DOCSLIB.ORG

And ALS-Inhibitors in Alopecurus Myosuroides Huds. and Their Causes

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
And ALS-Inhibitors in Alopecurus Myosuroides Huds. and Their Causes Analysis of the spatial and temporal dynamics of herbicide resistance to ACCase- and ALS-Inhibitors in Alopecurus myosuroides Huds. and their causes Von der Fakultat¨ Architektur, Bauingenieurwesen und Umweltwissenschaften der Technischen Universitat¨ Carolo-Wilhelmina zu Braunschweig zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigte Dissertation von Johannes Karlfried Manfred Bruno Herrmann geboren am 22.06.1987 aus Bautzen Eingereicht am: 10. Mai 2016 Disputation am: 11. Oktober 2016 Berichterstatter: Prof. Dr. Otto Richter Prof. Dr. Peter Zwerger 2016 Acknowledgements First, I would like to thank Dr. Martin Hess and Dr. Roland Beffa from Bayer Crop- Science. Both of them provided me with good support and feedback about my work, giving valuable comments and helping me fully understand the topic of herbicide re- sistance in all its respects. Dr. Martin Hess supported me additionally with the field sampling, carrying out of experiments and with interviewing farmers. As one of the founders of the project he had already collected data prior to the start of my PhD work, enabling me to work with a great amount of data right from the start. I also like to thank Dr. Harry Strek for his valuable comments about this project. I would also like to thank my supervisors Professor Dr. Otto Richter and Professor Dr. Peter Zwerger for their useful comments and ideas relating to the work. I would like to express my appreciation for Bayer CropScience for funding of this work and for letting me use their facilities. Special thanks go to the team at Bayer CropScience in Frankfurt, namely Ragnhild Paul, Falco Peter, Julia Unger, Thomas Schubel, Veronika Brabetz, Rebecka D¨ucker, Dr. Susana Gonzalez and Andrea Figge for their help in car- rying out greenhouse experiments and laboratory work. Their input and mental support throughout the work was invaluable to me. I would also like to thank the team at the JKI in Braunschweig, namely Dr. Lena Ulber, Dr. Henning Nordmeyer and Dagmar Rissel, for their useful comments. Special thanks to Dr. Janin Rummland for her help in the development of the simulation model. Finally special thanks to all the farmers that let us sample their fields and provided me with management data of their fields. Contents 1 Introduction and Aim of the Study 1 1.1 Introduction . .2 1.2 Literature Review . .3 1.2.1 Alopecurus myosuroides . .3 1.2.2 ALS- and ACCase-Inhibitors . .8 1.2.3 Herbicide Resistance . .9 1.2.4 Development of herbicide resistance . 11 1.3 Goals of the Study . 13 1.4 Thesis Layout . 14 2 Temporal and Spatial Evolution of Herbicide Resistance to ACCase- and ALS-Inhibitors at the Field Level in Alopecurus myosuroides Huds. 15 2.1 Introduction . 16 2.2 Material & Methods . 20 2.2.1 Site Selection . 20 2.2.2 Seed Sampling . 21 2.2.3 Greenhouse Bioassay . 22 2.2.4 Laboratory Analysis . 24 2.2.5 Statistical Analysis . 24 2.3 Results . 27 2.3.1 Infestation . 27 2.3.2 Greenhouse Results . 29 2.3.3 Laboratory Results . 35 2.3.4 Population Analysis . 42 2.4 Discussion . 47 3 Analysis of Field History and Soil Information to Determine the Driving Factors behind Herbicide Resistance to ALS-Inhibitors in Alopecurus myosuroides Huds. 59 3.1 Introduction . 60 3.2 Material & Methods . 63 3.2.1 Soil Map Analysis-Derivation of Variables . 63 3.2.2 Field History Analysis . 67 3.3 Results . 72 3.3.1 Soil Data . 72 3.3.2 Field History Data . 76 3.4 Discussion . 86 3.4.1 Soil characteristics . 87 3.4.2 Crop Management . 88 4 Predicting Herbicide Resistance: A Comparison of Different Methods to Forecast an Event of Resistance to ALS-Inhibitors in Alopecurus myosuroides Huds. 95 4.1 Introduction . 96 4.2 Material & Methods . 100 4.2.1 Supervised Learning . 100 4.2.2 Resistance Simulation Model . 100 4.3 Results . 114 4.3.1 Supervised Learning . 114 4.3.2 Simulation Model . 117 4.3.3 Prediction Outcomes . 122 4.4 Discussion . 141 4.4.1 Supervised Learning . 141 4.4.2 Simulation Model . 142 4.4.3 Comparing the overall prediction accuracy . 145 4.4.4 Limitations of the prediction and outlook . 145 5 Conclusions and Perspectives 149 5.1 Conclusions . 150 5.2 Future Work . 156 6 Summary 159 7 Appendix 163 7.1 Soil Map Legend . 