Go to the content. | Move to the navigation | Go to the site search | Go to the menu | Contacts | Accessibility

| Create Account

Mascanzoni, Elisa (2018) Epidemiology of herbicide resistance evolution in rice weeds and variability in Echinochloa spp. [Ph.D. thesis]

Full text disponibile come:

[img]
Preview
PDF Document - Submitted Version
8Mb

Abstract (italian or english)

Resistance issues has geometrically increased in the last ten years and now are affecting the production of all most important crops worldwide. Italy is the first European rice producer with about 230,000 ha cultivate mostly in the north west of the Po Valley. To date six weeds have evolved resistance in this crop, among these the most troublesome is Echinochloa spp. Two main purposes were followed in the research: first it was performed an epidemiological study at a scale as large as the main rice area, second a classification study was done on Echinochloa spp. including a dose response experiment of different species belonging to this genus to herbicides. The aim of epidemiological study was to investigate, at municipality level, the association between herbicide resistance cases identified so far and three important agronomic predictors: water seeding rate, soil texture and rotation rate. The analyses was performed using different statistical tests: discriminant analyses and logistic regressions were used to find the degree of association between resistance presence and the predictors. Neural Network approach was used to calculate the risk of resistance evolution on the base of the predictors. Neural Network was able to predict the evolution of resistance in 70% of municipalities when all 6 resistant weeds were considered in the analyses, 30% when Echinochloa spp. alone was considered, losing part of its predictive power maybe due to the smaller quantity of resistance cases present in the database. Maps in Qgis were composed to graphically display the diffusion of the three predictors in the area of the study and the risk of resistance evolution. A resistance screening was performed on 20 randomly collected populations of Echinochloa spp in the area where GIRE had never recorded resistant cases. 16 populations resulted resistant to ALS+ACCase showing that in in these areas resistance is present, but kept at an acceptable level for the farmers. The second part of the research was focused on the recognition of Echinochloa species, matching already published dichotomical keys, with molecular discrimination performed through DNA barcoding approach. Forty accessions of Echinochloa spp. were collected in 2015 from single plant, they were classified and tested for penoxsulam sensitivity. Among the sensible accessions ten were chosen for reproduction in order to obtain a bigger quantity of seeds for further studies. Penoxsulam screening was repeated on these accessions to confirm sensitivity. Morphological classification and molecular marker discrimination were performed both on original and reproduced accessions, with consistent results. Results show that according to Costea & Tardif (2002) only E. crus-galli and E. oryzicola were present among our accessions, according to Tabacchi et al. (2006) there were E. crus-galli, E. oryzicola and E. phyllopogon. Five DNA regions of the chloroplast were analyzed: matK was able to highlight genetic differences between the “white” species, besides distinguishing E. crus-galli, providing a good, but not complete match with Tabacchi et al. (2006) classification. rbcL region instead, differentiating only E. oryzicola from E. crus-galli provided a 100% match with Costea & Tardif (2002) classification. A Specie-Specific PCR protocol was set up on matK gene to discriminate among the “white” Echinochloa species in a single PCR reaction. Dose response experiment was performed twice on 9 accessions using three different herbicides: cyhalofop – butyl, penoxsulam and florpyrauxifen benzyl, both in greenhouse and in outside conditions: although results were variable, especially in the outside experiment, it was clear that the three herbicides had similar efficacy on all Echinochloa species. Results obtained show that planning herbicides strategies on the base of the Echinochloa spp. present on field could be wrong.

Abstract (a different language)

La resistenza agli erbicidi è un problema che è cresciuto esponenzialmente negli ultimi 10 anni e riguarda tutte le più importanti colture al mondo. L’Italia è il primo produttore europeo di riso con 230.000 ha localizzati nel nord-ovest della Pianura Padana. Ad oggi 6 infestanti hanno evoluto popolazioni resistenti in riso in Italia, fra queste la più importante è Echinochloa spp. La ricerca si è svolta su due binari paralleli: il primo è uno studio epidemiologico a larga scala sulla principale area riso, il secondo è uno studio sulla classificazione di Echinochloa spp. che comprendente esperimenti di dose risposta su varie specie di Echinochloa spp.agli erbicidi. Obiettivo dello studio epidemiologico era analizzare, a livello comunale, il grado di associazione fra presenza di resistenza e tre importanti predittori agronomici: percentuale di semina in acqua, tessitura del suolo e percentuale di rotazione. Le analisi sono state fatte con più approcci statistici: l’analisi discriminante e la regressione logistica hanno permesso di individuare un alto grado di associazione fra la presenza di resistenza ed i predittori. L’approccio Neural Network ha permesso di calcolare il rischio di evolvere popolazioni resistenti sulla base dei predittori: 70% quando tutte le infestanti resistenti sono state considerate, 30% quando solo i casi di Echinochloa spp. sono stati inclusi, perdendo parte del potere predittivo forse a causa della minore quantità di casi di resistenza inclusi nel database. In Qgis sono state create mappe per illustrare la diffusione dei tre predittori nell’area dello studio e il rischio di evoluzione della resistenza. 20 popolazioni di giavone sono state raccolte nei comuni dove non sono mai stati segnalati casi di resistenza al GIRE ed è stato fatto uno screening. 16 popolazioni sono risultate resistenti ACCase+ALS mostrando che in queste aree la resistenza è presente, ma viene tenuta ad un livello accettabile per gli agricoltori. La seconda parte della ricerca riguardava la classificazione delle diverse specie di giavone, abbinando la classificazione fenotipica alla discriminazione fatta attraverso marcatori molecolari usando il DNA barcoding. 40 accessioni di giavone sono state raccolte da piante singole nel 2015, sono state classificate morfologicamente e testate per la sensibilità al penoxsulam. Fra le piante sensibili, 10 sono state riprodotte per ottenere una quantità maggiore di seme. Lo screening con il penoxsulam è stato ripetuto sulle accessioni riprodotte a confermare la sensibilità. La classificazione morfologica e la discriminazione per marcatori molecolari sono state fatte sia sulle piante originali che sulle riprodotte, dando risultati consistenti: nelle popolazioni riprodotte usando Costea & Tardif (2002) sono presenti solo E. crus-galli ed E. oryzicola, usando Tabacchi et al. (2006) sono presenti E. crus-galli, E. oryzicola ed E. phyllopogon. Sono state analizzate 5 regioni di DNA cloroplastico: matK ha discriminato fra le diverse specie di giavone bianco, oltre a E. crus-galli offrendo un buon match, anche se incompleto, con la classificazione di Tabacchi et al. (2006). Le sequenze di rbcL invece hanno differenziato solo E. oryzicola da E. crus-galli, corrispondendo perfettamente a Costea & Tardif (2002) Un protocollo di PCR specie-specifica è stato impostato su matK gene per discriminare diverse specie di giavoni bianchi in una sola reazione di PCR. Gli esperimenti di dose-risposta sono stati fatti sia in serra che all’aperto su 9 accessioni con 3 erbicidi: cyhalofop – butyl, penoxsulam e florpyrauxifen benzyl: anche se i risultati sono stati variabili, soprattutto nell’esperimento all’aperto, è chiaro che i vari erbicidi hanno efficacia simile su tutte le specie di Echinochloa. I risultati ottenuti dimostrano che pianificare le strategie erbicide sulla base delle diverse specie di Echinochla possa essere erroneo.

