INTRODUCTION

 Potato (Solanum tuberosum L.) ranks third globally in food crop production after sugarcane and tomato (FAO, 2024). It forms the foundation of horticulture in Pakistan, where Punjab province produces more than 90 percent of the national potato output (Pakistan Bureau of Statistics, 2022). Despite its agricultural importance, potato production faces a persistent biotic challenge from early blight caused by the necrotrophic fungus Alternaria solani, which significantly threatens both production and tuber quality on a global scale (Chaerani and Voorrips, 2006; Cristina et al., 2021).

 Early blight manifests as concentric, target-shaped lesions on leaves, stems, and tubers, leading to premature defoliation, reduced photosynthetic capacity, and yield losses that can exceed 50% under favorable environmental conditions (Rotem, 1994; van der Waals et al., 2001). The disease is especially widespread in warm and humid climates prevalent in most parts of the Indo-Gangetic plains, including Punjab, Pakistan. Survival of A. solani in crop residues and soil, combined with high genetic variability, makes long-term disease management and resistance breeding challenging (Leiminger and Hausladen, 2012).

 The main methods of plant disease assessment are visual field observations and manual sampling. They offer useful information on disease occurrence and severity, but can be labour intensive, time consuming and observer dependent (Bock et al., 2010; Buja et al., 2021). Geographic Information Systems (GIS) can be used to make maps and analyse the spatial distribution of plant diseases and determine regions with relatively high disease levels. GIS-based analysis can also incorporate disease observations with environmental and geographical factors, if available, to aid in the surveillance and management of the disease, thereby helping in decision making (Tsouros et al., 2019; Mahanta et al., 2024).

 Accurate pathogen identification is critical for effective disease management. Although morphological characterization provides preliminary diagnostic information, molecular techniques such as Internal Transcribed Spacer (ITS) sequencing have been widely employed for identification of Alternaria solani and closely related species with high specificity (Woudenberg et al., 2014; El-Ganainy et al., 2021; Ayad et al., 2019). GIS applications in agriculture, reviewed by Mathenge et al. (2022), further support spatial analysis of disease distribution.

 In Punjab, despite frequent reports of early blight outbreaks in potato-growing fields, there is a general lack of systematic research on the spatial distribution of the disease, molecular identity of local isolates, and science-based disease management. No GIS-based epidemiological and molecular study of early blight has been conducted in this region, representing a critical knowledge gap. Accordingly, there is an urgent need for spatially explicit, science-based approaches integrating field surveillance, GIS-based mapping, and molecular diagnostics.

 The objectives of this study were to: (1) quantify the disease incidence, prevalence, and plant mortality of potato early blight across 40 sites in four major potato-producing districts of Punjab, Pakistan; (2) generate GIS-based spatial distribution maps to identify disease hotspots and spatial patterns of disease intensity; (3) characterize Alternaria solani isolates through colony morphology and conidial microscopy; and (4) confirm pathogen identity through ITS-based PCR amplification, BLAST analysis, and Maximum Likelihood phylogenetic inference.

MATERIALS AND METHODS

Survey and Sample Collection: A field survey was conducted at 40 potato-growing sites across four districts of Punjab, Pakistan: Multan, Khanewal, Sahiwal, and Okara. Ten sites were surveyed in each district. The survey was conducted across both urban and rural potato-growing regions to assess disease incidence, prevalence, and severity during the potato growing season.

 Each location was subdivided into quadrants systematically in an X formation, and samples were picked on the lines that cut across. Specifically, leaves with the typical symptoms of the disease turn into rounded, irregular spots about 2-5 mm in diameter, and their color is dark brown to black; thus, were selected in the field specifically. Infected potato leaves and roots, as well as soil in rhizosphere, were sampled with a sharp knife and put in polythene sampling bags. All bags were well labeled with the date and locality where the sample was taken. The obtained samples were further taken to the Plant Pathology Laboratory, BZU Multan, where they were cultured and their pathotypes of A. solani determined.

Assessment of Early Blight Disease: During surveys, 50–150 plots with 100–500 plants per plot using an X pattern were observed at each site. The level of disease was determined by visual estimation of the percentage of plant tissue showing early blight symptoms. For consistency and standardization, a disease rating scale of 0–5 was used (James, 1971), with modifications as described by Chaudhary et al. (2021) (Table 1).

