Study on Pathogen Identification and Biocontrol Fungi Screening of Oat Sheath Rot

Abstract

Oat sheath rot disease significantly reduces commercial oat yields, yet research on its incidence, causative pathogens, and control strategies remains limited, particularly in China. This study investigated the occurrence of oat sheath rot in major oat-producing regions of Northern China. Here, we isolated and identified two species of primary pathogenic fungi, Scopulariopsis brevicaulis and Alternaria alternata. Next, we conducted pathogenicity tests to confirm their role in the progression of oat sheath rot disease. Subsequently, we screened putative biocontrol fungi and identified Trichoderma harzianum and Trichoderma koningii as effective antagonistic biocontrol fungi. Both species demonstrated strong inhibitory effects against two primary pathogens through competitive interactions, with T. koningii achieving 100% inhibition in one test. Overall, T. harzianum and T. koningii both exerted strong inhibitory effects against pathogenic fungi via different forms of competition. Most importantly, infection experiments showed that T. harzianum and T. koningii both exerted strong antifungal effects against the pathogenic fungi that cause oat sheath rot. Taken together, our findings provide a foundation for developing biological control strategies to mitigate oat sheath rot in oat cultivation in China.

1. Introduction

Oat sheath rot disease is known to cause large reductions in commercial oat (Avena sativa) yields. At present, there remains relatively little research on the incidence of oat sheath rot, the extraction and identification of pathogens, or prevention and control measures, especially in China, a major oat exporter. This has resulted in a general failure to establish a corresponding prevention and control system. At the same time, excessive use of chemical agents can produce fungal resistance, leave pesticide residues, and cause other production or environmental issues.

Oat (Avena sativa, Gramineae) is a major cereal crop whose leaves and straw are juicy and tender with good palatability [1]. Moreover, the crude protein, crude fat, and nitrogen-free content of the straw is generally high, and fiber content, which is difficult to digest, is generally low. Oat is an excellent forage plant for supplementary feeding of livestock during the winter and spring and is critical for disaster-resistant livestock protection in pastoral areas [2]. At present, oat is produced on a large scale both as an annual grain and as a forage crop in China. It exhibits significant drought resistance, cold tolerance, adaptability to barren conditions, and salt tolerance [3] and plays a crucial role in agricultural rotation systems in China [4,5].

As the cultivation area of oat crops has expanded, the impact of various diseases on oat yield and quality has intensified, having now become one of the primary limiting factors for further development of the oat industry [6]. Oat diseases directly reduce oat yield and quality, causing decreases in crude protein, fat, and soluble sugars while increasing the crude fiber content of oats, which together cause reduced palatability and digestibility [7].

In recent years, a novel disease known as oat sheath rot was found on cultivated oats, wild oats, and highland barley in high-altitude (2400~3200 m) regions of central, western, and southern Gansu Province (i.e., in Tongwei, Tianzhu, and Hezuo) [8]. A previous study found that this oat sheath rot generally occurred sporadically in fields, with rates of diseased ears in severely diseased fields reaching 20%~30% of all plants [9]. The disease mainly occurs during the late growth period [10], which endangers oat leaf sheaths. Once infected, plants show brown and irregularly shaped lesions. In severe cases, the disease can spread to the entire leaf sheath [11,12], resulting in leaf sheath desiccation or necrosis, thereby significantly impacting oat yield [13].

At present, the use of chemical agents to control crop disease remains the main measure used during commercial agricultural production. However, the large-scale use of chemical pesticides is known to cause various problems, including induced resistance of pathogens to specific pesticides, changes in pathogen biotype, increased ineffectiveness of alternate fungicides, high levels of fungicide residues on postharvest products, and other negative effects on the environment and human health [14], including environmental degradation, drug resistance, and large-scale soil hardening. During China’s promotion of eco-friendly agricultural and sustainable development policies, it is necessary to seek greener cultivation methods that can ensure both adequate yield and food and environmental safety [15,16,17,18]. Recent years have identified new ways to prevent and control plant disease by isolating and identifying pathogenic fungi from hosts and applying biocontrol fungal strains to inhibit pathogen proliferation and promote normal host growth [19,20,21].

Therefore, this study conducted research into fungal biocontrol in the main oat-producing areas of China. By investigating disease in the field, we first isolated and identified pathogenic bacteria, then screened efficient and low-toxicity biocontrol fungi. In doing so we identified key pathogens causing oat sheath rot disease in major oat-producing areas and provide a basic protocol for the biological control of this disease.

