Embrittlement of electrical steel laminations by nitrogen pick-up during heat treatment

Heat treated electrical steel laminations have shown evidence of low ductility behavior, characterized by a small number of bends till fracture, on repeated bending tests. The laminations were produced using a new grade of electrical steel with much lower aluminum content than usual. The problem happens when the oxygen potential (measured by the dew point of the atmosphere) of the heat treatment atmosphere is abnormally high. Furthermore, ductility can be restored by a low-oxygen potential heat treatment. Although the heat treatment resulted in a loss of ductility, the magnetic properties were not deteriorated. The low ductility samples always show intergranular fracture, whereas the un-treated laminations fracture by cleavage. The low ductility is associated with the formation of silicon manganese nitride precipitates formed at grain boundaries, although they are not the cause of the low ductility. Ductility could be restored by a low dew point heat treatment but the inclusions remained in the grain boundaries. The low ductility and its recovery must be ascribed to the presence of nitrogen atoms segregated to the grain boundaries when the heat treatment atmosphere has a high oxygen potential. The lack of aluminum in the composition of the steel hinders the scavenging effect of this element on nitrogen atoms in solution in the steel. (C) 2009 Elsevier Ltd. All rights reserved.

Spatial portability of numerical models of leaf wetness duration based on empirical approaches

Leaf wetness duration (LWD) models based on empirical approaches offer practical advantages over physically based models in agricultural applications, but their spatial portability is questionable because they may be biased to the climatic conditions under which they were developed. In our study, spatial portability of three LWD models with empirical characteristics - a RH threshold model, a decision tree model with wind speed correction, and a fuzzy logic model - was evaluated using weather data collected in Brazil, Canada, Costa Rica, Italy and the USA. The fuzzy logic model was more accurate than the other models in estimating LWD measured by painted leaf wetness sensors. The fraction of correct estimates for the fuzzy logic model was greater (0.87) than for the other models (0.85-0.86) across 28 sites where painted sensors were installed, and the degree of agreement k statistic between the model and painted sensors was greater for the fuzzy logic model (0.71) than that for the other models (0.64-0.66). Values of the k statistic for the fuzzy logic model were also less variable across sites than those of the other models. When model estimates were compared with measurements from unpainted leaf wetness sensors, the fuzzy logic model had less mean absolute error (2.5 h day(-1)) than other models (2.6-2.7 h day(-1)) after the model was calibrated for the unpainted sensors. The results suggest that the fuzzy logic model has greater spatial portability than the other models evaluated and merits further validation in comparison with physical models under a wider range of climate conditions. (C) 2010 Elsevier B.V. All rights reserved.

Suitability of relative humidity as an estimator of leaf wetness duration

Leaf wetness duration (LWD) is a key parameter in agricultural meteorology since it is related to epidemiology of many important crops, controlling pathogen infection and development rates. Because LWD is not widely measured, several methods have been developed to estimate it from weather data. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results, but their complexity is a disadvantage for operational use. Alternatively, empirical models have been used despite their limitations. The simplest empirical models use only relative humidity data. The objective of this study was to evaluate the performance of three RH-based empirical models to estimate LWD in four regions around the world that have different climate conditions. Hourly LWD, air temperature, and relative humidity data were obtained from Ames, IA (USA), Elora, Ontario (Canada), Florence, Toscany (Italy), and Piracicaba, Sao Paulo State (Brazil). These data were used to evaluate the performance of the following empirical LWD estimation models: constant RH threshold (RH >= 90%); dew point depression (DPD); and extended RH threshold (EXT_RH). Different performance of the models was observed in the four locations. In Ames, Elora and Piracicaba, the RH >= 90% and DPD models underestimated LWD, whereas in Florence these methods overestimated LWD, especially for shorter wet periods. When the EXT_RH model was used, LWD was overestimated for all locations, with a significant increase in the errors. In general, the RH >= 90% model performed best, presenting the highest general fraction of correct estimates (F(C)), between 0.87 and 0.92, and the lowest false alarm ratio (F(AR)), between 0.02 and 0.31. The use of specific thresholds for each location improved accuracy of the RH model substantially, even when independent data were used; MAE ranged from 1.23 to 1.89 h, which is very similar to errors obtained with published physical models for LWD estimation. Based on these results, we concluded that, if calibrated locally, LWD can be estimated with acceptable accuracy by RH above a specific threshold, and that the EXT_RH method was unsuitable for estimating LWD at the locations used in this study. (C) 2007 Elsevier B.V. All rights reserved.

Estimating hourly net radiation for leaf wetness duration using the Penman-Monteith equation

