Introduction

Cereals, regarded as fundamental agricultural products are considered a food security issue in the world and in our country. In Türkiye, cereals account for 50% of the 243 million decares of arable land, and wheat is in first place with a 67% share of cereal land1.

Pre-harvest diseases and cereal pests have a major impact on the technological quality and yield of wheat, as well as on the genetic characteristics of the variety and the growing conditions. One of the key threats to wheat yield and quality in Türkiye is a group of insect species commonly referred to as wheat bugs, including the sunn pest (Eurygaster integriceps), the cereal ground beetle (Zabrus tenebrioides), the Hessian fly (Mayetiola destructor), and the striped bug (Aelia rostrata), all of which are known to cause significant damage by reducing grain quality, yield, and seed viability2,3,4,5,6,7,8,9,10.

Sunn pest, Eurygaster integriceps Puton (Hemiptera: Scutelleridae), is a major threat to wheat production in West and Central Asia and Eastern Europe11,12,13,14. They cause severe damage to crops by feeding on leaves, stems and grain. Sunn pest pierces plant tissue with its stylets and injects digestive enzymes through the salivary canal to liquefy the tissue into nutrient-rich slurry15,16.

The digestive damage caused by Eurygaster integriceps is primarily due to the proteolytic and amylolytic enzymes secreted during feeding. Using its stylet, the insect pierces the wheat grain and injects salivary secretions rich in enzymes such as trypsin-like proteases and α-amylases, which degrade gluten proteins and starch15,16. These enzymatic reactions lead to the breakdown of gluten structure, resulting in dough with poor elasticity and loaf volume, as well as undesirable baking characteristics such as rapid browning, weak texture, and off-flavor17,18,19. The enzymatic activity can be so severe that livestock reject the straw of infected plants20.

Proteolytic and amylolytic enzymes injected into wheat grains destroy the gluten and reduce the baking quality of the flour16,18,19,21. The resulting bread overcooks rapidly, fails to rise and often has an off-flavor. Advisers in Iran and Afghanistan reported that livestock refused to eat wheat straw after harvesting in fields with Sunn pest20. Sunn pest has been in the spotlight for many years because of the damage it causes to wheat in Türkiye, causing huge economic losses22,23,24. If the necessary control measures are not taken, Sunn pest (Eurygaster spp.) can cause product losses of up to 100% due to dry heart leaves damage (kurtbogazı), white spike damage (akbaşak) and suction damage in grain8.

Whereas in the 1950s the problem of Sunn pest was confined to the southeastern and southern Anatolian regions of Türkiye, today it threatens about 3/4 of the wheat production area. The reason why the Sunn pest degrades the technological quality of wheat is the digestive secretion with high proteolytic enzyme activity that this pest releases into the grain when it feeds by sucking on the wheat. This enzyme breaks down gluten proteins, causing a significant reduction in wheat quality3,25.

Globally, the management of Sunn pest primarily relies on integrated pest management (IPM) strategies that combine chemical, biological, and cultural control methods. In regions inside and outside of Türkiye, chemical insecticides are commonly used to manage E. integriceps populations, although their application is often limited by environmental and health concerns. Biological control agents, such as natural parasitoids and predators, are also being explored to reduce pest populations in an environmentally sustainable manner. Additionally, cultural practices like crop rotation, resistant wheat varieties, and early harvesting are promoted in several countries to limit the impact of Sunn pest on wheat production. Ongoing research focuses on improving these methods and developing more effective and sustainable pest control solutions.

Existing research on E. integriceps management highlights the importance of understanding its seasonal behavior and feeding patterns, which directly influence sampling and control strategies. For example, knowledge of its egg-laying and migration periods helps to determine the optimal times for pest sampling. In addition, integrated pest management (IPM) strategies that combine chemical, biological, and cultural methods, have been widely adopted. These methods, including the use of natural enemies and resistant wheat varieties, guided the approach used in this study, and ensured alignment with global pest management practices.

Based on this framework, the objectives of the study are threefold: (i) to assess spatial and temporal variation in Eurygaster integriceps population parameters (nymph density, egg parasitism rate, suction damage) across major wheat-producing provinces in Southeastern Türkiye; (ii) to investigate the statistical relationships among these parameters using regression and clustering analyses; and (iii) to derive practical implications for region-specific and data-supported pest management strategies.