2. Materials and Methods

2.1. Survey and Sample Collection

2.1.1. Investigation Site

A systematic survey was conducted in Tongwei County (Dingxi City, Gansu Province), Tianzhu County (Wuwei City, Gansu Province), Shandan County (Zhangye City, Gansu Province), and Shangdu County (Wulanchabu City, Inner Mongolia). The distribution of survey sites and general information are shown in Figure 1 and

Table 1. Sample plot overview.

Sample Area			Elevation (m)	Mean Annual Temperature (°C)	Rain Fall (mm)	Sunshine Hours (h)	Frost-Free Period (d)
Province	City	County
Gansu	Dingxi	Tongwei	2319	7.7	500	2430	147
Gansu	Wuwei	Tianzhu	2770	1.0	400	2495	146
Gansu	Zhangye	Shandan	2146	0.2	360	2823	155
Neimenggu	Wulanchabu	Shangdu	1400	3.1	350	2970	105

.

2.1.2. Test Materials

A total of 32 materials were tested; the details are shown in

Table 2. Tested oat materials.

Numbering	Growing Location	Variety Name	Numbering	Growing Location	Variety Name
1	Tongwei	Longyan 3	17	Shandan	Longyan 3
2	Tongwei	Longyan 5	18	Shandan	Longyan 5
3	Tongwei	Wuyou 1	19	Shandan	Wuyou 1
4	Tongwei	Aiwo	20	Shandan	Aiwo
5	Tongwei	Yanwang	21	Shandan	Yanwang
6	Tongwei	Baiyan 7	22	Shandan	Baiyan 7
7	Tongwei	Qingyan 1	23	Shandan	Qingyan 1
8	Tongwei	Heimeike	24	Shandan	Heimeike
9	Tianzhu	Longyan 3	25	Shangdu	Longyan 3
10	Tianzhu	Longyan 5	26	Shangdu	Longyan 5
11	Tianzhu	Wuyou 1	27	Shangdu	Wuyou 1
12	Tianzhu	Aiwo	28	Shangdu	Aiwo
13	Tianzhu	Yanwang	29	Shangdu	Yanwang
14	Tianzhu	Baiyan 7	30	Shangdu	Baiyan 7
15	Tianzhu	Qingyan 1	31	Shangdu	Qingyan 1
16	Tianzhu	Heimeike	32	Shangdu	Heimeike

.

2.1.3. Field Investigation of Sheath Rot

Three oat planting areas in Gansu Province (i.e., Tongwei, Tianzhu, and Shandan Counties) and one in Inner Mongolia (Shangdu County) were investigated in this study. At each site, a 667 m² plot was selected in an experimental field during the oat growing season. A five-point sampling method was then used to avoid sampling a 5 m band along the edge of the plot, and 20 oat plants were randomly selected at each point for sampling [22].

2.1.4. Disease Classification

The grading standard for oat sheath rot was set according to a framework published by Pushpam et al. (

Table 3. Severity grading standard of oat sheath rot disease.

Severity Grade	Grading Standard
0	No lesion
I	There are brown spots on the leaf sheath, and the area is not more than 25%.
II	The lesion area on the leaf sheath ranges from 25% to 50%.
III	The lesion area on the leaf sheath ranges from 50% to 75%.
IV	Leaf sheath disease, spot area range of 75%.

) [23]. IBM SPSS Statistics 26 software was used to determine the statistical significance of differences in group mean values. Oat sheath rot severity was classified according to the criteria listed below.

Calculation formula:

$$R=TD÷TS\times 100\%$$

(1)

$$S=\left[\sum _{i=1}^n(X_i\times S_i)÷(X_n\times S_n)\right]\times 100$$

(2)

Here, M indicates morbidity; TD, the total number of diseased plants; TS, the total number of survey plants; S, the disease index; X_i, the number of leaf sheaths of each disease grade; S_i, the value of severity grade i; X_n, the total number of leaf sheaths investigated; and S_n, the maximum severity grade value.

2.1.5. Resistance Evaluation

Next, oat sheath rot resistance was evaluated as follows (

Table 4. Evaluation method of oat sheath rot resistance [24].

Disease Index	Resistance
Disease index = 0	Immunity (I)
0.1 < Disease index ≤ 20	Highly resistant (HR)
20.1 < Disease index ≤ 40	Resistant (R)
40.1 < Disease index ≤ 60 I	Moderately resistant (MR)
60.1 < Disease index ≤ 80	Susceptible (S)
80.1 < Disease index ≤ 100	High sensitive (HS)

).

2.2. Pathogen Isolation and Identification

2.2.1. Separation and Purification

After a pair of scissors was sterilized, a small piece of tissue was cut along the edge of a lesion on a diseased plant. First, the lesion sample was soaked in 0.5% sodium hypochlorite for 1–2 min, then washed with 75% alcohol for 30 s before being washed three times in sterile water. Finally, samples were placed on sterilized filter paper to dry, and the lesion was placed downward on a potato glucose agar medium for culturing at 25 °C for 3 days. After the mycelium growth on the plate was visible, a sample of mycelium taken from the edge of the original culture was used to inoculate a new medium for culturing at a constant temperature. After seven days of cultivation, the resulting mycelium was mixed with sterile water to make a fungal spore suspension from which single spores were isolated. Purified colonies were compressed into 5 mm cakes that were inoculated onto an inclined medium that was placed in a refrigerator at 4 °C until further use [25].

2.2.2. Morphological Identification

Pathogen strains isolated and purified at early life stages were cultured on PDA plates at 25 °C for 5–7 days. Next, colony morphology and spore size were observed under a microscope, photographed, and recorded. Finally, we used the morphological taxonomic key developed by Siebold, M et al. for preliminary strain identification [26].

2.2.3. Identification Using Molecular Biological Analyses

In parallel, we extracted DNA from the pathogenic bacteria extracted in the early stage, then sent DNA sequence information regarding the ITS region of the pathogen genome. The primers used in this study were ITS1 (5′-TCCGTAGGTGAACCTGCGG-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′). Comparative analysis of pathogen ITS sequences was performed using NCBI-BLAST. After preliminary species identification, we developed specific primers for each corresponding strain, as shown in

Table 5. Sequences used for secondary sequencing.

Pathogenic Fungus	Primer Name and Sequence		PCR Length
WL3	NS1 (5′-GTAGTCATATGCTTGTCTC-3′)	NS4 (5′-CTTCCGTCAATTCCTTTAAG-3′)	About 1500 bp
WL6	H2P1 (5′-GTTCGCCCACCACTAGGACAAAC-3′)	H2P4 (5′-AGACGCCCAACACCAAGCAAAG-3′)

:

In this process, RNASE was used to process total DNA, and 5 μL was added during the extraction process. After the extraction of DNA was completed, the purity and concentration of DNA were identified by gel electrophoresis of DNA. The amplified PCR product was subjected to agarose gel electrophoresis to obtain an identification gel (Figure 2).

Using these primers we conducted another sequencing analysis as above, after which a phylogenetic tree was constructed using MEGA version 7.0.

2.2.4. Pathogenicity Testing

Next, we evaluated microbe pathogenicity in BBCH35-39 during the oat growing season. The oat variety used for all pathogenicity analyses was Longyan 3 (this variety was selected to reduce the randomness of variety selection in field trials and reduce the chance of trials). We began this procedure by extracting pathogenic fungus in the early stage before transferring it to a new plate, in which it was cultured at 25 °C for 4–5 days. After plate inoculates grew into complete colonies, the pathogenic fungus was inoculated onto oat plants. During inoculation, it was necessary to disinfect and sterilize the oat leaf sheath with alcohol, then use the scalpel to perform a minimally invasive scratch wound on the leaf sheath. Finally, a 5 mm fungi cake was taken, wrapped with a preservative film (to prevent falling or the contamination of the wound), then closely attached to the wound on the oat leaf sheath. We then observed the wound every two days, since, when the fungal cake infects the wound, it creates an obvious lesion. After infection, the pathogenic fungus present at the diseased site was extracted according to the method described in Section 2.2. Finally, we performed two pathogenic fungus extractions to verify whether they yielded the same strain [27].

2.3. Screening of Biocontrol Fungi

2.3.1. Pathogenic Fungi

We tested two strains of pathogenic fungi, Aspergillus brevis and Alternaria spp. Both were extracted from infected young plants before being preserved at the College of Grassland Science, Gansu Agricultural University.

2.3.2. Biocontrol Fungi

The antagonistic (biocontrol) fungi Trichoderma harzianum (HC), Trichoderma koningii (KN), and Gliocladium roseum (FZ) were extracted from young plants before being stored at the pathology laboratory of the College of Prataculture, Gansu Agricultural University.

