耦合气象影响因素和Logistic方程的水稻纹枯病发病等级动态预测模型研究

本文基于植保、气象等数据研究水稻纹枯病发病等级-时间动态的预测方法和模型。利用2010年-2016年湖南省12个县的植保调查数据和气象观测值,以水稻纹枯病流行机理为基础将Logistic方程与构建的温度影响模块和湿度影响模块耦合,建立Logistic-RICEBLA病害预测模型。通过对模型参数进行调优、训练和验证,实现对水稻纹枯病发病等级的动态预测。结果表明,Logistic-RICEBLA模型能够较好地响应温度、湿度等气象条件的变化,模型预测结果与实际的水稻纹枯病发病等级-时间变化曲线具有较高的一致性。经验证,模型预测结果在单时相上精度达到R~2=0.68,RMSE=1,容错准确率P＿bias=88%,表明预测值与实际发病等级的误差基本控制在±1级范围内。在多时相整体趋势的验证方面,模型预测的病害流行曲线下面积(AUDPC)与病害实际发展的AUDPC保持高度一致性,决定系数(R~2)达到0.86,表明模型能给出纹枯病在水稻不同生育期发病等级变化的整体趋势。本研究构建的Logistic-RICEBLA模型能由简单的气象数据和植保数据驱动,对水稻纹枯病发病等级进行动态预测,有助于在植保管...     

玉米弯孢菌叶斑病传播梯度模型

 根据植物病害流行学原理,采用人工接种方法,在田间造成玉米弯孢菌叶斑病不同的发病梯度,分析连续2年玉米弯孢菌叶斑病传播动态。利用SPSS统计软件构建了此病害的传播梯度模型,结果表明指数模型是沈阳地区玉米弯孢菌叶斑病传播梯度的最佳模型。接种2个月,掖单13玉米弯孢菌叶斑病传播梯度最佳模型是x=9.606×EXP (-0.2829×d),海试16最佳模型是x=7.154×EXP (-0.2351×d)(x:病情指数,d:距菌源中心的距离)。预测玉米弯孢菌叶斑病在2个月最远传播距离为28 m;传播速度为0.4~0.5 m/d。     

玉米灰斑病空间流行动态模拟模型组建及传播距离研究

 以玉米品种郑单958为试材接种灰斑病菌,在田间形成不同发病梯度,分析病害传播的空间流行动态,利用SAS9.13统计软件分别构建病害传播梯度的一维、二维和三维模型。结果表明:1.指数模型和GOMPERTZ模型是沈阳地区玉米灰斑病单向传播梯度的最佳模拟模型;2.高斯模型是模拟病害平行于垄向和垂直于垄向方向传播的最佳模型;3.含有(x²+y²)形式的圆形模型和含有a (x²+y²)+bx+cy+d形式的椭圆形模型是模拟病害在二维平面上传播过程的最佳模型。通过模型推导得到病害的传播距离约20~50m。     

小麦条锈病春季流行规律的数理分析(Ⅰ)

1.小麦条锈病春季流行强度是越冬菌量、流行速度和传播效能三变数的函数、2.流行速度的实地测算可利用普朗克(Van der Plank1960)的公式:r=(230)/(t_2-t_1)log (mx_2(1-x_1))/(x_1(1-x_2))……………………………………………(Ⅰ)其中 x_1为 t_1时的病情(大面积平均普遍率)x_2为 t_2时的病情m 为 t_1→t_2期间小麦绿色面积增长倍数。3.根据以统一方法、多点进行的系统调查资料,用“相关”的统计方法,导出了早春一个月内的雨量、雨日、气温对流行速度的影响的回归方程:r=1.16x 0.1y 0.95z-2.6……………………………………………(Ⅱ)r=流行速度,x=早春一个月内雨日。     

可编程序计算器在农业病虫测报中的应用(中)

六、积分运算通过积分运算可以求得不同时间范围内,病害流行曲线下面积(AUDPC)或害虫的累计虫量等项工作。1.病害流行曲线下面积的计算例7:依据林傅光先生1955年研究资料,在马铃薯晚疫病严重发生田块,15天内田间病株率可以从1%发展至100%,根据病害发生曲线指数增长方程分析结果 y_0=0.01 r=0.2734,     