Table 1. Rating Scale for The Assessment of Early Blight of Potato

Disease prevalence, disease incidence, and crop mortality were calculated using the standard epidemiological formulas as defined by Campbell and Madden (1990) and Nutter et al., (1991):

Statistical Analysis: This study was observational in design. Disease incidence, prevalence, and plant mortality data collected from 40 survey sites across four districts were analyzed using one-way ANOVA in Statistix 8.1 to test for significant differences among districts (α = 0.05). The assumptions of ANOVA (normality and homogeneity of variance) were verified using residual plots and Levene's test, respectively. Proportional data (disease incidence and prevalence) were arcsine square-root transformed prior to analysis. Pairwise mean comparisons were performed using Fisher's Least Significant Difference (LSD) test (Steel and Torrie, 1997). Pearson's correlation coefficient (r) was calculated to assess the strength and direction of linear relationships among disease incidence, disease prevalence, and plant mortality using Statistix 8.1. Correlation strength was interpreted as: |r| ≥ 0.70 = strong; 0.40 ≤ |r| < 0.70 = moderate; |r| < 0.40 = weak. Direction was expressed as positive or negative. Correlations were considered statistically significant at p ≤ 0.05.

GIS-based Spatial Analysis: The spatial distribution of disease intensity and the examination of potential associations between environmental factors and severity of the disease were examined with the help of GIS techniques, including the buffer analysis and interpolation. The data about the disease and GPS coordinates on each location were measured, and the data set was saved and processed through ArcGIS after weeding out unreliable data points. The table data were transferred into a point feature layer by using the Display XY Data tool and using the appropriate latitude and longitude options. The severity of the disease was demonstrated on the map using equal intervals with the help of a color range that the points received due to the disease rating, and a clear legend to define the levels of severity. The map that was developed finally had all the necessary features like a title, north arrow and a scale bar and a detailed legend showing which all the map layers were in detail, such that the information would be easily read. Mapping processes were conducted with the ArcGIS software version 10.8, and other GIS techniques that had been reported by Mahanta et al. (2024).

Pathogen Isolation and Purification: Leaf samples were surface-sterilized by rinsing with distilled water, treating with 0.5% sodium hypochlorite solution for 2 minutes (Barnett and Hunter, 1998), and rinsing again with sterile distilled water to remove surface contaminants. Leaf pieces (5 mm²) including both diseased and healthy tissue were plated onto potato dextrose agar (PDA) supplemented with streptomycin sulfate (100 μg mL⁻¹) under laminar airflow conditions (Dhingra and Sinclair, 1995). Plates were incubated at 28 ± 1°C under a 12-h light/dark cycle. Emerged fungal colonies were purified by single-spore isolation and transferred to PDA slants for storage as reference cultures.

Morphological Characterization of the A. solani: Morphological observations were conducted after the cultures grew to three weeks, following the procedure outlined by Yu et al. (2003). Morphological characteristics were noted and included colony size, color, shape of the colony edges and patterns found on the colony surface (rings or zones). Spores’ shape, color, size, and the count of septa were viewed using a Zeiss Primo Star microscope with an AxioCam ERC 5s camera under observation at 40x and 100x magnifications.

Molecular Characterization of the Pathogen

DNA extraction: DNA of the pathogenic fungus was isolated by employing the CTAB technique outlined by Doyle and Doyle (1987). Tris-HCl, NaCl, EDTA, CTAB and double-distilled water were combined to create an extraction buffer; b-mercaptoethanol was added after heating was performed to activate the solution and enhance the yield of DNA. Pure cultures of A. solani were used to obtain fungal mycelium collected and ground into fine powder using liquid nitrogen. The mycelium was scraped off in Eppendorf tubes filled with CTAB buffer and b-mercaptoethanol, scraped and incubated at 65 degrees Celsius with frequent shaking to ensure full cell lysis and release of DNA. The incubation was followed by the addition of chloroform-isoamyl alcohol to separate the DNA and the impurities. Centrifuged them and the DNA in the clear upper layer moved to new tubes. The solutions were then kept at -20 degree centigrade to allow the removal of DNA by the addition of cold isopropanol. The liquid phase was discarded after centrifugation, and the pellet containing DNA was washed with 70% ethanol in order to eliminate the traces of salts and contaminants. The pellet was subsequently dried in the air and put into the double-distilled water. The DNA solution was transferred to -80 degree Celsius and kept at that temperature to be used in the future.

Polymerase Chain Reaction (PCR): The ITS of the 5.8S rDNA was also amplified with the primers of the ITS4 and ITS5 according to an established technique with slight modifications. The internal transcribed spacer (ITS) region of the nuclear ribosomal DNA was amplified using primers ITS5 (5′-GGAAGTAAAAGTCGTAACAAGG-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′). A total of 26.5 ul reaction mixture was prepared in each PCR reaction (Table 2). The reactions were performed with the aid of the BK-AI Standard Thermal Cycler (Biobase), and the temperature and time parameters of each step of the amplification are provided in Table 3.

Table 2. Recipe for Reaction Mixture of PCR

This reaction mixture was composed of all the vital materials necessary to carry out a successful DNA amplification in the process of PCR.

Table 3. PCR conditions for the amplification of A. solani isolates

Gel Electrophoresis: A 1.25% (w/v) agarose gel was prepared by dissolving 1.5 g of agarose in 120 mL of Tris-acetate-EDTA (TAE) buffer. The mixture was kept in the hot water until the agarose was fully dissolved, and then the mixture was poured into the gel casting tray and left until fully solidified, after which the combs were removed and the tray locks opened. The solid gel, and the tray were transferred to the electrophoresis, where TAE buffer was added until the gel was submerged by a 2-4 mm layer of buffer as per the normal procedure, as explained by Sambrook and Russell (2001). Each DNA sample was mixed with 6X loading buffer using a gentle twirl, and 20 uL of the DNA sample and the loading buffer were added to the agarose gel using individual wells. Moreover, 10 ul of primers ITS4 and ITS5 were placed in each well, as in El-Ganainy et al. (2021).

 Amplification results were verified by loading 6 uL of the PCR product into a 1% ethidium bromide-stained agarose gel and running it at 85 volts for 1 hr. Once the dye moved to the terminal of the gel, the power supply was reverted off, and the cables were pulled out. The lid of the electrophoresis chamber was then opened, and the gel was finally examined under UV light with the use of a gel documentation system.

Genome Sequencing and Phylogenetic Analysis: Purified PCR products from all five isolates were submitted to Macrogen Inc. (Seoul, Republic of Korea) for Sanger sequencing using the ITS4 forward primer. The resulting sequences were deposited in the NCBI GenBank database (Accession Numbers: PZ544711, PZ544780, and three additional accession numbers pending GenBank processing; to be updated in the final proof). Sequence alignment and BLAST searches were conducted against the NCBI database to identify closely related reference sequences. Phylogenetic analysis was performed in MEGA 11 (Tamura et al., 2021) using the Maximum Likelihood method with the Tamura-Nei substitution model (Tamura and Nei, 1993) and 1,000 bootstrap replicates. Curvularia lunata (FG129235) was used as the outgroup.

RESULTS

Survey of 4 Potato Growing Districts in Punjab: A survey conducted on the characteristics of the disease in 40 sites in 4 districts in Punjab, Pakistan, to investigate the severity of the disease, incidence, prevalence and mortality rate reveals different findings. Table 4 contains details of various sites, such as the GPS coordinates. The disease symptoms on potato plants were assessed by documenting the disease scores based on the disease rating scale (Figure 1; Table 1).

Table 4. Details of survey and sampling sites

Figure 1: Disease rating scale explaining the different levels of infection due to Early Blight of Potato

 One-way ANOVA revealed significant differences among districts for all three disease parameters (p ≤ 0.05). Okara recorded the highest disease incidence (56.7%), disease prevalence (54.8%), and plant mortality (51.8%), followed by Khanewal (50.4%; 53.3%; 50.5%), Multan (47.2%; 45.8%; 46.0%), and Sahiwal (40.9%; 38.3%; 37.8%). For incidence, all four district means differed significantly (LSD = 2.028). For prevalence and mortality alike, all district pairs differed significantly except Okara and Khanewal (54.8% vs 53.3% and 51.8% vs 50.5%, differences below the LSD) (Table 5).

Table 5. Mean disease incidence (%), disease prevalence (%), and plant mortality (%) of potato early blight across four surveyed districts of Punjab, Pakistan, with LSD pairwise comparisons (ANOVA, p ≤ 0.05).

LSD = 2.028

 Pearson correlation analysis revealed a strong positive correlation between disease incidence and disease prevalence (r = 0.7159, p ≤ 0.05). A moderate positive correlation was observed between disease incidence and plant mortality (r = 0.6446, p ≤ 0.05). Disease prevalence and plant mortality exhibited a strong positive correlation (r = 0.8588, p ≤ 0.05) (Table 6).

