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.     

Prevalence and distribution of Potato mop‐top virus in Scotland

This study was undertaken to determine the current occurrence in Scottish seed potato crops of Potato mop‐top virus (PMTV), which is transmitted by Spongospora subterranea and causes spraing (brown arcs and lines) in the flesh of potato tubers, rendering them unsaleable. In 2004, a stratified survey of four commonly grown cultivars was conducted, while in 2007 and 2008, only samples from powdery scab‐affected crops were collected. The incidence of crops in which infection by PMTV was present was 37·5% in the stratified survey in 2004, but was greater in surveys in which tubers with powdery scab were tested (47·2% in 2007 and 44·6% in 2008). Similarly, the frequency of crops with incidences of more than 10% tuber infection was lower (9·4%) in 2004 than in 2007 (25·4%) and in 2008 (26·2%). Significant differences in crop infection were found amongst the four major seed‐producing regions and the counties within these regions. The incidence of crop and tuber infection was least for class Pre‐basic seed potatoes and greatest for class Super Elite 3 and Elite seed potatoes. The results indicate that the prevalence of PMTV has not increased since surveys in the early 1970s.     

Quantifying the effects of fungicides and disease resistance on greenhouse gas emissions associated with wheat production

A method is presented to quantify the net effect of disease management on greenhouse gas (GHG) emissions per hectare of crop and per tonne of crop produce (grain, animal feed, flour or bioethanol). Calculations were based on experimental and survey data representative of UK wheat production during the period 2004–06. Elite wheat cultivars, with contrasting yields and levels of disease resistance, were compared. Across cultivars, fungicides increased yields by an average of 1·78 t ha^?1 and GHG emissions were reduced from 386 to 327 kg CO₂ eq. t^?1 grain. The amount by which fungicides increased yield – and hence reduced emissions per tonne – was negatively correlated with cultivar resistance to septoria leaf blotch (Mycosphaerella graminicola, anamorph Septoria tritici). GHG emissions of treated cultivars were always less than those of untreated cultivars. Without fungicide use, an additional 0·93 Mt CO₂ eq. would be emitted to maintain annual UK grain production at 15 Mt, if the additional land required for wheat production displaced other UK arable crops/set aside. The GHG cost would be much greater if grassland or natural vegetation were displaced. These additional emissions would be reduced substantially if cultivars had more effective septoria leaf blotch resistance. The GHGs associated with UK fungicide use were calculated to be 0·06 Mt CO₂ eq. per annum. It was estimated that if it were possible to eliminate diseases completely by increasing disease resistance without any yield penalty and/or developing better fungicides, emissions could theoretically be reduced further to 313 kg CO₂ eq. t^?1 grain.     

A general model of plant-virus disease infection incorporating vector aggregation

In plant-virus disease epidemiology, dynamical models have invariably incorporated a bilinear inoculation rate that is directly proportional to both the abundance of healthy (susceptible) hosts and the abundance of infective vectors. Similarly, the acquisition rate is usually assumed to be directly proportional to the abundance of nonviruliferous vectors and that of infectious hosts. These bilinear assumptions have been questioned for certain human diseases, and infection rates that incorporate power parameters of the variables have been proposed. Here, infection rates for plant-virus diseases that are of a more general form than the familiar bilinear terms are examined. For such diseases, the power parameter can be regarded as a measure of the spatial aggregation of the vectors or as a coefficient of interference between them, depending on the context.
Field data of cassava mosaic virus disease (CMD) incidence were examined. When vector population density and disease incidence were high, disease progress curves over the first 6 months from planting could not be explained using models with bilinear infection rates. Incorporation of the new infection terms allowed the range of observed disease progress curve types to be described. New evidence of a mutually beneficial interaction between the viruses causing CMD and the whitefly vector, Bemisia tabaci , has shown that spatial aggregation of the vectors is an inevitable consequence of infection, particularly with a severe virus strain or a sensitive host. Virus infection increases both vector fecundity and the density of vectors on diseased plants. It is postulated that this enhances disease spread by causing an increased emigration rate of infective vectors to other crops. Paradoxically, within the infected crop, vector aggregation reduces the effective contact rate between vector and host and therefore the predicted disease incidence is less than when a bilinear contact rate is used.     

