Incorporation of weed spatial variability into the weed control decision-making process

Floral surveys were carried out on a field of 28 m × 100 m on the nodes of a regular 2 m × 2 m grid, using a rectangular sampling area of 25 cm × 30 cm. In total, 765 units were sampled, each one characterized by the spatial co-ordinates and the number of seedlings of different weed species. The spatial representation of the weeds was obtained with kriging. Simulations were carried out for Amaranthus spp., which had the highest frequency and density (221 plants m^?2), and Portulaca oleracea L., a species that combined a more aggregated distribution with a medium–high density (27 plants m^?2). The results obtained clearly indicated that the usefulness of geostatistical procedures depends on the type of question posed by the user. If the goal is to estimate weed density and, consequently, crop yield loss, kriging appears to overburden the decision-making process, without improving the estimates obtained. This procedure becomes useful for obtaining weed infestation maps to be used for intermittent spraying applications. The reliability of these maps increases with the number of samples used for kriging. With the more aggregated species, at least 50 samples are required to obtain an infestation map. The reduction in the area to be treated depends on the threshold level adopted and on the number of samples used for kriging. With a threshold around the break-even point for most post-emergence treatments, this reduction varies from 10% to 40% with maps obtained from 50 and 175 samples respectively. The usefulness of infestation maps obtained with kriging for improving the decision-making process is strictly dependent on the weed patch dynamics: if these patches remain relatively stable over time, kriging can be carried out periodically without overburdening the decision-making process, whereas, if they are not stable, maps need to be drawn up each year, with a significant increase in costs.     

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.     

Spatial sampling to detect an invasive pathogen outside of an eradication zone

Invasive pathogens are known to cause major damage to the environments they invade. Effective control of such invasive pathogens depends on early detection. In this paper we focus on sampling with the aim of detecting an invasive pathogen. To that end, we introduce the concept of optimized spatial sampling, using spatial simulated annealing, to plant pathology. It has been mathematically proven (15) that this optimization method converges to the optimum allocation of sampling points that give the largest detection probability. We show the benefits of the method to plant pathology by (i) first illustrating that optimized spatial sampling can easily be applied for disease detection, and then we show that (ii) combining it with a spatially explicit epidemic model, we can develop optimum sample schemes, i.e., optimum locations to sample that maximize the probability of detecting an invasive pathogen. This method is then used as baseline against which other sampling methods can be tested for their accuracy. For the specific example case of this paper, we test (i) random sampling, (ii) stratified sampling as well as (iii) sampling based on the output of the simulation model (using the most frequently infected hosts as sample points), and (iv) sampling the hosts closest to the outbreak point.     

Comparison of disease patterns assessed by three independent surveys of cassava mosaic virus disease in Rwanda and Burundi

Cassava mosaic disease (CMD) seriously affects cassava yields in Africa. This study compared the spatial distribution of CMD using three independent surveys in Rwanda and Burundi. Geostatistical techniques were used to interpolate the point‐based surveys and predict the spatial distributions of different measures of the disease. Correlative relationships were examined for 35 environmental and socio‐economic spatial variables of which 31 were correlated to CMD intensity, with the highest correlation coefficients for latitude (?0·47), altitude (?0·36) and temperature (+0·36). The most significant explanatory variables were entered in separate linear regression models for each of the surveys. The models explained 54%, 44% and 22% of the variation in CMD. The residuals of the regression models were interpolated using kriging and added to the regression models to map CMD across both countries. Significant differences were calculated in some areas after correcting for interpolation error. An important explanation of the differences is interaction between the CMD pandemic and the dates of the three surveys. Large relative prediction errors obtained in the regression kriging procedure show the need to improve the survey design and decrease measurement error. Improved maps of crop diseases such as CMD could aid targeting of control interventions and thereby contribute to increasing crop yields. This study validated the unique character of each of the survey approaches adopted and underlines the importance of specific interpretation of results for CMD management. The study emphasizes the need for optimization of sampling designs and survey protocols to maximize the potential of regression kriging.     

Sampling in Precision IPM: When the Objective Is a Map

ABSTRACT Measuring and understanding spatial variation of pests is a fundamental component of population dynamics. The resulting maps can drive spatially variable pest management, which we define as precision integrated pest management (IPM). Precision IPM has the potential to reduce insecticide use and slow the rate of resistance development because of the creation of temporally dynamic refuges. This approach to IPM requires sampling in which the objective is to measure spatial variation and map pest density or pressure. Interpolation of spatially referenced data is reviewed, and the influence of sampling design is suggested to be critical to the mapped visualization. Spatial sampling created problems with poor precision and small sample sizes that were partially alleviated with choosing sampling units based on their geostatistical properties, adopting global positioning system technology, and mapping local means. Mapping the probability of exceeding a threshold with indicator kriging is discussed as a decision-making tool for precision IPM. The different types of sampling patterns to deploy are discussed relative to the pest mapping objective.     

