Production of guava seedlings with increasing water salinity and nitrogenpotassium fertilizations

Rodrigues Filho, Ricardo A.; Nobre, Reginaldo G.; Lima, Geovani S. de; Moraes, Francimar M. da S.; Soares, Lauriane A. dos A.; Teixeira, Antonio D. da S.; Peixoto, Tayd D. C.; Vasconcelos, Emanoel dos S.

doi:10.1590/1983-21252023v36n420rc

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Agricultural Engineering • Rev. Caatinga 36 (4) • Oct-Dec 2023 • https://doi.org/10.1590/1983-21252023v36n420rc copy

Production of guava seedlings with increasing water salinity and nitrogenpotassium fertilizations

Produção de mudas de goiaba com aumento da salinidade da água e fertilização com nitrogênio e potássio

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Salt stress affects the development of several crops, being one of the limiting factors for irrigated agriculture in the semiarid region, where nitrogen and potassium fertilization can be an alternative for agricultural production. This study aimed to evaluate the viability of using water with different salinities associated with varying amounts of nitrogen and potassium fertilization in the production of seedlings of the guava cultivar Paluma in an experiment conducted in experimental areas of the Federal Rural University of the Semi-arid Region (UFERSA), campus of Caraúbas. The research was carried out from February to June 2021. The study was set up in a randomized block design and analyzed in a 5 × 4 factorial scheme with four replicates, and two plants per plot. Treatments were established by combining different levels of electrical conductivity of the irrigation water (ECw): 0.3, 1.1, 1.9, 2.7, and 3.5 dS m^-1, with Combinations (C) of nitrogen (N) and potassium (K₂O) levels of recommended fertilization: C1 = 70% N + 50% K₂O, C2 = 100% N + 75% K₂O, C3 = 130% N + 100% K₂O, and C4 = 160% N + 125% K₂O. Irrigation with electrical conductivity levels up to 2.1 dS m^-1 favored seedling growth for the guava cv. Paluma. Fertilization combinations C1 and C2 promoted the greatest increases in growth and biomass for guava seedlings cv. Paluma 125 days after sowing. Fertilizer combinations did not mitigate the harmful effects of salt stress from irrigation water on the production of young guava cv. Paluma.

Keywords
Psidium guajava L; Salt stress; Fertilizer

	Sand	Silt		Clay	Textural classification		ECse dS m^-1	pHse H2O	OM
	%									g kg^-1
	44.7		41.1	14.3	Loam		0.68	6.03		37
P	_K+	Na⁺	_Ca2+	_Mg 2+	Al³⁺ (H +Al)	SB	t	CEC	V	m	ESP
mg dm^-3	cmol_c dm^-3								%
134.2	0.87	0.15	17.11	1.14	0.0 0.58	19.27	19.27	19.27	97	0	0.78

Source of variation			Mean squares
Source of variation	NL	PH	SD	LA	AGR_LA	RGR_LA
Salinity levels (SL)	35.125^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	59.101^ns	1.004^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	21613.269^{^*}	17.633^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.0001^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}
Linear regression	126.025^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	139.129^ns	3.378^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	78594.431^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	62.575^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.0003^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}
Quadratic regression	0.071ns	33.325^ns	0.437^ns	992.886^ns	3.698^ns	0.00007^{^*}
NK combination	22.817^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	73.427^ns	1.070^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	33087.144^{^*}	6.705^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.00007^{^*}
Interaction (SL × NK)	4.608ns	57.186^ns	0.122^ns	4023.606^ns	1.741^ns	0.00002^ns
Blocks	15.250^{^*}	237.622^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.437^{^*}	47258.423^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	2.609ns	0.00013^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}
Residual	5.273	35.736	0.1333	6035.59	2.1903	1.904e^-5
Mean	21.125	48.743	4.26	541.01	6.103	0.0159
CV (%)	10.87	12.26	8.57	14.36	24.25	27.44

Source of variation			Mean squares
Source of variation	DML	DMS	DMR	TDM	DQI
Salinity levels (SL)	0.7694^{^*}	1.824^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.448^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.	7.839^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.045^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}
Linear regression	2.137^{^*}	5.177^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	1.402^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.	24.235^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.155^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}
Quadratic regression	0.465^ns	0.030^ns	0.149ns	0.793^ns	0.009^ns
NK combination	0.684^ns	0.529^{^*}	0.132ns	3.426^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}	0.025^{^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.}
Interaction (SL × NK)	0.254ns	0.138^ns	0.162^ ,* respectively non-significant, significant at p < 0.01, and significant at p < 0.05; CV= coefficient of variation.	0.758^ns	0.007^{^*}
Blocks	0.233^ns	0.342^ns	0.164^{^*}	1.562^ns	0.009^{^*}
Residual	0.330	0.148	0.045	0.767	0.003
Mean	3.555	2.086	1.487	7.123	0.468
CV (%)	16.16	18.43	14.23	12.29	11.97

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E-mail: caatinga@ufersa.edu.br

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[1] Conflict of interest: The authors declare no conflict of interest related to the publication of this manuscript.