Effect of Seed Treatment and Sowing Time on Microdochium spp. Caused Root Rot in Winter Wheat Cultivars

Abstract

Microdochium species are harmful pathogens of winter cereals, causing snow mould and stem base diseases such as root rot. With changing climatic conditions, including prolonged wet autumns and mild winters, addressing pathogens that thrive at low positive temperatures has become increasingly important. Integrated strategies, including optimized sowing times, resistant cultivars, and the use of seed treatment fungicides have been suggested as effective approaches to mitigate Microdochium-induced damage. Field trials were conducted between 2021 and 2024 using five winter wheat cultivars treated with different seed treatment fungicides and sown at either optimal or delayed sowing times. Laboratory analyses identified Microdochium spp. as the dominant pathogens on the stem base across all trial years. Disease severity assessments indicated that seed treatment fungicides were generally effective against root rot, with products containing fludioxonil and SDHI group fungicides delivering the best performance. While disease pressure varied between optimal and late sowing experiments, late-sown winter wheat exhibited slightly reduced damage in most years. Additionally, some of the tested winter wheat cultivars demonstrated better performance against Microdochium spp. damage compared to others, highlighting the importance of selecting resistant cultivars. This study provides valuable insights into the control of Microdochium spp. under changing climatic conditions, particularly during the early growth stages of winter wheat.

1. Introduction

Winter wheat is one of the most important crops in the world and the most grown cash crop in Lithuania. With climate warming, agricultural producers are increasingly facing unpredictable conditions. Warmer and longer autumns, milder winters and higher temperatures in summer provide better conditions for the development of agricultural crops, but also for pests and diseases that damage plants [1].

Stem base is affected (damaged) by a complex of pathogenic fungi including Oculimacula spp., Fusarium spp., Microdochium spp., and others [2,3]. Among these, Microdochium nivale and M. majus are the most damaging species causing snow mould and are widely found inflicting varied types of harm on cereals in Lithuania [2]. Pink snow mould, caused by M. nivale, appears with extensive growth of white or pink mycelium leading to leaf and stem decay, thus weakening plants during winter or early spring. This fungal pathogen is commonly found in grain cereals, where it disrupts seed germination and contributes to seedling mortality, which can result in significant yield losses [4]. It is widely documented that snow mould pathogens such as Microdochium spp. causes most of the damage to wintering cereals under extended periods of snow coverage, particularly when snow falls on unfrozen soil [5,6]. In addition, the proliferation of pathogens is significantly influenced by prolonged damp and cool autumnal weather, along with fluctuating snow cover that includes volatile thawing events during mild winters [7]. However, Microdochium species are now considered facultative snow mould causing agents that can thrive throughout the entire growing season of host plants [8], leading to symptoms such as seedling blight, foot/root rot, leaf and head blight.

Microdochium spp. as a pathogen of winter wheat can be controlled by agronomic measures such as the tillage method, crop rotation [9], optimal sowing rate, optimal sowing timing [10,11], cultivar selection [12], and the use of chemical inputs, specifically seed treatment fungicides [13].

Matušinsky et al. [14] suggests that sowing date is a crucial aspect when trying to mitigate stem base diseases spread, pointing out that early sowing prolongs the period when plants (shoots) are prone to get infected by the pathogens. Thus, a prolonged interval between plant emergency and significant temperature drop can result in higher infection rate [15]. Depending on the overwintering conditions of the year and the timing of sowing, early sown winter wheat may be more susceptible to spring mould than late-sown winter wheat [16]. It is recorded that later sowing time can reduce M. nivale infection in winter rye [17]. Altering sowing time is one of the most efficient and cost-effective agronomic practices for mitigating the negative impacts of climate change [10,11].

Cultivar selection is another essential agronomic practice for Microdochium spp. control. Well-documented studies confirmed genetic variations among cultivars contributing to different resilience under abiotic stress and low temperatures [18]. Wheat cultivars with higher frost resistance also display higher resistance to M. nivale [19]. Gaudet et al. [20] found that longer hardening periods promote a higher expression of a broader range of defense-related genes, which are associated with mechanisms that enhance resistance to M. nivale and other pathogens. Additionally, other researchers associate resistance to M. nivale with the increased production of phenolic compounds, which might inhibit fungal growth and strengthen plant defenses [21]. However, breeding resistant cultivars remains an ongoing challenge for breeders [18].

Currently, there are several seed treatment fungicides registered for Microdochium spp. control in Lithuania. These products come solo or as a mixture of undermentioned active ingredients, fludioxonil, demethylation inhibitor fungicides (tebuconazole, triticonazole, prothioconazole, difenoconazole), and succinate dehydrogenase inhibitor fungicides—sedaxane and fluxapyroxad [22]. Over the years, fludioxonil has consistently demonstrated strong effectiveness in controlling Microdochium spp., with significant sensitivity reported in various studies [2,13,23], while tebuconazole has shown limited and inconsistent control [2,23]. However, seed treatment fungicides should be used with caution, as extensive use of fungicides can also lead to the increased resistance of pathogen to specific compounds, as has already been noticed with strobilurins [24].