辽宁花生品种对疮痂病抗性及流行时间动态分析

为明确辽宁花生产区栽培品种对疮痂病抗性差异及病害发生流行规律,通过田间小区试验,采用五点调查法对不同花生品种疮痂病田间发生情况和时间流行动态进行了系统研究.结果表明,供试18个品种花生疮痂病病情指数存在显著差异,其中主栽品种白沙1016病情指数最高,可达34.5,新花2号病情指数最低,仅为15.7.根据花生疮痂病相对抗性评价标准,供试品种整体分为3类,高感品种4份,感病品种6份,中抗品种8份,未发现免疫和高抗品种.病害发生规律表现为:7月初为始发期,7月下旬至8月下旬为病害盛发期,8月末至9月上旬为病害衰退期.Logistic模型能够较好地描述花生疮痂病病情指数随时间的流行动态,依据模型公式也推导出主栽品种白沙1016的病情指数最大,日增长量为0.89.     

植病流行学(三)

(六)植病流行的时间动态植病流行的时间动态是流行学研究的中心课题之一,它在理论上和应用上都具有重要意义。研究时间动态主要研究病害流行速度及其变化规律。一、流行的类型及其防治策略 1.流行速度潜能和类型不同病害的流行速度不同,有的差异很大。据报道,在最适的条件下,一个生长季中病害数量增长倍数     

菜豆根腐病流行结构的特征描述

病害增长曲线(DPC)可用来表示某些特征可定义的流行病。分析这些特征,是定量流行学的主要内容,它能提供流行系统各组份间相互关系方面的重要信息。 Vanderplank曾确定出几个重要的曲线要素(curye elements),如病害起始时间、病害发展速率。kranz以更为完整的特征描叙来刻划了13个左右对称的病害增长曲线要素。刻划病害增长曲线时各要素的相     

植物病害管理战略决策的一个量化模型

 从植物病害流行结构分析入手,导出了以Logistic模式描述病害流行系统管理战略决策的数学模型:Z=In((1-X₀)/X₀)-r*t,并给出了相应的使用准则。初步试用的结果表明:它不仅十分简便和经济,而且是值得信赖的。     

沈阳地区葡萄霜霉病流行时间动态及其气象影响因子分析

 通过2012-2014年田间小区试验,对沈阳地区葡萄霜霉病自然发病情况进行了系统调查和对比分析,并对影响葡萄霜霉病流行动态的气象因素进行了相关性分析。结果表明,沈阳地区葡萄霜霉病的季节流行曲线是典型的单峰S形曲线。应用SPSS19.0软件分析,明确了Logistic模型能够反映沈阳地区葡萄霜霉病流行时间动态情况。同时,推导了病害流行阶段:指数增长期为7月上旬至7月下旬,该时期为最佳药剂防治时期;逻辑斯蒂增长期为7月下旬至8月下旬;衰退期为8月下旬至葡萄生育末期。不同生长季病害发生日期、流行阶段天数和最大病情指数虽各不相同,但与Logistic模型推导趋势基本一致。各个流行阶段病害的表观侵染速率表现为:始发期＞盛发期＞衰退期。始发期和盛发期的是决定整个生长季葡萄霜霉病流行程度的关键时期。气象因素对葡萄霜霉病的流行有明显影响,其中表观侵染速率与7 d平均相对湿度、7 d累计降雨量和7 d叶面湿润时数成显著正相关,而与7 d平均气温呈显著负相关,以上4个气象因素是影响沈阳地区葡萄霜霉病流行的主导因子。     

Simulation of Epidemics Caused by Venturia inaequalis (Cooke) Aderh.1

We discuss simulation of apple scab epidemics based on analog computer models, multivariate regression analyses, and systems analyses. Details of underlying models and their scope for applications are emphasized. Monomolecular growth functions by Bertalanffy and Gompertz in analog computers permit fast simulation of disease progress curves. The models derived from multivariate analyses of field experiments on apple scab epidemics simulate closely the changes in infection rates. EPIVEN, our comprehensive and self–generating simulator of apple scab epidemics is reviewed and compared to a reduced model, also based on elements of the system “apple scab epidemic”.     

