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Four years validation of decision support optimising herbicide dose in cereals under Spanish conditions
Abstract
The Danish decision support system Crop Protection Online (CPO) optimises herbicide weed control. CPO recommends specific herbicide solutions to achieve a required level of control. The aim is to apply herbicides as little as possible but as much as necessary. CPOWeeds is a version of CPO adjusted to conditions in North-eastern Spain. The predicted efficacies and the yield obtained with CPOWeeds were validated in winter cereal field trials from 2010 to 2013. All CPOWeeds treatments were related to the efficacies obtained with standard herbicide treatments decided upon by local advisors. The predictions from CPOWeeds were compared to the actually achieved efficacies in the field trials for the nine weed species at different developmental stages and for 84.2% of the comparisons the obtained efficacies were equal to or higher than predicted. The average difference between predicted and observed efficacies was 2.35 percentage points. Yield was measured in three trials and the recommendations from CPOWeeds were maintaining yield. There were two situations where CPOWeeds were performing suboptimal. One is in the early weed growth stages, as the model is not yet prepared to account for water stress on root action herbicides applied at 10-11 BBCH. The second situation was in fields with a prior unidentified population of resistant Alopecurus myosuroides. For key species in winter cereals in Spain, such as Avena sterilis, Lolium rigidum and Papaver rhoeas, CPOWeeds achieved a satisfactory control level. It was concluded that the use of CPOWeeds allowed optimisation of the herbicide application with a very high robustness. The recommendations were satisfactorily for the conditions of the Northeast of Spain and have the potential to decrease the amount of applied herbicides by at least 30%. Therefore, it can be an important tool in Integrated Weed Management.
... The tools sort and present the available herbicide solutions in different way as a response to the questions asked by the user. Several names for the system have been used in the English literature, including PC Plant Protection (Denmark), Plant Protection Online (Denmark), VIPS-Ugras (Norway), CPOWeeds (Spain) and DSSHerbicide (Poland, Germany) [11,12,18]. In each country, the CPO-versions are adjusted according to the herbicide availability in the specific country and parameterised accordingly, whereas the algorithms and calculations follow the same concept. ...
... The experiences hitherto have shown that CPO-Weeds has potential for substantial reductions in herbicide use, if used as intended. Practical experiments in various countries have estimated reduction potentials between 20 and 40 % compared to labelled rates or standard recommendations [11,14,18]. ...
Crop Protection Online—Weeds (CPO-Weeds) is a decision support system for weed control developed in Denmark and later adjusted to conditions in several other countries. In Denmark, the DSS includes all major crops and available herbicides. The background for developing CPO-Weeds was a political motivation for reducing pesticide use and the concept of factor-adjusted doses. It was never the intention to build a sophisticated scientific model, but rather to design a simple user-friendly system. It is a knowledge-driven DSS, which offers herbicide dose suggestions based on a large database of the existing knowledge of herbicides and herbicide efficacies. The required weed control level in CPO-Weeds is based on expert evaluations, a herbicides dose-response model and an additive dose model to calculate possible mixtures of herbicides targeted a specific weed population. The herbicide dose model is a two parameter dose-response model, which is modified to include the effects of temperature, weed growth stage and influence of drought. The development has been driven by an ambition of offering a robust system with relatively low amounts of input variables and limited need for experimental parameter generation. CPO-Weeds offers overview and guidance for field specific spraying solutions, and the system has proved able to recommend herbicide doses with considerable reductions compared to label rates. Furthermore, CPO-Weeds offers a variety of tools that summarises knowledge of herbicides for a wide range of questions asked by practical weed managers, e.g. efficacy profiles of each herbicide, efficacy of users own herbicide mixtures, weed identification key and guidance for spraying strategy. The experiences have shown that even though CPO-Weeds are considered robust and trustworthy by both farmers and advisors there is a relatively low number of farmers subscribing to the system. A survey revealed that the DSS falls in between the strategies of many farmers; either the farmers relies completely on own experiences or advisory services or they considers the full crop rotation in their weed management. The latter is not supported by CPO-Weeds, which focus on a single season. The long term consequences of herbicide recommendations is only included in the need to limit input to soil seed bank. Another limiting factor for an increased practical use of CPO-Weeds is the need for field monitoring of weed populations, which can be a time consuming task and requires extensive weed recognising abilities of the farmer at the very early growth stages of weeds. The intention of CPO-Weeds was to provide recommendations for the full spraying season of a field, but experiences have shown that the system has several uses. Many farmers spray with a standard solution in the autumn in winter crops and then use the DSS for spring sprayings. The relatively simple input requirements also make the DSS suitable for teaching purposes and for farmers starting to grow new crops in their rotation as a learning tool.
