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Simulation of expected future efficacy from HRAC group B herbicides against loose silky-bent grass, depending on cropping factors
Get PDFDas hessische Windhalm-Resistenzmonitoring aus dem Jahr 2014 zeigte, dass Resistenzen gegenüber Herbiziden der HRAC-Klasse B landesweit verbreitet sind. Daten von am Monitoring beteiligten Schlägen wurden herangezogen, um Bewirtschaftungsparameter wie z. B. Saattermin, Anteil Pflug in der Fruchtfolge u.a. zu identifizieren, welche den Wirkungsgrad und damit die Bildung von Resistenzen beeinflussen können. Korrelationen zwischen Bewirtschaftungsparametern und dem jeweils im Biotest erzielten Wirkungsgrad gegen Windhalm wurden berechnet. Daraufhin folgte ein Modellbildungsprozess, um Wirkungsgrade in Abhängigkeit der o. g. Einflussfaktoren simulieren zu können. Die Modellergebnisse zeigen, dass beispielsweise eine Erhöhung des Anteils von Herbiziden der HRAC-Klasse B in der Fruchtfolge zu einem hohen Wirkungsverlust führt. Um diesen Effekt zu neutralisieren, müsste der Pfluganteil steigen und der Anteil an Winterungen in der Fruchtfolge sinken. Das Modell sollte auf weitere Datensätze angewendet und weiter trainiert werden, um Strategien zur Resistenzvermeidung voran zu treiben.Results from the resistance monitoring on loose silky-bent grass conducted in 2014 in Hessen showed that resistance against herbicides from HRAC group B is disseminated throughout the federal state. Cropping data like seeding date, ploughing intensity etc. where collected for fields involved in the monitoring in order to identify the impact of such cropping practices on herbicide efficacy and evolution of resistance. Correlations were calculated between field cropping data and corresponding efficacy rates against loose silky-bent grass, generated from bioassays conducted in 2014. A simulation model was developed and validated to forecast efficacy rates in dependence of the collected cropping data. The simulation results demonstrate high efficacy losses if the proportion of herbicide from the HRAC group B will increase in the crop rotation. To neutralize the effect, ploughing intensity would have to increase and proportion of winter cereals should decrease. The simulation model should be applied to further datasets and further trained, to forward anti-resistance strategies
Predicting hormesis in mixtures of herbicidal compounds – where are we and how far can we go?
Get PDFDie Vorhersage des Auftretens und des Ausmaßes von stimulierenden Wirkungen subtoxischer Dosierungen (Hormesis) in Herbizidmischungen ist eine anspruchsvolle und notwendige Aufgabe, da Herbizid-Expositionen in der Praxis häufig in Mischungen und bei niedrigen Dosierungen erfolgen können, z.B. bei Abdrift, Anwendungsfehlern, Schutz durch Mulchauflagen, Herbizidresistenz oder kleinräumiger Heterogenität der Applikationsmenge. Während Mischungswirkungen im toxischen Dosisbereich zuverlässig modelliert und vorhergesagt werden können, fehlt für die Auswertung im hormetischen Dosisbereich bisher ein einfacher statistischer Ansatz. Es zeigte sich, dass eine Vorhersage von Hormesis-induzierenden Dosierungen durch Modelle, die für monotone Dosis-Wirkungszusammenhänge entwickelt wurden, hinreichend möglich ist. Im Gegensatz dazu gestaltet sich die Vorhersage der Amplitude der Stimulation, als eines der Hauptmerkmale der Hormesis, als schwierig. Derzeit stehen keine mechanistischen Modelle zur Verfügung um die Amplitude in Mischungen vorherzusagen, noch gibt es ein allgemein akzeptiertes statistisches Modell. Dennoch wurden einige vielversprechende Versuche unternommen, die hormetische Amplitude in Mischungen von herbiziden Wirkstoffen vorherzusagen. Diese Versuche zeigen, dass es grundsätzlich möglich ist, eine hormetische Stimulation in Mischungen zu modellieren und dadurch wertvolle Einblicke in das Phänomen der Herbizid-Hormesis zu gewinnen. Die Errungenschaften dieser Versuche werden zusammengefasst und zukünftiger Forschungsbedarf und Grenzen werden diskutiert.Predicting the occurrence and expression of stimulatory effects of subtoxic doses of phytotoxins or herbicides (hormesis) in mixtures is a challenging and needed task, considering that herbicide exposures in practice often occur in mixtures at low doses due to drift deposition, errors in application, protection by mulch, herbicide resistance, small-scale dose heterogeneity, and other