164 7.2 Maps of ACCase- and ALS-inhibitor distribution among years . 165 List of Figures 2.1 Excerpt of the Hohenlohe area with the three locations H, M and Z in circles 20 2.2 Maps of relative infestation levels across all samples for fields in the core area of location H, M and Z. Q10= <10% quantile, Q25= <25% quan- tile, Q50 = 25% <x >75% quantile, Q75= >75% quantile, Q90= >90% quantile. The number of observations considered is indicated for every field. 30 2.3 Observed control to ALS-inhibitors and three ACCase-inhibitors tested in the greenhouse. Differences are shown across different crops and infestation levels. Red, yellow and green background color indicate the three rating groups, "Low", "Reduced", "High", that were used previously. 36 2.4 Closely related clusters (>90% AU) in color for the three sample locations. Grey color indicates fields that did not show high similarity to any other field and are therefore independent at the accuracy level assessed. 44 3.1 Distribution of soil groups (clusters) among the three components analyzed byPCA .................................... 75 3.2 Maps of the eight soil clusters extracted using Kmeans clustering and their distribution in the core fields of location H, M and Z . 77 3.3 Boxplot of individual PCA scores of the 6 management clusters on the 5 Principal Component (PC) from table 3.15 . 83 4.1 Model Scheme with corresponding equation (Eq.) and tables (Tab.) used at each point . 102 4.2 Representation of Soil Clusters for Z038 among the cells of the Cellular Automaton (CA) . 104 4.3 Variance importance plot ranking the variables by mean accuracy decrease (left) and mean decrease gini (Gini Index, right) . 114 4.4 Frequency of prediction of resistance for 36 fields using a trained random forest model. The test dataset consists out of of 16, 7 and 13 fields that were classified as Resistant (R), developing resistance (I) and Sensitive (S) respectively. 116 4.5 Simulation Output for field "Z038" with total number of plants and the respective portion of homozygous(AA), heterozygous (Aa) and wildtype (aa) plants . 119 4.6 Annual Number and Distribution of homozygous(AA), heterozygous (Aa) and wildtype (aa) plants within the field simulated by CA . 121 4.7 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field H012. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.123 4.8 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field H289. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.124 4.9 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field H362. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.125 4.10 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field M001. Field Management by the farmer indicates information about the crop, tillage and herbicide regime. 126 4.11 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field M007. Field Management by the farmer indicates information about the crop, tillage and herbicide regime. 127 4.12 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field M017. Field Management by the farmer indicates information about the crop, tillage and herbicide regime. 128 4.13 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field M018. Field Management by the farmer indicates information about the crop, tillage and herbicide regime. 129 4.14 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field M027. Field Management by the farmer indicates information about the crop, tillage and herbicide regime. 130 4.15 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field Z002. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.131 4.16 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field Z009. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.132 4.17 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field Z023. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.133 4.18 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field Z029. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.134 4.19 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field Z116. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.135 4.20 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field Z279. Field Management by the farmer indicates information about the crop, tillage and herbicide regime.136 4.21 Comparison of the predicted resistance status by random forest and sim- ulation model to observational data for field Z284.
