Diagnosis of soil fertility and banana crop nutrition in the state of Santa Catarina

Soil fertility and nutritional imbalance are among the main limitations in banana production. The present study aimed to diagnose soil fertility and nutrition of bananas trees cultivated in the state of Santa Catarina, southern Brazil. Leaf and soil samples were collected, and banana yield was quantified in 2018 and 2019 in 53 orchards in the regions of ‘Vale do Itajaí’ and northern coast of Santa Catarina. Although orchards had satisfactory yield (42 t /ha/year), limitations related to soil fertility and banana crop nutrition were evident. The observed yield is 59.6 % of its full potential. Liming is still recurrent, mainly before orchard establishment. Fertilizer application is widespread, but in many cases, doses are overestimated. Ca, Zn, Cu, and B contents increased in leaves emitted in periods of higher soil water availability and higher temperatures. The results highlight the influence of weather conditions on the supply of these nutrients to banana plants. Nutritional factors are the main limiting factors in banana production in the state of Santa Catarina, compared to non-nutritional factors.

Index terms
Fruit culture; banana production; banana crop nutrition; nutritional management

Authorship

Gelton Geraldo Fernandes Guimarães Gelton Geraldo Fernandes Guimarães E-mail:geltonguimaraes@epagri.sc.gov.br

PhD in Soils and Plant Nutrition, Researcher at the Agricultural Research and Rural Extension Company of Santa Catarina -EPAGRI, Experimental Station of Itajaí, Itajaí-SC, Brazil. - Email: geltonguimaraes@epagri.sc.gov.br Estação Experimental de Itajaí-EPAGRIBrasilItajaí, SC, Brasil PhD in Soils and Plant Nutrition, Researcher at the Agricultural Research and Rural Extension Company of Santa Catarina -EPAGRI, Experimental Station of Itajaí, Itajaí-SC, Brazil. - Email: geltonguimaraes@epagri.sc.gov.br

http://orcid.org/0000-0001-9029-5241

José Aridiano Lima de Deus

PhD in Soils and Plant Nutrition, Extension Agent at the Rural Development Institute of Paraná – IAPAR-EMATER, Rondon-PR, Brazil. Email: josearidiano@idr.pr.gov.br Extension Agent at the Rural Development Institute of Paraná – IAPAR-EMATERBrasilRondon, PR, Brasil PhD in Soils and Plant Nutrition, Extension Agent at the Rural Development Institute of Paraná – IAPAR-EMATER, Rondon-PR, Brazil. Email: josearidiano@idr.pr.gov.br

http://orcid.org/0000-0002-3777-7337

Gelton Geraldo Fernandes Guimarães E-mail:geltonguimaraes@epagri.sc.gov.br

SCIMAGO INSTITUTIONS RANKINGS

Figures | Tables | Formulas

Figures (4)
Tables (7)
Formulas (4)

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Figure 1
Banana production distribution in the state of Santa Catarina and identification of evaluated municipalities. Source: adapted from (CEPA, 2018).

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Figure 2
Accumulated precipitation and average air temperature from August 2018 to July 2019 in the study area. Data from Epagri/Ciram.

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Figure 3
Frequency of orchards evaluated with nutrient deficiency, balance or excess according to the new nutrient sufficiency ranges in banana leaves of the ‘Cavendish’ subgroup for the state of Santa Catarina.

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Figure 4
Analysis of principal components relating nutrient contents in banana leaves in two collection periods (September to December 2018 “2018/II” or January to April 2019 “2019/I”).

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Table 1
Minimum, average, maximum values, standard deviation of the mean (S.D.) and coefficient of variation (C.V.) of the chemical soil parameters of commercial banana orchards of Santa Catarina (n = 53).

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Table 2
Frequency of evaluated orchards (n = 53) in relation to the classification of soil chemical parameters.

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Table 3
Minimum, average, maximum values, standard deviation of the mean (S.D.) and coefficient of variation (C.V) of the nutrient content of banana leaves and productivity of ‘Cavendish’ banana harvested in commercial orchards of Santa Catarina (n = 53).

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Table 4
Pearson correlation matrix between nutrient content in soil, banana leaf and bunch weight of 53 commercial banana orchards cultivated in Santa Catarina.

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Table 5
Adjusted equations for the relationship between relative productivity (y) of the frontier population as a function of the CND (x) indexes of macro and micronutrients.

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Table 6
Estimated maximum productivity averages as a function of CND indexes for each nutrient and reduced productivity of banana plantations in Santa Catarina due to nutritional and non-nutritional factors.

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Table 7
Average nutrient contents in banana leaves collected from September to December 2018 "2018/II" or from January to April 2019 "2019/I" and production of bananas from plants sampled in the respective periods.

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Figure 1 Banana production distribution in the state of Santa Catarina and identification of evaluated municipalities. Source: adapted from (CEPA, 2018).

Figure 2 Accumulated precipitation and average air temperature from August 2018 to July 2019 in the study area. Data from Epagri/Ciram.

