Performance of commercial okra genotypes at salinity levels

doi:10.21203/rs.3.rs-4680386/v1

Download PDF

Research Article

Performance of commercial okra genotypes at salinity levels

https://doi.org/10.21203/rs.3.rs-4680386/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Okra is a vegetable grown in Brazil. In arid and semiarid regions of the country, to guarantee production, farmers use irrigation, which often comes from low-quality water sources and high salinity levels, which can lead to a reduction in crop yields if the genotypes used are not tolerant to salinity stress. The aim of this study was to assess the performance of commercial okra genotypes subjected to different salinity levels. To this end, an experiment was conducted in a greenhouse at the Federal Institute of Alagoas - Piranhas Campus using a randomized block design in a factorial scheme (12 × 5), with 60 treatments and three replications, totaling 180 experimental plots. Twelve okra genotypes were evaluated: Apuim, Santa Cruz 47, Valença, IAC Midori, Clemson Americano 80, Speedy, Quiabel, Carcará, Estrela, Cariri, Guará and Canindé. The genotypes were subjected to five levels of salinity, corresponding to 0.0 dS m^− 1 (control), 2.5 dS m^− 1, 5.0 dS m^− 1, 7.5 dS m^− 1 and 10.0 dS m^− 1. All the genotypes showed a reduction in the values of the characteristics studied as the salt level increased, with the exception of the root length variable. It was possible to conclude that the Carcará, Cariri and IAC Midori genotypes obtained the best performances for most of the variables studied, regardless of the salinity levels, and that the genotypes showed no tolerance from the 2.5 dS m^− 1 level onward.

Abiotic stress

Malvaceae

Tolerance

The okra (Abelmoschus esculentus (L.) Moench) originated in Africa, belongs to the Malvaceae family and is traditionally grown in tropical regions (Galati et al. 2013). Due to its high versatility, this fruit vegetable is appreciated in Brazilian cuisine and used in the production of various typical dishes, as well as having good nutritional value, a characteristic that highlights its use in food (Filgueira 2012).

This vegetable has a vegetative stage from 0 to 64 days after sowing and a reproductive stage from 65 to 120 days after sowing, but its fruiting period can exceed 200 days depending on the growing season (Galati 2010). It is a crop that adapts well to arid and semiarid conditions with an expected average yield per hectare of between 20 and 40 tons, which depends on environmental conditions, with biotic and abiotic stresses reducing its performance (Modesto 2018).

Among the abiotic stresses, salinization reduces crop yields and, above certain limits, definitively eliminates production. Salinity, therefore, has become one of the most serious factors limiting agricultural production, especially in areas irrigated under conditions of high temperature and low rainfall, where the problem can be further aggravated by the use of poor-quality irrigation water (Azevedo et al. 2017).

Barros et al. (2021) stated that the use of saline water in irrigation is often the only alternative for making agricultural production viable, and despite being a solution to this problem, the use of this water in agriculture can cause soil salinization, especially if there is no proper irrigation management. The accumulation of salts in water and/or soil contributes to hindering the absorption of water by plants, resulting in water deficit, in which plants wilt, leaves develop a dark bluish green color, leaves are reduced in size and leaf area, and ion homeostasis changes (Sales 2020).

Almost all crops important to humans are sensitive to high concentrations of salts in irrigation water and soil; the presence of salts in the soil is one of the most significant abiotic stresses in agriculture, and it is vital to study genotypes that are more tolerant to salinity to increase the yield and quality of crops in this environment (Liang et al. 2018).

Due to the importance of the crop, studying genotypes that may be more tolerant to saline conditions could provide alternatives for planting vegetables on soils located in semiarid regions and increase the possibility of using saline or brackish water with appropriate irrigation management. The aim of this study was to evaluate the performance of commercial okra genotypes subjected to different salinity levels.

Location and period of the experiment

The experiment was carried out in a greenhouse at the Federal Institute of Alagoas (IFAL) - Piranhas Campus between 2021 and 2022. The study area is located at coordinates 9°37'25.183“S and 37°45'59.897” O, at an altitude of 181 meters; the region's climate has semiarid conditions and is, according to the Köppen climate classification, hot and dry (BSh), steppe type, with a rainy season starting in March and lasting until July and average annual rainfall between 400 and 600 mm (Santos et al. 2017).