... It is time consuming and expensive to develop and validate a DSS and the benefits of adopting an existing validated system in other countries are obvious. CPO has been adjusted to conditions in several other countries in different crops including Latvia (Vanaga and Zarina, 2008), Norway (Netland et al., 2005), and Spain (Montull et al., 2014). The required adjustments for a new country are mainly herbicide availability and legal restrictions, but in some cases different parameters concerning efficacies and required target levels are implemented. ...
... Further validation will show if DSSHerbicide can be validated for parts of Germany less similar to Danish conditions. As other versions of CPO have already shown promising results in both more Northern and Southern European countries, it is reasonable to expect robust results on a wider geographical scale (Montull et al., 2014;Netland et al., 2005). ...
... Most of the developed weed management decision support systems have severe limitations to the spatial variation of weed population [43][44][45]. DSS can significantly impact improving weed management strategies and strong communication between researcher, producers, and farmers as agricultural decision-makers [46]. Although weed management decision support systems are designed to simulate the stocks and flows between agricultural systems, they need to be controlled in order to collect the required data to run the models; farmers have more tendency toward the usage of low-cost herbicides [47,48]. ...
The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world.
Keywords: weed herbicide resistance; robotics; smart agriculture; artificial intelligent; remote sensing; digital technologies; machine vision
... • In Spain, about 30% in winter cereal crops and maize (Montull et al. 2014). ...
Integrated weed control is mandatory in the current legislative framework for sustainable plant protection programmes. The advent of synthetic pesticides in the 1950s allowed farmers to simplify cropping systems and forego more complicated crop protection strategies, especially in cereal production. Moreover, the awareness of the necessity to decrease pesticide use has been raised considerably since the mid-1980s in Europe. In this work, a Danish Decision Support System (DSS) for Field-Specific Crop Management is presented. This DSS, known as Crop Protection Online (CPO) and later IPMwise, optimizes herbicide weed control by providing recommendations of specific herbicide solutions to achieve a required control level. It has been developed since the 1980s, and the actual version (IPMwise) has recently been adapted to the edaphic and climatic conditions of Spain.
... Towards decision-making processes in weed control, Parsons et al. (2009) developed a model-based decision support system to assist arable farmers and consultants. Montull et al. (2014) validated the Danish decision support system Crop Protection Online (CPO) and found that its herbicide recommendations were satisfactory for the conditions of Northeast of Spain and had the potential to decrease the amount of applied herbicides by at least 30%, while Sønderskov et al. (2015) compared the results of two herbicide decision support systems with CPO's outputs after field experimental trials in winter wheat. ...
Site-specific weed management presupposes the careful monitoring and mapping of weed infestation areas. Cut-edge sensor technologies coupled with geographical information systems (GIS) provide the means for reliable decision-making concerning weed management even in sub-field level. In present research, two different spectral sensing systems were engaged in order to digitally map weed patches as grown in four different cotton fields in Central Greece. The systems used were a set of two Crop Circle multispectral sensors ACS-430 and a digital camera Nikon D300S. The spaces between cotton rows were scanned and photographed with the two systems accordingly. Raw recorded data were stored and analyzed in GIS environment producing spatially interpolated maps of red-edge normalized difference vegetation index (NDVI) and weed cover percentage values. Both mapping approaches were satisfactorily related to weed distribution as occurred in the fields; however, the photographic method tended to underestimate weed populations. Correlation of red-edge NDVI and weed cover values, at the points where photographs were taken, as revealed by Pearson’s correlation coefficient was high (r > 0.83) and statistically significant at the 0.01 level. A first-degree linear equation adequately modeled (R² > 0.7) the between value pair relations, strengthening the validity of the two methodologies in spatially monitoring weed patches. The methodologies and the technologies used in the study can be used for yearly mapping weed flora in cotton cultivation and potentially constitute a means of rationalizing herbicide application in terms of doses and spatio-temporal decision-making.