2.3.3. Screening of Biocontrol Fungi

Preliminary screening of the biocontrol fungi was performed to create 5 mm fungal cakes of activated pathogens and inoculate them in PDA medium. The biocontrol strains were then inoculated around the pathogens using a cross-crossing method. Each treatment was repeated three times. After three days of culturing in a biological incubator held at a constant temperature of 28 °C, the strains of fungus that produced an inhibitory zone against the pathogen were selected for further experiments. Next, the biocontrol fungal strains were rescreened using the plate confrontation method. The specific method used was as follows. First, strains obtained via activation screening were inoculated and stored. One side of the PDA medium was inoculated with a 5 mm sample of sheath rot pathogen located 3 cm from the edge of the plate. At the same time, a sample of a biocontrol fungus obtained from the preliminary screening was inoculated on the other side of the plate as above. The control group was then inoculated with single colonies. Each treatment had three technical replicates before all samples were cultured at 28 °C. The progress of the confrontation was observed and recorded every three days. Finally, those biocontrol strains that showed the best inhibitory effect were selected for further testing.

Formula for calculating antifungal rate:

$$F(\%)={\displaystyle \frac{Cd-Pd}{Cd}}\times 100$$

(3)

Here, F, fungi suppression rate; Cd, control colony diameter; Pd, processing colony diameter.

2.3.4. Antagonistic Ability of Biocontrol Fungi

Next, the plate confrontation method was used to select a 9 cm culture dish. The biocontrol fungi were pre-cultured in a biochemical incubator at 20 °C for seven days, after which a fungal cake (5 mm) was taken at the edge of the medium and inoculated into the PDA medium (3 cm from the edge of the plate). Simultaneously, the extracted pathogenic fungus was inoculated on the other side of the plate according to the method outlined above. Briefly, a control group was inoculated with single colonies of biocontrol and pathogenic fungi, and each treatment was set up with five replicates. Mycelia were then cultured at 20 °C in a biological incubator, and mycelial growth was observed and recorded every three days. When a colony inhibition zone appeared between two colonies, we measured the width of the colony inhibition zone as well as the colony diameter. When two fungi come into contact, the gap between the pathogen and the biocontrol bacteria is the bacteriostatic zone, and the distance from the edge of the bacteriostatic zone to the other side of the colony was taken to represent the colony diameter.

The calculation formula for the bacteriostatic rate used here is the same as that used in Section 2.3.3.

2.3.5. Mechanism of Action of Biocontrol Fungus on Sheath Rot Pathogen

Next, we co-cultured antagonistic fungi and oat sheath rot pathogens using the confrontation culture method at a constant temperature of 25 °C; here all samples were observed daily. When the two colonies intersected, we selected the pathogen hyphae present at the junction and observed their interactions under an optical microscope [23,28].

2.4. Statistical Analyses

SPSS version 25.0 (IBM SPSS, Chicago, IL, USA) was used for all statistical analyses of experimental data. Duncan multiple range tests were used to assess statistical significance of differences in group means. Finally, Origin 2021 was used for all mapping analyses.

3. Results

3.1. Field Symptoms and Investigation of Oat Sheath Rot Disease

Oat sheath rot is a major disease limiting oat production in China (Figure 3). This disease mostly occurs at the jointing stage and harms the leaf sheath of oats, resulting in irregular brown lesions. In severe cases, it can extend to the entire leaf sheath, causing dry rot of the leaf sheath and potentially death, thereby severely impacting oat production.

In 2023, a field survey of sheath rot was conducted on 32 oat plants cultivated in four counties in Gansu Province and Inner Mongolia. The results of this study showed that the sheath rot disease occurred widely throughout the area surveyed (

Table 6. Occurrence of sheath rot in the main oat-producing areas.