With regard to the warming trend in autumn and winter temperatures in temperate regions, it is expedient to address the change in long-established agronomic practices, particularly sowing time, to better manage Microdochium spp. severity [1]. A combination of all aforementioned practices could grant the ultimate desired control of winter wheat root rot. Therefore, the aim of this study was to evaluate the most common agronomic practices, such as varying sowing times, cultivar selection, and the use of seed treatment fungicides, that can mitigate the effects of winter wheat root rot and improve crop resilience under changing environmental conditions.

2. Materials and Methods

2.1. Experimental Design

Four-year research was conducted in the period of 2021–2024 in the experimental fields at the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, located in central Lithuania. Five popular and widely used winter wheat cultivars: ‘Ada’, ‘KWS Emil’, ‘Etana’, ‘Skagen’, and (from 2022) ‘Patras’ were selected for this study. Four commercial seed treatment fungicides, currently registered in Lithuania and consisting of various active ingredients, were used at the dose rates recommended by the manufacturers (

Table 1. Seed treatment fungicides and treatment doses used in the trial scheme.

Seed Treatment Fungicide	Active Ingredients, g L−1	Dose, L t−1
Bariton Super	Protioconazole 50; Tebuconazole 10; Fludioxonil 37.5	1.0
Kinto Plus	Fluxapyroxad 33.3; Triticonazole 33.3; Fludioxonil 33.3	2.0
Maxim 025 FS	Fludioxonil 25	1.5
Vibrance Star	Sedaxane 25; Fludioxonil 25; Triticonazole 20	2.0

). Prior to sowing, seeds were treated using the laboratory seed treatment drum ‘Wintersteiger Hege 11’ (Wintersteiger, Austria) with a slurry mixture of fungicide and water to ensure uniform seed coverage. Two sowing times were chosen; optimal sowing time in Lithuania is considered to be the end of the second decade of September, and the first part of the field trials was sown around 20th September each year (±2 days). Late sowing time was scheduled around 10th of October (±2 days). The trial was conducted in Central Lithuania (55.39411, 23.87544) in a field with soil classified as Cambisols (loam, drained, Endocalcaric, Endogleyic) [25]. In the 2020–2021 growing season, the winter wheat was sown after peas, while later year experiments were sown after winter rape. Each year soil was conventionally tilled (ploughed and cultivated) and fertilized with NPK 6-18-34 at 200 kg ha⁻¹ prior to sowing. Weeds were controlled using the herbicides Legacy 500 SC (diflufenican) at 0.25 L ha⁻¹. Sowing was carried out using a plot drilling machine Wintersteiger Kubota (Wintersteiger, Austria) in a randomized complete block design with four replicates (plots were side by side, blocks on top of each other). The seeding rate for each year was calculated to achieve 450 viable seeds per square meter, based on the seed germination rate and thousand-grain weight. Plots measured 15 m² (1.5 × 10 m) with ten rows distanced 15 cm each and 2 m distance between replicates.

2.2. Severity of Root Rot

Root rot assessments were carried out in early spring during tillering of winter wheat (at the growth stage BBCH: 21–29 [26]) by evaluating 30 plants from each plot taken randomly [27]. The severity of root rot was determined using a scale of 0 to 3 based on damage at the plant’s stem base, where 0—completely healthy plant, 1—slightly browned or blackened lower part of the plant, 2—browned or blackened lower part of the plant and roots, but the stem is strong, 3—browned or blackened lower part of the plant and roots, with a softened stem, and/or the plant is dead [28]. The root rot severity index (I) is calculated using the formula: I = ((Σ(B·a))/AK)·100, where A—evaluated seedling or stem number, B—seedling or stem number damaged by root rots, a—seedling or stem number damaged by the same score, K—the greatest score of the scale (0–3), Σ—number of seedling or stem damaged by the same score and scale score sum of the values [29].

$$I={\displaystyle \frac{\mathit{\Sigma }(B·a)}{AK}}·100$$

2.3. Pathogen Identification In Vitro

To identify predominant seedling root rot pathogens on the stem base, the PDA medium method was used. Stem base segments were taken from untreated plots in each cultivar. The collected plants were washed under running water and dried on filter paper, then sterilised in 5% NaClO solution for 2 min. After washing with sterile water and subsequently being dried, samples were placed in Petri dishes on PDA medium supplemented with antibiotics. Petri dishes were then incubated for 3 days in the dark and 4 days in UV at 15 °C. Light and dark periods were alternated at 12 h intervals [14]. After that, the pathogens were identified by microscopy by the morphological characteristics described by Mathur and Kongsdal [30,31]. The analyses resulted in an estimate of the percentage of seedling infestation by genus.

2.4. Meteorological Conditions

Meteorological data were sourced from the Dotnuva meteorological station (

Table 2. Average temperature and sums of precipitation throughout the study period (2021–2024).