新疆泽普县小麦白粉病流行的时间动态及预测模型

2012年-2019年对泽普县春季小麦白粉病田间发生情况进行了系统调查, 并对数据进行了分析和模型拟合, 明确了当地白粉病春季发生和流行的特点?其病害春季流行曲线为典型的单峰S形曲线, 符合Logistic或Gompertz模型?在此基础上, 通过Pearson相关系数法分析了多年来该地小麦不同生育期白粉病病情指数与66个主要气象因子之间的相关关系, 筛选出影响小麦白粉病发生流行的关键气象因子为1月下旬平均日照时间?2月下旬平均气温?1月上旬-3月上旬平均气温和10月下旬-4月中旬平均日照时间, 并采用多元回归分析法建立了基于关键气象因子的小麦扬花期?灌浆初期和灌浆中期的病害预测模型?此研究结果可为当地小麦白粉病的防控提供技术支撑?     

Comparative analysis of flexible two-parameter models of plant disease epidemics

ABSTRACT Eleven previously published models of plant disease epidemics, given as differential equations with a rate and a shape parameter, are compared using general model characteristics as well as their usefulness in fitting observed data. Six out of the eleven models can be solved analytically resulting in epidemic growth functions, while the others can be solved only numerically. When all 11 differential equations were fitted to two data sets, all models showed a similar goodness of fit, although the shape parameter in some models could not be estimated very precisely. With respect to useful characteristics (exponential population growth at the beginning, ability to generate monomolecular disease progression, and flexibility of the inflection point), the models of Fleming, Kosman-Levy, Birch, Richards and Waggoner, and Rich are recommended. Formulas were established to calculate the point of inflection as well as the weighted absolute and relative rate, respectively, depending on the shape and rate parameter. These formulas allow transformation of the parameter values of one model into those of another model in many cases. If the two models are required to have the same temporal position of the disease progress curve, then the initial disease level at the start of the epidemic or the time when the inflection point is reached have to be transformed.     

Effect of Sowing Date, Host Cultivar, and Race of Fusarium oxysporum f. sp. ciceris on Development of Fusarium Wilt of Chickpea

ABSTRACT Microplots experiments were carried out at Córdoba, southern Spain, from 1986 to 1989 to determine the effects of sowing date in the management of Fusarium wilt of chickpea as influenced by virulence of the pathogen race and by cultivar susceptibility. A total of 108 epidemics of the disease were described, analyzed, and compared to assess the degree of disease control. The epidemics were characterized by five curve elements: final disease intensity index (DII), standardized area under DII progress curve, time to epidemic onset, time to inflection point (t(ip)), and the DII value at t(ip), the last two parameters being estimates from the Richards function adjusted by nonlinear regression analysis. The structure of Fusarium wilt epidemics was examined by conducting multivariate principal components and cluster analyses. From these analyses, three factors accounting for 98 to 99% of the total variance characterized the DII progress curves and provided plausible epidemiological interpretations. The first factor included the t(ip) and the time to disease onset and can be interpreted as a positional factor over time. This factor accounted for the largest proportion of the total variance and may, therefore, be considered as the main factor for analysis of Fusarium wilt epidemics. The second factor concerns the standardized area under DII progress curves and the final DII of the epidemics. The third factor identified the uniqueness of the estimated value for the point of inflection of the DII progress curve over time. Our results indicate that for each year of experiment epidemic development was related mainly to the date of sowing. Thus, for chickpea crops in southern Spain, advancing the sowing date from early spring to early winter can slow down the development of Fusarium wilt epidemics, delay the epidemic onset, and minimize the final amount of disease. However, the net effect of this disease management practice may also be influenced, though to a lesser extent, by the susceptibility of the chickpea cultivar and the virulence and inoculum density of the Fusarium oxysporum f. sp. ciceris race.     