causes. While joint effects in toxin mixtures can be straightforwardly modelled and predicted at toxic doses, the evaluation at stimulatory doses lacks a common statistical approach. Prediction of effective hormetic doses can be adequately facilitated by adopting jointaction models that have been developed for monotonic responses. In contrast, prediction of the magnitude of hormesis as one of the key quantitative features of hormesis is not so easy. Currently, there are no mechanistic models available that could be adopted to predict the hormetic magnitude in mixtures nor is there a generally accepted model available. Nevertheless, some promising attempts were made to predict the hormetic magnitude in herbicidal mixtures demonstrating the fundamental possibility of modelling hormesis in mixtures and providing valuable insights into the phenomenon. The success of these attempts is summarized and future research needs and limits are discussed
Effects of catch crops on the weed vegetation in maize - an introduction to the study
Get PDFIn der Landwirtschaft Ostfrieslands ist ein langjähriger Anbau von Silomais auf der gleichen Fläche üblich. Diese Art der Bewirtschaftung führt im Laufe der Jahre trotz intensiven Herbizideinsatzes zur Selektion schwer zu bekämpfender Unkrautarten.In unterschiedlichen Untersuchungen konnte gezeigt werden, dass durch pflanzenbauliche Maßnahmen eine Beeinflussung der Unkrautvegetation im Maisanbau möglich ist. Ziel dieser Arbeit ist es zu überprüfen, ob im Maisdaueranbau durch das wiederholte Einbringen einer Untersaat in Verbindung mit dem Einsatz blattaktiver Herbizide eine Selektion schwer zu bekämpfender Unkrautarten verhindert und der Unkrautdruck insgesamt reduziert wird.In den Jahren 2017, 2018 und 2019 werden an zehn Standorten mit langjährigem Maisanbau Versuche mit identischem Design angelegt. Auf den Flächen werden die Parzellen der drei Varianten „Festuca-Untersaat“, „Lolium-Untersaat“ und „ohne Untersaat“ als Blockanlage mit drei Wiederholungen angeordnet. Der Herbizideinsatz erfolgt an allen Standorten gleich und wurde an die jeweilige Untersaat angepasst. Im Laufe des Maisanbaues werden fünf Unkrauterhebungen durchgeführt, bei denen der Einfluss der Varianten auf die Unkrautvegetation überprüft wird. Es werden kurz- und langfristige Auswirkungen auf die Unkrautvegetation erfasst.In the East Friesian agriculture a long-term cultivation of silo maize on the same arable area is usual. Over the years this way of cultivation causes always a selection of weeds which are difficult to eradicate despite of intensive use of herbicides.In various studies, it has already been shown that the way of crop cultivation can influence the weed vegetation in maize. The aim of this experiment is to test, if the selection of weed species which are difficult to control can be prevented and the weed pressure can be reduced by the repeated introduction of catch crops in combination with leaf-active herbicides. In the years 2017, 2018 and 2019, tests with identical design will be applied to ten sites with long-term maize cultivation. The plots of the three treatments "Festuca-Untersaat (undersown Festuca)", "Lolium-Untersaat (undersown Lolium)" and "ohne Untersaat (without undersown crops)" are set-up in three repeated blocks. The herbicide application is a universal measure at all sites and is adapted to the variants. During maize cultivation, five weed assessments will be carried out, in which the influence of the variants on the weed vegetation is determined. Short- and long-term effects on the weed vegetation will be recorded
Flufenacet an interesting mix partner for Viper™ Compact and GF-1546 against grass weeds in autumn
Get PDFViperTM Compact bestehend aus den drei Wirkstoffen Penoxsulam (15 g/l), Florasulam (3,75 g/l) und Diflufenican (100 g/l) ist ein breit wirksames, im Herbst einzusetzendes Herbizid, zur Bekämpfung von Windhalm, sowie ein- und zweikeimblättrigen Unkräutern in Winterweizen, Wintergerste, Winterroggen und Wintertriticale. Ein weiteres Herbizid ist GF-1546 welches aus der Wirkstoffkombination von Penoxsulam (15 g/l) und Diflufenican (100 g/l) besteht. Penoxsulam und Florasulam gehören der HRAC-Gruppe B (ALSHemmer) an, Diflufenican der HRAC-Gruppe F1. Da viele Windhalmpopulationen ein hohes Resistenzrisiko gegenüber Herbiziden der HRAC-Gruppe B aufweisen, ist es im Rahmen des Resistenzmanagements geboten Herbizide als Mischpartner