Load more

Recommended publications
  • Within-Species Variation in Grass Weeds in Sweden
    Within-species Variation in Grass Weeds in Sweden Dormancy, Herbicide Response, Genetic Relationships Liv Åkerblom Espeby Faculty of Natural Resources and Agricultural Sciences Department of Crop Production Ecology Uppsala Doctoral Thesis Swedish University of Agricultural Sciences Uppsala 2010 Acta Universitatis agriculturae Sueciae 2010:35 ISSN 1652-6880 ISBN 978-91-576-7448-7 © 2010 Liv Åkerblom Espeby, Uppsala Print: SLU Service/Repro, Uppsala 2010 Within-species Variation in Grass Weeds in Sweden. Dormancy, Herbicide Response, Genetic Relationships Abstract Variation within a weed species enables it to persist through varying conditions and is thus an important component of weediness. In this thesis, intra-specific variation in two agronomically important attributes - herbicide susceptibility and seed dormancy - are studied in Swedish Apera spica-venti (L.) Beauv. and Alopecurus myosuroides Huds., both serious annual weeds in winter cereals, and with many cases of herbicide resistance. Swedish Elymus repens (L.) Gould, a perennial, rhizomatous grass, is investigated for its genetic variability and variation in glyphosate response. The susceptibility to new and established herbicides in greenhouse studies in the two annual grasses ranged 0.5-4 orders of magnitude among populations, which mostly came from fields with no previous suspicion of resistance. The greatest variation was found in A. myosuroides in response to fenoxaprop-P-ethyl (an old herbicide in the sense that it had been used for a decade), with significant correlation with response to flupyrsulfuron-Na (a newly introduced herbicide). One fifth of the A. spica-venti populations were significantly less susceptible to isoproturon (old) than a susceptible reference population, but without correlation in response to sulfosulfuron (new).
    [Show full text]
  • Weed Suppressive Ability of Cover Crop Mixtures Compared to Repeated Stubble Tillage and Glyphosate Treatments
    agriculture Article Weed Suppressive Ability of Cover Crop Mixtures Compared to Repeated Stubble Tillage and Glyphosate Treatments Alexandra Schappert *, Miriam H. Messelhäuser, Marcus Saile, Gerassimos G. Peteinatos and Roland Gerhards Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, 70599 Stuttgart, Germany; [email protected] (M.H.M.); [email protected] (M.S.); [email protected] (G.G.P.); [email protected] (R.G.) * Correspondence: [email protected]; Tel.: +49-711-459-23444 Received: 15 August 2018; Accepted: 13 September 2018; Published: 15 September 2018 Abstract: The utilization of an effective stubble management practice can reduce weed infestation before and in the following main crop. Different strategies can be used, incorporating mechanical, biological, and chemical measures. This study aims at estimating the effects of cover crop (CC) mixtures, various stubble tillage methods, and glyphosate treatments on black-grass, volunteer wheat and total weed infestation. Two experimental trials were conducted in Southwestern Germany including seven weed management treatments: flat soil tillage, deep soil tillage, ploughing, single glyphosate application, dual glyphosate application, and a CC mixture sown in a mulch-till and no-till system. An untreated control treatment without any processing was also included. Weed species were identified and counted once per month from October until December. The CC mixtures achieved a black-grass control efficacy of up to 100%, whereas stubble tillage and the single glyphosate treatment did not reduce the black-grass population, on the contrary it induced an increase of black-grass plants. The dual glyphosate application showed, similar to the CC treatments, best results for total weed and volunteer wheat reduction.
    [Show full text]
  • 11 the Evolutionary Strategy of Claviceps
    Pažoutová S. (2002) Evolutionary strategy of Claviceps. In: Clavicipitalean Fungi: Evolutionary Biology, Chemistry, Biocontrol and Cultural Impacts. White JF, Bacon CW, Hywel-Jones NL (Eds.) Marcel Dekker, New York, Basel, pp.329-354. 11 The Evolutionary Strategy of Claviceps Sylvie Pažoutová Institute of Microbiology, Czech Academy of Sciences Vídeòská 1083, 142 20 Prague, Czech Republic 1. INTRODUCTION Members of the genus Claviceps are specialized parasites of grasses, rushes and sedges that specifically infect florets. The host reproductive organs are replaced with a sclerotium. However, it has been shown that after artificial inoculation, C. purpurea can grow and form sclerotia on stem meristems (Lewis, 1956) so that there is a capacity for epiphytic and endophytic growth. C. phalaridis, an Australian endemite, colonizes whole plants of pooid hosts in a way similar to Epichloë and it forms sclerotia in all florets of the infected plant, rendering it sterile (Walker, 1957; 1970). Until now, about 45 teleomorph species of Claviceps have been described, but presumably many species may exist only in anamorphic (sphacelial) stage and therefore go unnoticed. Although C. purpurea is type species for the genus, it is in many aspects untypical, because most Claviceps species originate from tropical regions, colonize panicoid grasses, produce macroconidia and microconidia in their sphacelial stage and are able of microcyclic conidiation from macroconidia. Species on panicoid hosts with monogeneric to polygeneric host ranges predominate. 329 2. PHYLOGENETIC TREE We compared sequences of ITS1-5.8S-ITS2 rDNA region for 19 species of Claviceps, Database sequences of Myrothecium atroviride (AJ302002) (outgroup from Bionectriaceae), Epichloe amarillans (L07141), Atkinsonella hypoxylon (U57405) and Myriogenospora atramentosa (U57407) were included to root the tree among other related genera.
