Effective Strategies for Managing Wheat Diseases: Mapping Academic Literature Utilizing VOSviewer and Insights from Our 15 Years of Research

Abstract

Wheat pathogens pose a significant risk to global wheat production, with climate change further complicating disease dynamics. Effective management requires a combination of genetic resistance, cultural practices, and careful use of chemical controls. Ongoing research and adaptation to changing environmental conditions are crucial for sustaining wheat yields and food security. Based on selective academic literature retrieved from the Scopus database and analyzed by a bibliographic software such as the VOSviewer we discussed and focused on various aspects of current and future strategies for managing major wheat pathogens and diseases such as Tan spot, Septoria tritici blotch, Fusarium head blight, etc. Chemical management methods, such as the use of fungicides, can be effective but are not always preferred. Instead, agronomic practices like crop rotation and tillage play a significant role in managing wheat diseases by reducing both the incidence and severity of these diseases. Moreover, adopting resistance strategies is essential for effective disease management.

1. Introduction

Wheat diseases pose a significant threat to wheat production, leading to yield losses and impacting global food security. Throughout history, wheat farmers have encountered various diseases, including rust, smuts, and blights. Notably, during the Middle Ages, the ergot fungus, Claviceps purpurea, which causes ergotoxicosis, ergot poisoning, and Saint Anthony’s fire, had profound historical effects in Europe. This fungus particularly affected individuals who consumed rye bread contaminated with it [1,2]. Ergot alkaloids, produced by the fungus C. purpurea, are a significant concern for wheat and other cereal grains due to their toxic effects on humans and animals. The study in Northern Italy demonstrated the occurrence of those mycotoxins (ergot alkaloids) in major and minor cereals such as wheat [3]. Another research of ergot alkaloid occurrence in Italian wheat shows that climate change will increase the frequency of extreme weather events and mitigate the potential risks associated with mycotoxin-producing fungi such as Claviceps fungi [4]. Moreover, the European Food Safety Authority has highlighted the need to develop risk assessment strategies for the presence of ergot alkaloids in food, especially with the growing consumption of plant-based foods [5]. Climate change can significantly impact the prevalence of certain diseases. For instance, changes in humidity may affect the life cycles of pathogens like C. purpurea, which thrives in cool, moist conditions. Additionally, warmer temperatures can accelerate the development of other pathogens, such as Pyrenophora tritici-repentis. This particular pathogen is highly influenced by temperature and has been shown to increase both the number and severity of lesions on wheat plants [6]. Based on the above, climate change is likely to exacerbate the emergence and severity of historical diseases such as ergot disease, as well as contemporary diseases like tan spot, by altering environmental conditions and pathogen dynamics.

Managing ergot effectively involves strategies during both the planning phase and the growing season. In the planning phase, it is important to select seeds that are free of ergot and less susceptible to the disease. We should avoid open-flowering varieties and those with long flowering periods. During the growing season, regular inspection of crops for symptoms of ergot should occur before harvest. Additionally, applying foliar fungicides can help mitigate fungal growth and disease development. Research indicates that applying foliar fungicides, such as propiconazole, tebuconazole, and benzovindiflupyr before anthesis significantly reduces the incidence of disease and increases plant yield [7]. Further studies have shown that fungicides applied during the growing season can also decrease the prevalence of ergot. For instance, combinations such as prothioconazole + metconazole, pydiflumetofen + propiconazole, and azoxystrobin + propiconazole have been found to significantly reduce both the total ergot body weight and ergot alkaloid production in wheat [8]. So, fungicides are a primary control method, for managing plant health and reducing disease incidence.

These active ingredients, particularly propiconazole, can also be effective against rust and other foliar diseases caused by the genera Pyrenophora or Septoria. However, the decline in plowing practices, due to a shift towards reduced cultivation and no-tillage methods, may lead to an increase in ergot prevalence (ahdb.org.uk, accessed on 17 November 2024). This trend has also been observed with other diseases, such as Tan spot of Wheat. Tan spot, caused by the fungus P. tritici-repentis, has become a significant disease affecting winter wheat globally, largely due to changing climate conditions and the adoption of new resource-conserving technologies. While wheat straw does not directly contribute to increased ergot risk, its residue acts as a natural inoculum source (pseudothecia/ascospores) for P. tritici-repentis, resulting in higher disease incidence and severity [9]. Removing or reducing straw residue through tillage or burning can decrease tan spot severity [10]. Moreover, research indicates that under minimum and no-tillage systems with crop residues, tan spot epidemics can begin earlier compared to conventional tillage systems, and higher levels of disease severity are often recorded during the filling stage [11,12].

Throughout history, agronomists have faced the challenge of managing wheat crop diseases by developing control strategies for various pathogens that have different infection mechanisms and host interactions, such as C. purpurea or P. tritici-repentis. Pyrenophora tritici-repentis is a residue-borne pathogen that affects wheat leaves [13,14], while C. purpurea infects the ovaries of grasses and cereals, leading to the production of toxic sclerotia [15]. Although integrated disease management strategies are essential for both pathogens, the specific control measures for P. tritici-repentis are generally not applicable to C. purpurea because of their different infection mechanisms and host interactions. Therefore, it is crucial to understand how specific pathogens impact crops and the susceptibility of those crops before implementing control measures against these pathogens.

In addition to ergot, stem rust has historically been a significant disease affecting wheat. In Roman times, various authors documented the impact of rust on wheat and barley production. Unlike tan spots, stem rust is a windborne pathogen with a shallow dispersal gradient, meaning it spreads over long distances through airborne spores rather than crop residues [13]. Today, the development of rust-resistant wheat cultivars through gene pyramiding, along with the application of rust-active fungicides, are crucial strategies to mitigate the impact of important rust diseases in wheat [16,17].

