Sampling procedures for spittlebug adults in pastures of Brachiaria decumbens

Foi conduzido um estudo sobre amostragem de adultos de cigarrinhas em pastagens de Brachiaria decumbens para se determinar o melhor número de batidas em uma amostra de rede entomológica. Considerando-se a variação relativa (EP/X) X 100, e o número total de batidas na amostragem, encontra-se 10 batidas/amostra ser melhor do que 5, 20 ou 40. O número de amostras necessárias para determinado nível de variação relativa nas populações de cigarrinhas > 16 adultos/10 batidas foi o mesmo, ao passo que, nas densidades 16 spittlebug adults/10 sweeps remained the same, whereas at densities < 16, the number increased inversely. A regression model to convert relative estimates obtained by sweep-net method to the absolute estimate was presented. Considering the degree of precision and the time spent in sampling, the sweep-net method was superior to cage method. The distribution pattern of numbers of spittlebug adults in samples of 10 sweep-net sweeps, generally fitted the Poisson series. A sequential plan presented here would reduce the sampling time over the conventional (fixed sample numbers) sampling

Planos de amostragem de ovos de cigarrinhas em pastagens de Brachiaria decumbens

A sampling study of spittlebug eggs in pastures of Brachiaria decumbens was conducted by using a sample unit of 15 x 15 cm. The number of samples required for a certain level of precision was inversely proportional to population density. A crude estimate of number of samples necessary for 10, 15 and 20% of precision were 133,59 and 33, respectively. A study of sampling variation showed that differences between plots were much more important than the block differences; therefore plot to plot variation must be considered while sampling spittlebug eggs. The distribution pattern of numbers of eggs per 225 cm2 of pasture fitted the negative binomial series. The sequential sampling plan presented here would reduce the sampling time over the conventional (fixed sample numbers) sampling.Foi conduzido um estudo sobre amostragem de ovos de cigarrinhas em pastagens de Brachiaria decumbens com o uso de uma unidade de amostragem de 15 x 15 cm. O número de amostras necessárias para um certo nível de precisão foi inversamente relacionado à densidade da população. Uma estimativa grosseira mostrou a necessidade de 133 amostras para se obter um nível de 10% de precisão, 59 para 15% e 33 para 20%. Um estudo sobre a variação na amostragem mostrou que a variação entre as parcelas foi mais importante do que entre os blocos; e assim sendo, a variação entre parcelas deve ser considerada na amostragem de ovos de cigarrinhas. O número de ovos por 225 cm2 de área de pastagens mostrou uma distribuição do tipo binomial negativa. Um plano de amostragem tipo sequencial, apresentado no presente trabalho, reduziria o tempo gasto na amostragem em comparação à amostragem convencional onde o número de amostras é fixo

Fusarium wilt incidence and common bean yield according to the preceding crop and the soil tillage system

The objective of this work was to evaluate the effects of preceding crops and tillage systems on the incidence of Fusarium wilt (Fusarium oxysporum f. sp. phaseoli) and common bean (Phaseolus vulgaris) yield. The cultivar BRS Valente was cultivated under center‑pivot irrigation in the winter seasons of 2003, 2004 and 2005, after several preceding crops established in the summer seasons. Preceding crops included the legumes Cajanus cajan (pigeon pea), Stylosanthes guianensis, and Crotalaria spectabilis; the grasses Pennisetum glaucum (millet), Sorghum bicolor (forage sorghum), Panicum maximum, and Urochloa brizantha; and a consortium of maize (Zea mays) and U. brizantha (Santa Fé system). Experiments followed a strip‑plot design, with four replicates. Fusarium wilt incidence was higher in the no‑tillage system. Higher disease incidences corresponded to lower bean yields in 2003 and 2004. Previous summer cropping with U. brizantha, U. brizantha + maize consortium, and millet showed the lowest disease incidence. Therefore, the choice of preceding crops must be taken into account for managing Fusarium wilt on irrigated common bean crops in the Brazilian Cerrado

Outline of fungi and fungus-like taxa

This article provides an outline of the classification of the kingdom Fungi (including fossil fungi. i.e. dispersed spores, mycelia, sporophores, mycorrhizas). We treat 19 phyla of fungi. These are Aphelidiomycota, Ascomycota, Basidiobolomycota, Basidiomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Entorrhizomycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota. The placement of all fungal genera is provided at the class-, order- and family-level. The described number of species per genus is also given. Notes are provided of taxa for which recent changes or disagreements have been presented. Fungus-like taxa that were traditionally treated as fungi are also incorporated in this outline (i.e. Eumycetozoa, Dictyosteliomycetes, Ceratiomyxomycetes and Myxomycetes). Four new taxa are introduced: Amblyosporida ord. nov. Neopereziida ord. nov. and Ovavesiculida ord. nov. in Rozellomycota, and Protosporangiaceae fam. nov. in Dictyosteliomycetes. Two different classifications (in outline section and in discussion) are provided for Glomeromycota and Leotiomycetes based on recent studies. The phylogenetic reconstruction of a four-gene dataset (18S and 28S rRNA, RPB1, RPB2) of 433 taxa is presented, including all currently described orders of fungi.Fil: Wijayawardene, N. N.. Qujing Normal University; ChinaFil: Hyde, K. D.. Mae Fah Luang University; TailandiaFil: Al-Ani, L. K. T.. University of Baghdad; IraqFil: Tedersoo, L.. University of Tartu; EstoniaFil: Haelewaters, D.. University of South Bohemia; República Checa. Purdue University; Estados Unidos. Universidad Autónoma de Chiriquí; PanamáFil: Becerra, Alejandra Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Schnittler, M.. Ernst Moritz Arndt University Greifswald; AlemaniaFil: Shchepin, O. N.. The Komarov Botanical Institute of the Russian Academy of Sciences; RusiaFil: Novozhilov, Y. K.. The Komarov Botanical Institute of the Russian Academy of Sciences; RusiaFil: Silva-Filho, A.G. S.. Universidade Federal do Rio Grande do Norte; BrasilFil: Gentekaki, E.. Mae Fah Luang University; TailandiaFil: Liu, P.. Jilin Agricultural University; ChinaFil: Cavender, J. C.. Ohio University; Estados UnidosFil: Kang, Y.. Guizhou Medical University; ChinaFil: Mohammad, S.. Iranian Research Organization for Science and Technology; IránFil: Zhang, L. F.. Qujing Normal University; ChinaFil: Xu, R. F.. Qujing Normal University; ChinaFil: Li, Y. M.. Qujing Normal University; ChinaFil: Dayarathne, M. C.. Guizhou University; ChinaFil: Ekanayaka, A. H.. Mae Fah Luang University; TailandiaFil: Wen, T. C.. Guizhou University; ChinaFil: Deng, C. Y.. Guizhou Academy of Science; ChinaFil: Pereira, O. L.. Universidade Federal de Viçosa; BrasilFil: Navathe, S.. Agharkar Research Institute; IndiaFil: Hawksworth, D. L.. The Natural History Museum; Reino UnidoFil: Fan, X. L.. Beijing Forestry University; ChinaFil: Dissanayake, L. S.. Guizhou University; ChinaFil: Kuhnert, E.. Leibniz University Hannover; AlemaniaFil: Grossart, H. P.. Leibnitz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Thines, M.. Senckenberg Biodiversity and Climate Research Centre; Alemani

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores