incidence of leaves with scab could be predicted accurately from the incidence of shoots with scab. The incidence-density relationships developed in this study could be used in making practical disease-management decisions when incidence of leaves with scab is less than 35%.     

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.     

The Role of Psychophysics in Phytopathology: The Weber–Fechner Law Revisited

The accuracy and precision of disease severity assessment data might be improved if there was a better understanding of how the laws of psychophysics actually relate to the theory and practice of phytopathometry. In this regard, we utilized a classical method developed in the field of psychophysics (the method of comparison stimuli) to test Horsfall and Barratt’s claim that raters cannot accurately discriminate disease severity levels between 25% and 50% because, according to the Weber–Fechner law, visual acuity is proportional to the logarithm of the intensity of the stimulus. We show for two pathosystems, wheat leaf rust and grapevine downy mildew, that raters can accurately discriminate disease severity levels between 25% and 50%, and that although Weber’s law appears to hold true, Fechner’s law does not. Furthermore, based upon our results, the relationship between actual (true) disease severity (X) and disease severity estimated by raters (Y) is linear, not logarithmic as proposed by Horsfall and Barratt.     

Effects of quadrat size and shape, initial epidemic conditions, and spore dispersal gradient on spatial statistics of plant disease epidemics

ABSTRACT The spatiotemporal spread of plant diseases was simulated using a stochastic model to study the effects of initial conditions (number of plants initially infected and their spatial pattern), spore dispersal gradient, and size and shape of sampling quadrats on statistics describing the spatiotemporal dynamics of epidemics. The spatial spread of disease was simulated using a half-Cauchy distribution with median dispersal distance mu (units of distance). A total of 54 different quadrat types, including 23 distinct sizes ranging from 4 to 144 plants, were used to sample the simulated epidemics. A symmetric form of the binary power law with two parameters (alpha, beta) was fitted to the sampled epidemic data using each of the 54 quadrats for each replicate simulation run. The alpha and beta estimates were highly correlated positively with each other, and their estimates were comparable to those estimated from observed epidemics. Intraclass correlation (kappa) was calculated for each quadrat type; kappa decreased exponentially with increasing quadrat size. An asymmetric form of the binary power law with three parameters (alpha (1), beta(1), beta(2)) was used to relate kappa to the disease incidence (p); beta1 was highly correlated to beta: beta1 approximately beta - 1. In general, initial conditions and quadrat size affected alpha, beta, alpha(1), beta(1), and beta(2) greatly. The parameter estimates increased as quadrat size increased, and the relationships were described well by a linear regression model on the logarithm of quadrat size with the slope or intercept parameters dependent on initial conditions and mu. Compared with initial conditions and quadrat size, the overall effects of mu and quadrat shape were generally small, although within each quadrat size and initial condition they could be substantial. Quadrat shape had the greatest effect when the quadrat was long and thin. The relationship of the index of dispersion (D) to p and quadrat size was determined from the alpha and beta estimates. D was greatest when p was 0.5 and decreased when p approached 0 or 1. It increased with quadrat size and the rate of the increase was maximum when p was 0.5 and decreased when p approached 0 or 1.     

Spatial analysis and incidence–density relationships for downy mildew on hop

The spatial pattern of downy mildew (Pseudoperonospora humuli) on hop (Humulus lupulus) was characterized over 4 years to aid in deriving an appropriate incidence–density relationship. From 472 disease assessments (datasets), discrete distributions were fitted to the datasets to determine aggregation of disease density. Where distributions were able to be fitted, the Poisson distribution fitted 4% of the datasets and the negative binomial distribution fitted 87% of the datasets. Larger‐scale patterns of disease were assessed by autocorrelation and runs analysis; both indicated aggregation of diseased plants was less common than aggregation of disease within plants. Taylor’s power law indicated disease density was aggregated and related to mean disease density in all years. Disease incidence and density were linked by saturation‐type relationships based on the zero term of the negative binomial distribution or an empirical regression. Certain individual datasets were not described well by any incidence–density model, particularly when disease density was greater than about 0·8 diseased shoots per plant with the cultivar Cascade. When applied to 56 validation datasets, 88% of the variation in observed disease incidence was explained by the incidence–density models. Under conditions where sampling would be implemented for disease management, the requisite conditions appear to be in place for a binomial sampling plan for downy mildew.     

Estimating Taylor's power law parameters for weeds and the effect of spatial scale

The spatial variability of weeds within fields was studied for six sets of count data. Heterogeneity for a given mean population density was measured using the variance of the counts between sample units at different locations; relatively large values of sample variance imply aggregation. The dependence of variance on mean was measured using the relationship known as Taylor's power law, ubiquitous in animal ecology but seldom used for plant populations. This was fitted to an extensive set of plant counts and 69 estimates of its parameters b, an index of aggregation, and log₁₀a were computed. Estimates were corrected for bias when the number of samples was small. Overall, b varied between 1.32 and 2.61, and log₁₀a varied between -0.85 and 1.58. agreeing well with previous estimates for both plant and animal populations. Parameter estimates varied with sample size and spatial sample scale, but unpredictably. Parameter values when species counts were combined were compared with individual species analyses. Knowledge of the likely range of these parameters for weed populations provides an important basis for future modelling of the relationship between weed density and crop yield loss.     

