02108naa a2200253 a 450000100080000000500110000800800410001910000200006024500930008026000090017350001070018252012260028965000160151565300250153165300210155665300310157765300370160865300320164570000230167770000220170070000170172270000220173977300930176121005362019-02-25       2018    bl uuuu     u00u1 u    #d1 aIGARASHI, W. T.  aEstimation of soybean leaf wetness from meteorological variables.h[electronic resource]  c2018  aTítulo em português: Estimativa do molhamento foliar da soja a partir de variáveis meteorológicas.  aThe objective of this work was to determine models for the estimation of leaf wetness percentage at three heights in the soybean (Glycine max) canopy, using meteorological variables from stations installed at the crop site and at an agrometeorological station. The experiment was conducted in three harvest seasons, in an area cropped with soybean, in the municipality of Londrina, in the state of Paraná, Brazil. To collect the meteorological variables, electronic trees were installed at four heights (0.3, 0.6, 0.9, and 1.7 m) in the crop and a station was installed in an agrometeorological station. The data were separated according to days with and without rain, and the analyses of correlation and of simple and multiple regressions were carried out, in order to obtain models with equations for leaf wetness estimation. Most of the equations that did not use the data of the sensors installed at 1.7 m, especially those of the models based on variables only from the agrometeorological station, presented low reliability. The models obtained with meteorological data only from the soybean crop show high reliability and use a lower amount of variables, which makes them a good alternative for wetness estimation.  aGlycine Max  aLeaf wetness sensors  aModelo empírico  aPercentage of leaf wetness  aPercentagem de molhamento foliar  aSensor de molhamento foliar1 aSILVA, M. A. de A.1 aFRANÇA, J. A. de1 aIGARASHI, S.1 aSAAB, O. J. G. A.  tPesquisa Agropecuária Brasileira, Brasília, DFgv. 53, n. 10, p. 1087-1092, Oct. 2018.