Our research experience [9], shows that in Mediterranean climate e.g., Greece common leaf spot diseases of wheat such as Tan spot caused by the fungus P. tritici-repentis and Septoria leaf blotch caused by the fungus Zymoseptoria tritici, establish early disease due to the survival of the pathogen (-s) in the wheat residue (debris and stubble). Research indicates that Z. tritici is significantly affected by weather conditions, especially humidity and precipitation [18]. Our observations showed that the presence of wheat straw retains moisture, particularly early in the season, which can increase the pathogen severity. This early season disease requires fungicide application to be early and poses the need even for a second fungicide application to protect flag leaves or early anthesis if the control should be based on the time of Fusarium graminearum infection (unpublished data). Besides our experience, another research [19], showed that two applications of trifloxystrobin + propiconazole fungicide product applied at GS31 + GS39—Zadoks growth stage, the first node (GS31) or flag leaf emergence (GS39) become detectable—had the highest net returns compared with the single applications at GS31 or GS39 only as our data shows [9]. In general, a single fungicide application was most often just as profitable [9], as applying a fungicide twice and depended primarily on location, cultivar, and timing of disease development [20].

According to Miedaner & Juroszek [21], wheat productivity is under threat from global climate change, which will impact disease resistance breeding in wheat in Northwestern Europe. The review by Miedaner & Juroszek [21] highlights several challenges presented by global climate change, including (i) increased risk of diseases, (ii) re-emergence of old pathogens, (iii) highly adaptive pathogens like P. striiformis, (iv) emergence of new, unknown pathogens, and (v) changes in Fusarium species complexes. On the fungicide front, the authors pose a dilemma, suggesting that rising temperatures might accelerate the development of pathogen resistance against fungicides [9,20]. To effectively address the impact of global climate change, we must acknowledge that the extensive use of fungicides has resulted in the development of resistance in pathogens such as Z. tritici (synonyms: Mycosphaerella graminicola, Septoria tritici), reducing the effectiveness of chemical control [22].

In general, foliar fungal diseases of wheat (winter wheat) cause crop loss worldwide and include leaf rust Puccinia triticina Erikss., and stripe rust P. striiformis Westend f. sp. tritici Erikss, Septoria leaf blotch Z. tritici (Desm) Quaedvkueg & Crous; syn. M. graminicola (Fuckel) J. Schröt; syn. S. tritici Roberge in Desmaz., Stagonospora glume blotch Parastagonospora nodorum (Berk.) Quaedvlieg, Verkley & Crous; syn. Phaeosphaeria nodorum (E. Müll.) Hedjaroude; syn. Stagonospora nodorum (Berk.) E. Castell. & E.G. Germano, tan spot P. tritici- repentis (Died.) Dreschsler; syn. Drechslera tritici-repentis (Died.) Shoemaker, spot blotch Cochliobolus sativus (S. Ito & Kurib.) Drechsler ex Dastur; syn. Bipolaris sorokiniana, and powdery mildew Blumeria graminis (DC.) Speer f. sp. tritici emend. É.J. Marchal [20].

Besides the above mentions, for effective disease management strategies apart from chemical control [9,23,24,25,26] and host plant resistance [27,28,29], epidemiological models can contribute to predicting and managing wheat diseases. The incidence and severity of wheat diseases may vary with season, region, variety, weather, inoculum load, and resistance level of the host cultivars [30,31,32,33,34,35]. So, epidemiological models can be used to predict and manage wheat diseases by characterizing disease dynamics, describing resistance, in predicting disease onset [36].

So, current methods and technologies for managing wheat diseases have evolved the chemical control, the host plant resistance, and the use of epidemiological models. However, the challenges posed by climate change and the need for innovative disease management highlight the need for future strategies and new approaches that will be based on the information of past and present strategies for wheat disease management. In this article, we answer these questions using data from the literature, which we process with the VOSviewer program, version 1.6.20 (Figure 1, and Figures 3–9). We also share our observations from 15 years of research experience (Figure 2). In detail, the aim of this review paper is to provide emphasis on the latest research about the most important fungal diseases that are threatening winter wheat crops and discuss important factors that facilitate their establishment and their future control.

2. Materials and Methods

2.1. Electronic Databases

To identify unique studies on the most significant fungal diseases affecting wheat, such as Septoria leaf blotch and tan spot, we conducted a comprehensive review of the relevant literature using the Scopus database. Our screening focused on four specific time periods: 1993–2003, 2004–2014, 2015–2022, and 2023–2024 due to significant events related to climate change, primarily focusing on the impact of temperature on ice (https://history.aip.org/climate/timeline.htm, accessed on 17 November 2024). This approach was designed to achieve our objective of identifying critical fungal pathogens that pose a threat to winter wheat crops over time. For our search in Scopus, we used the query “wheat AND pathogens” to address our first objective. The bibliographic data from Scopus were compared with our field-based and laboratory observations conducted in the region of Thessaly (Central Greece), as illustrated in Figure 2.

Further, to provide critical scientific ideas of wheat disease management (second objective), we utilized the Scopus database to explore research and review academic articles. For the Scopus general search, we used review questions such as:

• “Septoria AND tritici AND blotch” (VOSviewer visualization can be seen in Figure 3)
• “Pyrenophora AND tritici-repentis” (VOSviewer visualization can be seen in Figure 4)
• “Septoria AND nodorum AND blotch” (VOSviewer visualization can be seen in Figure 5)
• “Climate change AND wheat AND diseases” (VOSviewer visualization can be seen in Figure 6)
• “wheat AND disease AND propiconazole” (VOSviewer visualization can be seen in Figure 7)
• “wheat AND disease AND prothioconazole” (VOSviewer visualization can be seen in Figure 8)
• “wheat AND diseases AND algorithm” (VOSviewer visualization can be seen in Figure 9)

2.2. Mapping SCOPUS Literature with VOSviewer

To address our objectives of identifying critical fungal phytopathogens that threaten winter wheat crops and providing essential scientific insights for wheat disease management, we utilized the Scopus database to explore relevant research and academic review articles. We then analyzed the Scopus documents using VOSviewer version 1.6.20 mapping software, as demonstrated by Vagelas and Leontopoulos [37]. The VOSviewer analysis employed the Overlay Visualization tool, allowing us to create an overlay map that illustrates the changing patterns of terms over time. This map offers insights into the evolution of specific research areas. From our analysis, we examined recent scientific research topics, including the issue of “phosphotransferase” shown in Figure 3, which we believe has the potential to become a significant topic of future research.

3. Results

3.1. Mapping SCOPUS Literature with VOSviewer

3.1.1. Address the Most Important Pathogens That Are Threatening Winter Wheat Crop 1st Query

For the query “wheat AND pathogens” in Scopus, we created five research datasets based on the following time periods: 1928 to 1992, 1993 to 2003, 2004 to 2014, 2015 to 2023, and 2023 to 2024. These datasets were analyzed using VOSviewer and are presented in Figure 1a–e. The search yielded the following information: The years 1993, 2004, 2015, and 2024 were selected due to significant events related to climate change, primarily focusing on the impact of temperature on ice (https://history.aip.org/climate/timeline.htm). In 1993, ice cores from Greenland indicated that significant climate changes can happen within a single decade, at least on a regional scale. By 2003, various observations raised alarm about the potential for ice sheet collapses in West Antarctica and Greenland to cause sea levels to rise more quickly than previously thought. In 2015, researchers concluded that the collapse of the West Antarctic ice sheet might be irreversible, potentially resulting in meters of sea-level rise over the coming centuries. As of 2024, the mean global temperature (measured as a five-year average) is 14.9 °C, the highest in tens of thousands of years. The concentration of CO₂ in the atmosphere has reached 425 ppm, the highest level in millions of years.

For the period 1928 to 1992, the Scopus bibliographic database yielded a total of 335 documents, and the main pathogens were: Septoria nodorum, Septoria, and Phaeosphaeria nodorum (causing the disease Septoria nodorum blotch); Gaeumannomyces graminis (causing the disease, take-all); Rhizoctonia solani (causing Rhizoctonia Root and Crown Rot); Erysiphe graminis (causing the disease powdery mildew of wheat); Fusarium culmorum, Fusarium spp., (causing the disease crown rot of wheat); P. striformis (causes stripe rust on wheat); and Stem rust (caused by P. graminis f. sp. tritici), Figure 1a.

For the period 1993 to 2003, the Scopus bibliographic database yielded a total of 865 documents, and the main pathogens were: Septoria, Mycosphaerella, M. graminicola, Septoria tritici (causing Septoria tritici leaf blotch of wheat); Stagonospora (called Stagonosopora nodorum blotch or glume blotch); Powdery mildew; Rhizoctonia, R. solani, R. cerealis (the causal agent of sharp eyespot); Thanatephorus cucumeris (R.solani Kuhn. Teleomorph), wheat rust diseases—pathogens such as Puccinia, P. striformis, P. recondita f. sp. tritici; Gaeumannomyces; P. tritici-repentis (teleomorph-causes tan spots), Drechslera (anamorph)—(called tan spot, yellow leaf spot, yellow leaf blotch or helminthosporiosis); Bipolaris sorokiniana (Cochliobolus sativus anamorph)—(cause seedling blight, foot and root rot, head and leaf spot of cereals and grasses); and Fusarium, Gibberella zeae (the F. graminearum anamorph) Figure 1b.

For the period 2004 to 2014, the Scopus bibliographic database yielded a total of 1932 documents, and the main pathogens were: G. zeae, F. graminearum (causal agent of Fusarium Head Blight in wheat); F. culmorum; Microdochium nivale (cause a seedling blight in winter wheat); F. poae (one of the most common Fusarium head blight causal agents in wheat); Cochliobolus, B. sorokiniana; G. graminis var. tritici (Take-all disease); P. tritici-repentis (the causal agent of tan spot of wheat); wheat rust diseases—P. graminis f. sp. tritici (Wheat stem rust), P. striformis, P. triticina (Wheat leaf rust); and Tilletia indica (causing Karnal bunt of wheat seeds) Figure 1c.

For the period 2015 to 2022, the Scopus bibliographic database yielded a total of 2873 documents, and the main pathogens were: Fusarium, F. graminearum, Gibberella zeae; P. striformis, P. triticina; Rhizoctonia, T. cucumeris; and Septoria Figure 1d. For the period 2023 to 2024, the Scopus bibliographic database yielded a total of 900 documents, and the main pathogens were: Fusarium, F. graminearum, Z. tritici (Septoria tritici blotch); P. tritici-repentis (tan spot); P. graminis (Stem rust) and, P. striformis (wheat stripe—yellow—rust), Figure 1e.

According to Figure 1e, the primary fungal diseases affecting wheat currently include F. graminearum, Z. tritici, P. tritici-repentis, P. graminis, and P. striiformis. That is true in the Mediterranean, Central Greece region of Thessaly, Z. tritici and P.tritici-repentis are more severe fungal pathogens in wheat cropping systems that develop and mature on crop residue on the soil surface [9]. Both pathogens are part of a leaf spot complex that includes tan spot and Septoria leaf blotch (see Figure 2a). Pyrenophora tritici-repentis (anamorph: D. tritici-repentis) produce conidiophores (Figure 2b), and Z. tritici produce pycnidia (Figure 2c) on the leaf surface.

From 1993 to 2024 (Figure 1a–e), the analyzed data employing VOSviewer identifies that the dominant pathogens primarily belong to the genera Septoria and Pyrenophora. Specifically, our field 15-year observation data reveals that these two genera have been the most prevalent in central Greece during this period (Figure 2).

Concerning the two pathogens, our data (Figure 2), indicate that P. tritici-repentis becomes more severe over time (Figure 2d). Figure 2d consisted of wheat samples collected from selected sampling fields from the region of Thessaly (Central Greece) during the wheat growing season (period) 2010/11, 2015/16, and 2022/23. All samples were analyzed with microscopy (Figure 2d) and confirmed with a metabarcoding analysis [9].