	September	October	November	December	January	February	March
Average temperature, °C
2020–2021	15.0	10.2	5.3	0.6	−3.6	−5.9	1.9
2021–2022	11.7	8.1	3.9	−3.6	−0.2	1.1	1.8
2022–2023	10.6	9.8	2.8	−3.1	0.3	−0.3	2.5
2023–2024	16.6	7.9	2.2	0.2	−4.4	2.3	4.3
Long term average 1924–2023	12.2	6.9	2.0	−2.2	−4.6	−4.1	−0.5
Sum of precipitation, mm
2020–2021	14.8	49.4	33.3	24.4	49.4 (28) *	2.2 (27) *	16.3 (2) *
2021–2022	28.2	35.0	87.9 (10) *	24.4 (28) *	35.4 (4) *	49.9 (4) *	2.9
2022–2023	28.8	19.2	25.5 (13) *	42.6 (19) *	52.1 (17) *	20.1 (24) *	37.3 (17) *
2023–2024	8.6	98	30.8	41.1	24.2 (27) *	54.5 (9) *	10.6
Long term average 1924–2023	50.0	49.6	44.9	38	32.0	26.2	28.1

*—number in parentheses represents days per month with snow coverage in the field.

). Weather conditions varied dramatically from year to year, in average temperatures and total precipitation. The period of 2020–2021 featured a warmer-than-average autumn, followed by a colder winter with low overall precipitation, except for February. During 2021–2022, the autumn was cooler, followed by a warmer winter, with precipitation near average levels in most months except for a dry November. In 2022–2023, temperatures were close to average, with relatively wetter conditions overall. The period of 2023–2024 also saw fluctuating winter temperatures and precipitation. Snow covers favourable for snow mould spread occurred in the periods of 2020–2021 and 2022–2023 with 57 days and a high of 77 days of plants covered with snow, respectively. In contrast, only 46 days of snow coverage were recorded in 2021–2022, dropping to 36 days in 2023–2024.

2.5. Statistical Analysis

Prior to conducting statistical analyses, tests for normality and homogeneity of variances were performed to ensure the data met the assumptions for parametric testing using IBM SPSS Statistics (IBM Inc., Armonk, NY, USA). The Shapiro–Wilk test assessed normality, while Levene’s test verified the homogeneity of variances across groups. Both tests confirmed that the data satisfied the assumptions for analysis of variance (ANOVA).

Given that the intensity of seedling root rot varied between years, data were analysed separately for each year. A t-test was performed for each year to evaluate the effects of sowing date (optimal vs. late) on root rot severity within each cultivar and seed treatment fungicide (STF) combination, as well as to compare the prevalence of predominant pathogens (Microdochium spp. and Fusarium spp.) on winter wheat stem base in early spring. Additionally, one-way ANOVA was conducted to test for significant differences between STF within the same cultivar and sowing date, and also between cultivars within the same STF and sowing date. Post hoc comparisons were made using Duncan’s multiple range test to identify specific group differences. In addition to the year-by-year analysis, a three-way ANOVA assessed the main effects and interactions between cultivar, fungicide treatment, and sowing date on root rot severity. All statistical analyses were conducted using SPSS, with significance set at p < 0.05. Results are presented as means ± standard deviation (SD). Figures and tables were generated using Microsoft Excel (Microsoft, Redmond, WA, USA).

3. Results

3.1. Pathogens on Winter Wheat Stem Base

Table 3. Percentage of predominant pathogens found on winter wheat stem bases collected in early spring, 2021–2024.

		Pathogens on Stem Base, %
Sowing Time	Cultivar	2022			2023			2024
		Microdochium spp.	Fusarium spp.	sig.	Microdochium spp.	Fusarium spp.	sig.	Microdochium spp.	Fusarium spp.	sig.
Optimal	Ada	41.3 ± 6.3	2.5 ± 2.9	<0.001	18.8 ± 6.3	0.0 ± 0.0	<0.001	3.8 ± 2.5	1.3 ± 2.5	0.207
	KWS Emil	15.0 ± 9.1	1.3 ± 2.5	0.530	27.5 ± 8.7	0.0 ± 0.0	0.008	2.5 ± 2.9	2.5 ± 2.9	1.000
	Etana	25.0 ± 9.1	3.8 ± 4.8	0.006	26.3 ± 6.3	0.0 ± 0.0	<0.001	2.5 ± 2.9	0.0 ± 0.0	0.134
	Skagen	40.0 ± 9.1	10.0 ± 5.8	0.001	11.3 ± 2.5	13.8 ± 7.5	0.356	0.0 ± 0.0	1.3 ± 2.5	0.391
	Patras	16.3 ± 8.5	3.8 ± 4.8	0.032	25.0 ± 10	6.3 ± 4.8	0.0148	1.3 ± 2.5	0.0 ± 0.0	0.391
Late	Ada	41.3 ± 11.1	21.3 ± 7.5	0.024	66.3 ± 8.5	3.8 ± 2.5	<0.001	5.0 ± 4.1	2.5 ± 2.9	0.356
	KWS Emil	5.0 ± 4.1	5.0 ± 4.1	1.000	60.0 ± 7.1	31.3 ± 13.1	0.008	5.0 ± 0.0	0.0 ± 0.0	NA
	Etana	3.8 ± 4.8	2.5 ± 2.9	0.670	8.8 ± 4.8	8.8 ± 7.5	1	2.5 ± 2.9	0.0 ± 0.0	NA
	Skagen	30.0 ± 9.1	11.3 ± 6.3	0.015	37.5 ± 11.9	28.8 ± 10.3	0.309	0.0 ± 0.0	0.0 ± 0.0	0.215
	Patras	13.8 ± 4.8	5.0 ± 5.8	0.043	86.3 ± 8.5	12.5 ± 2.9	<0.001	1.3 ± 2.5	1.3 ± 2.5	0.391

