Acta agriculturae Slovenica, 121/4, 1–13, Ljubljana 2025 doi:10.14720/aas.2025.121.4.21860 Original research article / izvirni znanstveni članek Assessing the impact of seasonal variability on irrigation water quality and suitability for agricultural use in wet and dry conditions Arsalan Azeez MARIF 1, 2, Akram Othman ESMAIL 3 Received February 02, 2025; accepted September 24, 2025. Delo je prispelo 2. februar 2025, sprejeto 24. september 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 3 Soil and Water department, College of Agricultural Engineering sciences, Selahaddin University-Erbil, Erbil Kurdistan Region, Iraq Assessing the impact of seasonal variability on irrigation wa- ter quality and suitability for agricultural use in wet and dry conditions Abstract: This study investigates the seasonal variation in water quality for irrigation from 57 wells in Sulaimani City, using two classification models: Ayers & Westcot (1985) and Maia & Rodrigues (2012). Key water parameters such as pH, electrical conductivity (EC), sodium adsorption ratio (SAR), bicarbonates (HCO₃-), and (Cl-) concentrations were analyzed. Results showed that during the wet season, 45 wells had no re- strictions (NR), while 12 had slight to moderate restrictions (S- MR). In the dry season, 29 wells were classified as NR and 28 as S-MR. Water quality was generally favorable for irrigation in the wet season but required management strategies for wells with higher EC and SAR, particularly in the dry season when salinity and ion concentrations increased. The Ayers & West- cot classification reflected seasonal variations in EC, SAR, and bicarbonates, with water quality declining slightly in the dry season, leading to more wells classified as S-MR. Using the Ir- rigation Water Quality Index by Maia & Rodrigues, some wells shifted from “Good” to “Excellent” in the dry season due to changes in EC levels. These results highlight the need for con- tinuous water quality monitoring and adaptive irrigation man- agement to optimize water use and prevent soil salinization in regions with seasonal variability. Key words: irrigation water quality, EC, pH, season varia- tion Ocenjevanje vpliva sezonske spremenljivosti na kakovost vode za namakanje in primernost za kmetijsko uporabo v vla- žnih in sušnih razmerah Izvleček: Študija je preučevala sezonske razlike v kako- vosti vode za namakanje iz 57 vodnjakov v mestu Sulaimani z uporabo dveh klasifikacijskih modelov: Ayers & Westcot (1985) ter Maia & Rodrigues (2012). Analizirani so bili ključ- ni parametri vode, kot so pH, električna prevodnost (EC), ad- sorpcijsko razmerje natrija (SAR), koncentracija bikarbonatov (HCO₃-) in klorida (Cl-). Rezultati so pokazali, da med deževno sezono 45 vodnjakov ni imelo omejitev (NR), 12 pa je imelo rahle do zmerne omejitve (S-MR). V sušnem obdobju je bilo 29 vodnjakov razvrščenih kot NR in 28 kot S-MR. Kakovost vode je bila na splošno ugodna za namakanje v deževnem obdobju, vendar so bile potrebne strategije upravljanja za vodnjake z ve- čjima EC in SAR, zlasti v sušnem obdobju, ko sta se povečali slanost in koncentracija ionov. Klasifikacija Ayers & Westcot je odražala sezonska nihanja EC, SAR in bikarbonatov, pri čemer se je kakovost vode nekoliko zmanjšala v sušnem obdobju, za- radi česar je več vodnjakov razvrščenih kot S-MR. Z uporabo indeksa kakovosti vode za namakanje Maie & Rodriguesa so se nekateri vodnjaki v sušnem obdobju spremenili iz »dobrih« v »odlične« zaradi sprememb EC. Ti rezultati poudarjajo potrebo po stalnem spremljanju kakovosti vode in prilagodljivem upra- vljanju namakanja za optimizacijo rabe vode in preprečevanje zasoljevanja tal v regijah s sezonsko spremenljivostjo. Ključne besede: kakovost vode za namakanje, EC, pH, sezonska nihanja Acta agriculturae Slovenica, 121/4 – 20252 A. A. MARIF and A. O. ESMAIL 1 INTRODUCTION The quality of irrigation water plays a critical role in determining the success and sustainability of agricultural practices (Laoufi et al.,2025: Marif and Esmail, 2023). As global climate patterns fluctuate and regions experi- ence more extreme weather conditions, understanding how seasonal variability affects irrigation water quality has become increasingly important. Seasonal changes, particularly between wet and dry conditions, can sig- nificantly alter the chemical composition and suitability of water for irrigation (Panday et al., 2025: Marif, 2023). In agricultural settings, the assessment of water quality is crucial for maintaining soil health, ensuring optimal crop growth, and minimizing the risks of salinization, nutrient imbalances, and toxicity (Sharma and Pillai, 2025; Surucu et al., 2020). The use of water classifica- tion systems allows farmers and policymakers to make informed decisions on water management and irrigation practices, ensuring long-term agricultural productivity. This article delves into the impact of seasonal variability on irrigation water quality and suitability, specifically ex- amining how wet and dry seasons influence water chem- istry and its classification according to two prominent models (Ayers and Westcot, 1985, Maia and Rodrigues, 2012). The Maia and Rodrigues, 2012 model is one of the most widely applied approaches for evaluating irrigation water quality, as it provides a comprehensive framework that integrates a range of important parameters. These include salinity levels, pH balance, and the concentration of essential and potentially harmful ions, which together determine the degree of water suitability for agricultural use