This pathogen (P. tritici-repentis) survives on crop residue (straw) during the fall and early winter, with pseudothecia causing primary infections on wheat leaves. Based on that, we believe that climate change effects both with conservation tillage practices can contribute to a rise in infections of P. tritici-repentis, highlighting the need for integrated management strategies in agriculture.

3.1.2. Address Wheat Diseases Management 2nd Query

The Scopus bibliographic database yielded a total of 752 documents for “Septoria AND tritici AND blotch”, 612 documents for “Pyrenophora AND tritici-repentis”, 252 documents for “Septoria AND nodorum AND blotch”, 543 documents for “Climate change AND wheat AND diseases”, 126 documents for “wheat AND disease AND propiconazole”, 121 documents for “wheat AND diseases AND prothioconazole”, 438 documents for “wheat AND diseases AND algorithm”,. Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 show the co-keyword network of the keywords visualized using the bibliometric analysis software VOSviewer version 1.6.20.

Figure 3 illustrates the overlay visualization related to the Scopus query “Septoria AND tritici AND blotch”. The colors in Figure 3 represent the topics from the period of 2005–2020, ranging from purple (2005) to green (2020). Keywords in Figure 3, such as “phosphotransferase” highlighted in yellow (Figure 3, top left arrow), were chosen for further analysis. The analysis revealed that keywords (Figure 3, arrows) like “phosphotransferase”, “QTL”, “fungicide resistance”, “fungicide sensitivity”, “cyp51”, and “mefentrifluconazole” have the potential to become new research topics in the future and are discussed in this paper.

Figure 3. Profile map of “Septoria AND tritici AND blotch” query created using VOSviewer, based on publications from 2005 to 2020. Arrows showed the most important points discussed in this article.

Figure 3. Profile map of “Septoria AND tritici AND blotch” query created using VOSviewer, based on publications from 2005 to 2020. Arrows showed the most important points discussed in this article.

According to Derbyshire et al. [38], Z. tritici, the causal agent of the Septoria tritici blotch (STB) disease of wheat, is an apoplastic fungal pathogen. This means that it does not penetrate plant cells at any stage of infection and has a long initial period of symptomless leaf colonization. During this phase, it is unclear to what extent the fungus can access host plant nutrients or communicate with plant cells. It was found that several important primary and secondary metabolite pathways in fungi are regulated by the post-translational activator phosphopantetheine transferase, which provides an essential co-factor for lysine biosynthesis and the activities of non-ribosomal peptide synthases and polyketide synthases. This indicates the importance of lysine biosynthesis for virulence. As a result, phosphotransferase and the lysine biosynthesis pathway could be potential targets for fungicides effective against Z. tritici [39,40].

Fungicide resistance in Z. tritici is a significant concern, with mutations in target genes and overexpression of transporters contributing to resistance development. The efficacy of fungicides has decreased due to resistance development, emphasizing the need for new approaches to control Z. tritici [39]. Based on the above we will conclude that we need to understand more about the mechanisms of fungicide action on phosphotransferases, their contribution to fungicide resistance, and the current strategies for targeting phosphotransferases for the development of effective antifungal compounds. It is important to mention that phosphopantetheine transferase (-ases) (Ppt), is essential for the growth and development of fungi, as their deletion resulted in altered hyphal growth, branching patterns, and even sexual form development [41]. Research showed that those (Ppt) provide an essential co-factor for lysine biosynthesis, and this reveals Ppt, and the lysine biosynthesis pathway, could be a potential target for fungicides effective against Z. tritici [38,42]. Lysine biosynthesis involves multiple enzymes and pathways, such as the diaminopimelate and α-aminoadipate pathways, and is essential for bacterial growth like Escherichia coli [43]. Based on this, we believe that the importance of lysine biosynthesis in bacterial survival and the potential for novel antibiotic targets suggests its relevance to understanding and combating pathogens like Z. tritici.

As Z. tritici leads to significant yield losses, the use of resistant wheat varieties for Z. tritici management [44], and the identification of quantitative trait loci (QTL) for STB resistance in wheat populations is a sustainable strategy in addressing the pathogen’s impact [45].

Based on the research [39,46,47], it is evident that Z. tritici has developed resistance to various fungicides, particularly azoles that act as demethylation inhibitors (DMI), and succinate dehydrogenase inhibitors (SDHIs) fungicides. Further studies have identified specific mutations in the CYP51 locus, a key gene associated with azole resistance, highlighting the genetic basis of resistance in Z. tritici [47]. As fungicide resistance in Z. tritici populations poses a significant threat to wheat production [48,49], it is important to emphasize detailed monitoring of fungicide sensitivity and the development of resistance in Z. tritici populations.

The azole fungicides are considered to have a moderate risk of resistance development, falling between the low-risk multi-site fungicides and the higher-risk QoIs and SDHIs. This group includes over 37 fungicides from different subclasses such as triazoles, imidazoles, and triazolinthiones, all of which target 14a-demethylase. However, for STB (Septoria tritici blotch) control, only a small number of azole fungicides are commonly used, including prothioconazole, tebuconazole, and the novel azole mefentrifluconazole. According to Kildea et al. [50], European Z. tritici populations exhibit a wide range of sensitivity to mefentrifluconazole. Under glasshouse conditions reductions in the efficacy of mefentrifluconazole were observed in those strains exhibiting the lowest in vitro sensitivities. The authors concluded that the future use of mefentrifluconazole should take these findings into consideration to minimize the selection of these strains [50].

Figure 4 illustrates the overlay visualization related to the Scopus query “Pyrenophora AND tritici-repentis”. The colors in Figure 4 represent topics from 2005 to 2020, ranging from purple for 2005 to green for 2020. Keywords like “chromosomal mapping”, “ts1”, “toxa”, “toxa gene”, and “haplotypes” are indicated in Figure 2 with arrows. These keywords have the potential to become new research topics in the future and will be discussed below.

Figure 4. Profile map of “Pyrenophora AND tritici-repentis” query created using VOSviewer, based on publications from 2005 to 2020. Arrows showed the most important points discussed in this article.

