Snow manipulation: Adaptability and limitations of physical control of snow mold and frost damage in pastures

Snow manipulation: Adaptability and limitations of physical control of snow mold and frost damage in pastures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Snow manipulation: Adaptability and limitations of physical control of snow mold and frost damage in pastures Seiji Shimoda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6229443/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims : Soil temperature regulation through snow manipulation can mitigate overwintering diseases and enhance pasture growth. Identifying optimal temperature conditions for healthy overwintering is crucial, particularly in cold-resistant species such as timothy (TY). This study evaluated the effects of snow manipulation on overwintering damage and disease control in TY, orchardgrass (OG), and perennial ryegrass (PR). Methods : Snow manipulation was applied to pasture plots, and its impact on soil temperature and disease severity was assessed. The metric Mild-T, representing the number of days with soil temperatures near 0°C, was analyzed to determine its role in thermal disease control. Disease severity and total overwintering damage were compared across species. Results : Snow manipulation reduced Mild-T, which correlated with lower disease severity, particularly in TY. In plots with < 20 consecutive Mild-T days, maximum disease severity was the lowest in TY (0.13) compared to OG (1.78) and PR (1.63). However, total overwintering damage was influenced by disease and frost resistance. Although snow mold reduction did not consistently decrease overall damage, PR exhibited severe frost damage, making it highly vulnerable to snow manipulation. Contrastingly, OG compensated for overwintering losses because of its strong regrowth ability, maintaining similar dry matter production. Conclusion : Snow manipulation effectively controls overwintering diseases but may increase frost damage in less cold-resistant species. TY demonstrated the greatest adaptability, while PR showed high susceptibility to frost injury. OG maintained productivity despite overwinter losses. The findings highlight the need to consider disease and frost resistance when applying snow manipulation as a disease control strategy. Snow manipulation Soil temperature Overwintering damage Disease severity Frost resistance Pasture species Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Snow cover reduction is prominent in middle and high latitudes (Screen 2014 ), leading to colder soils and potentially fatal damage to overwintering plants. Decadal climate change-induced snow cover reduction often increases the severity of frost damage (e.g., heaving and desiccation) owing to the continuation of snow-free periods and freeze-thaw cycles (Gaudet et al. 2001 ). Additionally, delayed spring regrowth can occur because of the delayed thawing of frost soil after snowmelt and delayed soil temperature rise caused by shallow winter snow cover and soil frost in overwintering crops (Shimoda and Hirota 2018 ; Zhu et al. 2022 ; Iwasaki et al., 2025 ). Frost damage from heaving and desiccation typically occurs during late winter or early spring. Frost heaving occurs when saturated soils freeze with little or no snow cover. A reduction in seasonal snow accumulation may have a negative impact on the winter survival of overwintering vegetation, especially in regions where crops rely on adequate snow cover to survive harsh winters (Bélanger et al. 2002 ). Various indices assess overwintering conditions, with cumulative negative temperatures serving as a typical frost damage index for overwintering crops (Zhang et al. 2018; Qian et al. 2024 ). Grass pasture species have different cold tolerances and varieties based on their risk of winter damage. Baron and Bélanger ( 2020 ) ranked timothy (TY; Phleum pratensis ) as having medium or high cold tolerance, orchardgrass (OG; Dactylis glomerata L.) as having low tolerance, and perennial ryegrass (PR; Lolium perenne ) as having relatively low tolerance. Temperate grass pastures introduced to Hokkaido, northern Japan, often endure severely cold winters. Notably, OG and PR experienced extensive frost damage in the mid-1970s (Araki 1975 ; Kozeki and Noshiro 1985), prompting a shift toward winter-hardy TY, which increased from about 50–90% of the seeding ratio over 30 years. The latest seeding ratio of TY reached 76% (Hokkaido 2024 ), reflecting adaptation to summer warming. Although a thick snowpack can protect overwintering plants from frost damage, it can also reduce the subsequent growth of plants owing to infection by pathogens (e.g., snow mold). Snow mold, a disease of grasses and winter cereals exposed to snow in cold-temperate regions caused by several cold-adapted fungi, attacks overwinter grasses and crops in the Northern Hemisphere (Hsiang et al. 1999 ; Hoshino et al. 2010 ). Persistent snow cover insulates the soil, increases the contact of leaf surfaces with the soil, and creates a dark, humid environment favorable for snow mold development (Bruehl and Cunfer 1971 ). The key characteristic of snow mold fungi is the ability to spread at temperatures near 0°C under snow cover. The number of snow cover days is a well-known environmental indicator of snow mold (Ozaki 1979 ) because the temperature under the snow is maintained at 0°C during thick snow cover (Shimoda et al. 2023a ). Currently, snow mold control relies on chemical pesticides for disease control in winter wheat and turf. The chemical use of pasture grass is difficult from the viewpoints of cost and environmentally friendly management of forage use. Therefore, sustainable physical control methods based on an understanding of the environmental responses of snow mold are needed. In this study, we hypothesized that controlling soil temperature through snow manipulation could decrease overwintering diseases and improve pasture growth. The first objective of this study was to determine the temperature required to control the severity of snow mold. Next, we considered whether snow mold control simultaneously causes frost heaving and freezing death owing to low temperatures. The optimal approach involves determining effective temperatures for pasture grass survival and selecting appropriate snow manipulation methods while acknowledging inevitable frost damage. In Hokkaido, farmers use agricultural machinery to regulate snow depth over large areas to freeze volunteer potatoes (Shimoda et al. 2023b ). Snow manipulation is a feasible agricultural management option for local farmers (Hirota and Kobayashi 2019 ; Shimoda et al. 2021 ), and this study contributes to its practical application. Frost damage and snow mold, which tend to develop in low-temperature areas, are rarely studied separately, leaving their environmental dependencies poorly understood. Material and methods Site description and meteorological data collection The study site was located at the Memuro Experiment Station of the Hokkaido Agricultural Research Center (NARO/HARC) (42.888 N, 143.074 E; elevation 94 m above sea level) in Memuro, eastern Hokkaido, Japan. The annual mean air temperature and rainfall measurements taken at the station were 6.4°C and 945 mm, respectively, from 2012 to 2021 (Shimoda et al. 2023c ). The region has a humid continental climate (Köppen Dfb, hemiboreal) and features volcanic ash soil (andosol). The number of days with mean air temperatures below − 10°C was 17 in 2018/19, 24 in 2019/20, and 22 in 2020/21. To monitor the soil temperatures during winter, we installed thermometers with data loggers (Thermo buttons 22 L; Plug & Track Inc., Willems, France) at 2-h intervals from November 2018 in each block at depths of 0.02 m. In the no-treatment field area, a snow depth meter (Model SDM-311; Niigata Electric Co., Niigata, Japan) certified by the Japan Meteorological Agency was used to measure snow depth at 09:00 JST. For the snow compaction and snow removal treatments, we measured snow depth using a scale before and after treatments or every 10 d. Snow mold and freezing index Environmental indicators for snow mold and frost damage include the number of snow cover days. Gray snow molds, such as Typhula ishikariensis (Hoshino et al. 2022 ), thrive in deep snow regions with extended snow cover (Matsumoto and Hsiang 2016 ). Shimoda et al. ( 2023a ) suggested that snow mold is greater with longer periods at soil temperatures near 0°C. Sclerotinia borealis prevails on plants that are predisposed to cold and are subsequently covered with thick snow. This study defines a ‘mild temperature’ (Mild-T) index as the number of days with a daily mean soil temperature of 0 ± 0.5°C. Mild-T serves as a general indicator for various snow mold species, which require a transition period at 0°C after exposure to lower temperatures. Cumulative freezing index (CFI) is widely used to assess cold stress, representing the cumulative degree-days below 0°C from July 1 to June 30 of the following year. The CFI is also a reasonable environmental indicator of the occurrence of S. borealis , which requires a period of cold exposure before snow accumulation. In this study, the freeze-thaw index was calculated using soil temperature data. Disease and frost damage severity To determine disease severity, we investigated pastures that overwintered in each plot 2 to 3 weeks after snowmelt. Causal pathogens were identified based on disease symptoms, and plant damage was evaluated specifically for fungal infections. Two snow mold pathogens, T. ishikariensis and S. borealis , were found in the field. Plant damage was scored individually using the following scale: 0 = no damage, 1 = half of the leaves dead, 2 = all leaves dead, 3 = less than half of the shoots dead, and 4 = all shoots dead. Snow compaction