With this study, some parameters (number of nymphs, egg parasitism rates, sucking rates, etc.) that are important for the management of the pest were determined in wheat growing areas in the Southeast Anatolia region of Türkiye between 2016 and 2021. These detected parameters were analyzed with advanced statistical programs and the interrelationships of some of these parameters, which are important in the management of the pest, were revealed.

Furthermore, although this study was conducted in Southeastern Türkiye, the findings have broader implications for global wheat production regions with similar dryland conditions. Understanding spatial variation in Sunn pest population dynamics and biological control potential can aid in the development of more efficient, region-specific pest management strategies worldwide, contributing to improved cereal production sustainability.

Materials and methods

The research material consists of wheat found in the study areas and adults, eggs and nymphs of Sunn pest, the most important pest of wheat. In addition, 1/4 m2 iron frames were used in the Sunn pest surveys.

Study area

The research was carried out in the provinces of Adıyaman, Batman, Diyarbakır, Mardin, Siirt, Şanlıurfa and Şırnak in the Southeastern Anatolia Region of Türkiye, where Sunn pest management is carried out, grain cultivation is high and Sunn pest is a problem. This region is characterized by semi-arid climatic conditions, with average annual temperatures ranging between 15 and 20 °C and annual precipitation between 400 and 700 mm26. The topography of the region varies from plains to low-altitude plateaus (average elevation 500–900 m)27. Wheat cultivation in these areas typically follows traditional dry farming practices. Sowing is generally carried out between October and November, and harvesting occurs between June and early July. Common wheat varieties include ‘‘Gün 91’’, ‘‘Ceyhan 99’’, and ‘‘Zerahat’’, depending on the province. Fertilization is minimal, and most fields are rain fed, without supplemental irrigation application during the survey period. These cultivation patterns are consistent with dryland cereal production systems reported in the region28. Field surveys were conducted in wheat fields in these seven provinces between 2016 and 2022 (Fig. 1). In these agroecosystems, sustainable production systems are maintained. During the sampling periods, no herbicides, fungicides or insecticides were applied in the agroecosystems.

Fig. 1
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Location of the sampled ecosystems. Map created by the corresponding author using Paintmaps, an online map tool (https://paintmaps.com/map-charts/217/Turkey-map-chart, accessed July 2024).

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Sampling of Sunn pest, Eurygaster integriceps

For the sampling of adults, eggs and nymphs, 1/4 m2 frames (0.5 m × 0.5 m = 1/4 m2) were used. In order to minimise the effect of the edges, the wheat field was first entered diagonally. This ¼ m2 frame was then randomly placed in the wheat field and the adults, nymphs and eggs that entered it were counted. In each wheat field, a minimum of 10 randomly placed ¼ m² frames were used to sample adult, egg, and nymph populations. The number of frames varied depending on field size but followed national survey standards (Table 1)29. Observations were conducted with a loupe (tarkim 30X) for egg parasitism assessment and grain suction damage evaluation. A handheld GPS device (Garmin eTrex 10) was used to record the coordinates of each sampling location. All data were manually logged on standardized field data sheets and later digitized for analysis. Sampling was repeated once weekly during critical pest stages.

Table 1 The number of frames taken depending on the field size when counting of Sunn pest adults, eggs and nymphs.
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Studies of valuation survey area (da)

This study was carried out to determine the density of overwintered adults (Fig. 2) in wheat fields after the descent of the pest from its wintering grounds has been largely completed. Depending on the temperature and rainfall conditions (Temperature and precipitation have a significant impact on the Sunn pest population. Warmer temperatures accelerate their life cycle and increase reproduction, while extreme heat can reduce survival. Precipitation affects habitat and food availability, with dry conditions typically reducing pest survival. These climatic factors are critical to predicting and managing sunn pest populations.) in the provinces, studies to determine the valuation survey area began in March. Wheat fields in the provinces were visited within 7–10 days. Surveys in all provinces were completed in March (e.g. Adıyaman and Diyarbakır provinces on 7–17 March, Mardin and Şanlıurfa provinces on 17–27 March). This was used to collect data for the nymph survey29.

Fig. 2
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Overwintered adults of Sunn pest, Eurygaster integriceps.