Treatments and statistical design

Twelve commercial okra genotypes were evaluated (free-pollinated varieties Apuim, Santa Cruz-47, Valença, IAC Midori and Clemson Americano 80; and the hybrids Speedy, Quiabel, Carcará, Estrela, Cariri, Guará and Canindé) subjected to five different and ascending levels of salinity, corresponding to 0.0 dS m^− 1, 2.5 dS m^− 1, 5.0 dS m^− 1, 7.5 dS m^− 1 and 10.0 dS m^− 1. A randomized block design was used in a factorial scheme (12 x 5) with 60 treatments and three replications, resulting in a total of 180 experimental plots.

Conducting the experiment

To begin the experimental study, the 11-liter pots that held the plants were filled with Luvissolo Crômico soil with a loamy loam texture collected in the region. The pots were randomized and placed on top of bricks in a greenhouse covered with white agricultural plastic to reduce the effects of rainwater on the treatments evaluated.

It is important to note that the soil analysis was carried out before the start of the experiment and showed the following results in cmolc/dm³: Na (0.034), Ca (9.8), Mg (5.8) K (0.04), H + Al (2.6), Al (0.0), and S (15.6); in %: V (86), m (0), n (0), Corg. (1.7), and M.O. (2.9); the pH in water was 6.4, and the P concentration was 5.4 mg/dm³. Based on the soil analysis data, the soil was fertilized with 20 kg of N ha^− 1 from the ammonium sulfate source, 200 kg of P₂O₅ ha^− 1 from the simple superphosphate source and 100 kg of K₂O ha^− 1 from the potassium chloride source.

The genotypes were then sown directly into the pots, which were already labeled and previously fertilized, with enough seeds to ensure germination. Irrigation during germination, emergence and initial development of the seedlings was carried out with pure water. Fifteen days after sowing, thinning was carried out, and the most vigorous plants remained in the pots.

For irrigation, the salt levels were produced using sodium chloride (NaCl) and calcium chloride (CaCl₂) and adjusted in the water using a conductivity meter to check the electrical conductivity corresponding to the treatments evaluated. The quantity of salts used in each preparation (for dissolution in 80 L of water, carried out every two days) can be seen below: 2.5 dS m^− 1 (11.80 g of NaCl and 2.89 g of CaCl₂); 5.0 dS m^− 1 (20.54 g of NaCl and 8.74 g of CaCl₂); 7.5 dS m^− 1 (27.80 g of NaCl and 16.10 g of CaCl₂); and 10.0 dS m^− 1 (34.00 g of NaCl and 24.00 g of CaCl₂). It should be noted that the values used in the formulation were obtained from the RAS (sodium adsorption ratio).

Irrigation with saline water began after thinning and was carried out manually on a daily basis according to the crop's observed need by installing tensiometers in the pots at random so that the reading was always around the field capacity value (a reading of approximately 10 kPa was used).

The genotypes were grown until their productive period, with periodic fertilization according to the crop's needs. The top dressing was applied 30 days after sowing with 35 kg of N ha^− 1 ammonium sulfate and 24 kg of K₂O ha^− 1 potassium chloride (Trani et al. 2013). During the course of the experiment, aphids, mealybugs, small caterpillars and leaf miners appeared, which were alternatively controlled with the periodic application of tobacco syrup and detergent, as well as mechanical control.

The fruit produced was harvested daily after the first fruit appeared, with the operation continuing until the thirtieth day, with the plants being removed from the pots after the last harvest. As precocity is an individual characteristic of each genotype, the time it takes for the fruit to appear is different, so the removal period for some genotypes is different since the 30-day harvest period was respected for all materials. During harvest, a size standard of 10 to 14 centimeters was adopted for the removal of commercial fruit.

