The effects of sooty mold on photosynthesis and mesophyll structure of mahogany (Swietenia macrophylla King., Meliaceae) Efeitos da fumagina sobre a fotossíntese e a estrutura do mesofilo de mogno

The aim of present study was to evaluate the effects of the sooty mold on anatomy and photochemical activity of mahogany (Swietenia macrophylla) leaves. The photochemical features of shade-developed leaves with or without sooty mold were compared to those of sun leaves using chlorophyll a fluorescence measurements. Leaf anatomy was also evaluated using conventional techniques. The degree of blockage of the photosynthetic active photon flux density (PPFD) by sooty mold and its effect on photochemistry were evaluated. Sun leaves showed thick mesophyll with palisade parenchyma disposed in a uniseriate layer, whereas shade leaves showed narrow mesophyll, independently of sooty mold presence. The effective quantum yield (deltaF/Fm') and the apparent electron transport rate (ETR) of sun leaves were higher than those of shade leaves. The values of ETR suggested that photochemistry saturation occurred at lower PPFD in shade-grown plants. Lower values of the deltaF/Fm' and, consequently, lower values of ETR were observed in leaves with sooty mold. A reduction of 40% of the incident light was seen due to physical blockage by sooty mold which is presumably responsible for an additional decrease of ETR values. Our data indicated that sooty mold did not directly damage the leaf, but reduce leaf photochemistry capacity, by decreasing light availability.<br>O objetivo do presente estudo foi avaliar os efeitos da fumagina na anatomia e a atividade fotoquímica em folhas de mogno (Swietenia macrophylla King., Meliaceae). Folhas com e sem fumagina desenvolvidas na sombra foram comparadas com as de folhas de sol, para verificar as diferenças em parâmetros fotoquímicos utilizando-se medidas de fluorescência. As amostras de folhas destinadas a estudos anatômicos foram processadas segundo técnicas convencionais. A intensidade de bloqueio da radiação densidade de fótons fotossinteticamente ativos (DFFA) pela fumagina e seu efeito sobre a atividade fotoquímica foram avaliados. As folhas de sol têm mesofilo espesso e parênquima paliçádico unisseriado enquanto nas folhas de sombra o mesofilo é delgado, independentemente da presença ou não de fumagina. O rendimento quântico efetivo (deltaF/Fm') e a taxa aparente de transporte de elétrons (ETR) das folhas de sol foram superiores às das folhas de sombra. Os valores de ETR sugerem que, nas plantas crescidas na sombra, a saturação da atividade fotoquímica ocorre em menores valores de DFFA. Observaram-se menores valores de deltaF/Fm' nas folhas com fumagina e, conseqüentemente menores valores de ETR. A presença de fumagina promoveu bloqueio de 40% na luz incidente e, conseqüentemente, presume-se um decréscimo adicional nos valores de ETR. Pelos dados, verifica-se que a presença de fumagina não promoveu danos diretos nas folhas, mas reduz a capacidade fotoquímica por diminuir a disponibilidade de luz

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression*

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level