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Egg parasitism studies

The purpose of this survey is to determine the parasitism rates in that unit (wheat field). Egg parasitism studies were conducted in areas with an average density of overwintered adults per square meter of 0.8 and above, according to the valuation survey results. This study was started when 20–30% of the Sunn pest eggs had reached the anchor stage (Fig. 3a). To protect the parasitoids in the nature, eggs were marked and monitored in situ rather than collected from the units. General sampling was carried out in April, when adult females of Sunn pest were intensively laying eggs in wheat fields, and 10 egg packages were taken as a basis. Then, by counting the number of egg packages with and without parasites (Fig. 3b, c), the parasitism rate of the Sunn pest eggs was determined30. This stage was selected because parasitoid activity occurs primarily during the egg stage, and determining parasitism rates allows assessment of the effectiveness of natural biological control agents under field conditions.

Fig. 3
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Eggs of Sunn pest, Eurygaster integriceps (a) anchor stage of eggs, (b) unparasitised eggs, (c) parasitised eggs.

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Studies of nymph survey area (da)

In areas where valuation and parasitoid surveys had been completed, nymph surveys (within 10 days at the latest) were carried out to make the final decision before management. In the Sunn pest management, nymph (Fig. 4a, b,c) surveys were carried out by sampling method in all areas with a density of overwintered adults of 0.8 per m². Areas with 10 or more nymphs per m2 were included in the spraying programme as a result of nymph surveys. The management of the nymphs must be completed without delay8,29,30. Nymph surveys specifically targeted the 1st to 3rd instar stages, as national pest management guidelines recommend insecticide application only during these stages due to their higher susceptibility and lower mobility. Recording nymph stages is thus essential for determining the optimal timing for intervention.

Fig. 4
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Nymphs of Sunn pest, Eurygaster integriceps (a) 1st instar nymphs just emerged from egg package, (b) 2nd stage nymphs, (c) 3nd stage nymphs.

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Studies of average nymph density (m2)

First, the number of nymphs per m² was determined in the wheat fields where nymph surveys were carried out. These densities were then calculated for the province and average nymph density determined30.

Studies of management area (da)

The most important thing in the pest management of the Sunn pest is to use chemical control against the nymphs in the 1st − 3rd period (Fig. 4a, b,c). There is no treatment available for adult of Sunn pest. Once the nymph surveys were completed, wheat fields with a density of 10 or more nymphs per m² were identified and included in the spraying program30.

Studies of provincial suction rate (%)

The damage caused by the Sunn pest to the wheat grain can be seen by the black or brownish spot that it leaves on the grain. A white, mealy appearance begins to form around this point. This is the point at which the pest dips its proboscis into the wheat. It absorbs the essence of the wheat from the endosperm. This light-colored part is crushed when pressed with a fingernail (Fig. 5a, b).

Fig. 5
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Damage of Sunn pest, Eurygaster integriceps (a) wheat grains being sucked, (b) sucked and non-sucked grains.

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While detecting suction grains, 10 separate samples, 100 each, were prepared to detect the sample from each wheat field. These samples were then examined (with or without suction) with the naked eye or with a magnifying glass, and the suction rate was determined on a field basis. These suction rates were then calculated for the province and provincial suction rate (%) determined30,31. 

Data analysis

To visualize the valuation survey area (da), nymph survey area (da), management area (da), average nymph density (nymph/m²) and suction rate (%), the online program SankeyMATIC (https://sankeymatic.com/build/) was used to collect data. Regression and correlation analyses (SPSS 17.0 packaged software program) were used to determine whether there was a relationship between egg parasitism rate, nymph density and suction rate. In addition, heat map clustering (average nymph density, provincial suction rate) (SRplot: A free online platform for data visualization and graphing) and network graph (average nymph density, egg parasitism rate, provincial suction rate) (PAST 4.03 packaged software program) analyses were performed.

Results

In the provinces of Adıyaman, Diyarbakır, Mardin and Şanlıurfa, valuation and nymph surveys were carried out for Sunn pest, and then the management areas of these provinces were determined between 2016 and 2021. Over the years, including valuation, nymph, and management areas, ranged from 1,054,216 ha to 1,274,844 ha across most years, with a peak in 2021 at 1,351,732 ha. Diyarbakır had the highest cumulative survey and management area (2,588,753 ha), followed by Şanlıurfa (2,206,223 ha), Mardin (1,581,602 ha), and Adıyaman (883,514 ha). Specifically, valuation surveys were concentrated in Diyarbakır province (1,017,378 ha), nymph surveys in Şanlıurfa (897,044 ha) and management surveys in Diyarbakır (844,799 ha). A relationship was observed between the valuation survey area and both nymph and management areas, except in the case of Diyarbakır, where the relationship between nymph survey and management areas was partial (Fig. 6).