Table 6. Pearson correlation among disease incidence, prevalence, and mortality

* Indicates statistically significant Pearson correlation coefficient at p ≤ 0.05 (two-tailed). Positive r values indicate a positive linear association between variables

GIS-based Distribution of Potato Early Blight Disease in Selected Districts of Punjab, Pakistan: GIS maps were used to show the distribution and the severity of the disease amongst the areas under study, and they were used to depict the general patterns and magnitude of the disease in the regions that were under the study.

Incidence (%) of Potato Early Blight Disease in Selected Districts of Punjab, Pakistan: Experiments revealed definite variations in the percent diseases incidence in different localities, indicating that not all the localities were impacted by the disease. In general, the lowest disease incidence was observed in Sahiwal and Khanewal through spatial analysis. The highest incidence of disease was reported in Khanewal (Moza Nabipur Kabirwala 68%), 41/3R (67%) and Adda Gambir (62%) in Okara. Conversely, the disease was observed to be lower at Gopalpur (21%) at Multan, followed by 97/6R (22) at Sahiwal, meaning that the sites were less susceptible to the disease. There was several other sites that had moderate to high disease incidence that provided further insight into the total disease situation in the area. As an example, 6 /1 Shergharh Road, Depalpur had a disease incidence of 61%. Likewise, 55/2L Okara, 19 Kasi Jhandaywala, 40/D Depalpur, Umerpur, Chak No. 171/10-R, 5 Faiz, and 69/4R Yousafwala had values of incidence of diseases in the range of 51% 59% (Figure 2).

Figure 2. GIS map of Disease Incidence (%) of Early Blight of Potato in selected potato-cultivating areas of Multan, Khanewal, Sahiwal, and Okara districts of Punjab, Pakistan.

Prevalence (%) of Potato Early Blight Disease in Selected Districts of Punjab, Pakistan: The highest site-level disease prevalence was recorded at 41/3R in Okara (62%), followed by Moza Nabipur Kabirwala in Khanewal (61%). Adda Gambir in Okara and 56/5L in Sahiwal each recorded 59% disease prevalence. On the contrary, Gopalpur (17) in Multan, then 10/11L Chichawatni (18), and 97/6R (19) in Sahiwal had the lowest disease prevalence. There were a few other sites that had moderate and high percentages of Early blight, thus giving more reasons for the general occurrence of the disease. Chak No. 12/AH and 6/1 Shergarh Road Depalpur, both had a prevalence of 58%. On the same note, 55/2L Okara had a 57 percent prevalence, whereas 40/D Depalpur had a 55 percent prevalence. The prevalence of the disease was 54 percent in the sites 24/2L Adda Kissan Okara and Umerpur, and 52 percent in 19 Kasi Jhandywala (Figure 3).

Figure 3. GIS map of Disease Prevalence (%) of Early Blight of Potato in selected potato-cultivating areas of Multan, Khanewal, Sahiwal, and Okara districts of Punjab, Pakistan.

Mortality (%) of Potato Early Blight Disease in Selected Districts of Punjab, Pakistan: The death of the potato plants due to Early blight during the field conditions was evaluated, but the results showed an obvious difference between the sampled sites. The highest mortality of plants was observed at the Moza Nabipur Kabirwala (64%) at Khanewal, and 41/3R (61) and Adda Gambir (60) in Okara. However, there were also lower levels of mortality of plants in some sites, such as Gopalpur (19%) in Multan and 10/11 Chichawatni (19%) in Sahiwal, with 97/6R (22) and Muhammadpur (24) in Sahiwal, respectively. There were also other sites that had moderate plant mortality. Both Chak No. 12/AH and 6/1 Shergarh road Depalpur registered mortality rates of 59%. Similarly, mortality rates at 56/5L Sahiwal were found to be 56, 55/2L Okara was 55 and 53 at 19 Kasi Jhandywala (Figure 4).

Figure 4. GIS map of Plant Mortality (%) due to Early Blight of Potato in selected potato-cultivating areas of Multan, Khanewal, Sahiwal, and Okara districts of Punjab, Pakistan.

Morphological Characteristics of Alternaria solani Isolates

Colony Morphology of A. solani Isolates: The morphological variations in A. solani isolates in PDA media were distinct at various periods of incubation until the ninth day. Isolate A3 was the largest producer of the colony against A1, A5, A2, and A4. Colors also varying in the colonies and were whitish, brownish-grey, and blackish-grey. The color of the colony changed slowly to darker with an increase in the time of growth (Figure 5; Table 7).