    [Show full text]
  • ISTA List of Stabilized Plant Names 7Th Edition
    ISTA List of Stabilized Plant Names th 7 Edition ISTA Nomenclature Committee Chair: Dr. M. Schori Published by All rights reserved. No part of this publication may be The Internation Seed Testing Association (ISTA) reproduced, stored in any retrieval system or transmitted Zürichstr. 50, CH-8303 Bassersdorf, Switzerland in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior ©2020 International Seed Testing Association (ISTA) permission in writing from ISTA. ISBN 978-3-906549-77-4 ISTA List of Stabilized Plant Names 1st Edition 1966 ISTA Nomenclature Committee Chair: Prof P. A. Linehan 2nd Edition 1983 ISTA Nomenclature Committee Chair: Dr. H. Pirson 3rd Edition 1988 ISTA Nomenclature Committee Chair: Dr. W. A. Brandenburg 4th Edition 2001 ISTA Nomenclature Committee Chair: Dr. J. H. Wiersema 5th Edition 2007 ISTA Nomenclature Committee Chair: Dr. J. H. Wiersema 6th Edition 2013 ISTA Nomenclature Committee Chair: Dr. J. H. Wiersema 7th Edition 2019 ISTA Nomenclature Committee Chair: Dr. M. Schori 2 7th Edition ISTA List of Stabilized Plant Names Content Preface .......................................................................................................................................................... 4 Acknowledgements ....................................................................................................................................... 6 Symbols and Abbreviations ..........................................................................................................................
    [Show full text]
  • Alopecurus Myosuroides ALOPECURUS MYOSUROIDES HUDS
    IWMPRAISE — H2020-SFS-2016-2017/H2020-SFS-2016-2 – Inspiration sheet no. 12 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727321 Scientific name: Alopecurus myosuroides ALOPECURUS MYOSUROIDES HUDS. English name: Blackgrass Italian name: Coda di volpe French name: Vulpin des champs Danish name: Agerrævehale Blackgrass German name: Acker-Fuchsschwanz Dutch name: Akkervossestaart, Duist Spanish name: Cola de zorra Slovene name: Njivski lisičji rep AN ETERNAL WEED Blackgrass is a common annual grass in winter crops. Blackgrass is classified as a segetal weed species (found exclusively in cultivated fields) and was undoubtedly introduced at the dawn of agriculture. Since the 1960s, it has become one of the most troublesome weeds in Europe. First dispersed by combines, blackgrass is now found throughout the northern half of France. Its dispersal in the region was favoured by the development of resistance to grass herbicides. A similar situation exists in Great Britain, Belgium, Germany and even in Turkey. Agronomists have known about the plant since the early 20th century and in spite of the known ‘weaknesses’ in its biological characteristics, which should in theory render it easy to control, it is a major weed in European crop systems. Figure 1 - Geographical distribution ofAlopecurus BOTANY - ECOLOGY myosuroides in Europe and western Asia Family: Grasses (= Poaceae) (after Van Himme & Bulcke, 1975) Life cycle: annual plant (therophyte), strictly cross- pollinated. Emergence is mainly in the autumn, with a second peak of emergence at the end of winter. Its persistence and survival in fields is therefore linked exclusively to seed production and seed stock.