Fig. 6
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Valuation survey area, nymph survey area and management area interaction network.

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Average nymph densities (nymphs / m2) by year revealed the highest in 2019 (34.57), followed by 2017 (34.12), 2016 (33.75), 2021 (31.64), 2018 (31.40) and 2020 (30.09). Across the six-year period, Adıyaman exhibited the highest average nymph density (85.03), followed by Şanlıurfa (52.82), Diyarbakır (39.82) and Mardin (17.9). Yearly averages for Adıyaman ranged from 12.4 to 15.8 nymphs/m2 (Fig. 7).

Fig. 7
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Year, province, average nymph density (nymph/m2) interaction network.

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Suction rates (%) were highest in 2021 (4.35), followed by 2019 (4.33), 2017 (3.71), 2016 (3.63), 2018 (3.54) and 2020 (3.39). Cumulative six-year suction rates were highest in in Şanlıurfa (9.10), followed by Adıyaman (6.13), Diyarbakır (5.86) and Mardin (1.86). Annual values showed highest density was found in the provinces of Şanlıurfa (1.36 in 2016, 1.72 in 2017, 1.50 in 2018, 1.30 in 2019, 1.40 in 2020, and 1.82 in 2021) and Adıyaman (1.43 in 2019). The high rate of suction in these provinces is due to the high density of nymphs in these provinces (Fig. 8).

Fig. 8
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Year, province, suction rate (%) interaction network.

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Heat map clustering analyses revealed temporal and spatial similarities in both nymph density and suction rates (Fig. 9). Heat map clustering clearly discriminated the dependent/independent variables by sorting them, with a color range (+ 1 to − 1; red to blue) indicating the values obtained. While the values in the red columns marked with + 1 were found to be the highest, the values in the blue columns marked with − 1 were found to be the lowest. The results of the heat map clustering showed that the years 2017 and 2020 with 2018; 2017, 2018 and 2020 with 2019; 2017, 2018, 2019 and 2020 with 2021; 2017, 2018, 2019, 2020 and 2021 with 2016 were similar in terms of the relationship between average nymph density and years. Looking at the relationship between average nymph density and province, Adıyaman and Mardin provinces, Diyarbakır and Şanlıurfa provinces were found to be similar (Fig. 9a). In terms of the relationship of suction rate by year, it was found that 2021 and 2020 were similar to 2017; 2017, 2020 and 2021 to 2018; 2017, 2018, 2020 and 2021 to 2016; 2016, 2017, 2018, 2020 and 2021 to 2019. Looking at the relationship between the suction rate and the province, the provinces of Mardin and Şanlıurfa, Adıyaman and Diyarbakır were found to be similar (Fig. 9b).

Fig. 9
figure 9

Heat maps of (A) average nymph density (nymph/m2)—provinces and years, (B) suction rate (%)—provinces and years parameters.

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In 2021 and 2022, a positive spatial relationship was found between nymph densities among provinces: Adıyaman, Batman, and Siirt; Diyarbakır and Şanlıurfa; and Mardin and Şırnak (Fig. 10). For average egg parasitism rates (%), spatial correlations were observed between Adıyaman, Şanlıurfa, and Batman; and between Siirt, Şırnak, and Diyarbakır. No correlation was observed for Mardin (Fig. 11). Regarding suction rate relationships, positive spatial relationship existed between Adıyaman and Batman, and between Diyarbakır, Mardin, and Şanlıurfa; Siirt and Şırnak showed no significant relationship (Fig. 12).

Fig. 10
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Analysis of the network graph of applications. Average density of nymphs (nymphs / m2)—the provinces interaction networks.

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Fig. 11
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Analysis of the network graph of applications. Average egg parasitism rates (%)—the provinces interaction networks.

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Fig. 12
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Analysis of the network graph of applications. Average suction rate (%)—the provinces interaction networks.