and crop productivity (Marif and Esmail, 2023). By considering multiple factors simultaneously, the model allows for a more accurate classification of water qual- ity compared to traditional single-parameter methods. Seasonal variations also play a crucial role in shaping ir- rigation water quality, especially in regions with distinct wet and dry periods. During the wet season, rainfall con- tributes to the dilution of contaminants and lowers the salinity of surface and groundwater sources. This natu- ral dilution effect can improve the chemical balance of irrigation water, reducing the risks of soil salinization and ion toxicity. As a result, water resources that may be marginal or unsuitable during dry periods can become more favorable for irrigation in the rainy season. In con- trast, the dry season may exacerbate water quality issues, such as higher salinity, due to the lower availability of water and increased evaporation rates (Mohsen and Al- Mohammed, 2023). By analyzing the differences in water quality classifications during these two distinct periods, this model offers valuable insights into how agricultural irrigation practices should be adapted to seasonal condi- tions, ensuring the optimal use of water resources and minimizing negative environmental impacts (Rajab and Esmail, 2022). On the other hand, the global classification system developed by (Ayers and Westcot, 1985) offers a more universally applicable and standardized framework for assessing irrigation water quality, making it highly valu- able for agricultural management across diverse regions. This system evaluates water quality primarily through critical parameters such as electrical conductivity (EC), which reflects the salinity level of water; the sodium ad- sorption ratio (SAR), which indicates the potential for sodium-related soil structural problems; and the con- centrations of specific ions that may affect plant growth or soil health (Yan et al., 2024). By incorporating these parameters, the model provides a clear basis for deter- mining whether water is suitable for irrigation under dif- ferent environmental and cropping conditions. Seasonal variability strongly influences the results derived from this system. In wet conditions, for instance, excess rainfall can contribute to the dilution of salts and ions in water bodies, resulting in lower EC values and, consequently, an improvement in water quality according to this classi- fication. Conversely, in the dry season, high evaporation rates tend to concentrate salts and dissolved ions in ir- rigation sources, leading to increased EC values that can make the water less suitable for sustainable crop irriga- tion (Gupta and Kumar, 2024). Because of its adaptability and wide acceptance, this classification system has been extensively applied to guide irrigation practices glob- ally, particularly in regions with diverse soils, climates, and agricultural needs. It not only supports farmers and decision-makers in identifying risks associated with poor-quality irrigation water but also assists in planning management strategies that minimize long-term soil degradation. Therefore, understanding how this system evaluates water quality under both wet and dry seasonal conditions is crucial, as it provides essential information for improving water use efficiency, protecting soil health, and ultimately optimizing agricultural productivity un- der varying climatic scenarios (Hanoon et al., 2021).The primary aim of this article is to assess and compare the seasonal impact on irrigation water quality by classifying water using both the (Maia and Rodrigues, 2012) model and the (Ayers and Westcot, 1985) classification system. By analyzing the changes in water quality between wet and dry seasons, the article seeks to provide a compre- hensive evaluation of how different seasonal conditions influence the suitability of water for agricultural use. Through this comparison, the article aims to offer prac- tical recommendations for water management strategies Acta agriculturae Slovenica, 121/4 – 2025 3 Assessing the impact of seasonal variability on irrigation water quality and suitability for agricultural use in wet and dry conditions that can adapt to seasonal changes, ensuring sustainable agricultural practices across diverse climatic zones. 2 MATERIALS AND METHODS 2.1 STUDY AREA AND SAMPLING LOCATIONS The present study was conducted across 57 deep wells strategically distributed within the Sulaimani Gov- ernorate, encompassing a wide range of geographic zones and hydrogeological formations. These wells were care- fully selected to represent areas with varying land uses, agricultural practices, and irrigation demands, thereby providing a comprehensive and representative overview of groundwater quality in the region. The inclusion of wells from locations with different topographies, soil characteristics, and cultivation intensities was particu- larly important to capture both spatial variability and the combined influence of natural hydrogeological condi- tions and anthropogenic activities such as fertilizer ap- plication, intensive irrigation, and land management. To ensure temporal reliability and account for seasonal and short-term fluctuations in groundwater chemistry, water samples were systematically collected every two weeks from each well throughout the study period. This bi- weekly sampling approach allowed for continuous moni- toring of changes in water quality, such as variations in salinity, pH, and ionic concentrations, which are often