FERNANDES, J. L.

GHINI, R.

HAMADA, E.

PEDRO JÚNIOR, M. J.

ROSSI, P.

English

Repository Open Access to Scientific Information from Embrapa

Hamada et al.26Sci. Agric. (Piracicaba, Braz.), v.65, special issue, p.26-31, December 2008SPATIAL AND TEMPORAL VARIABILITY OFLEAF WETNESS DURATION IN THESTATE OF SÃO PAULO, BRAZILEmília Hamada1*; Raquel Ghini1; Jeferson Lobato Fernandes2; Mário José Pedro Júnior3;Paulo Rossi41Embrapa Meio Ambiente, C.P. 69 - 13820-000 - Jaguariúna, SP - Brasil.2UNICAMP/FEAGRI, Programa de Pós-Graduação em Planejamento e Desenvolvimento Rural Sustentável, C.P. 6011 - 13083-875 - Campinas, SP - Brasil.3IAC/APTA/SAA, C.P. 28 - 13012-970 - Campinas, SP - Brasil.4Embrapa Cerrados, C.P. 08223 - 73310-970 - Planaltina, DF - Brasil.*Corresponding author <emilia@cnpma.embrapa.br>ABSTRACT: One of the main factors influencing the occurrence of plant diseases is the leaf wetnessduration (LWD). However, this climatic parameter is not generally and systematically recorded atmeteorological stations, and the alternative to obtain an estimate for LWD is the use of mathematicalmodels. The objective of this study was to estimate LWD for the State of São Paulo, on the basis ofthe number of hours with relative humidity equal to or higher than 90%, and also plot them on a mapwith help of the Geographical Information System (GIS) tool. Using daily relative humidity data fromten meteorological stations of the State, for six years, adjustment equations were obtained fordifferent LWD periods (hours day–1) (R2 from 0.58 to 0.81) and of number of days (ND) per monthwith LWD for a period equal to or higher than ten consecutive hours (R2 from 0.57 to 0.75), both asfunctions of the mean relative humidity. The mean LWD and the mean ND varied among the differentregions of the State and different periods of the year. The smallest estimated values of mean annualLWD and ND were observed for the west region of the State, and the highest values for the coastalregion.Key words: plant diseases, relative humidity, Geographical Information System, regression equationVARIABILIDADE ESPACIAL E TEMPORAL DA DURAÇÃODO PERÍODO DE MOLHAMENTO FOLIAR NOESTADO DE SÃO PAULO, BRASILRESUMO: A duração do período de molhamento (DPM) foliar é um dos principais fatores para aocorrência de doenças de planta. No entanto, esse parâmetro climático não é geralmente registradosistematicamente nas estações meteorológicas, tendo-se como alternativa estimar a DPM por meiode modelos matemáticos. Este trabalho teve como objetivo estimar a DPM para o estado de SãoPaulo, baseando-se no número de horas com umidade relativa igual ou superior a 90%, e tambémespacializá-la utilizando as ferramentas de Sistema de Informações Geográficas (SIG). Utilizandoresultados diários de umidade relativa de dez estações meteorológicas do estado, de seis anos,foram obtidas para diferentes períodos as equações de ajuste de DPM (horas dia–1) (R2 de 0,58 a0,81) e de número de dias (ND) ao mês com DPM por um período igual ou superior a dez horasconsecutivas (R2 de 0,57 a 0,75), ambas em função da umidade relativa média. A DPM média e NDmédia variaram entre as regiões do estado e nos períodos do ano. Os menores valores estimados deDPM e de ND médias anuais foram observados na região oeste do estado e os maiores valores naregião litorânea.Palavras-chave: doenças de plantas, umidade relativa, Sistema de Informações Geográficas, equaçãode regressãoINTRODUCTIONLeaf wetness can be defined as water in liq-uid form present on the surface of leaves (Klemm etal., 2002) and leaf wetness duration (LWD) as thelength of time this water remains on the leaf surface.LWD is one of the main factors influencing the ap-pearance of plant diseases (Kim et al., 2002); and, inVariability of leaf wetness duration 27Sci. Agric. (Piracicaba, Braz.), v.65, special issue, p.26-31, December 2008general, the occurrence and severity of diseases aremore correlated with LWD than with rainfall (Reis,2004). For example, many pathogens require the pres-ence of water in the liquid form on the surface of theleaves in order to germinate their propagules and in-fect the host tissues (Gillespie & Kidd, 1978). Undercontrolled environmental conditions, among the atmo-spheric variables, LWD was most dependent on rela-tive humidity and relatively insensitive to temperature,net radiation and wind speed (Magarey et al., 2005).Both field measurements and models have beenused to estimate LWD. Under field conditions, LWDis commonly measured with sensors, but these pro-vide an indirect measurement and there is variabilitybetween different makes of the sensors. Also the useof sensors for field measurements performed by grow-ers is still restricted, due to their cost and difficultiesinvolving handling, installation, maintenance and datadownloading (Kim et al., 2002). On the other hand,on a regional scale, this parameter still lacks a quanti-tative and systematic description (Klemm et al., 2002).Most of the climatic stations worldwide do not recordLWD and do not have sensors available (Madeira etal., 2002). Additionally, this variable is not easily mea-sured (Santos, 2006).Since surface wetness is not routinely mea-sured by most National Weather Services, the abilityto derive surface wetness data is of importance forexperimentation or for operational use where fieldmeasurements are not available (Magarey et al.,2005). Models can, therefore, serve as an alternativeto determine LWD without the difficulties inherent tocollecting data.The objectives of this study were to estimateLWD, as a function of relative humidity, a parameterwhich is more easily found in meteorological stationrecords, for climatological purposes and also to pro-duce maps of mean monthly LWD and number of daysper month with the presence of LWD, for a periodequal to or higher than ten consecutive hours (ND),using a Geographical Information System - GIS.MATERIAL AND METHODSData were obtained from ten meteorologicalstations located in cities with different climatic char-acteristics of the State of São Paulo: Adamantina(21.685° S; 51.073° W), Capão Bonito (24.006° S;48.349° W), Campinas (22.906° S; 48.349° W), Jaú(22.296° S; 48.558° W), Pariquera-Açú (24.715° S;47.881° W), Pindamonhangaba (22.924° S; 45.462°W), Pindorama (21.186° S; 48.907° W), Ubatuba(23.434° S; 51.073° W), Ribeirão Preto (21.178° S;47.810° W), and Votuporanga (20.423° S; 49.973°W). The chosen cities are distributed evenly over theState of São Paulo (Figure 1). The years 1982, 1983,1990, 1994, 1996, and 2000 were chosen becausethey had been considered typically humid (1983), dry(1994) and normal (1982, 1990, 1996, and 2000)years.Figure 1 - Location of the meteorological stations used for adjustment of exponential functions, in the State of São Paulo, Brazil.Hamada et al.28Sci. Agric. (Piracicaba, Braz.), v.65, special issue, p.26-31, December 2008LWD was obtained on a daily basis for eachof these years and for each county using data fromhygrothermograph charts and adopting an empiricalmethod, assuming the occurrence of leaf wetnesswhen the air relative humidity was above 90%. Thisis a simple method and has become popular for thepurpose of estimating LWD (Kim et al., 2002; Santos,2006; Sentelhas et al., 1993; Sentelhas et al., 2004).For every month, the quantity of hours inwhich relative humidity was equal to or higher than90% (LWD) and the number of days per month inwhich the relative humidity of the air was equal to orhigher than 90%, for a period equal to or higher thanten consecutive hours (ND) were registered. After-wards, the data were related to the mean monthly rela-tive humidity to obtain the LWD and the ND adjust-ment equations, using the linear, quadratic, and expo-nential regression methods in order to allow estimatesof these parameters as a function of relative humidity,aiming the development of spatial distribution maps,considering different periods: a) April to September, dryand cold season; b) October to March, wet and warmseason; and c) Annual.The Geographical Information Systems soft-ware Idrisi 32, developed by the Clark University - USAwas used to create maps of relative humidity for theperiods, using data from the climatological normalsfrom 1961 to 1990, in the State of São Paulo, obtainedfrom the Climatic Research Unit (2006), originally witha spatial resolution of 10’ × 10’ of latitude and longi-tude. The software Surfer was used to apply the or-dinary kriging interpolation method and to generaterelative humidity surface maps, with a spatial resolu-tion of 0.01º × 0.01º of latitude and longitude.Following, LWD and ND maps were developedfor the State of São Paulo for the periods: i) April toSeptember; ii) October to March; and iii) Annual, ap-plying the adjustment equations over the relative hu-midity maps, in the arithmetic module.RESULTS AND DISCUSSIONTo estimate LWD and ND as a function ofrelative humidity, regression equations were developed.The relationships between LWD and ND and relativehumidity are shown in Figures 2 and 3, respectively.High values of coefficients of determination were ob-served (R2 from 0.57 to 0.81), indicating that LWDand ND can be estimated from relative humidity. Forlower values of mean monthly LDW and ND, theequations give more precise estimates than to highervalues. The obtained equations relating values of LWDand ND to relative humidity (RH) showed best fitthrough an exponential function as follows:a) April to September:LWD = 0.0219 e 0.0744 RH R2 = 0.81  (1)ND = 0.0051 e 0.0975 RH R2 = 0.75  (2)b) October to March:LWD = 0.0506 e 0.0640 RH R2 = 0.58  (3)ND = 0.0175 e 0.0828 RH R2 = 0.57  (4)c) Annual:LWD = 0.0269 e 0.0718 RH R2 = 0.75  (5)ND = 0.0067 e 0.0945 RH R2 = 0.72  (6)Figure 2 - Relationship between leaf wetness duration (LWD)and relative humidity for different periods: (a) April-September; (b) October-March; and (c) Annual.Variability of leaf wetness duration 29Sci. Agric. (Piracicaba, Braz.), v.65, special issue, p.26-31, December 2008The equations to estimate ND are similar to theexponential equation developed by Pedro Júnior et al.(1989) to map the Microciclus ulei potential of infec-tion of the different ecological zones for the State ofSão Paulo.The mean relative humidity was characterizedboth spatially and seasonally (Figure 4), considering adrier season (autumn and winter) and a more humidseason (spring and summer). In general, the drierweather was observed in the northwest and north ofthe State, with large areas where the mean relative hu-Figure 3 - Relationship between number of days (ND) per monthwith LWD for a period equal to or higher than tenconsecutive hours and relative humidity: (a) April-September; (b) October-March; and (c) Annual.y = 0.0051e0.0975xR² = 0.749905101520253040 50 60 70 80 90 100Mean monthly relative humidity (%)(a)Mean monthly ND (days per month)y = 0.0175e0.0828xR2 = 0.566905101520253040 50 60 70 80 90 100Mean monthly relative humidity (%)(b)Mean monthly ND (days per month)y = 0.0067e0.0945xR² = 0.716605101520253040 50 60 70 80 90 100Mean monthly relative humidity (%)(c)Mean monthly ND (days per month)midity ranged from 60% to 70%. For the most humidmonths, with relative humidity above 70%, were ob-served throughout most the State (Figure 4).The estimated mean LWD values changedamong the different regions of the State and seasonsof the year as expected, since they are direct func-tions of the relative humidity (Figure 5). The smallestestimated LWD values (1.5 to 3.0 hours day–1) oc-curred predominantly from April to September (Fig-ure 5). However, a predominance of areas with LWDFigure 4 - Mean monthly relative humidity (%) for the State ofSão Paulo for different periods of the year. Adaptedfrom Climatic Research Unit (2006).Hamada et al.30Sci. Agric. (Piracicaba, Braz.), v.65, special issue, p.26-31, December 2008higher than 6 hours day–1, from October to March wasobserved. In general, the south and southeast regionshad the highest LWD values (more than 7.5 hoursday–1) throughout the year.Autumn and winter, including the months fromApril to September, showed the smallest estimated NDvalues (Figure 6), in which with large areas present-ing less than five days per month of ND. During springand summer, from October to March, higher ND val-ues were observed, showing a large area within theState presenting five to ten days per month.Dalla Marta et al. (2007) proposed an expertagrometeorological system to evaluate the developmentof Plasmopara viticola, an important disease of grape-vine, on a regional scale, in part of Italy and Slovenia.A model for LWD based on the energy balance ap-proach was applied using hourly meteorological datacollected by a network of automatic weather stations.Nevertheless, a dense network of meteorological sta-tions