Predicting light leaf spot (Pyrenopeziza brassicae) risk on winter oilseed rape (Brassica napus) in England and Wales, using survey, weather and crop information

Data from surveys of winter oilseed rape crops in England and Wales in growing seasons with harvests in 1987–99 were used to construct statistical models to predict, in autumn (October), the incidence of light leaf spot caused by Pyrenopeziza brassicae on winter oilseed rape crops the following spring (March/April), at both regional and individual crop scales. Regions (groups of counties) with similar seasonal patterns of incidence (percentage of plants affected) of light leaf spot were defined by using principal coordinates analysis on the survey data. At the regional scale, explanatory variables for the statistical models were regional weather (mean summer temperature and mean monthly winter rainfall) and survey data for regional light leaf spot incidence (percentage of plants with affected pods) in July of the previous season. At the crop scale, further explanatory variables were crop cultivar (light leaf spot resistance rating), sowing date (number of weeks before/after 1 September), autumn fungicide use and light leaf spot incidence in autumn. Risk of severe light leaf spot (> 25% plants affected) in a crop in spring was also predicted, and uncertainty in predictions was assessed. The models were validated using data from spring surveys of winter oilseed rape crops in England and Wales from 2000 to 2003, and reasons for uncertainty in predictions for individual crops are discussed.     

Prediction of Pests,Diseases and Losses: Principles and Computer Applications1

Efficacy of plant protection is closely related to an appreciation of the seasonal development and population changes of harmful organisms. This necessitates national surveys on pest population dynamics and on the epiphytotics of plant diseases. From these surveys, regional forecasts can be produced to give advice to the farmers. This work needs highly sophisticated systems to cover both numerical and spatial aspects of pest situations. Thus, there is an urgent need to exploit computer applications in solving forecasting problems. However, the use of computers for forecasting the incidence of harmful organisms and losses is in its initial phase. At present, single pest and disease models prevail. Numerical aspects have been approached both empirically and experimentally. Empirical models, based on multiple regression analysis, are relatively simple and easy to use for various harmful organisms. However, simulation models of harmful organisms are expected to lead to much more reliable results, although, so far, little experience is available on their predictive powers in crop protection. Spatial aspects have been approached with a routine computer mapping technique, which seems to be an effective tool for recognizing and forecasting distribution trends, while loss predictions can be based on crop models, incorporating submodels forecasting the incidence of harmful organisms. The prospect for operational use, in forecasting, of the computer techniques mentioned is discussed.     

基于GIS的内蒙古兴安盟地区热量资源空间分布特征研究

采用半经验半理论回归模拟方法,建立了内蒙古兴安盟地区40个表征热量资源的空间分布模型,模型全部通过0.01显著性检验,其中90%的模型同代检验和87.5%的模型模拟检验相对误差在-5%～5%之内.不同时间尺度空间分布模型之间具有较好的一致性,所建模型能够较好的模拟该地区的热量资源空间分布.在此基础上,应用GIS技术和1...     

Detecting the spatial component of variation in the weed community at the farm scale with variation partitioning by canonical correspondence analysis

Variation partitioning by canonical correspondence analysis (CCA) was applied to analyse spatial variation in the species composition of a weed community for an area of farmland in southern Finland. The farmland, covering 450 ha, was sampled with a 60 m × 60 m grid. Data on weed species were collected along with the following groups of explanatory variables: spatial variables (the terms of second-order polynomial trend surface regression equation generated on the x and y co-ordinates of sample quadrats: x, y, x², xy, y², x²y and xy²), farmer variables (nine farms), soil variables (four soil types and pH value), crop variables (barley, oats, sugarbeet, potato and turnip rape) and physical variables (area of field, altitude, slope and aspect in four directions). The main variation in species composition along the first and second CCA axes was caused by interplay between farmer and crop variables. Farmer and crop variables explained more of the variation than did soil or physical variables. All the variables were to some extent spatially structured. The spatial variables contributed 54.5% of the total variation, of which pure spatial variation accounted for 12.2%. The highest covariation with spatial variables was detected with farmer (33.7%) and crop variables (25.7%). Variation partitioning by CCA is recommended for studying the relationship between the spatial variation in weed communities and explanatory variables.     

Relationship between disease severity and escape of Pseudoperonospora cubensis sporangia from a cucumber canopy during downy mildew epidemics