and removal practices Our experimental design allowed for environmental control and variety validation in agronomic plots of approximately 10 m 2 in the field. Experiments were conducted using a randomized complete block design with three replicates. A total of 4 × 3 plots were established: untreated control (Cont), snow compaction in early winter (SC1), compaction in both early winter and midwinter (SC2), and snow removal (Rem). For snow compaction, a tractor (EDR-PKCPS6; Massey Ferguson, Beauvais, France) equipped with tire pressure rollers (600/65R38; Agri-index Co., Memuro, Japan) was used. Snow removal, which is generally impractical for field crops due to increased frost injury risk after snow cover loss (Gossen et al. 2001 ), was performed using a wheel loader (WA30; Komatsu, Tokyo, Japan). To minimize direct plant damage, snow compaction preceded removal in Rem plots. The snow was compacted and removed in the east-west direction, with a width of approximately 3.5 m. Based on preliminary trials (Shimoda et al. 2015 ), we designed the yuki-fumi schedule as follows: after about 0.1 m depth of snow, snow compaction was performed once or twice in SC1 and until the snow depth exceeded 50 cm in SC2. Most treatment dates aligned with those reported for winter wheat fields from 2019 to 2021 (Shimoda et al. 2023a ). Pastures and growth investigation The test pasture species were TY, OG, and PR. Each species included early, medium, and late growth varieties: ‘Kunpuu’ (KUN), ‘Natsuchikara’ (NCH), and ‘Natsupirika’ (NPI) for TY; ‘Esajiman’ (ESA), ‘Harujiman’ (HAR), and ‘Toyomidori’ (TOY) for OG; and ‘Chinita’ (CHI), ‘Poroko’ (POR), and ‘Douto 1’ (DOU) for PR. These grass varieties are widely cultivated in Hokkaido, Japan. Seeds were sown at 20g m –2 by hand in late August of each year. The impact of snow mold on forage grass crops is difficult to assess because of the regenerative ability of grasses following biotic and abiotic stresses (Bonesmo et al., 2014 ). Therefore, we used first-cut aboveground production as a growth index after overwintering. A 1.0 × 2.0 m section was harvested, fresh weight measured using a scale (SJ-WP; A&D Co., Tokyo, Japan), and a 200 g sample dried for 1 week to estimate dry matter production. Rapid post-snowmelt growth is essential for early grazing, influencing first-cut hay yield at the heading stage. The heading date was recorded as the first occurrence of heading within a 1.0 × 2.0 m area at the center of each plot ( n = 3). Statistical analyses All analyses were conducted using R v. 4.3.2 software. Differences in disease and frost damage severity, and dry matter production among treatments were tested. Statical differences were evaluated by Tukey’s honestly significant difference (HSD) test (p < 0.05). Results Environmental conditions during winter The years of low snowfall in December and January resulted in markedly lower soil temperatures, and snow compaction accelerated temperature depletion. The maximum snow depth was lower in the winter of 2018/19 than in other years (Fig. 1 a). By midwinter, snow depth was nearly zero and remained below 0.20 m throughout winter, causing sharp soil temperature fluctuations regardless of treatment. In the winter of 2019/20, soil temperature rapidly decreased below − 10°C in Rem. The difference in soil temperatures between the snow removal and compaction treatments was higher in 2019/20 (Fig. 1 b) than in 2020/21 (Fig. 1 c). Snow cover began later in the winter of 2020/21 than in the other years. Damage severity Overwintering damage in TY was minimal in 2019 despite the short snow cover period. However, in 2021, the KUN and NCH varieties showed significantly higher damage severity in Cont plots than in other treatments (Fig. 2 a). At the OG site, there was no significant difference among the treatments (Fig. 2 b). In PR, compacted and snow-removed plots showed significantly higher damage in POK in 2019, DOU in 2020, and CHI in both 2019 and 2020 (Fig. 2 c). Disease severity was low in 2019 across all treatments, remaining below 0.5 regardless of variety. In contrast, in 2021, the Cont plot had high disease incidence across all pasture species, with one TY, two OGs, and all PRs showing severity greater than 2 (Fig. 3 ). Disease severity was significantly higher in the Cont plot compared with snow-compacted plots, with removal treatments also reducing severity in one PR variety in 2020 and one TY, one OG, and two PR varieties in 2021. S. borealis was prevalent in most snow mold-infested plots. The plots experienced less than 20 consecutive days of Mild-T, except for the Cont plot in 2020/21 (Fig. 4). The response of disease severity to consecutive days of Mild-T showed a difference in disease severity among pasture species, and disease severity in TY was lower than that in OG and PR, especially for shorter days of Mild-T. When Mild-T lasted fewer than 20 consecutive days, disease severity remained below 0.13 in TY, while it reached 1.78 in OG and 1.63 in PR. Severity generally increased with prolonged Mild-T across most plots, though ESA of OG showed a decreasing trend. We also observed that the CFI and Mild-T index showed similar trends, with cooler soil temperatures resulting in lighter snow mold damage (Appendix A1). Snow mold rarely occurred when CFI was below 200°C-day in TY, while OG had slightly higher severity than PR under similar conditions. The severity of frost damage was mostly due to frost heaving in OG and leaf death in PR. There was no significant difference in frost damage among the treatments in TY and OG, but there was significantly higher damage in PR for the SC2 or Rem plots in 2019 and 2020. Additionally, there was significant frost damage in OG and PR, although the relationship with the CFI was unclear (Fig. 5 ). Growth speed and production Growth speeds at the TY site were less affected by snow manipulation than those at the OG and PR sites. Additionally, growth delay was related to low temperatures due to snow manipulation in PR. The heading period in Cont was similar to that in the snow manipulation plots, and 11 of the 27 (40%) snow manipulation plots had earlier headings than Cont (Fig. 6 a). In OG and PR, most varieties showed later heading in the manipulation plots than in the control plots (Fig. 6 b, c), with delays exceeding 10 d in POK of OG and HAR of PR. In TY, there was no difference in production among the treatments in 2020 and 2021 (Fig. 7 a). Dry matter production in OG was similar among treatments except for ‘TOY’ in 2021 (Fig. 7 b). The adverse effect of snow manipulation on dry matter productivity was largest in PR, and most varieties showed significantly higher production in the Cont plots than in the snow manipulation plots (Fig. 7 c). In particular, snow removal had a large adverse effect on PR. The Rem plots exhibited less than 100 g m − 2 production across all years. Discussion Snow manipulation effectively suppressed snow mold by regulating soil temperatures; however, significant frost damage unrelated to disease offset its positive effects on growth. Reduction of snow mold The effects of snow manipulation varied somewhat among the pasture species. The highly cold-tolerant species TY avoided frost injury, while low temperatures from snow manipulation nearly eliminated snow mold. The response of disease severity to consecutive days of Mild-T indicated that TY had a higher overwintering tolerance than OG and PR. The effectiveness of snow manipulation in reducing snow mold in OG and PR was consistent with previous findings in wheat (Shimoda et al. 2023a ), suggesting that maintaining soil temperatures at 0 ± 0.5°C for over 30 d is a threshold for fatal damage (severity > 2). Sclerotinia borealis requires temperatures of − 2 to − 3°C followed by heavy snow to develop (Ozaki 1979 ). Snow manipulation shortened periods near 0°C, limiting the conditions favorable for snow mold pathogens. Across species, snow manipulation showed a declining trend in disease severity. Differences in disease response to Mild-T suggest overwintering disease tolerance in each species, but few varietal differences were detected. Some varieties may differ in their thermal response to disease, but reductions in disease often coincided with increased frost damage, making total damage severity dependent on both disease resistance and frost resistance. Varietal differences in frost resistance is often influenced by pre-winter growth management (Zhang et al. 2024 ), contributing to uncertainty in the relationship between Mild-T and disease severity. Changes in snow mold occurrence with snow compaction indicated the effectiveness of Mild-T as a soil temperature indicator for disease risk assessment. Frost damage by snow manipulation The difference in the degree of frost damage depended mainly on the species. Low-temperature damage from snow manipulation was the primary cause of the reduced growth at the PR and OG sites. Even in 2019, a year of low snow mold incidence, snow manipulation significantly reduced productivity at these sites. The adverse effects of low temperatures were more pronounced in PR than in OG. Since OG generally has greater cold tolerance than PR (Koseki and Noshiro 1985 ), our results reflect the general overwintering characteristics of pasture species. In our experiment, frost heaving was the major form of frost damage in OG. Frost heaving can damage plant roots in pastures, especially under conditions with inconsistent freezing and thawing cycles around 0°C. In Hokkaido, breeding efforts have focused on developing OG varieties with strong spring regrowth to compensate for overwintering losses (Sanada et al. 2010 ). In this study, the spring regrowth of OG mitigated the effects of cold injury due to snow manipulation. Our results lead us to consider the following question: Can snow cover