Efeito de fungicidas para o controle da Ramularia areola na cultura do algodoeiro

Abstract

Entre as doenças que causam manchas foliares no algodoeiro, a mancha de ramulária tem se tornado importante em ambientes tropicais, porque as condições climáticas são favoráveis para o desenvolvimento do patógeno. Objetivou-se nesse trabalho avaliar o desempenho dos fungicidas difenoconazol, trifenil hidróxido de estanho, trifloxistrobina, protioconazol e mancozebe, isolados ou em misturas, no controle da mancha de ramulária no algodoeiro. O experimento foi conduzido na safra 2014/2015, em uma fazenda localizada no município de Sapezal, estado do Mato Grosso. O delineamento experimental foi em blocos casualizados e quatro repetições, com parcelas subdivididas no tempo. RESUMOrealizadas sete avaliações de níveis de severidade da mancha da ramulária nos sete tratamentos. Os fungicidas foram aplicados oito vezes, em intervalos de 15 dias. Ao final do período experimental, foi avaliada a produtividade e a eficiência de controle dos fungicidas em comparação com a testemunha. A aplicação de fungicidas reduziu os danos de produtividade devido ao ataque do fungo Ramularia areola. O trifenil hidróxido de estanho, isolado ou em mistura com o difenoconazol permitiu maior controle da severidade da mancha de ramulária do que a combinação de trifloxistrobina + protioconazol com difenoconazol, ou difenoconazol com mancozebe. ABSTRACTAmong the diseases that cause leaf spots in the cotton crop, ramularia leaf spot has become important in tropical environments since the weather conditions are favorable to the pathogen development. The aim of this study was to evaluate the performance of fungicides difenoconazole, triphenyltin hydroxide, trifloxystrobin, prothioconazole and mancozeb, alone or in mixtures, in controlling ramularia leaf spot in the cotton crop. evaluations of ramularia spot severity levels were performed with seven treatments. Fungicides were applied eight times, at 15-day intervals. At the end of the experimental period, the yield and the control effectiveness of fungicides were evaluated in comparison with control. Fungicide applications reduced yield damage caused by the attack of the fungus Ramularia areola. Triphenyltin hydroxide alone or in combination with difenoconazole allowed greater control of ramularia spot severity than the combination of trifloxystrobin + prothioconazole with difenoconazole, or diphenoconazole with mancozeb. Keywords: Gossypium hirsutum , Ramularia areola, DMI, dithiocarbamatesA mancha de ramulária, causada pelo fungo Ramularia areola G.F. Atk., 1890, forma assexual ou anamórfica e sexual ou teleomófica, Mycosphaerella areaola Ehrlich &F.A. Wolf, 1932, é uma das doenças de importância econômica para o sistema de produção do algodoeiro no Brasil (13). Essa doença era considerada um problema fitossanitário secundário para o algodoeiro e só ocorria no final do ciclo da cultura. No entanto, com a adoção de cultivares mais produtivos e suscetíveis à doença, a mancha da ramulária passou a ser identificada também nos estádios iniciais da cultura, em torno de 60 dias após o plantio. A doença ...

Ramularia leaf spot effect on yield and fiber quality of cotton submitted to fungicide application

“…Similar studies were conducted to verify the fungicide efficiency against the RLS in different regions of Brazil. Lopes et al (2017) verified that fentin hydroxide more efficiently controlled RLS in Mato Grosso, compared to trifloxystrobin + prothioconazole and difenoconazole. These results agree with those obtained by Andrade Junior and Galbieri (2016), who evaluated the efficiency of different fungicides in the municipalities of Campo Verde and Sorriso in the state of Mato Grosso.…”

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

“…Modeling and analysis of classifiers based on unbalanced data can lead to the obtention of a tendentious model for the classification of more frequent classes [ 64 ]. According to López et al [ 66 ]; Ramyachitra and Manikandan [ 67 ], the problem of unbalanced data may be inherent in the way classification is carried out, reflecting the high cost of obtention of data, low availability of data, or even problems in labeling arising from noise during the process of manual classification of the data.…”

Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery

“…Initially considered only a secondary phytosanitary problem and occurring only at the final stages of the production cycle, highly productive cotton cultivars are now affected as well in initial stages. In conjunction with a possible increasing resistance against fungicides [40], an effective corrective measure can demand up to ten repetitive applications [25], requiring the detection and mapping of ramularia blight imperative as early as possible.…”