Figure 3 Frequency of orchards evaluated with nutrient deficiency, balance or excess according to the new nutrient sufficiency ranges in banana leaves of the ‘Cavendish’ subgroup for the state of Santa Catarina.

Figure 4 Analysis of principal components relating nutrient contents in banana leaves in two collection periods (September to December 2018 “2018/II” or January to April 2019 “2019/I”).

Table 1 Minimum, average, maximum values, standard deviation of the mean (S.D.) and coefficient of variation (C.V.) of the chemical soil parameters of commercial banana orchards of Santa Catarina (n = 53).

	Clay	pH¹	P	K	Ca	Mg	H + Al	CEC²	Al (m)³	V⁴	MO	Zn	Cu	Mn	Fe
	%		mg/dm³		................... cmolc/dm³ ...............				............ % ...............			.............. mg/dm³ ..............
Minimum	23.0	4.2	6.7	84.0	0.3	0.5	1.4	9.4	0.0	31.4	1.6	0.8	0.4	3,0	2,3
Average	33.2	5.3	85.7	221.1	6.1	2.1	4.8	13.8	6.1	64.9	3.9	14.0	1.7	18,9	119,9
Maximum	42.0	6.5	214.4	364.6	9.2	3.6	10.0	18.4	39.1	89.8	6.6	35.9	4.4	65,6	306,7
S.D.	4.5	0.5	46.8	68.1	1.9	0.7	1.7	2.2	8.0	12.2	1.1	9.0	0.9	15,2	74,0
C.V (%)	13.5	9.4	54.6	30.8	31.5	35.3	35.6	16.0	132.5	18.7	27.7	64.8	52.9	80,6	61,7

¹
pH in water;
²
Cation Exchange Capacity at pH 7
³
Aluminum saturation;
⁴
Base saturation.

Table 2 Frequency of evaluated orchards (n = 53) in relation to the classification of soil chemical parameters.

Class	pH¹	P	K	MO	Ca	Mg	CEC²	V³	Zn	Cu	Mn	Fe
Class	..................................................................... % ........................................................................
Very low	8	0	0	-	2	0	0	15	0	0	11	2
Low	57	4	0	8	9	6	68	43	2	2	43	4
Average	34	2	2	75	42	15	32	34	0	17	28	0
High	2	9	34	17	47	79	0	8	34	72	17	4
Very high	-	85	64	-	-	-	-	-	64	9	0	91

¹
pH in water;
²
Cation Exchange Capacity at pH 7;
³
Base saturation.

Table 3 Minimum, average, maximum values, standard deviation of the mean (S.D.) and coefficient of variation (C.V) of the nutrient content of banana leaves and productivity of ‘Cavendish’ banana harvested in commercial orchards of Santa Catarina (n = 53).

	N	P	K	Ca	Mg	S	Fe	Mn	Zn	Cu	B	Productivity
	............................. g/kg ...................................						......................... mg/kg ...........................					t/ha/year
Minimum	16.4	2.0	48.8	4.6	2.7	1.2	49.4	63.4	10.2	0.9	8.7	18.9
Average	22.3	2.3	64.1	8.2	3.4	1.7	131.0	442.6	26.1	5.7	17.9	42.5
Maximum	27.3	2.7	79.6	12.4	4.4	2.5	527.8	1143.6	46.7	11.5	37.5	71.8
S.D.	2.9	0.2	7.5	1.9	0.4	0.3	75.4	296.3	10.5	2.6	9.6	11.2
C.V (%)	13.0	6.7	11.7	23.0	11.4	15.1	57.5	66.9	40.2	46.2	54.0	26.4

Table 4 Pearson correlation matrix between nutrient content in soil, banana leaf and bunch weight of 53 commercial banana orchards cultivated in Santa Catarina.

Variables	P_soil	K_soil	Ca_ soil	Mg_ soil	N_leaf	P_ leaf	K_ leaf	Ca_ leaf	Mg_ leaf	S_ leaf	Bunch weight
OM	0.055	-0.079	-0.136	0.012	0.338¹	0.096	0.104	-0.338	-0.025	0,440	-0,181
P_soil		0.503	0.395	0.136	-0.063	0.260	0.304	-0.083	-0.180	0,049	0,434
K_ soil			0.300	0.311	0.011	-0.032	0.219	-0.105	-0.198	-0,192	0,357
Ca_ soil				0.570	0.006	0.104	0.221	0.206	0.094	0,129	0,292
Mg_ soil					-0.083	0.148	0.094	0.052	0.300	0,037	0,035
N_leaf						-0.158	0.463	-0.475	-0.116	0,481	-0,051
P_ leaf							0.224	0.184	0.036	0,139	0,337
K_ leaf								-0.460	-0.346	0,276	0,364
Ca_ leaf									0.435	-0,273	-0,085
Mg_ leaf										-0,079	-0,269
S_ leaf											0,020

¹
Values in bold are different from 0 with significance level of a = 0.05.

Table 5 Adjusted equations for the relationship between relative productivity (y) of the frontier population as a function of the CND (x) indexes of macro and micronutrients.