Statistiche Download
EPrint type:Ph.D. thesis
Tutor:Sattin, Maurizio
Supervisor:Masin, Roberta
Ph.D. course:Ciclo 31 > Corsi 31 > SCIENZE DELLE PRODUZIONI VEGETALI
Data di deposito della tesi:29 November 2018
Anno di Pubblicazione:18 November 2018
Key Words:Resistance, Epidemiology, classification, Echinochloa spp., neural network, barcoding
Settori scientifico-disciplinari MIUR:Area 07 - Scienze agrarie e veterinarie > AGR/02 Agronomia e coltivazioni erbacee
Struttura di riferimento:Dipartimenti > Dipartimento di Agronomia Animali Alimenti Risorse Naturali e Ambiente
Codice ID:11446
Depositato il:08 Nov 2019 09:06
Simple Metadata
Full Metadata
EndNote Format

Bibliografia

I riferimenti della bibliografia possono essere cercati con Cerca la citazione di AIRE, copiando il titolo dell'articolo (o del libro) e la rivista (se presente) nei campi appositi di "Cerca la Citazione di AIRE".
Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

Altop, E. K., & Mennan, H. (2011). Genetic and morphologic diversity of Echinochloa crus-galli populations from different origins. Phytoparasitica, 39(1), 93–102. Cerca con Google

Aoki, D., & Yamaguchi, H. (2008). Genetic relationship between Echinochloa crus-galli and Echinochloa oryzicola accessions inferred from internal transcribed spacer and chloroplast DNA sequences. Weed Biology and Management, 8(4), 233–242. https://doi.org/10.1111/j.1445-6664.2008.00303.x Vai! Cerca con Google

Ash, C. (2018). Meeting resistance. Science, 360(6390), 726 LP-727. Retrieved from http://science.sciencemag.org/content/360/6390/726.abstract Vai! Cerca con Google

Asíns, M. J., Carretero, J. L., Del Busto, A., Carbonell, E. A., & De Barreda, D. G. (1999). Morphologic and Isozyme Variation in Barnyardgrass (Echinochloa) Weed Species. Weed Technology, 13(02), 209–215. https://doi.org/10.1017/S0890037X00041622 Vai! Cerca con Google

Baker, H. G. (1965). Characteristics and modes of origin of weeds. The Genetics of Colonizing Species: Proc. 1st Internat, Union Biol Sci., Asilomar, California. Academic Press Inc., N.Y. Cerca con Google

Barrett, S. C. H., & Wilson, B. F. (1983). Colonizing ability in the Echinochloa crus-galli complex (barnyard grass). II. Seed biology. Canadian Journal of Botany, 61(2), 556–562. Cerca con Google

Barzman, M., Bàrberi, P., Birch, A. N. E., Boonekamp, P., Dachbrodt-Saaydeh, S., Graf, B., … Kudsk, P. (2015). Eight principles of integrated pest management. Agronomy for Sustainable Development, 35(4), 1199–1215. Cerca con Google

Beckie, H. J., & Harker, K. N. (2017). Our top 10 herbicide‐resistant weed management practices. Pest Management Science, 73(6), 1045–1052. Cerca con Google

Benvenuti, S., Dinelli, G., & Bonetti, A. (2004). Germination ecology of Leptochloa chinensis: a new weed in the Italian rice agro-environment. Weed Research, 44(2), 87–96. https://doi.org/10.1111/j.1365-3180.2003.00376.x Vai! Cerca con Google

Benvenuti, S., Macchia, M., & Bonari, E. (1997). Ecophysiology of germination and emergence of Echinochloa crus-galli L. seeds. Rivista Di Agronomia, 31(4), 925–933. Cerca con Google