NA—not applicable. Numbers with “±” represents standard deviation.

presents the mean percentage of pathogen presence on the stem base of different cultivars during early spring across the years 2022–2024. The data indicate that, in 2022 and 2023, the majority of the cultivars were predominantly infected by Microdochium spp. Insignificant differences between the presence of Microdochium spp. and Fusarium spp. were observed in plots where a generally low percentage of pathogens was detected on PDA media in both 2022 and 2023. In contrast, the 2024 data showed inconsistencies, with a generally low percentage of pathogens found on stem bases that year, although Microdochium spp. was found slightly more often.

3.2. Optimal Sowing Time

Table 4. Winter wheat root rot severity index in different cultivars under different seed treatment sown on optimal sowing time, 2021–2024.

Year	Seed Treatment Fungicide	Cultivar
		Ada	Etana	KWS Emil	Patras	Skagen	Mean
2021	Control	61.9 aA ± 6.3	47.2 bcA ± 2.3	52.2 bA ± 5.9	NA	43.9 cA ± 3.8	51.3 A ± 8.3
	Bariton Super	56.7 aAB ± 7.4	42.2 bA ± 2.0	48.6 abA ± 4.0	NA	42.5 bA ± 6.4	47.5 AB ± 7.8
	Kinto Plus	45.6 abC ± 7.8	42.2 abA ± 1.6	49.7 aA ± 4.3	NA	41.4 bA ± 3.4	44.7 B ± 5.5
	Maxim	47.8 aBC ± 8.7	41.7 aA ± 5.5	52.8 aA ± 2.9	NA	47.5 aA ± 9.7	47.4 AB ± 7.6
	Vibrance Star	45.3 abC ± 1.9	42.5 bA ± 5.0	51.1 aA ± 4.8	NA	41.9 bA ± 3.2	45.2 B ± 5.1
	Mean	51.4 a ± 9.2	43.2 b ± 3.9	50.9 a ± 4.3	NA	43.4 b ± 5.7
2022	Control	31.4 bA ± 3.3	39.2 aA ± 1.4	32.2 bA ± 3.7	34.2 bA ± 1.9	40.3 aA ± 3.7	35.4 A ± 4.6
	Bariton Super	31.4 aA ± 2.6	29.2 aBC ± 4.6	27.2 aAB ± 1.4	31.7 aA ± 3.5	31.7 aB ± 1.4	30.2 BC ± 3.2
	Kinto Plus	31.1 abA ± 2.4	24.2 bcC ± 5.8	21.9 cB ± 8.7	31.4 abA ± 1.4	32.8 aB ± 1.9	28.3 C ± 6.2
	Maxim	31.4 aA ± 1.7	34.2aAB ± 1.9	26.4 bAB ± 5.3	31.4 aA ± 1.7	33.3 aB ± 2.4	31.3 A ± 3.8
	Vibrance Star	31.1 abA ± 1.3	25.3 cC ± 2.9	27.8 bcAB ± 4.0	31.9 aA ± 2.3	31.1 abB ± 0.1	29.4 BC ± 3.4
	Mean	31.3 ab ± 2.1	30.4 b ± 6.6	27.1 c ± 5.7	32.1 ab ± 2.3	33.8 a ± 3.9
2023	Control	51.1 aA ± 4.7	49.4 aA ± 3.2	35.8 bA ± 3.6	26.4 cA ± 4.6	46.4 aA ± 9.6	41.8 A ± 10.8
	Bariton Super	34.7 aB ± 4.2	39.7 aA ± 5.5	35.1 aA ± 6.4	21.4 bA ± 2.9	38.9 aAB ± 4.0	34.0 B ± 8
	Kinto Plus	34.4 bB ± 2.9	41.9 aA ± 4.2	24.4 cB ± 4.0	15.3 dB ± 2.5	33.3 bB ± 3.3	29.9 B ± 9.9
	Maxim	35.6 aB ± 2.2	42.8 aA ± 12.8	21.1 bB ± 1.5	10.8 bB ± 4.8	42.5 aAB ± 6.8	30.6 B ± 14.3
	Vibrance Star	35.0 bB ± 4.6	43.6 aA ± 5.0	20.3 cB ± 3.7	10.6 dB ± 3.7	34.7 bB ± 5.8	28.8 B ± 12.8
	Mean	38.2 b ± 7.5	43.5 a ± 7.1	27.3 c ± 7.9	16.9 d ± 7.2	39.2 ab ± 7.5
2024	Control	27.8 bcA ± 4.7	33.1 bA ± 2.3	48.3 aA ± 6.6	20.6 cA ± 6.7	25.6 bcA ± 9	31.1 A ± 11.3
	Bariton Super	16.9 bcBC ± 1.9	23.9 bB ± 2.3	35.0 aB ± 7.3	13.9 cAB ± 2.1	14.4 cB ± 7.2	20.8 B ± 9.2
	Kinto Plus	13.6 bC ± 4.2	23.9 aB ± 3.5	28.9aB ± 3.0	9.7 bB ± 3.9	12.2 bB ± 6.5	17.7 B ± 8.5
	Maxim	19.4 bB ± 2.9	28.9 aAB ± 3.7	33.3 aB ± 4.2	15.3bcAB ± 3.4	13.1 cB ± 3.1	22.0 B ± 8.6
	Vibrance Star	18.1 bBC ± 2.9	25.6 aB ± 4.0	32.5 aB ± 6.9	13.3 bcAB ± 5.5	7.8 cB ± 2.4	19.4 B ± 9.9
	Mean	19.2 c ± 5.7	27.1 b ± 4.6	35.6 a ± 8.6	14.6 d ± 5.5	14.6 d ± 8.2