    [Show full text]
  • First Report of Alopecurus Arundinaceus, A
    Journal of Plant Pathology (2006), 88 (1), 121-125 Edizioni ETS Pisa, 2006 121 DISEASE NOTE DISEASE NOTE FIRST REPORT OF ALOPECURUS FIRST RECORD OF CHESTNUT ARUNDINACEUS, A. MYOSUROIDES, BLIGHT IN IRAN HORDEUM VIOLACEUM, AND PHLEUM PRATENSE AS HOSTS OF CLAVICEPS M. Niknejad Kazempour1, S.A. Khodaparast1, PURPUREA POPULATION G2 IN TURKEY M. Salehi2, B. Amanzadeh2, A. Nejat-Salary3 and B.K. Shiraz2 C. Eken1, S. Pazoutova2, A. Honzatko 2 and S. Yıldız1 1 Department of Plant Pathology, Faculty of Agricultural Sciences, University of Guilan, P.O. 1 Department of Plant Protection, Faculty of Agriculture, Box 41635, 1314 Rasht, Iran Atatürk University, 25240 Erzurum, Turkey 2 Natural Resources and Domestic Animals Research, 2 Guilan Province, Iran Institute of Microbiology CAS, Videnska 1083, 3 14220 Prague 4, Czech Republic Natural Resources and Domestic Animals Research, Alborz-Karaj, Iran Ergot (Claviceps purpurea (Fr.) Tul.) was observed on Diseased chestnut trees (Castanea sativa) were ob- Alopecurus arundinaceus Poir., Alopecurus myosuroides served in surveys conducted from 2001 to 2005 in the Huds., Hordeum violaceum Boiss. et Huet and Phleum forests of Visroud, Imamzadeh Ebrahim, Fuman, and pratense L. in Erzurum, Turkey, during late spring and Shafaroud, the main growing areas of chestnut in Iran. summer of 2002 and 2003. The early stage of ergot is rec- Affected plants had declining or dead branches and ognized by an appearance of thick conidia-laden “honey- sprouts with yellow or brown wilted leaves. Elliptical dew”, which exudes from infected florets. In the late yellowish-brown cankers with a cracked surface and stage of infection an elongated, hard, purplish-black scle- rough appearance were present on trunks and branches rotium develops.
    [Show full text]
  • Blackgrass: Alopecurus Myosuroides Huds
    PNW 377 • January 1992 Blackgrass Alopecurus myosuroides Huds. S. Aldrich-Markham Blackgrass (Alopecurus cally because it starts rapid myosuroides Huds.), also growth early in the spring, known as slender foxtail, ranks, competing with the grain for with wild oats and annual resources. In test plots in ryegrass, among the most Washington,DATE. uncontrolled important grass weeds in blackgrass reduced winter winter cereal production in wheat yields by as much as Europe. It infests other winter OF42%, with an average yield crops there as well, including loss of 25%. Most of the grass seed, rapeseed, and abundant blackgrass seeds are forage legumes. shed before the grain is har- vested. Although it’s not yet wide- OUT spread in the United States, it’s been introduced into a number of states, including IS IDENTIFICATION Washington and Oregon. The seedheads of blackgrass There are several infestations Figure 2.—Blackgrass is a are dense and cylindrical, in eastern Washington, in winter annual that may grow similar to those of meadow to 3 feet tall. foxtail (Alopecurus pratensis L.), a perennial species commonly Lincoln,information: Spokane, and planted for pasture in wet Whitman counties. Plants areas (figure 1). have been found within a mile of the Idaho border. The Blackgrass seedheads have a only infestations in Oregon smaller diameter in propor- are in the Willamette Valley, tion to their length than in Yamhill County near the meadow foxtail and are more currentborder with Polk County. tapered at each end. They’re PUBLICATIONBlackgrass has become a usually reddish-purple in serious problem in these color, giving the appearance, areas, where winter wheat is from a distance, of a “black an important crop.