Measured variables

After the experimental period, according to Oliveira Silva et al. (2023), the plants were carefully removed from the pots, and some agronomic characteristics were assessed, such as the number of leaves per plant (NF), number of fruits per plant (NFP), fruit length (CF), fruit diameter (DF), average fruit mass (MMFR), plant height (AP), stem diameter (DC), root system length (CR), aerial part green matter (MVPA), dry matter of the aerial part (MSPA), green matter of the roots (MVR), green matter of the fruit (MVFR), dry matter of the fruit (MSFR) and productivity (PROD), which were determined through the weight of the fruit in 1 ha, using a spacing of 0.8 × 0.4 m. To measure the dry matter variables, an oven was used for 72 hours at 65°C.

Statistical analysis

Analysis of variance was carried out on the data obtained for all the variables; regressions were carried out for the quantitative factor (salinity levels). For the qualitative factor (genotype), grouping tests (Scott‒Knott) were carried out at 5% probability. The computer application Sisvar (Ferreira 2013) was used to carry out the statistical analyses.

Tables 1, 2 and 3 summarize the analyses of variance and their respective coefficients of variation for the performance of commercial okra genotypes at different salinity levels. For the source of variation in the genotypes, all the variables showed a significant difference according to the F test at 1% probability, which indicates variability between the genotypes. Similar results were found by Oliveira Silva et al. (2023), who, in their assessment of five commercial okra genotypes subjected to different levels of salinity, also found significant differences in all the variables, considering the source of variation in the genotypes.

Table 1

Summary of the analyses of variance (mean square values) and coefficients of variation for the variables measured in the performance of okra genotypes at different salinity levels.
Source of variation	QM
Source of variation	GL	SD	PH	NL	RL	GMAP
Genotypes (G)	11	14.93**	557.90**	100.13**	191.72**	19133.65**
Salinity levels (SL)	4	9.52**	248.48**	37.07**	49.31**	3808.66**
G x SL	44	1.00^ns	24.69^ns	2.80^ns	5.15^ns	262.92^ns
Blocks	2	1.70	263.68	13.86	7.39	2052.71
Residue	118	1.01	47.24	3.63	8.69	316.56
CV (%)		8.78	13.49	22.21	20.92	20.64
Note: ns: Not significant at 5% probability according to the F test; **: significant at 1% probability according to the F test; GL: degrees of freedom; CV: coefficient of variation; SD: stem diameter; PH: plant height; NL: number of leaves; RL: root length; GMAP: green mass of the aerial part.

For the salinity factor, only the fruit diameter and length variables showed no significant difference according to the F test at 5% probability. Furthermore, for all the variables analyzed, there was no significant interaction between the factors under study, indicating that there was no dependence between the factors analyzed (genotype × salinity level), so there was no need to separate them since the genotypes showed the same behavior when subjected to different salinity levels.

The lack of interaction between the factors in this study is different from that found by Oliveira Silva et al. (2023), who observed a relationship of dependence between commercial genotypes and salinity levels only for fruit dry mass. These different results highlight the need for more studies on this subject since changing the genotype and the level of salinity can produce different results. According to Borém et al. (2021), each genotype can respond differently to environmental factors.

Table 2

Summary of the analyses of variance (mean square values) and coefficients of variation for the variables measured in the performance of okra genotypes at different salinity levels.
Source of variation	QM
Source of variation	GL	GRM	APDM	NFP	GFM	DFM
Genotypes (G)	11	48.49**	396.01**	76.56**	6377.95**	109.86**
Salinity levels (SL)	4	52.75**	258.75**	48.65**	8135.62**	125.12**
G x SL	44	3.79^ns	10.01^ns	2.54^ns	239.38^ns	4.04^ns
Blocks	2	20.25	228.50	18.40	2639.62	54.39
Residue	118	4.10	13.77	3.91	527.25	9.04
CV (%)		22.99	27.34	24.10	36.07	37.24
Note: ns: Not significant at 5% probability according to the F test; **: significant at 1% probability according to the F test; GL: degrees of freedom; CV: coefficient of variation; GRM: green root mass; APDM: aerial part dry mass; NFP: number of fruits per plant; GFM: green fruit mass; DFM: dry fruit mass.