influenced by rainfall, irrigation intensity, and evapora- tion rates. In addition, the geographic coordinates and elevation details of all wells were carefully recorded and are presented in Figure 1 and Table 1, serving as a spa- tial reference for data interpretation and for facilitating future monitoring programs. As part of this disserta- Figure 1. Study area of fifty-seven wells with their GPS readings in Utm system Wells Number Well Name Elevation (Meter) GPS points Depth (m) N E L1 Turka 744 518312 3942218 90 L2 Palka Rash 727 522025 3947156 180 L3 TakTak 726 519937 3946535 76 L4 Gazalan 646 518352 3947459 54 L5 Khan Ali /Goshqut 684 492339 3925659 94 L6 Ali Zangana 680 492565 3924852 75 L7 Sofi hassan 721 490323 3925264 100 L8 Kani Shaitan 914 500478 3945133 60 L9 Darikali 873 478610 3927687 100 L10 Bazian 793 485490 3936873 100 L11 Sharawany Allahi 882 483690 3937843 54 L12 Kazhzwa Village shar bazher 1116 442901 3933460 80 L13 Barzinja Village 1313 500000 3873043 95 L14 Kanisard S1 896 450996 3946263 102 L15 GorgadarS1 1083 448567 3942290 70 L16 GorgadarS2 972 448900 3941182 110 L17 GorgadarS3 969 449026 3941126 60 L18 kani sard S2 903 451016 3946170 80 Table 1: Study area of fifty-seven wells with their GPS readings in Utm system Acta agriculturae Slovenica, 121/4 – 20254 A. A. MARIF and A. O. ESMAIL scientifically informed, region-specific water manage- ment strategies that enhance agricultural sustainabil- ity in the governorate. tion, this robust sampling framework forms the meth- odological foundation for assessing irrigation water quality, identifying potential risks to soil health and crop productivity, and supporting the development of L19 Twa soran 560 500699 4010969 120 L20 Girdjan S1 542 518366 4006752 100 L21 Girdjan S2 542 518366 4006752 286 L22 Chwarqurna 542 514571 4009056 120 L23 Dolabafra 559 495160 4009491 86 L24 Uch tapan 549 419334 3915725 150 L25 Qalijo Village 557 435322 3914364 60 L26 Qawella Village 778 428382 3926247 96 L27 Hajikadir no.17 515 420780 3937212 50 L28 Kanispika Parkhy 577 427844 3917653 60 L29 Hajiqadir well no.11 527 420556 3911950 100 L30 Kazhzwa Sharazwr Village 562 439646 3913727 70 L31 Mindol /Lano Nursery 553 427563 3915927 71 L32 Nawgrdan Village 510 418146 3909220 90 L33 Swrdash 1027 490620 3968490 30 L34 Homarqawm 1044 487683 3966574 48 L35 Piramagrwn 807 486883 3954699 150 L36 Kanimeran 790 489995 3961328 150 L37 Gokhlan 1259 404897 3954728 150 L38 Hangazhal 1277 415101 3953043 75 L39 Garmik 1259 414124 3954094 96 L40 Barrawa 1231 413262 3958718 165 L41 Basharaty KhwarwS1 528 413359 3902496 141 L42 Shashk 557 410009 3904340 150 L43 Sargat 1047 399304 3905726 50 L44 Golp 732 403559 3901782 30 L45 TapiSafay khwarwS1 546 411185 3905487 153 L46 TapiSafay khwarw S2 522 412231 3905443 107 L47 Baroy Shahid 941 473936 3926803 160 L48 Braimawa S1 951 470244 3921389 150 L49 Braimawa S2 951 470148 3921467 100 L50 Braimawa S3 958 470154 3921525 113 L51 Hargena 954 469135 3919985 80 L52 Tangisar Village 860 473726 3920645 110 L53 Wandarena Village 1338 500000 3651287 63 L54 Zerinjoy sarw 552 500000 3873043 57 L55 Sarzal 969 500000 3873043 75 L56 Bakhtiary 815 591253 3873500 100 L57 Berashka 504 419307 3908612 110 Acta agriculturae Slovenica, 121/4 – 2025 5 Assessing the impact of seasonal variability on irrigation water quality and suitability for agricultural use in wet and dry conditions 2.2 WATER SAMPLING Water samples were systematically collected from 57 deep wells across the study area during the wet season (May to June), with the sampling depths corresponding to the specific hydrogeological characteristics of each well and each 2-week (14 days) samples were taken as out- lined in Table 1. To ensure the reliability and representa- tiveness of the samples, collection was performed using clean, sterilized polyethylene bottles, which effectively minimize the risk of contamination during handling and transport. Prior to sampling, each well was thoroughly purged by pumping 2–3 times its well volume, a stand- ard practice designed to remove stagnant water from the borehole and ensure that only fresh groundwater was ob- tained for analysis. This step was particularly important for wells of varying depths, as indicated in Table 1, since deeper aquifers may show different chemical composi- tions compared to shallower sections. Once collected, the samples were subjected to a detailed physicochemical analysis focusing on parameters critical for evaluating ir- rigation water quality. This included pH, measured using a calibrated portable pH meter, and electrical conductiv- ity (EC), determined in situ with a portable conductivity meter to assess salinity levels. Furthermore, the chemical composition of the water was analyzed for major cations (Ca²+, Mg²+, Na+, K+) and major anions (HCO₃-, SO₄²-, Cl-, NO₃-), which were quantified in mmolc  l-1 follow- ing standard analytical methods recommended for water quality assessment. This rigorous methodological frame- work not only ensured the accuracy and comparability of results but also provided a strong scientific basis for interpreting groundwater quality variations in relation to depth, hydrogeological setting, and agricultural suit- ability. 2.3 CALCULATION WATER QUALITY INDEX (IWQI) ACCORDING TO MAIA AND RO- DRIGUES, 2012 The main steps for determining IWQI was summa- rized as follow: 2.3.1 Calculating the deviation from the reference values for each variable, considering normal distribution of data, the Z-test was applied for data standardization as follow: Where: Zi = Standardized value of the studied parameter. Xi = Value of the property determined at the water source. = Mean value of the variable evaluated from the reference population. SD = Standard deviation of the parameter determined from the reference population. 