Acta agriculturae Slovenica, 121/2, 1–12, Ljubljana 2025
doi:10.14720/aas.2025.121.2.22269 Original research article / izvirni znanstveni članek
Water quality effects on germination of okra seed (Abelmoschus esculen-
tus L.)
Arsalan Azeez MARIF 1, 2
Received March 26, 2025; accepted May 29, 2025
Delo je prispelo 26. marec 2025, sprejeto 29. maj 2025
1 Garden Design Department, Bakrajo Technical Institute BTI, Sulaimani Polytechnic University SPU, Sulaimani, Kurdistan Region, Iraq 
2 Correspondence Author: arsalan.marif@spu.edu.iq
Water quality effects on germination of okra seed (Abel-
moschus esculentus L.)
Abstract: This study, conducted at Bakrajo Technical In-
stitute in 2023, assessed the water quality of 24 resources using 
the Irrigation Water Quality Index (IWQI). Results revealed 
two categories: “Excellent” (19 resources, IWQI 91.4-96.5) and 
“Good” (5 resources, IWQI 70-90). Water in the “Excellent” 
category was highly suitable for irrigation, while the “Good” 
category was of lower quality but still acceptable. Electrical 
conductivity (EC) was identified as a key factor influencing 
the IWQI, with higher EC correlating with lower water qual-
ity. Principal Component Analysis (PCA) and Agglomerative 
Hierarchical Clustering (AHC) were used to classify resources 
based on cation, anion, and heavy metal content. A negative 
correlation between EC and IWQI emphasized the importance 
of monitoring EC for irrigation purposes. The study also found 
weak, non-significant correlations between pH, EC, and germi-
nation ratio, but noted that higher IWQI values and lower EC 
levels generally promoted better seed germination. The find-
ings highlight the value of advanced models in water quality 
classification, offering essential insights for agricultural water 
management.
Key words: water quality, germination ratio, okra, irriga-
tion water, pH, EC
Kakovost vode vpliva na kalitev semen jedilnega osleza (Abel-
moschus esculentus L.)
Izvleček: V raziskavi, izvedeni na Bakrajo Technical In-
stitute, v letu 2023 je bila ocenjena kakovost 24 vodnih virov 
z indeksom kakovosti vode (IWQI). Rezultati so odkrili dve 
kategoriji, “odlična” (19 virov, IWQI 91.4-96.5) in “dobra” (5 
virov, IWQI 70-90). Voda iz virov “odlična” je bila zelo prim-
erna za namakanje, med tem, ko je bila voda iz kategorije 
“dobra” slabše kakovosti vendar še sprejemljiva. Električna 
prevodnost (EC) je bila prepoznana kot ključni dejavnik, ki 
vpliva na indeks kakovosti vode (IWQI), pri čemer so večje 
vrednosti EC korelirale s slabšo kakovostjo vode. Analiza 
glavnih komponent (PCA) in hierarhično aglomerativno 
grozdenje (AHC) sta bila uporabljena za klasifikacijo vodnih 
virov glede na vsebnost kationov, anionov in težkih kovin. 
Ugotovljena je bila negativna korelacija med EC in IWQI, kar 
poudarja pomen monitoringa EC za namene namakanja. Ra-
ziskava je tudi odkrila šibko, neznačilno povezavo med pH, 
EC in deležem kalitve, pri čemer so večje vrednosti IWQI in 
manjše vrednosti EC navadno pospeševale boljšo kalitev. Ta 
odkritja pojasnjujejo vrednost naprednejših modelov pri kla-
sifikaciji kakovosti vod in ponujajo bistven vpogled v kmeti-
jsko upravljanje z vodo.
Ključne besede: kakovost vode, kalitev, jedilni oslez, 
voda za namakanje, pH, EC
Acta agriculturae Slovenica, 121/2 – 20252
A. A. MARIF
1 INTRODUCTION
Water quality plays a crucial role in determining 
both agricultural productivity and the overall health of 
ecosystems. It directly impacts plant growth, germina-
tion, and the subsequent development of crops, influenc-
ing both yield and quality (Yuan et al., 2024: Wakchaure 
et al., 2023). The importance of water in agriculture is un-
derscored by its effect on various physiological processes 
within plants, such as nutrient uptake, photosynthesis, 
and transpiration. For crops like okra (Abelmoschus escu-
lentus L.), which is widely cultivated in tropical regions, 
water quality can significantly alter germination rates 
and early vegetative growth, ultimately affecting overall 
crop performance. Okra is a staple vegetable in many 
countries, with a global cultivation area of approximately 
2.5 million hectares, yielding 10.5 million tons annually 
(Ibrahim, 2024): Food and Agriculture Organization of 
the United Nations, 2018).
Seed priming, a pre-sowing treatment of seeds us-
ing water or other solutions, is an important technique to 
enhance germination and improve early seedling growth. 
When combined with high-quality irrigation water, seed 
priming has been shown to accelerate seedling emer-
gence, leading to more robust and healthy plants. Water 
quality, defined by parameters such as salinity, pH, dis-
solved oxygen, and presence of contaminants, directly 
affects seed priming outcomes. Numerous studies have 
demonstrated that water with high salinity or other un-
desirable characteristics can hinder seedling emergence 
and reduce crop yields. Okra is not only valuable for its 
role in agriculture but also an important source of nutri-
tion, providing essential vitamins, minerals, and dietary 
fiber. Given the crop’s significance, understanding how 
different water qualities impact okra germination and 
seedling development is vital for optimizing agricultural 
practices, particularly in regions where water resources 
may be limited or of varying quality. Previous studies 
have highlighted the influence of water quality on seed 
germination and vegetative growth across different re-
gions (Rima, 2021).
The aim of the present research is to assess the water 
quality from 25 different water resources, classifying the 
irrigation water based on the Sulaimani Irrigation Wa-
ter Quality Index (SIWI) proposed by Marif and Esmael 
(2023), as well as the classification system by Todd (1966). 
Additionally, this study utilizes advanced statistical tech-
niques such as Principal Component Analysis (PCA) and 
Cluster Analysis to classify the water resources further 
and investigate their impact on okra seed germination. 