For the coefficients of variation, in a general and comprehensive way for agricultural experiments, according to Gomes (1985) and Ferreira (2018), some variables obtained values above 20%; however, this occurrence is understandable and considered normal since some genotypes presented fruit and others did not, causing greater variance. The explanation for some genotypes being more productive in a short time than others is precocity, which is inherent to the genotype (Costa et al 2018). These classifications proposed for the coefficients of variation are still wide-ranging because they do not take into account the particularities of the species studied and because they do not distinguish between the nature of the variables observed (Gurgel 2013). This is because there is no precise coefficient that serves as an evaluation criterion in okra experiments.

Table 3

Summary of the analyses of variance (mean square values) and coefficients of variation for the variables measured in the performance of okra genotypes at different salinity levels.
Source of variation	QM
Source of variation	GL	AFM	FD	FL	PROD
Genotypes (G)	11	54.59**	69.79**	11.46**	6228471.45**
Salinity levels (SL)	4	9.77**	5.52^ns	4.20^ns	7944941.96**
G x SL	44	3.28^ns	4.67^ns	2.30^ns	233775.39^ns
Blocks	2	3.22	0.24	3.09	2577760.96
Residue	118	3.16	4.23	2.14	514892.82
CV (%)		16.12	14.08	13.82	20.46
Note: ns: Not significant at 5% probability according to the F test; **: significant at 1% probability according to the F test; GL: degrees of freedom; CV: coefficient of variation; AFM: average fruit mass; FD: fruit diameter; FL: fruit length; PROD: productivity.

Tables 4 and 5 show the averages and the Scott Knott grouping test at 5% probability for the performance of commercial okra genotypes at different salinity levels. For the SD variable, there were significant differences at 5% probability according to the Scott–Knott grouping test, indicating the formation of four groups. The group with the best performance included the IAC Midori and Santa Cruz varieties, with an average of 13.35 mm, while the group with the worst performance was composed of the Apuim, Canindé, Guará and Quiabel hybrids, with a group average of 10.65 mm.

Table 4

Means and the Scott Knott grouping test at 5% probability for the performance of commercial okra genotypes at different salinity levels.
Genotypes	SD (mm)	PH (cm)	NL (un.)	RL (cm)	GMAP (g)	GRM (g)	APDM (g)
Apuim	10.71^d	43.30^d	6.60^d	14.92^a	52.74^f	7.38^d	8.40^e
Canindé	10.82^d	46.16^d	7.80^c	5.88^d	56.36^f	5.92^d	9.25^e
Carcará	11.65^c	57.26^b	6.66^d	15.94^a	92.93^c	10.65^b	14.62^c
Cariri	12.13^b	49.57^c	8.86^c	15.26^a	92.87^c	9.92^b	15.12^c
C. Americano 80	11.14^c	52.12^c	7.33^d	17.78^a	81.91^d	9.85^b	13.91^c
Estrela	11.15^c	48.38^d	7.20^d	16.63^a	73.36^e	8.84^c	11.41^d
Guará	10.90^d	66.50^a	8.26^c	11.16^b	69.79^e	6.76^d	11.55^d
IAC Midori	13.35^a	53.31^c	12.73^b	15.34^a	140.01^b	12.00^a	20.45^b
Quiabel	10.18^d	48.44^d	7.73^c	16.88^a	55.64^f	7.11^d	9.11^e
Santa Cruz-47	13.36^a	51.10^c	14.92^a	14.45^a	171.37^a	10.25^b	25.69^a
Speedy	11.29^c	46.76^d	8.00^c	8.96^c	67.18^e	8.46^c	9.29^e
Valença	11.14^c	48.33^d	6.86^d	15.94^a	80.33^d	8.62^c	14.02^c
Note: Averages followed by the same letters in the same column do not differ according to the Scott–Knott test at 5% probability. C: Clemson; SD: Stem diameter; PH: Plant height; NL: Number of leaves; RL: Root length; GMAP: Green mass of the aerial part; GRM: Green root mass; APDM: Aerial part dry mass.