基于景观结构和3S技术的干旱区绿洲生态风险分析——以高台县为例

以干旱区绿洲的典型区域-张掖市高台县为研究对象,基于景观结构中的景观干扰度指数和景观脆弱度指数构建区域生态风险指数,并利用空间分析方法对生态风险指数进行变量空间化。通过对生态风险指数采样结果进行半变异函数分析和空间插值,绘制了研究区域2000和2010年生态风险分布图,并对区域生态风险结构分布变化和空间分布变化进行分析。结果表明:生态风险强度为0.1-0.3的区域主要分布在水域、建设用地与耕地集中的人工绿洲区;生态风险强度为0.3-0.4的区域主要分布在人工绿洲外围的过渡带;以盐碱地、戈壁及沙地景观为主的各种荒漠景观类型所在区域的生态风险强度在0.4-0.65之间。     

Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imaging: Panicumeffusum in oilseed rape stubble

The potential accuracy of using airborne multispectral imaging to map weed patches rapidly in a fallow field has been evaluated. An image of a field of oilseed rape ( Brassica napus L.) stubble interspersed with Panicum effusum R. Br. was acquired using a four-camera airborne digital imaging system; recording in the infrared, red, green and blue wavebands. The image was converted into georectified weed maps using supervised and unsupervised classification procedures. Comparison of the airborne-derived maps with an accurate weed map compiled from a detailed ground survey demonstrated that weed:non-weed classification and mapping accuracies of better than 87% are possible. The limitations of assessing the accuracy of classified imagery using ground-truth data of similar spatial resolution are discussed.     

Evaluating field‐scale sampling methods for the estimation of mean plant densities of weeds

The weed flora (comprising seven species) of a field continuously grown with soyabean was simulated for 4 years, using semivariograms established from previous field observations. Various sampling methods were applied and compared for accurately estimating mean plant densities, for differing weed species and years. The tested methods were based on (a) random selection wherein samples were chosen either entirely randomly, randomly with at least 10 or 20 m between samples, or randomly after stratifying the field; (b) systematic selection where samples were placed along diagonals or along zig‐zagged lines across the field; (c) predicted Setaria viridis (L.) P. Beauv seedling maps which were used to divide the field into low‐ and high‐density areas and to choose the largest sample proportion in the high‐density area. For each method, sampling was performed with 5–40 samples. Systematic methods generally resulted in the lowest estimation error, followed by the random methods and finally by the predicted‐map methods. In case of species over‐ or under‐represented along the diagonals or the zig‐zag sampling line, the systematic methods performed badly, especially with low sample numbers. In those instances, random methods were best, especially those imposing a minimal distance between samples. Even for S. viridis, the methods based on predicted S. viridis maps were not satisfactory, except with low sample numbers. The relationships between sampling error and species characteristics (mean density, variability, spatial structures) were also studied.     

中国马铃薯晚疫病监测预警系统“China-blight”的组建及运行

马铃薯晚疫病是严重威胁世界马铃薯生产和粮食安全的重要病害之一,同时也是植物病害中流行速度最快的病害之一。由于品种多不抗病,目前国内外主要依靠化学防治控制该病害。为了提高用药的时效性,将信息技术与植物病害流行学原理相结合,设计并组建了中国马铃薯晚疫病监测预警系统"China-blight"(www.china-blight.net)。该系统由"中国晚疫病实时分布"、"未来48小时不同区域晚疫病菌侵染危险性预测"和"晚疫病化学防治决策支持系统"等子系统构成,此外还包括"晚疫病防治方法"、"品种抗病性"、"化学药剂库"、"其他病虫害"、"问题与经验交流"和"用户田间管理电子档案"等知识信息与服务功能。通过对2009年我国北方马铃薯一作区6-7月份病害侵染时段出现次数与晚疫病实际发生情况进行比较,预测信息与病害实际发生程度相符,该系统可以用于对马铃薯晚疫病田间防治的指导。     

A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.     

