Suitability of relative humidity as an estimator of leaf wetness duration

Sentelhas, Paulo César; Marta, Anna Dalla; Orlandini, Simone; Santos, Eduardo A.; Gillespie, T. J.

doi:10.1016/j.agrformet.2007.09.011

Cited by 131 publications

(100 citation statements)

References 28 publications

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“…The big-leaf model of Zhang et al (2003b) predicts surface wetness semimechanistically and distinguishes dew from rain based on precipitation data for rain and on nighttime cloud cover and friction velocity for dew formation (Janssen and Romer, 1991;Brook et al, 1999). In this study, as the cloud cover information was missing, we performed sensitivity tests for the presence of dew by using the constant relative humidity (RH) threshold method for estimating dew condensation (Sentelhas et al, 2008). Various RH thresholds have been used as proxies to determine canopy wetness over forests and grasslands (Tsai et al, 2010;Wichink Kruit et al, 2008Flechard et al, 2011).…”

Section: Modeling Of the Dry Deposition Velocity For Each Idaf Sitementioning

confidence: 99%

Dry deposition of nitrogen compounds (NO2, HNO3, NH3), sulfur dioxide and ozone in west and central African ecosystems using the inferential method

et al. 2013

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Abstract. This work is part of the IDAF program (IGAC-DEBITS-AFRICA) and is based on the long-term monitoring of gas concentrations (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) established at seven remote sites representative of major African ecosystems. Dry deposition fluxes were estimated by the inferential method using on the one hand surface measurements of gas concentrations (NO 2 , HNO 3 , NH 3 , SO 2 and O 3 ) and on the other hand modeled exchange rates. Dry deposition velocities (V d ) were calculated using the big-leaf model of Zhang et al. (2003b). The bidirectional approach is used for NH 3 surface-atmosphere exchange (Zhang et al., 2010). Surface and meteorological conditions specific to IDAF sites have been used in the models of deposition. The seasonal and annual mean variations of gaseous dry deposition fluxes (NO 2 , HNO 3 , NH 3 , O 3 and SO 2 ) are analyzed.Along the latitudinal transect of ecosystems, the annual mean dry deposition fluxes of nitrogen compounds range from −0.4 to −0.8 kg N ha −1 yr −1 for NO 2 , from −0.7 to −1.0 kg N ha −1 yr −1 for HNO 3 and from −0.7 to −8.3 kg N ha −1 yr −1 for NH 3 over the study period (1998-2007). The total nitrogen dry deposition flux (NO 2 +HNO 3 +NH 3 ) is more important in forests (−10 kg N ha −1 yr −1 ) than in wet and dry savannas (−1.6 to −3.9 kg N ha −1 yr −1 ). The annual mean dry deposition fluxes of ozone range between −11 and −19 kg ha −1 yr −1 in dry and wet savannas, and −11 and −13 kg ha −1 yr −1 in forests. Lowest O 3 dry deposition fluxes in forests are correlated to low measured O 3 concentrations, lower by a factor of 2-3, compared to other ecosystems. Along the ecosystem transect, the annual mean of SO 2 dry deposition fluxes presents low values and a small variability (−0.5 to −1 kg S ha −1 yr −1 ). No specific trend in the interannual variability of these gaseous dry deposition fluxes is observed over the study period.

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Section: Modeling Of the Dry Deposition Velocity For Each Idaf Sitementioning

confidence: 99%

Dry deposition of nitrogen compounds (NO2, HNO3, NH3), sulfur dioxide and ozone in west and central African ecosystems using the inferential method

et al. 2013

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“…Similar results were obtained by Sentelhas et al (2004c) when estimating LWD with the CART/SLD model for a cotton crop in São Paulo State, Brazil. As in the RH work of Sentelhas et al (2007b), these findings demonstrate that adaptation of LWD estimation models with empirical characteristics to new climates and regions can be accomplished relatively easily.…”

Section: Sources Of Measurement Error -mentioning

confidence: 64%

“…On the other hand, although empirical models can be highly accurate for sites and regions where they are developed, they may not be as portable as physical models because empirical models depend on best-fit rather than physical principles. However, Sentelhas et al (2007b), in validating an empirical model that used hours of RH>90% as a surrogate for LWD, showed that adjusting the RH threshold to account for regional climate differences resulted in LWD estimates that were as accurate as those derived from physical models. The distinction made between physical and empirical models is an oversimplification.…”

Section: Sources Of Measurement Error -mentioning

confidence: 99%

Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study

Duttweiler

Batzer

Taylor

et al. 2008

Sci. agric. (Piracicaba, Braz.)