This research will provide a comprehensive understand-
ing of how various water qualities influence the early 
No. Location GPS Coordinate Altitude
1 Kani Ababaile 35.18235 45.98377 915
2 Well Ababaile 35.18235 45.98377 910
3 Kani Faqe 
Byara S1
35.2250909 46.11599645 1087
4 Mergapan 35.78892 45.26581 1226.85
5 Peryadi _chm-
chamal
35.51728 44.86407 697.58
6 Bakrajo 35.550052 45.358326 743.36
7 Kani panka S1 35.3816374 45.7082884 549
8 Kani panka S2 35.3816374 45.7082884 549
9 Sharbazher S1 35.720167 45.502614 827.28
10 Sharbazher S2 35.695679 45.528932 846.55
11 Krbchna 1 35.283103 45.276888 989
12 Krbchna 2 35.27582 45.26 786 933
13 Khwrmal S1 35.2971744 46.03963419 567
14 Bnawela 35.3647.1 453351.2 1130
15 Sitak 35.593677 45.517543 1126
16 Qarachatan 354410.3 450842.2 807
17 Khwrmal S2 35.30378 46.03852 567
18 Byara S2 35.23021 46.1212 1117
19 Awesar 35.21647 46.18916 1671
20 Tapatolaka 35.29402 45.95852 508
21 Sulaimani Rain 
water 
35.557215 45.47464 1002
22 Kani sard 35.3932.4 453229 896
23 Bakrajo Tap 
water
35.550052 45.358326 743.36
24 Shene 36.1327 450425 570
Tabel 1: Study area description and GPS Coordination’s
Figure 1: Study Area Map
Acta agriculturae Slovenica, 121/2 – 2025 3
Water quality effects on germination of okra seed (Abelmoschus esculentus L.)
stages of okra cultivation and offer insights into improv-
ing irrigation practices for better crop performance.
2 MATERIALS AND METHODS
2.1 STUDY AREA
The research was conducted at the Bakrajo Techni-
cal Institute Field, located in the Sulaimani Governorate 
of the Kurdistan Region, Iraq. This field site is situated 
in Sulaimani City, which lies at an elevation of 888 me-
ters above sea level. The geographical coordinates of the 
location are approximately 35.39705° N latitude and 
45.28260° E longitude, as shown in Figure 1 and detailed 
in Table 1. The specific location’s altitude and geographic 
position are important factors in determining the cli-
mate, soil conditions, and overall environmental char-
acteristics of the study area, all of which play significant 
roles in influencing the results and interpretation of the 
research. Sulaimani’s climate is influenced by its semi-
No. Location PH
EC TDS Na Ca 2+ Mg2+ Na+ K+ CO3
- HCO3
- SO4
-- Cl- NO3
-
dS m-1 mS.cm-1 % mg l-1
1 Kani Aba-
baile
7.3 0.58 368.36 8.0 1.14 1.083 0.172 0.021 0 1.967 0.219 0.014 0.013
2 Well, ababaile 7.1 0.57 362.83 9.1 1.28 1.156 0.234 0.010 0 1.901 0.474 0.199 0.018
3 Kani Faqe 
Byara S1
7.1 0.54 346.58 8.8 1.17 1.142 0.179 0.044 0 1.820 0.215 0.243 0.075
4 Mergapan 7.1 1.01 645.67 9.1 2.20 1.190 0.335 0.006 0 1.059 1.310 1.029 0.171
5 Shwan _chm-
chamal
7.2 0.83 533.85 14.5 1.48 1.124 0.397 0.044 0 1.803 0.077 1.086 0.065
6 Bakrajo 7.3 1.02 649.64 12.8 1.20 1.125 0.190 0.151 0 1.984 0.216 0.329 0.021
7 Kani panka 
S1
7.4 0.54 344.13 3.5 1.65 1.250 0.101 0.005 0 1.984 0.500 0.429 0.013
8 Kani panka 
S2
7.3 0.60 386.82 4.5 1.30 1.100 0.108 0.006 0 1.746 0.328 0.191 0.013
9 Shakhasur 7.1 0.45 288.45 6.5 1.24 1.138 0.158 0.008 0 1.910 0.191 0.286 0.022
10 Sharbazher 
tagaran
7.2 0.44 284.50 6.6 1.30 1.243 0.165 0.014 0 1.929 0.654 0.029 0.032
11 Krbchna 1 7.3 1.48 946.58 16.7 1.95 1.821 0.219 0.536 0 1.115 1.890 1.186 0.129
12 Krbchna 2 7.2 0.70 450.67 3.5 1.35 1.123 0.087 0.003 0 1.931 0.431 0.091 0.023
13 Khwrmal S1 7.1 1.99 1273.61 6.8 2.10 2.103 0.248 0.059 0 1.459 1.550 1.229 0.177
14 Bnawela 7.2 0.69 443.46 5.8 1.30 1.271 0.148 0.010 0 1.820 0.595 0.029 0.022
15 Sitak 7.1 0.74 475.24 8.9 1.12 1.283 0.220 0.015 0 1.900 0.088 0.629 0.013
16 Qarachatan 7.3 0.43 275.03 4.0 1.31 1.212 0.078 0.026 0 1.787 0.398 0.243 0.083
17 Khwrmal S2 7.2 0.50 318.08 19.0 1.81 1.159 0.626 0.070 0 1.838 0.763 0.571 0.099
18 Byara S2 7.1 0.52 329.94 4.7 1.23 1.010 0.104 0.006 0 1.484 0.475 0.243 0.025
19 Awsar 7.1 0.37 237.74 8.1 1.05 0.890 0.143 0.028 0 1.203 0.805 0.029 0.043
20 Tapatolaka 7.2 0.58 372.74 8.8 1.393 0.910 0.200 0.023 0 1.216 0.683 0.322 0.324
21 Rain water 7.1 0.34 218.88 2.2 1.1 0.920 0.030 0.014 0 1.323 0.353 0.280 0.028
22 Kani sard 7.2 0.32 204.80 6.0 0.7 0.430 0.059 0.013 0 0.436 0.446 0.186 0.048
23 Bakrajo Tap 
water
7.3 0.25 162.56 10.3 0.411 0.410 0.082 0.012 0 0.492 0.165 0.169 0.063
24 Shene 7.1 0.321 205.44 8.5 0.332 0.367 0.054 0.011 0 0.321 0.210 0.154 0.043
Table 2: Water cations and anions analysis
Acta agriculturae Slovenica, 121/2 – 20254
A. A. MARIF
arid conditions, with distinct seasonal variations that 
can impact agricultural practices and ecological studies. 