Fundamental to the development of models to predict the spread of cucurbit downy mildew is the ability to determine the escape of Pseudoperonospora cubensis sporangia from infected fields. Aerial concentrations of sporangia, C (sporangia m^?3), were monitored using Rotorod samplers deployed at 0·5 to 3·0 m above a naturally infected cucumber canopy in two sites in central and eastern North Carolina in 2011, where disease severity ranged from 1 to 40%. Standing crop of sporangia was assessed each morning at 07·00 h EDT and ranged from 320 to 16 170 sporangia m^?2. Disease severity and height above the canopy significantly (P < 0·0001) affected C with mean concentration (C_m) being high at moderate disease. Values of C_m decreased rapidly with canopy height and at a height of 2·0 m, C_m was only 7% of values measured at 0·5 m when disease was moderate. Daily total flux (F_D) was dependent on disease severity and ranged from 5·9 to 2242·3 sporangia m^?2. The fraction of available sporangia that escaped the canopy increased from 0·028 to 0·171 as average wind speed above the canopy for periods of high C increased from 1·7 to 3·6 m s^?1. Variations of C_m and F_D with increasing disease were well described (P < 0·0001) by a log‐normal model with 15% as the threshold above which C_m and F_D decreased as disease severity increased. These results indicate that disease severity should be used to adjust sporangia escape in spore transport simulation models that are used to predict the risk of spread of cucurbit downy mildew.     

Quantifying cropping practices in relation to inoculum levels of Fusarium graminearum on crop stubble

This study was conducted in 58 producer‐field locations in Manitoba from 2003 to 2006 to understand how cropping practices influence Fusarium graminearum inoculum levels on stubble of various crops, including wheat, collected from the soil surface. Colonies per m² (CN) were determined and converted to base‐10 logarithm values (log₁₀CN). Mean log₁₀CN of the sampled field for various crops and groups of crops grown in the 3 years prior to sampling were tested to find significant differences. Average log₁₀CN values were also used to determine significant differences between tillage systems and the effect of number of years. Average log₁₀CN values for zero and minimum tillage systems were not different but were significantly higher than values for conventional tillage. A series of three crop rotation scenarios were tested using weighted log₁₀CN values for crop, tillage, their interaction and their squared terms in step‐wise regression models to identify which model was the best predictor of log₁₀CN. This was selected as the cropping practice index (CPI) model and was expressed as: CPI = 1·98423 + 0·55975 (C₂ × C₁ × T)² + 0·4390 (C₂ × T)², where C₁, C₂ and T represent the weighted log₁₀CN values for crops grown 1 and 2 years previously and tillage system, respectively. R² value for this model was 0·933 (P < 0·0001). The reliability of the CPI model was tested using jack‐knife full cross‐validation regression. The resulting R² was 0·899. The CPI model was tested using data collected from seven wheat fields in 2006 (R² = 0·567). The relationship between CPI and FHB index (R² = 0·715) was significant.     

Field models for the prediction of leaf infection and latent period of Fusicladium oleagineum on olive based on rain,temperature and relative humidity

Although much is known about the effect of climatic conditions on the development of peacock leaf spot of olive, field‐operational models predicting disease outbreaks are lacking. With the aim of developing such models, a 10‐year survey was conducted to relate leaf infection to climate parameters that can be easily monitored in the field. As outbreaks of disease are known to be linked to rain, models were evaluated for their ability to predict whether infection would occur following a rain event, depending on air temperature and duration of relative humidity above 85%. A total of 134 rain events followed by confirmed leaf infection and 191 rain events not followed by detectable infection were examined. The field data were adequately fitted (both specificity and sensitivity >0·97) with either a multilayer neural network or with two of six tested regression models describing high boundary values of high humidity duration, above which no infection occurred over the temperature range, and low boundary values below which no infection occurred. The data also allowed the selection of a model successfully relating the duration of latent period (time between infection and the first detection of leaf spots) as a function of air temperature after the beginning of rain (R² > 0·98). The predictive abilities of these models were confirmed during 2 years of testing in commercial olive orchards in southern France. They should thus provide useful forecasting tools for the rational application of treatments and foster a reduction in fungicide use against this major disease of olive.     

Weed vegetation in the north-western Balkans: diversity and species composition

Surveys of weed vegetation of the western Balkan peninsula (1939–2006) were used to study changes in species composition. A large data set of arable weed vegetation was compiled and analysed with direct and indirect ordination, regression and beta (β) diversity analysis. Five environmental variables (altitude, season, year, crop, phytogeographical region) were used to determine broad-scale changes in weed species composition. The most important parameter was phytogeography and the second was crop. Altitude and season were found to be less important, although significant, which contrasts with results from Central and Northern Europe. β-diversity was higher in cereals and in summer, while decline along the altitudinal gradient previously demonstrated in Central Europe, was not observed. In southern parts of the studied area, thermophilous species have shifted to higher altitudes. The results and ranking of importance of environmental and spatial variables are discussed in relation to similar studies in Northern and Central Europe.     