Four groups were also formed for the pH variable, where the Guará hybrid differed significantly from the other genetic materials, showing the best performance (66.50 cm). The genotypes belonging to the group with the worst performance were Apuim, Canindé, Estrela, and Quiabel. Speedy and Valença did not differ significantly from each other.

With regard to NL, four groups were formed, where the Santa Cruz-47 variety had the highest number of leaves (14.92 pcs), differing significantly from the other groups, while the Apuim, Carcará, C. Americano 80, Estrela and Valença genotypes made up the group with the lowest performance for this variable, with an average of 6.93 pcs. For the RL, four groups were formed, with the Canindé hybrid composing the worst performing group (5.88 cm), while two intermediate groups were also formed, each composed of the Speedy hybrid and the Guará hybrid, with averages of 8.96 cm and 11.16 cm, respectively. The other genotypes made up the group with the best performance and did not differ significantly from each other.

For the GMAP variable, six groups were formed, where the Santa Cruz variety was the group with the best performance (171.37 g), while the Apuim, Canindé and Quiabel genotypes made up the group with the lowest green mass of the aerial part, with an average of 54.91 g, which did not differ significantly from each other. For the GRM variable, groups were formed in which the IAC Midori genotype made up the best performing group, with an average of 12 g, while the Apuim, Canindé, Guará and Quiabel genotypes made up the lowest performing group, which did not differ significantly from each other and had an average of 6.79 g. With regard to APDM, five groups were formed, where the Santa Cruz variety had the highest dry mass of the aerial part, with an average of 25.69 g, while the Apuim, Canindé, Quiabel and Speedy genotypes had the lowest performance for this variable, not differing significantly from each other, with an average of 9.01 g.

Four groups were formed for the NFP variable, where the Apuim, Canindé, Estrela, Quiabel, Speedy and Valença genotypes had the best performance, not differing significantly from each other, with an average of 7.70 units. The lowest number of fruits per plant (1.53 units) was produced by the Santa Cruz variety, which made up the worst performing group.

Table 5

Means and the Scott Knott grouping test at 5% probability for the performance of commercial okra genotypes at different salinity levels.
Genotypes	NFP (un.)	GFM (g)	DFM (g)	AFM (g)	FD (mm)	FL (cm)	PROD (kg ha^− 1)
Apuim	6.86^a	65.79^b	8.19^b	9.61^d	13.67^c	10.87^a	2.055,92^a
Canindé	7.46^a	85.86^a	10.68^a	11.51^c	15.95^b	10.64^a	2.683,36^a
Carcará	5.33^b	61.53^b	7.77^b	11.55^c	15.30^b	10.72^a	1.922,50^b
Cariri	5.20^b	60.76^c	8.17^b	11.70^c	15.10^b	10.63^a	1.899,00^b
C. Americano 80	4.00^c	61.45^b	7.18^b	15.40^a	19.60^a	10.38^a	1.920,33^b
Estrela	9.06^a	89.78^a	11.37^a	9.87^d	13.51^c	10.97^a	2.805,77^a
Guará	3.53^c	45.36^c	11.16^b	13.06^b	15.99^b	11.14^a	1.417,58^c
IAC Midori	3.80^c	43.43^c	5.47^c	11.28^c	14.58^c	10.87^a	1.357,42^c
Quiabel	7.13^a	72.97^b	9.29^b	10.05^d	13.66^c	10.95^a	2.280,48^a
Santa Cruz	1.53^d	17.65^d	2.22^d	8.07^e	10.57^d	7.90^b	551.63^d
Speedy	8.06^a	76.69^a	10.16^a	9.39^d	13.08^c	11.16^a	2.396,79^a
Valença	7.66^a	82.68^a	10.94^a	10.75^c	14.25^c	10.82^a	2.583,88^a
Note: Averages followed by the same letters in the same column do not differ according to the Scott–Knott test at 5% probability. C: Clemson; NFP: Number of fruits per plant; GFM: Green fruit mass; DFM: Dry fruit mass; AFM: Average fruit mass; FD: Fruit diameter; FL: Fruit length; PROD: Productivity.