Berti, A., Onofri, A., Zanin, G., & Sattin, M. (2001). Sistema integrato di gestione della lotta alle malerbe. In A. Berti & G. Zanin (Eds.), Malerbologia (pp. 659–710). Bologna: Pàtron editore, IT. Cerca con Google

Bouhache, M., & Bayer, D. E. (1993). Photosynthetic response of flooded rice (Oryza sativa) and three Echinochloa species to changes in environmental factors. Weed Science, 41(4), 611–614. Cerca con Google

Burton, J. D., Gronwald, J. W., Keith, R. A., Somers, D. A., Gengenbach, B. G., & Wyse, D. L. (1991). Kinetics of inhibition of acetyl-coenzyme A carboxylase by sethoxydim and haloxyfop. Pesticide Biochemistry and Physiology, 39(2), 100–109. https://doi.org/https://doi.org/10.1016/0048-3575(91)90130-E Vai! Cerca con Google

Cantele, A., Zanin, G., & Zuin, M. C. (1985). Resistenza cloroplastica alle triazine: attuale estensione del fenomeno e prospettive. L’informatore Agrario, 41(9), 153–168. Cerca con Google

Carretero, J. L. (1981). El género" Echinochloa" Beauv. en el suroeste de Europa. In Anales del Jardín Botánico de Madrid. Real Jardín Botánico, (pp. 91–108). Real Jardín Botánico. Cerca con Google

CBOL Plant Working Group, C. P. W., Hollingsworth, P. M., Forrest, L. L., Spouge, J. L., Hajibabaei, M., Ratnasingham, S., … Little, D. P. (2009). A DNA barcode for land plants. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 12794–12797. https://doi.org/10.1073/pnas.0905845106 Vai! Cerca con Google

Chase, M. W., Cowan, R. S., Hollingsworth, P. M., van den Berg, C., Madriñán, S., Petersen, G., … Carine, M. (2007). A proposal for a standardised protocol to barcode all land plants. Taxon, 56(2), 295–299. Cerca con Google

Claerhout, S., Dewaele, K., De Riek, J., Reheul, D., & De Cauwer, B. (2016). Morphological and genetic variability of local Echinochloa accessions and the link with herbicide sensitivity. Weed Research, 56(2), 137–148. https://doi.org/10.1111/wre.12192 Vai! Cerca con Google

Clayton, W. D., & Renvoize, S. A. (1986). Genera Graminum. Grasses of the world. Genera Graminum. Grasses of the World., 13. Cerca con Google

Coissac, E., Hollingsworth, P. M., Lavergne, S., & Taberlet, P. (2016). From barcodes to genomes: extending the concept of DNA barcoding. Molecular Ecology, 25(7), 1423–1428. Cerca con Google

Collavo, A., & Sattin, M. (2012). Resistance to glyphosate in Lolium rigidum selected in Italian perennial crops: bioevaluation, management and molecular bases of target‐site resistance. Weed Research, 52(1), 16–24. Cerca con Google

Collavo, A., & Sattin, M. (2014). First glyphosate‐resistant L olium spp. biotypes found in a European annual arable cropping system also affected by ACC ase and ALS resistance. Weed Research, 54(4), 325–334. Cerca con Google

Costea, M., & Tardif. (2002). taxonomy of the most common weedy european echinochloa species (poaceae: panicoideae) with special emphasis on characters of the lemma and caryopsis. SIDA, Contributions to Botany, 20(2), 525–548. Retrieved from http://www.jstor.org/stable/41968068 Vai! Cerca con Google

Das, S. K., Kumar, A., Das, B., & Burnwal, A. (2013). On soft computing techniques in various areas. Computer Science & Information Technology (CS & IT), 3, 59–68. Cerca con Google

DE CAUWER, B., ROMBAUT, R., BULCKE, R., & REHEUL, D. (2012). Differential sensitivity of Echinochloa muricata and Echinochloa crus-galli to 4-hydroxyphenyl pyruvate dioxygenase- and acetolactate synthase-inhibiting herbicides in maize. Weed Research, 52(6), 500–509. https://doi.org/10.1111/j.1365-3180.2012.00944.x Vai! Cerca con Google

de Wet, J. M. J., Prasada Rao, K. E., Mengesha, M. H., & Brink, D. E. (1983). Domestication of mawa millet (Echinochloa colona). Economic Botany, 37(3), 283–291. https://doi.org/10.1007/BF02858883 Vai! Cerca con Google

Délye, C. (2013). Unravelling the genetic bases of non‐target‐site‐based resistance (NTSR) to herbicides: a major challenge for weed science in the forthcoming decade. Pest Management Science, 69(2), 176–187. Cerca con Google

Délye, C., Jasieniuk, M., & Le Corre, V. (2013). Deciphering the evolution of herbicide resistance in weeds. Trends in Genetics, 29(11), 649–658. https://doi.org/https://doi.org/10.1016/j.tig.2013.06.001 Vai! Cerca con Google

Délye, C., Pernin, F., & Scarabel, L. (2011). Evolution and diversity of the mechanisms endowing resistance to herbicides inhibiting acetolactate-synthase (ALS) in corn poppy (Papaver rhoeas L.). Plant Science, 180(2), 333–342. https://doi.org/https://doi.org/10.1016/j.plantsci.2010.10.007 Vai! Cerca con Google

Derks, J., & Tomasi, C. (2015). Peri‐implant health and disease. A systematic review of current epidemiology. Journal of Clinical Periodontology, 42, S158–S171. Cerca con Google