Table 7. Details of Colony Morphology of the isolates of Alternaria solani

Figure 5. Colony Morphology among different isolates of A. solani isolated from infected potato

Microscopic Features of A. solani Isolates: It was clearly seen that the various isolates of A. solani had distinct differences in their microscopic characteristics. Generally, the conidia length (not including the beak) of the five isolates was between 15.28 and 41.24 µm and the width between 7.56 and 17.34 µm. The horizontal septa were 3 to 7 branches, but the vertical were less frequent (Figure 6; Table 8).

Table 8. Different microscopic characteristics of five A. solani isolates

Figure 6. Microscopic characterization of A. solani isolates at 100X

Molecular Characterization of A. solani isolates: All the A. solani isolates were capable of being amplified using the primer sets ITS4 and ITS5. The DNA fragments that were amplified had a size of about 1200 base pairs (bp). The amplification results are presented in Figure 7, with lane 1 (M) marking the molecular size marker to compare the size of the fragments. Clearly, DNA bands of approximately 1200 bp were observed in lanes 2 (A1) and 3 (A2) corresponding to A. solani isolates. Similarly, lanes 4 (A3), 5 (A4) and 6 (A5) had amplified DNA fragments with sizes of about 1200 bp. These results prove the amplification of the target gene region to be successful in all the tested isolates.

Figure 7. PCR amplification of the ITS region from Alternaria solani isolates. Lane 1 (M): DNA size marker; lanes 2–6: isolates A1, A2, A3, A4, and A5, respectively. All isolates produced an amplicon of approximately 1200 bp.

 The ITS sequences were subjected to BLAST analysis to identify the fungi, with sequence identities ranging from 99.1 to 99.8 per cent to the reference sequences (Table 9) that were Alternaria solani from Pakistan and India. These results gave molecular evidence which correlated with the morphological identification of the isolates. However, because ITS alone may not fully resolve closely related Alternaria species, multilocus analysis using additional markers, such as GAPDH, TEF1-α, RPB2, and Alt a 1, would provide stronger species-level confirmation.

Table 9. Comparison of current 5 isolates with reference sequences from NCBI BLAST results

Figure 8. Phylogenetic tree derived from the Maximum Likelihood method displaying the relationship of the sequences obtained from the isolates of Alternaria collected during the study and the sequences of the reference Alternaria isolates from Genbank. MEGA 11 software was used for phylogenetic analysis with substitution model: Tamura–Nei, with 1,000 bootstrap replicates. Curvularia lunata (FG129235) was used as the outgroup. Study isolates are highlighted in red.

DISCUSSION

 This study provides a comprehensive evaluation of the distribution, intensity, and pathogen diversity of potato early blight in key growing regions of Punjab, Pakistan, integrating GIS-based epidemiological mapping with morphological, microscopic, and molecular characterization of A. solani isolates. GIS-based spatial analysis revealed marked variability in early blight disease incidence, prevalence, and plant mortality across the four surveyed districts of Punjab. Okara and Khanewal consistently emerged as high-risk districts, while Multan and parts of Sahiwal exhibited comparatively lower disease pressure. This spatial heterogeneity in disease distribution is consistent with patterns documented in other regions for potato and tomato crops using spatial analysis (Hussain et al., 2019; Ding et al., 2021). Such variation likely reflects local interactions among host susceptibility, pathogen inoculum pressure, and environmental conditions. High disease incidence at Moza Nabipur Kabirwala (68%) and Adda Gambir (62–67%) indicates early establishment and rapid spread of A. solani, which is characteristic of this pathogen under conducive conditions (Chaudhary et al., 2021).

 In the current study, prevalence was more than 55% in some places of Okara and Khanewal, which implies that the pathogen was established widely. The prevalence is high, which implies the A. solani inoculum can stay in crop remnants and soil, which is also in line with its saprophytic survival strategy (Schmey et al., 2024). On the other hand, due to the small prevalence in Gopalpur and Chichawatni, this could be due to a positive cultural practice, or varietal resistance or unfavorable microclimatic conditions. The values of plant mortality in hotspots in areas which are considered to be 60-64% demonstrate the devastating nature of early blight in cases where it is not controlled. Extensive defoliation and loss of plants have a direct impact on reducing photosynthetic capacity and tuber bulking, leading to significant loss of yield. Losses of 5-50% of the yield have been commonly reported in periods of severe epidemics of early blight (Chaudhary et al., 2021). The fact that high incidence, prevalence, and mortality in this study share a strong spatial overlap also goes to the extent of verifying the suitability of GIS mapping as a decision-support tool in targeted disease intervention. The maps of spatial interpolation help identify clear areas that need priority management, which approve earlier research that underlines an effective use of GIS as the effective method of plant disease surveillance (Hussain et al., 2019; Ding et al., 2021). These tools are especially important in agricultural systems with a limited number of resources, as they can help to control diseases more precisely and implement fungicides rationally, which will help minimize the costs of production and minimize the impact on the environment.