2.3.2 Calculating the IWQI for the studied param- eters such as (Ca2+, Mg2+, Na+, K+, HCO3 -, SO4 2-, Cl-, and NO3 -) by using the following equations WQIi = The Index value for the characteristic of the studied water quality. Zi = The standardized variable value. Where: WQIi is the Water Quality Index for the characteristic, and IWQI stands for Irrigation Water Quality Index. Table 2 2.3.3 Ayers and Westcot, 1985 Model The (Ayers and Westcot, 1985)model focuses on the salinity and sodicity of irrigation water. The key param- eters for the calculation include EC, SAR, and Na %. The steps for calculating the IWQI according to this model are: 1. Electrical Conductivity (EC) Classification: Based on the EC, the water is classified into one of the following categories: – Low salinity (EC ≤ 0.7 dS m-1) – Medium salinity (0.7 < EC ≤ 2 dS m-1) – High salinity (EC > 2 dS m-1) 2. Sodium Absorption Ratio (SAR) Classification: Based on SAR, the water is classified as: – Low SAR (SAR ≤ 3) – Medium SAR (3 < SAR ≤ 6) IWQi or WQIi Restriction WQIi or IWQI ≤ 1.96 1- (Excellent) 1.96 < WQIi or IWQI ≤ 5.88 2- (Good) 5.88 < Wii or IWQI ≤ 9.80 3- (Average) WQIi or IWQI > 9.80 4- (Poor) Table 2: Shows irrigation water classes depending on irrigation water quality index (IWQI) (Maia and Rodrigues, 2012) Acta agriculturae Slovenica, 121/4 – 20256 A. A. MARIF and A. O. ESMAIL – High SAR (SAR > 6) 3. Na  % Classification: Sodium percentage is used to assess the water’s potential to cause soil permeability problems. The classification is as follows: – Low Na % (Na % ≤ 20) – Medium Na % (20 < Na % ≤ 40) – High Na % (Na % > 40) 4. Overall water quality: The final classification is determined by the intersection of the EC, SAR, and Na% classifications, using a salinity-sodicity diagram from (Ayers and Westcot, 1985) 5. Data Analysis The results from both models were analyzed and compared for consistency. The IWQI values from both models were categorized into water quality classes for ir- rigation. 2.4 STATISTICAL ANALYSIS The collected data were analyzed using the statisti- cal software XLSTAT (version 2019.2.2.59614) to assess seasonal variations in water quality. Analysis of variance (ANOVA) was applied to detect significant differences in the measured parameters between seasons, ensuring a clear understanding of how water quality fluctuates over time. In addition, correlation analysis was performed to identify the strength and direction of relationships among the different water quality parameters, such as pH, electrical conductivity, dissolved salts, and nutrient concentrations. This approach not only revealed whether the seasonal changes were statistically significant but also provided insights into how certain variables are interre- lated, thereby offering a more comprehensive evaluation of the overall water quality dynamics. 3 RESULTS AND DISCUSSIONS 3.1 IRRIGATION WATER CLASSIFICATION DE- PENDING ON GLOBAL CLASSIFICATION (AYERS AND WESTCOT, 1985) IN WET AND DRY SEASON The classification of wells based on key irrigation water quality parameters—such as pH, electrical conduc- tivity (EC), sodium adsorption ratio (SAR), bicarbonate (HCO₃-), and chloride (Cl-) concentrations—revealed clear seasonal variations and corresponding restrictions on water use in the study area. During the wet season, the majority of wells (45 in total) were categorized as having no restriction (NR), while 12 wells fell into the slight to moderate restriction (S-MR) category. As shown in Table 3, the pH values for these wells ranged from 6.7 to 7.8, EC values were between 0.30 and 0.70 dS m-1, and SAR levels varied from 0.02 to 1.01 (mmolec  l-1)¹/². Accord- ing to the classification framework outlined by Ayers and Westcot (1985), such low EC and SAR values correspond to unrestricted water quality that is generally favorable for irrigation, whereas slight to moderate restrictions in- dicate that careful management is necessary due to el- evated parameter values. A similar trend was observed in the dry season, when 29 wells remained within the NR class and 28 shifted into the S-MR category, again dem- onstrating the seasonal sensitivity of groundwater qual- ity. Although most wells displayed consistent parameter ranges across both seasons, certain wells (e.g., 45, 54, and 57) consistently maintained NR status, suggesting greater resilience to seasonal variability. By contrast, wells such as 12 and 55 recorded higher EC, SAR, and chloride levels, which contributed to S-MR classification and pose risks for soil structure and long-term crop perfor- mance. These findings are consistent with the results of Fadl et al. (2024) and Meena et al. (2024), who similarly reported that seasonal fluctuations in water quality di- rectly affect irrigation suitability. The observed seasonal dynamics highlight the necessity of continuous monitor- ing and adaptive management, as emphasized by Kisekka (2024), to prevent adverse impacts on soil fertility and crop yields. Furthermore, the broader significance of this research aligns with the conclusions of Zhang et al. (2024), Marif (2023), and Marif and Esmail (2023), who underline that systematic water quality assessments are fundamental for sustaining irrigation practices in regions experiencing strong climatic seasonality. 