is not common in Brazil, on a regional or territo-rial scale. Plant disease zoning has recently been fa-Figure 5 - Leaf wetness duration (LWD) in hour day–1 for theState of São Paulo for different periods of the year.Figure 6 - Number of days (ND) per month with LWD for aperiod equal to or higher than ten consecutive hoursfor the State of São Paulo.Variability of leaf wetness duration 31Sci. Agric. (Piracicaba, Braz.), v.65, special issue, p.26-31, December 2008Received November 01, 2007Accepted September 08, 2008cilitated by technological progress and GIS, which hasmade the manipulation of large georeferenced databases relatively straightforward (Hijmans et al., 2000).Once LWD and ND are important for plant dis-ease development, maps of theses parameters for theState of São Paulo may help planners to choose areaswhere disease occurrence is expected to cause lessdamage to the crop. The proposed methodology al-lowed estimating LWD for the State of São Paulo, fromrelative humidity measurements, as well as estimatingthe ND, using the GIS tools. Results of this study madeit possible to apply LWD and ND information on a re-gional scale. The exponential equations obtained to es-timate LWD and ND as a function of relative humid-ity, which can easily be found in most of the standardweather stations, can be used as a tool to map andunderstand disease progress in different regions.ACKNOWLEDGEMENTSTo CNPq, for grant and scholarship given toauthors and to Embrapa (Macroprograma), for finan-cial support.REFERENCESCLIMATIC RESEARCH UNIT. Ten minute climatology: relativehumidity. Available at: http://www.cru.uea.ac.uk/cru/data/tmc.htm. Accessed 10 May 2006.DALLA MARTA, A.; GANI, M.; CICOGNA, A.; DIETRICH, S.;ORLANDINI, S.; SANTORELLI, E. Transboundary system forgrapevine downy mildew risk evaluation on a territorial scale(P. Rada project). EPPO Bulletin, v.37, p.290-294, 2007.GILLESPIE, T.J.; KIDD, G.E. Sensing duration of leaf moistureretention using electrical impedance grids. Canadian Journalof Plant Science, v.58, p.179-187, 1978.HIJMANS, R.J.; FORBES, G.A.; WALKER, T.S. Estimating theglobal severity of potato late blight with GIS-linked diseaseforecast models. Plant Pathology, v.49, p. 697-705, 2000.KIM, K.S.; TAYLOR, S.E.; GLEASON, M.L.; KOEHLER, K.J.Model to enhance site-specific estimation of leaf wetnessduration. Plant Disease, v.86, p.179-185, 2002.KLEMM, O.; MILFORD, C.; SUTTON, M.A.; SPINDLER, G.;PUTTEN, E. van. A climatology of leaf surface wetness.Theoretical and Applied Climatology, v.71, p.107-117,2002.MAGAREY, R.D.; RUSSO, J.M.; SEEM, R.C.; GADOURY, D.M.Surface wetness duration under controlled environmentalconditions. Agricultural and Forest Meteorology, v.128,p.111-122, 2005.MADEIRA, A.C.; KIM, K.S.; TAYLOR, S.E.; GLEASON, M.L. Asimple cloud-based energy balance model to estimate dew.Agricultural and Forest Meteorology, v.111, p.55-63, 2002.PEDRO JÚNIOR, M.J.; ORTOLANI, A.A.; CHIAVEGATTO,O.M.D.P. Umidade relativa do ar para indicação do potencial deinfecção do M. ulei em seringal adulto no estado de São Paulo.In: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA,6., Maceió, 1989. Anais. Piracicaba: Sociedade Brasileira deAgrometeorologia, 1989. p.374-382.REIS, E.M. Previsão de doenças de plantas. Passo Fundo: UPF,2004. 316p.SANTOS, E.A. Duração do período de molhamento foliar: medidacom sensores eletrônicos, variabilidade espacial em culturas eestimativa com modelos empíricos. Piracicaba: USP/ESALQ,2006. 56p. (Dissertação - Mestrado).SENTELHAS, P.C.; GILLESPIE, T.J.; MONTEIRO, J.E.B.A.;ROWLANDSON, T. Estimating leaf wetness duration on a cottoncrop from meteorological data. Revista Brasileira deAgrometeorologia, v.12, p.235-245, 2004.SENTELHAS, P.C.; PEDRO JÚNIOR., M.J.; FELÍCIO, J.C.Estimativa da duração do período de molhamento para o trigo.Revista Brasileira de Agrometeorologia, v.1, p.117-122,1993.

Spatial and temporal variability of leaf wetness duration in the State of São Paulo, Brazil.

http://www.alice.cnptia.embrapa.br/bitstream/doc/31799/1/2008AP30.pdf

Spatial and temporal variability of leaf wetness duration in the State of São Paulo, Brazil.

Abstract

Similar works

Full text

Available Versions

Repository Open Access to Scientific Information from Embrapa