Conventional weed control methods are based on uniform treatments of the whole field, however, weeds are not distributed uniformly within fields, which means that the uniform distribution of herbicides is inappropriate. Considerable research has been conducted on different aspects of site-specific weed management in the past three decades from fundamental studies on the spatial distribution of weeds to the applied development and testing of new technologies for weed detection and site-specific control. Despite the available technologies and knowledge, there has been little practical adoption of these technologies mainly due to the lack of automated weed detection methods. However, significant progress has been made in the past few years. This chapter describes the general principles of automated weed detection and how these principles can be used in an agricultural context. Two cases demonstrate the progress of spot-application of herbicides based on automated weed detection using RGB cameras mounted on unmanned aerial vehicles (UAV) and ground vehicles.
The adoption of herbicides as a weed control strategy has allowed farmers to reduce the short-term effects of biological adversities on crop yields. However, they have also jeopardized agroecosystem sustainability by causing negative alterations of social and environmental subsystems. The physicochemical properties of herbicides (volatility, adsorption, or water solubility) can make them persist in the soil, air, and water, changing the structure and function of key environmental compartments. The occurrence of herbicide-resistant weed populations has generated a positive feedback loop requiring the application of higher doses, aggravating negative externalities. Hence, the economic benefits of herbicides as a unique control strategy substantially decrease in time. In addition, the dependence of agricultural systems on external inputs generates an herbicidal “lock-in” process that hinders the transition towards more sustainable integrated management systems. Therefore, there is a pressing need to elucidate the principal aspects of environmental risk analysis of herbicide use in agroecosystems. The objectives of this chapter are: (1) to introduce key concepts related to the construction and application of environmental risk indicators with a focus on agricultural system risk assessment, (2) to list the potentially negative effects associated with the use of herbicides, (3) to understand the processes that regulate herbicides’ fate and behavior in farming systems, (4) to highlight the importance of decision support systems (DSS) in reducing herbicide use in favor of integrated weed management (IWM), and (5) to understand the decision-making logic behind the increasing adoption of chemical weed control despite its negative socio-environmental effects.
In recent years, precision agriculture and precision weed control have been developed aiming at optimising yield and cost while minimising environmental impact. Such solutions include robots for precise hoeing or spraying. The commercial success of robots and other precision weed control techniques has, however, been limited, partly due to a combination of a high acquisition price and low capacity compared to conventional spray booms, limiting the usage of precision weeding to high-value crops. Nonetheless, conventional spray booms are rarely used optimally. A study by Jørgensen et al. (2007) has shown that selecting the right herbicides can lead to savings by more than 40 percent in cereal fields without decreasing the crop yield when using conventional sprayers. Therefore, in order to utilise conventional spray booms optimally, a preliminary analysis of the field is necessary. This analysis should determine which weeds are present in the field and the density of those weeds so that herbicides targeting those weeds may be selected.
Researchers have sought to detect and classify weeds and crops in images, but studies are limited regarding the number of plant species that can be discriminated and the flexibility of the camera setup.
In the present PhD thesis, requirements for the camera set-up are loosened, allowing the use of consumer grade cameras or even cell phones for weed species localisation and identification in images from conventionally grown fields. In total 4 537 images have been collected over three growth seasons from Danish fields. In these images 31397 plants are annotated with names, from which the 17 most frequent species are selected for automated classifiiiication. The automated classification consists of two steps: Initially, weeds are located in images after which, the weeds are classified.
Three types of weed localisation approaches are tested: Two approaches that perform a pixel-wise segmentation of plants, and one approach, that detects regions in images containing weeds. Common for all three approaches is that they aim at overcoming some of the challenges when working with images from fields: Namely changes in lighting, soil types, and plant stress due to lack of nutrition. The first of the suggested approaches segments plant material from the soil by using fuzzy C -means clustering combined with a threshold value for each pixel, which depends on the neighbourhood pixels, which helps to detect non-green stem regions. The second approach uses a fully convolutional neural network for segmenting pixels in three categories: Soil, weeds, and crops. The Neural Network is trained solely on modelled images but can segment weeds from maize with an intersection-over-union of between 0.69 and 0.93 for weeds and maize. Rather than segmenting images, the third approach produces region proposals that indicate weed locations in images. This method also uses a fully convolutional neural network, that enables it to detect weed instances in wheat fields despite occluding leaves.