3.2 CLASSIFICATION OF IRRIGATION WATER DEPENDING ON CATIONS AND ANIONS CONCENTRATION USING PRINCIPAL’S COM- PONENT ANALYSIS (PCA) IN WET SEASON In the dry season, the classification of irrigation water from various wells based on the guidelines from (Ayers and Westcot, 1985) reveals important insights into water quality for agricultural use. According to the data presented in Table 4, water from 29 wells showed no restrictions (NR) for irrigation, with electrical conduc- tivity (EC) values ranging from 0.35 to 0.69 dS m-1 and a pH ranging from 6.6 to 7.9. Additionally, the sodium adsorption ratio (SAR) for these wells ranged from 0.014 to 0.317 mmole l-1^1/2, indicating that they are generally safe for irrigation purposes with minimal adverse effects on soil properties. In contrast, water from 28 wells fell under the slight to moderate restriction (S-MR) category, with EC values between 0.76 and 1.76 dS m-1 , and SAR Acta agriculturae Slovenica, 121/4 – 2025 7 Assessing the impact of seasonal variability on irrigation water quality and suitability for agricultural use in wet and dry conditions values ranging from 0.034 to 0.757 mmole l-1. While still usable for irrigation, the water from these wells may lead to some long-term soil salinity issues or mild changes in the water’s sodium content. The classification system also accounts for additional factors such as bicarbonate (HCO3-) and chloride (Cl-) concentrations, which fur- ther influence water suitability (Hammoumi et al., 2024, Benaissa et al., 2024). For instance, the water from well 57, with a bicarbonate concentration exceeding 8.5 mmole l-1, was classified as having severe restrictions (S), which is a significant concern for its agricultural use. The detailed water classifications provided in Table 4 high- light the variability in water quality across wells and pro- vide a comprehensive view of how EC, SAR, and other factors interact to determine irrigation suitability. This data is crucial for managing water resources efficiently in regions dependent on irrigation, ensuring that water used for agricultural purposes does not negatively impact soil health or crop yields in the long term(Ali et al., 2024, Ishola, 2024a). This table summarizes the classification of water samples based on various parameters, providing a clear overview of how water quality varies and its suita- bility for agricultural use. The classification system, based on EC, SAR, pH, and ionic concentrations, is essential for understanding how water quality can impact irrigation practices and long-term soil health, as discussed in the 2024 context by(Ayers and Westcot, 1985). Table 5 presents the seasonal variation in the clas- sification of irrigation water quality based on the criteria provided by (Ayers and Westcot, 1985) comparing data between the wet and dry seasons for various water pa- rameters, including Electrical Conductivity (EC), pH, sodium adsorption ratio (SAR), bicarbonates (HCO3 -), and chloride (Cl-). The table also includes the number of wells categorized under different classifications during the wet and dry seasons (Abugu et al., 2024; Hamed Al Maliki et al., 2024). In the wet season, most of the wells (45) fall under the “NR” (Normal) category, which in- dicates that the water quality is within acceptable limits for irrigation, as reflected by relatively balanced levels of EC, pH, SAR, HCO3 -, and Cl-. In contrast, during the dry season, the number of wells classified as “NR” decreases slightly to 29, suggesting that water quality deteriorates in terms of salinity (EC) and ion concentrations, pos- sibly due to reduced water availability or concentration effects as water levels drop (Hailu et al., 2024). The “S- MR” (Slightly Marginally Restricted) classification is observed in 12 wells during the wet season, with a no- ticeable increase to 28 wells in the dry season, indicat- ing that the water quality becomes marginally less suit- able for irrigation due to an increase in certain factors like SAR or bicarbonates, which can affect soil structure and crop health (Muthu et al., 2024). The number of wells categorized as “Severe” remains at zero during the wet Water clasess Well Number pH EC dS m-1 SAR (mmolc ll -1)1/2 No .of wells No Restrictions (NR) 1, 2, 3, 4, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 53, 54, 55, 56, and 57 6.8 to 7.8 0.30 to 0.70 0.02 to 1.01 45 Slight to Moderate (SM) 5, 6, 7, 11, 15, 25, 47, 48, 49, 50, 51, and 52 6.7 to 7.6 0.76 to 1.76 0.06 to 0.56 12 Table 3: Classification of irrigation water according to international method Ayers & Westcote (1985) in wet season Water clasess Well number pH EC dS m-1 SAR (mmolc l -1)1/2 No .of wells No Restrictions (NR) 2, 9, 10, 11, 19, 22, 23, 27, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 53, 54, 55, 56 and 57 6.6 to 7.9 0.35 to 0.69 0.014 to 0.317 29 Slight to Moderate (SM) 1, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 17, 18, 20, 21, 24, 25, 26, 28, 31, 33, 47, 48, 49, 50, 51 and 52 6.6 to 7.9 0.70 to 1.70 0.034 to 0.757 28 Table 4: Classification of irrigation water according to international method (Ayers and Westcot, 1985) in dry season Acta agriculturae Slovenica, 121/4 – 20258 A. A. MARIF and A. O. ESMAIL season but increases to one in the dry season, which may reflect worsening water conditions, such as high salin- ity or sodium levels that severely affect crop growth and soil permeability. This seasonal variation is significant in terms of irrigation management, as the increased salin- ity and ion concentration in the dry season may lead to more challenges in managing irrigation practices, with the need for monitoring water quality and potentially modifying irrigation techniques to avoid long-term soil degradation or crop yield reductions. Thus, the seasonal fluctuations in water quality, including the higher EC, pH, SAR, and bicarbonates in the dry season, point to a direct link between seasonal changes in water availabil- ity and irrigation water quality, highlighting the need for adaptive management strategies in areas that experience such variations(Ishola, 2024b, Semar et al., 2024). 