These geographic and climatic features, combined with 
the region’s unique environmental factors, provide valu-
able insights into the scope of the research, highlighting 
the importance of this field site in understanding local 
agricultural systems, climate adaptation, and ecological 
sustainability in the context of the Kurdistan Region.
2.2 WATER SAMPLING COLLECTING 
The water sampling process for evaluating the water 
quality index in this research followed these steps:
First step: Water samples were collected from 24 
different locations or wells (labeled 1 to 24) in the study 
area. These samples were analyzed and compared based 
on their electrical conductivity (EC) and pH values.
Second step: A total of 24 water samples were se-
lected for the study.
Third step: The water from the 24 selected wells or 
water resources was tested for physicochemical proper-
ties and some heavy metals. Additionally, 24 of these 
samples were used for germination experiment with okra 
seeds. The samples were classified according to various 
methods outlined in Tables 2 and 3. Each water sample 
was collected in a 1.5-liter container for physicochemical 
analysis and was also used for germinating okra seeds in 
the laboratory at Bakrajo Technical Institute, maintained 
at a temperature of 25 °C.
2.3 WATER ANALYSIS AND COMPUTING THE 
IRRIGATION WATER QUALITY INDEX 
(IWQI).
The water analysis was conducted as follows:
2.3.1 pH and electrical conductivity (EC) measure-
ment
A portable pH meter (Hanna pH H 98107) was used 
No Location
Fe Mn Cu Co Ni Zn Cd Cr
mg l-1
1 Kani Ababaile 0.011 0.036 0.02383 0.00150 0.007 0.016 0.00090 0.00156
2 Well ababaile 0.014 0.020 0.01257 0.00105 0.004 0.012 0.00326 0.00152
3 Kani Faqe Byara S1 0.019 0.033 0.02368 0.00201 0.005 0.002 0.00427 0.00300
4 Mergapan 0.031 0.046 0.02214 0.00213 0.005 0.012 0.00105 0.00190
5 Shwan _chmchamal 0.022 0.029 0.02334 0.00195 0.006 0.003 0.00166 0.00115
6 Bakrajo 0.025 0.004 0.03474 0.00184 0.007 0.006 0.00179 0.00210
7 Kani panka S1 0.057 0.014 0.01360 0.00415 0.001 0.046 0.00215 0.00331
8 Kani panka S2 0.016 0.032 0.02377 0.00120 0.004 0.001 0.00158 0.00393
9 Shakhasur 0.017 0.026 0.02286 0.00117 0.004 0.001 0.00275 0.00104
10 Sharbazher tagaran 0.014 0.026 0.01256 0.00111 0.004 0.03110 0.00569 0.00211
11 Krbchna 1 0.045 0.037 0.02890 0.00166 0.001 0.00164 0.00244 0.00664
12 Krbchna 2 0.023 0.016 0.03490 0.00156 0.005 0.00532 0.00530 0.00332
13 Khwrmal S1 0.033 0.039 0.02225 0.00174 0.003 0.00131 0.00353 0.00091
14 Bnawela 0.002 0.023 0.01124 0.00190 0.002 0.00210 0.00231 0.00100
15 Sitak 0.013 0.039 0.01560 0.00138 0.037 0.00313 0.00174 0.00125
16 Qarachatan 0.010 0.014 0.01856 0.00112 0.001 0.00210 0.00171 0.00210
17 Khwrmal S2 0.013 0.038 0.01530 0.00110 0.001 0.00132 0.00217 0.00132
18 Byara S2 0.007 0.002 0.01260 0.00200 0.002 0.00142 0.00132 0.00142
19 Awsar 0.024 0.023 0.01220 0.00416 0.004 0.00673 0.00388 0.00273
20 Tapatolaka 0.029 0.034 0.01490 0.00125 0.006 0.00598 0.00129 0.00218
21 Rain water 0.022 0.033 0.02230 0.00118 0.003 0.00031 0.00140 0.00031
22 Kani sard 0.019 0.035 0.01410 0.00108 0.003 0.00104 0.00138 0.00144
23 Bakrajo Tap water 0.003 0.039 0.01530 0.00196 0.002 0.00091 0.00126 0.00091
24 Shene 0.016 0.034 0.01250 0.00110 0.002 0.00560 0.00135 0.05600
Table 3: Water heavy metals analysis
Acta agriculturae Slovenica, 121/2 – 2025 5
Water quality effects on germination of okra seed (Abelmoschus esculentus L.)
to measure the pH of the water sample, and an EC me-
ter (HI981311) was employed to determine the electrical 
conductivity, following the standard methods described 
in the APHA (1998) guidelines for water quality analysis.
2.3.2 Cation and anion analysis
The concentrations of various cations calcium 
(Ca²+), magnesium (Mg²+), potassium (K+), and sodium 
(Na+)) and anions carbonate (CO₃²-, bicarbonate (HCO₃-
), sulfate (SO₄²-), nitrate (NO₃-), and chloride (Cl-)) were 
measured to assess the chemical composition of the wa-
ter.
2.3.3 Heavy metal concentration analysis
The concentrations of heavy metals, including 
cobalt (Co²+), copper (Cu²+), iron (Fe²+), manganese 
(Mn²+), zinc (Zn²+), chromium (Cr²+), cadmium (Cd²+), 
and nickel (Ni²+), were determined using a Shimadzu 
ICP-9820 inductively coupled plasma atomic emission 
spectrometer (ICP-AES), made in Japan, which is ca-
pable of detecting trace amounts of these metals in the 
water. The results from these analyses are summarized in 
Table 3.
2.4 GERMINATION RATIO CALCULATIONS
A germination assessment is often the most reliable 
method to evaluate whether a seed is ready for planting. 
For this particular test, local varieties of okra seeds were 
used. Germination in these seeds typically begins after 
approximately 4 days, provided that the seeds are kept 
under optimal temperature and humidity conditions. 
The germination rate can be calculated using a specific 
equation, which helps to quantify the proportion of seeds 
that successfully sprout. The data collected during this 
experiment is summarized in Table 4, where the calcu-
lated germination ratio is recorded for each observation. 