Devine, M. D., & Shukla, A. (2000). Altered target sites as a mechanism of herbicide resistance. Crop Protection, 19(8–10), 881–889. Cerca con Google

Diggle, A. J., Neve, P. B., & Smith, F. P. (2003). Herbicides used in combination can reduce the probability of herbicide resistance in finite weed populations. Weed Research, 43(5), 371–382. https://doi.org/10.1046/j.1365-3180.2003.00355.x Vai! Cerca con Google

Doyle, J. J., & Doyle, J. L. (1987). CTAB DNA extraction in plants. Phytochemical Bulletin, 19, 11–15. Cerca con Google

Drábková, L., Kirschner, J., & Vlček, Č. (2006). Phylogenetic relationships within Luzula DC. and Juncus L. (Juncaceae): A comparison of phylogenetic signals of trnL-trnF intergenic spacer, trnL intron and rbcL plastome sequence data. Cladistics, 22(2), 132–143. https://doi.org/10.1111/j.1096-0031.2006.00095.x Vai! Cerca con Google

Duke, S. O. (2012). Why have no new herbicide modes of action appeared in recent years? Pest Management Science, 68(4), 505–512. https://doi.org/10.1002/ps.2333 Vai! Cerca con Google

Epp, J. B., Alexander, A. L., Balko, T. W., Buysse, A. M., Brewster, W. K., Bryan, K., … Yerkes, C. N. (2016). The discovery of ArylexTM active and RinskorTM active: Two novel auxin herbicides. Bioorganic & Medicinal Chemistry, 24(3), 362–371. https://doi.org/https://doi.org/10.1016/j.bmc.2015.08.011 Vai! Cerca con Google

Evans, J. A., Tranel, P. J., Hager, A. G., Schutte, B., Wu, C., Chatham, L. A., & Davis, A. S. (2015). Managing the evolution of herbicide resistance. Pest Management Science, 72(1), 74–80. https://doi.org/10.1002/ps.4009 Vai! Cerca con Google

Ferrero, A., Tinarelli, A., Capri, E., & Karpouzas, D. G. (2008). Pesticide risk assessment in Rice paddies: theory and practice. Elsevier, Radwarweg. Cerca con Google

Ferrero A, Vidotto F, 2007. Weeds and weed management in Italian rice fields. In: A. Ferrero, F. Vidotto (eds.). Agro-economical traits of rice cultivation in Europe and India.pp. 55–72. ISBN: 88-86960-83-2. Cerca con Google

Ferrero, A., & Vidotto, F. (2010). History of rice in Europe. Rice: Origin, Antiquity and History. Science Publishers Enfield, New Hampshire, 341–372. Cerca con Google

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. Cerca con Google

Cerca con Google

Franklin, K. A., & Lindberg, E. (2015). Obstructive sleep apnea is a common disorder in the population—a review on the epidemiology of sleep apnea. Journal of Thoracic Disease, 7(8), 1311. Cerca con Google

Gaines, T. A., Zhang, W., Wang, D., Bukun, B., Chisholm, S. T., Shaner, D. L., … Westra, P. (2010). Gene amplification confers glyphosate resistance in Amaranthus palmeri. Proceedings of the National Academy of Sciences, 107(3), 1029 LP-1034. Retrieved from http://www.pnas.org/content/107/3/1029.abstract Vai! Cerca con Google

Gealy, D. R., Mitten, D. H., & Rutger, J. N. (2003). Gene Flow Between Red Rice (Oryza sativa) and Herbicide-Resistant Rice (O. sativa): Implications for Weed Management. Weed Technology, 17(3), 627–645. https://doi.org/DOI: 10.1614/WT02-100 Vai! Cerca con Google

Gong, Q., Zhang, J., & Wang, J. (2018). Application of GIS-Based Back Propagation Artificial Neural Networks and Logistic Regression for shallow Landslide Susceptibility Mapping in South China-Take Meijiang River Basin as an Example. The Open Civil Engineering Journal, 12(1). Cerca con Google

Gonzalez‐Andujar, J. L., Chantre, G. R., Morvillo, C., Blanco, A. M., & Forcella, F. (2016). Predicting field weed emergence with empirical models and soft computing techniques. Weed Research, 56(6), 415–423. Cerca con Google

Gould, F., Brown, Z. S., & Kuzma, J. (2018). Wicked evolution: Can we address the sociobiological dilemma of pesticide resistance? Science, 360(6390), 728 LP-732. Retrieved from http://science.sciencemag.org/content/360/6390/728.abstract Vai! Cerca con Google

Grossmann, K. (2010). Auxin herbicides: current status of mechanism and mode of action. Pest Management Science, 66(2), 113–120. https://doi.org/10.1002/ps.1860 Vai! Cerca con Google

Hay, J. R. (1974). Gains to the grower from weed science. Weed Science, 439(22). Cerca con Google

Heap, I. (2013). The international survey of herbicide resistant weeds. Online. Internet. Ian Heap Corvallis, OR, USA. Cerca con Google

Heap, I. (2014a). Global perspective of herbicide‐resistant weeds. Pest Management Science, 70(9), 1306–1315. Cerca con Google

Heap, I. (2014b). Herbicide Resistant Weeds BT - Integrated Pest Management: Pesticide Problems, Vol.3. In D. Pimentel & R. Peshin (Eds.) (pp. 281–301). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-7796-5_12 Vai! Cerca con Google

Heap, I., & Duke, S. O. (2018). Overview of glyphosate‐resistant weeds worldwide. Pest Management Science, 74(5), 1040–1049. Cerca con Google