Nutrient	Equation	R²	****** of data
N	y = -6.176x² + 0.868x + 100.25	0.92	11
P	y = -6.687x² - 0.836x + 100.05	0.89	11
K	y = -5.210x² - 0.941x + 100.71	0.92	12
Ca	y = -9.144x² + 2.825x + 100.43	0.92	14
Mg	y = -6.297x² + 2.155x + 100.77	0.95	12
S	y = -8.239x² - 0.689x + 101.84	0.94	17
Fe	y = -10.314x² + 6.644x + 101.29	0.88	13
Mn	y = -12.385x² + 4.174x + 100.21	0.89	16
Cu	y = -5.907x² - 4.301x + 99.96	0.91	10
Zn	y = -5.760x² - 5.928x + 100.39	0.80	13
B	y = -8.976x² + 2.309x + 96.54	0.95	13
R¹	y = -3.993x² + 0.670x + 94.76	0.89	11

¹
Represents the sum of all other elements not measured in the plant tissue analysis.

Table 6 Estimated maximum productivity averages as a function of CND indexes for each nutrient and reduced productivity of banana plantations in Santa Catarina due to nutritional and non-nutritional factors.

Municipalities	- - - - - - - - - - - - - - - - - - -Maximum estimated productivity- - - - - - - - - - - - - - - - - - -												Y_obs.²	↓LN³	↓LÑN⁴
Municipalities	N	P	K	Ca	Mg	S	Fe	Mn	Cu	Zn	B	R¹	Y_obs.²	↓LN³	↓LÑN⁴
	................................................................................................... % .........................................................................................
Balneário Piçarras	89.6	91.8	92.9	87.3	93.8	95.6	66.5	91.5	95.8	67.8	80.3	84.7	74,6	45,3	19,4
Corupá	70.1	97.3	96.6	91.9	92.2	93.1	83.2	82.5	87.8	89.7	81.1	71.8	51,3	50,3	17,7
Jaraguá do sul	96.2	94.9	95.5	99.0	87.9	94.7	97.0	86.5	79.0	98.7	84.4	78.2	54,4	35,8	23,5
Luiz Alves	96.1	95.6	93.7	94.7	85.4	99.3	88.9	97.2	94.9	97.8	92.7	85.4	58,7	24,0	21,3
Massaranduba	71.4	92.5	95.6	94.3	96.3	94.2	57.4	88.8	93.7	74.4	88.7	76.5	70,3	45,0	9,7
São João ** Itaperiú	80.6	95.4	96.1	85.6	94.8	94.8	85.0	87.2	97.8	72.3	79.0	80.0	59,3	42,1	34,1
Schroeder	95.1	97.3	92.9	97.1	99.3	94.8	95.2	86.1	96.0	95.5	80.2	89.3	48,7	25,1	26,2

***** Catarina	85.6	95.0	94.8	92.8	92.8	95.2	81.9	88.5	92.1	85.2	83.8	80.8	59,6	38,2	21,7

¹
R: Maximum estimated productivity as a function of CND “IR” indexes.
²
Yobs.:Relative productivity observed in the field;
³
?LN: reduction in relative productivity due to nutritional factors;
⁴
?LÑN: reduction in relative productivity due to non-nutritional factors.

Table 7 Average nutrient contents in banana leaves collected from September to December 2018 "2018/II" or from January to April 2019 "2019/I" and production of bananas from plants sampled in the respective periods.

Collection	N		P			K	Ca		Mg		S
	..................................................... g/kg .....................................................................
2018/II	23.6 ± 2.0		2.29 ± 0.16			66.6 ± 6.6	7.3 ± 1.15		3.3 ± 0.39		1.76 ± 0.25
2019/I	19.3 ± 2.3		2.34 ± 0.15			58.3 ± 6.1	10.4 ± 1.38		3.5 ± 0.35		1.47 ± 0.08
Collection	Fe	Mn			Zn			Cu		B	Mean bunch weight
	.................................................. mg/** ...........................................										kg
2018/II	142.9 ± 79.8			471.3 ± 319	20.1 ± 3.3			4.4 ± 1.7		11.8 ± 2.0	33.3 ± 8.5
2019/I	103.3 ± 56.9			376.0 ± 228	40.1 ± 7.7			8.8 ± 1.7		31.7 ± 4.4	33.3 ± 9.8

N u t r i t i o n a l L i m i t a t i o n (↓ L N) : Y m a x i m u m - Y r e a c h a b l e

N o n - n u t r i t i o n a l L i m i t a t i o n (↓ L Ñ N) : Y r e a c h a b l e - Y o b s e r v e d

N u t r i t i o n a l L i m i t a t i o n (↓ L N) : 100 - 70 = 30 %

N o n - n u t r i t i o n a l L i m i t a t i o n (↓ L Ñ N) : 70 - 50 = 20 %

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Diagnosis of soil fertility and banana crop nutrition in the state of Santa Catarina

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[1] Gelton Geraldo Fernandes Guimarães E-mail:geltonguimaraes@epagri.sc.gov.br