Estimation of soybean leaf wetness from meteorological variables

Igarashi, Wagner Teigi; Silva, Marcelo Augusto de Aguiar e; França, José Alexandre de; Igarashi, Seiji; Saab, Otávio Jorge Grigoli Abi

doi:10.1590/S0100-204X2018001000001

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AGROMETEOROLOGY • Pesq. agropec. bras. 53 (10) • Oct 2018 • https://doi.org/10.1590/S0100-204X2018001000001 copy

Estimation of soybean leaf wetness from meteorological variables

Estimativa do molhamento foliar da soja a partir de variáveis meteorológicas

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract:

The 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.

Index terms:
Glycine max; empirical models; leaf wetness sensors; percentage of leaf wetness

Embrapa Secretaria de Pesquisa e Desenvolvimento; Pesquisa Agropecuária Brasileira Caixa Postal 040315, 70770-901 Brasília DF Brazil, Tel. +55 61 3448-1813, Fax +55 61 3340-5483 - Brasília - DF - Brazil
E-mail: pab@embrapa.br

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[TFN1] ⁽¹⁾LWP, leaf wetness percentage; X1, average air temperature at the agrometeorogical station; X2, air temperature at the crop site; X3, air minimum temperature at the agrometeorological station; X4, air relative humidity at the meteorological station; X5, air relative humidity at the crop site; X6, maximum wind at the meteorological station; X7, average wind at the agrometeorological station; and X8, leaf wetness percentage at 1.7 m in the crop site.

Model	Multiple linear regression equations⁽¹⁾	R²	p-value
1	$1 LWP (0.3 m) = 127.54954 - 6.24579 X 2 + 2.96710 X 3 + 1.33636 X 4 - 1.37747 X 5 - 1.59242 X 7$	0.83	<0.0001
	$2 LWP (0.6 m) = 147.91539 - 6.98593 X 2 + 3.32035 X 3 + 1.47015 X 4 - 1.57807 X 5 - 2.13025 X 7$	0.84	<0.0001
	$3 LWP (0.9 m) = 177.17620 - 8.00164 X 2 + 3.81110 X 3 + 1.47731 X 4 - 1.89404 X 5 + 0.68229 X 6 - 1.83347 X 7$	0.79	<0.0001
2	$4 LWP (0.3 m) = 47.98549 - 2.61945 X 2 + 1.11239 X 3 + 0.61727 X 4 - 0.40517 X 5 - 0.99359 X 7 + 0.53482 X 8$	0.88	<0.0001
	$5 LWP (0.6 m) = 50.13201 - 2.52919 X 2 + 1.04093 X 3 + 0.58639 X 4 - 0.38313 X 5 - 1.39430 X 7 + 0.65728 X 8$	0.90	<0.0001
	$6 LWP (0.9 m) = 38.31986 - 1.60554 X 2 + 0.54198 X 3 + 0.21463 X 4 - 0.19071 X 5 + 0.25791 X 6 - 1.14043 X 7 + 0.93820 X 8$	0.94	<0.0001
3	$7 LWP (0.3 m) = 67.10633 - 5.39906 X 1 + 3.27491 X 3 + 0.41065 X 4 - 1.67444 X 7$	0.78	<0.0001
	$8 LWP (0.6 m) = 79.23311 - 6.06410 X 1 + 3.69005 X 3 + 0.40955 X 4 - 2.22394 X 7$	0.79	<0.0001
	$9 LWP (0.9 m) = 9621120 - 7.04302 X 1 + 4.33005 X 3 + 0.20289 X 4 + 0.67379 X 6 - 1.9488 X 7$	0.71	<0.0001
4	$10 LWP (0.3 m) = 42.30313 - 1.79715 X 3 + 0.57961 X 4 - 1.56077 X 7$	0.77	<0.0001
	$11 LWP (0.6 m) = 51.37474 - 2.00677 X 3 + 0.59932 X 4 - 2.09627 X 7$	0.77	<0.0001
	$12 LWP (0.9 m) = 65.24187 - 2.26839 X 3 + 0.41771 X 4 - 1.86773 X 7$	0.69	<0.0001
5	$13 LWP (0.3 m) = 21.95868 - 1.38547 X 2 + 0.34909 X 5 + 0.72227 X 8$	0.86	<0.0001
	$14 LWP (0.6 m) = 16.44007 - 1.27356 X 2 + 0.37750 X 5 + 0.86804 X 8$	0.87	<0.0001
	$15 LWP (0.9 m) = 19.27378 - 0.88018 X 2 + 0.11950 X 5 + 1.04020 X 8$	0.93	<0.0001
6	$16 LWP (0.3 m) = 8.10063 + 1.07836 X 8$	0.77	<0.0001
	$17 LWP (0.6 m) = 7.58987 + 1.21806 X 8$	0.81	<0.0001
	$18 LWP (0.9 m) = 3.444349 + 1.225426 X 8$	0.91	<0.0001