Numbers with “±” represent standard deviation. Lowercase letters indicate statistically significant differences between cultivars within the same year. Uppercase letters indicate statistically significant differences between seed treatment fungicides within the same year. NA—not applicable.

shows root rot severity presented in different cultivars and years under different seed treatment sown on optimal sowing time. Data of cultivar ‘Patras’ are absent in 2021 because they were used from 2022 and onwards. The data suggested that disease pressure varied slightly throughout the years of the experiment as weather conditions and snow coverage periods were inconsistent each winter. During the winters of 2020–2021 and 2022–2023, prolonged snow coverage led to increased disease severity compared to the winters of 2021–2022 and 2023–2024, which had shorter or inconsistent periods of snow coverage. In 2021 disease pressure ranged from a high severity index of 41.4% in Kinto Plus-treated ‘Skagen’ to a staggering 61.9% in untreated ‘Ada’, which exhibited the highest disease severity among the cultivars assessed. In general, the previously mentioned cultivar ‘Skagen’ together with ‘Etana’ were slightly less damaged by root rots in 2021. Despite very high disease pressure, most of the seed treatment fungicides were effective against root rots, even though in some cases efficacy was low compared to untreated seed plots. Maxim-treated ‘Skagen’ and ‘KWS Emil’ winter wheat seedlings were slightly more damaged by the disease.

Contrary to the year before, in 2022 the general disease severity index was slightly lower throughout all experimental plots sown at optimal time and varied from 21.9% in Kinto Plus-treated seed plots to 40.3% in untreated seed ‘Skagen’ plots. In addition, overall seed treatment fungicide efficacy was slightly less predominant, as in some cases treated and untreated seed plots presented similar damage levels, especially in cultivar ‘Ada’ plots. Somewhat more distinct differences in contrast to treated and untreated plots were established in cultivars ‘Etana’ and ‘KWS Emil’ Kinto Plus-treated plots. Similarly to the spring of 2021, in the 2023 experiment with the optimal sowing date, root rot severity was high yet diverse with regard to different cultivars. The highest severity index was recorded in untreated seed ‘Ada’ plots reaching up to 51.1%, whereas, in contrast, the least damaged optimal-time-sown cultivar ‘Patras’ averaged a 20.4% severity index in the untreated control. It is essential to highlight that the cultivar ‘Patras’ exhibited a significantly improved control of root rot when treated with Maxim and Vibrance Star, resulting in severity indices of 10.6% and 10.8%, respectively, which represent approximately a 60% reduction compared to untreated plots.

Effective disease control was also observed in optimally sown plots during the experiment of 2024. A more pronounced reduction in disease severity indexes was recorded for Kinto Plus-treated seed ‘Patras’, decreasing from 20.6% to 9.7%, and for ‘Skagen’ seed treated with Vibrance Star, decreasing from 25.6% to 7.8%. Furthermore, this year’s data reveal increased variability in disease pressure among different cultivars. Generally, disease pressure was slightly lower compared to earlier years but still remained quite high, especially in cultivar ‘KWS Emil’ untreated seed plots where disease severity averaged 48.8%. Just as in the year 2023, cultivar ‘Patras’ was the least damaged by root rot compared to other used cultivars in this experiment.

3.3. Late Sowing Time

Late sowing time root rot severity indexes in different cultivars are presented in

Table 5. Winter wheat root rot severity index in different cultivars under different seed treatment sown in late sowing time, 2021–2024.