Modelling within‐field spatial variability of crop biomass – weed density relationships using geographically weighted regression

The objective of this study is to offer a new framework for exploring and modelling the spatial variation in crop biomass – weed density relationships, adapting geographically weighted regression (GWR) to include a non‐linear regression model. The relationship between crop biomass and weed density is usually modelled by non‐linear regression models, in which the spatial heterogeneity of the relationship is ignored, although the effect of weeds on crop can differ in relation to topographic and edaphic variability. GWR attempts to capture spatial variability by calibrating a regression model to each location in space. We show the application of the method in different cereal cropping systems, with one or two weed species. The results indicate that GWR can significantly improve model fitting over non‐linear least squares (NLS) in some situations. Furthermore, the parameter estimates can be mapped to illustrate local spatial variations in the regression relationship under study and eventually to relate the spatial variability of the model to the environmental heterogeneity. We discuss the value of the GWR for analysing the observed spatial variability and for improving model development and our understanding of spatial processes.     

The current status of insecticide resistance in Musca domestica in England and Wales and the implications for housefly control in intensive animal units

Following the 1984–85 housefly (Musca domestica L.) resistance survey, a similar survey was carried out between January 1990 and April 1992 in order to monitor changes in UK housefly resistance. Samples of fly populations were collected from 35 farms throughout England and Wales. Dose-response data were obtained by topical application and feeding test methods. For both methods the knockdown (KD) after 48 h was used for all the dose-response comparisons. The ranges of resistance factors for the topical application method were for methomyl, 1·6–20·0 at KD₅₀ and 4·5–34·4 at KD₉₅; for azamethiphos, 2·5–58·5 at KD₅₀ and 5·0–2604 at KD₉₅; for pyrethrins+piperonyl butoxide, 1·2–9·6 at KD₅₀ and 1·6–14·7 at KD₉₅; and for permethrin, 2·2–118·8 at KD₅₀ and 4·3–200·0 at KD₉₅. The ranges of the resistance factors for the feeding tests were for methomyl, 1·2–56·1 at KD₅₀ and 3·1–80·0 at KD₉₅, and for azamethiphos, 3·9–125·0 at KD₅₀ and 4·4–380·0 at KD₉₅. The means of resistance factors for all of the insecticides showed increases over those obtained in a previous survey carried out in 1984–85. The significance of the results for housefly control in intensive animal units in the UK is discussed.     

基于小波神经网络和BP神经网络的麦蚜发生期预测对比

为建立更准确、稳定的病虫害预测预报模型,减少农作物病虫害损失、提高农作物产量与质量,运用主成分分析法从42个基础气象因子中整合形成8个新的自变量输入模型,采用试凑法对网络关键参数进行筛选,用2002-2011年数据进行网络训练,建立了以Morlet小波函数为传递函数的小波神经网络模型,并与以Sigmoid函数为传递函数的BP神经网络模型进行了比较.在小波神经网络训练过程中,有6年拟合精度在90％以上,平均拟合精度为89％,预测结果MAPE值为4.1939,MSE值为5.9764;在BP神经网络的训练过程中,有4年拟合精度超过90％,平均拟合精度仅为81.07％,预测结果中MAPE值为6.4694,MSE值为8.2457.从训练结果看,小波神经网络更能准确描述麦蚜发生期的变化规律,其拟合能力较BP神经网络好;从预测精度和模型的稳定性来看,小波神经网络好于BP神经网络.     

Evaluation of models to predict take-all incidence in winter wheat as a function of cropping practices,soil, and climate

The incidence and severity of take-all, caused by Gaeumannomyces graminis var. tritici (Ggt), in susceptible crops depend on climate, soil characteristics and cropping practices. Take-all can be controlled by modifying crop rotation, crop management and fungicide treatment. When available, fungicides are used as a seed treatment and are partially effective. There is currently no reliable method for helping farmers to optimise their choice of cropping system to improve take-all control. In this study, we defined 16 models, based on various mathematical functions and input variables, for predicting disease incidence in a wheat crop as a function of soil characteristics, climate, crop rotation and crop management. The parameters of these models were estimated from field experiments carried out at six sites in the north of France over a ten-year period. The root mean squared error of prediction (RMSEP) values of the models were estimated by cross validation and compared. RMSEP was in the range 16.34–65.93% and was higher for the models based on multiplicative functions. The lowest RMSEP value was obtained for a dynamic model simulating disease incidence during the crop cycle and which included among input variables the percentage of diseased plants determined at GS30.     