    [Show full text]
  • Alopecurus Myosuroides (Poaceae) – Nowy Gatunek Dla Flory Kotliny
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Jagiellonian Univeristy Repository Fragm. Flor. Geobot. Polonica 17(1): 179–203, 2010 NOTATKI BOTANICZNE Alopecurus myosuroides (Poaceae) – gatunek nowy dla fl ory Kotliny Sandomierskiej Alopecurus myosuroides Huds. (wyczyniec polny) pochodzi z obszaru śródziemnomorsko- irano-turańskiego (ZAJĄC 1979, 1987). Aktualnie gatunek ten występuje w Europie, połu- dniowo-zachodniej, środkowej i wschodniej Azji, północnej Afryce, Australii i na Nowej Zelandii oraz w Ameryce (CLARKE 1980; HULTÉN & FRIES 1986; JEHLÍK i in. 1998; SHEN- GLIAN & PHILLIPS 2006; CRINS 2007). W Polsce roślina ta jest antropofi tem, zaliczanym do grupy archeofi tów (ZAJĄC 1979), przy czym jego pojawy w niektórych częściach naszego kraju mają jedynie efemeryczny charakter (RUTKOWSKI 2003). Dokładne rozmieszczenie tego gatunku na terenie naszego kraju jest nadal niewystarczająco poznane. Wiele infor- macji na temat występowania A. myosuroides zestawili w ostatnim czasie DAJDOK i SZCZĘ- ŚNIAK (2009). W Polsce wyczyniec polny występuje głównie na niżu. W górach znany jest B Wiązownica Ujezna Pełkinie San 2 km A Jarosław Ryc. 1. Lokalizacja nowego stanowiska Alopecurus myosuroides Huds. w Polsce (A) i w okolicy Jarosławia (B) Fig. 1. Localization of the new station of Alopecurus myosuroides Huds. in Poland (A) and in the area of Jarosław (B) 180 Fragm. Flor. Geobot. Polonica 17(1), 2010 ze stanowisk zlokalizowanych na Pogórzu Kaczawskim (KWIATKOWSKI 2006) i Przedgórzu Sudeckim (DAJDOK i SZCZĘŚNIAK 2009). Z informacji zawartych w polskiej literaturze bota- nicznej oraz w bazie ATPOL wynika, że jego występowania nie stwierdzono jak dotąd ani w Kotlinie Sandomierskiej ani na obszarze polskich Karpat.
    [Show full text]
  • Assessing Non-Chemical Weeding Strategies Through Mechanistic Modelling of Blackgrass (Alopecurus Myosuroides Huds.) Dynamics Nathalie Colbach, D.A.G
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Archive Ouverte en Sciences de l'Information et de la Communication Assessing non-chemical weeding strategies through mechanistic modelling of blackgrass (Alopecurus myosuroides Huds.) dynamics Nathalie Colbach, D.A.G. Kurstjens, Nicolas Munier-Jolain, A. Dalbiès, Thierry Doré To cite this version: Nathalie Colbach, D.A.G. Kurstjens, Nicolas Munier-Jolain, A. Dalbiès, Thierry Doré. As- sessing non-chemical weeding strategies through mechanistic modelling of blackgrass (Alopecurus myosuroides Huds.) dynamics. European Journal of Agronomy, Elsevier, 2010, 32 (3), pp.205-218. 10.1016/j.eja.2009.11.005. hal-01173174 HAL Id: hal-01173174 https://hal.archives-ouvertes.fr/hal-01173174 Submitted on 14 Aug 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Assessing non-chemical weeding strategies through mechanistic 2 modelling of blackgrass ( Alopecurus myosuroides Huds.) dynamics 3 4 N. Colbach 1,2,3 , D.A.G. Kurstjens 4, N.M. Munier-Jolain 1,2,3 , A. Dalbiès 5,6 , T. Doré 5,6 5 6 1 INRA, UMR 1210 Biologie et Gestion des Adventices, F-21000 Dijon, France 7 2 AgroSup Dijon, UMR 1210 Biologie et Gestion des Adventices, F-21000 Dijon, France 8 3 Université de Bourgogne, UMR 1210 Biologie et Gestion des Adventices, F-21000 Dijon, France 9 4 Wageningen University, Farm Technology Group, P.O.
    [Show full text]
  • Alopecurus Myosuroides Huds.) Bruno Chauvel, Nicolas Munier-Jolain, Anne Letouzé, David Grandgirard
    Developmental patterns of leaves and tillers in a black-grass population (Alopecurus myosuroides Huds.) Bruno Chauvel, Nicolas Munier-Jolain, Anne Letouzé, David Grandgirard To cite this version: Bruno Chauvel, Nicolas Munier-Jolain, Anne Letouzé, David Grandgirard. Developmental patterns of leaves and tillers in a black-grass population (Alopecurus myosuroides Huds.). Agronomie, EDP Sciences, 2000, 20 (3), pp.247-257. 10.1051/agro:2000124. hal-00886018 HAL Id: hal-00886018 https://hal.archives-ouvertes.fr/hal-00886018 Submitted on 1 Jan 2000 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Agronomie 20 (2000) 247–257 247 © INRA, EDP Sciences 2000 Original article Developmental patterns of leaves and tillers in a black-grass population (Alopecurus myosuroides Huds.) Bruno CHAUVEL*, Nicolas MUNIER-JOLAIN, Anne LETOUZÉ, David GRANDGIRARD Unité de Malherbologie et Agronomie, INRA, 17 rue Sully, BP 1540, 21034 Dijon Cedex, France Agriculture and Environment (Received 28 July 1999; revised 16 December 1999; accepted 18 January 2000) Abstract – In an agricultural system with low herbicide input, prediction of the timing of annual weed developmental stages would be useful to predict weed growth rate and seed production, or to determine the appropriate timing for the application of foliar herbicides in order to optimize their use and avoid secondary effects of herbicide treatment such as water pollution or herbicide resistance.