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Disease-warning systems are decision support tools designed to help growers determine when to apply control measures to suppress crop diseases. Weather data are nearly ubiquitous inputs to warning systems. This contribution reviews ways in which weather data are gathered for use as inputs to disease-warning systems, and the associated logistical challenges. Grower-operated weather monitoring is contrasted with obtaining data from networks of weather stations, and the advantages and disadvantages of measuring vs. estimating weather data are discussed. Special emphasis is given to leaf wetness duration (LWD), not only because LWD data are inputs to many disease-warning systems but also because accurate data are uniquely challenging to obtain. It is concluded that there is no single "best" method to acquire weather data for use in disease-warning systems; instead, local, regional, and national circumstances are likely to influence which strategy is most successful. Key words: integrated pest management, site-specific weather data, disease forecasting, disease prediction, sustainable agriculture OBTENÇÃO DE DADOS METEOROLÓGICOS PARA SISTEMAS DE ALERTA FITOSSANITÁRIO: O CASO DA DURAÇÃO DO PERÍODO DE MOLHAMENTO FOLIARRESUMO: Os sistemas de alerta fitossanitário são ferramentas de suporte à decisão desenvolvidos para ajudar os agricultures a determinar o melhor momento da aplicação das medidas de controle para combater as doenças de plantas. As variáveis meteorológicas são dados de entrada quase que obrigatórios desses sistemas. Este trabalho apresenta uma revisão sobre os meios pelos quais as variáveis meteorológicas são coletadas para serem usadas como dados de entrada em sistemas de alerta fitossanitário e sobre os desafios associados à logística de obtenção desses dados. Essa revisão compara o monitoramento meteorológico ao nível do produtor, nas propriedades agrícolas, com aquele feito ao nível de redes de estações meteorológicas, assim como discute as vantagens e desvantagens entre medir e estimar tais variáveis meteorológicas. Especial ênfase é dada à duração do período de molhamento foliar (DPM), não somente pela sua importância como dado de entrada em diversos sistemas de alerta fitossanitário, mas também pelo desafio de se obter dados acurados dessa variável. Pode-se concluir, após ampla discussão do assunto, que não há um método único e melhor para se obter os dados meteorológicos para uso em sistemas de alerta fitossanitário; por outro lado, as circunstâncias a nível local, regional e nacional provavelmente influenciam a estratégia de maior sucesso. Palavras chave: manejo integrado de doenças, dados meteorológicos específicos do local, previsão de doenças, estimativa de doenças, agricultura sustentável

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“…The estimation method taken into account to assess LWD was the one that considers the number of hours with a relative humidity above 90% (NHRH > 90%) (SENTELHAS et al, 2008). Among the aforementioned dates only the first one was not utilized for statistical and epidemiological analyses, since there were no infection symptoms on the assessed plants in the field at that date.…”

Section: Methodsmentioning

confidence: 99%

Incidence and severity of white mold for soybean under different cultural practices and local meteorological conditions

Beruski¹,

Pereira²,

Jaccoud-Filho³

et al. 2015

Biosci. J.

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Suitability of relative humidity as an estimator of leaf wetness duration

Cited by 131 publications

References 28 publications

Dry deposition of nitrogen compounds (NO<sub>2</sub>, HNO<sub>3</sub>, NH<sub>3</sub>), sulfur dioxide and ozone in west and central African ecosystems using the inferential method

Dry deposition of nitrogen compounds (NO<sub>2</sub>, HNO<sub>3</sub>, NH<sub>3</sub>), sulfur dioxide and ozone in west and central African ecosystems using the inferential method

Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study

Incidence and severity of white mold for soybean under different cultural practices and local meteorological conditions

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Suitability of relative humidity as an estimator of leaf wetness duration

Cited by 131 publications

References 28 publications

Dry deposition of nitrogen compounds (NO&lt;sub&gt;2&lt;/sub&gt;, HNO&lt;sub&gt;3&lt;/sub&gt;, NH&lt;sub&gt;3&lt;/sub&gt;), sulfur dioxide and ozone in west and central African ecosystems using the inferential method

Dry deposition of nitrogen compounds (NO&lt;sub&gt;2&lt;/sub&gt;, HNO&lt;sub&gt;3&lt;/sub&gt;, NH&lt;sub&gt;3&lt;/sub&gt;), sulfur dioxide and ozone in west and central African ecosystems using the inferential method

Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study

Incidence and severity of white mold for soybean under different cultural practices and local meteorological conditions

Contact Info

Product

Resources

About

Dry deposition of nitrogen compounds (NO<sub>2</sub>, HNO<sub>3</sub>, NH<sub>3</sub>), sulfur dioxide and ozone in west and central African ecosystems using the inferential method

Dry deposition of nitrogen compounds (NO<sub>2</sub>, HNO<sub>3</sub>, NH<sub>3</sub>), sulfur dioxide and ozone in west and central African ecosystems using the inferential method