be artificially managed for pasture management? A previous study indicated that snow compaction can control snow mold without causing yield loss in cold-tolerant wheat varieties (Shimoda et al. 2023a ). Snow removal is used in turf management to suppress gray snow mold, which requires prolonged snow cover (Hsiang et al 1999 ). However, in pasture grass, the priority should be evaluating its agronomic feasibility and potential benefits for forage production rather than just disease suppression. Pasture grass is a perennial crop; consequently, growth delays owing to snow manipulation can result in shorter growing periods and lower annual yields. In cold-tolerant species, such as TY, snow compaction is a relatively effective method for preventing fatal overwintering diseases without apparent growth delays. In OG, growth delays did not contribute to the loss of first-cut dry mass production. Previous studies on winter wheat showed that low spring soil temperatures in snow-compacted fields delayed heading by a few days, but subsequent growth compensated for the loss of spring dry matter production (Shimoda and Hirota 2018 ). Similarly, at the OG sites, high regrowth ability compensated for overwinter loss, resulting in no significant difference in dry matter production. For pastures containing cold-sensitive species, snow manipulation offers no clear agricultural benefit. Improved cold hardiness and cultivation methods may increase the chances of successful snow manipulation. Improved cold-sensitive varieties or cultivation may increase the likelihood of successful snow manipulation. In general, survival depends on pre-wintering growth, so the potential of snow manipulation by improving pre-wintering growth needs further investigation. Snow manipulation as a pasture management strategy Cold-sensitive pasture species lack sufficient resistance to temperatures below − 10°C, making overwintering cold damage a key concern before considering snow mold mitigation. In such cases, avoiding intervention may be the best strategy to maximize production. At the study site, daily mean air temperatures fell below − 10°C for 17 to 24 d. Historically, this region has been at the survival threshold for both OG and PR (Araki 1975 ; Kozeki and Noshiro 1985), and the addition of colder winters to current climatic conditions further threatens healthy growth. In such a cold region, snow removal can reduce snow mold damage but also increases the risk of fatal frost damage, particularly in PR owing to its high frost sensitivity. This study discussed pasture adaptation to snow manipulation under current climatic conditions. Global warming has led to rising temperatures in spring and early winter, leading to extreme snowpack patterns in snow-covered regions worldwide (Pulliainen et al. 2020 ), including northern Japan (Shimoda et al. 2024 ). Some studies have indicated that snowfall decreases the risk of winter injury through potential soil heaving and ice encasement (Qian et al. 2024 ). Identifying optimal temperature ranges for winter management can inform future agricultural adaptations to climate change. Conclusions The study attempts effective physical control practice snow manipulations for overwintering without fatal disease infection and frost damage of pasture grasses. Snow manipulations: compaction and removal have been conducted to control the snow depth and their thermal insulation, and decreased soil temperature after practice. Our findings demonstrate that manipulating snow cover significantly alters soil temperature dynamics, reducing the number of days with near-freezing temperatures, an important factor in overwintering disease progression. This reduction in thermally conducive conditions for pathogens led to lower disease severity, particularly in timothy, which exhibited the highest adaptability to snow manipulation. However, our results also reveal that the benefits of disease reduction must be weighed against potential increases in frost damage, particularly in frost-sensitive species such as perennial ryegrass. Thermal index of Mild-T shows clear differences in the severity of disease among species, while cold damage is unstable in its response to temperature. By integrating soil temperature modulation with plant resilience traits, our study advances a mechanistic understanding of plant responses to winter stress and contributes to broader discussions on optimizing plant production systems under variable climatic conditions. Abbreviations TY: timothy OG: orchardgrass PR: perennial ryegrass Mild-T: mild temperature CFI: Cumulative freezing index Cont: untreated control SC1: snow compaction in early winter SC2: compaction in both early winter and midwinter Rem: snow removal KUN: Kunpuu NCH: Natsuchikara NPI: Natsupirika ESA: Esajiman HAR: Harujiman TOY: Toyomidori CHI: Chinita POR: Poroko DOU: Douto 1 Declarations Acknowledgments The authors thank Dr. Sanada (NARO/HARC) for his support in the experimental plan and Maiko Omote (NARO/HARC) for their support in the field investigation. Funding This work was supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science (MEXT/JSPS), KAKENHI [grant number 24K01885]. Declaration of Competing Interest The authors declare no conflict of interest. Contributions Seiji Shimoda: Writing original draft, Visualization, Methodology. References Araki T (1975) Heavy infestation of pasture snow mold in Hokkaido, Japan. Plant Prot 29:484–488 (in Japanese). https://www.jppn.ne.jp/jpp/s_mokuji/19751204.pdf Baron VS, Bélanger G (2020) Climate, climate-change and forage adaptation. In: Moore KJ, Collins M, Nelson CJ, Redfearn DD (eds) Forages: the science of grassland agriculture, vol II, 7th edn. John Wiley & Sons Limited, pp 151–186. https://doi.org/10.1002/9781119436669.ch8 Bélanger G, Rochette P, Castonguay Y, Bootsma A, Mongrain D, Ryan DA (2002) Climate change and winter survival of perennial forage crops in eastern Canada. Agron J 94:1120–1130. https://doi.org/10.2134/agronj2002.1120 Bruehl GW, Cunfer B (1971) Physiologic and environmental factors that affect the severity of snow mold of wheat. Phytopathology 61:792–799. https://doi.org/10.1094/Phyto-61-792 Bonesmo H, Baron VS, Young D, Bélanger G, Jing Q (2014) Adapting the CATIMO grass model to meadow bromegrass grown in western Canada. Can J Plant Sci 94:61–71. https://doi.org/10.4141/cjps2013-183 Hokkaido (2024) Distribution of Forage Crop Seeds in Hokkaido. https://www.pref.hokkaido.lg.jp/ns/tss/feed/shiryousyushi.html Gaudet DA, Laroche A, Puchalski B (2001) Effect of plant age on water content in crowns of fall rye and winter wheat cultivars differing in snow mold resistance. Can J Plant Sci 81:541–550. https://doi.org/10.4141/P00-002 Gossen BD, Hsiang T, Murray TD (2001) Managing snow mold diseases of winter cereals and turf. In: Iriki G, Tronsmo, Matsumoto, Yoshida N (eds) Low Temperature Plant Microbe Interactions Under Snow. Hokkaido National Agricultural Experiment Station, Sapporo, pp 181–192. https://doi.org/10.4141/cjps2013-183 Hirota T, Kobayashi K (2019) The roles of farmers, scientists, and extension staff in technology development for soil frost control as an adaptation to climate change in Tokachi, Hokkaido, Japan. In: Iizumi T (eds), Adaptation to climate change in agriculture. Springer. Springer Singapore, Singapore, p 211–228. https://doi.org/10.1007/978-981-13-9235-1_14 Hoshino T, Terami F, Tkachenko OB, Tojo M, Matsumoto N (2010) Mycelial growth of the snow mold fungus, Sclerotinia borealis, improved at low water potentials: an adaption to frozen environment. Mycoscience 51:98–103. https://doi.org/10.1007/S10267-009-0013-3 Hoshino T, Tkachenko OB, Tojo M, Tronsmo AM, Kasuya T, Matsumoto N (2022) Taxonomic revision of the Typhula ishikariensis complex. Mycoscience 63:118–130. https://doi.org/10.47371/mycosci.2022.03.003 Hsiang T, Matsumoto N, Millett SM (1999) Biology and management of Typhula snow molds of turfgrass. Plant Dis 83:788–798. https://doi.org/10.1094/PDIS.1999.83.9.788 Iwasaki C, Shimoda S, Sugiura R, Kikui G, Sugikawa Y (2025) Cascade prediction of winter wheat developmental stages simply considering growth in snowy season. J Agric Meteorol 81:44–55. https://doi.org/10.2480/agrmet.D-21-00001 Koseki JI, Noshiro M (1985) Winter hardiness of grasses and its enhancement. JARQ 18:215–221. https://doi.org/10.6090/jarq.18.215 Matsumoto N, Hsiang T (2016) Snow mold: the battle under snow between fungal pathogens and their plant. Hosts. Springer, New York. https://doi.org/10.1007/978-981-10-0758-3 Ozaki M (1979) Ecological study of selerotinia snow blight disease of orchardgrass. Bol Hokkaido Prefect Agric Exp Stn 42:55–65 Pulliainen J, Luojus K, Derksen C, Mudryk L, Lemmetyinen J, Salminen M, Ikonen J, Takala M, Cohen J, Smolander T, Norberg J (2020) Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature 581:294–298. https://doi.org/10.1038/s41586-020-2258-0 Qian B, Jing Q, Jégo G, Bélanger G, Smith W, VanderZaag A, Shang J, Liu J, Grant B, Crépeau M (2024) Warmer future climate in Canada—implications for winter survival of perennial forage crops. Can J Plant Sci 104:320–335. https://doi.org/10.1139/cjps-2023-0192 Sanada Y, Tamura K, Yamada T (2010) Relationship between water-soluble carbohydrates in fall and spring and vigor of spring regrowth in orchardgrass. Crop Sci 50:380–390. https://doi.org/10.2135/cropsci2009.01.0031 Screen JA (2014) Arctic amplification decreases temperature variance in northern mid-to high-latitudes. Nat Clim Change 4:577–582. https://doi.org/10.1038/nclimate2268 Shimoda S, Hirota T (2018) Planned snow compaction approach (yuki-fumi) contributes toward balancing wheat yield and the frost-kill of unharvested potato tubers (yuki-fumi). Agric Meteorol 262:361–369. https://doi.org/10.1016/j.agrformet.2018.07.030 Shimoda S, Hirota T, Onodera M (2023b) Promotion