3.3 IRRIGATION WATER CLASSIFICATION US- ING (MAIA AND RODRIGUES, 2012) MODEL IN WET SEASON In the wet season, the water quality from the wells showed distinct classifications based on the Irrigation Water Quality Index (IWQI) values, with results falling into the “excellent,” “good,” “average,” and “poor” catego- ries, reflecting the variation in suitability for irrigation. Specifically, 16 wells exhibited an “excellent” water qual- ity, with IWQI values ranging from 1.34 to 1.92, which is consistent with water having low electrical conduc- tivity (EC) of 0.30 to 0.55 dS m-1.(Martínez et al., 2024, Lal et al., 2024) These findings indicate that the water from these wells is ideal for irrigation, as it falls within the ideal range for nutrient delivery and minimal salin- ity. On the other hand, 32 wells were classified as “good” with IWQI values between 2.09 and 5.87, associated with EC levels ranging from 0.30 to 1.72 dS  m-1. Although still within acceptable limits for irrigation, this classifica- tion suggests that these waters may require more care- ful management to avoid long-term soil salinization. A further 7 wells showed “average” quality, with IWQI values spanning from 5.89 to 8.90 and EC between 0.35 to 1.22 dS m-1, indicating that these waters can be used for irrigation, but may necessitate specific soil amend- ments or more intensive monitoring. Finally, two wells were classified as “poor,” with IWQI values of 21.55 to 22.79 and EC values of 0.55 to 1.76 dS m-1. Such water would be considered less suitable for irrigation without significant treatment or blending with higher quality sources, as the high IWQI reflects potential salinity risks to soil and crops. These results align with the findings of (Rodríguez-Aguilar et al., 2024) and (Scheibel et al., 2024) who also observed similar variations in water qual- ity in different seasonal conditions. Table 6, as presented, outlines these classifications based on (Maia and Rodri- gues, 2012, MARIF and ESMAIL, 2023) model in the wet season, demonstrating the range of EC and IWQI val- ues that characterize each water class, underscoring the variability in irrigation water quality across the studied wells(Ferreira et al., 2024). 3.4 IRRIGATION WATER CLASSIFICATION US- ING MAIA & RODIREGUES ( 2012) MODEL IN DRY SEASON In the dry season, the irrigation water from vari- ous wells was classified according to the model estab- lished by (Maia and Rodrigues, 2012) revealing a broad spectrum of water quality as reflected by the Irrigation Water Quality Index (IWQI) values. Specifically, the ir- rigation water from 37 wells was categorized as excel- lent, showing IWQI values ranging from 0.35 to 1.79 and electrical conductivity (EC) values between 0.39 and 0.85 dS m-1, suggesting that these wells provide op- timal water quality for irrigation. Seventeen wells were classified as good, with IWQI values ranging from 2.05 to 4.00 and EC values between 0.69 and 1.23 dS m-1, in- dicating that while the water quality is still suitable for irrigation, it may require more management to avoid potential adverse effects on crops (Kisekka, 2024). One well was classified as average, with an IWQI of 5.98 and W a t e r Class Wet season Dry season EC pH SAR HCO3 - Cl- EC pH SAR HCO3 - Cl- No. of wells in wet season No. of wells in dry season NR 45 57 57 2 54 29 57 57 0 57 S-MR 12 0 0 55 3 28 0 0 56 0 Severe 0 0 0 0 0 0 0 0 1 0 Table 5: Sesonal varaiation of classification of irrigation water according to (Ayers and Westcot, 1985) compariosn in wet and dry season Acta agriculturae Slovenica, 121/4 – 2025 9 Assessing the impact of seasonal variability on irrigation water quality and suitability for agricultural use in wet and dry conditions an EC of 1.29 dS  m-1, reflecting a less desirable qual- ity for irrigation, where water management strategies become more crucial (Muthu et al., 2024). Finally, two wells were rated as poor, with a fixed IWQI of 10.72 and EC values ranging from 1.12 to 1.70 dS m-1, mak- ing the water from these wells unsuitable for irriga- tion without treatment or careful management due to the higher risk of salinity affecting crop growth. The IWQI values for these classifications were consistent with the findings of (SHARMA, 2024) and (Sharma et al., 2024), who reported similar trends in water qual- ity assessments(Saeed et al., 2024). This classification provides a clear understanding of water quality across the studied wells and is crucial for guiding sustainable irrigation practices. The following table summarizes the detailed classification of the irrigation water based on (Maia and Rodrigues, 2012). This classification under- scores the variability in irrigation water quality within the region and its potential impact on crop production, highlighting the need for tailored water management strategies based on the IWQI and EC values in different wells (Shaw and Sharma, 2024). The seasonal variations significantly