At post-harvest period, quality of corn seeds may be influenced by several important factors such as: presence of harmful microorganisms, chemical treatments, host species genotype and storage conditions. The objective of this study was to evaluate the performance of corn seeds, hybrids 2B 688 and 2B 710, with high incidence of fungus Fusarium verticillioides and treated with mixtures of fungicides thiophanate-methyl + pyraclostrobin (50 mL a.i .100 kg-1 of seeds) and carbendazim + thiram + micronutrients (100 mL a.i .100 kg-1 of seeds) during six months storage. Performance assessments of seeds were carried out at 0, 30, 60, 90, 120, and 180 days storage. The incidence of F. verticillioides, as well as physiological quality, germination, vigor, stand of plants, emergence speed index, and dry matter weight were assessed. It has been verified that seed treatment with fungicide mixtures was efficient for ensuring seed physiological quality of both genotypes and to reduce incidence of F. verticillioides on treated seeds. By contrast, analysis between treatments with fungicides, within each period assessed and each treatment as compared to control along storage period was verified clear benefits on emergence of seeds after treatment with fungicides.
N a região sul de Minas concentrase grande número de produtores de alface, os quais, em sua maioria, utilizam algum tipo de material como cobertura de canteiro. Entretanto, existe pouca informação sobre os materiais disponíveis na região e que podem ser utilizados como cobertura de solo.Tanto a cobertura com plástico quanto com restos vegetais têm sido exploradas com os objetivos de reduzir a evaporação da água na superfície do solo; diminuir as oscilações de temperatura do solo (ARAÚJO et al., 1993); permitir o controle de plantas invasoras; oferecer proteção aos frutos, evitando seu contato direto com o solo; obter maior precocidade da colheita e capacidade de influir diretamente, de maneira positiva, sobre a incidência de pragas e doenças (CASTELLANE, 1995 vegetais contribuem ainda como reserva considerável de nutrientes, cuja disponibilização pode ser rápida e intensa, dependendo, dentre outros fatores, do regime de chuvas (ROSOLEM et al. 2003); da relação C/N (ROBINSON, 1988) além de reduzir a lixiviação dos nutrientes e a compactação do solo.Avaliando a utilização de materiais como cobertura morta do solo no cultivo de pimentão, Queiroga et al. (2002) verificaram que o diâmetro, número, massa de fruto e a produção foram afetados pela cobertura morta, sendo a palha de carnaúba superior aos demais materiais usados como cobertura. Segundo os autores, este fato deve-se à melhor conservação da umidade do solo, menor incidência de plantas daninhas, redução da temperatura do solo e ao fornecimento de nutrientes às plantas, devido a sua rápida decomposição.Ao avaliar o efeito da cobertura morta sobre o comportamento de cultivares de alface no município de Mossoró, Maia Neto (1988) verificou que a cobertura morta proporcionou aumentos na produção e na massa mé-dia de plantas das cultivares Brasil 221, Babá de Verão e Vitória, e também reduziu a massa de matéria fresca das plantas invasoras.De acordo com Reghin et al. (2002), o uso da cobertura com agrotextil preto proporcionou maior produção de alface (cv. Veneza Roxa) quando comparado à cobertura com palha de arroz, (153,68 g e 127,71 g, respectivamente). A palha de arroz picada não apresentou resposta favorável como cobertura de canteiro, pois permitiu o desenvolvimento de RESUMOCom o objetivo de avaliar o efeito de diferentes coberturas de canteiro sobre as características agronômicas de cultivares de alface (Lactuca sativa L.) tipo lisa, foi realizado um experimento na Universidade Vale do Rio Verde em Três Corações (MG). O delineamento experimental utilizado foi de blocos ao acaso em esquema fatorial 5 x 2, proveniente da combinação de cinco tipos de cobertura (plástico preto, capim braquiária seco, casca de arroz, casca de café e solo nu) e duas cultivares de alface tipo lisa (Regina e Elisa), com 3 repetições. A colheita foi realizada 42 dias após o transplantio, sendo avaliados a produção total (t ha -1 ), produção comercial, massa média por planta, diâmetro médio de cabeça, diâmetro médio de caule, nú-mero médio de folhas e massa média de raiz. Foi obser...
As pragas e doenças no cafeeiro, ocasionadas por bicho-mineiro, broca-do-café, ferrugem e cercosporiose, chegam a atingir ate 50% de uma lavoura cafeeira, podendo causar grandes prejuízos aos cafeicultores. Sendo assim, os sistemas inteligentes são de suma importância para predizer esses danos ao cafeeiro. As redes neurais articiais do tipo perceptron multicamadas, foram os sistemas inteligentes utilizados neste trabalho para prever a porcentagem de ocorrência de pragas e incidência de doenças no cafeeiro. Foram utilizados dados meteorológicos, tais como: temperaturas mínima e máxima, precipitação pluviométrica, umidade relativa do ar, incidência de raios solares e pressão atmosféerica como variáveis de entrada do modelo. O valor dos dados referentes as pragas e doenças se deram quantitativamente e foram coletados no Campo Experimental da EPAMIG de São Sebastião do Paraíso, no sul de Minas Gerais. Foram empregadas as méetricas estatísticas RMSE e R2 para vericar o quão o modelo de rede neuralarticial proposto está predizendo as manifestações de pragas e doenças adequadamente.
The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the “Coffee-Leaf-Miner Index” was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR–BLUE and NIR–RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The “Coffee-Leaf-Miner Index” presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R2 range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, “Coffee-Leaf-Miner Index” can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing.
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