to farmers of snow compaction (Yuki-Fumi) on winter wheat to control volunteer potatoes without depending on chemical materials. JARQ 57:261–268. https://doi.org/10.6090/jarq.57.261 Shimoda S, Kanaya M, Kominami Y, Tsuji H (2023c) Decline in tuber number and weight of potato (Solanum tuberosum L) associated with drought in 2020 and 2021 in a humid region of eastern Hokkaido. J Agric Meteorol 79:59–68. https://doi.org/10.2480/agrmet.D-22-00031 Shimoda S, Onodera M, Okumura O, Araki H, Kimura A, Chiba K, Kusano Y, Hoshi H, Tamura S, Suda T, Kominami Y, Nakatsuji T, Hirota T (2021) Effects of snow compaction yuki-fumi on soil frost depth and volunteer potato control in potato–wheat rotation system in Hokkaido. Plant Prod Sci 24:186–197. https://doi.org/10.1080/1343943X.2020.1828950 Shimoda S, Shimazaki Y, Ikenaga S, Kawakita S, Nakajima M, Seki M (2024) Uneven changes in air and crown temperatures associated with snowpack changes affect the phenology of overwintering cereals. Sci Total Environ 953:175750. https://doi.org/10.1016/j.scitotenv.2024.175750 Shimoda S, Terasawa Y, Kanaya M (2023a) Control of snow mold damage of winter wheat by snow compaction (Yuki-fumi). Soil Till Res 225:105554. https://doi.org/10.1016/j.still.2022.105554 Shimoda S, Yazaki T, Nishio Z, Hamasaki T, Hirota T (2015) Possible soil frost control by snow compaction on winter wheat fields. J Agric Meteorol 71:276–281. https://doi.org/10.2480/agrmet.D-15-00001 Zhang Z, Wang M, Huo X, Mao W, Gu Y, Cao G, Aidaituli M (2024) Construction and analysis of freezing damage indices for winter wheat during the overwintering period in northern Xinjiang, China. Theor Appl Climatol 155:1381–1394. https://doi.org/10.1007/s00704-023-04696-7 Zhu P, Kim T, Jin Z, Lin C, Wang X, Ciais P, Mueller ND, Aghakouchak A, Huang J, Mulla D, Makowski D (2022) The critical benefits of snowpack insulation and snowmelt for winter wheat productivity. Nat Clim Chang 12:485–490. https://doi.org/10.1038/s41558-022-01327-3 Appendix A1 Appendix A1 is not available with this version. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6229443","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435292899,"identity":"2503209d-45c8-4a72-a9a5-646c1c9dcad4","order_by":0,"name":"Seiji Shimoda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYHACxgcMNnJQNg9xWpgNGNKMSdPCJoHQQgyQ7z/+rOJHgkE+fwP7xQcMMncIazG4kWN2syfBwHLGAZ5iAwaeZ0RokeBhu8H7448BUHmaBAPPYeIcVvgnwYAELQwHEsyYecBa2I8RpwXoF2NpGaAWicM8zAYJxPgF6LCHH98AtfC3tz988LGHiBBDAGYeA4bEngOkaGFgf8DA8IM0LaNgFIyCUTAyAABlKDR2FY/ToQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2989-6966","institution":"National Agricultural Research Center for Western Region, National Agriculture and Food Research Organization (NARO)","correspondingAuthor":true,"prefix":"","firstName":"Seiji","middleName":"","lastName":"Shimoda","suffix":""}],"badges":[],"createdAt":"2025-03-14 23:01:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6229443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6229443/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80780254,"identity":"c2283a04-ea48-4674-a7b4-b377417ba3cd","added_by":"auto","created_at":"2025-04-17 04:27:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88057,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal changes in snow depth, soil temperature (at depths of 0.02 m and 0.20 m), and treatment dates for a) 2018/19, b) 2019/20, and c) 2020/21\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/12b87c035b9a818e4fb7c2a1.png"},{"id":80780255,"identity":"6c4d9883-d648-4267-9cd7-0ecbafa9da09","added_by":"auto","created_at":"2025-04-17 04:27:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41064,"visible":true,"origin":"","legend":"\u003cp\u003eTotal damage severity in a) TY, b) OG, and c) PR for 3 years under different snow manipulation. Cont, control; SC1, snow compaction in early winter; SC2, compaction in the midwinter; Rem, snow removal throughout winter. Error bars represent the standard error of the mean (\u003cem\u003en\u003c/em\u003e= 3). Different letters denote significant differences among treatments and between years (p \u0026lt; 0.05, Tukey’s honestly significant difference test)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/389a5593b1bb65fa9a1da7ed.png"},{"id":80780988,"identity":"8a62707c-5a4d-4ee2-84ae-a29b04f4b66f","added_by":"auto","created_at":"2025-04-17 04:35:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46192,"visible":true,"origin":"","legend":"\u003cp\u003eDisease severity and frost damage severity over 3 years in a) TY, b) OG, and c) PR under different snow manipulation treatments: control (Cont), early-winter snow compaction (SC1), midwinter snow compaction (SC2), and snow removal (Rem). Error bars represent the standard error of the mean for disease and frost damage severity (\u003cem\u003en \u003c/em\u003e= 3). Different letters denote significant differences among treatments and between years (p \u0026lt; 0.05, Tukey’s honestly significant difference test). Bar graphs are color-coded to differentiate between pathogens (\u003cem\u003eS. borealis\u003c/em\u003e and \u003cem\u003eT. ishikariensis\u003c/em\u003e) and frost damage types (freeze dry leaves and frost heaving)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/d39ec7c71359e8cb54238af5.png"},{"id":80780257,"identity":"e20524d3-a61a-47ea-bce5-846b038f63fc","added_by":"auto","created_at":"2025-04-17 04:27:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21571,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between disease severity in winter wheat cultivars and the number of days with soil temperatures range of –0.5 °C to 0.5 °C. Plot colors represent different treatments (black: control (Cont); blue: early-winter snow compaction (SC1); orange: midwinter snow compaction (SC2); white: snow removal (Rem)) over 3 years for a) TY, b) OG, and c) PR. Colors indicate differences in treatments black: control (Cont); blue: early-winter snow compaction (SC1); orange: midwinter snow compaction (SC2); white: snow removal (Rem)) over 3 years for a) TY, b) OG, and c) PR. Varietal differences are shown in the form of plots. Square, triangle and circle plots indicate ‘Kunpuu,’ ‘Natsuchikara,’ and ‘Natupirika’ in TY, ‘Esajiman,’ ‘Harujiman,’ and ‘Toyomidori’ in OG, and ‘Tinita,’ ‘Poroko,’ and ‘Doto 1’ in PR\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/8e2649d94717e9ab7e404dee.png"},{"id":80780261,"identity":"f9f4924d-c420-451e-acb2-48f2b19ed133","added_by":"auto","created_at":"2025-04-17 04:27:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24844,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between frost damage severity in winter wheat cultivars and the CFI. Plot colors indicate differences in treatments (black: control (Cont); blue: early-winter snow compaction (SC1); orange: midwinter snow compaction (SC2); white: snow removal (Rem)) over 3 years for a) TY, b) OG, and c) PR. Varietal differences are shown in the form of plots. Square, triangle, and circle plots indicate ‘Kunpuu,’ ‘Natsuchikara,’ and ‘Natupirika’ in TY, ‘Esajiman,’ ‘Harujiman,’ and ‘Toyomidori’ in OG, and ‘Tinita,’ ‘Poroko,’ and ‘Doto 1’ in PR\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/a4ea296d9dd2117fc4ed726f.png"},{"id":80780263,"identity":"a0d8b902-03ce-43fe-ac34-b529e36d7646","added_by":"auto","created_at":"2025-04-17 04:27:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":180474,"visible":true,"origin":"","legend":"\u003cp\u003eDelay of heading in snow manipulations to control in a) TY, b) OG, and c) PR\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/f10bd99d6c39cf6430e8ef14.png"},{"id":80780992,"identity":"dd497566-4b7e-4ac2-9a2c-799c3964bf46","added_by":"auto","created_at":"2025-04-17 04:35:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54775,"visible":true,"origin":"","legend":"\u003cp\u003eDry matter production under different snow manipulation treatments: control (Cont; white bars), early-winter snow compaction (SC1; orange bars), midwinter snow compaction (SC2; gray bars), and snow removal (Rem). Error bars represent the standard error of the mean (\u003cem\u003en\u003c/em\u003e= 3). Different letters denote significant differences among treatments and between years (p \u0026lt; 0.05, Tukey’s honestly significant difference test)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/8f49381ee19091d4e18023c6.png"},{"id":82901342,"identity":"32bf2c54-0756-46d8-b017-894cc041afc2","added_by":"auto","created_at":"2025-05-16 13:29:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":753308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6229443/v1/086e9b5e-2382-4109-9c9c-fe8f680f0ff3.pdf"}],"financialInterests":"","formattedTitle":"Snow manipulation: Adaptability and limitations of physical control of snow mold and frost damage in pastures","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSnow cover reduction is prominent in middle and high latitudes (Screen \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), leading to colder soils and potentially fatal damage to overwintering plants. Decadal climate change-induced snow cover reduction often increases the severity of frost damage (e.g., heaving and desiccation) owing to the continuation of snow-free periods and freeze-thaw cycles (Gaudet et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Additionally, delayed spring regrowth can occur because of the delayed thawing of frost soil after snowmelt and delayed soil temperature rise caused by shallow winter snow cover and soil frost in overwintering crops (Shimoda and Hirota \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Iwasaki et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Frost damage from heaving and desiccation typically occurs during late winter or early spring. Frost heaving occurs when saturated soils freeze with