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Morphological and physiochemical changes of jojoba under water pollution stress condition

M. S. Aboryia

M. S. Aboryia

Horticulture Department (Pomology), Faculty of Agriculture, Damietta UniversityNew Damietta, Egypt
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Aboryia, M. S.
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Mohamed Saleh M. Ali

Mohamed Saleh M. Ali

Horticulture Department (Pomology), Faculty of Agriculture, Suez Canal UniversityIsmailia, Egypt
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Ali, Mohamed Saleh M.
, 
Ahmed F. Elshiekh

Ahmed F. Elshiekh

Horticulture Department (Pomology), Faculty of Agriculture, Suez Canal UniversityIsmailia, Egypt
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Elshiekh, Ahmed F.
, 
Basmah M. Alharbi

Basmah M. Alharbi

Department of Biology, Faculty of Science, University of Tabuk, Biodiversity Genomics Unit, Faculty of Science, University of TabukTabuk,
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Alharbi, Basmah M.
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Ibrahim Eid Elesawi

Ibrahim Eid Elesawi

Agricultural Biochemistry Department, Faculty of Agriculture, Zagazig UniversityZagazig, Egypt
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Elesawi, Ibrahim Eid
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Ahmed M. Fikryi

Ahmed M. Fikryi

Horticulture Department (Pomology), Faculty of Agriculture, Zagazig UniversityZagazig, Egypt
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Fikryi, Ahmed M.
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Ahmed A. Helaly

Ahmed A. Helaly

Vegetable and Floriculture Department, Faculty of Agriculture, Mansoura UniversityMansoura, Egypt
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Helaly, Ahmed A.
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Fatma R. Ibrahim

Fatma R. Ibrahim

Vegetable and Floriculture Department, Faculty of Agriculture, Mansoura UniversityMansoura, Egypt
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Ibrahim, Fatma R.
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Eman A. swedan

Eman A. swedan

Horticulture Department (Floriculture), Faculty of Agriculture, Damanhour UniversityDamanhour, Egypt
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swedan, Eman A.
, 
Hany G. Abd El-Gawad

Hany G. Abd El-Gawad

Department of Horticulture, Faculty of Agriculture, Ain Shams UniversityCairo, Egypt
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Abd El-Gawad, Hany G.
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Samy F. Mahmoud

Samy F. Mahmoud

Department of Biotechnology, College of Science, Taif UniversityTaif city, Saudi Arabia
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Mahmoud, Samy F.
 und 
El-Sayed A. EL-Boraie

El-Sayed A. EL-Boraie

Horticulture Department (Floriculture), Faculty of Agriculture, Damietta UniversityNew Damietta, Egypt
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EL-Boraie, El-Sayed A.
  
01. Aug. 2024
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Artikel-Kategorie: ORIGINAL ARTICLE
Online veröffentlicht: 01. Aug. 2024
Seitenbereich: 259 - 281
Eingereicht: 29. Feb. 2024
Akzeptiert: 19. Juni 2024
DOI: https://doi.org/10.2478/fhort-2024-0016
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© 2024 M. S. Aboryia et al., published by Sciendo
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Figure 1.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on the NSI% (A), JHI% (B) and JSDI% (C). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. JHI%, jojoba height increase percentage; JSDI, jojoba stem diameter increase percentage; NSI%, number of shoots increase percentage; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on the NSI% (A), JHI% (B) and JSDI% (C). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. JHI%, jojoba height increase percentage; JSDI, jojoba stem diameter increase percentage; NSI%, number of shoots increase percentage; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.

Figure 2.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on the NLI% (A), LT (mm) (B), LA (cm2) (C) and VQ (D). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. LA, leaf area; LT, leaf thickness; NLI%, Number of leaves increase percentage; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water; VQ, Visual quality.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on the NLI% (A), LT (mm) (B), LA (cm2) (C) and VQ (D). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. LA, leaf area; LT, leaf thickness; NLI%, Number of leaves increase percentage; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water; VQ, Visual quality.

Figure 3.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on LFW (g) (A), LDW (g) (B) and SCC (mg · g−1 DW) (C). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. LDW, leaf dry weight; LFW, leaf fresh weight; RADW, raw agricultural drainage water; SCC, soluble carbohydrate content; TSDW, treated sewage drainage water.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on LFW (g) (A), LDW (g) (B) and SCC (mg · g−1 DW) (C). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. LDW, leaf dry weight; LFW, leaf fresh weight; RADW, raw agricultural drainage water; SCC, soluble carbohydrate content; TSDW, treated sewage drainage water.

Figure 4.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on HM concentrations as Ni ppm (A), Al ppm (B), Cr ppm (C) and Cd ppm (D). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. HM, Heavy metal; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on HM concentrations as Ni ppm (A), Al ppm (B), Cr ppm (C) and Cd ppm (D). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. HM, Heavy metal; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.