Figure 4. Profile map of “Pyrenophora AND tritici-repentis” query created using VOSviewer, based on publications from 2005 to 2020. Arrows showed the most important points discussed in this article.

Genetic mapping is crucial for understanding the basis of genetic and biochemical diseases, providing genetic markers for disease identification [51]. Based on this chromosomal mapping has been conducted in P. tritici-repentis, and genetic markers associated with sensitivity to effector genes have been identified [52,53]. According to Corsi et al. [52], the genetic mapping of sensitivity to the P. tritici-repentis effector ToxB has led to the development of a diagnostic genetic marker, contributing to the understanding of pathogenicity. In the current research [52], the P. tritici-repentis sensitivity locus has been towards to Tsc2 where all other P. tritici-repentis effector sensitivity locus that has been cloned is Tsn1, at which recessive alleles confer insensitivity to the effector ToxA.

It is well known that P. tritici-repentis (Ptr) produces specific phytotoxins encoded by the ToxA and ToxB genes. The ToxA gene is responsible for inducing necrosis in sensitive wheat genotypes [54,55]. According to Leišová-Svobodová et al. [56], the ToxA gene was transferred from another wheat pathogen the S. nodorum. The ToxA-Tsn1 system represents an inverse gene-for-gene relationship, where the presence of the Tsn1 gene in the host indicates susceptibility to ToxA-carrying pathogens. Based on the above we believe that understanding the molecular interactions between Ptr ToxA and host factors is crucial for developing resistant wheat cultivars and effective crop protection strategies [57].

Based on “haplotypes” research shows that P. tritici-repentis exhibits significant genetic diversity, in detail research showed that the presence of effector genes, such as ToxA and ToxB, varies among P. tritici-repentis isolates [58]. In their review paper [58], it was shown that ToxA is encoded by the single-copy gene ToxA and causes necrosis on sensitive wheat cultivars, while ToxB causes chlorosis and is encoded by the multicopy gene ToxB. The authors also concluded that races capable of producing the P. tritici-repentis ToxA are predominant in the global P. tritici-repentis population. Additionally, various researchers worldwide have reported new P. tritici-repentis races.

Figure 5 is probably “apocalyptic”. The majority of the yellow dots are related to genetic referrals and provide relevant information that has been addressed in Figure 3 and Figure 4. From Figure 5, it can be seen that the dominant topics or keywords were “quantitative trait loci”, “host-plant interaction”, “nectrotrophic”, “plant protein”, “sntox1”, “toxa”, “tsn1”, “ptrtoxa”, “disease predisposition” and many others related to genetic, genes and chromosomes.

Figure 5. Profile map of “Septoria AND nodorum AND blotch” query created using VOSviewer, based on publications from 2005 to 2020. Arrows showed the most important points discussed in this article.

Figure 5. Profile map of “Septoria AND nodorum AND blotch” query created using VOSviewer, based on publications from 2005 to 2020. Arrows showed the most important points discussed in this article.

Based on the keyword “disease predisposition” literature shows that the Genetic predisposition (in general), refers to an increased probability of developing a particular disease due to a person’s genetic framework. It does not directly cause a disease but elevates the risk compared to the general population. This predisposition is influenced by complex interactions between different genes, and behavioral, environmental, and genetic factors [59]. According to host-pathogen interaction research over the last two decades has shown that the wheat- P. nodorum pathosystem mostly follows an inverse gene-for-gene model, with the fungus producing necrotrophic effectors (NEs) that interact with specific host gene products encoded by dominant sensitivity genes [60].

The interaction between NEs (e.g., NE genes of SnToxA and SnTox3) and host genes leads to programmed cell death, providing nutrients for the pathogen to grow and sporulate. Parastagonospora nodorum hijacks multiple and diverse host targets to cause disease, and NE gene expression plays a key role in disease severity [60]. The literature [60,61] also shows that the P. nodorum NEs of SnToxA and SnTox3 can manipulate hormonal signaling pathways by influencing plant microRNAs to regulate the susceptibility of the plant. Specifically, SnTox3 mainly suppressed the expression of three miRNAs: miR159, miR393, and miR408. Additionally, the SnToxA NE suppressed miR166 expression. Research showed [61] that SnToxA hijacked the Salicylic acid signaling pathway and manipulated it for fungal growth and development. Furthermore, in the same research [61], data showed that both SnToxA and SnTox3 suppressed the expression of miR408, which is regulated by cytokinins and salicylic acid, during the development of protective reactions by wheat against P. nodorum. In our opinion, these findings provide valuable insights into the intricate interactions between P. nodorum and wheat plants associated with NEs and wheat susceptibility genes. Further, the disease is determined by various resistance components and their interaction with environmental factors, such as temperature and relative humidity [62]. So, it is essential to comprehend these interactions for the development of successful breeding strategies for Septoria nodorum blotch-resistant wheat cultivars.

Figure 6 illustrates that for the “Climate change AND wheat AND diseases” query, important and up-to-date keywords include “deep learning”, “learning systems”, “gene editing”, “genome editing”, and “speed breeding”. Among these keywords, we consider “speed breeding” to be the most important. Speed breeding, a technique utilizing longer photoperiod times and higher temperatures, is being adopted by wheat breeders to fast-track cultivar development. Speed breeding, which accelerates plant development, has been used to assess Fusarium head blight severity and mycotoxin accumulation in wheat varieties, leading to efficient disease screening and evaluation under speed breeding conditions [21,63]. Plant breeders have been struggling to improve wheat resistance against major diseases through selection and conventional breeding techniques [63].

Figure 6. Profile map of “Climate change AND wheat AND diseases” query created using VOSviewer, based on publications from 2014 to 2022.

Figure 6. Profile map of “Climate change AND wheat AND diseases” query created using VOSviewer, based on publications from 2014 to 2022.