Hebert, P. D. N., Cywinska, A., & Ball, S. L. (2003). Biological identifications through DNA barcodes. Proceedings of the Royal Society of London B: Biological Sciences, 270(1512), 313–321. Cerca con Google

Hess, M., Barralis, G., Bleiholder, H., Buhr, L., Eggers, T. H., Hack, H., & Stauss, R. (1997). Use of the extended BBCH scale—general for the descriptions of the growth stages of mono; and dicotyledonous weed species. Weed Research, 37(6), 433–441. https://doi.org/10.1046/j.1365-3180.1997.d01-70.x Vai! Cerca con Google

Hicks, H. L., Comont, D., Coutts, S. R., Crook, L., Hull, R., Norris, K., … Freckleton, R. P. (2018). The factors driving evolved herbicide resistance at a national scale. Nature Ecology & Evolution, 2(3), 529–536. https://doi.org/10.1038/s41559-018-0470-1 Vai! Cerca con Google

Hilu, K. W., & Liang, gping. (1997). The matK gene: sequence variation and application in plant systematics. American Journal of Botany, 84(6), 830–839. https://doi.org/10.2307/2445819 Vai! Cerca con Google

Holm, L. G., Plucknett, D. L., Pancho, J. V, & Herberger, J. P. (1977). The world’s worst weeds. Distribution and biology. University Press of Hawaii. Cerca con Google

Holt, J. S., & Lebaron, H. M. (1990). Significance and Distribution of Herbicide Resistance. Weed Technology, 4(01), 141–149. https://doi.org/10.1017/S0890037X00025148 Vai! Cerca con Google

Holzner, W. (1982). Concepts, categories and characteristics of weeds BT - Biology and ecology of weeds. In W. Holzner & M. Numata (Eds.) (pp. 3–20). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-017-0916-3_1 Vai! Cerca con Google

Hoste, I. (2004). The naturalisation history of echinochloa muricata in belgium, with notes on its identity and morphological variation. Belgian Journal of Botany, 137(2), 163–174. Retrieved from http://www.jstor.org/stable/20794550 Vai! Cerca con Google

Huang, Y., Wang, J., Yang, Y., Fan, C., & Chen, J. (2017). Phylogenomic Analysis and Dynamic Evolution of Chloroplast Genomes in Salicaceae. Frontiers in Plant Science, 8, 1050. https://doi.org/10.3389/fpls.2017.01050 Vai! Cerca con Google

Jasieniuk, M., Brûlé-Babel, A. L., & Morrison, I. N. (1996). The Evolution and Genetics of Herbicide Resistance in Weeds. Weed Science, 44(1), 176–193. Retrieved from https://www.jstor.org/stable/4045802 Vai! Cerca con Google

Jordan, N. R., & Davis, A. S. (2015). Middle-way strategies for sustainable intensification of agriculture. BioScience, 65(5), 513–519. Cerca con Google

Juraimi, A. S., Uddin, M. K., Anwar, M. P., Mohamed, M. T. M., Ismail, M. R., & Man, A. (2013). Sustainable weed management in direct seeded rice culture: A review. Australian Journal of Crop Science, 7(7), 989. Cerca con Google

Kress, W. J., & Erickson, D. L. (2012). DNA Barcodes: Methods and Protocols BT - DNA Barcodes: Methods and Protocols. In W. J. Kress & D. L. Erickson (Eds.) (pp. 3–8). Totowa, NJ: Humana Press. https://doi.org/10.1007/978-1-61779-591-6_1 Vai! Cerca con Google

Lazcano, C., Gómez-Brandón, M., Revilla, P., & Domínguez, J. (2013). Short-term effects of organic and inorganic fertilizers on soil microbial community structure and function. Biology and Fertility of Soils, 49(6), 723–733. Cerca con Google

Lee, S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477–1491. Cerca con Google

Mansourian, S., Darbandi, E. I., Mohassel, M. H. R., Rastgoo, M., & Kanouni, H. (2017). Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran. Crop Protection, 93, 43–51. Cerca con Google

Cerca con Google

Mascanzoni E, Perego A., Marchi N., Scarabel L., Panozzo S., Ferrero A., Acutis M. & Sattin M. (2018) Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale. Agronomy for sustainable development, accepted on 21st November 2018 Cerca con Google

https://dx.doi.org/10.1007/513593-018-0548-9 Vai! Cerca con Google

Maun, M. A., & Barrett, S. C. H. (1986). The biology of canadian weeds.: 77. Echinochloa crus-galli (L.) Beauv. Canadian Journal of Plant Science, 66(3), 739–759. https://doi.org/10.4141/cjps86-093 Vai! Cerca con Google

Maxwell, B. D., Roush, M. L., & Radosevich, S. R. (1990). Predicting the Evolution and Dynamics of Herbicide Resistance in Weed Populations. Weed Technology, 4(01), 2–13. https://doi.org/10.1017/S0890037X0002488X Vai! Cerca con Google

Michael, P. W. (1983). Taxonomy and distribution of Echinochloa species with special reference to their occurrence as weeds of rice. In Proceeding of the Conference on Weed Control in Rice (Vol. 31, pp. 291–306). Cerca con Google

Mortensen, D. A., Egan, J. F., Maxwell, B. D., Ryan, M. R., & Smith, R. G. (2012). Navigating a critical juncture for sustainable weed management. BioScience, 62(1), 75–84. Cerca con Google