    [Show full text]
  • State of NEW MEXICO 2014 Wetland Plant List
    4/2/14 & n s p State of NEW MEXICO 2014 Wetland Plant List Lichvar, R.W., M. Butterw ick, N.C. Melvin, and W.N. Kirchner. 2014. The National Wetland Plant List: 2014 Update of Wetland Ratings. Phytoneuron 2014-41: 1-42. http://wetland_plants.usace.army.mil/ Sisyrinchium demissum Greene (Stif f Blue-Ey ed-Grass) Photo: Lewis E. Epple User Notes: 1) Plant species not listed are considered UPL for w etland delineation purposes. 2) A few UPL species are listed because they are rated FACU or w etter in at least one Corps region. 3) Some state boundaries lie w ithin tw o or more Corps Regions. If a species occurs in one region but not the other, its rating w ill be show n in one column and the other column w ill be BLANK. Approved for public release; distribution is unlimited. 1/24 4/2/14 State of NEW MEXICO 2014 Wetland Plant List Total Species = 1506 AW GP WMVC OBL 273 266 266 FACW 337 330 320 FAC 338 329 345 FACU 450 390 430 UPL 100 126 116 Regional Totals 1498 1441 1477 Scientific Name Authorship AW GP WMVC Common Name Abies bifolia A. Murr. FACU FACU Rocky Mountain Alpine Fir Abutilon theophrasti Medik. UPL UPL FACU Velv etleaf Acer glabrum Torr. FAC FAC FACU Rocky Mountain Maple Acer grandidentatum Nutt. FACU FAC FACU Cany on Maple Acer negundo L. FACW FAC FAC Ash-Leaf Maple Acer saccharinum L. FAC FAC FAC Silv er Maple Achillea millefolium L. FACU FACU FACU Common Yarrow Achnatherum hymenoides (Roemer & J.A.
    [Show full text]
  • Growth Stage of Alopecurus Myosuroides Huds. Determines the Efficacy of Pinoxaden
    plants Communication Growth Stage of Alopecurus myosuroides Huds. Determines the Efficacy of Pinoxaden Ana Pintar 1,*, Zlatko Sveˇcnjak 2 , Valentina Šoštarˇci´c 1, Josip Laki´c 1, Klara Bari´c 1, Dragojka Brzoja 1 and Maja Š´cepanovi´c 1 1 Department of Weed Science, University of Zagreb Faculty of Agriculture, Svetošimunska Cesta 25, 10 000 Zagreb, Croatia; [email protected] (V.Š.); [email protected] (J.L.); [email protected] (K.B.); [email protected] (D.B.); [email protected] (M.Š.) 2 Department of Field Crops, Forages and Grasslands, University of Zagreb Faculty of Agriculture, Svetošimunska Cesta 25, 10 000 Zagreb, Croatia; [email protected] * Correspondence: [email protected]; Tel.: +385-1-2393-606 Abstract: Alopecurus myosuroides Huds. is an important pinoxaden-resistant grass weed in many countries of Europe. Recently, the low efficacy of pinoxaden was reported in winter cereals in Croatia, but a preliminary dose–response trial showed no herbicide resistance for the investigated weed pop- ulation. Therefore, a two-year experiment was conducted under greenhouse conditions to determine the efficacy of various pinoxaden doses (20, 40 and 80 g a.i. ha−1) on weed visual injuries and biomass reduction after herbicide application at different growth stages. As expected, the maximum weed biomass reduction (97.3%) was achieved by applying the highest dose (80 g a.i. ha−1) at the earliest −1 growth stage (ZCK 12–14). A pinoxaden dose of 20 g a.i. ha resulted in satisfactory weed biomass reduction (88.9%) only when applied at ZCK 12–14. The recommended dose (40 g a.i.
    [Show full text]