einzusetzen, welche aus einer weniger resistenzgefährdeten HRAC-Gruppe stammen. Ein praxisüblicher Mischpartner ist hierbei der Wirkstoff Flufenacet aus der HRAC-Gruppe K3. In 2015 wurden in Feldversuchen Mischungen von ViperTM Compact (0.5 - 0.75 l/ha) mit Flufenacet (125 - 240 g/ha) getestet. Während ViperTM Compact sensitive Gräserpopulationen sicher erfasst, konnten nun durch die Zugabe von Flufenacet auch weniger sensitive Apera spica-venti (APESV) Biotypen erfolgreich bekämpft werden. Desweiteren konnte mit der erhöhten Flufenacet Aufwandmenge von 240 g/ha + ViperTM Compact, Ackerfuchsschwanz erfolgreich bekämpft werden. Insgesamt war die Mischung verträglich in den getesteten Kulturen. Die Tankmischung von ViperTM Compact + Flufenacet bietet somit eine hohe Wirksamkeit gegenüber Ungräsern und Unkräutern bei gleichzeitig verringertem Resistenzrisiko gegenüber Ungräsern.ViperTM Compact herbicide consists of the three active ingredients penoxsulam (15 g/L), florasulam (3.75 g/L) and diflufenican (100 g/L). It is a broad-spectrum herbicide used to control loose silky-bent (Apera spica venti), mono- and dicotyledonous weeds in winter wheat, winter barley, winter rye and winter triticale in the autumn. Penoxsulam and florasulam belong to the HRAC group B (ALS inhibitor), diflufenican to the HRAC group F1. Many loose silky-bent populations have a high risk of developing resistance to herbicides in the HRAC group B. For an effective resistance management, it is necessary to use herbicides from low resistance risk groups as mixing partner. A common mixing partner is the active substance flufenacet from the HRAC group K3.In 2015, mixtures of ViperTM Compact (0.5 - 0.75 L/ha) with flufenacet (125-240 g/ha) were tested in field trials. While ViperTM Compact is able to control sensitive grass populations, the addition of flufenacet was able to successfully control less sensitive Apera spica-venti (APESV) biotypes. Furthermore, with the increased flufenacet application rate of 240 g/ha + ViperTM Compact, blackgrass was also controlled successfully. Overall the mixture was selective in the tested cultures. The tank mix of ViperTM Compact + flufenacet thus offers a high effectiveness against grasses and weeds, while at the same time reducing the risk of resistance development
Precision harrowing using a bispectral camera and a flexible tine harrow
Get PDFIn dieser Studie wird ein justierbarer Kamerastriegel vorgestellt und getestet. Während des Striegelns kann die Intensität automatisch an die ortsspezifische Verunkrautung angepasst werden. Hierbei wurden die drei Striegeleinstellungen leicht (20 %), mittel (40 %) und stark (60 %) getestet. Vor der Überfahrt mit dem Kamerastriegel wurden in jedem Plot die Unkrautdichte und -zusammensetzung erhoben. Diese Informationen dienten einem Decision Support System, basierend auf Fuzzylogic, zur Anpassung des Einstellwinkels der Striegelzinken. Somit konnten Bereiche mit großer Unkraut- und Getreidedichte aggressiver gestriegelt werden als Bereiche mit geringer Biomasse.Die Handhabung und Effektivität des Systems zur Unkrautregulierung wurde in Sommergerste auf einem Feld an der Universität Hohenheim getestet. Hierzu wurde in einer Variante die maximale Striegelintensität (60 %), wie sie bei nicht automatisch justierbaren Maschinen angewandt wird getestet. Diese wurde verglichen mit der automatischen Striegeleinstellung, welche durch das Decision Support System vorgenommen wurde. Zusätzlich wurden eine unbehandelte Kontroll- und eine Herbizidvariante angelegt. Die Bonituren zur Erfassung der Verunkrautung und Biomasse erfolgten vor und nach der Behandlung, um die Wirksamkeit der verschiedenen Verfahren zu ermitteln. Das automatische System erwies sich als praxistauglich und lieferte vergleichbare Ergebnisse wie der fest voreingestellte Striegel, obwohl mit geringerer Intensität gestriegelt wurde. Zweikeimblättrige Unkräuter wurden mit dem automatischen Kamerastriegel ebenso gut behandelt, wie in der Herbizidvariante.In the given study an adjustable harrowing system is presented and tested. The automatic harrow can increase or decrease the harrowing intensity during operation. A gentle (20%), medium (40%) and aggressive (60%) harrow intensity was chosen. Prior to the application, measurements were performed in each plot concerning the weed density and composition. With this information, a Decision Support System based on fuzzy