Moser, H., & Lee, M. (1994). RFLP variation and genealogical distance, multivariate distance, heterosis, and genetic variance in oats. Theoretical and Applied Genetics, 87(8), 947–956. Cerca con Google

Nissen, S. J., Masters, R. A., Lee, D. J., & Rowe, M. L. (1995). DNA-based marker systems to determine genetic diversity of weedy species and their application to biocontrol. Weed Science, 43(3), 504–513. Cerca con Google

Norris, R. F. (1996). Morphological and phenological variation in barnyardgrass (Echinochloa crus-galli) in California. Weed Science, 804–814. Cerca con Google

Norsworthy, J. K., Ward, S. M., Shaw, D. R., Llewellyn, R. S., Nichols, R. L., Webster, T. M., … Burgos, N. R. (2012). Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Science, 60(SP1), 31–62. Cerca con Google

Oerke, E.-C. (1994). Estimated crop losses due to pathogens, animal pests, and weeds. Crop Production and Crop Protection. Elsevier Science Publishing, New York, NY, 535–597. Cerca con Google

Oerke, E.-C. (2006). Crop losses to pests. The Journal of Agricultural Science, 144(01), 31. https://doi.org/10.1017/S0021859605005708 Vai! Cerca con Google

Onofri, A. (2004). Bioassay97: EXCEL Add-in per l’elaborazione statistica del dosaggio biologico con erbicidi. In A. D. Marta & S. Orlandini (Eds.), Proceedings of III Giornate di Studio su Metodi numerici, statistici e informatici nella difesa delle colture agrarie e delle foreste: ricerca ed applicazioni (pp. 202–206). Firenze, IT. Cerca con Google

Onofri, A., & Covalleri, G. (2001). Definizione, cenni storici e statistiche. In P. Catizone & G. Zanin (Eds.), Malerbologia (pp. 303–308). Pàtron editore, IT. Cerca con Google

Onofri, A., & Pannacci, E. (2011). A simplified step-by-step guide to non-linear regression analysis of herbicide bioassay, by using a spreadsheet. Cerca con Google

Orson, J. H. (1999). The cost to the farmer of herbicide resistance. Weed Technology, 607–611. Cerca con Google

Osca, J. M. (2013). Expansion of Leptochloa fusca ssp. uninervia and Leptochloa fusca ssp. fascicularis in rice fields in Valencia, eastern Spain. Weed Research, 53(6), 479–488. https://doi.org/10.1111/wre.12046 Vai! Cerca con Google

Osuna, M. D., Vidotto, F., Fischer, A. J., Bayer, D. E., De Prado, R., & Ferrero, A. (2002). Cross-resistance to bispyribac-sodium and bensulfuron-methyl in Echinochloa phyllopogon and Cyperus difformis. Pesticide Biochemistry and Physiology, 73(1), 9–17. https://doi.org/https://doi.org/10.1016/S0048-3575(02)00010-X Vai! Cerca con Google

Panozzo, S. (2012). Basis of herbicide resistance in two troublesome summer weeds, Echinochloa crus-galli and Sorghum halepense. Cerca con Google

Panozzo, S., Colauzzi, M., Scarabel, L., Collavo, A., Rosan, V., & Sattin, M. (2015). iMAR: An Interactive Web-Based Application for Mapping Herbicide Resistant Weeds. PloS One, 10(8), e0135328. Cerca con Google

Panozzo, S., Scarabel, L., Collavo, A., & Sattin, M. (2015). Protocols for Robust Herbicide Resistance Testing in Different Weed Species. Journal of Visualized Experiments : JoVE, (101), 52923. https://doi.org/10.3791/52923 Vai! Cerca con Google

Pignatti, S. (1982). Flora d’Italia. Bologna.: Edagricole. Retrieved from citeulike-article-id:13503767 Cerca con Google

Pirola, A. (1965). Appunti per il riconoscimento delle Echinochloe italiane (Giavone). Il Riso, 14(3), 204–208. Cerca con Google

Porceddu, E., Sattin, M., & Zanin, G. (1997). Herbicide resistance in Italy: evolution and current situation. Agricoltura Mediterranea, 127, 97–105. Cerca con Google

Powles, S. B., & Matthews, J. M. (1992). Multiple herbicide resistance in annual ryegrass (Lolium rigidum): a driving force for the adoption of integrated weed management. In Resistance’91: Achievements and Developments in Combating Pesticide Resistance (pp. 75–87). Springer. Cerca con Google

Powles, S. B., & Yu, Q. (2010). Evolution in Action: Plants Resistant to Herbicides. Annual Review of Plant Biology, 61(1), 317–347. https://doi.org/10.1146/annurev-arplant-042809-112119 Vai! Cerca con Google

Powles, S., & Shaner, D. L. (2001). Herbicide resistance and world grains. (CRC Press, Ed.). Boca Raton, FL. Cerca con Google

EPPO, (2015), PP 1/213 (4) Resistance risk analysis. EPPO Bull, 45: 371-387. doi:10.1111/epp.12246 Cerca con Google

Preston, C. (2004). Herbicide Resistance in Weeds Endowed by Enhanced Detoxification: Complications for Management. Weed Science, 52(3), 448–453. Retrieved from http://www.jstor.org/stable/4046944 Vai! Cerca con Google

Primack, R. B., & Kang, H. (1989). Measuring fitness and natural selection in wild plant populations. Annual Review of Ecology and Systematics, 20(1), 367–396. Cerca con Google

Ratnasingham, S., & Hebert, P. D. N. (2007). BOLD: The Barcode of Life Data System (http://www. barcodinglife. org). Molecular Ecology Notes, 7(3), 355–364. Vai! Cerca con Google