 The morphological differences distinctly existed between the A. solani isolates cultivated on the PDA medium, especially in the size and growth rate of the colonies. Isolate A3 exhibited the largest colony diameter (95.56 mm), followed by A1 (92.71 mm), A5 (90.20 mm), A2 (85.54 mm), and A4 (83.50 mm). These differences indicate variation in colony growth among the studied isolates under the experimental conditions (Ayad et al., 2019; El-Ganainy et al., 2021). Morphological character alone as a species identifying character is, however, becoming deemed unreliable since there is an overlap between large-spored Alternaria species. Further microscopic characterization indicated that there was a difference in conidium size, septation and shape among isolates. The obtained values of conidial length (15.28-41.24 um) and width (7.56-17.34 um) are in line with classical descriptions of A. solani (El-Ganainy et al., 2021). This variability can potentially affect the efficiency of the spore dispersal and capacity to infect, and lead to differences in the severity of the disease between places.

 Although ITS sequence analysis provided molecular evidence consistent with the morphological identification of the studied isolates as A. solani, this marker alone may not adequately distinguish closely related Alternaria species. Future investigations incorporating additional molecular markers, including GAPDH, TEF1-α, RPB2, and Alt a 1, would improve species-level resolution and provide a more robust assessment of genetic diversity (Woudenberg et al., 2014). This result is consistent with previous morphological and molecular studies of Alternaria isolates associated with potato and other solanaceous crops (El-Ganainy et al., 2021). Specifically, isolate PZ544711 showed 99.81% sequence similarity with A. solani isolate OK036375 from India, whereas isolate PZ544780 showed 99.15% similarity with A. solani isolate OK036371 from India. In the Maximum Likelihood phylogenetic tree, both study isolates clustered with their respective closely related A. solani reference sequences (Figure 8). The low ITS sequence variation observed among the study isolates and selected reference sequences is broadly consistent with previous reports of genetic variation within A. solani populations (Van der Waals et al., 2004; Chaerani et al., 2017). However, because ITS alone may not fully resolve closely related Alternaria species, multilocus analysis using additional markers, such as GAPDH, TEF1-α, RPB2, and Alt a 1, would provide stronger species-level confirmation. Although ITS sequence analysis, together with morphological characteristics, provided preliminary support for the identification of the study isolates as A. solani, ITS alone may not provide sufficient resolution to distinguish closely related Alternaria species. Multilocus phylogenetic analysis incorporating additional markers, such as GAPDH, TEF1-α, RPB2, and Alt a 1, would provide more robust species delimitation and phylogenetic inference in future studies (Woudenberg et al., 2014).

 Areas like Khanewal and Okara, which are susceptible to fungal attacks, need to be given more vigilance, use of fungicides at the right time and use of resistant cultivars where feasible. Additionally, the reported genetic variation of A. solani isolates underscores the need to change fungicides with varying modes of action in order to postpone the emergence of resistance. The lack of completely resistant potato varieties highlights the necessity of the use of integrated disease management strategies that combine the cultural practice, resistant germplasm, and the use of rationally applied fungicides.

 The findings underscore the need for region-specific, integrated disease management approaches in Punjab's potato sector. Areas such as Khanewal and Okara require enhanced surveillance, timely fungicide application with rotations among different modes of action to mitigate resistance development, and deployment of resistant cultivars where available. The calendar-based fungicide application currently practiced in the region is economically unfeasible and environmentally unsustainable (Khan et al., 2003; Ganie et al., 2013). GIS-based disease maps provide a spatially explicit decision-support framework to guide targeted, rational interventions. The observed genetic similarity among isolates does not preclude intraspecific variation in aggressiveness, which should be investigated in future studies alongside multi-season disease monitoring

Conclusions: The current study combined the spatial assessment and morphological, microscopic and ITS-molecular characterization of potato early blight in four major potato growing districts of Punjab, Pakistan and implemented it using a GIS-based approach. Spatial analysis revealed Okara and Khanewal as comparatively high level of disease intensity (site-level disease incidence of 68%, disease prevalence of 62% and plant mortality of 64%). Variation between the fungal isolates studied was found by morphological and microscopic examinations. The molecular evidence of 99.1–99.8% similarity with the reference sequences of Alternaria solani as determined by ITS sequence analysis confirmed the identification of the isolates. Maps of disease distribution using GIS provide a spatial context to assist in the identification of disease management priorities for disease surveillance. A detailed multilocus molecular characterisation and a multi-season epidemiological investigation are suggested to aid in pathogen identification and the development of disease-management strategies in Punjab.