The three methods for weed segmentation and localisation solve four problems in the field of camera based weed detection: handling of changing environments, handling of non-green plant stems, segmentation of weeds and crops that are overlapping, and instance detection in cereal fields with occluding leaves.
Following the detection of the weeds, the weed species are to be determined. For solving this problem, a convolutional neural network is used, which classifies the weeds with an overall accuracy of 87 percent for 17 species despite a severe degree of leaf occlusion.
Because of the ability to handle weed detection and classification in natural environments, these methods can potentially reduce the investment of farmers, and thus lead to a higher adoption rate than existing precision weed control techniques, resulting in huge potential savings regarding herbicide consumption.
Injury to weeds from sublethal doses of POST herbicides may reduce the effect of weed interference on crop yield. Information on how herbicide dose influences weed mortality, growth, and seed production is needed to assess the potential benefit of applying reduced herbicide doses. Field experiments were conducted at Mead, NE, in 2001 and 2002 to quantify velvetleaf mortality, growth, and corn–velvetleaf interference in response to varying doses of three POST herbicides. Untreated velvetleaf at six densities (0, 1, 3, 6, 12, and 20 plants m−1 corn row) was grown in mixture with corn to establish a baseline corn–velvetleaf interference relationship. Treated velvetleaf at a density of 20 plants m−1 row received one of five doses of dicamba, halosulfuron, or flumiclorac. Untreated velvetleaf height, biomass, and seed capsule production were greater in 2002 than 2001 and declined with increasing velvetleaf density in both years. Corn yield was not affected by untreated velvetleaf in 2001, but yield loss increased with increasing velvetleaf density in 2002. Mortality of herbicide-treated velvetleaf was 56% greater in 2001 than 2002 and increased with increasing herbicide dose. Maximum height of treated velvetleaf was similar for all treatments in 2001 but declined with increasing herbicide dose in 2002. Biomass and seed production of treated velvetleaf varied among herbicides in 2002 and decreased with increasing dose. Corn yield was not influenced by velvetleaf in 2001, but yield loss in response to herbicide-treated velvetleaf declined with increasing herbicide dose in 2002. Results show that the assumption that weeds surviving herbicide application are as competitive as untreated weeds is incorrect. Reduction in growth and resource consumption by herbicide-damaged weeds reduced the negative effects of weeds on corn. Nomenclature: Dicamba; halosulfuron; flumiclorac; velvetleaf; Abutilon theophrasti Medic. ABUTH; corn; Zea mays L.
Public concern of possible effects of pesticides on human health and the environment has lead to an increased pressure on
farmers to optimise their use of pesticides. Reducing pesticide doses below the recommended doses whenever possible is a straightforward
approach to reduce the risk of adverse side effects. To adopt this approach decision-making has to be improved. The parameters
to consider optimising herbicide doses are: weed flora and growth stage, crop competitiveness, climatic conditions, application
technique, formulation/adjuvant and combination with other pesticides. In Denmark this information is provided to farmers
through the decision support system ‹Plant Protection Online’. Based on input on weed species and densities, climatic conditions,
soil type, crop cultivar and expected yield ‹Plant Protection Online’ will provide farmers with optimum herbicide solution
usually with doses lower than standard recommended doses.
Experiments were conducted in growth chambers by growing oats ( Avena sativa L. ‘Ansi’) in water culture and soil with admixed TCA (trichloroacetic acid), pyrazon [5-amino-4-chloro-2-phenyl-3-(2 H )-pyridazinone], and two mixtures (pyrazon: less TCA; 1:25 and 1:50). The sigmoid log-dose relationship for dry-matter yield was fitted with a four-parameter logistic equation. Generally, the mixtures appeared to be less active than would be expected under an additive dose model. Around GR 50 the departure of joint action of the mixtures from the reference model was almost similar in the two growth media. At higher dose levels the mixtures approached the reference model, and the bioactivity of one mixture (1:25) in the water culture experiment even showed a higher activity than would be expected from the reference model.