influence the classification of water quality across different wells, as evidenced by the data from 15 and 6 wells during the dry season. Specifically, during the dry season, water from these wells, which initially fell under the “Good” and “Average” categories, shifted to the “Excellent” cat- egory. This suggests an improvement in water quality, likely driven by changes in key water parameters such as electrical conductivity (EC) and the chemical composi- tion of the water, as discussed by (Haq and Muhammad, 2023). Conversely, during the wet season, water quality was predominantly categorized as “Good,” with 32 wells in this category, while the number of wells classified as “Excellent” remained lower at 16. However, a notable shift occurred during the transition from the wet season to the dry season, where 21 wells, which had been clas- sified as “Good” or “Average” in the wet season, were reclassified into the “Excellent” category. This transition reflects a decrease in the number of wells in the “Good” and “Average” categories, resulting in an overall increase in the number of wells classified as “Excellent” during the dry season. Such shifts in classification could be at- tributed to the changes in water chemistry, as noted in the seasonal variational classification provided by (Maia and Rodrigues, 2012) and the findings in Table 8, which highlight a clear increase in water quality classification during the dry season. 4 DISCUSSION Water clasess Well number EC dS m-1 IWQI No .of wells Excellent 10, 19, 23, 27, 35, 36, 37, 38, 39, 40, 42, 43, 44, 54, 55 and 56 0.30 to 0.55 1.34 to 1.92 16 Good 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 16, 17, 20, 21, 22, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 41, 45, 46, 47, 49, 53 and 57 0.30 to 1.72 2.09 to 5.87 32 Average 14, 15, 18, 48, 50, 51 and 52 0.35 to1.22 5.89 to 8.90 7 Poor 12 and 7 0.55 to 1.76 21.55 to 22.79 2 Table 6: Classification of irrigation water according to (Maia and Rodrigues, 2012)model in wet season Water clasess Well number EC dS m-1 IWQI No of wells Excellent 2, 5, 10, 11, 13, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 53, 54, 55, 56 and 57 0.39 to 0.85 0.35 to 1.79 37 Good 1, 3, 4, 6, 8, 9, 14, 15, 16, 17, 18, 33, 47, 48, 49, 50 and 51 0.69 to 1.23 2.05 to 4.00 17 Average 52 1.29 5.98 1 Poor 7 to 12 1.12 to 1.70 10.72 to 10.72 2 Table 7: Classification of irrigation water according to (Maia and Rodrigues, 2012) model in dry season Acta agriculturae Slovenica, 121/4 – 202510 A. A. MARIF and A. O. ESMAIL The study of water quality in Sulaimani City (2023), which classified irrigation water suitability across wet and dry seasons using the models of Maia and Rodrigues (2012) and Ayers and Westcot (1985), opens an impor- tant discussion about the broader implications for sus- tainable irrigation management in the region. The results clearly demonstrated that water quality parameters such as EC, SAR, pH, HCO₃-, and Cl- fluctuate seasonally, highlighting the need for long-term monitoring systems that can capture inter-annual trends and provide more reliable insights into how these fluctuations influence soil health and crop productivity over time. Establishing such monitoring networks across different wells would not only help track temporal shifts in water quality but also strengthen adaptive strategies for irrigation plan- ning. Additionally, the study revealed significant spa- tial differences among wells, with certain wells showing higher resilience to seasonal changes, thereby empha- sizing the importance of developing site-specific irriga- tion management strategies. Such localized approaches, supported by decision support tools that integrate water quality data with crop requirements, soil conditions, and seasonal forecasts, could enhance water-use efficiency and minimize risks of soil salinization. Another key is- sue raised by the findings is the lack of direct assessment of long-term impacts of irrigation water on soil proper- ties. In areas classified as having slight to moderate re- strictions, elevated levels of EC, SAR, and bicarbonates could gradually lead to soil salinity, poor structure, and reduced fertility, ultimately threatening crop yields. Therefore, future research should focus on linking irriga- tion water quality with soil health outcomes and testing mitigation practices such as soil amendments. Moreo- ver, while this study employed established classification models, the discussion highlights the potential value of incorporating modern analytical tools like machine learning to improve the precision and predictive capac- ity of irrigation water assessments. By training models on historical datasets and integrating them into real-time decision-making platforms, researchers and practition- ers could achieve more dynamic and accurate manage- ment outcomes. Finally, given that climate change is projected to alter rainfall regimes, evaporation rates, and overall water availability, its likely influence on irrigation water quality cannot be overlooked. Anticipating shifts in salinity, ion concentrations, and related parameters under future climatic conditions is critical to safeguard agricultural sustainability. Overall, these findings under- line that sustainable irrigation management in Sulaimani requires a comprehensive and integrated approach that combines long-term monitoring, site-specific strategies, soil health assessments, advanced predictive modeling, and climate change considerations. 