Spatiotemporal variation in airborne sporangia of Phytophthora infestans: characterization and initiatives towards improving potato late blight risk estimation

This study investigated the value of using real‐time monitoring of Phytophthora infestans airborne inoculum as a complement to decision support systems (DSS). The experiment was conducted during the 2010, 2011 and 2012 potato production seasons in two locations in New Brunswick, Canada. Airborne sporangia concentrations (ASC) of P. infestans were monitored using 16 rotating‐arm spore samplers placed 3 m above the ground. The first cases of late blight (2010 and 2011) were detected 6–7 days after the first ASC peak, and all samplers captured their first sporangia within the same week (at 3‐ and 9‐day periods). The cumulative ASC curve and the risk curves from two DSS (PLANT‐Plus and Pameseb Late Blight) had the same shape but different magnitudes. In both locations, the negative binomial distribution fitted the data better than the Poisson distribution, which is indicative of heterogeneity, and based on Taylor's power law, the heterogeneity increased with increasing ASC. Therefore, the present results suggest that spore‐sampling network devices may be a suitable approach for early detection of incoming inoculum and, when combined with DSS, represent a potential aid for targeting the optimal time to apply a disease‐control product. In this context, cumulative ASC can be a counterweight to the DSS risk estimate: a high risk combined with significant ASC will trigger fungicide spraying. Moreover, spore sampling can be used to assess the efficiency of management strategies by means of examining the area under the inoculum progress curve.     

Effects of fungicide movement between plots in field experiments

Interactions between plots of spring barley which were either unsprayed, or sprayed with triadi mefon or tridemorph to control powdery mildew ( Erysiphe graminis f.sp. hordei ) were measured in experiments of balanced design in 1978 and 1979. The average amounts of mildew in untreated plots with triadimefon-reated neighbours on the predominaotly up-wind side were usually less than half those in untreated plots with tridemorph-treated neighbours on the up-wind side. Triadiniefon-treated plots had most effect on the adjacent sides of their untreated neighbours but there was evidence that their influence extended across the whole widths of these neighbouring plots. Amounts of mildew in tridemorph-treated plots were increased by untreated neighbours.
Differences in amounts of disease between these plots of spring barley with differently treated neighbours had no significant effects on grain yield. However, in a rando'mized block experiment with spring wheat in 1975 there was evidence that similar inter-plot interactions caused consider able bias in the yield data. Possible causes of the observed effects are discussed.     

Heterogeneity of the aerial concentration and deposition of ascospores of <Emphasis Type="Italic">Venturia inaequalis</Emphasis> within a tree canopy during the rain

Scab is an important disease of apple and its control depends almost exclusively on frequent use of fungicides. Primary scab infection in the spring assumes several steps: ascospore maturation, liberation of ascospores that become airborne, deposition on susceptible tissues, and infection. However, the spatial heterogeneity of ascospores within the tree canopy is unknown. Aerial concentration of ascospore (ACA), ascospore concentration in rain water (ACR) and ascospore deposition (AD) were therefore measured at six heights (20–257 cm from the ground) with rotating-arm air samplers, funnels, and greased glass slides, respectively, during five rain events in 2001 and in 2002. In addition, ACR and AD were measured at eight locations within tree canopy at 196 cm height. Apple scab was assessed at the end of the primary infection period in each sampling location within the apple tree. A similar experimental design was used in 2003 to study the spatial heterogeneity of both AD and primary scab lesions. ACA and AD decreased with increasing height, while ACR increased with increasing height. Based on both variance to mean ratio and the power law relationship in both years, the ACR was heterogeneous, while AD was heterogeneous only during the peaks of ascospore release. The ACR was significantly higher at the centre of the trees and the AD was significantly higher at the centre and at the western edge of the trees. Only the cumulative AD was significantly correlated with apple scab lesions at the same location (r = 0.83). In 2003, a similar pattern of spatial heterogeneity within the tree canopy was observed for AD and primary scab lesion counts and there was a linear relationship (R ² = 0.84) between these two variables. It was concluded that ACR and AD within the tree canopy are not randomly distributed at least during peaks of ascospore release and that AD is a good estimate of primary scab lesion development. This spatial heterogeneity should be considered when estimating ascospore deposition using mathematical models or when quantifying ascosporic inoculum using spore samplers.     

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.     

Some Observations on the Basic Principles Involved in Ultra-low-volume Spray Application

Abstract

Between 1982 and 1985, Nigerian cassava fields were periodically sampled through three crop growing seasons to analyze the within and between‐plant distribution of CM as affected by weather (dry and rainy season) using the mean crowding statistics and Taylor's power law. Enumerative and binomial sampling plans were developed which take into consideration the seasonal changes in the spatial distribution patterns of CM. The costs of the two procedures were compared to select the most cost‐efficient sampling plan.     