little or no snow cover. A reduction in seasonal snow accumulation may have a negative impact on the winter survival of overwintering vegetation, especially in regions where crops rely on adequate snow cover to survive harsh winters (Bélanger et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Various indices assess overwintering conditions, with cumulative negative temperatures serving as a typical frost damage index for overwintering crops (Zhang et al. 2018; Qian et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGrass pasture species have different cold tolerances and varieties based on their risk of winter damage. Baron and Bélanger (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) ranked timothy (TY; \u003cem\u003ePhleum pratensis\u003c/em\u003e) as having medium or high cold tolerance, orchardgrass (OG; \u003cem\u003eDactylis glomerata\u003c/em\u003e L.) as having low tolerance, and perennial ryegrass (PR; \u003cem\u003eLolium perenne\u003c/em\u003e) as having relatively low tolerance. Temperate grass pastures introduced to Hokkaido, northern Japan, often endure severely cold winters. Notably, OG and PR experienced extensive frost damage in the mid-1970s (Araki \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Kozeki and Noshiro 1985), prompting a shift toward winter-hardy TY, which increased from about 50–90% of the seeding ratio over 30 years. The latest seeding ratio of TY reached 76% (Hokkaido \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), reflecting adaptation to summer warming.\u003c/p\u003e \u003cp\u003eAlthough a thick snowpack can protect overwintering plants from frost damage, it can also reduce the subsequent growth of plants owing to infection by pathogens (e.g., snow mold). Snow mold, a disease of grasses and winter cereals exposed to snow in cold-temperate regions caused by several cold-adapted fungi, attacks overwinter grasses and crops in the Northern Hemisphere (Hsiang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hoshino et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Persistent snow cover insulates the soil, increases the contact of leaf surfaces with the soil, and creates a dark, humid environment favorable for snow mold development (Bruehl and Cunfer \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). The key characteristic of snow mold fungi is the ability to spread at temperatures near 0°C under snow cover. The number of snow cover days is a well-known environmental indicator of snow mold (Ozaki \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) because the temperature under the snow is maintained at 0°C during thick snow cover (Shimoda et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Currently, snow mold control relies on chemical pesticides for disease control in winter wheat and turf. The chemical use of pasture grass is difficult from the viewpoints of cost and environmentally friendly management of forage use. Therefore, sustainable physical control methods based on an understanding of the environmental responses of snow mold are needed.\u003c/p\u003e \u003cp\u003eIn this study, we hypothesized that controlling soil temperature through snow manipulation could decrease overwintering diseases and improve pasture growth. The first objective of this study was to determine the temperature required to control the severity of snow mold. Next, we considered whether snow mold control simultaneously causes frost heaving and freezing death owing to low temperatures. The optimal approach involves determining effective temperatures for pasture grass survival and selecting appropriate snow manipulation methods while acknowledging inevitable frost damage. In Hokkaido, farmers use agricultural machinery to regulate snow depth over large areas to freeze volunteer potatoes (Shimoda et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Snow manipulation is a feasible agricultural management option for local farmers (Hirota and Kobayashi \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shimoda et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and this study contributes to its practical application. Frost damage and snow mold, which tend to develop in low-temperature areas, are rarely studied separately, leaving their environmental dependencies poorly understood.\u003c/p\u003e "},{"header":"Material and methods","content":"\u003cp\u003eSite description and meteorological data collection\u003c/p\u003e\u003cp\u003eThe study site was located at the Memuro Experiment Station of the Hokkaido Agricultural Research Center (NARO/HARC) (42.888 N, 143.074 E; elevation 94 m above sea level) in Memuro, eastern Hokkaido, Japan. The annual mean air temperature and rainfall measurements taken at the station were 6.4°C and 945 mm, respectively, from 2012 to 2021 (Shimoda et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023c\u003c/span\u003e). The region has a humid continental climate (Köppen Dfb, hemiboreal) and features volcanic ash soil (andosol). The number of days with mean air temperatures below − 10°C was 17 in 2018/19, 24 in 2019/20, and 22 in 2020/21.\u003c/p\u003e\u003cp\u003eTo monitor the soil temperatures during winter, we installed thermometers with data loggers (Thermo buttons 22 L; Plug \u0026amp; Track Inc., Willems, France) at 2-h intervals from November 2018 in each block at depths of 0.02 m. In the no-treatment field area, a snow depth meter (Model SDM-311; Niigata Electric Co., Niigata, Japan) certified by the Japan Meteorological Agency was used to measure snow depth at 09:00 JST. For the snow compaction and snow removal treatments, we measured snow depth using a scale before and after treatments or every 10 d.\u003c/p\u003e\u003cp\u003eSnow mold and freezing index\u003c/p\u003e\u003cp\u003eEnvironmental indicators for snow mold and frost damage include the number of snow cover days. Gray snow molds, such as \u003cem\u003eTyphula ishikariensis\u003c/em\u003e (Hoshino et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), thrive in deep snow regions with extended snow cover (Matsumoto and Hsiang \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Shimoda et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) suggested that snow mold is greater with longer periods at soil temperatures near 0°C. \u003cem\u003eSclerotinia borealis\u003c/em\u003e prevails on plants that are predisposed to cold and are subsequently covered with thick snow. This study defines a ‘mild temperature’ (Mild-T) index as the number of days with a daily mean soil temperature of 0 ± 0.5°C. Mild-T serves as a general indicator for various snow mold species, which require a transition period at 0°C after exposure to lower temperatures. Cumulative freezing index (CFI) is widely used to assess cold stress, representing the cumulative degree-days below 0°C from July 1 to June 30 of the following year. The CFI is also a reasonable environmental indicator of the occurrence of \u003cem\u003eS. borealis\u003c/em\u003e, which requires a period of cold exposure before snow accumulation. In this study, the freeze-thaw index was calculated using soil temperature data.\u003c/p\u003e\u003cp\u003eDisease and frost damage severity\u003c/p\u003e\u003cp\u003eTo determine disease severity, we investigated pastures that overwintered in each plot 2 to 3 weeks after snowmelt. Causal pathogens were identified based on disease symptoms, and plant damage was evaluated specifically for fungal infections. Two snow mold pathogens, \u003cem\u003eT. ishikariensis\u003c/em\u003e and \u003cem\u003eS. borealis\u003c/em\u003e, were found in the field. Plant damage was scored individually using the following scale: 0 = no damage, 1 = half of the leaves dead, 2 = all leaves dead, 3 = less than half of the shoots dead, and 4 = all shoots dead.\u003c/p\u003e\u003cp\u003eSnow compaction and removal practices\u003c/p\u003e\u003cp\u003eOur experimental design allowed for environmental control and variety validation in agronomic plots of approximately 10 m\u003csup\u003e2\u003c/sup\u003e in the field. Experiments were conducted using a randomized complete block design with three replicates. A total of 4 × 3 plots were established: untreated control (Cont), snow compaction in early winter (SC1), compaction in both early winter and midwinter (SC2), and snow removal (Rem). For snow compaction, a tractor (EDR-PKCPS6; Massey Ferguson, Beauvais, France) equipped with tire pressure rollers (600/65R38; Agri-index Co., Memuro, Japan) was used. Snow removal, which is generally impractical for field crops due to increased frost injury risk after snow cover loss (Gossen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), was performed using a wheel loader (WA30; Komatsu, Tokyo, Japan). To minimize direct plant damage, snow compaction preceded removal in Rem plots. The snow was compacted and removed in the east-west direction, with a width of approximately 3.5 m. Based on preliminary trials (Shimoda et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we designed the yuki-fumi schedule as follows: after about 0.1 m depth of snow, snow compaction was performed once or twice in SC1 and until the snow depth exceeded 50 cm in SC2. Most treatment dates aligned with those reported for winter wheat fields from 2019 to 2021 (Shimoda et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePastures and growth investigation\u003c/p\u003e\u003cp\u003eThe test pasture species were TY, OG, and PR. Each species included early, medium, and late growth varieties: ‘Kunpuu’ (KUN), ‘Natsuchikara’ (NCH), and ‘Natsupirika’ (NPI) for TY; ‘Esajiman’ (ESA), ‘Harujiman’ (HAR), and ‘Toyomidori’ (TOY) for OG; and ‘Chinita’ (CHI), ‘Poroko’ (POR), and ‘Douto 1’ (DOU) for PR. These grass varieties are widely cultivated in Hokkaido, Japan. Seeds were sown at 20g m\u003csup\u003e–2\u003c/sup\u003e by hand in late August of each year.