Leaf wetness duration (LWD) is related to plant disease occurrence and is therefore a key parameter in agrometeorology. As LWD is seldom measured at standard weather stations, it must be estimated in order to ensure the effectiveness of warning systems and the scheduling of chemical disease control. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results for operational use. However, the requirement of net radiation (Rn) is a disadvantage foroperational physical models, since this variable is usually not measured over crops or even at standard weather stations. With the objective of proposing a solution for this problem, this study has evaluated the ability of four models to estimate hourly Rn and their impact on LWD estimates using a Penman-Monteith approach. A field experiment was carried out in Elora, Ontario, Canada, with measurements of LWD, Rn and other meteorological variables over mowed turfgrass for a 58 day period during the growing season of 2003. Four models for estimating hourly Rn based on different combinations of incoming solar radiation (Rg), airtemperature (T), relative humidity (RH), cloud cover (CC) and cloud height (CH), were evaluated. Measured and estimated hourly Rn values were applied in a Penman-Monteith model to estimate LWD. Correlating measured and estimated Rn, we observed that all models performed well in terms of estimating hourly Rn. However, when cloud data were used the models overestimated positive Rn and underestimated negative Rn. When only Rg and T were used to estimate hourly Rn, the model underestimated positive Rn and no tendency was observed for negative Rn. The best performance was obtained with Model I, which presented, in general, the smallest mean absolute error (MAE) and the highest C-index. When measured LWD was compared to the Penman-Monteith LWD, calculated with measured and estimated Rn, few differences were observed. Both precision and accuracy were high, with the slopes of the relationships ranging from 0.96 to 1.02 and R-2 from 0.85 to 0.92, resulting in C-indices between 0.87 and 0.93. The LWD mean absolute errors associated with Rn estimates were between 1.0 and 1.5h, which is sufficient for use in plant disease management schemes.

Influence of soybean phenological stage and leaflets age on infection by Phakopsora pachyrhizi

Influence of soybean phenological stage and leaflets age on infection by Phakopsora pachyrhizi This work was conducted to study the influence of soybean growth stage and leaf age on the infection of Phakopsora pachyrhizi, the soybean rust pathogen. Soybean plants (cv. BRS 154 and BRS 258) at the V(3), R(1) and R(5) growth stages were inoculated with a 1 x 10(5) urediniospores per mL suspension. After a period of 24 hours in dew chambers, all plants were removed from the chambers and placed under greenhouse conditions for 20 days. Mean latent period (PLM) and disease severity were estimated. The susceptibility of trifoliate leaves to soybean rust was estimated on cv. BRS 154 at the growth stage R5. Pathogen inoculation was done at the first four trifoliate leaves. Fifteen days after inoculation, leaflets of each trefoil were evaluated for disease severity, lesion mean size and infection frequency. Plants` growth stage did not influence the PLM. Cultivars BRS 154 and BRS 258 presented PLM of 8 and 9 days, respectively. There was no difference in disease severity at the growth stages V(3) and R(1), but those values were higher than at the R(5) growth stage, 8 days after inoculation. The oldest trefoil showed the highest disease values.

Spatial heterogeneity of leaf wetness duration in apple trees and its influence on performance of a warning system for sooty blotch and flyspeck

To determine the effect of sensor placement on the performance of a disease-warning system for sooty blotch and flyspeck (SBFS), we measured leaf wetness duration (LWD) at 12 canopy positions in apple trees, then simulated operation of the disease-warning system using LWD measurements from different parts of the canopy. LWD sensors were placed in four trees within one Iowa orchard during two growing seasons, and in one tree in each of four orchards during a single growing season. The LWD measurements revealed substantial heterogeneity among sensor locations. In all data sets, the upper, eastern portion of the canopy had the longest mean daily LWD, and was the first site to form dew and the last to dry. The lower, western portion of the canopy averaged about 3 It less LWD per day than the top of the canopy, and was the last zone where dew formed and the first to dry off. On about 25% of nights when dew occurred in the top of the canopy, no dew formed in the lower, western canopy. Intracanopy variability of LWD was more pronounced when dew was the sole source of wetness than on days when rainfall occurred. Daily LWD in the upper, eastern portion of the canopy was slightly less than reference measurements made at a 0.7-m height over turfgrass located near the orchard. When LWD measurements from several canopy positions were input to the SBFS warning system, timing of occurrence of a fungicide-spray threshold varied by as much as 30 days among canopy positions. Under Iowa conditions, placement of an LWD sensor at an unobstructed site over turfgrass was a fairly accurate surrogate for the wettest part of the canopy. Therefore, such an extra-canopy LWD sensor might be substituted for a within-canopy sensor to enhance operational reliability of the SBFS warning system.

Thermal Characteristics of the Mud Nests of the Social Wasp Polybia spinifex (Hymenoptera; Vespidae)

The thermal characteristics of mud nests of Polybia spinifex were investigated by measuring internal and surface temperatures of the nests. The nests had a layer of mud envelope and consisted of mud with fine sand particles. The envelope had a vertically long slit-like entrance hole. The mud nests had high thermal conductivities (0.51-0.67 W/(m degrees C)) comparable to brick, rather than insulation materials of wasps` nests such as paper and wood. It was revealed that the long entrance radiated more heat from the thereto-image. The rate of thermal radiation (emissivity) of the nest surface was 0.80, and the value was similar to that of sand. The internal temperatures of the nests were high from top (T(n1), temperature difference between ambient temperature (T(a)) was 10 degrees C) to bottom (T(n3), difference, 5 degrees C) at noon. On the other hand, the temperature distributions were reversed during the night. Temperature T(n1) was lower by 1 degrees C than T(a), possibly from nightly dew on the top surface, whereas, at the middle point (T(n2),) and T(n3), temperatures were higher by 1 degrees C compared to T(a). Temperature fluctuations (ranges between maximum and minimum temperature) at T(n2) and T(n3) were similar to that of T(a), whereas the values at T(n1) and T(s) were higher than that of T(a).