Dinâmica de inóculo de Alternaria solani, efeito da densidade de plantio na intensidade da pinta preta e requeima e previsão dessas doenças em tomateiro e batateira

• Ano de defesa: 2006
• Autor(a) principal: Batista, Diógenes da Cruz
• Orientador(a): Maffia, Luiz Antônio
• Banca de defesa: Brommonschenkel, Sérgio Hermínio , Silva, Carlos Henrique Osório , Teixeira, Hudson , Reis, Ailton
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Doutorado em Fitopatologia
• Departamento: Etiologia; Epidemiologia; Controle
• País: BR
• Palavras-chave em Português: Epidemiologia; Manejo; Controle
• Palavras-chave em Inglês: Epidemiology; Management; Control
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOSSANIDADE::FITOPATOLOGIA
• Link de acesso: http://locus.ufv.br/handle/123456789/1075

Several basic and applied epidemiological aspects, relative to early (Alternaria solani) and late (Phytophthora infestans) were studied to generate useful information for the management of these diseases in tomato and potato. The basic studies were conducted aiming to determine: dynamics of A. solani conidia in the air throughout the year; the survival of the pathogen associated with tomato crop debris (leaflets and stems) kept either buried or on soil surface; and the influence of planting spacing on the intensity of early blight on both hosts and of late blight on potato. Applied epidemiological studies involved the validation of forecast systems for tomato early (FAST, CUFAST, and TOMCAST) and potato late (BLITECAST, SIMCAST, NEGFRY, and Wallin) blight, and the development of a simple model, based only on precipitation, for the forecast of tomato early blight. Highest concentration of conidia in the air was detected during spring-summer and a peak (62 conidia/m3) was recorded in October. The number of conidia was negatively correlated with relative humidity (RH) and the number of hours with RH above 90%, and it was positively correlated with the number of hours with RH below 70% and temperature above 20oC. The viability of A. solani quickly declined when crop debris were buried. After 90 days, sporulation in buried leaflet debris was up to 10 times lower than in those kept at the soil surface. The maximum survival time was 242 and 272 days, in leaflets and stems kept on the surface, respectively. There was no difference regarding disease severity or area under the disease progress curve (AUDPC) for early blight in tomato or potato plots planted at three different spacing, with or without chlorothalonil application. For late blight, the more spaced plots had the lowest AUDPC values. The ratio between average AUDPC values from unsprayed spaced and dense plots was 2.49 and 1.15, in Bintje and Monalisa , respectively. Performance of early and late blight forecast systems varied according to the season. For early blight, AUDPC values in spring-summer and summer-fall, in plots treated according CUFAST (64.0 and 359.2, respectively) and FAST (123.2 and 340.3, respectively) did not differ from the calendar (82.5 e 225.7, respectively), however the number of sprays was reduced with both systems. In both seasons, TOMCAST did not differ from the unsprayed control. The BLITECAST, SIMCAST, NEGFRY, and Wallin systems triggered fungicide sprays in experiments carried out during the summer, despite no late blight epidemics developed in the plots. In the fall, AUDPC values for BLITECAST (19.5), SIMCAST (97.7), NEGFRY (193.1), and Wallin (69.7) did not differ from the calendar (63.5). One fungicide spray was saved with Wallin and NEGFRY, but compared to the calendar, SIMCAST and BLITECAST recommended one and two sprays more, respectively. A simple model, based only on precipitation, was developed using logistic regression to assess the risk on early blight on tomato. The equation to estimate the probability (p) of an increase of two percentual points in early blight severity was: ln (p/(1-p))= -0.6208 -0.1373X1 + 0.5767X2 -0.0413X1X2 + 0.0042X2 1+ 0.0979X2 2, where X1 is total precipitation and X2 is the cumulative number of hours with precipitation during the last four days and u is a random effect assumed normally distributed with mean equal to zero and variance ó2= 8.90. This models still needs to be validated under field conditions, however, together with the other information generated in this study it will potentially contribute to improve disease management.