\u003c/p\u003e\u003cp\u003eThe impact of snow mold on forage grass crops is difficult to assess because of the regenerative ability of grasses following biotic and abiotic stresses (Bonesmo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, we used first-cut aboveground production as a growth index after overwintering. A 1.0 × 2.0 m section was harvested, fresh weight measured using a scale (SJ-WP; A\u0026amp;D Co., Tokyo, Japan), and a 200 g sample dried for 1 week to estimate dry matter production. Rapid post-snowmelt growth is essential for early grazing, influencing first-cut hay yield at the heading stage. The heading date was recorded as the first occurrence of heading within a 1.0 × 2.0 m area at the center of each plot (\u003cem\u003en\u003c/em\u003e = 3).\u003c/p\u003e\u003cp\u003eStatistical analyses\u003c/p\u003e\u003cp\u003eAll analyses were conducted using R v. 4.3.2 software. Differences in disease and frost damage severity, and dry matter production among treatments were tested. Statical differences were evaluated by Tukey’s honestly significant difference (HSD) test (p \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eEnvironmental conditions during winter\u003c/p\u003e\u003cp\u003eThe years of low snowfall in December and January resulted in markedly lower soil temperatures, and snow compaction accelerated temperature depletion. The maximum snow depth was lower in the winter of 2018/19 than in other years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). By midwinter, snow depth was nearly zero and remained below 0.20 m throughout winter, causing sharp soil temperature fluctuations regardless of treatment. In the winter of 2019/20, soil temperature rapidly decreased below − 10°C in Rem. The difference in soil temperatures between the snow removal and compaction treatments was higher in 2019/20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) than in 2020/21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Snow cover began later in the winter of 2020/21 than in the other years.\u003c/p\u003e\u003cp\u003eDamage severity\u003c/p\u003e\u003cp\u003eOverwintering damage in TY was minimal in 2019 despite the short snow cover period. However, in 2021, the KUN and NCH varieties showed significantly higher damage severity in Cont plots than in other treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). At the OG site, there was no significant difference among the treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In PR, compacted and snow-removed plots showed significantly higher damage in POK in 2019, DOU in 2020, and CHI in both 2019 and 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eDisease severity was low in 2019 across all treatments, remaining below 0.5 regardless of variety. In contrast, in 2021, the Cont plot had high disease incidence across all pasture species, with one TY, two OGs, and all PRs showing severity greater than 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Disease severity was significantly higher in the Cont plot compared with snow-compacted plots, with removal treatments also reducing severity in one PR variety in 2020 and one TY, one OG, and two PR varieties in 2021. \u003cem\u003eS. borealis\u003c/em\u003e was prevalent in most snow mold-infested plots.\u003c/p\u003e\u003cp\u003eThe plots experienced less than 20 consecutive days of Mild-T, except for the Cont plot in 2020/21 (Fig.\u0026nbsp;4). The response of disease severity to consecutive days of Mild-T showed a difference in disease severity among pasture species, and disease severity in TY was lower than that in OG and PR, especially for shorter days of Mild-T. When Mild-T lasted fewer than 20 consecutive days, disease severity remained below 0.13 in TY, while it reached 1.78 in OG and 1.63 in PR. Severity generally increased with prolonged Mild-T across most plots, though ESA of OG showed a decreasing trend. We also observed that the CFI and Mild-T index showed similar trends, with cooler soil temperatures resulting in lighter snow mold damage (Appendix A1). Snow mold rarely occurred when CFI was below 200°C-day in TY, while OG had slightly higher severity than PR under similar conditions.\u003c/p\u003e\u003cp\u003eThe severity of frost damage was mostly due to frost heaving in OG and leaf death in PR. There was no significant difference in frost damage among the treatments in TY and OG, but there was significantly higher damage in PR for the SC2 or Rem plots in 2019 and 2020. Additionally, there was significant frost damage in OG and PR, although the relationship with the CFI was unclear (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGrowth speed and production\u003c/p\u003e\u003cp\u003eGrowth speeds at the TY site were less affected by snow manipulation than those at the OG and PR sites. Additionally, growth delay was related to low temperatures due to snow manipulation in PR. The heading period in Cont was similar to that in the snow manipulation plots, and 11 of the 27 (40%) snow manipulation plots had earlier headings than Cont (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In OG and PR, most varieties showed later heading in the manipulation plots than in the control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, c), with delays exceeding 10 d in POK of OG and HAR of PR.\u003c/p\u003e\u003cp\u003eIn TY, there was no difference in production among the treatments in 2020 and 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Dry matter production in OG was similar among treatments except for ‘TOY’ in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). The adverse effect of snow manipulation on dry matter productivity was largest in PR, and most varieties showed significantly higher production in the Cont plots than in the snow manipulation plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). In particular, snow removal had a large adverse effect on PR. The Rem plots exhibited less than 100 g m\u003csup\u003e− 2\u003c/sup\u003e production across all years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSnow manipulation effectively suppressed snow mold by regulating soil temperatures; however, significant frost damage unrelated to disease offset its positive effects on growth.\u003c/p\u003e\u003cp\u003eReduction of snow mold\u003c/p\u003e\u003cp\u003eThe effects of snow manipulation varied somewhat among the pasture species. The highly cold-tolerant species TY avoided frost injury, while low temperatures from snow manipulation nearly eliminated snow mold. The response of disease severity to consecutive days of Mild-T indicated that TY had a higher overwintering tolerance than OG and PR. The effectiveness of snow manipulation in reducing snow mold in OG and PR was consistent with previous findings in wheat (Shimoda et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), suggesting that maintaining soil temperatures at 0 ± 0.5°C for over 30 d is a threshold for fatal damage (severity \u0026gt; 2). \u003cem\u003eSclerotinia borealis\u003c/em\u003e requires temperatures of − 2 to − 3°C followed by heavy snow to develop (Ozaki \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Snow manipulation shortened periods near 0°C, limiting the conditions favorable for snow mold pathogens. Across species, snow manipulation showed a declining trend in disease severity.\u003c/p\u003e\u003cp\u003eDifferences in disease response to Mild-T suggest overwintering disease tolerance in each species, but few varietal differences were detected. Some varieties may differ in their thermal response to disease, but reductions in disease often coincided with increased frost damage, making total damage severity dependent on both disease resistance and frost resistance. Varietal differences in frost resistance is often influenced by pre-winter growth management (Zhang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), contributing to uncertainty in the relationship between Mild-T and disease severity. Changes in snow mold occurrence with snow compaction indicated the effectiveness of Mild-T as a soil temperature indicator for disease risk assessment.\u003c/p\u003e\u003cp\u003eFrost damage by snow manipulation\u003c/p\u003e\u003cp\u003eThe difference in the degree of frost damage depended mainly on the species. Low-temperature damage from snow manipulation was the primary cause of the reduced growth at the PR and OG sites. Even in 2019, a year of low snow mold incidence, snow manipulation significantly reduced productivity at these sites. The adverse effects of low temperatures were more pronounced in PR than in OG. Since OG generally has greater cold tolerance than PR (Koseki and Noshiro \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), our results reflect the general overwintering characteristics of pasture species.\u003c/p\u003e\u003cp\u003eIn our experiment, frost heaving was the major form of frost damage in OG. Frost heaving can damage plant roots in pastures, especially under conditions with inconsistent freezing and thawing cycles around 0°C. In Hokkaido, breeding efforts have focused on developing OG varieties with strong spring regrowth to compensate for overwintering losses (Sanada et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, the spring regrowth of OG mitigated the effects of cold injury due to snow manipulation.\u003c/p\u003e\u003cp\u003eOur results lead us to consider the following question: Can snow cover be artificially managed for pasture management? A previous study indicated that snow compaction can control snow mold without causing yield loss in cold-tolerant wheat varieties (Shimoda et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Snow removal is used in turf management to suppress gray snow mold, which requires prolonged snow cover (Hsiang et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, in pasture grass, the priority should be evaluating its agronomic feasibility and potential benefits for forage production rather than just disease suppression.