Numbering	Place of the Investigation	Material	Grade of the Disease					Diseased Plant Rate (%)	Disease Index	Resistant Type
			0	I	II	III	IV
1	Tongwei County	Longyan 3	100	0	0	0	0	0	0	I
2	Tongwei County	Longyan 5	98	1	1	0	0	2	0.75	HR
3	Tongwei County	Wuyou 1	97	2	1	0	0	3	1	HR
4	Tongwei County	Aiwo	100	0	0	0	0	0	0	I
5	Tongwei County	Yanwang	97	2	1	0	0	3	1	HR
6	Tongwei County	Baiyan 7	99	0	1	0	0	1	0.5	HR
7	Tongwei County	Qingyan 1	96	1	2	1	0	4	2	HR
8	Tongwei County	Heimeike	100	0	0	0	0	0	0	I
9	Tianzhu County	Longyan 3	99	1	0	0	0	1	0.25	HR
10	Tianzhu County	Longyan 5	100	0	0	0	0	0	0	I
11	Tianzhu County	Wuyou 1	99	0	0	1	0	1	0.75	HR
12	Tianzhu County	Aiwo	99	1	0	0	0	1	0.25	HR
13	Tianzhu County	Yanwang	100	0	0	0	0	0	0	I
14	Tianzhu County	Baiyan 7	100	0	0	0	0	0	0	I
15	Tianzhu County	Qingyan 1	97	2	1	0	0	3	1	HR
16	Tianzhu County	Heimeike	96	2	2	0	0	4	1.5	HR
17	Shandan County	Longyan 3	96	1	3	0	0	4	1.75	HR
18	Shandan County	Longyan 5	99	1	0	0	0	1	0.25	HR
19	Shandan County	Wuyou 1	97	1	2	0	0	3	1.25	HR
20	Shandan County	Aiwo	100	0	0	0	0	0	0	I
21	Shandan County	Yanwang	100	0	0	0	0	0	0	I
22	Shandan County	Baiyan 7	99	1	0	0	0	1	0.25	HR
23	Shandan County	Qingyan 1	100	0	0	0	0	0	0	I
24	Shandan County	Heimeike	100	0	0	0	0	0	0	I
25	Shangdu County	Longyan 3	100	0	0	0	0	0	0	I
26	Shangdu County	Longyan 5	100	0	0	0	0	0	0	I
27	Shangdu County	Wuyou 1	100	0	0	0	0	0	0	I
28	Shangdu County	Aiwo	95	1	2	2	0	5	2.75	HR
29	Shangdu County	Yanwang	99	0	1	0	0	1	0.5	HR
30	Shangdu County	Baiyan 7	100	0	0	0	0	0	0	I
31	Shangdu County	Qingyan 1	92	5	1	2	0	8	3.25	HR
32	Shangdu County	Heimeike	97	2	1	0	0	3	1	HR

). Of the sites under study, Shangdu County (Ulanqab City) had the most serious outbreak of this disease, with an average disease rate of 2.13% and an average disease index of 0.94. This was followed by Tongwei County (Dingxi City), which showed an average disease rate of 1.63% and an average disease index of 0.66. Plants sourced from Shandan County (Zhangye City) had the lowest incidence of this rot, with an average disease rate of 1.13% and an average disease index of 0.44.

The average disease index of each variety was calculated by finding the mean of the sheath rot survey results from the main oat-producing areas of Gansu Province and Inner Mongolia (Figure 4). As shown in Figure 3, we observed significant differences in the average disease index of sheath rot among different varieties from the same survey site (p < 0.05). For example, the disease index of Qingyan 1 in Tongwei County was 2.00 and showed high resistance (HR), while Longyan 3, Aiwo, and Heimeike showed no disease (I), with significant differences among the varieties (p < 0.05). Moreover, the disease index of Wuyou 1 in Shandan County was 1.25 and also showed high resistance (HR), whereas Aiwo, Yanwang, Qingyan 1, and Heimeike did not show disease (I); once again we observed significant differences among varieties (p < 0.05).

In the same variety, we consistently observed significant differences in average disease index at different survey sites (p < 0.05). For example, the average disease index of Evo in Tianzhu County was 0.25, whereas that of Evo in Shangdu County was 2.75. Furthermore, this difference was statistically significant (p < 0.05). In addition, the average disease index of Qingyan 1 in Tongwei County was 2, while its average disease index in Shangdu County was 3.25, and this difference was also significant (p < 0.05).

3.2. Fungal Isolates

Fungal isolates were extracted from all collected strains, and 11 strains with inconsistent appearances were isolated and purified. These pathogenic strains were numbered WL1-WL11.

3.2.1. Determination of Pathogenicity

Next, the isolated pathogenic strains were inoculated into live oats, and we observed that WL3 and WL6 caused different degrees of symptoms. The remaining strains did not cause plant diseases. Symptoms after inoculation by pathogenic fungus are shown in Figure 3. On the third day after inoculation, disease spots began to appear on oat plant stems, approximately 3–7 cm below the flag leaf of the oat leaf sheath. Over time, this lesion area began to expand, the infected site became chlorotic, and light brown lesions gradually emerged. However, none of these symptoms were observed in control plants. Finally, after seven days of inoculation, disease symptoms were significantly aggravated in inoculated plants (Figure 5).