Evaluation of fungicides for control of eyespot disease and yield loss nationships in winter wheat

Following the discovery of resistance to benzimidazole fungicides in the cereal eyespot pathogen Pseudocercosporella herpotrichoides in the UK in 1981, and an initial in vitro screen to select the fungicides with greatest activity against the pathogen, 40 field experiments were carried out between 1983 and 1986 to evaluate alternative fungicides for control of eyespot. At the majority of experimental sites, benomyl-resistant strains of the pathogen were present, and carbendazim did not control eyespot. Prochloraz was the most effective fungicide, reducing the eyespot index by 30–60%. There was no extra benefit from adding carbendazim to prochloraz. Flusilazole was almost as effective as prochloraz, but other fungicides had little or no effect. At sites with a high incidence of eyespot, prochloraz, with or without the addition of carbendazim, generally gave the largest yield increase. The mean yield increases each year were in the range 0·36–0·85 t/ha, and the greatest yield increase at any site was 2·27 t/ha. Most other fungicides increased yield, but carbendazim did not from 1984 to 1986. There were also yield increases at many sites with a low incidence of eyespot. Yield increases were associated with increases in thousand-grain weight at the majority of sites, but in only a few instances were there associated increases in specific weight. Prochloraz application at GS30-31 was cost effective at 71% of sites. At most sites, in regression of yield on eyespot, eyespot accounted for less than 25% of the variance in yield. The mean relationship between severe eyespot lesions and yield loss was such that each 1% increase in the percentage of tillers affected by severe eyespot was associated with a yield loss of 0·21 %. There was a significant positive correlation between eyespot at GS75 and GS30-31, and between yield increase from prochloraz treatment (at GS30-31) and eyespot at GS75, but not between yield increase and eyespot at GS30-31. The ADAS threshold for fungicide application of 20% tillers affected at GS30-31 was a reliable indicator of the cost-effectiveness of treatment at 60% of sites.     

Relationship between sweet orange yield and intensity of Citrus Variegated Chlorosis

Citrus Variegated Chlorosis (CVC) is currently present in approximately 40% of citrus plants in Brazil and causes an annual loss of around 120 million US dollars to the Brazilian citrus industry. Despite the fact that CVC has been present in Brazil for over 20 years, a relationship between disease intensity and yield loss has not been established. In order to achieve this, an experiment was carried out in a randomized block design in a 3 × 2 factorial scheme with 10‐year‐old Natal sweet orange. The following treatments were applied: irrigation with 0, 50 or 100% of the evapotranspiration of the crop, combined with natural infection or artificial inoculation with Xylella fastidiosa, the causal agent of CVC. The experiment was evaluated during three seasons. A negative exponential model was fitted to the relationships between yield versus CVC severity and yield versus Area Under Disease Progress Curve (AUDPC). In addition, the relationship between yield versus CVC severity and canopy volume was fitted by a multivariate exponential model. The use of the AUDPC variable showed practical limitations when compared with the variable CVC severity. The parameter values in the relationship of yield–CVC severity were similar for all treatments unlike in the multivariate model. Consequently, the yield–CVC intensity relationship (with 432 data points) could be described by one single model: y = 114·07 exp(?0·017 x), where y is yield (symptomless fruit weight in kg) and x is disease severity (R² = 0·45; P < 0·01).     

Molecular epidemiology of cassava mosaic disease in Madagascar

Cassava is the staple food for hundreds of millions of people in Africa but its cultivation is seriously constrained by cassava mosaic disease (CMD) in Madagascar, and in Africa in general. This study identified the cassava mosaic geminiviruses (CMGs) involved in CMD in Madagascar and their associated epidemiological characteristics from countrywide surveys. Molecular characterization of CMGs in Madagascar revealed an unprecedented diversity and co‐occurrence of six viruses: African cassava mosaic virus (ACMV), East African cassava mosaic Cameroon virus (EACMCV), East African cassava mosaic Kenya virus (EACMKV), East African cassava mosaic virus (EACMV), South African cassava mosaic virus (SACMV) and the recently described Cassava mosaic Madagascar virus (CMMGV). Distinct geographical distributions were observed for the six viruses. While ACMV was more prevalent in the central highlands, EACMV and EACMKV were prevalent in lowlands and coastal regions. Both EACMCV and SACMV occurred in almost all the localities visited. PCR diagnosis revealed that mixed infection (up to four co‐infected viruses) occurred in 21% of the samples and were associated with higher symptom severity scores. Pairwise comparisons of virus associations showed that EACMCV was found in mixed infections more often than expected while ACMV and SACMV were mostly found in single infections. A greater abundance of whiteflies was observed in lowland and coastal areas. Nevertheless, infected cuttings remain the primary source of CMD propagation (95%) in Madagascar.