Novel molecular breeding techniques, such as marker-assisted selection (MAS), quantitative trait loci (QTL), genome-wide association studies (GWAS), and the CRISPR/Cas-9 system, have been fruitful for developing broad-spectrum disease resistance in wheat. Integrated disease management, including the use of decision support systems, cultural practices, and fungicides, varies across different countries due to differences in cropping systems, climatic conditions, and disease prevalence [64,65,66]. Based on the above we believe that the use of advanced technologies such as deep learning and image processing for disease detection in wheat crops holds promise in improving crop yields and reducing losses due to diseases.

The “wheat AND disease AND propiconazole” query indicates that important and up-to-date keywords are “meta-analysis”, “cytology”, “stereoselectivity”, “stereoisomers”, “Fusarium pseudograminearum”, and “fungicide sensitivity” (Figure 7). With a focus on “fungicide sensitivity”, it was found that propiconazole, a systemic fungicide, effectively controls powdery mildew disease in wheat. Additionally, a study examining the sensitivity of F. graminearum to propiconazole indicated that Propiconazole sensitivity showed a high heritability (H² = 0.97), with mean EC₅₀ values ranging from 5.4 to 62.2 mg L⁻¹ [67]. This funding suggests that genes outside of the CYP51 family may make the most important contribution to demethylation inhibitors (DMIs) resistance, such as propiconazole, in F. graminearum [67].

Figure 7. Profile map of “wheat AND disease AND Propiconazole” query created using VOSviewer, based on publications from 2014 to 2022.

Figure 7. Profile map of “wheat AND disease AND Propiconazole” query created using VOSviewer, based on publications from 2014 to 2022.

Based on the “cytotoxicity” and “stereoselectivity” keywords, a study evaluated the stereoselectivity of propiconazole (PRO) enantiomers and found that cis (−)-2S,4R-PRO exhibited the lowest cytotoxicity in various cell lines. The same study suggested that applying cis (−)-2S,4R-PRO for agriculture would significantly reduce environmental risk [68]. However, another study reported that propiconazole stereoisomers exhibited different degrees of bioactivity and acute toxicity toward various pathogens and non-target organisms [69].

Figure 8 illustrates that for the “wheat AND disease AND prothioconazole” query, important and up-to-date keywords include “septoria tritici blotch”, “qoi”, “cross-resistance”, “resistance mechanism”, “demethylation inhibitor”, “fusarium pseudograminearum”, “cytology”, “meta-analysis”.

Figure 8. Profile map of “wheat AND disease AND prothioconazole” query created using VOSviewer, based on publications from 2014 to 2022.

Figure 8. Profile map of “wheat AND disease AND prothioconazole” query created using VOSviewer, based on publications from 2014 to 2022.

DMI fungicides, such as tebuconazole, propiconazole, and metconazole, have been widely used to control wheat diseases like Fusarium head blight and septoria tritici blotch [70,71]. Continuous use of DMIs has led to a gradual loss of sensitivity of Z. tritici to several active ingredients of the triazole group, causing a shift towards reduced sensitivity of the pathogen to DMI fungicides [70,71].

Studies have shown that DMI resistance is associated with mutations in the CYP51 gene, which encodes the fungal sterol 14α-demethylase target protein for these [72]. Moreover, the frequency of different CYP51 haplotypes, associated with decreased sensitivity to DMIs [49], has been monitored across European wheat-growing areas, indicating an ongoing evolution of CYP51 in the Z. tritici population [72].

From the above is concluded that the use of demethylation inhibitors in controlling wheat diseases has led to the development of resistance mechanisms in wheat pathogens at the molecular level, impacting disease development and spread. So, the need for effective anti-resistance strategies to prolong the efficacy of these fungicides and mitigate their environmental implications is fundamental.

Figure 9 illustrates that for the “wheat AND diseases AND algorithm” query, important and up-to-date keywords include “convolutional neural networks”, “deep learning”, “learning systems”, “images classification”, “detection algorithms”, “remote-sensing”, “object detection”, “yolov5”, and others such as “detection accuracy”, “disease index”.

Figure 9. Profile map of “wheat AND diseases AND algorithm” query created using VOSviewer, based on publications from 2014 to 2022.

Figure 9. Profile map of “wheat AND diseases AND algorithm” query created using VOSviewer, based on publications from 2014 to 2022.

Based on the above, convolutional neural networks (CNN), are used for wheat disease detection by analyzing leaf photos and extracting complex information from them. CNNs can generalize, and process data on environmental effects, such as residual water stains on healthy leaves. This important issue has been found to cause misclassifications in field images, impacting the accuracy of disease detection [73,74,75]. CNN models have shown superior performance in image-based disease detection and classification tasks, surpassing the accuracy of other related traditional techniques for wheat disease detection e.g., human-based where farmers perform field surveys to identify the wheat disease [73,74,75,76]. We believe that using CNNs for wheat disease detection could be a promising technique for automatically identifying various wheat diseases. The system could be trained with a self-built dataset of important leaf photos with the most important leaf disease symptoms.

For the system mentioned above to achieve high classification accuracy of wheat diseases perhaps we need to use deep learning models. Moreover, for accurately classifying various wheat diseases, such as Septoria, Powdery mildew, Pyrenophora, etc., the deep learning models should be further trained to achieve the recognition of the specific disease based on characteristic disease symptoms. Based on the literature different deep-learning models, including VGG19, ResNet50, EfficientNet, and others, have been evaluated for wheat disease classification, with VGG19 emerging as a top-performing deep-learning model [77,78]. Further, we need to take care of the limitations of deep-learning models such as the requirement of large datasets for training [79].

As the literature shows, learning systems are effective in automating the diagnosis of wheat diseases, leading to the potential for improving crop management and increasing crop yield quantity and quality [79,80]. Advanced technologies like deep learning and image processing are being used for disease recognition, involving steps such as image preprocessing, segmentation, feature extraction, and classification [66,81].

Probably, systems such as CNN and deep learning models have the potential to automate disease diagnosis, improve crop yield, and provide timely and accurate disease detection, offering significant benefits for wheat farmers and crop management. However, we believe that there is a need for further research to address challenges in disease severity recognition and to develop hybrid algorithms that link disease classification and severity recognition.