logic was used in order to trigger an appropriate tine angle movement. Thus, areas with high crop and weed densities were applied with more aggressive harrowing treatments and areas with lower weed densities with a gentler treatment.A spring barley field was adopted to evaluate the suitability and effectiveness of the system at the University of Hohenheim, Germany. A harrow application was conducted at the maximum permitted harrow intensity (60%), as the farmer would have applied it on the field and an automatic adaptation, based on the results of the decision support system. A further herbicide treatment and an untreated control were also included. Weed counting was performed prior to and after the application, along with biomass cuts and yield in order to estimate the treatment efficacy. The automatic system performed well, providing similar results as the nonautomatic harrowing, but with lower intensity levels. Dicotyledonous weeds were, in both mechanical applications, reduced as well as by the herbicide application, without any significant differences
Survey of efficacy trials for Conviso® One in sugar beet
Get PDFDas Herbizid Conviso One mit den beiden Wirkstoffen Foramsulfuron 50 g l-1 und Thiencarbazone-Methyl 30 g l-1 (HRAC-Gruppe B) benötigt im Zuckerrübenanbau komplementär eine resistente Sorte (sortenspezifische Selektivität). Erfahrungen mit diesen Wirkstoffen in Mais zeigen, dass eine hohe Wirksamkeit auch bei Unkräutern in späteren Wachstumsstadien gegeben ist, während die bisher im Zuckerrübenanbau eingesetzten Herbizide die höchste Effizienz im Keimblattstadium der Unkräuter haben. Um Erkenntnisse bezüglich der Wirkdauer im Boden, der Sensitivität von Unkräutern in verschiedenen Wachstumsstadien und des optimalen Einsatztermins zu gewinnen, wurden in den Jahren 2013 und 2014 vom Institut für Zuckerrübenforschung mehrere Feldversuche angelegt. Dabei wurden Unkräuter der Arten Chenopodium album, Brassica napus, Galium aparine, Matricaria chamomilla und Polygonum convolvulus ausgesät, um die Bodenwirksamkeit (Applikation vor Aussaat der Unkräuter) sowie die Wirksamkeit nach Spritzapplikation in unterschiedlichen Stadien der Unkräuter zu testen. Weitere Versuche auf Praxisflächen unter standorttypischer Verunkrautung dienten zur Ermittlung der Wirksamkeit von Conviso One gegenüber einer Standard- Herbizidstrategie sowie gegenüber Tankmischungen und Spritzfolgen unter Zugabe von Conviso One. Die Ergebnisse zeigen eine abnehmende Wirksamkeit mit zunehmenden Entwicklungsstadien der Unkräuter, vor allem bei C. album. Im Vergleich zu praxisüblichen Herbizidstrategien, die eine erste Behandlung im Keimblattstadium der Unkräuter vorsehen, kann bei Conviso One ein wirksamer Einsatz bis BBCH 14 von C. album erfolgen. Verglichen mit einer Standard-Herbizidstrategie kann durch den Einsatz von Conviso One eine höhere Wirksamkeit gegenüber schwer bekämpfbaren Unkrautarten wie Mercurialis annua und Durchwuchs-Kartoffel (Solanum tuberosum) erreicht werden und die Anzahl der notwendigen Spritzapplikationen sinkt. Die Bodenwirksamkeit betrug im Mittel der Versuche und Jahre 15-20 Tage.The ALS-inhibitor herbicide Conviso One (foramsulfuron 50 g L-1 + thiencarbazone-methyl 30 g L-1, HRAC B) requires a corresponding resistant variety when used in sugar beet cultivation. Experiences with these active ingredients in maize show a high efficacy even at later development stages of weeds, whereas active ingredients applied in current sugar beet cultivation cause highest efficacy at the cotyledonous stage. To acquire insights concerning soil activity, sensitivity of weeds at various development stages and the optimum application timing of Conviso One, numerous field trials were conducted in 2013 and 2014 by the Institute of Sugar Beet Research, Göttingen. Weed plants of the species Chenopodium album, Brassica napus, Galium aparine, Matricaria chamomilla and Polygonum convolvulus were sown to test soil activity (application prior to sowing of weeds) and efficacy after spraying at various development stages of weeds. Additional field trials on naturally infested sites built the basis to investigate efficacy of Conviso One compared to standard herbicides and compared to combinations of Conviso One and standard herbicides in spraying sequence or tank mixture. The results indicate decreasing efficacy when development of weeds increases, especially for C. album. Compared to current herbicide strategies, which require application at the cotyledonous stage of weeds, effective application of Conviso One can take place until BBCH 14 of C. album. Conviso One caused higher efficacy against difficult to control weeds as Mercurialis annua and volunteer-potato (Solanum tuberosum) than the standard herbicide treatment and the number of applications decreased. Soil activity lasted 15-20 days on average of the field trials