Injury to weeds from sublethal doses of POST herbicides may reduce the effect of weed interference on crop yield. Information on how herbicide dose influences weed mortality, growth, and seed production is needed to assess the potential benefit of applying reduced herbicide doses. Field experiments were conducted at Mead, NE, in 2001 and 2002 to quantify velvetleaf mortality, growth, and corn–velvetleaf interference in response to varying doses of three POST herbicides. Untreated velvetleaf at six densities (0, 1, 3, 6, 12, and 20 plants m-1 corn row) was grown in mixture with corn to establish a baseline corn–velvetleaf interference relationship. Treated velvetleaf at a density of 20 plants m-1 row received one of five doses of dicamba, halosulfuron, or flumiclorac. Untreated velvetleaf height, biomass, and seed capsule production were greater in 2002 than 2001 and declined with increasing velvetleaf density in both years. Corn yield was not affected by untreated velvetleaf in 2001, but yield loss increased with increasing velvetleaf density in 2002. Mortality of herbicide-treated velvetleaf was 56% greater in 2001 than 2002 and increased with increasing herbicide dose. Maximum height of treated velvetleaf was similar for all treatments in 2001 but declined with increasing herbicide dose in 2002. Biomass and seed production of treated velvetleaf varied among herbicides in 2002 and decreased with increasing dose. Corn yield was not influenced by velvetleaf in 2001, but yield loss in response to herbicide-treated velvetleaf declined with increasing herbicide dose in 2002. Results show that the assumption that weeds surviving herbicide application are as competitive as untreated weeds is incorrect. Reduction in growth and resource consumption by herbicide-damaged weeds reduced the negative effects of weeds on corn.
The influence of weather on the efficacy of dichlorprop-P/MCPA and tribenuron-methyl on annual weeds in spring barley was studied in the field during 4 years at six locations in southern Sweden. The herbicides were applied at one-eighth to three-quarters of the full dose at three application times with approximately 6-day intervals. Weather stations, placed in the experiments, recorded climatic data. Maximum herbicide efficacy was obtained when spraying 1 week after the cotyledon stage with half or three-quarters of the recommended dose. Dose-response curves were estimated and the ED80 doses were calculated. On average, the herbicide dose required to obtain an effect of 80% was about 40% of the recommended dose. The influence of weather was analysed for seven different periods: 7 and 2 days before and after herbicide application, 1 day before and after application, and the day of herbicide treatment. The most pronounced effects of weather were found for the day of and the day before herbicide application, revealing the strong influence of weather on herbicide uptake and plant metabolism. High air temperature and low global radiation during the day of treatment reduced the ED80 dose of dichlorprop-P/MCPA, whereas the ED80 dose of tribenuron-methyl increased. For both herbicides, precipitation and high soil temperature increased the ED80 dose, which reflects the importance of rain on herbicide uptake and the effect of soil temperature on plant growth. The R2 values were higher in the analyses of dichlorprop-P/MCPA than of tribenuron-methyl, indicating that the effect of dichlorprop-P/MCPA was more weather-dependent than that of tribenuron-methyl. Separate analyses of the ED80 doses for Brassica napus L., Chenopodium album L. and Stellaria media (L.) Vill. generally resulted in increased R2 values, but otherwise gave results similar to those for the total weed population. Although the analyses revealed significant effects of individual weather factors on herbicide efficacy, it was not possible to discern a consistent and causal relationship between weather and herbicide performance.
Crop Protection Online (CPO) is a decision support system, which integrates decision algorithms
quantifying the requirement for weed control and a herbicide dose model. CPO was designed to be
used by advisors and farmers to optimize the choice of herbicide and dose. The recommendations
from CPO for herbicide application in spring barley in Denmark were validated through field
experiments targeting three levels of weed control requirement. Satisfactory weed control levels at
harvest were achieved by a medium control level requirement generating substantial herbicide
reductions (~ 60% measured as the Treatment Frequency Index (TFI)) compared to a high level of
required weed control. The observations indicated that the current level of weed control required is
robust for a range of weed scenarios. Weed plant numbers 3 wk after spraying indicated that the
growth of the weed species were inhibited by the applied doses, but not necessarily killed, and that an
adequate level of control was reached later in the season through crop competition.