With regard to the GFM variable, four groups were established, where the Canindé, Estrela, Speedy and Valença genotypes had the highest green fruit mass values, which did not differ significantly from each other, with an average of 83.75 g; the Santa Cruz genotype had the worst performance for the variable (17.65 g). For DFM, the behavior was similar to that of the previous variable, with the formation of four groups in which the Canindé, Estrela, Speedy and Valença genotypes obtained the best results, with a group average of 10.78 g, which was not significantly different from each other; the Santa Cruz genotype showed the worst performance for the variable (2.22 g).

With regard to MFA, five groups were formed, where the group with the highest average fruit mass was formed by the C. americana 80 genotype. For Americano 80, with an average of 15.40 g, the Santa Cruz genotype was part of the group with the lowest value for the variable (8.07 g). For the DF variable, four groups were formed, where the highest value obtained for this trait was from the C. americana 80 genotype, with an average of 19.07 g. The average length of the Americano 80 genotype was 19.60 mm. With regard to FL, two groups were formed, where the Santa Cruz genotype was the group with the lowest value for the variable (7.90 cm) and the other genotypes had higher values for fruit length, which did not differ significantly from each other, with an average of 10.83 cm. This proximity of averages was because the size criterion for harvesting the fruit at the commercial stage was preestablished, which, for most varieties, is 10 to 14 centimeters (Lana et al. 2020).

For the yield obtained 30 days after harvest for each genetic material, there were significant differences at 5% probability according to the Scott–Knott grouping test, indicating the formation of four groups. The best performing group included the Apuim, Canindé, Estrela, Quiabel, Speedy and Valença genotypes, with a group average of 2.467,69 kg ha^− 1. The group with the worst performance was composed of the Santa Cruz-47 variety, which had an average yield of 551.626 kg ha^− 1.

In relation to the salinity levels, as previously mentioned, all the variables showed a significant difference according to the F test at 1% probability, with the exception of fruit diameter and fruit length. Figure 1 shows the quadratic regression equation corresponding to the SD response pattern as a function of salinity for the genotypes studied; according to the results obtained, it can be inferred that the stem diameter decreased as the salinity of the irrigation water increased, with the highest value (12.36 mm) occurring in the plots where water without salinity was applied and the lowest value (11.05 mm) occurring at a salinity level of 10 dS m^− 1.

In the work by Unlûkara et al. (2008), the diameter of the okra stem decreased significantly from an electrical conductivity of 5 dS m^− 1, as did that in the work by Nascimento et al. (2017), where it was observed that the treatment with the highest level of irrigation water salinity (5 dS m^− 1) had a significantly lower diameter than the other treatments throughout the experimental period for the Santa Cruz-47 variety.

For the PH source, the behavior of the genotypes was similar to that of the variables discussed above, where there is an indication of a decreasing quadratic regression with salinity levels, i.e., as salinity levels increase, plant height decreases. In their study, Ferreira et al. (2012) observed greater okra plant height at low salinity levels, which is related to the nondeleterious effect that low-salinity water provides. Nascimento et al. (2017) also showed that okra plants irrigated with water (0.26 dS m^− 1) grew significantly more than those in other treatments with increasing salinity.

For the NL variable, there is a negative linear regression equation with salinity levels, where NL decreases with increasing salinity, with a reduction of approximately 25% between the minimum and maximum levels of electrical conductivity of the irrigation water. Kamaluldeen et al. (2014) showed that in okra, the number of leaves was one of the most affected variables when the crop was grown in both soil and saline water. An analysis of the variables related to the roots revealed that LR was the only characteristic of the varieties that increased with increasing salinity.

The opposite result was found in the work of Sousa et al. (2020), who studied the morphophysiological characteristics of okra plants subjected to saline stress in soil with organic fertilizer, where root growth decreased linearly with increasing salinity from 1.0 to 5.0 dS m^− 1, showing a decrease of 5.9 cm (25.76%). For GRM, there was a decrease in this variable as the salt concentration increased. In a study by Sousa (2007), a decrease in root mass from 7 g to 3 g was observed when cowpeas were subjected to irrigation levels of 0.5 dS m^− 1 and 4.5 dS m^− 1, respectively.