1.5 Distribution of residues of neonicotinoids in the hive and in bees in relation to bee health
Get PDFA field study was done to search for residues of neonicotinoids in 15 honeybee hives, in 5 apiaries to determine if any bee loss or symptoms of stress were associated with such residues. The apiaries were adjacent to corn or soybean crop fields in southern Ontario, and Quebec, Canada. Samples of healthy adult bees, larvae, impaired bees with symptoms of intoxication, black bees and dead bees were analysed for acetamiprid, clothianidin, imidacloprid, thiamethoxam, and the metabolite TZNG. Neither the concentrations of the individual compounds found nor the aggregate exposures to multiple compounds were associated with any evidence of stress or bee loss. Extensive diagnostic tests were done to monitor mites and diseases, and hive weights were monitored. Viruses were frequently found in all bee sample types. Over 90% of impaired bees had viruses, but 20% or less had any of the test compounds and only at low levels (<0.05 ng/bee) of neonicotinoids. 77% of black bees had viruses but none of the test compounds was detected in these bees. Method verification, distribution of residues in the colony, assessment of hive scale results, calculation of the combined effects, implications for diagnosis, and risk assessment will be discussed.A field study was done to search for residues of neonicotinoids in 15 honeybee hives, in 5 apiaries to determine if any bee loss or symptoms of stress were associated with such residues. The apiaries were adjacent to corn or soybean crop fields in southern Ontario, and Quebec, Canada. Samples of healthy adult bees, larvae, impaired bees with symptoms of intoxication, black bees and dead bees were analysed for acetamiprid, clothianidin, imidacloprid, thiamethoxam, and the metabolite TZNG. Neither the concentrations of the individual compounds found nor the aggregate exposures to multiple compounds were associated with any evidence of stress or bee loss. Extensive diagnostic tests were done to monitor mites and diseases, and hive weights were monitored. Viruses were frequently found in all bee sample types. Over 90% of impaired bees had viruses, but 20% or less had any of the test compounds and only at low levels (<0.05 ng/bee) of neonicotinoids. 77% of black bees had viruses but none of the test compounds was detected in these bees. Method verification, distribution of residues in the colony, assessment of hive scale results, calculation of the combined effects, implications for diagnosis, and risk assessment will be discussed
1.6 Simple modelling approaches to refine exposure for bee risk assessment based on worst case assumptions
Get PDFThe risk assessment for plant protection products to bees has attracted a lot of attention over the past five years or more. Current estimates of exposure (e.g. EFSA, 2013) are based on 90th percentile concentrations of active substances present in pollen and nectar in the field. Although suitable for acute risks, in field concentrations are not suitable for chronic assessment especially for honey bees which feed from colony stores before making foraging flights or for larvae which are fed from in-hive food stores via nurse bees. Other areas of exposure such as to pollen and nectar in following crops or to guttation may also be better estimated by use of simple exposure models.We will present simple methods based worst case assumptions to model chronic adult and larval honey bee exposure to spray applications of plant protection products (PPP) which take into account in-hive storage of pollen and nectar and also approaches to model exposure levels in succeeding crops and guttation water.Case studies will be presented demonstrating how these worst case model exposure estimates can be used in refining the risk assessment for bees offering a robust, worst case and cost effective alternative to field studies. Having better robust modelled exposure estimates for in-hive food reserves