Concerning “image classification”, Wheat diseases, such as powdery wheat, can be classified using machine learning and deep learning methods. Those models have a high testing accuracy of 97.88% [82], or 97.47% validation accuracy on the training set of images and a testing accuracy of 98.42% on the testing set, as presented by Abdalla et al. [83]. Another research shows that pre-trained deep-learning models for detecting and classifying wheat diseases such as EfficientNetB0, VGG16, and ResNet50 detect wheat diseases, achieving a high accuracy rate of 99.37% with the EfficientNetB0 model. This model performance was enhanced through data augmentation and multistage fine-tuning techniques. Moreover, EfficientNetB0 outperformed other models, including Generalized Focal Loss WheatNet (GFLWheatNet) and YOLO v5s, in terms of detection accuracy and efficiency [84,85]. Besides the above-mentioned results, the proposed YOLOv5s models achieved high mean average precision scores ranging from 95.92% to 97.15% for the detection of wheat diseases such as powdery mildew and Fusarium head blight [86,87], indicating their effectiveness in accurately identifying crop diseases.

In conclusion, the above results by Zhao et al. [86] and Gao et al. [87] provide evidence of the successful application of YOLO v5s for the detection of common wheat diseases such as Fusarium head blight and Wheat Powdery mildew. Probably the proposed YOLOv5s models, which achieved high mean average precision for wheat diseases such as Fusarium head blight and Wheat Powdery mildew indicate promising models able to detect other important wheat diseases such as Septoria tritici blotch or Tan spot of Wheat.

4. Discussion with Overview of the Collected Data

The results (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9) of this article indicate that the key pathogens requiring attention due to climate change and conservation tillage practices primarily involve the genera Septoria and Pyrenophora. The following paragraphs will mainly discuss these two pathogens and explore potential solutions based on existing literature.

It is well known that foliar diseases significantly impact wheat (a C3 plant) by affecting its photosynthetic efficiency and overall yield. As mentioned above, foliar diseases like Tan spot, caused by P. tritici-repentis, rapidly disrupt photosynthetic processes, leading to reduced photosynthesis and increased oxidative stress [88,89,90,91]. Moreover, foliar diseases, such as leaf rust, Septoria leaf blotch, and Tan spot, significantly reduce above-ground biomass and grain yield in wheat crops [92]. Even more foliar fungal diseases can impact wheat crop growth rate differently, modifying nitrogen dynamics and carbohydrate accumulation in the grain, ultimately affecting milling and end-use quality [92,93,94]. So, the impact of foliar diseases on wheat yield and quality can be managed through the selection of suitable genotypes and the application of fungicides [95,96].

Based on the above we can conclude that C3 photosynthesis in wheat is crucial for crop growth and productivity. Still, it also renders wheat plants susceptible to various foliar diseases such as Septoria leaf blotch, and Tan spot. Understanding the molecular mechanisms underlying this interaction, considering environmental factors, and implementing effective management strategies are essential for wheat crop management and breeding programs. In general, fungicides contribute to wheat yield gains through increased grain filling rates, by prolonging the filling period, or both relating to the extension of the green area duration of the flag leaf and particularly due to the prolongation of grain filling per day based on the extra flag leaf life which is varied amongst cultivars [97].

Wheat is usually categorized as a classical C3 plant. However, recent evidence suggests that the pericarp of wheat grains has structural similarities with classical C4 plants [98]. This suggests that the plant may rely more on C4 photosynthesis under water stress conditions (due to climate change) during the grain-filling stage. The authors [98] concluded that during terminal drought, photosynthates from the pericarp, glumes, and awns become crucial when other parts of the plant lose their photosynthetic capacity. Based on Figure 5, we believe that this may have significant implications for managing foliar diseases of wheat in the face of climate change or for addressing grain pathogens such as P. nodorum or species of Fusarium and mainly P. tritici-repentis that affect wheat.

Late foliar diseases that appear during the grain filling period can reduce radiation interception and absorption, leading to a decrease in assimilation supply. This reduction can induce lower grain weight and yield [99]. The relationship between the source-sink ratio and growth per grain has been studied, and it was found that wheat cultivars with a higher source-sink ratio have a greater capacity to maintain yield performance when the crop is affected by late foliar disease [99]. This can help avoid the negative effects on grain, as originally described in the present study. Furthermore, studies on the determination of wheat grain quality have shown that early nitrogen-sulfur fertilization can enhance the influence of the source-sink ratio on wheat grain quality [100]. This is an important finding, as it suggests that minimizing foliar diseases of wheat from tillering growth stages and during the stem elongation growth stages could contribute to modifying the source-sink ratio and effectively increasing grain weight during the grain filling period.

Additionally, this research article highlights the need to better understand the mechanisms by which fungicides affect phosphotransferases as Figure 3 shows. It emphasizes the role of these enzymes in contributing to fungicide resistance, as well as current strategies for targeting phosphotransferases to develop effective antifungal compounds. In detail, fungicides can affect various cellular processes, including those involving phosphotransferases. These enzymes play a role in intermediary metabolism, energy transduction, nucleic acid biosynthesis, and regulation of cellular processes. Understanding how fungicides interact with these enzymes can help in designing more effective treatments. Even more, phosphotransferases and other enzymes can contribute to fungicide resistance through mechanisms such as target site alteration. These adaptations allow fungi to survive despite the presence of fungicides, making it essential to monitor and manage resistance effectively. Developing antifungal compounds that target phosphotransferases involves understanding their role in fungal metabolism and identifying inhibitors that can effectively disrupt their function.

Based on the results of Figure 4, Figure 6, and Figure 7, and as Z. tritici has developed resistance to various fungicides, particularly to azoles we believe that is important to understand the key points on azole resistance in Z. tritici such as (i) mutations in the CYP51 gene, (ii) cross-resistance depending on specific CYP51 mutation and (iii) manage resistance thought integrated pest management (IPM) strategies [39,101].