5 CONCLUSION In conclusion, this study has highlighted the sig- nificant seasonal variations in water quality, as classi- fied according to (Ayers and Westcot, 1985) for irriga- tion purposes. The analysis of wells during both the wet and dry seasons revealed notable shifts in water quality, particularly in terms of Electrical Conductivity (EC), So- dium Adsorption Ratio (SAR), and other ionic concen- trations such as bicarbonates and chloride. During the wet season, most wells showed no restrictions, indicat- ing favorable water conditions for irrigation. However, the dry season saw an increase in slight to moderate re- strictions, suggesting that water quality may deteriorate with reduced water availability or concentration effects, especially concerning salinity and sodium levels. These findings underline the necessity for continuous monitor- ing and adaptive water management strategies to ensure sustainable irrigation practices and mitigate potential long-term impacts on soil health and crop productivity. Furthermore, the use of the Irrigation Water Qual- ity Index (IWQI) by (Maia and Rodrigues, 2012) provid- ed additional insight into the variability of water quality across the studied wells. The classification of water qual- ity into categories ranging from “Excellent” to “Poor” Seasons Water classification according to (Maia and Rodrigues, 2012) Excellent Good Average Poor Wet season 16 32 7 2 Dry season 37 17 1 2 No. of wells 21 15 6 0 Variations Increase Decrease Decrease 0 Table 8: The Seasonal variational classification of irrigation water according to (Maia and Rodrigues, 2012) Acta agriculturae Slovenica, 121/4 – 2025 11 Assessing the impact of seasonal variability on irrigation water quality and suitability for agricultural use in wet and dry conditions revealed how seasonal shifts in key parameters such as EC and pH can influence the suitability of water for ir- rigation. In the wet season, water from many wells was classified as good, while the dry season saw some im- provements in water quality, with more wells categorized as “Excellent.” These shifts emphasize the importance of understanding the dynamic nature of water quality and its impact on irrigation efficiency. By incorporating both seasonal and index-based classifications, this study con- tributes valuable knowledge to the management of irriga- tion water resources, providing guidance for farmers to adjust their irrigation strategies in response to changing environmental conditions and to optimize agricultural productivity throughout the year. Recommendations Incorporation of Advanced Analytical Techniques (Machine Learning) for Classification and Prediction – Rationale: The classification of irrigation water quality is done using traditional models, which could be enhanced by incorporating modern data analysis methods. Advanced techniques like machine learning could provide more accurate, real-time assessments of water quality and pre- dict future trends based on historical data. – Future Work: Develop a machine learning-based model that can predict irrigation water qual- ity based on various parameters (EC, SAR, pH, HCO₃-, and Cl-). This model could be trained us- ing historical data from wells and incorporated into a web-based platform for real-time decision support in irrigation management. – Evaluation of the Effects of Climate Change on Irrigation Water Quality – Rationale: The study provides insights into sea- sonal variations in water quality but does not ex- plore how climate change may affect future water quality. As climate change is likely to alter pre- cipitation patterns, evaporation rates, and water availability, these changes could exacerbate sa- linity and ion concentration issues in irrigation water. – Future Work: Conduct a study that integrates climate change projections with current water quality data to assess how future climatic condi- tions might influence irrigation water suitability. This could involve modeling the effects of tem- perature increases, altered rainfall patterns, and changing evaporation rates on the water quality parameters critical for irrigation. Acknowledgment We would like to express our sincere gratitude to the Head of the Garden Design Department, the Dean, and the Vice Dean of the Bakrajo Technical Institute (BTI), as well as the faculty of the Soil and Water Department, College of Agricultural Engineering Sciences, Salahad- din University-Erbil, Kurdistan Region, Iraq, for their invaluable support and guidance throughout the course of this research. Their continuous encouragement, con- structive feedback, and dedication to academic excel- lence were instrumental in shaping the direction and quality of this work. We are also deeply appreciative of the institutional resources and the highly supportive re- search environment provided by BTI, which created the necessary foundation for carrying out this study effec- tively. Conducted in Sulaimani, Kurdistan Region, Iraq, this research was successfully completed owing to their sustained commitment and professional support. Data availability statement All data are included in the manuscript. 6 REFERENCES Abugu, H. O., Egbueri, J. C., Agbasi, J. C., Ezugwu, A. L., Omeka, M. E., Ucheana, I. A. & Aralu, C. C. (2024). Hy- drochemical characterization of ground and surface water for irrigation application in Nigeria: A review of progress. Chemistry Africa, 1-26. Al Maliki, A., Kumar, U. S., Falih, A. H., Sultan, M., Al-Naemi, A., Alshamsi, D., Arman, H., Ahmed, A. & Sabarathinam, C. (2024). 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