Morphogenetic and structural traits of tillers and herbage accumulation of Tanganyika grass under shading levels

CARVALHO, Carlos Augusto Brandão de; SILVA, Pedro Henrique Ferreira da; ZANELLA, Pablo Gilliard; DIAVÃO, Jaciara; PAIVA, Adenilson José

doi:10.1590/S1519-99402100452022

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Revista Brasileira de Saúde e Produção Animal

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Forestry and Grassland • Rev. bras. saúde prod. anim. 23 • 2022 • https://doi.org/10.1590/S1519-99402100452022 copy

Morphogenetic and structural traits of tillers and herbage accumulation of Tanganyika grass under shading levels

Características morfogênicas e estruturais de perfilhos e acúmulo de forragem do capim-tanganica sob níveis de sombreamento

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This study aimed to evaluate the effect of tree shading levels on tillers’ morphogenetic and structural traits, besides the herbage accumulation of Tanganyika grass ( Megathyrsus maximus Jacq. cv. Tanganyika). For that, an experiment was carried out from December 2010 to March 2012, under a completely randomized design, with four treatments (shading levels) and five repetitions. Phyllochron (PHY), leaf and stem elongation rates (LER and SER, respectively), number of leaves per tiller (NLT), leaf blade length (LBL), stem length (ST), tiller population density (TPD), leaf (LGR) and stem growth rates (SGR), senescence rate (SR) and herbage accumulation rate (HAR) were assessed. Excepted by the LER and NLT, the shading levels influenced the other morphogenetic variables (P<0.05), positively or negatively. Except in the spring, the TPD linearly increased because of the shading levels (P<0.05). At tiller level, except in the spring, the LBL linearly increased with the shading levels (P<0.05). In general, the SL linearly decreased with the shading levels. Regarding the growth rates, summer II and spring provided greater values, and the lowest one occurred in autumn (P<0.05). The adjustments of both morphogenetic and structural traits ensured the Tanganyika grass a great adaptation to the shaded environment.

forage production; Megathyrsus maximus; morphogenesis; shaded environments

Season	Shading levels

	FS (%)	SS (%)	MS (%)	HS (%)
Summer I	0	26	46	54
Autumn	0	40	67	74
Spring	0	27	54	59
Summer II	0	31	47	56

Season	Shading level¹				SEM	Regression equation	R²

	FS	SS	MS	HF
	PHY (days leaf^-1 tiller¹)
Summer I	9.4^A	6.8^C	7.2^A	7.4^AB	0.9	Ŷ= 7.7^ns	-
Autumn	11.4^A	16.0^A	7.2^A	7.4^AB	1.3	Ŷ= 10.5^ns	-
Spring	6.6^B	8.4^C	7.6^A	9.2^A	0.7	Ŷ= 6.896 + 0.0301x*	0.54
Summer II	11.4^A	11.8^B	7.6^A	7.0^B	0.7	Ŷ= 12.24 - 0.0833x*	0.67
	LER (cm tiller^-1 day^-1)
Summer I	0.38^B	0.47^B	0.58^A	0.49^B	0.06	Ŷ= 0.48^ns	-
Autumn	0.23^B	0.29^B	0.58^A	0.49^B	0.03	Ŷ= 0.40^ns	-
Spring	0.70^A	0.68^A	0.73^A	0.74^A	0.05	Ŷ= 0.71^ns	-
Summer II	0.48^B	0.50^B	0.68^A	0.64^AB	0.05	Ŷ= 0.58^ns	-
	SER (cm tiller^-1 day^-1)
Summer I	0.10^B	0.10^B	0.20^A	0.18^B	0.03	Ŷ= 0.0861 + 0.0019x**	0.73
Autumn	0.10^B	0.04^B	0.20^A	0.18^B	0.02	Ŷ= 0.0658 - 0.0014x*	0.41
Spring	1.00^A	0.40^A	0.38^A	0.51^A	0.09	Ŷ= 0.8672 + 0.0084x*	0.62
Summer II	0.19^B	0.24^A	0.30^A	0.31^A	0.04	Ŷ= 0.1851+ 0.0022x**	0.96