\u003c/p\u003e\u003cp\u003ePasture grass is a perennial crop; consequently, growth delays owing to snow manipulation can result in shorter growing periods and lower annual yields. In cold-tolerant species, such as TY, snow compaction is a relatively effective method for preventing fatal overwintering diseases without apparent growth delays. In OG, growth delays did not contribute to the loss of first-cut dry mass production. Previous studies on winter wheat showed that low spring soil temperatures in snow-compacted fields delayed heading by a few days, but subsequent growth compensated for the loss of spring dry matter production (Shimoda and Hirota \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, at the OG sites, high regrowth ability compensated for overwinter loss, resulting in no significant difference in dry matter production. For pastures containing cold-sensitive species, snow manipulation offers no clear agricultural benefit. Improved cold hardiness and cultivation methods may increase the chances of successful snow manipulation. Improved cold-sensitive varieties or cultivation may increase the likelihood of successful snow manipulation. In general, survival depends on pre-wintering growth, so the potential of snow manipulation by improving pre-wintering growth needs further investigation.\u003c/p\u003e\u003cp\u003eSnow manipulation as a pasture management strategy\u003c/p\u003e\u003cp\u003eCold-sensitive pasture species lack sufficient resistance to temperatures below − 10°C, making overwintering cold damage a key concern before considering snow mold mitigation. In such cases, avoiding intervention may be the best strategy to maximize production. At the study site, daily mean air temperatures fell below − 10°C for 17 to 24 d. Historically, this region has been at the survival threshold for both OG and PR (Araki \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Kozeki and Noshiro 1985), and the addition of colder winters to current climatic conditions further threatens healthy growth. In such a cold region, snow removal can reduce snow mold damage but also increases the risk of fatal frost damage, particularly in PR owing to its high frost sensitivity. This study discussed pasture adaptation to snow manipulation under current climatic conditions. Global warming has led to rising temperatures in spring and early winter, leading to extreme snowpack patterns in snow-covered regions worldwide (Pulliainen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), including northern Japan (Shimoda et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some studies have indicated that snowfall decreases the risk of winter injury through potential soil heaving and ice encasement (Qian et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Identifying optimal temperature ranges for winter management can inform future agricultural adaptations to climate change.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe study attempts effective physical control practice snow manipulations for overwintering without fatal disease infection and frost damage of pasture grasses. Snow manipulations: compaction and removal have been conducted to control the snow depth and their thermal insulation, and decreased soil temperature after practice. Our findings demonstrate that manipulating snow cover significantly alters soil temperature dynamics, reducing the number of days with near-freezing temperatures, an important factor in overwintering disease progression. This reduction in thermally conducive conditions for pathogens led to lower disease severity, particularly in timothy, which exhibited the highest adaptability to snow manipulation. However, our results also reveal that the benefits of disease reduction must be weighed against potential increases in frost damage, particularly in frost-sensitive species such as perennial ryegrass. Thermal index of Mild-T shows clear differences in the severity of disease among species, while cold damage is unstable in its response to temperature. By integrating soil temperature modulation with plant resilience traits, our study advances a mechanistic understanding of plant responses to winter stress and contributes to broader discussions on optimizing plant production systems under variable climatic conditions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTY: timothy\u003c/p\u003e\n\u003cp\u003eOG: orchardgrass\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePR: perennial ryegrass\u003c/p\u003e\n\u003cp\u003eMild-T: mild temperature\u003c/p\u003e\n\u003cp\u003eCFI: Cumulative freezing index\u003c/p\u003e\n\u003cp\u003eCont: untreated control\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSC1: snow compaction in early winter\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSC2: compaction in both early winter and midwinter\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRem: snow removal\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKUN: Kunpuu\u003c/p\u003e\n\u003cp\u003eNCH: Natsuchikara\u003c/p\u003e\n\u003cp\u003eNPI: Natsupirika\u003c/p\u003e\n\u003cp\u003eESA: Esajiman\u003c/p\u003e\n\u003cp\u003eHAR: Harujiman\u003c/p\u003e\n\u003cp\u003eTOY: Toyomidori\u003c/p\u003e\n\u003cp\u003eCHI: Chinita\u003c/p\u003e\n\u003cp\u003ePOR: Poroko\u003c/p\u003e\n\u003cp\u003eDOU: Douto 1\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr. Sanada (NARO/HARC) for his support in the experimental plan and Maiko Omote (NARO/HARC) for their support in the field investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science (MEXT/JSPS), KAKENHI [grant number 24K01885].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeiji Shimoda: Writing original draft, Visualization, Methodology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAraki T (1975) Heavy infestation of pasture snow mold in Hokkaido, Japan. Plant Prot 29:484\u0026ndash;488 (in Japanese). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jppn.ne.jp/jpp/s_mokuji/19751204.pdf\u003c/span\u003e\u003cspan address=\"https://www.jppn.ne.jp/jpp/s_mokuji/19751204.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaron VS, B\u0026eacute;langer G (2020) Climate, climate-change and forage adaptation. In: Moore KJ, Collins M, Nelson CJ, Redfearn DD (eds) Forages: the science of grassland agriculture, vol II, 7th edn. John Wiley \u0026amp; Sons Limited, pp 151\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/9781119436669.ch8\u003c/span\u003e\u003cspan address=\"10.1002/9781119436669.ch8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026eacute;langer G, Rochette P, Castonguay Y, Bootsma A, Mongrain D, Ryan DA (2002) Climate change and winter survival of perennial forage crops in eastern Canada. Agron J 94:1120\u0026ndash;1130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/agronj2002.1120\u003c/span\u003e\u003cspan address=\"10.2134/agronj2002.1120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruehl GW, Cunfer B (1971) Physiologic and environmental factors that affect the severity of snow mold of wheat. Phytopathology 61:792\u0026ndash;799. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/Phyto-61-792\u003c/span\u003e\u003cspan address=\"10.1094/Phyto-61-792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonesmo H, Baron VS, Young D, B\u0026eacute;langer G, Jing Q (2014) Adapting the CATIMO grass model to meadow bromegrass grown in western Canada. Can J Plant Sci 94:61\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4141/cjps2013-183\u003c/span\u003e\u003cspan address=\"10.4141/cjps2013-183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHokkaido (2024) Distribution of Forage Crop Seeds in Hokkaido. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pref.hokkaido.lg.jp/ns/tss/feed/shiryousyushi.html\u003c/span\u003e\u003cspan address=\"https://www.pref.hokkaido.lg.jp/ns/tss/feed/shiryousyushi.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaudet DA, Laroche A, Puchalski B (2001) Effect of plant age on water content in crowns of fall rye and winter wheat cultivars differing in snow mold resistance. Can J Plant Sci 81:541\u0026ndash;550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4141/P00-002\u003c/span\u003e\u003cspan address=\"10.4141/P00-002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGossen BD, Hsiang T, Murray TD (2001) Managing snow mold diseases of winter cereals and turf. In: Iriki G, Tronsmo, Matsumoto, Yoshida N (eds) Low Temperature Plant Microbe Interactions Under Snow. Hokkaido National Agricultural Experiment Station, Sapporo, pp 181\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4141/cjps2013-183\u003c/span\u003e\u003cspan address=\"10.4141/cjps2013-183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirota T, Kobayashi K (2019) The roles of farmers, scientists, and extension staff in technology development for soil frost control as an adaptation to climate change in Tokachi, Hokkaido, Japan. In: Iizumi T (eds), Adaptation to climate change in agriculture. Springer. Springer Singapore, Singapore, p 211\u0026ndash;228. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-13-9235-1_14\u003c/span\u003e\u003cspan address=\"10.1007/978-981-13-9235-1_14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoshino T, Terami F, Tkachenko OB, Tojo M, Matsumoto N (2010) Mycelial growth of the snow mold fungus, Sclerotinia borealis, improved at low water potentials: an adaption to frozen environment. Mycoscience 51:98\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S10267-009-0013-3\u003c/span\u003e\u003cspan address=\"10.1007/S10267-009-0013-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoshino T, Tkachenko OB, Tojo M, Tronsmo AM, Kasuya T, Matsumoto N (2022) Taxonomic revision of the Typhula ishikariensis complex. Mycoscience 63:118\u0026ndash;130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47371/mycosci.2022.03.003\u003c/span\u003e\u003cspan address=\"10.47371/mycosci.2022.