This test is essential for determining the viability of the 
seeds before planting.
………………………… (1)
2.5 IRRIGATION WATER QUALITY CALCULA-
TION 
2.5.1 Irrigation water quality calculation according to 
modified SIWi 2023 (Marif and Esmael, 2023)
The irrigation water quality calculation, according 
to the modified SIWi 2023 as modified by (Marif and Es-
mael, 2023), involves a detailed analysis of several water 
quality parameters, including salinity, pH, sodium, chlo-
ride, and other essential factors that affect crop growth 
and soil health. This method incorporates updated 
thresholds and classifications to assess the suitability of 
water for irrigation, ensuring its compatibility with spe-
cific soil types and crop needs. In this context, the quality 
of irrigation water is categorized based on these criteria, 
which are then cross-referenced with the standards listed 
in Table 5 of the modified SIWi 2023. This table provides 
a classification system that ranks water quality into dif-
ferent categories, helping to determine whether the wa-
ter is suitable for different agricultural purposes, these 
calculations and classifications are critical for managing 
Table 4: Germination ratio of the study area
No Location Germination Ratio %
1 Kani Ababaile 88.3
2 Well ababaile 90
3 Kani Faqe Byara S1 86.5
4 Mergapan 87.0
5 Shwan _chmchamal 85
6 Bakrajo 88
7 Kani panka S1 81.7
8 Kani panka S2 80
9 Shakhasur 83.0
10 Sharbazher tagaran 85.0
11 Krbchna 1 86.67
12 Krbchna 2 88.3
13 Khwrmal S1 81.0
14 Bnawela 82.0
15 Sitak 80.0
16 Qarachatan 83.3
17 Khwrmal S2 79
18 Byara S2 83.0
19 Awesar 91.7
20 Tapatolaka 88.3
21 Rain water 92
22 Kani sard 83.3
23 Bakrajo Tap water 88.3
24 Shene 87.0
Acta agriculturae Slovenica, 121/2 – 20256
A. A. MARIF
irrigation practices, preventing soil degradation, and 
maximizing crop yield.
2.6 DATA ANALYSIS
The data were analyzed using XLSTAT 
2019.2.2.59614, a comprehensive statistical software 
tool. Principal Component Analysis (PCA) was applied 
to reduce the dimensionality of the data while retaining 
its most significant features, helping to uncover patterns 
and structure in complex datasets. Agglomerative Hi-
erarchical Clustering (AHC) was also utilized to group 
similar data points based on their characteristics, allow-
ing for the identification of distinct clusters or patterns 
within the data. Additionally, correlation analysis was 
performed to examine the relationships between vari-
ous variables, helping to understand the strength and di-
rection of their associations. Together, these analytical 
methods provided a thorough exploration of the data, 
generating valuable insights for further interpretation 
and decision-making.
3 RESULTS AND DISCUSSIONS
3.1 CLASSIFICATION OF WATER RESOURCES 
ACCORDING TO MODIFIED SIWI (MARIF & 
ESMAEL, 2023)
The study results classify the water resources into 
No Location IWQI Classes
1 kani Ababaile 92.1 Excellent
2 Well Ababaile 93.8 Excellent
3 kani Faqe Byara S1 93.1 Excellent
4 Mergapan 90.9 Good
5 Shwan _chmchamal 91.4 Excellent
6 Bakrajo 89.7 Good
7 kani panka S1 84.4 Good
8 kani panka S2 92.4 Excellent
9 Shakhasur 94.0 Excellent
10 sharbazher tagaran 93.3 Excellent
11 Krbchna 1 86.4 Good
12 Krbchna 2 91.5 Excellent
13 Khwrmal S1 85.5 Good
14 Bnawela 93.8 Excellent
15 Sitak 92.3 Excellent
16 Qarachatan 94.7 Excellent
17 khwrmal S2 92.5 Excellent
18 Byara S2 96.0 Excellent
19 Awesar 94.9 Excellent
20 Tapatolaka 92.5 Excellent
21 Rain water 95.0 Excellent
22 kani sard 96.5 Excellent
23 Bakrajo Tap water 96.3 Excellent
24 Shene 91.4 Excellent
No Location Classes
1 kani ababaile Suitable for Irrigation
2 well ababaile Suitable for Irrigation
3 kani faqe byara s1 Suitable for Irrigation
4 Mergapan Suitable for Irrigation
5 shwan _chmchamal Suitable for Irrigation
6 Bakrajo Suitable for Irrigation
7 kani panka s1 Suitable for Irrigation
8 kani panka s2 Suitable for Irrigation
9 Shakhasur Suitable for Irrigation
10 sharbazher tagaran Suitable for Irrigation
11 krbchna 1 Suitable for Irrigation
12 krbchna 2 Suitable for Irrigation
13 khwrmal s1 Suitable for Irrigation
14 Bnawela Suitable for Irrigation
15 Sitak Suitable for Irrigation
16 Qarachatan Suitable for Irrigation
17 khwrmal s2 Suitable for Irrigation
18 byara s2 Suitable for Irrigation
19 Awesar Suitable for Irrigation
20 Tapatolaka Suitable for Irrigation
21 rain water Suitable for Irrigation
22 kani sard Suitable for Irrigation
23 bakrajo tap water Suitable for Irrigation
24 Shene Suitable for Irrigation
Table 6: Irrigation water quality classification according 
(Todd,1966)
Table 5 Irrigation water quality calculation according to modi-
fied SIWi 2023(Marif and Esmael, 2023)
Acta agriculturae Slovenica, 121/2 – 2025 7
Water quality effects on germination of okra seed (Abelmoschus esculentus L.)
two distinct quality categories based on the Irrigation 
Water Quality Index (IWQI). Nineteen of the resources 
were categorized as “Excellent” for irrigation, demon-
strating IWQI values above 90, with individual values 
ranging from 91.4 to 96.5, indicating that these water 
sources are highly suitable for agricultural use. These 
resources, spread across various locations (1, 2, 3, 5, 8, 
9, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, and 24), 
exhibit optimal quality for irrigation, as reflected in their 
consistently high IWQI scores. In contrast, five water 
sources (locations 4, 6, 7, 11, and 13) were classified as 
“Good,” with IWQI values between 70 and 90, indicating 
that they are still suitable for irrigation but with slightly 
lower quality compared to the “Excellent” category. These 
lower IWQI scores correspond to higher electrical con-
ductivity (EC) values, as seen in the data, with EC emerg-
ing as a key factor influencing water quality. Specifically, 
the study found that water resources with lower EC val-
ues tended to have higher IWQI scores, while those with 
higher EC values showed lower IWQI values. This pat-
tern is consistent with findings from Dhaoui et al. (2023) 
and Benaafi et al. (2024), which also identified a strong 
correlation between EC levels and IWQI, underlining the 
critical role of EC in evaluating the suitability of water for 
irrigation purposes. This classification provides valuable 
insights into the variability of water quality across differ-
ent locations and highlights the importance of monitor-
ing EC as a predictor of irrigation water suitability.