Model	Multiple linear regression equations⁽¹⁾	R²	p-value
7	$19 LWP (0.3 m) = 51.52725 - 4.41955 X 2 + 1.42553 X 3 + 0.91607 X 4 - 0.15259 X 5 + 1.08750 X 6 - 2.27106 X 7$	0.74	<0.0001
	$20 LWP (0.6 m) = 80.24948 - 6.08890 X 2 + 2.50053 X 3 + 1.27673 X 4 - 0.66172 X 5 + 1.31052 X 6 - 2.80992 X 7$	0.78	<0.0001
	$21 LWP (0.9 m) = 106.44939 - 6.79510 X 2 + 2.90822 X 3 + 1.18250 X 4 - 0.90987 X 5 + 1.41759 X 6 - 2.76769 X 7$	0.72	<0.0001
8	$22 LWP (0.3 m) = 10.26596 - 1.38294 X 2 + 0.34842 X 4 + 0.25685 X 5 + 0.45553 X 6 - 1.02902 X 7$	0.82	<0.0001
	$23 LWP (0.6 m) = 25.06817 + 0.86709 X 1 - 2.37630 X 2 + 0.58684 X 4 - 0.15772 X 5 + 0.45321 X 6 - 1.16008 X 7$	0.88	<0.0001
	$24 LWP (0.9 m) = 40.39236 - 1.90715 X 2 + 0.60264 X 3 + 0.26998 X 4 - 0.25216 X 5 + 0.40093 X 6 - 0.77167 X 7$	0.91	<0.0001
9	$25 LWP (0.3 m) = 30.37948 - 2.17291 X 1 + 0.84028 X 4 + 0.81604 X 6 - 1.99454 X 7$	0.72	<0.0001
	$26 LWP (0.6 m) = 15.28498 - 2.04430 X 1 + 1.02122 X 4 + 0.87211 X 6 - 2.37127 X 7$	0.74	<0.0001
	$27 LWP (0.9 m) = 26.0749 - 3.9827 X 1 + 1.9503 X 3 + 0.7892 X 4 + 1.0743 X 6 - 2.4524 X 7$	0.67	<0.0001
10	$28 LWP (0.3 m) = 27.24802 - 2.17615 X 3 + 0.88124 X 4 - 1.00303 X 7$	0.72	<0.0001
	$29 LWP (0.6 m) = 9.0681 - 1.9626 X 3 + 1.0776 X 4 - 1.3087 X 7$	0.74	<0.0001
	$30 LWP (0.9 m) = 12.95904 - 1.88796 X 3 + 0.90155 X 4 - 1.15320 X 7$	0.66	<0.0001
11	$31 LWP (0.3 m) = 27.46739 - 1.87760 X 2 + 0.47607 X 5 + 0.62322 X 8$	0.81	<0.0001
	$32 LWP (0.6 m) = 57.17362 - 2.32626 X 2 + 0.19541 X 5 + 0.81299 X 8$	0.86	<0.0001
	$33 LWP (0.9 m) = 55.90168 - 1.69465 X 2 - 0.09315 X 5 + 0.92460 X 8$	0.91	<0.0001
12	$34 LWP (0.3 m) = 14.48372 + 0.96282 X 8$	0.73	<0.0001
	$35 LWP (0.6 m) = 11.12872 + 1.12187 X 8$	0.82	<0.0001
	$36 LWP (0.9 m) = 4.46975 + 1.08283 X 8$	0.89	<0.0001