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsiang T, Matsumoto N, Millett SM (1999) Biology and management of Typhula snow molds of turfgrass. Plant Dis 83:788\u0026ndash;798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/PDIS.1999.83.9.788\u003c/span\u003e\u003cspan address=\"10.1094/PDIS.1999.83.9.788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwasaki C, Shimoda S, Sugiura R, Kikui G, Sugikawa Y (2025) Cascade prediction of winter wheat developmental stages simply considering growth in snowy season. J Agric Meteorol 81:44\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2480/agrmet.D-21-00001\u003c/span\u003e\u003cspan address=\"10.2480/agrmet.D-21-00001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoseki JI, Noshiro M (1985) Winter hardiness of grasses and its enhancement. JARQ 18:215\u0026ndash;221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6090/jarq.18.215\u003c/span\u003e\u003cspan address=\"10.6090/jarq.18.215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsumoto N, Hsiang T (2016) Snow mold: the battle under snow between fungal pathogens and their plant. Hosts. Springer, New York. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-10-0758-3\u003c/span\u003e\u003cspan address=\"10.1007/978-981-10-0758-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzaki M (1979) Ecological study of selerotinia snow blight disease of orchardgrass. Bol Hokkaido Prefect Agric Exp Stn 42:55\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePulliainen J, Luojus K, Derksen C, Mudryk L, Lemmetyinen J, Salminen M, Ikonen J, Takala M, Cohen J, Smolander T, Norberg J (2020) Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature 581:294\u0026ndash;298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-020-2258-0\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2258-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian B, Jing Q, J\u0026eacute;go G, B\u0026eacute;langer G, Smith W, VanderZaag A, Shang J, Liu J, Grant B, Cr\u0026eacute;peau M (2024) Warmer future climate in Canada\u0026mdash;implications for winter survival of perennial forage crops. Can J Plant Sci 104:320\u0026ndash;335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1139/cjps-2023-0192\u003c/span\u003e\u003cspan address=\"10.1139/cjps-2023-0192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanada Y, Tamura K, Yamada T (2010) Relationship between water-soluble carbohydrates in fall and spring and vigor of spring regrowth in orchardgrass. Crop Sci 50:380\u0026ndash;390. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2009.01.0031\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2009.01.0031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScreen JA (2014) Arctic amplification decreases temperature variance in northern mid-to high-latitudes. Nat Clim Change 4:577\u0026ndash;582. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nclimate2268\u003c/span\u003e\u003cspan address=\"10.1038/nclimate2268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda S, Hirota T (2018) Planned snow compaction approach (yuki-fumi) contributes toward balancing wheat yield and the frost-kill of unharvested potato tubers (yuki-fumi). Agric Meteorol 262:361\u0026ndash;369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2018.07.030\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2018.07.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda S, Hirota T, Onodera M (2023b) Promotion to farmers of snow compaction (Yuki-Fumi) on winter wheat to control volunteer potatoes without depending on chemical materials. JARQ 57:261\u0026ndash;268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6090/jarq.57.261\u003c/span\u003e\u003cspan address=\"10.6090/jarq.57.261\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda S, Kanaya M, Kominami Y, Tsuji H (2023c) Decline in tuber number and weight of potato (Solanum tuberosum L) associated with drought in 2020 and 2021 in a humid region of eastern Hokkaido. J Agric Meteorol 79:59\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2480/agrmet.D-22-00031\u003c/span\u003e\u003cspan address=\"10.2480/agrmet.D-22-00031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda S, Onodera M, Okumura O, Araki H, Kimura A, Chiba K, Kusano Y, Hoshi H, Tamura S, Suda T, Kominami Y, Nakatsuji T, Hirota T (2021) Effects of snow compaction yuki-fumi on soil frost depth and volunteer potato control in potato\u0026ndash;wheat rotation system in Hokkaido. Plant Prod Sci 24:186\u0026ndash;197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1343943X.2020.1828950\u003c/span\u003e\u003cspan address=\"10.1080/1343943X.2020.1828950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda S, Shimazaki Y, Ikenaga S, Kawakita S, Nakajima M, Seki M (2024) Uneven changes in air and crown temperatures associated with snowpack changes affect the phenology of overwintering cereals. Sci Total Environ 953:175750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2024.175750\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2024.175750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda S, Terasawa Y, Kanaya M (2023a) Control of snow mold damage of winter wheat by snow compaction (Yuki-fumi). Soil Till Res 225:105554. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.still.2022.105554\u003c/span\u003e\u003cspan address=\"10.1016/j.still.2022.105554\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda S, Yazaki T, Nishio Z, Hamasaki T, Hirota T (2015) Possible soil frost control by snow compaction on winter wheat fields. J Agric Meteorol 71:276\u0026ndash;281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2480/agrmet.D-15-00001\u003c/span\u003e\u003cspan address=\"10.2480/agrmet.D-15-00001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Wang M, Huo X, Mao W, Gu Y, Cao G, Aidaituli M (2024) Construction and analysis of freezing damage indices for winter wheat during the overwintering period in northern Xinjiang, China. Theor Appl Climatol 155:1381\u0026ndash;1394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-023-04696-7\u003c/span\u003e\u003cspan address=\"10.1007/s00704-023-04696-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu P, Kim T, Jin Z, Lin C, Wang X, Ciais P, Mueller ND, Aghakouchak A, Huang J, Mulla D, Makowski D (2022) The critical benefits of snowpack insulation and snowmelt for winter wheat productivity. Nat Clim Chang 12:485\u0026ndash;490. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-022-01327-3\u003c/span\u003e\u003cspan address=\"10.1038/s41558-022-01327-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Appendix A1","content":"\u003cp\u003eAppendix A1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Snow manipulation, Soil temperature, Overwintering damage, Disease severity, Frost resistance, Pasture species","lastPublishedDoi":"10.21203/rs.3.rs-6229443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6229443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aims\u003c/strong\u003e: Soil temperature regulation through snow manipulation can mitigate overwintering diseases and enhance pasture growth. Identifying optimal temperature conditions for healthy overwintering is crucial, particularly in cold-resistant species such as timothy (TY). This study evaluated the effects of snow manipulation on overwintering damage and disease control in TY, orchardgrass (OG), and perennial ryegrass (PR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Snow manipulation was applied to pasture plots, and its impact on soil temperature and disease severity was assessed. The metric Mild-T, representing the number of days with soil temperatures near 0°C, was analyzed to determine its role in thermal disease control. Disease severity and total overwintering damage were compared across species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Snow manipulation reduced Mild-T, which correlated with lower disease severity, particularly in TY. In plots with \u0026lt; 20 consecutive Mild-T days, maximum disease severity was the lowest in TY (0.13) compared to OG (1.78) and PR (1.63). However, total overwintering damage was influenced by disease and frost resistance. Although snow mold reduction did not consistently decrease overall damage, PR exhibited severe frost damage, making it highly vulnerable to snow manipulation. Contrastingly, OG compensated for overwintering losses because of its strong regrowth ability, maintaining similar dry matter production.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Snow manipulation effectively controls overwintering diseases but may increase frost damage in less cold-resistant species. TY demonstrated the greatest adaptability, while PR showed high susceptibility to frost injury. OG maintained productivity despite overwinter losses. The findings highlight the need to consider disease and frost resistance when applying snow manipulation as a disease control strategy.\u003c/p\u003e","manuscriptTitle":"Snow manipulation: Adaptability and limitations of physical control of snow mold and frost damage in pastures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 04:27:08","doi":"10.21203/rs.3.rs-6229443/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a5de217-15b5-40d6-aced-38fa5eba6653","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-16T13:21:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 04:27:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6229443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6229443","identity":"rs-6229443","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}