Year	Seed Treatment Fungicide	Cultivar
		Ada	Etana	KWS Emil	Patras	Skagen	Mean
2021	Control	23.1 bAB ± 10.6	13.4 bA ± 2.1	45.0 aA ± 7.9	NA	38.9 aA ± 13.6	30.1 A ± 15.5
	Bariton Super	18.6 bB ± 2.9	6.3 cB ± 0.6	30.3 aB ± 9.6	NA	20.6 abB ± 9.1	18.9 B ± 10.7
	Kinto Plus	27.2 aAB ± 3.2	5.1 cB ± 1.2	24.2 abB ± 1.7	NA	20.0 bB ± 6.5	19.1 B ± 9.4
	Maxim	23.6 aAB ± 7.0	6.3 bB ± 1.2	30.0 aB ± 5.2	NA	25.8 aAB ± 9.7	21.4 B ± 11
	Vibrance Star	30.8 aA ± 6.2	5.0 cB ± 0.6	26.9 abB ± 4.5	NA	22.8 bB ± 5.3	21.4 B ± 11
	Mean	24.7 b ± 7.3	7.2 c ± 3.4	31.3 a ± 9.3	NA	25.6 b ± 10.9
2022	Control	22.8 bA ± 1.9	5.6 dA ± 1.3	21.4 bA ± 3.7	14.2 cA ± 4.0	32.5 aA ± 8.4	19.3 A ± 10.1
	Bariton Super	19.4 aA ± 5.6	8.3 bA ± 5.8	20 aAB ± 3.6	11.1 bA ± 3.3	12.8 bB ± 2.3	14.3 B ± 6.1
	Kinto Plus	19.4 aA ± 2.6	9.2 cA ± 2.3	15.3 bB ± 1.9	10.8 cA ± 3.9	12.2 bcB ± 2.2	13.4 B ± 4.4
	Maxim	20.6 aA ± 5.8	7.2 bA ± 5.0	15.6 abB ± 2.4	11.9 bA ± 5.4	14.4 abB ± 3.3	13.9 B ± 6.5
	Vibrance Star	20.0 aA ± 2.7	6.1 cA ± 0.6	15.0 bB ± 4.5	12.8 bA ± 2.3	12.5 bB ± 3.2	13.3 B ± 5.3
	Mean	20.4 a ± 3.8	7.3 d ± 4.2	17.4 ab ± 4.1	12.2 c ± 3.7	16.9 b ± 9
2023	Control	57.5 aA ± 1.7	32.5 cA ± 11	52.2 aA ± 7.4	49.2 abA ± 4.2	36.7 bcA ± 12.2	45.6 A ± 12.2
	Bariton Super	42.2 aB ± 8.0	15.8 bB ± 7.9	46.4 aAB ± 23.3	34.4 abB ± 5.4	31.1 abAB ± 4.4	34.0 B ± 15.2
	Kinto Plus	32.5 aC ± 7.9	10.0 bB ± 7.2	32.2 aB ± 2.4	25.0 aBC ± 6.7	31.4 aAB ± 4.5	26.2 B ± 10.3
	Maxim	35.0b BC ± 2.3	8.3 dB ± 4.3	44.7 aAB ± 4.4	31.1 bBC ± 6.3	23.9 cB ± 4.9	28.6 B ± 13.1
	Vibrance Star	34.2 abBC ± 5.1	15 cB ± 10.4	38.1 aAB ± 3.5	23.9 bcC ± 7.1	27.8 abAB ± 8.4	27.8 B ± 10.5
	Mean	40.3 a ± 10.7	16.3 c ± 11.6	42.7 a ± 12.3	32.7 b ± 10.8	30.2 b ± 8
2024	Control	11.7 bA ± 2.9	7.5 bA ± 3.7	21.9 aA ± 8.8	23.9 aA ± 2.6	8.1 bA ± 3.6	14.6 A ± 8.3
	Bariton Super	8.6 abAB ± 2.9	3.1 bAB ± 2.6	10.6 aB ± 3.5	8.6 abC ± 5.9	5.8 abA ± 2.5	7.3 B ± 4.2
	Kinto Plus	6.7 abB ± 2.7	3.1 bAB ± 3.0	10.6 aB ± 3.5	6.7 abC ± 3.0	5.6 bA ± 3.0	6.5 B ± 3.7
	Maxim	6.1 bcB ± 1.1	2.5 cB ± 1.4	14.4 aAB ± 4.7	17.2 aB ± 3.5	7.5 bA ± 1.9	9.6 B ± 6.1
	Vibrance Star	8.9 aAB ± 3.5	3.1 bAB ± 2.5	8.6 aB ± 3.6	9.4 aC ± 2.6	6.1 abA ± 2.1	7.2 B ± 3.6
	Mean	8.4 b ± 3.2	3.8 c ± 3.1	13.2 a ± 6.7	13.2 a ± 7.4	6.6 bc ± 2.6

Numbers with ‘±’ represent standard deviation. Lowercase letters indicate statistically significant differences between cultivars within the same year. Uppercase letters indicate statistically significant differences between seed treatment fungicides within the same year. NA—not applicable.