3.2 CLASSIFICATION OF WATER RESOURCES 
ACCORDING TO ACCORDING (TODD,1966)
 Water classification results indicated that all water 
resources (1, 2, 3, 4, ... to 24) were initially considered 
suitable for irrigation, with no variation observed across 
the resources. However, upon comparing the findings to 
the classification framework proposed by Todd (1966), 
it was evident that recent studies have led to significant 
changes in water classification due to the inclusion of ad-
ditional parameters and updated models for calculating 
the Irrigation Water Quality Index (IWQI). These chang-
es in classification can be attributed to the introduction 
of more comprehensive criteria for assessing water quali-
ty, which in turn influenced the classification of water re-
sources. The results presented in Table 6 align with recent 
research conducted by Mahammad and Islam (2024), as 
well as Laaraj et al. (2024), which highlights the evolv-
ing nature of water classification models as they adapt to 
new data and methodologies. This shift underscores the 
importance of using more advanced and detailed models 
to accurately reflect the quality and suitability of water 
resources for irrigation purposes, as these models pro-
vide a more nuanced understanding of water quality and 
its potential impact on agricultural practices.
3.3 CLASSIFICATION OF WATER RESOURCES 
ACCORDING TO CATIONS AND ANIONS 
USING PRINCIPAL’S COMPONENT ANALYSIS 
PCA
The classification of 24 water resources based on 
their cation and anion content using Principal Compo-
nent Analysis (PCA) reveals significant insights into wa-
ter quality variations across different locations. As shown 
in Figure 2, the water resources are divided into seven 
distinct classes, each represented by a unique shape. 
Class 1, depicted by a left arrow shape, corresponds to 
water resource number 13. Class 2, represented by a 
north arrow shape, includes water resource number 11. 
Class 3, with a circle shape, includes water resources 4 
and 17. Class 4, represented by a pentagon shape, is as-
signed to water resource number 20. Class 5, with a cyl-
inder shape, corresponds to water location number 19. 
Class 6, represented by a triangle shape, groups water 
resources 22, 23, and 24. Finally, Class 7, depicted by a 
square shape, includes a broad range of water locations, 
including 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, and 
21. PCA analysis indicates that Factor 1 (F1) accounts for 
48.17 % of the variation in water classification, while Fac-
tor 2 (F2) explains 14.53 %, together making up 62.70 % 
of the total variance. These findings are consistent with 
recent studies by Hammoumi et al. (2024), Arıman et al. 
(2024), and Ali et al. (2024), which similarly applied PCA 
to classify water resources, highlighting the effectiveness 
of this statistical approach in understanding water qual-
ity and variability across different regions. This analysis 
emphasizes the substantial influence of the first factor 
(F1), suggesting that cation-anion concentrations are the 
primary determinants in classifying water resources.
3.4 CLASSIFICATION OF WATER RESOURCES 
ACCORDING TO CATIONS AND ANIONS 
USING AGGLOMERATIVE HIERARCHICAL 
CLUSTERING (AHC)
The classification of water resources according to 
their cation and anion concentrations using Agglom-
erative Hierarchical Clustering (AHC) provides a struc-
tured approach to grouping water samples based on their 
chemical composition. As shown in Table 7 and Figure 
3, the AHC analysis divided the water resources into five 
distinct classes. Class 1, which includes 16 water resourc-
es (locations 1, 2, 3, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 
Acta agriculturae Slovenica, 121/2 – 20258
A. A. MARIF
23, and 24), exhibited relatively low variation in terms 
of ion concentration. In contrast, Classes 2 and 3 each 
included fewer resources, with Class 2 comprising only 
locations 4 and 6, and Class 3 encompassing resources 5, 
12, 14, and 15. Both Class 4 and Class 5 contained only 
one water resource each, located at positions 11 and 13, 
respectively. The lower variation observed in Classes 1 
and 3 suggests a more homogeneous ionic composition, 
whereas the higher variation in Classes 4 and 5 can be at-
tributed to the greater influence of electrical conductivity 
(EC), which likely caused more significant differentiation 
in the clustering. The impact of EC on water classifica-
tion is well-documented in previous studies (Marif and 
Esmail, 2023; Mishra et al., 2023; Djaafri et al., 2024), 
supporting the findings of this research. This clustering 
technique thus underscores the complex interplay of 
cations, anions, and EC in determining the quality and 
characteristics of water resources.