Renton, M., Busi, R., Neve, P., Thornby, D., & Vila‐Aiub, M. (2014). Herbicide resistance modelling: past, present and future. Pest Management Science, 70(9), 1394–1404. Cerca con Google

Editorial (2018). Resistance is … complex. (2018). Nature Ecology & Evolution, 2(3), 405. https://doi.org/10.1038/s41559-018-0495-5 Vai! Cerca con Google

Ross, M. A., & Lembi, A. C. (2009). Applied Weed Science, Third editions. Prentice Hall: Pearsons. Cerca con Google

Rüegg, W. T., Quadranti, M., & Zoschke, A. (2007). Herbicide research and development: challenges and opportunities. Weed Research, 47(4), 271–275. https://doi.org/10.1111/j.1365-3180.2007.00572.x Vai! Cerca con Google

Ruiz-Santaella, J. P., Bastida, F., Franco, A. R., & De Prado, R. (2006). Morphological and molecular characterization of different Echinochloa spp. and Oryza sativa populations. Journal of Agricultural and Food Chemistry, 54(4), 1166–1172. Cerca con Google

Ryan, G. F. (1970). Resistance of common groundsel to simazine and atrazine. Weed Science, 18(5), 614–616. Cerca con Google

Saari, L. L., Cotterman, J. C., & Thill, D. C. (2018). Resistance to acetolactate synthase inhibiting herbicides. In Herbicide resistance in plants (pp. 83–140). CRC Press. Cerca con Google

Sattin, M. (2005). Herbicide resistance in Europe: an overview. In Proc. British Crop Production Council International Conference Crop Science & Technology. Glasgow, UK (pp. 131–138). Cerca con Google

Sattin, M., Berti, A., & Zanin, G. (1995). Agronomic aspects of herbicide use. In V. Marco & F. Enzo (Eds.), Pesticide Risk in groundwater (pp. 45–70). CRC Lewis. Cerca con Google

Scarabel, L., Cenghialta, C., Manuello, D., & Sattin, M. (2012). Monitoring and management of imidazolinone-resistant red rice (Oryza sativa L., var. sylvatica) in Clearfield® Italian paddy rice. Agronomy, 2(4), 371–383. Cerca con Google

Scarabel, L., Cenghialta, C., Panozzo, S., Manuello, D., & Sattin, M. (2013). Resistance evolution and sustainability of the rice cropping system: the Italian case study. In Proceedings of Global Herbicide Resistance Challenge Conference, Fremantle, Western Australia, February (p. 105). Freemantle, Australia. Cerca con Google

Scarabel, L., Gasparetto, M. A., & Sattin, M. (2002). An Italian population of Echinochloa crus-galli resistant to propanil in paddy rice. In Proc. 12th EWRS Symposium, Wageningen (pp. 142–143). Cerca con Google

Scarabel, L., & Miniotti, E. (2018). Primi casi di Cyperus esculentus resistenti alle solfoniluree. Informatore Agrario, (17), 62. Cerca con Google

Seefeldt, S. S., Jensen, J. E., & Fuerst, E. P. (1995). Log-Logistic Analysis of Herbicide Dose-Response Relationships. Weed Technology, 9(02), 218–227. https://doi.org/10.1017/S0890037X00023253 Vai! Cerca con Google

Serra F., Fogliatto S., Vidotto F. (2018). Effect of salinity on Echinochloa crus-galli germination as affected by herbicide resistance. Italian Journal of Agronomy, 13: 221–228. doi:10.4081/ija.2018.1046 Cerca con Google

Shaner, D. L. (2014). Herbicide Handbook. (W. S. S. of America, Ed.) (10th Editi). Lawrence. Cerca con Google

Sharma, B., & Venugopalan, K. (2014). Comparison of neural network training functions for hematoma classification in brain CT images. IOSR Journal of Computer Engineering (IOSR-JCE), 16(1), 31–35. Cerca con Google

Shaw, D. R., Barrett, M., Schroeder, J., Asmus, A. B., Ervin, D., Jussaume, R. A., & Coble, H. (2018). Critical Next Steps in Combating Herbicide Resistance: Our View. Weed Science, 66(05), 559–561. https://doi.org/10.1017/wsc.2018.42 Vai! Cerca con Google

Smith, O. S., Smith, J. S. C., Bowen, S. L., Tenborg, R. A., & Wall, S. J. (1990). Similarities among a group of elite maize inbreds as measured by pedigree, F 1 grain yield, grain yield, heterosis, and RFLPs. Theoretical and Applied Genetics, 80(6), 833–840. Cerca con Google

Smith, R. J. (1988). Weed Thresholds in Southern U.S. Rice, Oryza sativa. Weed Technology, 2(3), 232–241. https://doi.org/DOI: 10.1017/S0890037X00030505 Vai! Cerca con Google

Sparacino, A. C., & Sgattoni, P. (1993). Le erbe infestanti il riso. Informatore agrario, 49, 37. Cerca con Google

Sudianto, E., Beng-Kah, S., Ting-Xiang, N., Saldain, N. E., Scott, R. C., & Burgos, N. R. (2013). Clearfield® rice: Its development, success, and key challenges on a global perspective. Crop Protection, 49, 40–51. Cerca con Google

Sutherland, S. (2004). What makes a weed a weed: life history traits of native and exotic plants in the USA. Oecologia, 141(1), 24–39. https://doi.org/https://doi.org/10.1007/s00442-004-1628-x Vai! Cerca con Google