The inoculation sites of strains WL3 and WL6 showed dryness and obvious lesions, indicating that their pathogenicity was strong. These symptoms were not observed in the control. We therefore isolated pathogenic fungi from the WL3 and WL6 lesions for further culturing. Microscopic examination revealed that the obtained isolates were consistent with the inoculated pathogenic fungus. Further molecular identification of reisolated pathogens revealed that they were the same strains as the original pathogens, indicating that both strains induce oat sheath rot.

3.2.2. Morphological Characteristics of Pathogens

We subsequently cultured the two isolated and purified pathogens in PDA medium for seven days, after which the colony and conidial morphologies were observed.

The WL3 strain cultured in PDA medium is shown in Figure 6. This colony was white during its early stages of development and became a powder culture with a yellow hazel edge in the center after a short time. The edge of the colony was not fixed, and the back of the Petri dish had a light yellow appearance. The mycelial surface was smooth. According to these morphological characteristics, the WL3 strain was preliminarily identified as S. brevicaulis.

Next, the WL6 strain cultured on PDA medium is shown in Figure 7. In general, isolated Alternaria spp. can have a variety of different colors, including light green, dark green, light gray, olive-brown-green, brown, and white. Their conidia can also have different shapes, such as long beaks and obtuse shapes with round apices. Moreover, these can have both long and small conidia, and some may have germination tubes and a round perspex and be of a medium size. Other reports of Alternaria isolates showed significant morphological variability in conidial length, conidial width, and the number of septa (transverse/longitudinal). According to the morphological characteristics of WL6 and in comparison to other strains, we initially identified WL6 as Alternaria alternate.

The remaining extracted microbial species were initially identified as Chaetomium globosum, Paecilomyces sp, Thielavia subthermophila, Aspergillus flavus, etc., but only WL3 and WL6 showed good pathogenicity in the later pathogenicity test. Therefore, the remaining extracted fungi were discarded, and only WL3 and WL6 pathogenic fungi were left to continue the study.

3.2.3. Molecular Identification of Pathogens

Using ITS sequencing, we found that the homologous sequence similarity of the two strains was >90%. Phylogenetic tree analysis of the pathogen sequences was then performed using MEGA version 7.0 (Figure 8). These results showed that the gene sequences of the WL3 and WL6 strains were closely related to C. brevis and A. alternata, respectively. When combined with analyses of their morphological characteristics (above) and verification as per Koch’s rule, we confirmed the identification of the pathogenic fungi WL3 and WL6 as Brevicorynespora spp. and Alternaria spp, respectively (sequence ID: WL3: OL589624.1 and WL6: MK370641.1)

3.3. Screening of Biocontrol Fungi

Next, we screened three antagonistic fungi using a cross-crossing method (Figure 9). After three days of culturing, we found that HC and KN exerted obvious antagonistic effects on both pathogens, whereas FZ showed poor antagonistic effects against both pathogens. Thus, we selected two putative biocontrol fungi, HC and KN, from this screening for further analysis.

3.3.1. Pathogen Inhibition Using Biocontrol Fungi

Next, we performed a rescreening using the two biocontrol fungi obtained from the initial screening (Figure 10). The results of the plate confrontation test showed that both HC and KN exerted antagonistic effects on both pathogens. In all cases, the pathogens and antagonistic fungi first grew on their own half of the PDA plate. G. catenatum grew quickly at first and gradually occupied the biocontrol plate. On the third day, the growth rate of G. catenatum decreased, and the antifungal zone gradually appeared. Subsequently, the biocontrol fungi continued to grow, while the pathogens stopped growing. By the fifth day, the biocontrol fungal colonies grew along the pathogen colonies and formed a ring. Over time, the pathogen was gradually suppressed and had been completely covered by the biocontrol fungi by Day 7 (Figure 11). At this point the surface of the culture dish showed the color of the antagonistic fungi. The relative inhibition rates of the two biocontrol strains are listed in

Table 7. The relative inhibition rates of the two biocontrol fungi on the 3rd, 5th, and 7th days against the two pathogens.

Antagonist	Time	Diameter of the Pathogen (mm)	Diameter of the Pathogen (mm)	Control Pathogen Diameter (mm)	Antifungal Rate (%)
HC-WL3	3 d	17	31	43	60.47
	5 d	10	76	55	81.82
	7 d	7	82	71	90.14
HC-WL6	3 d	22	30	32	31.25
	5 d	13	65	48	72.92
	7 d	9	80	77	88.31
KN-WL3	3 d	7	81	43	83.72
	5 d	11	77	55	80.00
	7 d	0	87	71	100.00
KN-WL6	3 d	17	48	32	46.88
	5 d	0	88	48	100.00
	7 d	0	88	77	100.00

.