Spread risk of Eutypella canker of maple in Europe1

Eutypella canker of maple, caused by the pathogen Eutypella parasitica, has recently been reported for the first time in Europe. A rule‐based model of Eutypella parasitica spread risk in Europe is presented. This model incorporates the effects of spatial and temporal variability of multiple variables on pathogen spread and establishment. Model predictions are based on current knowledge of host susceptibility, pathogen reproduction and pathogen transmission, with particular regard to the host species’ distribution and climate suitability. Maps of host species’ distributions and monthly weather conditions were spatially analyzed in a Geographic Information System using the magnitude and direction of each variable's effect on disease spread. Spread risk predictions were computed for each month and averaged to generate a cumulative risk map. The model was calibrated using data on the natural distribution of Eutypella canker in North America. Extensive areas covering the natural distribution of maples in Europe are at considerable risk from the Eutypella parasitica infection. The most endangered regions are broad areas of the Balkans, the Apennines, France, Central and Eastern Europe and the Caucasus.     

基于遥感数据的新疆开-孔河流域农业区种植结构提取

农作物种植结构是农作物空间格局的重要组成部分,是区域土地资源和水资源优化配置的基础。以新疆开―孔河流域农业区为研究区域,综合利用作物物候信息和2016年的MODIS NDVI时序曲线,获得不同作物生长差异明显的关键期,选择关键期的Landsat 8 OLI影像,构建主要作物提取知识规则,基于决策树方法开展农作物的分类识别。开―孔河农业区2016年主要作物种植面积为5.07×10^5hm^2,其中棉花种植面积最大,为1.97×10^5hm^2,玉米、小麦次之。博斯腾湖和开都河农业区以辣椒、玉米和小麦为主要作物,种植结构比较零散;孔雀河农业区种植结构比较单一,以棉花和香梨为主要作物。与仅利用时间序列的MODIS数据进行作物分类识别的结果对比表明,综合利用MODIS和Landsat数据的作物识别精度有显著提高,总体分类精度从62.58%提高到88.37%,kappa系数从0.53提高到0.86。该方法综合利用了MODIS数据的时序特征和Landsat数据较高的空间分辨率特征,有效地避免了MODIS数据空间分辨率不足而导致分类精度较差的情况,又避免了Landsat数据时间分辨率不足所引起的时相选择盲目性或数据冗余,在干旱区农业种植结构的提取领域具有一定的应用价值。     

Application of high-resolution weather data to pest risk assessment1

Landscape maps of pest risk in the northeastern USA were produced for a hypothetical case study of an exotic plant disease (soybean rust) during the 1990 season. Published relations between the disease and a recently available high-resolution weather database were used to generate the maps. The landscape maps depicted patterns of risk using color classes overlaid with county boundaries at a spatial resolution of 1 km. The potential geographical impact of the exotic disease was estimated by interpreting a series of maps during a growing season.     

Perspectives of spatial scale in a wildland forest epidemic

The challenge of observing interactions between plant pathogens, their hosts, and environmental heterogeneity across multiple spatial scales commonly limits our ability to understand and manage wildland forest epidemics. Using the forest pathogen Phytopthora ramorum as a case study, we established 20 multiscale field sites to analyze how host-pathogen-environment relationships vary across spatial scales of observation in a wildland pathosystem. We developed statistical models of disease intensity across five nested levels of spatial aggregation, from an individual host through four broader spatial extents of observation. Analyses were conducted from two spatial perspectives: a focal view, where disease intensity at one scale was examined as a function of broader-scale landscape conditions, and an aggregate view, where disease intensity and landscape conditions was observed at the same scale of spatial aggregation. For each perspective, separate models were developed to compare direct field measurements of host density versus less expensive remotely sensed estimates of host habitat as predictors of disease in landscape-scale studies. From both perspectives, models using direct measurements of host density performed better than models using remotely sensed estimates of host habitat across all four spatial extents. We found no significant difference in model performance at the individual level. From the focal view, the performance of host density models declined with increasing spatial extent, whereas the performance of host habitat models improved with spatial extent. These results illustrate how the scale of observation – both spatial extent and measurement detail – can influence conclusions drawn from epidemiological models of wildland pathosystems.     

Using sequential Gaussian simulation to assess the uncertainty of the spatial distribution of soil salinity in arid regions of Northwest China

Accurately mapping and monitoring the spatial distribution pattern of soil salinity is essential for sustainable soil management and decision-making. The kriging-based interpolation technique is generally used to map the spatial distribution of soil salinity; however, this technique neglects the variation caused by interpolation for each unsampled location. The sequential gaussian simulation (SGS) is an effective tool to collect mapping uncertainties at several locations simultaneously, which is not possible in the kriging-based technique. Soil electrical conductivity has been widely used as an index for soil salinity. Based on 0–100?cm soil profile from 117 locations in the Manas River basin, Northwest China, the SGS algorithm was used to assess the uncertainty of the spatial distribution of soil electrical conductivity. It was found that the SGS algorithm was reliable in reproducing the spatial distribution of soil electrical conductivity. The SGS algorithm reproduced the sample statistics reasonably well. The standard deviations of the samples generated by the SGS algorithm (0.463–0.508 (dS m^?1)) were closer to the actual samples (0.675 (dS m^?1)) than those generated by kriging (0.454 (dS m^?1)). Most of the study area was lightly affected by salinity. Around 30% of the study area was moderately affected, and the heavily affected areas were sporadically scattered across the study area. The spatial uncertainty at multiple point presented a declining trend as the critical probability at a single point increased. The spatial estimation of the soil electrical conductivity in multiple point was more robust than that in the local location because of the low uncertainty.     