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Regression and correlation analyses (2021–2022 data) revealed a moderate positive (r = 0.50–0.69) correlation (r = 0.535*, p < 0.05) between average nymph density (nymphs/ m2) and average egg parasitism rates (%). No correlation was found between nymph density and suction rate (Table 2). A significant linear relationship (p < 0.05) was confirmed between nymph density and egg parasitism rates via regression analysis (Table 3; Fig. 13).

Table 2 Correlation analysis between egg parasitism rates, nymph densities and sucking rates of Sunn pest.
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Table 3 Regression analysis between egg parasitism rates and nymph densities of Sunn pest.
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Fig. 13
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The relationship between average nymph densities and average egg parasitism rates of Sunn pest.

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Discussion

A review of previous studies revealed that no comprehensive analysis had been conducted on the valuation survey area, egg parasitism rate, nymph survey area, average nymph density, management area and suction rate of Eurygaster integriceps. In this study, these parameters were evaluated using a combination of statistical software (SPSS 17.0, PAST 4.03) and online visualization tools (SankeyMATIC, SRplot) to determine relationships quickly, accurately, and visually.

Although previous studies are valuable, they were mostly limited in scope and detail. For example, Khubenov32 reported that parasitoids significantly limited Sunn pest populations in Bulgaria. Şimşek and Sezer33 observed a positive relationship between nymph density and parasitism rate in Hatay. Similarly, Memişoğlu and Özer34 demonstrated a direct correlation between Eurygaster maura eggs and Trissolcus spp. parasitism in Ankara. In our study, statistical analysis confirmed a moderate positive correlation between nymph density (nymphs/m2) and egg parasitism rate (%). This density-dependent parasitoid behavior has also been supported by international studies. For example, Fourouzan and Farrokh-Eslamlou35 demonstrated that inundative releases of Trissolcus grandis in Iran resulted in 60–90% egg parasitism, and CABI reports egg parasitism levels of 30–80% under favorable conditions. These findings suggest that as egg densities rise, natural enemies respond by increasing parasitism, contributing significantly to population regulation35,36.

This relationship (positive correlation between egg parasitism rates and nymph densities) suggests that effective parasitism directly reduces hatching success, thereby lowering nymph populations. It may also reflect a density-dependent dynamic where higher egg availability attracts more parasitoids. Conversely, no significant correlation was found between nymph density and suction rate. This may be due to additional variables such as plant growth stage, local microclimate conditions (e.g., temperature, humidity), plant resistance, or selective feeding behavior. Additionally, delayed damage expression or plant compensation might also obscure visual symptoms of pest feeding. This lack of correlation between suction rate and nymph density is consistent with previous studies, which found that suction sampling efficiency is highly affected by environmental factors such as wind speed and insect behavior37. Nymphs may also avoid detection by hiding under foliage or adhering to the soil surface, especially in dry or hot regions.

Explaining regional disparities in these parameters provides context for our findings. For instance, Adıyaman recorded the highest nymph density despite limited survey coverage—likely due to focused sampling in localized outbreak areas. Mardin, on the other hand, showed 0% parasitism throughout the study, possibly due to intensive pesticide use reducing parasitoid populations or the region’s dry climate limiting parasitoid survival. Similar regional disparities were documented by Islamoğlu38, who reported very low parasitoid presence (3–7%) in adult Sunn pests in Adıyaman, supporting our findings of limited biological control pressure in this region. In contrast, regions with climates that are more favorable or less pesticide use have shown significantly higher natural parasitism.

Beyond statistical correlations, spatial analytical tools such as heatmap clustering and regression models also offer practical insights for region-specific pest management. The heatmap clustering provided valuable insight into regional similarities in Sunn pest parameters. For example, the clustering of Diyarbakır and Şanlıurfa suggests similar pest population dynamics, likely due to shared agroecological conditions or wheat cultivation practices. Such use of heatmaps and regression analyses has become common in pest management research, aiding the identification of spatial patterns and guiding intervention timing. Recent UAV-based studies have employed similar correlation heatmaps to align pest populations with vegetation indices, enhancing monitoring precision39. This supports the idea that pest management strategies can be regionally coordinated in such provinces. Moreover, the regression analysis between nymph density and egg parasitism rates revealed a moderate positive relationship. This relationship suggests that nymph density data collected early in the season can help predict parasitism activity and inform decisions about whether biological control may be sufficient or if chemical treatments are needed. These findings demonstrate that integrating spatial and statistical analyses enhances targeted, data-driven pest management planning.

Several previous studies have evaluated feeding damage and pest impact in different ways. For example, Atlı40 proposed suction thresholds based on wheat protein content. Duman and Doğanlar41 found no significant correlation between Sunn pest density and suction rates, similar to our findings. Likewise, Gözüaçık et al.42 and Dizlek and İslamoğlu31 examined yield losses and methodological differences in assessing suction damage. In our study, no statistical relationship was found between nymph density and suction rate. Koçak et al.43 emphasized the need for close monitoring of Eurygaster integriceps, which was found to be more damaging and widespread than other species in cereal fields. Mutlu et al.44 also reported substantial yield losses linked to overwintered and new generation pests. Our findings further revealed a positive correlation between the valuation survey area, nymph survey area, and management areas, supporting the need for integrated and localized pest management strategies.

To enhance Integrated Pest Management (IPM) strategies, combining biological control with spatial modeling is recommended. Aljaryian et al.45 used CLIMEX models to forecast Sunn pest distribution under climate change scenarios, emphasizing proactive planning. Likewise, IPM programs can benefit from incorporating parasitoid release programs or conservation of native enemies, particularly in areas with proven natural control potential.

Although the scope of this study is geographically limited to Southeastern Türkiye, its implications extend far beyond this region. Dryland wheat production systems similar to those studied here are prevalent across Central and Western Asia, North Africa, and parts of Eastern Europe, where Eurygaster integriceps remains a key pest. The analytical framework presented—combining spatial clustering, regression modeling, and multivariate analysis—provides a transferable methodology for pest population assessment and decision-making in IPM programs. Moreover, this study demonstrates how fine-scale spatial and temporal data can inform region-specific interventions, offering a model that can be adapted in similar agroecological contexts. Therefore, our findings not only improve local pest management practices but also contribute broadly to the advancement of data-driven IPM strategies under variable environmental conditions.

Conclusions

Understanding and knowing the valuation survey area, egg parasitism rate, nymph survey area, average nymph density, management area and sucking rate of Sunn pest that important pest is the first step in implementing appropriate measures to management their negative impacts. This study has shown that the relationship between the above-mentioned data is important to manage of the pest. In addition, the relationships between these values may vary from one year to another and from one province to another.

As a result of this study, the following conclusions have been drawn:

  • The average parasitism rate in 2021–2022 was found to be highest in Adıyaman (13.45% and 14.72%) and lowest in Diyarbakır (1.50%) and Mardin (0%).

  • It was found that the average nymph density (nymphs/m²) in 2021–2022 was highest in Batman province (14.30 and 9.79 nymphs/m²) and lowest in Diyarbakır (5.83 and 7.88 nymphs/m²) and Mardin (6.30 and 2.08 nymphs/m²) provinces respectively.

  • It was found that there is a linear relationship between the number of nymphs per square meter in wheat fields and the number of parasitized eggs, i.e. as the number of nymphs increases, so does the rate of parasitism.

  • It has been found that as the number of nymphs hatching from eggs laid by adult Sunn pest, Eurygaster integriceps adult increases, and so do suction rates, but there is no statistical relationship between the two.

Based on these findings, several practical recommendations for Sunn pest management can be made. In provinces such as Adıyaman, where both nymph density and egg parasitism rates are relatively high, biological control strategies—including the conservation or augmentation of natural enemies—should be prioritized. Conversely, provinces such as Mardin and Diyarbakır, where parasitism rates are low despite the presence of nymph populations, require enhanced monitoring and consideration of integrated pest management (IPM) practices. These suggestions are supported by the observed linear relationship between nymph density and parasitism rate and can help guide region-specific control strategies.

Although the current study was regionally focused, the methodological framework and key findings have relevance beyond Türkiye. Similar dryland agroecosystems across Asia, the Middle East, and parts of Europe face comparable pest management challenges. Therefore, spatially informed monitoring and control approaches, as exemplified here, can be adapted to other regions to enhance integrated pest management efficiency.

In the future, much more research on the pest is needed to understand the damage and management of the main wheat pest, Sunn pest, and to identify new strategies, as well as statistical evaluations to prove their scientific accuracy.