The results in Table 7 show that the inhibition rates of HC and KN showed good results by the fifth day of culture. Moreover, by the seventh day of culture, the inhibition rate of both antagonistic fungi was greater than 80%, and the inhibition rate of KN-WL3 and KN-WL6 was 100%.

3.3.2. Competition Between T. harzianum, T. koningii, and Pathogen Hyphae

Figure 12 shows optical microscope images of interactions between the biocontrol fungi T. harzianum and T. koningii and the mycelia of the two pathogens. Here, images were taken after 5 days of constant temperature culturing at 25 °C. Figure 12 (A3,A6) show that the biocontrol fungus tightly wound around the pathogen hyphae, causing the pathogens to distort and break, thereby seriously restricting the normal growth and development of pathogen hyphae. Figure 12 (B3,B6) show that biocontrol mycelia were closely attached to pathogen mycelia; this parallel growth likely prevented the pathogen mycelia from obtaining nutrients and continuing to grow. At this point, the pathogen mycelia began to darken, became thinner, then dissolved.

4. Discussion

The interaction between plant growth and disease is a complex biological process [16]. Plant diseases are often not caused by a single factor, but can be influenced by plant disease resistance, the environment of the planting area, the pathogenicity of the pathogen, and field management measures in place [17,18,19,20]. For example, in a previous study, Linghong [21] examined samples of rice variety Jinyou 463 planted in Wuyuan, Xinfeng, and Taihe Counties, and found that Wuyuan County plants were the most severely affected by rice blast. In addition, meteorological factors are also known to influence plant disease. For example, the temperature, rainfall, and relative humidity of different planting sites can lead to differences in disease occurrence [22]. By investigating the occurrence of powdery mildew in oat plants grown in 13 villages from eight different counties of Gansu Province, a study by Haoyang [25] found that even when the same variety was present at different survey sites, the disease could take very different formats. Among the oat plants in the six townships, the authors recorded the highest incidence frequency in Majiazhuang Village (Tianzhu County), while the incidence of Baiyan 7 in Zhongtang Village (Yongdeng County) was the highest of the four planting areas.

Overall, meteorological, vegetation, and soil factors are the most tightly connected to the occurrence and prevalence of plant diseases. If the critical period of pathogen proliferation and infection coincides with suitable environmental conditions, the result can be an epidemic of plant disease [21]. Atmospheric humidity and wind speed are also critically important for keeping cereal crops disease-free. As such, climate change can both directly affect pathogens but also make hosts more vulnerable to pathogens [25]. When the temperature rises or rainfall conditions change, pathogen life history, oversummering behavior, and reproductive diffusion ability can change, thereby affecting the occurrence and development of plant disease [22].

Crop plants can be affected by various diseases during any stage of their growth and development, mainly due to pathogen infection [27]. In nature, many fungi can cause crop diseases, including oat sheath rot, with diseases caused by pathogenic fungi accounting for 70–80% of all plant diseases. Furthermore, fungal plant diseases are widely distributed, are generally rapid-onset, are sometimes difficult to spot, and can cause serious harm. Fungal pathogens often invade the apoplast or plant cells to obtain nutrients, thus establishing a parasitic relationship that can cause plant disease [23,28].

Plant–pathogen interactions are an important class of complex biological processes affecting crop production. The resistance of plants to pathogenic microorganisms is not only affected by their own genes but also by their adaptations to different regions and by human protocols for plant management [29,30,31]. This study found slight differences in the types and quantities of pathogens extracted from each plant, which may reflect that pathogen infection is related to variety-specific disease resistance. In addition, disease severity can be related to sowing date, planting density, fertilizer application, plant variety, and irrigation management method. However, the pathogenic factors affecting plant disease and epidemics in different regions can also be quite different, and so the pathogenic microorganisms responsible in different regions may not be the same [31]. In this study, we investigated diseases of the same oat variety in four different regions and found that the main causes of differences in disease emergence and format were environmental factors and management measures that differed among regions.

Oat sheath rot caused by S. graminearum has been previously reported in oat crops [8]. However, the two pathogens found in this study that were found to cause oat sheath rot have not been previously identified. This may be because different sites contain different pathogen living environments. In this study, the symptoms of sheath rot caused by pathogens were similar to those observed in the field but were also slightly different. For example, the central color of lesions of susceptible plants cultivated in the laboratory was darker: light beige or light brown, with the halo region being an even darker brown. Moreover, the lesion area overall was smaller than observed in the field. We speculate that these differences merely reflect differences between experimental and field growth environments [32].