Research on P. tritici-repentis indicates that the presence of effector genes, such as ToxA and ToxB, varies among different isolates. Studies have demonstrated that the distribution of these effector genes can significantly differ based on geographic location [55,96,102]. This variability in effector gene presence adds to the complexity of managing tan spot disease, as different isolates may necessitate distinct management strategies [103]. Moreover, the interaction between necrotrophic effectors (NEs) like SnToxA and SnTox3 (Figure 4), and host genes is a critical aspect of the pathogenicity of P. nodorum. These effectors interact with specific host sensitivity genes, leading to programmed cell death in the host plant [104,105]. This PCD provides nutrients for the pathogen, facilitating its growth and sporulation. We believe that understanding these interactions is vital for developing effective disease management strategies and breeding resistant wheat varieties.

Further based on the results of Figure 3, novel molecular breeding techniques, such as marker-assisted selection (MAS), quantitative trait loci (QTL), genome-wide association studies (GWAS), and the CRISPR/Cas-9 system, have been fruitful in developing broad-spectrum disease resistance in wheat, ensuring better yields and food security. As mentioned above research indicates that genes outside of the CYP51 family may significantly contribute to demethylation inhibitor (DMI) resistance in F. graminearum [67]. We believe that understanding these additional genetic factors is essential for developing more effective fungicide resistance management strategies and ensuring the continued efficacy of DMIs like Propiconazole.

Additionally, the continuous use of demethylation inhibitors (DMIs) has indeed led to a gradual loss of sensitivity in Z. tritici to several active ingredients of the triazole group. This phenomenon probably is primarily due to the selection pressure exerted by the repeated application of these fungicides. As DMIs remain a valuable tool in managing Z. tritici, we believe management strategies such as fungicide rotation, combining chemical control with cultural practices, and monitoring important pathogen (-s) populations can enhance disease management and increase the significant contribution and efficacy of fungicides.

Our data (Figure 2) and Mapping academic literature utilizing VOSviewer (Figure 1, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9) indicate that climate change with conservation tillage practices can raise the infections of significant wheat pathogens such as P. tritici-repentis and Z. tritici. We believe that wheat residues are a primary source of inoculum for P. tritici-repentis, which contributes to the spread and severity of tan spot disease or the primary source of inoculum for Z. tritici. Therefore, managing crop residues is crucial, as the pathogen can survive on wheat stubble. We propose an integrated approach that combines crop rotation, stubble management, and the use of fungicides such as triazoles and SDHI fungicides. Specifically, we believe that certain crop management practices—including tillage, rotating crops with non-host plants, and applying fungicides like Systiva^® (BASF Hellas S.A., Marousi, Greece) during the early growth stage—are essential for effectively managing early developing wheat foliar diseases such as tan spot disease [9,106].

Based on Figure 7 and Figure 8, for foliar application, we believe that Propiconazole, whether used alone [107] or in combination with other fungicides, has demonstrated significant control of tan spots in wheat [108]. In a field trial, Propiconazole was shown to provide levels of control similar to those of other triazole and SDHI fungicides [108]. It is important to apply fungicides like Propiconazole not in intensive spraying, as excessive spraying can lead to reduced sensitivity to fungicides in the pathogens [109]. In addition to its efficacy against P. tritici-repentis, we believe that Propiconazole should be incorporated into integrated management strategies as mentioned above. Propiconazole works by inhibiting cytochrome P450 (CYP) enzymes, specifically targeting CYP51, which are essential for fungal cell wall formation. This inhibition disrupts the synthesis of ergosterol, a key component of the fungal cell membrane [110,111]. Prothioconazole another DMI fungicide, is also effective against P.tritici-repentis. Field trials in New Zealand demonstrated that Prothioconazole, both alone and in mixtures with other fungicides, provided effective control of tan spots.

Besides our results in Figure 7 and Figure 8, research indicates that the timing of DMI fungicide application is crucial [112]. For instance, applying fungicides such as Prothioconazole at the GS33 stage (when the third node is detectable) is important, as infections that are well-established before fungicide application can decrease control efficacy [108]. Azole fungicides, such as Prothioconazole, inhibit various cytochrome P450 enzymes, including CYP51, which plays a role in fungal sterol biosynthesis [113,114]. However, the inhibition of these enzymes can also impact the metabolism of xenobiotics in non-target organisms, raising environmental concerns [115]. So, this inhibition mechanism among azole fungicides disrupts essential metabolic processes in fungi, but it can also have broader implications for non-target species and the environment [116].

Research indicates, as shown in Figure 9, that convolutional neural networks (CNNs) have become a powerful tool for detecting wheat diseases by analyzing leaf photos [117]. CNNs have shown high accuracy in detecting various wheat diseases, such as rust, powdery mildew, and leaf blight [76,117]. Based on that, researchers are continuously working on optimizing CNN models to make them more efficient and accurate [117]. We believe that the continuous optimization of convolutional neural networks (CNNs) is crucial for enhancing their efficiency and accuracy in tasks for practical applications in agriculture like wheat disease detection, and beyond. We believe that image detection and classification can be utilized to create automated systems for the early detection and classification of significant diseases [118], such as tan spots in wheat fields, allowing for timely and accurate fungicide application.

5. Conclusions

In conclusion, effectively controlling wheat foliar pathogens such as P. tritici-repentis requires a combination of strategies. These include utilizing effective fungicides and implementing cultural practices such as managing over-wintering inoculum, tillage, and crop rotation. Genetic studies have identified specific genes that provide resistance to different races of P. tritici-repentis and influence resistance to the necrosis and chlorosis caused by the pathogen. Therefore, a combination of these strategies along with decision support systems is crucial for reducing disease risk and support decision-making. Climate change is expected to significantly influence the emergence and severity of plant diseases, including tan spot diseases. Adaptive management strategies such as using fungicides, breeding resistant varieties, implementing Integrated Pest Management (IPM) through crop rotation and timely fungicide applications, and removal of infected plant debris, are essential for mitigating these impacts and ensuring sustainable crop production.