In relation to the GMAP and APDM variables, there were quadratic regression equations as a function of the salinity of the irrigation water. The results indicate a decreasing quadratic regression for both variables, i.e., as the salt levels increase, the green and dry mass of the aerial part decreases. Abid et al. (2002) reported differences in the green and dry mass of okra plants; the greatest amount of green matter (61.82 g) was detected in the control group, which did not receive saline treatment, while the lowest amount (12.99 g) was detected in the treatment group that received water at the highest salinity concentration (6 dS m^− 1). The values for the dry mass of the aerial part followed the same trend (decrease with increasing levels of salinity).

For the NFP and average fruit mass AFM variables, there was a negative linear regression, while for GFM and DFM, there was a decreasing quadratic regression, i.e., all the fruit-related variables studied decreased as the salinity levels increased. For the PROD variable, according to the results obtained, it can be inferred that productivity decreased as the salinity of the irrigation water increased, with the highest value (2,700.14 kg ha^− 1) occurring in the plots where no saline treatment was applied and the lowest value (1,535.29 kg ha^− 1) occurring at a salinity level of 10 dS m^− 1. These behaviors are similar to those found by Pereira (2016) when studying melon fruit productivity under water salinity, where the plants showed a decreasing linear response with increasing water salinity levels up to 2.4 dS m^− 1, resulting in a 25.9% reduction in the number of fruits per plant.

All the genotypes showed a reduction in the values of the characteristics studied as the salt level increased, with the exception of the root length variable. It was possible to conclude that the Carcará, Cariri and IAC Midori genotypes obtained the best performances for most of the variables studied, regardless of the salinity levels, and that the genotypes showed no tolerance from the 2.5 dS m^− 1 level onward.

Funding

The authors declare that no funds, grants, or other support was received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or nonfinancial interests to disclose.

Author Contributions

All authors contributed to the conception and planning of the study. All authors have read and approved the final manuscript.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethical Statement

The okra genotypes evaluated in this experiment were purchased from agricultural stores in the region, precisely because they are commercial cultivars. Each genotype was identified according to the commercial name of the available cultivars. All the okra genotypes belong to the same family, Abelmoschus esculentus. It is worth noting that the authors confirm that all the methods were carried out in accordance with the relevant guidelines and regulations for research with commercial cultivars. And that the experimental protocols were approved by the University Research, since this work is the result of scientific initiation approved by the CNPQ. And finally, the commercial cultivars used in the study are in accordance with local or national guidelines, without the need for additional confirmation.