. The presented data suggested that the highest disease pressure in 2021 was recorded in ‘KWS Emil’ as the severity index reached up to 45% in untreated seed plots. The cultivar ‘Skagen’ was also quite damaged by the disease (severity in untreated control of 38.9%). Consistent with the findings from spring 2021, the late-sown cultivar ‘Skagen’ was relatively more damaged compared to other cultivars. Consequently, the efficacy of seed treatments was slightly higher compared to other used cultivars with a disease severity index of 32.5% in the untreated control, compared to 12.2 to 14.4% in the treated seed plots. In contrast, in the spring of 2022, late-sown ‘Etana’ plots had the lowest root rot infection level throughout this four-year experiment as the severity index in the untreated control was a meagre 5.6%. Moreover, it is important to highlight that, in the case of exceptionally low infection, seed treatment fungicide efficacy was not determined since the root rot severity index was insignificantly higher in treated seed plots. In accordance with the previously addressed data of late-sown wheat in 2021, the results reflect some consistency in 2023 with an overall level of disease pressure. Furthermore, similar trends regarding cultivars ‘KWS Emil’ and ‘Etana’ were established, as ‘Etana’ typically had a lower disease severity index of 32.2% in the untreated control, while ‘KWS Emil’ untreated seed winter wheat exhibited significant damage by the diseases resulting in 52.2%. Notably, plots of cultivar ‘Ada’ displayed an even higher severity index of root rot in the untreated control, reaching an extraordinary 57.7%—the highest value in late-sown winter wheat throughout the entire experiment period.

The results in the last year of late-sown winter wheat varied among different cultivars and seed treatments. The disease severity index varied among cultivars, ranging from the usual low of 7.5% in untreated plots of ‘Etana’ to 21.9% in ‘KWS Emil’ and 23.9% in ‘Patras’. A significant control was also established regarding the most seed treatment fungicides, with Kinto Plus demonstrating slightly greater general efficacy in late-sown winter wheat in 2024. Notably, despite the low disease severity in some of the cultivars, other used seed treatments also gave quite good efficacy.

3.4. Interactions Between Tested Factors

Looking into the interactions between the sowing time, cultivar, and seed treatment fungicide (

Table 6. The interaction between the sowing time, cultivar, and seed treatment fungicide, 2021–2024.

Interaction	DF	Mean Square	F Value	Significance	DF	Mean Square	F Value	Significance
	2021				2023
sowing time × cultivar	3	678.189	18.81	<0.001	4	3271.884	71.21	<0.001
sowing time × STF	4	59.486	1.65	0.166	4	77.353	1.68	0.157
cultivar × STF	12	20.055	0.56	0.873	16	63.578	1.38	0.157
sowing time × cultivar × STF	12	137.628	3.82	<0.001	16	66.589	1.45	0.126
	2022				2024
sowing time × cultivar	4	334.381	23.83	<0.001	4	893.191	49.74	<0.001
sowing time × STF	4	7.945	0.57	0.688	4	40.664	2.26	0.065
cultivar × STF	16	48.149	3.43	<0.001	16	26.523	1.48	0.115
sowing time × cultivar × STF	16	42.359	3.02	<0.001	16	31.440	1.75	0.043

), only sowing time and cultivar exhibited a consistent significant (p < 0.001) interaction throughout all the testing period (2021–2024). This shows that sowing timing affects the disease severity on different cultivars, with some being more susceptible under optimal sowing conditions, likely due to higher pathogen pressure or favorable conditions for disease development. In both 2021 and 2022, additional significant interaction was observed involving all three tested factors. This demonstrates the combined impact of fungicide treatments, cultivars, and sowing timings on the disease index. For instance, specific fungicides may have been more effective in late sowing, providing better protection to cultivars that are otherwise more prone to higher disease severity during this period. Exceptionally, a significant interaction between cultivar and seed treatment fungicide was detected only in 2022. This difference may be due to the slightly lower overall efficacy of seed treatment fungicides in both optimal and late-sown experiments, as well as the variation in average disease severity between sowing times. There were no significant interactions between sowing time and seed treatment fungicides throughout the testing period.

4. Discussion

In light of the predicted impacts of climate change, researchers are facing the need to renew or adapt traditional agronomic practices. The aim of these adaptations is to mitigate the spread of pests and optimize grain yields under changing environmental conditions [32]. As root rot can be caused by a complex of pathogens, we found it crucial to identify whether Microdochium spp. was the cause of the infection. Understanding the fungal spectrum is essential for improving stem base disease control [33]. An in vitro analysis of seedlings from control plots confirmed Microdochium spp. as the dominant pathogen in most years and both sowing times. Our data showed that fluctuations and deviation on the disease pressure throughout the research period were likely impacted by inconsistent weather conditions and snow coverage. In 2021, the disease severity index ranged dramatically from a distinctive high of 45.0% in untreated ‘KWS Emil’ sown late to a concerning 61.9% in untreated ‘Ada’ sown at an optimal time, reflecting a high level of disease pressure that year. The period of days with snow coverage was second to the highest recorded, totaling 57 days. This trend is also evident in the period 2022–2023 as snow coverage was observed for a considerable 77 days, thus increasing root rot severity from 51.1% in optimal-sown to 57.5% in late-sown untreated seed cv. ‘Ada’. Notably, disease severity was relatively lower in the springs of 2022 and 2024, with snow coverage lasting for 46 days and as low as 36 days in previous winters, respectively. The findings in this study agreed with those of Temirbekova et al. who emphasized that abiotic stresses caused by temperature fluctuations and snow coverage create more favourable conditions for pathogens to infect, meanwhile reducing the plant immune system [18].