3.5 CLASSIFICATION OF WATER RESOURCES 
ACCORDING TO HEAVY METAL CONTENTS 
USING PRINCIPAL’S COMPONENT ANALYSIS 
PCA
The classification of water resources based on heavy 
metal content using Principal Component Analysis 
(PCA) offers a comprehensive approach to understand-
ing the variability and quality of water bodies in relation 
to pollutants. In this study, 24 water resources were cat-
egorized into six distinct classes according to their heavy 
metal profiles, as demonstrated in Figure 4. These classes 
are represented by different geometric shapes, each cor-
responding to specific water resource locations. Class 1, 
marked by a circle, includes water resources at locations 
6 and 12, while Class 2, represented by a north arrow, 
encompasses resources at locations 5, 3, 8, 9, 11, 13, 16, 
and 21. Class 3, shown with a rectangular shape, includes 
resources at locations 1, 2, 4, 14, 15, 17, 18, 20, 22, and 
23. Class 4, marked by a pentagon, represents locations 
10 and 19, while Class 5, identified by a triangle, cor-
responds to location 7. Finally, Class 6, represented by 
a cylinder shape, is composed of resource 24. The PCA 
results, as depicted in Figure 2, show that the first factor 
(F1) accounts for 26.68% of the total variability affect-
ing water classification, while the second factor (F2) con-
tributes 16.59%. Together, these factors explain 43.28 % 
of the variance in the water classification, highlighting 
the significant influence of heavy metal content on wa-
ter quality. These findings align with previous research 
by Hammoumi et al. (2024) and Arıman et al. (2024), 
validating the effectiveness of PCA in classifying water 
Figure 2: Classification of water resources according to cations 
and anions using principal’s component analysis PCA 
Classes Class 1 Class 2 Class 3 Class 4 Class 5
Number of 
classes
16 2 4 1 1
Water 
Resources or 
Locations
1, 2, 3, 7, 8, 
9, 10, 16, 
17, 18, 19, 
20, 21, 22, 
23 and 24
4 and 6 5, 12, 14, 
and 15
11 13
Table 7: Classification of water resources according to cations 
and anions using agglomerative hierarchical clustering (AHC)
Figure 3: Classification of water resources according to cations 
and anions using agglomerative hierarchical clustering (AHC)
Acta agriculturae Slovenica, 121/2 – 2025 9
Water quality effects on germination of okra seed (Abelmoschus esculentus L.)
resources based on their contamination levels. This anal-
ysis underscores the importance of understanding the 
principal factors contributing to water quality variations 
and their implications for environmental monitoring and 
management.
3.6 CLASSIFICATION OF WATER RESOURCES 
ACCORDING TO HEAVY METAL CONTENTS 
USING AGGLOMERATIVE HIERARCHICAL 
CLUSTERING (AHC)
The classification of water resources based on heavy 
metal contents, utilizing Agglomerative Hierarchical 
Clustering (AHC), groups the water sources into nine 
distinct classes, as shown in Table 8 and Figure 5. Class 
1, which includes water sources 1, 3, 5, 8, 9, 17, 21, 22, 
and 23, represents the largest cluster with nine water re-
sources. Class 2, containing four locations (2, 14, 16, and 
19), and Class 3, which includes locations 4, 11, 13, and 
20, each contain four water resources. Class 4, consisting 
of only two locations (6 and 12), demonstrates a more 
limited variation in heavy metal content. In contrast, 
Classes 5, 6, 7, 8, and 9 are more distinct, each contain-
ing a single water resource—specifically, water sources 7, 
10, 15, 18, and 24, respectively. The classification shows 
low variability in heavy metal concentrations for Class 
1 and Class 3, which are likely influenced by similar en-
vironmental or anthropogenic factors, while a greater 
degree of variability is observed in Classes 5 through 9. 
This greater variation can be attributed to factors such as 
electrical conductivity (EC), which significantly impacts 
water quality classification by affecting the solubility 
and mobility of heavy metals in aquatic environments. 
These findings align with previous studies by Marif and 
Esmail (2023) and Mohsine et al. (2023), confirming that 
clustering based on heavy metal content provides a reli-
able method for assessing water quality, revealing both 
regional differences and the influence of chemical pro-
cesses on water resources.
3.7 CORRELATION COEFFICIENT BETWEEN PH, 
EC, IWQI AND GERMINATION RATIO
The correlation analysis presented in Tables 4 and 9 
reveals intriguing insights into the relationships between 
Figure 4: Classification of water resources according to heavy 
metal contents using principal’s component analysis PCA
Classes Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 9
Number of classes 9 4 4 2 1 1 1 1 1
Water resources or Locations 1, 3, 5, 8, 9, 
17, 21, 22, 
and 23
2, 14, 16, 
and 19 
4, 11, 13, 
and 20
6 and 12 7 10 15 18 24
Table 8: Classification of water resources according to cluster analysis of heavy metal contents
Figure 5: Classification of water resources according to heavy 
metal contents using agglomerative hierarchical clustering 
(AHC)
Acta agriculturae Slovenica, 121/2 – 202510
A. A. MARIF
pH, electrical conductivity (EC), irrigation water quality 
index (IWQI), and germination ratio. Notably, the study 
found a positive but non-significant relationship between 
the germination ratio and IWQI, with a correlation coef-
ficient (r) of 0.218, suggesting that although IWQI may 
have some influence on germination, its effect is weak and 
not statistically meaningful (Lal et al., 2024). Addition-
ally, a similarly weak but negative correlation between 
pH and the germination ratio (-0.157), and EC and the 
germination ratio (-0.196), indicates that as pH and EC 
increase, there is a slight decrease in the germination ra-
tio. However, these correlations are also non-significant, 
implying that other factors may be influencing the ger-
mination rate more strongly. A more robust and signifi-
cant negative correlation was observed between EC and 
IWQI, with an r value of -0.727, which implies a strong 
inverse relationship. This suggests that as EC (a measure 
of salinity) increases, the overall quality of irrigation wa-
ter as indicated by the IWQI declines. This negative sig-
nificant relationship is critical because high EC typically 
denotes saline water, which can have detrimental effects 
on plant growth and germination. In summary, while the 
correlations between pH, EC, and germination ratio are 
weak and non-significant, the strong negative relation-
ship between EC and IWQI underscores the importance 
of monitoring EC levels in maintaining irrigation water 
quality and optimizing germination success.
3.8 EFFECTS OF WATER QUALITY ON GERMINA-
TION RATIO
The data presented in Tables 10 and 4 provides valu-
able insights into the relationship between water quality 
and the germination ratio of okra seeds. The maximum 
germination ratio recorded was 92  %, observed under 
excellent water quality conditions with an electrical con-
ductivity (EC) of 0.34 dS m-¹ and a standard deviation 
of 3.72. In contrast, the minimum germination ratio of 
79 % was observed at a slightly higher EC of 0.5 dS m-¹, 
accompanied by the same standard deviation, indicating 
that even small variations in EC can influence seed ger-
mination. This decline in germination is likely due to the 
combined effects of EC and pH on water quality, which 
have been shown to impact the osmotic potential and the 
uptake of water by seeds, ultimately affecting their abil-
ity to sprout. Higher EC levels can cause osmotic stress, 
making it more difficult for the seed to absorb sufficient 
water, which is crucial for the germination process. This 
aligns with the findings of Seymen et al. (2023), Singh et 
al. (2023), and Nautiyal et al. (2023), who demonstrated 
that water quality parameters such as EC and pH are 
critical factors in seed viability and germination. In this 
study, the mean values for pH, EC, and IWQI (Irrigation 
Water Quality Index) were 7.19, 0.66 dS m-1, and 92.28, 
respectively, indicating that maintaining water quality 
within optimal ranges is essential for maximizing germi-
nation rates and ensuring successful crop establishment.