Figure 5.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on nitrogen content (mg · 100 g−1 DW) (A), phosphorus content (mg · 100 g−1 DW) (B) and potassium content (mg · 100 g−1 DW) (C). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100%, on nitrogen content (mg · 100 g−1 DW) (A), phosphorus content (mg · 100 g−1 DW) (B) and potassium content (mg · 100 g−1 DW) (C). The results are the average of two subsequent seasons (2022 and 2023), with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.

Figure 6.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100% on: chlorophyll a (μg · cm−2) (A), chlorophyll b (μg · cm−2) (B), total chlorophyll (μg · cm−2) (C) and carotenoids (μg · cm−2) (D). The results are the average of two subsequent seasons (2022 and 2023) with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100% on: chlorophyll a (μg · cm−2) (A), chlorophyll b (μg · cm−2) (B), total chlorophyll (μg · cm−2) (C) and carotenoids (μg · cm−2) (D). The results are the average of two subsequent seasons (2022 and 2023) with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.

Figure 7.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100% on proline (mg · g−1 FW) (A), IL (%) (B) and MDA (μM · g−1 FW) (C). The results are the average of two subsequent seasons (2022 and 2023) with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. IL%, ion leakage; MDA, malondialdehyde; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100% on proline (mg · g−1 FW) (A), IL (%) (B) and MDA (μM · g−1 FW) (C). The results are the average of two subsequent seasons (2022 and 2023) with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. IL%, ion leakage; MDA, malondialdehyde; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.

Figure 8.

The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100% on O2•− (mM · min−1 · g−1 FW) (A), H2O2 (mM · min−1 · g−1 FW) (B) and DPPH% (C). The results are the average of two subsequent seasons (2022 and 2023) with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. DPPH, 2,2-diphenyl-1-picrylhydrazyl; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.
The effect of different concentrations of RADW and TSDW at four levels, 25%, 50%, 75% and 100% on O2•− (mM · min−1 · g−1 FW) (A), H2O2 (mM · min−1 · g−1 FW) (B) and DPPH% (C). The results are the average of two subsequent seasons (2022 and 2023) with n = 3 for each season. According to Duncan’s multiple range test, each parameter’s mean values and standard error (±SE) are significantly different when each is followed by a different alphabetical letter at p ≤ 0.05. DPPH, 2,2-diphenyl-1-picrylhydrazyl; RADW, raw agricultural drainage water; TSDW, treated sewage drainage water.

Figure 9.

Person’s correlation matrix among the resistant-related parameters on jojoba. Values express the average values of the wastewater treatments on jojoba plant. The correlations are calculated by the Row-wise method. DPPH, 2,2-diphenyl-1-picrylhydrazyl; IL%, ion leakage; JHI%, jojoba height increase percentage; JSDI, jojoba stem diameter increase percentage; LA, leaf area; LDW, leaf dry weight; LT, leaf thickness; MDA, malondialdehyde; NLI, Number of leaves increase percentage; NSI%, number of shoots increase percentage; SCC, soluble carbohydrate content; VQ, Visual quality.
Person’s correlation matrix among the resistant-related parameters on jojoba. Values express the average values of the wastewater treatments on jojoba plant. The correlations are calculated by the Row-wise method. DPPH, 2,2-diphenyl-1-picrylhydrazyl; IL%, ion leakage; JHI%, jojoba height increase percentage; JSDI, jojoba stem diameter increase percentage; LA, leaf area; LDW, leaf dry weight; LT, leaf thickness; MDA, malondialdehyde; NLI, Number of leaves increase percentage; NSI%, number of shoots increase percentage; SCC, soluble carbohydrate content; VQ, Visual quality.

Figure 10.

First and second PCA scores plot of different waste water treatments (A). First. and second PCA scores plot of the correlation in analysed parameters (B). DPPH, 2,2-diphenyl-1-picrylhydrazyl; IL%, ion leakage; JHI%, jojoba height increase percentage; JSDI, jojoba stem diameter increase percentage; LA, leaf area; LDW, leaf dry weight; LT, leaf thickness; MDA, malondialdehyde; NLI, Number of leaves increase percentage; NSI%, number of shoots increase percentage; PCA, principal component analysis; RADW, raw agricultural drainage water; SCC, soluble carbohydrate content;TSDW, treated sewage drainage water; VQ, Visual quality.
First and second PCA scores plot of different waste water treatments (A). First. and second PCA scores plot of the correlation in analysed parameters (B). DPPH, 2,2-diphenyl-1-picrylhydrazyl; IL%, ion leakage; JHI%, jojoba height increase percentage; JSDI, jojoba stem diameter increase percentage; LA, leaf area; LDW, leaf dry weight; LT, leaf thickness; MDA, malondialdehyde; NLI, Number of leaves increase percentage; NSI%, number of shoots increase percentage; PCA, principal component analysis; RADW, raw agricultural drainage water; SCC, soluble carbohydrate content;TSDW, treated sewage drainage water; VQ, Visual quality.
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