ABID, M. et al. Response of Okra (Abelmoschus esculentus L.) to EC and SAR of Irrigation Water. 2002. 4(3).
AZEVEDO, P. R. L. et al. Efeito dos sais e da qualidade da água no solo e na planta. Revista de Agroecologia no Semiárido. 2017, 1(1), 1-12.
BARROS, M. P. et al. Tolerância de cultivares de pimentão (Capsicum annuum L.) à salinidade na germinação e crescimento inicial. Research, Society and Development. 2021, 10(4), 4. https://doi.org/10.33448/rsd-v10i4.13851
Borém, A. et al. Melhoramento de plantas. Oficina de Textos. 2021.
COSTA, K. D. DA S. Yield and Precocity Features of Okra Varieties in Piranhas–Alagoas State/Brazil. Journal of Experimental Agriculture International. 2020. 36–43. https://doi.org/10.9734/jeai/2020/v42i630535
ELIAS FERREIRA, L. Salinidade e seu efeito sobre a produção de grãos do quiabeiro Santa Cruz 47. Revista Verde de Agroecologia e Desenvolvimento Sustentável. 2012, 7(4), 22.
FERREIRA, D. F. Análises estatísticas por meio do Sisvar para Windows versão 4.0. Reunião anual da região brasileira da sociedade internacional de biometria. 2013, 45, 235.
FERREIRA, P.V. Estatística Experimental Aplicada às Ciências Agrárias. Viçosa: Editora UFV. 2018. 588p.
FILGUEIRA, F. A. R. Novo manual de olericultura: agrotecnologia moderna na produção e comercialização de hortaliças. 3 a edição. Viçosa: UFV. 2012. 418p.
GALATI, V. C. et al. Crescimento e acúmulo de nutrientes da cultura do quiabeiro. Semina: Ciências Agrárias. 2013, 34(1), 191–200. https://doi.org/10.5433/1679-0359.2013v34n1p191
GALATI, V. C. Crescimento e acúmulo de nutrientes em quiabeiro ‘Santa Cruz 47’. 2010. http://hdl.handle.net/11449/96892
GOMES, F. P. Curso de estatistica experimental. 1985. https://repositorio.usp.br/item/000773318
GURGEL, F. DE L., and SOARES, A. C. S. E. Coeficiente de variação como critério de avaliação em experimentos de milho. 2013. http://www.alice.cnptia.embrapa.br/handle/doc/974562
KAMALULDEEN, J. et al. A absorção e distribuição de íons revelam mecanismos de tolerância contrastantes para a salinidade do solo e da água no quiabo (Abelmoschus esculentus) e tomate (Solanum esculentum). Agricultural Water Management. 2014, 146, 95–104. https://doi.org/10.1016/j.agwat.2014.07.027
LANA, M. M. et al. Hortaliça como comprar, conservar e consumir: Quiabo. Embrapa. 2020. http://www.infoteca.cnptia.embrapa.br/handle/doc/1126016
LIANG, W. et al. Plant salt-tolerance mechanism: A review. Biochemical and Biophysical Research Communications. 2018, 495(1), 286–291. https://doi.org/10.1016/j.bbrc.2017.11.043
MODESTO, F. J. N. Crescimento, produção e consumo hídrico do quiabeiro submetido à salinidade em condições hidropônicas. 2018. http://ri.ufrb.edu.br/jspui/handle/123456789/3060
NASCIMENTO, P. Crescimento vegetativo do quiabeiro em função da salinidade da água de irrigação e da adubação nitrogenada. Colloq. Agrar. 2017, 10–15.
SALES, J. et al. Variabilidade espaço-temporal da qualidade da água em área de agricultura irrigada. Revista Brasileira de Agricultura Irrigada. 2020, 14, 4071–4085. https://doi.org/10.7127/rbai.v14n101167
SANTOS, G. et al. Análise da precipitação pluvial e temperatura do ar de Olho D’água do Casado, Delmiro Gouveia e Piranhas, Alagoas. Revista de Geociências do Nordeste. 2017, 3(1), Artigo 1. https://doi.org/10.21680/2447-3359.2017v3n1ID10845
SILVA, M. A. et al. Tolerância de genótipos de quiabeiro à salinidade. DELOS: Desarrollo Local Sostenible. 2023, 16(48), 3195–3211. https://doi.org/10.55905/rdelosv16.n48-015
SOUSA, G. G. Morphophysiological characteristics of okra plants submitted to saline stress in soil with organic fertilizer. Comunicata Scientiae, 2020, 11, e3241–e3241. https://doi.org/10.14295/cs.v11i0.3241
TRANI, P. E. et al. Adubação Orgânica de Hortaliças e Frutíferas. Campinas: Instituto Agronômico de Campinas. 2013.
ÜNLÜKARA, A. et al. Growth and Evapotranspiration of Okra (Abelmoschus Esculentus L.) as Influenced by Salinity of Irrigation Water. Journal of Irrigation and Drainage Engineering-asce - J IRRIG DRAIN ENG-ASCE. 2008, 134. https://doi.org/10.1061/(ASCE)0733-9437(2008)134:2(160)

No competing interests reported.

Download PDF

Reviewers invited by journal
19 Aug, 2024
Editor assigned by journal
06 Aug, 2024
Submission checks completed at journal
22 Jul, 2024
First submitted to journal
03 Jul, 2024

You are reading this latest preprint version

Performance of commercial okra genotypes at salinity levels

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Location and period of the experiment

Treatments and statistical design

Conducting the experiment

Measured variables

Statistical analysis

Results and discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1