Season	Shading level¹				SEM	Regression equation	R²
Season	FS	SS	MS	HF	SEM	Regression equation	R²
	LGR (kg ha^-1 dia^-1 MS)
Summer I	12^B	10^B	98^B	45^C	7	Ŷ= 5.0132 + 1.1504x*	0.45
Autumn	5^B	7^B	31^C	46^C	3	Ŷ= -0.9968 + 0.5137x**	0.76
Spring	34^A	34^A	182^A	106^B	11	Ŷ= 17.616 + 2.0395x**	0.62
Summer II	32^A	73^A	165^A	180^A	14	Ŷ= 19.891 + 2.7644x**	0.90
	SGR (kg ha^-1 dia^-1 MS)
Summer I	3^B	1^B	8^B	15^C	2	Ŷ= 0.3468 + 0.2033x**	0.61
Autumn	3^B	1^B	7^B	15^C	2	Ŷ= 0.5668 + 0.1311x*	0.50
Spring	27^A	7^B	43^A	33^B	5	Ŷ= 17.94 + 0.2731x*	0.24
Summer II	12^AB	51^A	27^A	83^A	9	Ŷ= 11.861 + 0.937x*	0.55
	SR (kg ha^-1 dia^-1 MS)
Summer I	1^B	1^B	44^B	12^A	5	Ŷ= 1.2772 + 0.5009x*	0.35
Autumn	3^B	1^B	7^D	12^A	2	Ŷ= 0.9733 + 0.1056x*	0.53
Spring	1^B	2^B	21^C	5^B	1	Ŷ= 0.1085 + 0.2102x*	0.38
Summer II	23^A	22^A	66^A	12^A	6	Ŷ= 31^ns	-
	HAR (kg ha^-1 dia^-1 MS)
Summer I	14^B	10^C	62^C	48^B	3	Ŷ= 6.6371 + 0.8528x**	0.65
Autumn	5^C	7^C	31^D	49^B	3	Ŷ= -1.4033 + 0.5393x**	0.74
Spring	60^A	39^B	204^A	134^AB	27	Ŷ= 35.665 + 2.1024x*	0.58
Summer II	21^B	102^A	126^B	251^A	19	Ŷ= 7.7592 + 3.4997x**	0.82

Season	Shading level¹				SEM	Regression equation	R²

	FS	SS	MS	HF
	NLT (leaves tiller^-1)
Summer I	2.3^B	2.6^A	2.2^B	2.3^B	0.1	Ŷ= 2.3^ns	-
Autumn	2.4^B	1.9^B	1.9^B	2.3^B	0.1	Ŷ= 2.1^ns	-
Spring	3.2^A	2.7^A	3.0^A	2.8^A	0.1	Ŷ= 2.9^ns	-
Summer II	2.4^B	2.3^A	2.2^B	2.6^A	0.1	Ŷ= 2.4^ns	-
	LBL (cm leaf^-1)
Summer I	9.8^B	11.2^C	12.6^C	12.7^B	0.9	Ŷ= 9.7971 + 0.0564x**	0.98
Autumn	12.1^B	13.6^B	12.6^C	12.7^B	0.9	Ŷ= 9.7391 + 0.0406x**	0.99
Spring	20.0^A	17.5^A	21.7^A	24.0^A	0.9	Ŷ= 20.8^ns	-
Summer II	12.0^B	14.3^B	18.6^B	14. 9^B	1.0	Ŷ= 11.7 + 1.3x*	0.38
	TPD (tillers m^-2)
Summer I	345^A	589^A	461^B	540^B	53	Ŷ= 398.12 + 2.7185x*	0.38
Autumn	424^A	564^A	532^B	537^B	53	Ŷ= 449.05 + 1.4409x**	0.61
Spring	275^A	452^A	1196^A	1056^A	53	Ŷ= 745^ns	-
Summer II	335^A	461^A	589^B	540^B	55	Ŷ= 338.8 + 4.2523x**	0.89
SL (cm)
Summer I	60^A	51^A	43^C	40^B	2	Ŷ= 60.22 - 0.3721x**	0.99
Autumn	48^C	36^C	47^B	35^C	1	Ŷ= 45.958 - 0.0985x*	0.22
Spring	54^B	45^B	59^A	51^A	2	Ŷ= 52^ns	-
Summer II	60^A	52^A	41^C	40^B	1	Ŷ= 60.907 - 0.3778x**	0.95

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