3.9 RELATION BETWEEN GERMINATION RATIO 
AND EC DS M-1
Figure 6 illustrates the inverse relationship be-
tween the germination ratio and electrical conductiv-
ity (EC, measured in dS m-1), showing a significant 
negative correlation. As EC increases, the germina-
tion ratio decreases dramatically, which can be at-
tributed to the detrimental effects of high salinity on 
seed germination. Electrical conductivity in soil is a 
direct measure of its salinity, and when the EC is high, 
it indicates that the soil solution has a higher concen-
tration of dissolved salts. These salts can create an os-
motic pressure that reduces the availability of water to 
seeds, impairing their ability to absorb water and thus 
hindering the germination process. This negative rela-
tionship is supported by the correlation coefficient (r 
= -0.07), which highlights the weak yet consistent in-
verse trend between EC and germination. These find-
ings align with those of Hossain et al. (2023) and Na-
her et al. (2024), who also observed similar impacts of 
salinity on seed germination. High salinity can induce 
Variables pH EC dS m-1 IWQI
Germina-
tion Ratio
pH 1
EC dS m-1 -0.012 1
IWQI -0.322 -0.727 1
Germination 
Ratio
-0.157 -0.196 0.218 1
Table 9: Correlation between pH, EC, IWQI and germination 
ratio 
Variable
Observa-
tions
Mini-
mum
Maxi-
mum Mean Std. deviation
PH 24 7.1 7.4 7.19 0.01
EC 24 0.26 1.2 0.66 0.40
IWQI 24 84.43 96.55 92.28 3.18
Germina-
tion Ratio
24 79 92 85.36 3.72
Table 10: Summary of water quality effect on germination 
ratio
Acta agriculturae Slovenica, 121/2 – 2025 11
Water quality effects on germination of okra seed (Abelmoschus esculentus L.)
physiological stress in seeds, affecting enzyme activ-
ity, cell membrane integrity, and nutrient uptake, all 
of which are essential for successful germination and 
early seedling development.
3.10 RELATION BETWEEN GERMINATION RATIO 
AND IRRIGATION WATER QUALITY INDEX 
(IWQI)
Figure 7 illustrates a positive correlation between 
germination ratio and the Irrigation Water Quality 
Index (IWQI), demonstrating that as the germination 
ratio increases, so does the IWQI, indicating better 
water quality. This relationship suggests that higher 
IWQI values, which reflect cleaner, more suitable wa-
ter for irrigation, promote better seed germination 
and overall plant growth. Conversely, a decrease in 
IWQI corresponds to lower germination ratios, high-
lighting the detrimental effects of poor water quality 
on seedling establishment. This finding is consistent 
with previous studies, such as those by Marif and Es-
mail (2023) and Mezlini et al. (2024), which empha-
size the significant role of water quality in agricultural 
productivity. Poor-quality irrigation water, character-
ized by high salinity, contamination, or imbalanced 
nutrient content, can impede seedling growth by cre-
ating osmotic stress, altering nutrient availability, or 
introducing toxic compounds, all of which negatively 
affect germination. Thus, maintaining a high IWQI is 
critical for ensuring successful crop establishment and 
maximizing agricultural yields.
4 CONCLUSIONS
 The study of irrigation water quality (IWQ) from 
various locations or water sources reveals significant varia-
tions in water quality parameters, with conductivity being 
one of the most influential factors. Electrical conductiv-
ity (EC) serves as an indicator of the ion concentration in 
water, reflecting the level of dissolved salts or minerals. In 
agricultural practices, water with high EC values can lead 
to salinity stress, which adversely affects plant growth and 
seed germination. Conversely, water with lower EC values, 
indicating fewer dissolved salts, tends to be more favorable 
for seedling establishment and plant growth. The observed 
variations in water quality across different sources can thus 
be attributed to the local environmental conditions, such 
as soil composition and water sources, which influence the 
ion concentrations and overall quality of irrigation water.
Furthermore, the study demonstrates a positive cor-
relation between irrigation water quality and the germina-
tion rate of okra seeds. As the IWQ index increases, par-
ticularly in relation to lower EC values, the germination 
ratio of okra seeds also increases. This suggests that water 
with lower salt concentrations provides a more conducive 
environment for seed sprouting, likely due to reduced os-
motic stress and enhanced water uptake. High-quality irri-
gation water, characterized by low EC values, ensures that 
seeds receive the optimal conditions necessary for proper 
germination, leading to higher success rates in seedling 
emergence. This finding underscores the importance of 
maintaining high-quality water for irrigation to promote 
healthy crop development and improve agricultural pro-
ductivity. Therefore, the results highlight that managing 
Figure 6: Relation between germination ratio and EC dS m-1 Figure 7: Relation between germination ratio and IWQI
Acta agriculturae Slovenica, 121/2 – 202512
A. A. MARIF
water salinity, by monitoring and controlling EC levels, is 
crucial for optimizing crop germination and growth.
5 REFERENCES
Ahmad, T., Muhammad, S., Umar, M., Azhar, M. U., Ahmed, 
A., Ahmed, A., & Ullah, R. (2024). Spatial distribution of 
physicochemical parameters and drinking and irrigation 
water quality indices in the Jhelum River, Pakistan. Envi-
ronmental Geochemistry and Health, 46(8), 263.
Ali, S., Verma, S., Agarwal, M. B., Islam, R., Mehrotra, M., Deo-
lia, R. K., . . . Raj, D. (2024). Groundwater quality assess-
ment using water quality index and principal component 
analysis in the Achnera block, Agra district, Uttar Pradesh, 
Northern India. Scientific Reports, 14(1), 5381.
Arıman, S., Soydan-Oksal, N. G., Beden, N., & Ahmadzai, H. 