Spatial heterogeneity,incidence-incidence and incidence-lesion density relationship of apple scab (<Emphasis Type="Italic">Venturia inaequalis</Emphasis>) in managed orchards

The spatial pattern of apple scab was characterized using 10 years of disease incidence and lesion density data collected in managed orchards located in Quebec, Canada. Distributional analyses indicated that scab incidence was better characterized by the beta-binomial than the binomial distribution in 53 and 65% of the data sets at the leaf and shoot scales, respectively. Median values of the beta-binomial parameter θ, a measure of small-scale aggregation, were near 0 (0.003 and 0.028) at both sampling scales, indicating that disease incidence was close to being randomly distributed (low degree of aggregation). For lesion density, the negative binomial distribution fitted the data better than the Poisson distribution in 86% of the data sets at the leaf scale. The median value of the index of dispersion k was 0.068, indicating that aggregation was present. For all apple scab measurements, the power law models provided a good fit to the data. The estimated slope and intercept parameters were significantly greater than 1 and 0, respectively, suggesting that spatial heterogeneity changed systematically with disease incidence. Results of a covariance analysis showed that spatial heterogeneity of scab incidence at both scales and lesion density was not dependent upon shoot type but that spatial heterogeneity of scab incidence and lesion density at the leaf scale was influenced by the sampling period. A hierarchical analysis showed that scab incidence at the tree scale increased as a saturation-type curve with respect to incidence at the leaf or shoot scales. A similar relationship was observed for incidences at the shoot and leaf scales. An effective sample size model based on the binary power law parameters (Madden and Hughes, Phytopathology 89:770–781, 1999) gave the best fit to the leaf and shoot data, respectively. The incidence-lesion density relationship at both scales was well described by a complementary log-log (CLL) and log transformation model ( R_adj² = 0.97 and R_adj² = 0.94 ) \left( {R_{{adj}}^2 = 0.97\,and\,R_{{adj}}^2 = 0.94} \right) , however, the models tended to underestimate lesion density. The information of the spatial relations of apple scab within and between hierarchical scales acquired from this study can be used in developing and evaluating practical disease management strategies and to improve apple scab assessments for fungicide or cultivar susceptibility trials.     

槐豆木虱若虫空间分布型及抽样技术研究

应用6种分布型指数法分析判定了槐豆木虱[Cyamophila willieti(Wu)]若虫在国槐(Sophora japonicaLinnaeus)上的空间分布型,利用Taylor(1961)幂法则和Iwao(1971)回归方程分析聚集原因,结果表明,槐豆木虱若虫在国槐上呈聚集分布,公共kc值为5.159 0,且符合负二项分布;其种群聚集原因是由昆虫本身行为习性所致。在此基础上,采用Iwao的方法确定了槐豆木虱若虫的田间理论抽样数和序贯抽样表。     

Survey Methods for Assessment of Citrus Tristeza Virus Incidence When Toxoptera citricida Is the Predominant Vector

ABSTRACT Citrus tristeza virus (CTV) incidence may be assessed by sampling groups of citrus trees, recording the groups as CTV positive (one or more infected trees) or CTV negative (no infected trees), and then calculating disease incidence at the scale of the individual tree by means of a formula involving incidence at the group scale and the number of trees per group. This procedure works well when the CTV status of a tree can be regarded as independent of the CTV status of other trees in the same group. This is the case when the main vector species is Aphis gossypii and the groups comprise four adjacent trees, because the spatial pattern of CTV incidence at the within-group scale can be regarded as random. However, when the main vector species is Toxoptera citricida, this simple procedure is not appropriate, because the spatial pattern of CTV incidence at the within-group scale cannot be regarded as random. Using field data and computer simulation, an alternative procedure for assessment of CTV incidence when the main vector species is T. citricida was devised and tested. In the alternative procedure, the sampling scheme is operationally identical to that used when the main vector species is A. gossypii, but the calculation of CTV incidence at the scale of the individual tree is based on incidence at the group scale and an effective sample size. The analysis of CTV-incidence data collected from a number of citrus blocks in reasonable geographical and temporal proximity and the use of CTV-detection methods more sensitive than the enzyme-linked immunosorbent assay used here are also discussed.     

纪念《进出境动植物检疫法》颁布实施25周年

《进出境动植物检疫法》是进出境动植物检疫的基本法。该法自实施25周年来,为把动植物检疫工作纳入法制管理轨道,防止动植物疫情疫病传入传出,保护农林业生产安全、人体健康和生态安全,促进对外经济贸易的发展发挥了积极作用。适时对该法进行修订,依法施检、以法促检、以检护法,将更有利于适应形势变化,接轨国际惯例,保障农林生产和生态安全,促进经济社会的发展。     

大兴区农业植物检疫行政执法工作现状及建议

植物检疫行政执法是植物检疫工作的重要组成部分,本文将北京市大兴区植物检疫行政执法工作进行梳理,阐述了植物检疫行政执法工作的主要做法和采取的措施,并对工作中面临的问题进行了总结,同时提出了推进植物检疫执法工作的建议。