The growth rate of antagonistic fungi is an important indicator that they can compete with pathogenic fungi. Thus, the faster the rate of growth of antagonistic fungi, the more potent its effect on inhibiting the growth of pathogenic fungi [33]. During the seven days of confrontation culture, the relative inhibition rates of the two antagonistic bacteria generally increased (Figure 10). Only KN-WL3 showed a slight downward trend on the fifth day, but this sample showed an upward trend on the seventh day. This result may indicate that the nutrients in its culture medium were not distributed evenly when the PDA plate was manually inverted, resulting in a faster colony growth rate on the nutrient-enriched part of the PDA plate and a slower colony growth rate on the nutrient-scarce part.

Chemical pesticides are fast and effective, but pose significant biological safety and environmental pollution risks when used over the long term. Vogel et al. [34] found that the detection probability of myclobutanil fungicide in rainwater collected during the rainy season in California was as high as 74%, with the concentrations of some samples reaching as high as 0.113 μg L-1. In a similar study, Ju et al. [35] found that the total fungi, total bacteria, and biomass carbon content of fluvio-aquic soil from Henan (China) decreased significantly after myclobutanil spraying. Moreover, Yu et al. [36] showed that triazole fungicides can cause thyroid endocrine disorders and affect gene transcription in zebrafish (Barchydanio rerio) fry.

Fungi use many mechanisms to control plant disease, including competitive functions such as parasitism, antagonism and induction of disease resistance. In this study, the antagonistic fungus T. harzianum was found to have a high biological control value; this is significant, since it is known to parasitize a variety of plant pathogens. The biological control effect of T. harzianum has been verified by many previous studies. Bin and Pingzhi [37,38] showed that T. harzianum can improve the rhizosphere soil environment, mainly by regulating nutrient competition, spatial competition, and heavy parasitism on tomato gray mold, leaf mold, Fusarium wilt, and brown spot. Lakhdari et al. [39] showed that T. harzianum could control tomato Fusarium wilt by inhibiting the growth of Fusarium oxysporum mycelia. Another study by Ahmad et al. [40] showed that T. harzianum inhibited the reproduction of tomato root-knot nematodes, while a different study looking at pepper showed that Trichoderma harzianum had an inhibitory effect on cucumber gray mold, powdery mildew, Rhizoctonia solani, Fusarium wilt, root rot, and root-knot nematodes, as well as a strong inhibitory effect on Phytophthora capsici, small sclerotia, Rhizoctonia solani, and other soil-borne pathogens [41]. Reports on the use of Trichoderma to control crop Fusarium wilt have mainly focused on T. harzianum, T. asperellum [42], and T. viride [43]. However, there are relatively few reports of Trichoderma koningii being used to prevent Fusarium wilt. In this study, Trichoderma koningii was used to inhibit oat sheath rot, and it showed a remarkable biocontrol effect.

G. roseum is another important biocontrol fungus. It has many advantages, including a fast growth rate, production of a large number of spores, and strong antagonistic ability. In addition to its heavy parasitism, antibiosis, bacteriolysis, and competitive inhibition effects against pathogens, it can also induce systemic resistance in target plants [44]. One study reported that Gliocladium roseum shows a strong parasitic ability and was able to inhibit a pathogen causing fruit branch disease by more than 60% [45]. Therefore, at present, accelerating the research of biocontrol agents and strengthening their production efficiency are top priorities. Moreover, developing the capacity to culture but inhibit the development of pathogens is required for testing biological control agents [46].

At present, few studies have examined the prevention and control of oat sheath rot. Therefore, our results provide a novel theoretical basis for further prevention and control of oat sheath rot.

5. Conclusions

This study investigated oat sheath rot in oat crops in northern China by isolating and identifying pathogenic fungi, as well as putative biocontrol agents capable of inhibiting their pathogenicity. Accordingly, we identified two pathogenic fungi, Scopulariopsis brevicaulis and Alternaria alternata; to the best of our knowledge, this is the first report that this strain can cause oat sheath rot in China. Moreover, this study also showed that Trichoderma harzianum and Trichoderma koningii both showed good inhibitory effects on the isolated pathogenic fungi in different competitive ways; they may therefore be suitable alternative biocontrol agents for the prevention and treatment of oat sheath rot. In general, the isolation and identification of pathogenic oat sheath rot fungi and the screening of biocontrol fungi is a significant benefit for the oat industry.