Use of multispectral radiometry for assessment of rhizoctonia blight in creeping bentgrass

ABSTRACT The ability to identify diseases early and quantify severity accurately is crucial in plant disease assessment and management. This study was conducted to assess changes in the spectral reflectance of sunlight from plots of creeping bentgrass during infection by Rhizoctonia solani, the cause of Rhizoctonia blight, and to evaluate multispectral radiometry as a tool to quantify Rhizoctonia blight severity. After inoculation of 6-year-old creeping bentgrass turf with R. solani anastomosis group 2-2, reflectance of sunlight from the foliar canopy was measured at light wavelengths of 460 nm (blue) to 810 nm (near infrared [NIR]), at 50-nm intervals. Visual estimates of disease severity and percentage of canopy reflectance were made daily throughout each of three epidemics of Rhizoctonia blight from the onset of visible symptoms until maximum disease severity was reached. In each experiment, linear regression analysis revealed a significant reduction in the percentage of NIR (760 and 810 nm) reflectance as disease severity increased. However, in the majority of analyses, regression models explained <50% of the variability between components. Multispectrum radiometry appears to function best when used to assess differences in disease severity at discrete points in time rather than over an entire epidemic.     

Spatiotemporal relationships between disease development and airborne inoculum in unmanaged and managed Botrytis leaf blight epidemics

Comparatively little quantitative information is available on both the spatial and temporal relationships that develop between airborne inoculum and disease intensity during the course of aerially spread epidemics. Botrytis leaf blight and Botrytis squamosa airborne inoculum were analyzed over space and time during 2 years (2002 and 2004) in a nonprotected experimental field, using a 6 x 8 lattice of quadrats of 10 x 10 m each. A similar experiment was conducted in 2004 and 2006 in a commercial field managed for Botrytis leaf blight using a 5 x 5 lattice of quadrats of 25 x 25 m each. Each quadrat was monitored weekly for lesion density (LD) and aerial conidium concentration (ACC). The adjustment of the Taylor's power law showed that heterogeneity in both LD and ACC generally increased with increasing mean. Unmanaged epidemics were characterized in either year, with aggregation indices derived from SADIE (Spatial Analysis by Distance Indices). For LD, the aggregation indices suggested a random pattern of disease early in the season, followed by an aggregated pattern in the second part of the epidemic. The index of aggregation for ACC in 2002 was significantly greater than 1 at only one date, while it was significantly greater than 1 at most sampling dates in 2004. In both years and for both variables, positive trends in partial autocorrelation were observed mainly for a spatial lag of 1. In 2002, the overall pattern of partial autocorrelations over sampling dates was similar for LD and ACC with no significant partial autocorrelation during the first part of the epidemic, followed by a period with significant positive autocorrelation, and again no autocorrelation on the last three sampling dates. In 2004, there was no significant positive autocorrelation for LD at most sampling dates while for ACC, there was a fluctuation between significant and non-significant positive correlation over sampling dates. There was a significant spatial correlation between ACC at given date (t(i)) and LD 1 week later (t(i + 1)) on most sampling dates in both 2002 and 2004 for the unmanaged and managed sites. It was concluded that LD and ACC were not aggregated in the early stage of epidemics, when both disease intensity and airborne conidia concentration were low. This was supported by the analysis of LD and ACC from a commercial field, where managed levels of disease were low, and where no aggregation of both variables was detected. It was further concluded that a reliable monitoring of airborne inoculum for management of Botrytis leaf blight is achievable in managed fields using few spore samplers per field.     