基于GIS的三江源地区物种多样性保护优先性分析

根据我们的多次科考调查和青海省对野生动物的调查结果等有关资料图件,对三江源地区的物种多样性和人类干扰因素等进行了分析和评估。选择9种鸟类、15种哺乳动物、20种植物作为本研究的指示物种,利用GIS制作了主要的珍稀濒危野生动植物分布图。选择放牧、人口、居民点、公路等因素作为生物多样性的主要干扰因素,制作出三江源地区干扰强度空间分布图。然后根据物种的丰富度和特有性、脆弱性和干扰程度等指标分析了三江源地区物种多样性保护优先性地区,做出了物种多样性保护优先地区空间分布图。     

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.     

植物病害时空流行动态模拟模型的构建

 一个描述在二维空间中单一种植或混合种植的植物群体内病害时、空流行动态的计算机随机模拟模型构建完成。模型由寄主、病原2个组分和病斑产孢、孢子传播、孢子着落、孢子侵染、病斑潜育、寄主生长、病害控制等一系列代表病害流行生物学过程的子模型构成。模型采用了面向对象的程序设计方法,用C++语言编写,能以病害流行曲线图、空间分布图、数据列表等方式显示模拟结果。测试结果表明:模型能反映植物病害流行过程的本质规律,既可作为植物病害流行学教学工具,帮助学生理解病害流行的时、空动态规律和不同因子对病害流行的影响,也可以作为研究工具,对流行学的某些理论问题进行模拟研究     

Spatial distribution and temporal development of fusarium crown and root rot of tomato and pathogen dissemination in field soil

ABSTRACT The spatial distribution and temporal development of tomato crown and root rot, caused by Fusarium oxysporum f. sp. radicis-lycopersici, were studied in naturally infested fields in 1996 and 1997. Disease progression fit a logistic model better than a monomolecular one. Geostatistical analyses and semivariogram calculations revealed that the disease spreads from infected plants to a distance of 1.1 to 4.4 m during the growing season. By using a chlorate-resistant nitrate nonutilizing (nit) mutant of F. oxysporum f. sp. radicis-lycopersici as a "tagged" inoculum, the pathogen was found to spread from one plant to the next via infection of the roots. The pathogen spread to up to four plants (2.0 m) on either side of the inoculated focus plant. Root colonization by the nit mutant showed a decreasing gradient from the site of inoculation to both sides of the inoculated plant. Simulation experiments in the greenhouse further established that this soilborne pathogen can spread from root to root during the growing season. These findings suggest a polycyclic nature of F. oxysporum f. sp. radicis-lycopersici, a deviation from the monocyclic nature of many nonzoosporic soilborne pathogens.     

Measurement of infection efficiency of a major wheat pathogen using time-resolved imaging of disease progress

Infection efficiency is a key epidemiological parameter that determines the proportion of pathogen spores able to infect and cause lesions once they have landed on a susceptible plant tissue. In this study, an improved method to measure infection efficiency of Zymoseptoria tritici using a replicated greenhouse experiment is presented. Zymoseptoria tritici is a fungal pathogen that infects wheat leaves and causes septoria tritici blotch (STB), a major disease of wheat worldwide. A novel experimental setup was devised, where living wheat leaves were attached to metal plates, allowing for time-resolved imaging of disease progress in planta. Because lesions were continuously appearing, expanding and merging during the period of up to 3 weeks, daily measurements were necessary for accurate counting of lesions. Reference membranes were also used to characterize the density and spatial distribution of spores inoculated onto leaf surfaces. In this way, the relationship between the number of lesions and the number of viable spores deposited on the leaves was captured and an infection efficiency of about 4% was estimated from the slope of this relationship. This study provides a proof of principle for accurate and reliable measurement of infection efficiency of Z. tritici. The method opens opportunities for determining the genetic basis of the component of quantitative resistance that suppresses infection efficiency. This knowledge would improve breeding for quantitative resistance against STB, a control measure considered more durable than deployment of major resistance genes.