The influence of weather on the efficacy of dichlorprop-P/MCPA and tribenuron-methyl on annual weeds in spritig barley was studied in the field during 4 years at six locations in southern Sweden. The herbicides were applied at one-eighth to three-quarters of the full dose at three application times with approximately 6-day intervals. Weather stations, placed in the experiments, recorded climatic data, Maximum herbicide efficacy was obtained when spraying 1 week after the cotyledon stage with half or three-quarters of the recommended dose. Dose-response curves were estimated and the ED80 doses were calculated. On average, the herbicide dose required to obtain an effect of 80% was about 40% of the recommended dose. The influence of weather was analysed for seven different periods: 7 and 2 days before and after herbicide application, 1 day before and after application, and the day of herbicide treatment. The most pronounced eftects of weather were found for the day of and the day before herbicide application, revealing the strong influence of weather on herbicide uptake and plant metabolism. High air tetnperature and low global radiation during the day of treattnent reduced the ED80 dose of dichlorprop-P/MCPA, whereas the ED80 dose of tribenuron-methyl increased. For both herbicides, precipitation and high soil temperature increased the ED80 dose, which reflects the importance of rain on herbicide uptake and the effect of soil temperature on plant growth. The R2 values were higher in the analyses of dichlorprop-P/MCPA than of tribenuron-methyl, indicating that the effect of dichlorprop-P/MCA was more weather-dependent than that of tribenuron-methyl. Separate analyses of the ED80 doses for Brassica napus L., Chenopodium, album L. and Stellaria media (L.) Vill. generally resulted in increased R2 values. but otherwise gave results similar to those for the total weed population. Although the analyses revealed significant effects of indivtdual weather factors on herbicide efficacy, it was not possible to discern a consistent and causal relationship between weather and herbicide performance.
A Danish decision support system (DSS) named Crop Protection Online (CPO) for integrated management of weeds in cereals and beet has been developed during the past 20 years. CPO is based on a model that runs in three main steps: model step 1 quantifies the level of weed control needed on a field level, model step 2 selects candidate herbicides and calculates dose rates to meet the need, and model step 3 calculates tank mixtures of herbicides with two to four mixing components, if advantageous. CPO has been developed in cereals and beet, and various prototype versions have been validated in 1679 field tests. CPO secured yield potentials, and the level of residual weeds was not increased when compared with reference treatments. The potential of CPO to reduce herbicide use has been observed in all model crops, but the potential was greatest in cereals. In spring cereal field trials highly infested with weeds, the present version of CPO suggested 35% of one full herbicide dose on average and in winter cereals CPO suggested 44% on average of one full dose. The results from validation trials demonstrate that CPO is capable of suggesting robust treatment options with a low input of herbicides. The system architecture of CPO has been exported to Poland and the Baltic countries, and the system is expected to be suitable for export to other countries too.
Two wetland plant species (Mimulus ringens L. [monkey-flower] and Bidens cernua L. [bur-marigold]), two terrestrial species (Sinapis arvensis L. [wild mustard] and Phaseolus vulgaris L. [beans-variety, Kentucky blue]), and one species found in both wet and dry habitats (Echinochloa crusgalli [L.] Beauv. [barnyardgrass]) were exposed to 1% (0.045 g a.i./ha) and 10% (0.45 g a.i./ha) of recommended label rate of metsulfuron methyl, a sulfonylurea herbicide used in western Canada. The objective of the study was to investigate the effect of metsulfuron methyl on these plant species and to determine the most sensitive phenological stage. Chemical analyses of herbicide residues showed that there was good correspondence between the quantity measured in the tank mix compared with that detected on glass fiber papers, the latter representing the dose reaching the test plants during the spray event. All species exhibited marked effects on the vegetative growth and reproductive performance when sprayed at 10% label rate. Less pronounced but significant effects were shown at 1% label rate. Seed weight was reduced for B. cernua and S. arvensis. The seedling stage was the most sensitive period for all species tested, although surviving plants sprayed at later stages showed considerable effects on the reproductive parts. All species tested would be at risk from small doses of metsulfuron methyl drifting away from the sprayed areas. This study highlights the shortcomings of the current testing schemes required prior to pesticide registration.
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here.
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