Temporal and spatial dynamics of watermelon gummy stem blight epidemics

The effect of the distance of initial inoculum on the intensity of watermelon gummy stem blight, caused by Didymella bryoniae, was studied in a naturally-infected rainfed commercial field. The shorter the distance from the focus, the sooner was disease onset and the earlier maximum disease levels were achieved. Maximum disease incidences were reached earlier than maximum severities, but eventually destructive levels were observed for both disease incidence and severity. Disease progressed at similar rates, irrespective of the radial distance from the focus. A detailed study of the disease temporal progress was conducted in inoculated rainfed experimental fields with commercial genotypes Crimson Sweet (susceptible, S) and Riviera (moderately resistant, R). The Gompertz model best described the disease progress curves, and estimated apparent infection rates were 0.049 and 0.020 respectively for S and R genotypes. In addition, spatial pattern studies were conducted during the dry season in overhead irrigated experimental plots, inoculated with point-source foci. Disease intensity gradients were better explained by the Exponential model than by the Power Law model. Gummy stem blight distribution was classified as aggregated by the Ordinary Runs procedure. Two different spatial autocorrelation methods (2DCorr and LCOR) revealed strong short distance spatial dependencies. Long distance positive correlations between quadrats were observed along with periods of higher progress rates. The dynamic patterns of the epidemics of gummy stem blight in watermelon described here are consistent with epidemics of polycyclic diseases with splash-dispersed spores.     

Temporal Progression of Bean Common Bacterial Blight (Xanthomonas Campestris pv. phaseoli) in Sole and Intercropping Systems

The effects of four planting patterns of bean (Phaseolus vulgaris) (bean only, maize–bean (MB), sorghum–bean (SB), and maize–bean–sorghum (MBS)) and four cropping systems (sole cropping, row, mixed, and broadcast intercropping) on the temporal epidemics of bean common bacterial blight (CBB) caused by Xanthomonas campestris pv. phaseoli were studied. The experiments were conducted during two consecutive spring and summer seasons in 1999 and 2000 in replicated field experiments. The Gompertz model described disease progress curves better than the logistic model. Intercropping delayed epidemic onset, lowered disease incidence and severity, and reduced the disease progress rate. The type of cropping system and planting pattern affected CBB incidence and severity at initial, final and overall assessments and also affected the rate of disease development. Statistical significance of treatment interactions based on disease assessments was found for incidence in all four experiments and for severity in three experiments. A slower disease progress rate and lower incidence and severity occurred on beans planted with maize or sorghum in row, mixed and broadcast intercropping than on bean planted alone. Incidence was reduced 36% and severity 20% in intercropping compared to sole cropping. The built-in disease delay and the slowing of the disease progress rate could provide protection for beans from severe CBB epidemics in intercropped systems. Variation between years appeared to be related to relative humidity (RH).     

Spatiotemporal Description of Epidemics Caused by Phoma ligulicola in Tasmanian Pyrethrum Fields

ABSTRACT Spatial and temporal patterns of foliar disease caused by Phoma ligulicola were quantified in naturally occurring epidemics in Tasmanian pyrethrum fields. Disease assessments (defoliation incidence, defoliation severity, incidence of stems with ray blight, and incidence of flowers with ray blight) were performed four times each year in 2002 and 2003. Spatial analyses based on distribution fitting, runs analysis, and spatial analysis by distance indices (SADIE) demonstrated aggregation in fields approaching their first harvest for all assessment times between September and December. In second-year harvest fields, however, the incidence of stems with ray blight was random for the first and last samplings, but aggregated between these times. Spatiotemporal analyses were conducted between the same disease intensity measures at subsequent assessment times with the association function of SADIE. In first-year harvest fields, the presence of steep spatial gradients was suggested, most likely from dispersal of conidia from foci within the field. The importance of exogenous inoculum sources, such as wind-dispersed ascospores, was suggested by the absence of significant association between defoliation intensity (incidence and severity) and incidence of stems with ray blight in second-year harvest fields. The logistic model provided the best temporal fit to the increase in defoliation severity in each of six first-year harvest fields in 2003. The logistic model also provided the best fit for the incidence of stems with ray blight and the incidence of flowers with ray blight in four of six and three of six fields, respectively, whereas the Gompertz model provided the best fit in the remaining fields. Fungicides applied prior to mid-October (early spring) significantly reduced the area under disease progress curve (P < 0.001) for defoliation severity, the incidence of stems with ray blight, and the incidence of flowers with ray blight for epidemics at all field locations. This study provides information concerning the epidemiology of foliar disease and ray blight epidemics in pyrethrum and offers insight on how to best manage these diseases.