(2024). Assessment of groundwater quality through hydro-
chemistry using principal components analysis (PCA) and 
water quality index (WQI) in Kızılırmak Delta, Turkey. Wa-
ter, 16(11), 1570.
Association, A. P. H. (1926). Standard methods for the exami-
nation of water and wastewater (Vol. 6): American Public 
Health Association.
Dhaoui, O., Agoubi, B., Antunes, I. M., Tlig, L., & Kharroubi, 
A. (2023). Groundwater quality for irrigation in an arid re-
gion—application of fuzzy logic techniques. Environmental 
Science and Pollution Research, 30(11), 29773-29789.
Djaafri, I., Seghir, K., Valles, V., & Barbiero, L. (2024). Regional 
hydro-chemistry of hydrothermal springs in Northeastern 
Algeria, case of Guelma, Souk Ahras, Tebessa and Khen-
chela Regions. Earth, 5(2), 214-227.
Fadl, M. E., ElFadl, D. M. A., Hussien, E. A. A., Zekari, M., 
Shams, E. M., Drosos, M., . . . Megahed, H. A. (2024). Ir-
rigation water quality asessment in Egyptian arid lands, 
utilizing irrigation water quality iondex and geo-spatial 
techniques. Sustainability, 16(14), 6259.
Hammoumi, D., Al-Aizari, H. S., Alaraidh, I. A., Okla, M. K., 
Assal, M. E., Al-Aizari, A. R., . . . Bejjaji, Z. (2024). Seasonal 
variations and assessment of surface water quality using 
water quality index (WQI) and principal component analy-
sis (PCA): A case study. Sustainability, 16(13), 5644.
Hossain, M., Sumon, M., Islam, M., Haque, M., & Ikbal, M. 
(2023). Germination capacity of different wheat genotypes 
under salt stress. Journal of Agroforestry and Environment, 
16(2), 114-123
Ibrahim, F.A., (2024). Assessing Regenerative Agricultural Tech-
niques on Soil Health, Crop Yield and Nutritional Quality of 
Okra (Abelmoschus esculentus). (Master’s thesis, Oklahoma 
State University). 
Laaraj, M., Benaabidate, L., Mesnage, V., & Lahmidi, I. (2024). 
Assessment and modeling of surface water quality for 
drinking and irrigation purposes using water quality in-
dices and GIS techniques in the Inaouene watershed, Mo-
rocco. Modeling Earth Systems and Environment, 10(2), 
2349-2374.
Lal, B., Shukla, A., Kumar, P., Singh, S., Singh, Y., & Chaturvedi, 
S. (2024). Evaluation of irrigation water quality under new-
ly weathered soil in hot and semi-humid region of central 
India using GIS. Environment, Development and Sustain-
ability, 26(5), 12901-12938.
Mahammad, S., & Islam, A. (2024). Surface water quality as-
sessment for drinking and irrigation using DEMATEL, 
entropy-based models and irrigation hazard indices. Envi-
ronmental Earth Sciences, 83(10), 332.
Marif, a., & esmail, a. (2023). Quality evaluation of water re-
sources for irrigation in Sulaimani governorate, Iraq. Ap-
plied Ecology & Environmental Research, 21(3).
Mezlini, W., Amor, R. B., Beneduci, A., Romdhane, I. B., Sham-
mas, M. I., Almazroui, M., & Attia, R. (2024). Effects of ir-
rigation water quality on soil physico-chemical proprieties: 
Case study in North-West of Tunisia. Earth Systems and 
Environment, 1-21.
Mishra, A., Rai, A., Mishra, P. K., & Rai, S. C. (2023). Evaluation 
of hydro-chemistry in a phreatic aquifer in the Vindhyan 
Region, India, using entropy weighted approach and geo-
chemical modelling. Acta Geochimica, 42(4), 648-672.
Mohsine, I., Kacimi, I., Abraham, S., Valles, V., Barbiero, L., 
Dassonville, F., . . . Jabrane, M. (2023). Exploring multiscale 
variability in groundwater quality: a comparative analysis 
of spatial and temporal patterns via clustering. Water, 15(8), 
1603.
Naher, N., MAHMUD, S., Bell, R., Alam, A., Hossain, M., & 
Chowdhury, A. (2024). Germination ability of different 
fieldpea (Pisum sativum L.) genotypes under salinity stress. 
Journal of the Indian Society of Coastal Agricultural Re-
search, 42(1).
Nakai, J. (2018). Food and agriculture organization of the Unit-
ed Nations and the sustainable development goals. Sustain-
able Development, 22, 1-450.
Nautiyal, P., Sivasubramaniam, K., & Dadlani, M. (2023). Seed 
dormancy and regulation of germination. Seed Science and 
Technology, 39-66.
Rima, R. (2021). Seed priming significantly affects germination 
and post germination early seedling growth in okra. Malay-
sian Journal of Halal Research, 4(1), 11-13.
Seymen, M., Yavuz, D., Eroğlu, S., Arı, B. Ç., Tanrıverdi, Ö. B., 
Atakul, Z., & Issı, N. (2023). Effects of different levels of wa-
ter salinity on plant growth, biochemical content, and pho-
tosynthetic activity in cabbage seedling under water-deficit 
conditions. Gesunde Pflanzen, 75(4), 871-884.
Singh, A., Sengar, R. S., Rajput, V. D., Agrawal, S., Ghazaryan, 
K., Minkina, T., . . . Habeeb, T. (2023). Impact of zinc oxide 
nanoparticles on seed germination characteristics in rice 
(Oryza sativa L.) under salinity stress. Journal of Ecological 
Engineering, 24(10).
Todd, D. K., & Mays, L. W. (2004). Groundwater hydrology, 
John Wiley & Sons.
Wakchaure, G., Minhas, P., Kumar, S., Khapte, P., Dalvi, S., 
Rane, J., & Reddy, K. S. (2023). Pod quality, yields responses 
and water productivity of okra (Abelmoschus esculentus L.) 
as affected by plant growth regulators and deficit irrigation. 
Agricultural Water Management, 282, 108267.
Yuan, X., Li, S., Chen, J., Yu, H., Yang, T., Wang, C., Huang, 
S., Chen, H. and Ao, X., (2024). Impacts of global climate 
change on agricultural production: a comprehensive re-
view. Agronomy, 14(7), p.1360.