can aid in the assessment of both single PPP stressors and interactions with multiple stressors (e.g. disease and Varroa mites).The risk assessment for plant protection products to bees has attracted a lot of attention over the past five years or more. Current estimates of exposure (e.g. EFSA, 2013) are based on 90th percentile concentrations of active substances present in pollen and nectar in the field. Although suitable for acute risks, in field concentrations are not suitable for chronic assessment especially for honey bees which feed from colony stores before making foraging flights or for larvae which are fed from in-hive food stores via nurse bees. Other areas of exposure such as to pollen and nectar in following crops or to guttation may also be better estimated by use of simple exposure models.We will present simple methods based worst case assumptions to model chronic adult and larval honey bee exposure to spray applications of plant protection products (PPP) which take into account in-hive storage of pollen and nectar and also approaches to model exposure levels in succeeding crops and guttation water.Case studies will be presented demonstrating how these worst case model exposure estimates can be used in refining the risk assessment for bees offering a robust, worst case and cost effective alternative to field studies. Having better robust modelled exposure estimates for in-hive food reserves can aid in the assessment of both single PPP stressors and interactions with multiple stressors (e.g. disease and Varroa mites)
1.13 Using respiratory physiology techniques in assessments of pesticide effects
Get PDFThe determination of sub-lethal effects of pesticides on beneficial insects is challenging topic because the vast number of different possible endpoints. Traditionally measured endpoints reflect the basic outcome but do not give any information about the mode of actions or the real non-harming dosages of the studied toxicants. Physiological changes, however, reflect even small deviations from normal state. The gas exchange patterns are sensitive cues to determine the sub-lethal toxicosis in insects. Methods of respiratory physiology have been used to detect sub-lethal toxic effects of many chemicals, but information for biological preparations is also needed, especially when bees are used in entomovectoring task. The aims of this study were i) to clarify which are the effects of three microbiological preparations on two bee species, honey bees Apis mellifera L. and bumble bees Bombus terrestris L. and ii) could we compare the effects of the same preparations on different bee species. We saw that honey bees and bumble bees react similarly on microbiological preparations, however the reaction strength differed. We found that kaolin affects the survival of bumble bees and honey bees as much as did entomopathogenic preparations, whereas pure spores of a non-hazardous fungus and wheat flour did not. Bumble bees seem to be more tolerant to microbiological preparations than honey bees.The determination of sub-lethal effects of pesticides on beneficial insects is challenging topic because the vast number of different possible endpoints. Traditionally measured endpoints reflect the basic outcome but do not give any information about the mode of actions or the real non-harming dosages of the studied toxicants. Physiological changes, however, reflect even small deviations from normal state. The gas exchange patterns are sensitive cues to determine the sub-lethal toxicosis in insects. Methods of respiratory physiology have been used to detect sub-lethal toxic effects of many chemicals, but information for biological preparations is also needed, especially when bees are used in entomovectoring task. The aims of this study were i) to clarify which are the effects of three microbiological preparations on two bee species, honey bees Apis mellifera L. and bumble bees Bombus terrestris L. and ii) could we compare the effects of the same preparations on different bee species. We saw that honey bees and bumble bees react similarly on microbiological preparations, however the reaction strength differed. We found that kaolin affects the survival of bumble bees and honey bees as much as did entomopathogenic preparations, whereas pure spores of a non-hazardous fungus and wheat flour did not. Bumble bees seem to be more tolerant to microbiological preparations than honey bees