The analysis on the root rot severity index also indicates that there were notable differences between cultivars. Consistently, the late-sown ‘Etana’ was recorded to be the least infected by the root rot, showing significantly lower disease severity throughout the research period in untreated plots. In 2023, ‘Etana’ untreated seed plots’ root rot severity index recorded only 5.6% in a clear contrast to 32.5% untreated seed plots of cv. ‘Skagen’. This resilience suggests that ‘Etana’ likely has traits that confer better resistance to root rot. Data coincide with the data of Temirbekova et al. that also identified more resistant cultivars [18]. Similarly, resistance to Microdochium spp. caused diseases observed by McBeath, thus increasing valuable insights for breeding programs [34]. This also highlights the importance of selection of more resistant cultivars. When addressing optimal sowing time, in the springs of 2023 and 2024, cv. ‘Patras’ stood out with clearly lower disease pressure compared to other used cultivars, therefore in part validating that cultivars resist pathogens differently under various conditions including different sowing time. Therefore, cultivar selection should align with the planned sowing time to minimize disease risk. Researchers indicate that resistance to Microdochium spp. is closely linked to a cultivar’s winter hardiness, suggesting that this trait should be prioritized when selecting cultivars [6].

The data revealed that optimal-sown cultivars experienced consistently higher root rot severity compared to those sown late. For example, in 2021, optimally sown ‘Ada’ recorded a severity index of 61.9%, compared to only 23.1% in the untreated control for the same cultivar sown late. These findings in part oppose previous assumptions that late sowing slows plant growth consequently increasing susceptibility to diseases such as root rots [35]. Conversely, exception was seen in 2023 with optimally sown untreated plots of ‘Ada’ being damaged slightly lower—51.1% compared to 57.5 in late-sown untreated plots. Apart from that, our data underscore the importance of later sowing times in managing disease pressure, as earlier sowing appears to exacerbate root rot severity in certain cultivars, particularly ‘Ada’ and ‘KWS Emil’. Previous research in Latvia also highlighted the importance of sowing time for Microdochium spp. control, indicating that a later sowing time resulted in a decreased pathogen spread [36]. Therefore, the timing of sowing is a critical factor in disease management strategies.

Seed treatment fungicide efficacy testing in Lithuania nearly a decade ago showed that a product based solely on fludioxonil had the highest control of snow mould [37]. A recent study from China also demonstrated the high efficacy of fludioxonil [38]. However, in the current research, the product containing only fludioxonil was less effective compared to products containing a mixture of fludioxonil and an SDHI fungicide (either fluxapyroxad or sedaxane). These two seed treatment fungicides were the most effective in the present research. This finding aligns with previous studies, which also indicated that fludioxonil and sedaxane are effective for controlling winter wheat seedling root rot [39]. As other research implies, although SDHI fungicides may be ineffective as solo treatments for controlling Microdochium-induced damage, they could be a valuable component in fungicide tank mixes with other active ingredients [40]. As mentioned in the introduction, the DMI fungicide tebuconazole has lower efficacy against Microdochium spp. Additionally, another study reported that the DMI fungicides tebuconazole and prothioconazole provide only moderate control of M. majus [41]. Consequently, the product containing a mixture of these two DMIs and fludioxonil exhibited only moderate performance in the current study.

5. Conclusions

This study found that fluctuating disease pressure, influenced by inconsistent weather and snow coverage, highlights the need to adapt agronomic practices to mitigate pest spread and optimize yields. Significant differences in root rot severity between cultivars were observed, with ‘Etana’ showing notable resilience to the disease, suggesting better resistance traits, while ‘Patras’ demonstrated lower disease pressure at optimal sowing times, underscoring the importance of selecting resistant cultivars and considering sowing timing. Later sowing times reduced root rot severity in certain cultivars, highlighting the importance of sowing timing in disease management strategies. Fludioxonil-based seed treatments, when combined with SDHI fungicides fluxapyroxad or sedaxane, demonstrated higher efficacy against snow mould and winter wheat seedling root rot, whereas seed treatment fungicides with a mixture of fludioxonil and both DMI fungicides tebuconazole and prothioconazole showed only a moderate control of Microdochium spp. To mitigate snow mould and minimize the potential damage of root rot, farmers should focus on optimizing sowing time, particularly by avoiding early sowing in temperate climate regions prone to changing weather patterns, as well as choosing more resistant cultivars available. Additionally, the selection of effective seed treatment fungicides should be approached with caution not to induce pathogen resistance to certain active ingredients. Further research is needed to develop resistant varieties and more resilient agronomic practices under changing environmental conditions.