Tabacchi, M. (2003). Caratterizzazione dei giavoni (Echinochloa spp.) delle risaie. PhD Thesis University of Turin. Cerca con Google

Tabacchi, M., Mantegazza, R., Spada, A., & Ferrero, A. (2006). Morphological traits and molecular markers for classification of Echinochloa species from Italian rice fields. Weed Science, 54(6), 1086–1093. https://doi.org/10.1614/WS-06-018R1.1 Vai! Cerca con Google

Taberlet, P., Coissac, E., Pompanon, F., Gielly, L., Miquel, C., Valentini, A., … Willerslev, E. (2006). Power and limitations of the chloroplast trn L (UAA) intron for plant DNA barcoding. Nucleic Acids Research, 35(3), e14–e14. Cerca con Google

Tatineni, V., Cantrell, R. G., & Davis, D. D. (1996). Genetic diversity in elite cotton germplasm determined by morphological characteristics and RAPDs. Crop Science, 36(1), 186–192. Cerca con Google

Taylor, G. R. (1997). Design ARMS and PCR Primers. In C. Press (Ed.), Laboratory Methods for the Detection of Mutations and Polymorphisms in DNA (pp. 47–48). Robert Stern. Cerca con Google

Van Nguyen, N., & Ferrero, A. (2006). Meeting the challenges of global rice production. Springer. Cerca con Google

Vidotto, F., Fogliatto, S., Milan, M., & Ferrero, A. (2016). Weed communities in Italian maize fields as affected by pedo-climatic traits and sowing time. European Journal of Agronomy, 74, 38–46. Cerca con Google

Vidotto, F., Tesio, F., Tabacchi, M., & Ferrero, A. (2007). Herbicide sensitivity of Echinochloa spp. accessions in Italian rice fields. Crop Protection, 26(3), 285–293. Cerca con Google

Viggiani, P., & Tabacchi, M. (2017). Piante Infestanti di Risaie e Canali. (Edagricole - New Business Media, Ed.). Bologna. Cerca con Google

White, T. J., Bruns, T., Lee, S., & Taylor, J. L. (1990). Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR Protocols: A Guide to Methods and Applications, 18(1), 315–322. Cerca con Google

Yabuno, T. (1983). Cytogenetical studies on the hybrids of Echinochloa oryzicola Vasing. and the Thai tetraploid strain of E. stagnina (Retz.) Beauv. with the West African species E. obtusiflora Stapf. Cytologia, 48(3), 597–604. Cerca con Google

Yamaguchi, H., Utano, A. Y. A., Yasuda, K., Yano, A., & Soejima, A. (2005). A molecular phylogeny of wild and cultivated Echinochloa in East Asia inferred from non-coding region sequences of trnT-L-F. Weed Biology and Management, 5(4), 210–218. https://doi.org/10.1111/j.1445-6664.2005.00185.x Vai! Cerca con Google

Yasuda, K., Yano, A., Nakayama, Y., & Yamaguchi, H. (2002). Molecular identification of Echinochloa oryzicola Vasing. and E. crus‐galli (L.) Beauv. using a polymerase chain reaction–restriction fragment length polymorphism technique. Weed Biology and Management, 2(1), 11–17. Cerca con Google

Ye, C.-Y., Lin, Z., Li, G., Wang, Y.-Y., Qiu, J., Fu, F., … Song, W. (2014). Echinochloa chloroplast genomes: insights into the evolution and taxonomic identification of two weedy species. PLoS One, 9(11), e113657. Cerca con Google

You, F. M., Huo, N., Gu, Y. Q., Luo, M., Ma, Y., Hane, D., … Anderson, O. D. (2008). BatchPrimer3: A high throughput web application for PCR and sequencing primer design. BMC Bioinformatics, 9(1), 253. https://doi.org/10.1186/1471-2105-9-253 Vai! Cerca con Google

Yu, Q., Abdallah, I., Han, H., Owen, M., & Powles, S. (2009). Distinct non-target site mechanisms endow resistance to glyphosate, ACCase and ALS-inhibiting herbicides in multiple herbicide-resistant Lolium rigidum. Planta, 230(4), 713–723. Cerca con Google

Yu, Q., & Powles, S. B. (2014). Metabolism-based herbicide resistance and cross-resistance in crop weeds: a threat to herbicide sustainability and global crop production. Plant Physiology, pp-114. Cerca con Google

Yuan, J. S., Tranel, P. J., & Stewart, C. N. (2007). Non-target-site herbicide resistance: a family business. Trends in Plant Science, 12(1), 6–13. https://doi.org/https://doi.org/10.1016/j.tplants.2006.11.001 Vai! Cerca con Google

Zhang, H., Tweel, B., & Tong, L. (2004). Molecular basis for the inhibition of the carboxyltransferase domain of acetyl-coenzyme-A carboxylase by haloxyfop and diclofop. Proceedings of the National Academy of Sciences of the United States of America, 101(16), 5910–5915. https://doi.org/10.1073/pnas.0400891101 Vai! Cerca con Google

Zhang, J., Fu, F., Liu, C., Lin, Z., Wang, Y., Ye, C., & Lu, Y. (2017). Chloroplast DNA markers for Echinochloa taxa. Weed Research, 57(6), 355–360. Cerca con Google

Zhang, Q., Zhang, J., Yan, D., & Bao, Y. (2013). Dynamic risk prediction based on discriminant analysis for maize drought disaster. Natural Hazards, 65(3), 1275–1284. Cerca con Google

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record