Authors contribution: All authors contributed equally to the conception, designing, writing, reviewing and approval of the final version of the manuscript.

Declarations of competing interest: The authors declare that they have no competing financial interests or personal relationships that could have influenced the work reported in this study.

Availability of data and materials: All data in this manuscript are available upon request from the corresponding author.

REFERENCES

Ayad, D., D. Aribi, B. Hamon, A. Kedad, P. Simoneau and Z. Bouznad (2019). Distribution of large-spored Alternaria species associated with early blight of potato and tomato in Algeria. Phytopathol. Mediterr. 58(1): 139-150. https://doi.org/10.14601/Phytopathol_Mediterr-23988

Barnett, H.L. and B.B. Hunter (1998). Illustrated Genera of Imperfect Fungi. 4th Ed. APS Press, St Paul, USA.

Campbell, C.L. and L.V. Madden (1990). Introduction to Plant Disease Epidemiology. John Wiley and Sons, New York, USA.

Bock, C., G. Poole, P. Parker and T. Gottwald (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit. Rev. Plant Sci. 29(2): 59-107. https://doi.org/10.1080/07352681003617285

Buja, I., E. Sabella, A. G. Monteduro, M. S. Chiriacò, L. De-Bellis, A. Luvisi and G. Maruccio (2021). Advances in Plant Disease Detection and Monitoring: From Traditional Assays to In-Field Diagnostics. Sens. 21(6): 2129-2141.https://doi.org/10.3390/s21062129

Chaerani, C., M. K. Kardin, S. Suhardi, E. Sofiari, R. V. van Ginkel, R. Groenwolt and R. E. Voorrips (2017). Variation in aggressiveness and AFLP among Alternaria solani isolates from Indonesia. Indones. J. Agric. Sci. 18(2): 51-62. https://doi.org/10.21082/ijas.v18n2.2017.p51-62

Chaerani, R. and R. E. Voorrips (2006). Tomato early blight (Alternaria solani): the pathogen, genetics, and breeding for resistance. J. Gen. Plant Pathol. 72(6): 335-347. https://doi.org/10.1007/s10327-006-0299-3

Chaudhary, A. K., J. Yadav, A. K. Gupta and K. Gupta (2021). Integrated disease management of early blight (Alternaria Solani) of potato. Trop. Agrobiodivers. 2(2): 77-81. https://doi.org/10.26480/trab.02.2021.77.81

Cristina, M., M. Sturzeanu and A. Din (2021). In vitro evaluation of fungicide and bio-fungicides against isolates of Alternaria solani Sorauer. Rom. Biotechnol. Lett. 26(1): 2295-2301.  https://doi.org/10.25083/rbl/26.1/2295.2301

Ding, S., K. Meinholz and A. J. Gevens (2021). Spatiotemporal distribution of potato-associated Alternaria species in Wisconsin. Plant Dis. 105(1): 149-155. https://doi.org/10.1094/PDIS-11-19-2290-RE

Dhingra, O.D. and J.B. Sinclair (1995). Basic Plant Pathology Methods. 2nd Ed. CRC Press, Boca Raton, USA.

El-Ganainy, S. M., S. E. El-Abeid, Y. Ahmed and Z. Iqbal (2021). Morphological and molecular characterization of large-spored Alternaria species associated with potato and tomato early blight in Egypt. Int. J. Agric. Biol. 25(5): 1101-1110. https://doi.org/10.17957/IJAB/15.1769

FAO (2024). FAOSTAT: Crops and Livestock Products. Food and Agriculture Organization of the United Nations, Rome, Italy. https://www.fao.org/faostat/en/#data/QCL

Ganie, S.A., M.Y. Ghani, Q. Nissar, N. Jabeen, Q. Anjum, F.A. Ahanger and A. Ayaz (2013). Status and symptomatology of early blight (Alternaria solani) of potato (Solanum tuberosum L.) in Kashmir valley. Afr. J. Agric. Res. 8(41): 5104–5115. https://doi.org/10.5897/AJAR2013.7338

