Helmintos parásitos de Telmatobius jelskii (Peters) (Anura, Leptodactylidae) de Lima, Perú Helminth parasites of Telmatobius jelskii (Peters) (Anura, Leptodactylidae) from Lima, Peru

<abstract language="eng">A quantitative research of parasites of 67 endemic frog Telmatobius jelskii (Peters, 1863) collected from Laguna Tucto (76°46'11"W, 10°39'11"S) where Pativilca River is originated was conducted, and was located in the Province of Oyon, high Andean area from the Department of Lima, Peru during September-October 2000. Of the frogs collected, 23 were females and 44 males. Male showed a length between 5.2 ± 0.5 cm (range = 4.0-6.4 cm) and female between 5.5 ± 1 cm (range = 3.9-7.6 cm) and were not found differences between both sexes. 86 specimens of parasite and three species in total during all the survey were collected. 28 hosts were infected (41.8%). twenty-five hosts (37.3%) showed infection with one parasite species, and three (4.5%) had two parasite species. Three parasite species were found: Gorgoderina parvicava Travassos, 1922 (Digenea: Gorgoderidae) (Prevalence = 40.3%; mean Intensity = 3.1; mean abundance = 1.2), Cylindrotaenia americana Jewell, 1916 (Cestoda: Proteocephalidae) (Prevalence = 3%; mean Intensity = 1; mean abundance = 0.02) and Aplectana hylambatis (Baylis, 1927) (Nematoda: Cosmocercidae) (Prevalence = 3%; mean Intensity = 1; mean abundance = 0.02). G. parvicava had an overdispersed distribution and was the dominant species. An effect of sex and length with prevalence and mean abundance of infection of G. parvicava was not found. The relationship of helminthes parasites with T. jelskii is discussed. G. parvicava and C. americana are new records for T. jelskii

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