Hussain, A., S. Ali, H. Abbas, H. Ali, A. Hussain and S. W. Khan (2019). Spatial distribution of early blight disease on tomato, climatic factors and bioefficacy of plant extracts against Alternaria solani. Acta Sci. Pol. 18(6): 29-38. https://doi.org/10.24326/asphc.2019.6.3

Doyle, J.J. and J.L. Doyle (1987). A Rapid DNA Isolation Procedure for Small Quantities of Fresh Leaf Tissue. Phytochem. Bull. 19(1): 11-15. https://worldveg.tind.io/record/33886?ln=en#files

Khan, M., A. Rashid and M. Iqbal (2003). Evaluation of Foliar Applied Fungicides Against Early Blight of Potato Under Field Conditions. Int. J. Agric. Biol. 5(4): 543-544.

James, C. (1971). A Manual of Assessment Keys for Plant Diseases. Canada Department of Agriculture  Publication No. 1458. Ottawa, Canada.

Nutter, F.W., P.S. Teng and F.M. Shokes (1991). Disease assessment terms and concepts. Plant Dis. 75(11): 1187–1188.

Leiminger, J. and H. Hausladen (2012). Early blight control in potato using disease-orientated threshold values. Plant Dis. 96(1): 124-130. https://doi.org/10.1094/pdis-05-11-0431

Mahanta, C., A. Prusty and P. Saha (2024). Advances in Agricultural Research Methodology (Chapter 36: Application of GIS in Agriculture). 2nd Ed. SP Publishing; New Delhi (India). 385-396. https://doi.org/10.5281/zenodo.11340751

Mathenge, M., B. G. J. S. Sonneveld and J. E. W. Broerse (2022). Application of GIS in Agriculture in Promoting Evidence-Informed Decision Making for Improving Agriculture Sustainability: A Systematic Review. Sustainability. 14(16): 1-15. https://doi.org/10.3390/su14169974

Pakistan Bureau of Statistics (2022). Agricultural Statistics of Pakistan 2021-22. Government of Pakistan.

Rotem, J. (1994). The genus Alternaria: biology, epidemiology, and pathogenicity. 7th Ed. American Phytopathological Society. St Paul (USA). 41. https://www.cabidigitallibrary.org/doi/full/10.5555/19942306197

Sambrook, J. and D. W. Russell (2001). Molecular Cloning (Chapter 8: In Vitro amplification of DNA by the polymerase chain reaction). 2nd Ed. Cold Spring Harbor Laboratory Press. New York (USA). 8.1-8.113.

Schmey, T., C. S. Tominello‐Ramirez, C. Brune and R. Stam (2024). Alternaria diseases on potato and tomato. Mol. Plant Pathol. 25(3): e13435. https://doi.org/10.1111/mpp.13435

Steel, R.G.D. and J.H. Torrie (1997). Principles and Procedures of Statistics: A Biometrical Approach. 3rd Ed. McGraw-Hill Book Company, New York, USA. 666 pp.

 Tamura, K. and M. Nei (1993). Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10(3): 512–526. https://doi.org/10.1093/oxfordjournals.molbev.a040023

Tamura, K., G. Stecher and S. Kumar (2021). MEGA 11: molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38(7): 3022–3027. https://doi.org/10.1093/molbev/msab120

Tsouros, D., S. Bibi and P. Sarigiannidis (2019). A Review on UAV-Based Applications for Precision Agriculture. Info. 10(11): 349. https://doi.org/10.3390/info10110349

van der Waals J. E., L. Korsten and T.A.S. Aveling (2001). A review of early blight of potato. Afr. Plant Prot. 7(2): 91-102. https://hdl.handle.net/10520/EJC87837

Van der Waals, J. E., L. Korsten and B. Slippers (2004). Genetic diversity among Alternaria solani isolates from potatoes in South Africa. Plant Dis. 88(9): 959-964. https://doi.org/10.1094/pdis.2004.88.9.959.

Woudenberg, J.H.C., M. Truter, J.Z. Groenewald and P.W. Crous (2014). Large-spored Alternaria pathogens in section Porri disentangled. Studies in Mycology 79: 1–47. https://doi.org/10.1016/j.simyco.2014.07.003

Yu, S.-H., H. Cho, B.-R. Kim and M. S. Park (2003). Morphological and Molecular Characterization of Alternaria Isolates from Solanaceous Crops. Kor. J. Mycol. 31(2): 103-113. https://doi.org/10.4489/KJM.2003.31.2.103